Impacts of Short-Term Solar Power Forecasts in System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ibanez, Eduardo; Krad, Ibrahim; Hodge, Bri-Mathias

2016-05-05

Solar generation is experiencing an exponential growth in power systems worldwide and, along with wind power, is posing new challenges to power system operations. Those challenges are characterized by an increase of system variability and uncertainty across many time scales: from days, down to hours, minutes, and seconds. Much of the research in the area has focused on the effect of solar forecasting across hours or days. This paper presents a methodology to capture the effect of short-term forecasting strategies and analyzes the economic and reliability implications of utilizing a simple, yet effective forecasting method for solar PV in intra-daymore » operations.« less

An Operational Short-Term Forecasting System for Regional Hydropower Management

NASA Astrophysics Data System (ADS)

Gronewold, A.; Labuhn, K. A.; Calappi, T. J.; MacNeil, A.

2017-12-01

The Niagara River is the natural outlet of Lake Erie and drains four of the five Great lakes. The river is used to move commerce and is home to both sport fishing and tourism industries. It also provides nearly 5 million kilowatts of hydropower for approximately 3.9 million homes. Due to a complex international treaty and the necessity of balancing water needs for an extensive tourism industry, the power entities operating on the river require detailed and accurate short-term river flow forecasts to maximize power output. A new forecast system is being evaluated that takes advantage of several previously independent components including the NOAA Lake Erie operational Forecast System (LEOFS), a previously developed HEC-RAS model, input from the New York Power Authority(NYPA) and Ontario Power Generation (OPG) and lateral flow forecasts for some of the tributaries provided by the NOAA Northeast River Forecast Center (NERFC). The Corps of Engineers updated the HEC-RAS model of the upper Niagara River to use the output forcing from LEOFS and a planned Grass Island Pool elevation provided by the power entities. The entire system has been integrated at the NERFC; it will be run multiple times per day with results provided to the Niagara River Control Center operators. The new model helps improve discharge forecasts by better accounting for dynamic conditions on Lake Erie. LEOFS captures seiche events on the lake that are often several meters of displacement from still water level. These seiche events translate into flow spikes that HEC-RAS routes downstream. Knowledge of the peak arrival time helps improve operational decisions at the Grass Island Pool. This poster will compare and contrast results from the existing operational flow forecast and the new integrated LEOFS/HEC-RAS forecast. This additional model will supply the Niagara River Control Center operators with multiple forecasts of flow to help improve forecasting under a wider variety of conditions.

The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Wu, Hongyu; Florita, Anthony R.

The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was comparedmore » through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Lastly, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.« less

The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

DOE PAGES

Wang, Qin; Wu, Hongyu; Florita, Anthony R.; ...

2016-11-11

The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was comparedmore » through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Lastly, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.« less

Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoff, Thomas Hoff; Kankiewicz, Adam

Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California Independent System Operator’s (ISO) load forecasts by integrating behind-the-meter (BTM) PV forecasts. The three, new, state-of-the-art solar irradiance forecasting models included: the infrared (IR) satellite-based cloud motion vector (CMV) model; the WRF-SolarCA model and variants; and the Optimized Deep Machine Learning (ODML)-training model. The first two forecasting models targeted known weaknesses in current operational solar forecasts. They were benchmarked against existing operational numerical weather prediction (NWP)more » forecasts, visible satellite CMV forecasts, and measured PV plant power production. IR CMV, WRF-SolarCA, and ODML-training forecasting models all improved the forecast to a significant degree. Improvements varied depending on time of day, cloudiness index, and geographic location. The fourth objective was to demonstrate that the California ISO’s load forecasts could be improved by integrating BTM PV forecasts. This objective represented the project’s most exciting and applicable gains. Operational BTM forecasts consisting of 200,000+ individual rooftop PV forecasts were delivered into the California ISO’s real-time automated load forecasting (ALFS) environment. They were then evaluated side-by-side with operational load forecasts with no BTM-treatment. Overall, ALFS-BTM day-ahead (DA) forecasts performed better than baseline ALFS forecasts when compared to actual load data. Specifically, ALFS-BTM DA forecasts were observed to have the largest reduction of error during the afternoon on cloudy days. Shorter term 30 minute-ahead ALFS-BTM forecasts were shown to have less error under all sky conditions, especially during the morning time periods when traditional load forecasts often experience their largest uncertainties. This work culminated in a GO decision being made by the California ISO to include zonal BTM forecasts into its operational load forecasting system. The California ISO’s Manager of Short Term Forecasting, Jim Blatchford, summarized the research performed in this project with the following quote: “The behind-the-meter (BTM) California ISO region forecasting research performed by Clean Power Research and sponsored by the Department of Energy’s SUNRISE program was an opportunity to verify value and demonstrate improved load forecast capability. In 2016, the California ISO will be incorporating the BTM forecast into the Hour Ahead and Day Ahead load models to look for improvements in the overall load forecast accuracy as BTM PV capacity continues to grow.”« less

Is the economic value of hydrological forecasts related to their quality? Case study of the hydropower sector.

NASA Astrophysics Data System (ADS)

Cassagnole, Manon; Ramos, Maria-Helena; Thirel, Guillaume; Gailhard, Joël; Garçon, Rémy

2017-04-01

The improvement of a forecasting system and the evaluation of the quality of its forecasts are recurrent steps in operational practice. However, the evaluation of forecast value or forecast usefulness for better decision-making is, to our knowledge, less frequent, even if it might be essential in many sectors such as hydropower and flood warning. In the hydropower sector, forecast value can be quantified by the economic gain obtained with the optimization of operations or reservoir management rules. Several hydropower operational systems use medium-range forecasts (up to 7-10 days ahead) and energy price predictions to optimize hydropower production. Hence, the operation of hydropower systems, including the management of water in reservoirs, is impacted by weather, climate and hydrologic variability as well as extreme events. In order to assess how the quality of hydrometeorological forecasts impact operations, it is essential to first understand if and how operations and management rules are sensitive to input predictions of different quality. This study investigates how 7-day ahead deterministic and ensemble streamflow forecasts of different quality might impact the economic gains of energy production. It is based on a research model developed by Irstea and EDF to investigate issues relevant to the links between quality and value of forecasts in the optimisation of energy production at the short range. Based on streamflow forecasts and pre-defined management constraints, the model defines the best hours (i.e., the hours with high energy prices) to produce electricity. To highlight the link between forecasts quality and their economic value, we built several synthetic ensemble forecasts based on observed streamflow time series. These inputs are generated in a controlled environment in order to obtain forecasts of different quality in terms of accuracy and reliability. These forecasts are used to assess the sensitivity of the decision model to forecast quality. Relationships between forecast quality and economic value are discussed. This work is part of the IMPREX project, a research project supported by the European Commission under the Horizon 2020 Framework programme, with grant No. 641811 (http://www.imprex.eu)

From Research to Operations: Transitioning Noaa's Lake Erie Harmful Algal Bloom Forecast System

NASA Astrophysics Data System (ADS)

Kavanaugh, K. E.; Stumpf, R. P.

2016-02-01

A key priority of NOAA's Harmful Algal Bloom Operational Forecast System (HAB-OFS) is to leverage the Ecological Forecasting Roadmap to systematically transition to operations scientifically mature HAB forecasts in regions of the country where there is a strong user need identified and an operational framework can be supported. While in the demonstration phase, the Lake Erie HAB forecast has proven its utility. Over the next two years, NOAA will be transitioning the Lake Erie HAB forecast to operations with an initial operating capability established in the HAB OFS' operational infrastructure by the 2016 bloom season. Blooms of cyanobacteria are a recurring problem in Lake Erie, and the dominant bloom forming species, Microcystis aeruginosa, produces a toxin called microcystin that is poisonous to humans, livestock and pets. Once the toxins have contaminated the source water used for drinking water, it is costly for public water suppliers to remove them. As part of the Lake Erie HAB forecast demonstration, NOAA has provided information regarding the cyanobacterial blooms in a biweekly Experimental HAB Bulletin, which includes information about the current and forecasted distribution, toxicity, potential for vertical mixing or scum formation, mixing of the water column, and predictions of bloom decline. Coastal resource managers, public water suppliers and public health officials use the Experimental HAB Bulletins to respond to and mitigate the impacts of cyanobacterial blooms. The transition to operations will benefit stakeholders through ensuring that future Lake Erie HAB forecast products are sustained, systematic, reliable, and robust. Once operational, the forecasts will continue to be assessed and improvements will be made based on the results of emerging scientific research. In addition, the lessons learned from the Lake Erie transition will be used to streamline the process for future HAB forecasts presently in development.

Interactive Forecasting with the National Weather Service River Forecast System

NASA Technical Reports Server (NTRS)

Smith, George F.; Page, Donna

1993-01-01

The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.

Towards Optimal Operation of the Reservoir System in Upper Yellow River: Incorporating Long- and Short-term Operations and Using Rolling Updated Hydrologic Forecast Information

NASA Astrophysics Data System (ADS)

Si, Y.; Li, X.; Li, T.; Huang, Y.; Yin, D.

2016-12-01

The cascade reservoirs in Upper Yellow River (UYR), one of the largest hydropower bases in China, play a vital role in peak load and frequency regulation for Northwest China Power Grid. The joint operation of this system has been put forward for years whereas has not come into effect due to management difficulties and inflow uncertainties, and thus there is still considerable improvement room for hydropower production. This study presents a decision support framework incorporating long- and short-term operation of the reservoir system. For long-term operation, we maximize hydropower production of the reservoir system using historical hydrological data of multiple years, and derive operating rule curves for storage reservoirs. For short-term operation, we develop a program consisting of three modules, namely hydrologic forecast module, reservoir operation module and coordination module. The coordination module is responsible for calling the hydrologic forecast module to acquire predicted inflow within a short-term horizon, and transferring the information to the reservoir operation module to generate optimal release decision. With the hydrologic forecast information updated, the rolling short-term optimization is iterated until the end of operation period, where the long-term operating curves serve as the ending storage target. As an application, the Digital Yellow River Integrated Model (referred to as "DYRIM", which is specially designed for runoff-sediment simulation in the Yellow River basin by Tsinghua University) is used in the hydrologic forecast module, and the successive linear programming (SLP) in the reservoir operation module. The application in the reservoir system of UYR demonstrates that the framework can effectively support real-time decision making, and ensure both computational accuracy and speed. Furthermore, it is worth noting that the general framework can be extended to any other reservoir system with any or combination of hydrological model(s) to forecast and any solver to optimize the operation of reservoir system.

A report from the Space Science and Engineering Center, the University of Wisconsin-Madison, Madison, Wisconsin

NASA Technical Reports Server (NTRS)

1985-01-01

Operational forecasters have habitually been plagued with the problems associated with acquisition, display, and dissemination of data used in preparing forecasts. The centralized storm information system (CSIS) experiment provided an operational forecaster with an interactive computer system which could perform these preliminary tasks more quickly and accurately than any human could. CSIS objectives pertaining to improved severe storms forecasting and warning procedures are addressed.

Operational water management of Rijnland water system and pilot of ensemble forecasting system for flood control

NASA Astrophysics Data System (ADS)

van der Zwan, Rene

2013-04-01

The Rijnland water system is situated in the western part of the Netherlands, and is a low-lying area of which 90% is below sea-level. The area covers 1,100 square kilometres, where 1.3 million people live, work, travel and enjoy leisure. The District Water Control Board of Rijnland is responsible for flood defence, water quantity and quality management. This includes design and maintenance of flood defence structures, control of regulating structures for an adequate water level management, and waste water treatment. For water quantity management Rijnland uses, besides an online monitoring network for collecting water level and precipitation data, a real time control decision support system. This decision support system consists of deterministic hydro-meteorological forecasts with a 24-hr forecast horizon, coupled with a control module that provides optimal operation schedules for the storage basin pumping stations. The uncertainty of the rainfall forecast is not forwarded in the hydrological prediction. At this moment 65% of the pumping capacity of the storage basin pumping stations can be automatically controlled by the decision control system. Within 5 years, after renovation of two other pumping stations, the total capacity of 200 m3/s will be automatically controlled. In critical conditions there is a need of both a longer forecast horizon and a probabilistic forecast. Therefore ensemble precipitation forecasts of the ECMWF are already consulted off-line during dry-spells, and Rijnland is running a pilot operational system providing 10-day water level ensemble forecasts. The use of EPS during dry-spells and the findings of the pilot will be presented. Challenges and next steps towards on-line implementation of ensemble forecasts for risk-based operational management of the Rijnland water system will be discussed. An important element in that discussion is the question: will policy and decision makers, operator and citizens adapt this Anticipatory Water management, including temporary lower storage basin levels and a reduction in extra investments for infrastructural measures.

Ensemble Streamflow Forecast Improvements in NYC's Operations Support Tool

NASA Astrophysics Data System (ADS)

Wang, L.; Weiss, W. J.; Porter, J.; Schaake, J. C.; Day, G. N.; Sheer, D. P.

2013-12-01

Like most other water supply utilities, New York City's Department of Environmental Protection (DEP) has operational challenges associated with drought and wet weather events. During drought conditions, DEP must maintain water supply reliability to 9 million customers as well as meet environmental release requirements downstream of its reservoirs. During and after wet weather events, DEP must maintain turbidity compliance in its unfiltered Catskill and Delaware reservoir systems and minimize spills to mitigate downstream flooding. Proactive reservoir management - such as release restrictions to prepare for a drought or preventative drawdown in advance of a large storm - can alleviate negative impacts associated with extreme events. It is important for water managers to understand the risks associated with proactive operations so unintended consequences such as endangering water supply reliability with excessive drawdown prior to a storm event are minimized. Probabilistic hydrologic forecasts are a critical tool in quantifying these risks and allow water managers to make more informed operational decisions. DEP has recently completed development of an Operations Support Tool (OST) that integrates ensemble streamflow forecasts, real-time observations, and a reservoir system operations model into a user-friendly graphical interface that allows its water managers to take robust and defensible proactive measures in the face of challenging system conditions. Since initial development of OST was first presented at the 2011 AGU Fall Meeting, significant improvements have been made to the forecast system. First, the monthly AR1 forecasts ('Hirsch method') were upgraded with a generalized linear model (GLM) utilizing historical daily correlations ('Extended Hirsch method' or 'eHirsch'). The development of eHirsch forecasts improved predictive skill over the Hirsch method in the first week to a month from the forecast date and produced more realistic hydrographs on the tail end of high flow periods. These improvements allowed DEP to more effectively manage water quality control and spill mitigation operations immediately after storm events. Later on, post-processed hydrologic forecasts from the National Weather Service (NWS) including the Advanced Hydrologic Prediction Service (AHPS) and the Hydrologic Ensemble Forecast Service (HEFS) were implemented into OST. These forecasts further increased the predictive skill over the initial statistical models as current basin conditions (e.g. soil moisture, snowpack) and meteorological forecasts (with HEFS) are now explicitly represented. With the post-processed HEFS forecasts, DEP may now truly quantify impacts associated with wet weather events on the horizon, rather than relying on statistical representations of current hydrologic trends. This presentation will highlight the benefits of the improved forecasts using examples from actual system operations.

Navigating the "Research-to-Operations" Bridge of Death: Collaborative Transition of Remotely-Sensed Snow Data from Research into Operational Water Resources Forecasting

NASA Astrophysics Data System (ADS)

Miller, W. P.; Bender, S.; Painter, T. H.; Bernard, B.

2016-12-01

Water and resource management agencies can benefit from hydrologic forecasts during both flood and drought conditions. Improved predictions of seasonal snowmelt-driven runoff volume and timing can assist operational water managers with decision support and efficient resource management within the spring runoff season. Using operational models and forecasting systems, NOAA's Colorado Basin River Forecast Center (CBRFC) produces hydrologic forecasts for stakeholders and water management groups in the western United States. Collaborative incorporation of research-oriented remote sensing data into CBRFC operational models and systems is one route by which CBRFC forecasts can be improved, ultimately for the benefit of water managers. Successful navigation of research-oriented remote sensing products across the "research-to-operations"/R2O gap (also known as the "valley of death") to operational destinations requires dedicated personnel on both the research and operations sides, working in a highly collaborative environment. Since 2012, the operational CBRFC has collaborated with the research-oriented Jet Propulsion Laboratory (JPL) under funding from NASA to transition remotely-sensed snow data into CBRFC's operational models and forecasting systems. Two specific datasets from JPL, the MODIS Dust Radiative Forcing in Snow (MODDRFS) and the MODIS Snow Covered-Area and Grain size (MODSCAG) products, are used in CBRFC operations as of 2016. Over the past several years, JPL and CBRFC have worked together to analyze patterns in JPL's remote sensing snow datasets from the operational perspective of the CBRFC and to develop techniques to bridge the R2O gap. Retrospective and real-time analyses have yielded valuable insight into the remotely-sensed snow datasets themselves, CBRFC's operational systems, and the collaborative R2O process. Examples of research-oriented JPL snow data, as used in CBRFC operations, are described. A timeline of the collaboration, challenges encountered during the journey across the R2O gap, or "valley of death", and solutions to those challenges are also illustrated.

Short Term Load Forecasting with Fuzzy Logic Systems for power system planning and reliability-A Review

NASA Astrophysics Data System (ADS)

Holmukhe, R. M.; Dhumale, Mrs. Sunita; Chaudhari, Mr. P. S.; Kulkarni, Mr. P. P.

2010-10-01

Load forecasting is very essential to the operation of Electricity companies. It enhances the energy efficient and reliable operation of power system. Forecasting of load demand data forms an important component in planning generation schedules in a power system. The purpose of this paper is to identify issues and better method for load foecasting. In this paper we focus on fuzzy logic system based short term load forecasting. It serves as overview of the state of the art in the intelligent techniques employed for load forecasting in power system planning and reliability. Literature review has been conducted and fuzzy logic method has been summarized to highlight advantages and disadvantages of this technique. The proposed technique for implementing fuzzy logic based forecasting is by Identification of the specific day and by using maximum and minimum temperature for that day and finally listing the maximum temperature and peak load for that day. The results show that Load forecasting where there are considerable changes in temperature parameter is better dealt with Fuzzy Logic system method as compared to other short term forecasting techniques.

Progress and challenges with Warn-on-Forecast

NASA Astrophysics Data System (ADS)

Stensrud, David J.; Wicker, Louis J.; Xue, Ming; Dawson, Daniel T.; Yussouf, Nusrat; Wheatley, Dustan M.; Thompson, Therese E.; Snook, Nathan A.; Smith, Travis M.; Schenkman, Alexander D.; Potvin, Corey K.; Mansell, Edward R.; Lei, Ting; Kuhlman, Kristin M.; Jung, Youngsun; Jones, Thomas A.; Gao, Jidong; Coniglio, Michael C.; Brooks, Harold E.; Brewster, Keith A.

2013-04-01

The current status and challenges associated with two aspects of Warn-on-Forecast-a National Oceanic and Atmospheric Administration research project exploring the use of a convective-scale ensemble analysis and forecast system to support hazardous weather warning operations-are outlined. These two project aspects are the production of a rapidly-updating assimilation system to incorporate data from multiple radars into a single analysis, and the ability of short-range ensemble forecasts of hazardous convective weather events to provide guidance that could be used to extend warning lead times for tornadoes, hailstorms, damaging windstorms and flash floods. Results indicate that a three-dimensional variational assimilation system, that blends observations from multiple radars into a single analysis, shows utility when evaluated by forecasters in the Hazardous Weather Testbed and may help increase confidence in a warning decision. The ability of short-range convective-scale ensemble forecasts to provide guidance that could be used in warning operations is explored for five events: two tornadic supercell thunderstorms, a macroburst, a damaging windstorm and a flash flood. Results show that the ensemble forecasts of the three individual severe thunderstorm events are very good, while the forecasts from the damaging windstorm and flash flood events, associated with mesoscale convective systems, are mixed. Important interactions between mesoscale and convective-scale features occur for the mesoscale convective system events that strongly influence the quality of the convective-scale forecasts. The development of a successful Warn-on-Forecast system will take many years and require the collaborative efforts of researchers and operational forecasters to succeed.

Introduction on the operational storm surge forecasting system in Korea Operational Oceanographic System (KOOS)

NASA Astrophysics Data System (ADS)

Kwon, Jae-Il; Park, Kwang-Soon; Choi, Jung-Woon; Lee, Jong-Chan; Heo, Ki-Young; Kim, Sang-Ik

2017-04-01

During last more than 50 years, 258 typhoons passed and affected the Korean peninsula in terms of high winds, storm surges and extreme waves. In this study we explored the performance of the operational storm surge forecasting system in the Korea Operational Oceanographic System (KOOS) with 8 typhoons from 2010 to 2016. The operation storm surge forecasting system for the typhoon in KOOS is based on 2D depth averaged model with tides and CE (U.S. Army Corps of Engineers) wind model. Two key parameters of CE wind model, the locations of typhoon center and its central atmospheric pressure are based from Korea Meteorological administrative (KMA)'s typhoon information provided from 1 day to 3 hour intervals with the approach of typhoon through the KMA's web-site. For 8 typhoons cases, the overall errors, other performances and analysis such as peak time and surge duration are presented in each case. The most important factor in the storm surge errors in the operational forecasting system is the accuracy of typhoon passage prediction.

Current and future data assimilation development in the Copernicus Atmosphere Monitoring Service

NASA Astrophysics Data System (ADS)

Engelen, R. J.; Ades, M.; Agusti-panareda, A.; Flemming, J.; Inness, A.; Kipling, Z.; Parrington, M.; Peuch, V. H.

2017-12-01

The European Copernicus Atmosphere Monitoring Service (CAMS) operationally provides daily forecasts of global atmospheric composition and regional air quality. The global forecasting system is using ECMWF's Integrated Forecasting System (IFS), which is used for numerical weather prediction and which has been extended with modules for atmospheric chemistry, aerosols and greenhouse gases. The system assimilates observations from more than 60 satellite sensors to constrain both the meteorology and the atmospheric composition species. While an operational forecasting system needs to be robust and reliable, it also needs to stay state-of-the-art to provide the best possible forecasts. Continuous development is therefore an important component of the CAMS systems. We will present on-going efforts on improving the 4D-Var data assimilation system, such as using ensemble data assimilation to improve the background error covariances and more accurate use of satellite observations. We will also outline plans for including emissions in the daily CAMS analyses, which is an area where research activities have a large potential to feed into operational applications.

Building the Sun4Cast System: Improvements in Solar Power Forecasting

DOE PAGES

Haupt, Sue Ellen; Kosovic, Branko; Jensen, Tara; ...

2017-06-16

The Sun4Cast System results from a research-to-operations project built on a value chain approach, and benefiting electric utilities’ customers, society, and the environment by improving state-of-the-science solar power forecasting capabilities. As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers.more » The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, the basis of the system beyond about 6 h. For short-range (0-6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short to mid-term irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach, and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. As a result, this paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.« less

Building the Sun4Cast System: Improvements in Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haupt, Sue Ellen; Kosovic, Branko; Jensen, Tara

The Sun4Cast System results from a research-to-operations project built on a value chain approach, and benefiting electric utilities’ customers, society, and the environment by improving state-of-the-science solar power forecasting capabilities. As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers.more » The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, the basis of the system beyond about 6 h. For short-range (0-6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short to mid-term irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach, and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. As a result, this paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.« less

Operational Forecasting and Warning systems for Coastal hazards in Korea

NASA Astrophysics Data System (ADS)

Park, Kwang-Soon; Kwon, Jae-Il; Kim, Jin-Ah; Heo, Ki-Young; Jun, Kicheon

2017-04-01

Coastal hazards caused by both Mother Nature and humans cost tremendous social, economic and environmental damages. To mitigate these damages many countries have been running the operational forecasting or warning systems. Since 2009 Korea Operational Oceanographic System (KOOS) has been developed by the leading of Korea Institute of Ocean Science and Technology (KIOST) in Korea and KOOS has been operated in 2012. KOOS is consists of several operational modules of numerical models and real-time observations and produces the basic forecasting variables such as winds, tides, waves, currents, temperature and salinity and so on. In practical application systems include storm surges, oil spills, and search and rescue prediction models. In particular, abnormal high waves (swell-like high-height waves) have occurred in the East coast of Korea peninsula during winter season owing to the local meteorological condition over the East Sea, causing property damages and the loss of human lives. In order to improve wave forecast accuracy even very local wave characteristics, numerical wave modeling system using SWAN is established with data assimilation module using 4D-EnKF and sensitivity test has been conducted. During the typhoon period for the prediction of sever waves and the decision making support system for evacuation of the ships, a high-resolution wave forecasting system has been established and calibrated.

Multi-platform operational validation of the Western Mediterranean SOCIB forecasting system

NASA Astrophysics Data System (ADS)

Juza, Mélanie; Mourre, Baptiste; Renault, Lionel; Tintoré, Joaquin

2014-05-01

The development of science-based ocean forecasting systems at global, regional, and local scales can support a better management of the marine environment (maritime security, environmental and resources protection, maritime and commercial operations, tourism, ...). In this context, SOCIB (the Balearic Islands Coastal Observing and Forecasting System, www.socib.es) has developed an operational ocean forecasting system in the Western Mediterranean Sea (WMOP). WMOP uses a regional configuration of the Regional Ocean Modelling System (ROMS, Shchepetkin and McWilliams, 2005) nested in the larger scale Mediterranean Forecasting System (MFS) with a spatial resolution of 1.5-2km. WMOP aims at reproducing both the basin-scale ocean circulation and the mesoscale variability which is known to play a crucial role due to its strong interaction with the large scale circulation in this region. An operational validation system has been developed to systematically assess the model outputs at daily, monthly and seasonal time scales. Multi-platform observations are used for this validation, including satellite products (Sea Surface Temperature, Sea Level Anomaly), in situ measurements (from gliders, Argo floats, drifters and fixed moorings) and High-Frequency radar data. The validation procedures allow to monitor and certify the general realism of the daily production of the ocean forecasting system before its distribution to users. Additionally, different indicators (Sea Surface Temperature and Salinity, Eddy Kinetic Energy, Mixed Layer Depth, Heat Content, transports in key sections) are computed every day both at the basin-scale and in several sub-regions (Alboran Sea, Balearic Sea, Gulf of Lion). The daily forecasts, validation diagnostics and indicators from the operational model over the last months are available at www.socib.es.

AN OPERATIONAL EVALUATION OF THE ETA-CMAQ AIR QUALITY FORECAST MODEL

EPA Science Inventory

The National Oceanic and Atmospheric Administration (NOAA), in collaboration with the Environmental Protection Agency (EPA), are developing an Air Quality Forecasting Program that will eventually result in an operational Nationwide Air Quality Forecasting System. The initial pha...

Ensemble Flow Forecasts for Risk Based Reservoir Operations of Lake Mendocino in Mendocino County, California: A Framework for Objectively Leveraging Weather and Climate Forecasts in a Decision Support Environment

NASA Astrophysics Data System (ADS)

Delaney, C.; Hartman, R. K.; Mendoza, J.; Whitin, B.

2017-12-01

Forecast informed reservoir operations (FIRO) is a methodology that incorporates short to mid-range precipitation and flow forecasts to inform the flood operations of reservoirs. The Ensemble Forecast Operations (EFO) alternative is a probabilistic approach of FIRO that incorporates ensemble streamflow predictions (ESPs) made by NOAA's California-Nevada River Forecast Center (CNRFC). With the EFO approach, release decisions are made to manage forecasted risk of reaching critical operational thresholds. A water management model was developed for Lake Mendocino, a 111,000 acre-foot reservoir located near Ukiah, California, to evaluate the viability of the EFO alternative to improve water supply reliability but not increase downstream flood risk. Lake Mendocino is a dual use reservoir, which is owned and operated for flood control by the United States Army Corps of Engineers and is operated for water supply by the Sonoma County Water Agency. Due to recent changes in the operations of an upstream hydroelectric facility, this reservoir has suffered from water supply reliability issues since 2007. The EFO alternative was simulated using a 26-year (1985-2010) ESP hindcast generated by the CNRFC. The ESP hindcast was developed using Global Ensemble Forecast System version 10 precipitation reforecasts processed with the Hydrologic Ensemble Forecast System to generate daily reforecasts of 61 flow ensemble members for a 15-day forecast horizon. Model simulation results demonstrate that the EFO alternative may improve water supply reliability for Lake Mendocino yet not increase flood risk for downstream areas. The developed operations framework can directly leverage improved skill in the second week of the forecast and is extendable into the S2S time domain given the demonstration of improved skill through a reliable reforecast of adequate historical duration and consistent with operationally available numerical weather predictions.

Decision Support on the Sediments Flushing of Aimorés Dam Using Medium-Range Ensemble Forecasts

NASA Astrophysics Data System (ADS)

Mainardi Fan, Fernando; Schwanenberg, Dirk; Collischonn, Walter; Assis dos Reis, Alberto; Alvarado Montero, Rodolfo; Alencar Siqueira, Vinicius

2015-04-01

In the present study we investigate the use of medium-range streamflow forecasts in the Doce River basin (Brazil), at the reservoir of Aimorés Hydro Power Plant (HPP). During daily operations this reservoir acts as a "trap" to the sediments that originate from the upstream basin of the Doce River. This motivates a cleaning process called "pass through" to periodically remove the sediments from the reservoir. The "pass through" or "sediments flushing" process consists of a decrease of the reservoir's water level to a certain flushing level when a determined reservoir inflow threshold is forecasted. Then, the water in the approaching inflow is used to flush the sediments from the reservoir through the spillway and to recover the original reservoir storage. To be triggered, the sediments flushing operation requires an inflow larger than 3000m³/s in a forecast horizon of 7 days. This lead-time of 7 days is far beyond the basin's concentration time (around 2 days), meaning that the forecasts for the pass through procedure highly depends on Numerical Weather Predictions (NWP) models that generate Quantitative Precipitation Forecasts (QPF). This dependency creates an environment with a high amount of uncertainty to the operator. To support the decision making at Aimorés HPP we developed a fully operational hydrological forecasting system to the basin. The system is capable of generating ensemble streamflow forecasts scenarios when driven by QPF data from meteorological Ensemble Prediction Systems (EPS). This approach allows accounting for uncertainties in the NWP at a decision making level. This system is starting to be used operationally by CEMIG and is the one shown in the present study, including a hindcasting analysis to assess the performance of the system for the specific flushing problem. The QPF data used in the hindcasting study was derived from the TIGGE (THORPEX Interactive Grand Global Ensemble) database. Among all EPS available on TIGGE, three were selected: ECMWF, GEFS, and CPTEC. As a deterministic reference forecast, we adopt the high resolution ECMWF forecast for comparison. The experiment consisted on running retrospective forecasts for a full five-year period. To verify the proposed objectives of the study, we use different metrics to evaluate the forecast: ROC Curves, Exceedance Diagrams, Forecast Convergence Score (FCS). Metrics results enabled to understand the benefits of the hydrological ensemble prediction system as a decision making tool for the HPP operation. The ROC scores indicate that the use of the lower percentiles of the ensemble scenarios issues for a true alarm rate around 0,5 to 0,8 (depending on the model and on the percentile), for the lead time of seven days. While the false alarm rate is between 0 and 0,3. Those rates were better than the ones resulting from the deterministic reference forecast. Exceedance diagrams and forecast convergence scores indicate that the ensemble scenarios provide an early signal about the threshold crossing. Furthermore, the ensemble forecasts are more consistent between two subsequent forecasts in comparison to the deterministic forecast. The assessments results also give more credibility to CEMIG in the realization and communication of flushing operation with the stakeholders involved.

A Diagnostics Tool to detect ensemble forecast system anomaly and guide operational decisions

NASA Astrophysics Data System (ADS)

Park, G. H.; Srivastava, A.; Shrestha, E.; Thiemann, M.; Day, G. N.; Draijer, S.

2017-12-01

The hydrologic community is moving toward using ensemble forecasts to take uncertainty into account during the decision-making process. The New York City Department of Environmental Protection (DEP) implements several types of ensemble forecasts in their decision-making process: ensemble products for a statistical model (Hirsch and enhanced Hirsch); the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) forecasts based on the classical Ensemble Streamflow Prediction (ESP) technique; and the new NWS Hydrologic Ensemble Forecasting Service (HEFS) forecasts. To remove structural error and apply the forecasts to additional forecast points, the DEP post processes both the AHPS and the HEFS forecasts. These ensemble forecasts provide mass quantities of complex data, and drawing conclusions from these forecasts is time-consuming and difficult. The complexity of these forecasts also makes it difficult to identify system failures resulting from poor data, missing forecasts, and server breakdowns. To address these issues, we developed a diagnostic tool that summarizes ensemble forecasts and provides additional information such as historical forecast statistics, forecast skill, and model forcing statistics. This additional information highlights the key information that enables operators to evaluate the forecast in real-time, dynamically interact with the data, and review additional statistics, if needed, to make better decisions. We used Bokeh, a Python interactive visualization library, and a multi-database management system to create this interactive tool. This tool compiles and stores data into HTML pages that allows operators to readily analyze the data with built-in user interaction features. This paper will present a brief description of the ensemble forecasts, forecast verification results, and the intended applications for the diagnostic tool.

Advancing Data Assimilation in Operational Hydrologic Forecasting: Progresses, Challenges, and Emerging Opportunities

NASA Technical Reports Server (NTRS)

Liu, Yuqiong; Weerts, A.; Clark, M.; Hendricks Franssen, H.-J; Kumar, S.; Moradkhani, H.; Seo, D.-J.; Schwanenberg, D.; Smith, P.; van Dijk, A. I. J. M.;

A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamann, Hendrik F.

The goal of the project was the development and demonstration of a significantly improved solar forecasting technology (short: Watt-sun), which leverages new big data processing technologies and machine-learnt blending between different models and forecast systems. The technology aimed demonstrating major advances in accuracy as measured by existing and new metrics which themselves were developed as part of this project. Finally, the team worked with Independent System Operators (ISOs) and utilities to integrate the forecasts into their operations.

First Assessment of Itaipu Dam Ensemble Inflow Forecasting System

NASA Astrophysics Data System (ADS)

Mainardi Fan, Fernando; Machado Vieira Lisboa, Auder; Gomes Villa Trinidad, Giovanni; Rógenes Monteiro Pontes, Paulo; Collischonn, Walter; Tucci, Carlos; Costa Buarque, Diogo

2017-04-01

Inflow forecasting for Hydropower Plants (HPP) Dams is one of the prominent uses for hydrological forecasts. A very important HPP in terms of energy generation for South America is the Itaipu Dam, located in the Paraná River, between Brazil and Paraguay countries, with a drainage area of 820.000km2. In this work, we present the development of an ensemble forecasting system for Itaipu, operational since November 2015. The system is based in the MGB-IPH hydrological model, includes hydrodynamics simulations of the main river, and is run every day morning forced by seven different rainfall forecasts: (i) CPTEC-ETA 15km; (ii) CPTEC-BRAMS 5km; (iii) SIMEPAR WRF Ferrier; (iv) SIMEPAR WRF Lin; (v) SIMEPAR WRF Morrison; (vi) SIMEPAR WRF WDM6; (vii) SIMEPAR MEDIAN. The last one (vii) corresponds to the median value of SIMEPAR WRF model versions (iii to vi) rainfall forecasts. Besides the developed system, the "traditional" method for inflow forecasting generation for the Itaipu Dam is also run every day. This traditional method consists in the approximation of the future inflow based on the discharge tendency of upstream telemetric gauges. Nowadays, after all the forecasts are run, the hydrology team of Itaipu develop a consensus forecast, based on all obtained results, which is the one used for the Itaipu HPP Dam operation. After one year of operation a first evaluation of the Ensemble Forecasting System was conducted. Results show that the system performs satisfactory for rising flows up to five days lead time. However, some false alarms were also issued by most ensemble members in some cases. And not in all cases the system performed better than the traditional method, especially during hydrograph recessions. In terms of meteorological forecasts, some members usage are being discontinued. In terms of the hydrodynamics representation, it seems that a better information of rivers cross section could improve hydrographs recession curves forecasts. Those opportunities for improvements are currently being addressed in the system next update.

Traffic flow forecasting for intelligent transportation systems.

DOT National Transportation Integrated Search

1995-01-01

The capability to forecast traffic volume in an operational setting has been identified as a critical need for intelligent transportation systems (ITS). In particular, traffic volume forecasts will directly support proactive traffic control and accur...

Experimental Forecasts of Wildfire Pollution at the Canadian Meteorological Centre

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Beaulieu, Paul-Andre; Chen, Jack; Landry, Hugo; Cousineau, Sophie; Moran, Michael

2016-04-01

Environment and Climate Change Canada's Canadian Meteorological Centre Operations division (CMCO) has been running an experimental North American air quality forecast system with near-real-time wildfire emissions since 2014. This system, named FireWork, also takes anthropogenic and other natural emission sources into account. FireWork 48-hour forecasts are provided to CMCO forecasters and external partners in Canada and the U.S. twice daily during the wildfire season. This system has proven to be very useful in capturing short- and long-range smoke transport from wildfires over North America. Several upgrades to the FireWork system have been made since 2014 to accommodate the needs of operational AQ forecasters and to improve system performance. In this talk we will present performance statistics and some case studies for the 2014 and 2015 wildfire seasons. We will also describe current limitations of the FireWork system and ongoing and future work planned for this air quality forecast system.

An evaluation of the real-time tropical cyclone forecast skill of the Navy Operational Global Atmospheric Prediction System in the western North Pacific

NASA Technical Reports Server (NTRS)

Fiorino, Michael; Goerss, James S.; Jensen, Jack J.; Harrison, Edward J., Jr.

1993-01-01

The paper evaluates the meteorological quality and operational utility of the Navy Operational Global Atmospheric Prediction System (NOGAPS) in forecasting tropical cyclones. It is shown that the model can provide useful predictions of motion and formation on a real-time basis in the western North Pacific. The meterological characteristics of the NOGAPS tropical cyclone predictions are evaluated by examining the formation of low-level cyclone systems in the tropics and vortex structure in the NOGAPS analysis and verifying 72-h forecasts. The adjusted NOGAPS track forecasts showed equitable skill to the baseline aid and the dynamical model. NOGAPS successfully predicted unusual equatorward turns for several straight-running cyclones.

Evaluation of Ensemble Water Supply and Demands Forecasts for Water Management in the Klamath River Basin

NASA Astrophysics Data System (ADS)

Broman, D.; Gangopadhyay, S.; McGuire, M.; Wood, A.; Leady, Z.; Tansey, M. K.; Nelson, K.; Dahm, K.

2017-12-01

The Upper Klamath River Basin in south central Oregon and north central California is home to the Klamath Irrigation Project, which is operated by the Bureau of Reclamation and provides water to around 200,000 acres of agricultural lands. The project is managed in consideration of not only water deliveries to irrigators, but also wildlife refuge water demands, biological opinion requirements for Endangered Species Act (ESA) listed fish, and Tribal Trust responsibilities. Climate change has the potential to impact water management in terms of volume and timing of water and the ability to meet multiple objectives. Current operations use a spreadsheet-based decision support tool, with water supply forecasts from the National Resources Conservation Service (NRCS) and California-Nevada River Forecast Center (CNRFC). This tool is currently limited in its ability to incorporate in ensemble forecasts, which offer the potential for improved operations by quantifying forecast uncertainty. To address these limitations, this study has worked to develop a RiverWare based water resource systems model, flexible enough to use across multiple decision time-scales, from short-term operations out to long-range planning. Systems model development has been accompanied by operational system development to handle data management and multiple modeling components. Using a set of ensemble hindcasts, this study seeks to answer several questions: A) Do a new set of ensemble streamflow forecasts have additional skill beyond what?, and allow for improved decision making under changing conditions? B) Do net irrigation water requirement forecasts developed in this project to quantify agricultural demands and reservoir evaporation forecasts provide additional benefits to decision making beyond water supply forecasts? C) What benefit do ensemble forecasts have in the context of water management decisions?

An operational global ocean forecast system and its applications

NASA Astrophysics Data System (ADS)

Mehra, A.; Tolman, H. L.; Rivin, I.; Rajan, B.; Spindler, T.; Garraffo, Z. D.; Kim, H.

2012-12-01

A global Real-Time Ocean Forecast System (RTOFS) was implemented in operations at NCEP/NWS/NOAA on 10/25/2011. This system is based on an eddy resolving 1/12 degree global HYCOM (HYbrid Coordinates Ocean Model) and is part of a larger national backbone capability of ocean modeling at NWS in strong partnership with US Navy. The forecast system is run once a day and produces a 6 day long forecast using the daily initialization fields produced at NAVOCEANO using NCODA (Navy Coupled Ocean Data Assimilation), a 3D multi-variate data assimilation methodology. As configured within RTOFS, HYCOM has a horizontal equatorial resolution of 0.08 degrees or ~9 km. The HYCOM grid is on a Mercator projection from 78.64 S to 47 N and north of this it employs an Arctic dipole patch where the poles are shifted over land to avoid a singularity at the North Pole. This gives a mid-latitude (polar) horizontal resolution of approximately 7 km (3.5 km). The coastline is fixed at 10 m isobath with open Bering Straits. This version employs 32 hybrid vertical coordinate surfaces with potential density referenced to 2000 m. Vertical coordinates can be isopycnals, often best for resolving deep water masses, levels of equal pressure (fixed depths), best for the well mixed unstratified upper ocean and sigma-levels (terrain-following), often the best choice in shallow water. The dynamic ocean model is coupled to a thermodynamic energy loan ice model and uses a non-slab mixed layer formulation. The forecast system is forced with 3-hourly momentum, radiation and precipitation fluxes from the operational Global Forecast System (GFS) fields. Results include global sea surface height and three dimensional fields of temperature, salinity, density and velocity fields used for validation and evaluation against available observations. Several downstream applications of this forecast system will also be discussed which include search and rescue operations at US Coast Guard, navigation safety information provided by OPC using real time ocean model guidance from Global RTOFS surface ocean currents, operational guidance on radionuclide dispersion near Fukushima using 3D tracers, boundary conditions for various operational coastal ocean forecast systems (COFS) run by NOS etc.

The Copernicus Atmosphere Monitoring Service: facilitating the prediction of air quality from global to local scales

NASA Astrophysics Data System (ADS)

Engelen, R. J.; Peuch, V. H.

2017-12-01

The European Copernicus Atmosphere Monitoring Service (CAMS) operationally provides daily forecasts of global atmospheric composition and regional air quality. The global forecasting system is using ECMWF's Integrated Forecasting System (IFS), which is used for numerical weather prediction and which has been extended with modules for atmospheric chemistry, aerosols and greenhouse gases. The regional forecasts are produced by an ensemble of seven operational European air quality models that take their boundary conditions from the global system and provide an ensemble median with ensemble spread as their main output. Both the global and regional forecasting systems are feeding their output into air quality models on a variety of scales in various parts of the world. We will introduce the CAMS service chain and provide illustrations of its use in downstream applications. Both the usage of the daily forecasts and the usage of global and regional reanalyses will be addressed.

Uncertainty quantification and reliability assessment in operational oil spill forecast modeling system.

PubMed

Hou, Xianlong; Hodges, Ben R; Feng, Dongyu; Liu, Qixiao

2017-03-15

As oil transport increasing in the Texas bays, greater risks of ship collisions will become a challenge, yielding oil spill accidents as a consequence. To minimize the ecological damage and optimize rapid response, emergency managers need to be informed with how fast and where oil will spread as soon as possible after a spill. The state-of-the-art operational oil spill forecast modeling system improves the oil spill response into a new stage. However uncertainty due to predicted data inputs often elicits compromise on the reliability of the forecast result, leading to misdirection in contingency planning. Thus understanding the forecast uncertainty and reliability become significant. In this paper, Monte Carlo simulation is implemented to provide parameters to generate forecast probability maps. The oil spill forecast uncertainty is thus quantified by comparing the forecast probability map and the associated hindcast simulation. A HyosPy-based simple statistic model is developed to assess the reliability of an oil spill forecast in term of belief degree. The technologies developed in this study create a prototype for uncertainty and reliability analysis in numerical oil spill forecast modeling system, providing emergency managers to improve the capability of real time operational oil spill response and impact assessment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Risky Business: Development, Communication and Use of Hydroclimatic Forecasts

NASA Astrophysics Data System (ADS)

Lall, U.

2012-12-01

Inter-seasonal and longer hydroclimatic forecasts have been made increasingly in the last two decades following the increase in ENSO activity since the early 1980s and the success in seasonal ENSO forecasting. Yet, the number of examples of systematic use of these forecasts and their incorporation into water systems operation continue to be few. This may be due in part to the limited skill in such forecasts over much of the world, but is also likely due to the limited evolution of methods and opportunities to "safely" use uncertain forecasts. There has been a trend to rely more on "physically based" rather than "physically informed" empirical forecasts, and this may in part explain the limited success in developing usable products in more locations. Given the limited skill, forecasters have tended to "dumb" down their forecasts - either formally or subjectively shrinking the forecasts towards climatology, or reducing them to tercile forecasts that serve to obscure the potential information in the forecast. Consequently, the potential utility of such forecasts for decision making is compromised. Water system operating rules are often designed to be robust in the face of historical climate variability, and consequently are adapted to the potential conditions that a forecast seeks to inform. In such situations, there is understandable reluctance by managers to use the forecasts as presented, except in special cases where an alternate course of action is pragmatically appealing in any case. In this talk, I review opportunities to present targeted forecasts for use with decision systems that directly address climate risk and the risk induced by unbiased yet uncertain forecasts, focusing especially on extreme events and water allocation in a competitive environment. Examples from Brazil and India covering surface and ground water conjunctive use strategies that could potentially be insured and lead to improvements over the traditional system operation and resource allocation are provided.

Pathways to designing and running an operational flood forecasting system: an adventure game!

NASA Astrophysics Data System (ADS)

Arnal, Louise; Pappenberger, Florian; Ramos, Maria-Helena; Cloke, Hannah; Crochemore, Louise; Giuliani, Matteo; Aalbers, Emma

2017-04-01

In the design and building of an operational flood forecasting system, a large number of decisions have to be taken. These include technical decisions related to the choice of the meteorological forecasts to be used as input to the hydrological model, the choice of the hydrological model itself (its structure and parameters), the selection of a data assimilation procedure to run in real-time, the use (or not) of a post-processor, and the computing environment to run the models and display the outputs. Additionally, a number of trans-disciplinary decisions are also involved in the process, such as the way the needs of the users will be considered in the modelling setup and how the forecasts (and their quality) will be efficiently communicated to ensure usefulness and build confidence in the forecasting system. We propose to reflect on the numerous, alternative pathways to designing and running an operational flood forecasting system through an adventure game. In this game, the player is the protagonist of an interactive story driven by challenges, exploration and problem-solving. For this presentation, you will have a chance to play this game, acting as the leader of a forecasting team at an operational centre. Your role is to manage the actions of your team and make sequential decisions that impact the design and running of the system in preparation to and during a flood event, and that deal with the consequences of the forecasts issued. Your actions are evaluated by how much they cost you in time, money and credibility. Your aim is to take decisions that will ultimately lead to a good balance between time and money spent, while keeping your credibility high over the whole process. This game was designed to highlight the complexities behind decision-making in an operational forecasting and emergency response context, in terms of the variety of pathways that can be selected as well as the timescale, cost and timing of effective actions.

Development and Use of the Hydrologic Ensemble Forecast System by the National Weather Service to Support the New York City Water Supply

NASA Astrophysics Data System (ADS)

Shedd, R.; Reed, S. M.; Porter, J. H.

2015-12-01

The National Weather Service (NWS) has been working for several years on the development of the Hydrologic Ensemble Forecast System (HEFS). The objective of HEFS is to provide ensemble river forecasts incorporating the best precipitation and temperature forcings at any specific time horizon. For the current implementation, this includes the Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFSv2). One of the core partners that has been working with the NWS since the beginning of the development phase of HEFS is the New York City Department of Environmental Protection (NYCDEP) which is responsible for the complex water supply system for New York City. The water supply system involves a network of reservoirs in both the Delaware and Hudson River basins. At the same time that the NWS was developing HEFS, NYCDEP was working on enhancing the operations of their water supply reservoirs through the development of a new Operations Support Tool (OST). OST is designed to guide reservoir system operations to ensure an adequate supply of high-quality drinking water for the city, as well as to meet secondary objectives for reaches downstream of the reservoirs assuming the primary water supply goals can be met. These secondary objectives include fisheries and ecosystem support, enhanced peak flow attenuation beyond that provided natively by the reservoirs, salt front management, and water supply for other cities. Since January 2014, the NWS Northeast and Middle Atlantic River Forecast Centers have provided daily one year forecasts from HEFS to NYCDEP. OST ingests these forecasts, couples them with near-real-time environmental and reservoir system data, and drives models of the water supply system. The input of ensemble forecasts results in an ensemble of model output, from which information on the range and likelihood of possible future system states can be extracted. This type of probabilistic information provides system managers with additional information not available from deterministic forecasts and allows managers to better assess risk, and provides greater context for decision-making than has been available in the past. HEFS has allowed NYCDEP water supply managers to make better decisions on reservoir operations than they likely would have in the past, using only deterministic forecasts.

Assessing the viability of `over-the-loop' real-time short-to-medium range ensemble streamflow forecasts

NASA Astrophysics Data System (ADS)

Wood, A. W.; Clark, E.; Mendoza, P. A.; Nijssen, B.; Newman, A. J.; Clark, M. P.; Arnold, J.; Nowak, K. C.

2016-12-01

Many if not most national operational short-to-medium range streamflow prediction systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow are automated, but others require the hands-on-effort of an experienced human forecaster. This approach evolved out of the need to correct for deficiencies in the models and datasets that were available for forecasting, and often leads to skillful predictions despite the use of relatively simple, conceptual models. On the other hand, the process is not reproducible, which limits opportunities to assess and incorporate process variations, and the effort required to make forecasts in this way is an obstacle to expanding forecast services - e.g., though adding new forecast locations or more frequent forecast updates, running more complex models, or producing forecast ensembles and hindcasts that can support verification. In the last decade, the hydrologic forecasting community has begun to develop more centralized, `over-the-loop' systems. The quality of these new forecast products will depend on their ability to leverage research in areas including earth system modeling, parameter estimation, data assimilation, statistical post-processing, weather and climate prediction, verification, and uncertainty estimation through the use of ensembles. Currently, the operational streamflow forecasting and water management communities have little experience with the strengths and weaknesses of over-the-loop approaches, even as the systems are being rolled out in major operational forecasting centers. There is thus a need both to evaluate these forecasting advances and to demonstrate their potential in a public arena, raising awareness in forecast user communities and development programs alike. To address this need, the National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the US Army Corps of Engineers, using the NCAR 'System for Hydromet Analysis, Research, and Prediction' (SHARP) to implement, assess and demonstrate real-time over-the-loop forecasts. We present early hindcast and verification results from SHARP for short to medium range streamflow forecasts in a number of US case study watersheds.

Development of a model-based flood emergency management system in Yujiang River Basin, South China

NASA Astrophysics Data System (ADS)

Zeng, Yong; Cai, Yanpeng; Jia, Peng; Mao, Jiansu

2014-06-01

Flooding is the most frequent disaster in China. It affects people's lives and properties, causing considerable economic loss. Flood forecast and operation of reservoirs are important in flood emergency management. Although great progress has been achieved in flood forecast and reservoir operation through using computer, network technology, and geographic information system technology in China, the prediction accuracy of models are not satisfactory due to the unavailability of real-time monitoring data. Also, real-time flood control scenario analysis is not effective in many regions and can seldom provide online decision support function. In this research, a decision support system for real-time flood forecasting in Yujiang River Basin, South China (DSS-YRB) is introduced in this paper. This system is based on hydrological and hydraulic mathematical models. The conceptual framework and detailed components of the proposed DSS-YRB is illustrated, which employs real-time rainfall data conversion, model-driven hydrologic forecasting, model calibration, data assimilation methods, and reservoir operational scenario analysis. Multi-tiered architecture offers great flexibility, portability, reusability, and reliability. The applied case study results show the development and application of a decision support system for real-time flood forecasting and operation is beneficial for flood control.

Canadian Operational Air Quality Forecasting Systems: Status, Recent Progress, and Challenges

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Davignon, Didier; Ménard, Sylvain; Munoz-Alpizar, Rodrigo; Landry, Hugo; Beaulieu, Paul-André; Gilbert, Samuel; Moran, Michael; Chen, Jack

2017-04-01

ECCC's Canadian Meteorological Centre Operations (CMCO) division runs a number of operational air quality (AQ)-related systems that revolve around the Regional Air Quality Deterministic Prediction System (RAQDPS). The RAQDPS generates 48-hour AQ forecasts and outputs hourly concentration fields of O3, PM2.5, NO2, and other pollutants twice daily on a North-American domain with 10-km horizontal grid spacing and 80 vertical levels. A closely related AQ forecast system with near-real-time wildfire emissions, known as FireWork, has been run by CMCO during the Canadian wildfire season (April to October) since 2014. This system became operational in June 2016. The CMCO`s operational AQ forecast systems also benefit from several support systems, such as a statistical post-processing model called UMOS-AQ that is applied to enhance forecast reliability at point locations with AQ monitors. The Regional Deterministic Air Quality Analysis (RDAQA) system has also been connected to the RAQDPS since February 2013, and hourly surface objective analyses are now available for O3, PM2.5, NO2, PM10, SO2 and, indirectly, the Canadian Air Quality Health Index. As of June 2015, another version of the RDAQA has been connected to FireWork (RDAQA-FW). For verification purposes, CMCO developed a third support system called Verification for Air QUality Models (VAQUM), which has a geospatial relational database core and which enables continuous monitoring of the AQ forecast systems' performance. Urban environments are particularly subject to AQ pollution. In order to improve the services offered, ECCC has recently been investing efforts to develop a high resolution air quality prediction capability for urban areas in Canada. In this presentation, a comprehensive description of the ECCC AQ systems will be provided, along with a discussion on AQ systems performance. Recent improvements, current challenges, and future directions of the Canadian operational AQ program will also be discussed.

The state of the art of flood forecasting - Hydrological Ensemble Prediction Systems

NASA Astrophysics Data System (ADS)

Thielen-Del Pozo, J.; Pappenberger, F.; Salamon, P.; Bogner, K.; Burek, P.; de Roo, A.

2010-09-01

Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Because of the specific characteristics of each catchment, varying data availability and end-user demands, the design of the best flood forecasting system may differ from catchment to catchment. However, despite the differences in concept and data needs, there is one underlying issue that spans across all systems. There has been an growing awareness and acceptance that uncertainty is a fundamental issue of flood forecasting and needs to be dealt with at the different spatial and temporal scales as well as the different stages of the flood generating processes. Today, operational flood forecasting centres change increasingly from single deterministic forecasts to probabilistic forecasts with various representations of the different contributions of uncertainty. The move towards these so-called Hydrological Ensemble Prediction Systems (HEPS) in flood forecasting represents the state of the art in forecasting science, following on the success of the use of ensembles for weather forecasting (Buizza et al., 2005) and paralleling the move towards ensemble forecasting in other related disciplines such as climate change predictions. The use of HEPS has been internationally fostered by initiatives such as "The Hydrologic Ensemble Prediction Experiment" (HEPEX), created with the aim to investigate how best to produce, communicate and use hydrologic ensemble forecasts in hydrological short-, medium- und long term prediction of hydrological processes. The advantages of quantifying the different contributions of uncertainty as well as the overall uncertainty to obtain reliable and useful flood forecasts also for extreme events, has become evident. However, despite the demonstrated advantages, worldwide the incorporation of HEPS in operational flood forecasting is still limited. The applicability of HEPS for smaller river basins was tested in MAP D-Phase, an acronym for "Demonstration of Probabilistic Hydrological and Atmospheric Simulation of flood Events in the Alpine region" which was launched in 2005 as a Forecast Demonstration Project of World Weather Research Programme of WMO, and entered a pre-operational and still active testing phase in 2007. In Europe, a comparatively high number of EPS driven systems for medium-large rivers exist. National flood forecasting centres of Sweden, Finland and the Netherlands, have already implemented HEPS in their operational forecasting chain, while in other countries including France, Germany, Czech Republic and Hungary, hybrids or experimental chains have been installed. As an example of HEPS, the European Flood Alert System (EFAS) is being presented. EFAS provides medium-range probabilistic flood forecasting information for large trans-national river basins. It incorporates multiple sets of weather forecast including different types of EPS and deterministic forecasts from different providers. EFAS products are evaluated and visualised as exceedance of critical levels only - both in forms of maps and time series. Different sources of uncertainty and its impact on the flood forecasting performance for every grid cell has been tested offline but not yet incorporated operationally into the forecasting chain for computational reasons. However, at stations where real-time discharges are available, a hydrological uncertainty processor is being applied to estimate the total predictive uncertainty from the hydrological and input uncertainties. Research on long-term EFAS results has shown the need for complementing statistical analysis with case studies for which examples will be shown.

The Rise of Complexity in Flood Forecasting: Opportunities, Challenges and Tradeoffs

NASA Astrophysics Data System (ADS)

Wood, A. W.; Clark, M. P.; Nijssen, B.

2017-12-01

Operational flood forecasting is currently undergoing a major transformation. Most national flood forecasting services have relied for decades on lumped, highly calibrated conceptual hydrological models running on local office computing resources, providing deterministic streamflow predictions at gauged river locations that are important to stakeholders and emergency managers. A variety of recent technological advances now make it possible to run complex, high-to-hyper-resolution models for operational hydrologic prediction over large domains, and the US National Weather Service is now attempting to use hyper-resolution models to create new forecast services and products. Yet other `increased-complexity' forecasting strategies also exist that pursue different tradeoffs between model complexity (i.e., spatial resolution, physics) and streamflow forecast system objectives. There is currently a pressing need for a greater understanding in the hydrology community of the opportunities, challenges and tradeoffs associated with these different forecasting approaches, and for a greater participation by the hydrology community in evaluating, guiding and implementing these approaches. Intermediate-resolution forecast systems, for instance, use distributed land surface model (LSM) physics but retain the agility to deploy ensemble methods (including hydrologic data assimilation and hindcast-based post-processing). Fully coupled numerical weather prediction (NWP) systems, another example, use still coarser LSMs to produce ensemble streamflow predictions either at the model scale or after sub-grid scale runoff routing. Based on the direct experience of the authors and colleagues in research and operational forecasting, this presentation describes examples of different streamflow forecast paradigms, from the traditional to the recent hyper-resolution, to illustrate the range of choices facing forecast system developers. We also discuss the degree to which the strengths and weaknesses of each strategy map onto the requirements for different types of forecasting services (e.g., flash flooding, river flooding, seasonal water supply prediction).

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jie; Cui, Mingjian; Hodge, Bri-Mathias

The large variability and uncertainty in wind power generation present a concern to power system operators, especially given the increasing amounts of wind power being integrated into the electric power system. Large ramps, one of the biggest concerns, can significantly influence system economics and reliability. The Wind Forecast Improvement Project (WFIP) was to improve the accuracy of forecasts and to evaluate the economic benefits of these improvements to grid operators. This paper evaluates the ramp forecasting accuracy gained by improving the performance of short-term wind power forecasting. This study focuses on the WFIP southern study region, which encompasses most ofmore » the Electric Reliability Council of Texas (ERCOT) territory, to compare the experimental WFIP forecasts to the existing short-term wind power forecasts (used at ERCOT) at multiple spatial and temporal scales. The study employs four significant wind power ramping definitions according to the power change magnitude, direction, and duration. The optimized swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental WFIP forecasts improve the accuracy of the wind power ramp forecasting. This improvement can result in substantial costs savings and power system reliability enhancements.« less

Communicating uncertainty in hydrological forecasts: mission impossible?

NASA Astrophysics Data System (ADS)

Ramos, Maria-Helena; Mathevet, Thibault; Thielen, Jutta; Pappenberger, Florian

2010-05-01

Cascading uncertainty in meteo-hydrological modelling chains for forecasting and integrated flood risk assessment is an essential step to improve the quality of hydrological forecasts. Although the best methodology to quantify the total predictive uncertainty in hydrology is still debated, there is a common agreement that one must avoid uncertainty misrepresentation and miscommunication, as well as misinterpretation of information by users. Several recent studies point out that uncertainty, when properly explained and defined, is no longer unwelcome among emergence response organizations, users of flood risk information and the general public. However, efficient communication of uncertain hydro-meteorological forecasts is far from being a resolved issue. This study focuses on the interpretation and communication of uncertain hydrological forecasts based on (uncertain) meteorological forecasts and (uncertain) rainfall-runoff modelling approaches to decision-makers such as operational hydrologists and water managers in charge of flood warning and scenario-based reservoir operation. An overview of the typical flow of uncertainties and risk-based decisions in hydrological forecasting systems is presented. The challenges related to the extraction of meaningful information from probabilistic forecasts and the test of its usefulness in assisting operational flood forecasting are illustrated with the help of two case-studies: 1) a study on the use and communication of probabilistic flood forecasting within the European Flood Alert System; 2) a case-study on the use of probabilistic forecasts by operational forecasters from the hydroelectricity company EDF in France. These examples show that attention must be paid to initiatives that promote or reinforce the active participation of expert forecasters in the forecasting chain. The practice of face-to-face forecast briefings, focusing on sharing how forecasters interpret, describe and perceive the model output forecasted scenarios, is essential. We believe that the efficient communication of uncertainty in hydro-meteorological forecasts is not a mission impossible. Questions remaining unanswered in probabilistic hydrological forecasting should not neutralize the goal of such a mission, and the suspense kept should instead act as a catalyst for overcoming the remaining challenges.

Oregon Washington Coastal Ocean Forecast System: Real-time Modeling and Data Assimilation

NASA Astrophysics Data System (ADS)

Erofeeva, S.; Kurapov, A. L.; Pasmans, I.

2016-02-01

Three-day forecasts of ocean currents, temperature and salinity along the Oregon and Washington coasts are produced daily by a numerical ROMS-based ocean circulation model. NAM is used to derive atmospheric forcing for the model. Fresh water discharge from Columbia River, Fraser River, and small rivers in Puget Sound are included. The forecast is constrained by open boundary conditions derived from the global Navy HYCOM model and once in 3 days assimilation of recent data, including HF radar surface currents, sea surface temperature from the GOES satellite, and SSH from several satellite altimetry missions. 4-dimensional variational data assimilation is implemented in 3-day time windows using the tangent linear and adjoint codes developed at OSU. The system is semi-autonomous - all the data, including NAM and HYCOM fields are automatically updated, and daily operational forecast is automatically initiated. The pre-assimilation data quality control and post-assimilation forecast quality control require the operator's involvement. The daily forecast and 60 days of hindcast fields are available for public on opendap. As part of the system model validation plots to various satellites and SEAGLIDER are also automatically updated and available on the web (http://ingria.coas.oregonstate.edu/rtdavow/). Lessons learned in this pilot real-time coastal ocean forecasting project help develop and test metrics for forecast skill assessment for the West Coast Operational Forecast System (WCOFS), currently at testing and development phase at the National Oceanic and Atmospheric Administration (NOAA).

JPSS Products, Applications and Training

NASA Astrophysics Data System (ADS)

Torres, J. R.; Connell, B. H.; Miller, S. D.

2017-12-01

The Joint Polar Satellite System (JPSS) is a new generation polar-orbiting operational environmental satellite system that will monitor the weather and environment around the globe. JPSS will provide technological and scientific improvements in environmental monitoring via high resolution satellite imagery and derived products that stand to improve weather forecasting capabilities for National Weather Service (NWS) forecasters and complement operational Geostationary satellites. JPSS will consist of four satellites, JPSS-1 through JPSS-4, where JPSS-1 is due to launch in Fall 2017. A predecessor, prototype and operational risk-reduction for JPSS is the Suomi-National Polar-orbiting Partnership (S-NPP) satellite, launched on 28 October 2011. The following instruments on-board S-NPP will also be hosted on JPSS-1: Visible Infrared Imaging Radiometer Suite (VIIRS), Cross-track Infrared Sounder (CrIS), Advanced Technology Microwave Sounder (ATMS), Ozone Mapping and Profiler Suite (OMPS) and the Clouds and Earth's Radiant Energy System (CERES). JPSS-1 instruments will provide satellite imagery, products and applications to users. The applications include detecting water and ice clouds, snow, sea surface temperatures, fog, fire, severe weather, vegetation health, aerosols, and sensing reflected lunar and emitted visible-wavelength light during the nighttime via the Day/Night Band (DNB) sensor included on VIIRS. Currently, there are only a few polar products that are operational for forecasters, however, more products will become available in the near future via Advanced Weather Interactive Processing System-II (AWIPS-II)-a forecasting analysis software package that forecasters can use to analyze meteorological data. To complement the polar products an wealth of training materials are currently in development. Denoted as the Satellite Foundational Course for JPSS (SatFC-J), this training will benefit NWS forecasters to utilize satellite data in their forecasts and daily operations as they discover their operational value in the NWS forecast process. As JPSS-1 launch nears, training materials will be produced in the form of modules, videos, quick guides, fact sheets, and hands-on exercises.

AIRS associated accomplishments at the JCSDA: First use of full spatial resolution hyperspectral data show significant improvements in global forecasts

NASA Astrophysics Data System (ADS)

Le Marshall, J.; Jung, J.; Lord, S. J.; Derber, J. C.; Treadon, R.; Joiner, J.; Goldberg, M.; Wolf, W.; Liu, H. C.

2005-08-01

The National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), and Department of Defense (DoD), Joint Center for Satellite Data Assimilation (JCSDA) was established in 2000/2001. The goal of the JCSDA is to accelerate the use of observations from earth-orbiting satellites into operational numerical environmental analysis and prediction systems for the purpose of improving weather and oceanic forecasts, seasonal climate forecasts and the accuracy of climate data sets. As a result, a series of data assimilation experiments were undertaken at the JCSDA as part of the preparations for the operational assimilation of AIRS data by its partner organizations1,2. Here, for the first time full spatial resolution radiance data, available in real-time from the AIRS instrument, were used at the JCSDA in data assimilation studies over the globe utilizing the operational NCEP Global Forecast System (GFS). The radiance data from each channel of the instrument were carefully screened for cloud effects and those radiances which were deemed to be clear of cloud effects were used by the GFS forecast system. The result of these assimilation trials has been a first demonstration of significant improvements in forecast skill over both the Northern and Southern Hemisphere compared to the operational system without AIRS data. The experimental system was designed in a way that rendered it feasible for operational application, and that constraint involved using the subset of AIRS channels chosen for operational distribution and an analysis methodology close to the current analysis practice, with particular consideration given to time limitations. As a result, operational application of these AIRS data was enabled by the recent NCEP operational upgrade. In addition, because of the improved impact resulting from use of this enhanced data set compared to that used operationally to date, provision of a realtime "warmest field" of view data set has been established for use by international NWP Centers.

Forecasting the short-term passenger flow on high-speed railway with neural networks.

PubMed

Xie, Mei-Quan; Li, Xia-Miao; Zhou, Wen-Liang; Fu, Yan-Bing

2014-01-01

Short-term passenger flow forecasting is an important component of transportation systems. The forecasting result can be applied to support transportation system operation and management such as operation planning and revenue management. In this paper, a divide-and-conquer method based on neural network and origin-destination (OD) matrix estimation is developed to forecast the short-term passenger flow in high-speed railway system. There are three steps in the forecasting method. Firstly, the numbers of passengers who arrive at each station or depart from each station are obtained from historical passenger flow data, which are OD matrices in this paper. Secondly, short-term passenger flow forecasting of the numbers of passengers who arrive at each station or depart from each station based on neural network is realized. At last, the OD matrices in short-term time are obtained with an OD matrix estimation method. The experimental results indicate that the proposed divide-and-conquer method performs well in forecasting the short-term passenger flow on high-speed railway.

Space Transportation System Meteorological Expert

NASA Technical Reports Server (NTRS)

Beller, Arthur E.; Stafford, Sue P.

1987-01-01

The STS Meteorological Expert (STSMET) is a long-term project to acquire general Shuttle operational weather forecasting expertise specific to the launch locale, to apply it to Shuttle operational weather forecasting tasks at the Cape Canaveral Forecast Facility, and ultimately to provide an on-line real-time operational aid to the duty forecasters in performing their tasks. Particular attention is given to the development of an approach called scenario-based reasoning, with specific application to summer thunderstorms; this type of reasoning can also be applied to frontal weather phenomena, visibility including fog, and wind shear.

Short-Term Load Forecasting Error Distributions and Implications for Renewable Integration Studies: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, B. M.; Lew, D.; Milligan, M.

2013-01-01

Load forecasting in the day-ahead timescale is a critical aspect of power system operations that is used in the unit commitment process. It is also an important factor in renewable energy integration studies, where the combination of load and wind or solar forecasting techniques create the net load uncertainty that must be managed by the economic dispatch process or with suitable reserves. An understanding of that load forecasting errors that may be expected in this process can lead to better decisions about the amount of reserves necessary to compensate errors. In this work, we performed a statistical analysis of themore » day-ahead (and two-day-ahead) load forecasting errors observed in two independent system operators for a one-year period. Comparisons were made with the normal distribution commonly assumed in power system operation simulations used for renewable power integration studies. Further analysis identified time periods when the load is more likely to be under- or overforecast.« less

Value of long-term streamflow forecast to reservoir operations for water supply in snow-dominated catchments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anghileri, Daniela; Voisin, Nathalie; Castelletti, Andrea F.

In this study, we develop a forecast-based adaptive control framework for Oroville reservoir, California, to assess the value of seasonal and inter-annual forecasts for reservoir operation.We use an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity hydrology model. The optimal sequence of daily release decisions from the reservoir is then determined by Model Predictive Control, a flexible and adaptive optimization scheme.We assess the forecast value by comparing system performance based on the ESP forecasts with that based on climatology and a perfect forecast. In addition, we evaluate system performance based onmore » a synthetic forecast, which is designed to isolate the contribution of seasonal and inter-annual forecast skill to the overall value of the ESP forecasts.Using the same ESP forecasts, we generalize our results by evaluating forecast value as a function of forecast skill, reservoir features, and demand. Our results show that perfect forecasts are valuable when the water demand is high and the reservoir is sufficiently large to allow for annual carry-over. Conversely, ESP forecast value is highest when the reservoir can shift water on a seasonal basis.On average, for the system evaluated here, the overall ESP value is 35% less than the perfect forecast value. The inter-annual component of the ESP forecast contributes 20-60% of the total forecast value. Improvements in the seasonal component of the ESP forecast would increase the overall ESP forecast value between 15 and 20%.« less

Operational specification and forecasting advances for Dst, LEO thermospheric densities, and aviation radiation dose and dose rate

NASA Astrophysics Data System (ADS)

Tobiska, W.; Knipp, D. J.; Burke, W. J.; Bouwer, D.; Bailey, J. J.; Hagan, M. P.; Didkovsky, L. V.; Garrett, H. B.; Bowman, B. R.; Gannon, J. L.; Atwell, W.; Blake, J. B.; Crain, W.; Rice, D.; Schunk, R. W.; Fulgham, J.; Bell, D.; Gersey, B.; Wilkins, R.; Fuschino, R.; Flynn, C.; Cecil, K.; Mertens, C. J.; Xu, X.; Crowley, G.; Reynolds, A.; Azeem, S. I.; Wiley, S.; Holland, M.; Malone, K.

2013-12-01

Space weather's effects upon the near-Earth environment are due to dynamic changes in the energy transfer processes from the Sun's photons, particles, and fields. Of the space environment domains that are affected by space weather, the magnetosphere, thermosphere, and even troposphere are key regions that are affected. Space Environment Technologies (SET) has developed and is producing innovative space weather applications. Key operational systems for providing timely information about the effects of space weather on these domains are SET's Magnetosphere Alert and Prediction System (MAPS), LEO Alert and Prediction System (LAPS), and Automated Radiation Measurements for Aviation Safety (ARMAS) system. MAPS provides a forecast Dst index out to 6 days through the data-driven, redundant data stream Anemomilos algorithm. Anemomilos uses observational proxies for the magnitude, location, and velocity of solar ejecta events. This forecast index is used by satellite operations to characterize upcoming geomagnetic storms, for example. LAPS is the SET fully redundant operational system providing recent history, current epoch, and forecast solar and geomagnetic indices for use in operational versions of the JB2008 thermospheric density model. The thermospheric densities produced by that system, driven by the LAPS data, are forecast to 72-hours to provide the global mass densities for satellite operators. ARMAS is a project that has successfully demonstrated the operation of a micro dosimeter on aircraft to capture the real-time radiation environment due to Galactic Cosmic Rays and Solar Energetic Particles. The dose and dose-rates are captured on aircraft, downlinked in real-time via the Iridium satellites, processed on the ground, incorporated into the most recent NAIRAS global radiation climatology data runs, and made available to end users via the web and smart phone apps. ARMAS provides the 'weather' of the radiation environment to improve air-crew and passenger safety. Many of the data products from MAPS, LAPS, and ARMAS are available on the SpaceWx smartphone app for iPhone, iPad, iPod, and Android professional users and public space weather education. We describe recent forecasting advances for moving the space weather information from these automated systems into operational, derivative products for communications, aviation, and satellite operations uses.

Supporting inland waterway transport on German waterways by operational forecasting services - water-levels, discharges, river ice

NASA Astrophysics Data System (ADS)

Meißner, Dennis; Klein, Bastian; Ionita, Monica; Hemri, Stephan; Rademacher, Silke

2017-04-01

Inland waterway transport (IWT) is an important commercial sector significantly vulnerable to hydrological impacts. River ice and floods limit the availability of the waterway network and may cause considerable damages to waterway infrastructure. Low flows significantly affect IWT's operation efficiency usually several months a year due to the close correlation of (low) water levels / water depths and (high) transport costs. Therefore "navigation-related" hydrological forecasts focussing on the specific requirements of water-bound transport (relevant forecast locations, target parameters, skill characteristics etc.) play a major role in order to mitigate IWT's vulnerability to hydro-meteorological impacts. In light of continuing transport growth within the European Union, hydrological forecasts for the waterways are essential to stimulate the use of the free capacity IWT still offers more consequently. An overview of the current operational and pre-operational forecasting systems for the German waterways predicting water levels, discharges and river ice thickness on various time-scales will be presented. While short-term (deterministic) forecasts have a long tradition in navigation-related forecasting, (probabilistic) forecasting services offering extended lead-times are not yet well-established and are still subject to current research and development activities (e.g. within the EU-projects EUPORIAS and IMPREX). The focus is on improving technical aspects as well as on exploring adequate ways of disseminating and communicating probabilistic forecast information. For the German stretch of the River Rhine, one of the most frequented inland waterways worldwide, the existing deterministic forecast scheme has been extended by ensemble forecasts combined with statistical post-processing modules applying EMOS (Ensemble Model Output Statistics) and ECC (Ensemble Copula Coupling) in order to generate water level predictions up to 10 days and to estimate its predictive uncertainty properly. Additionally for the key locations at the international waterways Rhine, Elbe and Danube three competing forecast approaches are currently tested in a pre-operational set-up in order to generate monthly to seasonal (up to 3 months) forecasts: (1) the well-known Ensemble Streamflow Prediction approach (ensemble based on historical meteorology), (2) coupling hydrological models with post-processed outputs from ECMWF's general circulation model (System 4), and (3) a purely statistical approach based on the stable relationship (teleconnection) of global or regional oceanic, climate and hydrological data with river flows. The current results, still pre-operational, reveal the existence of a valuable predictability of water levels and streamflow also at monthly up to seasonal time-scales along the larger rivers used as waterways in Germany. Last but not least insight into the technical set-up of the aforementioned forecasting systems operated at the Federal Institute of Hydrology, which are based on a Delft-FEWS application, will be given focussing on the step-wise extension of the former system by integrating new components in order to meet the growing needs of the customers and to improve and extend the forecast portfolio for waterway users.

Quantifying the Economic and Grid Reliability Impacts of Improved Wind Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Martinez-Anido, Carlo Brancucci; Wu, Hongyu

Wind power forecasting is an important tool in power system operations to address variability and uncertainty. Accurately doing so is important to reducing the occurrence and length of curtailment, enhancing market efficiency, and improving the operational reliability of the bulk power system. This research quantifies the value of wind power forecasting improvements in the IEEE 118-bus test system as modified to emulate the generation mixes of Midcontinent, California, and New England independent system operator balancing authority areas. To measure the economic value, a commercially available production cost modeling tool was used to simulate the multi-timescale unit commitment (UC) and economicmore » dispatch process for calculating the cost savings and curtailment reductions. To measure the reliability improvements, an in-house tool, FESTIV, was used to calculate the system's area control error and the North American Electric Reliability Corporation Control Performance Standard 2. The approach allowed scientific reproducibility of results and cross-validation of the tools. A total of 270 scenarios were evaluated to accommodate the variation of three factors: generation mix, wind penetration level, and wind fore-casting improvements. The modified IEEE 118-bus systems utilized 1 year of data at multiple timescales, including the day-ahead UC, 4-hour-ahead UC, and 5-min real-time dispatch. The value of improved wind power forecasting was found to be strongly tied to the conventional generation mix, existence of energy storage devices, and the penetration level of wind energy. The simulation results demonstrate that wind power forecasting brings clear benefits to power system operations.« less

Workstation-Based Real-Time Mesoscale Modeling Designed for Weather Support to Operations at the Kennedy Space Center and Cape Canaveral Air Station

NASA Technical Reports Server (NTRS)

Manobianco, John; Zack, John W.; Taylor, Gregory E.

1996-01-01

This paper describes the capabilities and operational utility of a version of the Mesoscale Atmospheric Simulation System (MASS) that has been developed to support operational weather forecasting at the Kennedy Space Center (KSC) and Cape Canaveral Air Station (CCAS). The implementation of local, mesoscale modeling systems at KSC/CCAS is designed to provide detailed short-range (less than 24 h) forecasts of winds, clouds, and hazardous weather such as thunderstorms. Short-range forecasting is a challenge for daily operations, and manned and unmanned launches since KSC/CCAS is located in central Florida where the weather during the warm season is dominated by mesoscale circulations like the sea breeze. For this application, MASS has been modified to run on a Stardent 3000 workstation. Workstation-based, real-time numerical modeling requires a compromise between the requirement to run the system fast enough so that the output can be used before expiration balanced against the desire to improve the simulations by increasing resolution and using more detailed physical parameterizations. It is now feasible to run high-resolution mesoscale models such as MASS on local workstations to provide timely forecasts at a fraction of the cost required to run these models on mainframe supercomputers. MASS has been running in the Applied Meteorology Unit (AMU) at KSC/CCAS since January 1994 for the purpose of system evaluation. In March 1995, the AMU began sending real-time MASS output to the forecasters and meteorologists at CCAS, Spaceflight Meteorology Group (Johnson Space Center, Houston, Texas), and the National Weather Service (Melbourne, Florida). However, MASS is not yet an operational system. The final decision whether to transition MASS for operational use will depend on a combination of forecaster feedback, the AMU's final evaluation results, and the life-cycle costs of the operational system.

A real-time evaluation and demonstration of strategies for 'Over-The-Loop' ensemble streamflow forecasting in US watersheds

NASA Astrophysics Data System (ADS)

Wood, Andy; Clark, Elizabeth; Mendoza, Pablo; Nijssen, Bart; Newman, Andy; Clark, Martyn; Nowak, Kenneth; Arnold, Jeffrey

2017-04-01

Many if not most national operational streamflow prediction systems rely on a forecaster-in-the-loop approach that require the hands-on-effort of an experienced human forecaster. This approach evolved from the need to correct for long-standing deficiencies in the models and datasets used in forecasting, and the practice often leads to skillful flow predictions despite the use of relatively simple, conceptual models. Yet the 'in-the-loop' forecast process is not reproducible, which limits opportunities to assess and incorporate new techniques systematically, and the effort required to make forecasts in this way is an obstacle to expanding forecast services - e.g., though adding new forecast locations or more frequent forecast updates, running more complex models, or producing forecast and hindcasts that can support verification. In the last decade, the hydrologic forecasting community has begun develop more centralized, 'over-the-loop' systems. The quality of these new forecast products will depend on their ability to leverage research in areas including earth system modeling, parameter estimation, data assimilation, statistical post-processing, weather and climate prediction, verification, and uncertainty estimation through the use of ensembles. Currently, many national operational streamflow forecasting and water management communities have little experience with the strengths and weaknesses of over-the-loop approaches, even as such systems are beginning to be deployed operationally in centers such as ECMWF. There is thus a need both to evaluate these forecasting advances and to demonstrate their potential in a public arena, raising awareness in forecast user communities and development programs alike. To address this need, the US National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the US Army Corps of Engineers, using the NCAR 'System for Hydromet Analysis Research and Prediction Applications' (SHARP) to implement, assess and demonstrate real-time over-the-loop ensemble flow forecasts in a range of US watersheds. The system relies on fully ensemble techniques, including: an 100-member ensemble of meteorological model forcings and an ensemble particle filter data assimilation for initializing watershed states; analog/regression-based downscaling of ensemble weather forecasts from GEFS; and statistical post-processing of ensemble forecast outputs, all of which run in real-time within a workflow managed by ECWMF's ecFlow libraries over large US regional domains. We describe SHARP and present early hindcast and verification results for short to seasonal range streamflow forecasts in a number of US case study watersheds.

WOD - Weather On Demand forecasting system

NASA Astrophysics Data System (ADS)

Rognvaldsson, Olafur; Ragnarsson, Logi; Stanislawska, Karolina

2017-04-01

The backbone of the Belgingur forecasting system (called WOD - Weather On Demand) is the WRF-Chem atmospheric model, with a number of in-house customisations. Initial and boundary data are taken from the Global Forecasting System, operated by the National Oceanic and Atmospheric Administration (NOAA). Operational forecasts use cycling of a number of parameters, mainly deep soil and surface fields. This is done to minimise spin-up effects and to ensure proper book-keeping of hydrological fields such as snow accumulation and runoff, as well as the constituents of various chemical parameters. The WOD system can be used to create conventional short- to medium-range weather forecasts for any location on the globe. The WOD system can also be used for air quality purposes (e.g. dispersion forecasts from volcanic eruptions) and as a tool to provide input to other modelling systems, such as hydrological models. A wide variety of post-processing options are also available, making WOD an ideal tool for creating highly customised output that can be tailored to the specific needs of individual end-users. The most recent addition to the WOD system is an integrated verification system where forecasts can be compared to surface observations from chosen locations. Forecast visualisation, such as weather charts, meteograms, weather icons and tables, is done via number of web components that can be configured to serve the varying needs of different end-users. The WOD system itself can be installed in an automatic way on hardware running a range of Linux based OS. System upgrades can also be done in semi-automatic fashion, i.e. upgrades and/or bug-fixes can be pushed to the end-user hardware without system downtime. Importantly, the WOD system requires only rudimentary knowledge of the WRF modelling, and the Linux operating systems on behalf of the end-user, making it an ideal NWP tool in locations with limited IT infrastructure.

Improving the Transition of Earth Satellite Observations from Research to Operations

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Lapenta, William M.; Jedlovec, Gary J.

2004-01-01

There are significant gaps between the observations, models, and decision support tools that make use of new data. These challenges include: 1) Decreasing the time to incorporate new satellite data into operational forecast assimilation systems, 2) Blending in-situ and satellite observing systems to produce the most accurate and comprehensive data products and assessments, 3) Accelerating the transition from research to applications through national test beds, field campaigns, and pilot demonstrations, and 4) Developing the partnerships and organizational structures to effectively transition new technology into operations. At the Short-term Prediction Research and Transition (SPORT) Center in Huntsville, Alabama, a NASA-NOAA-University collaboration has been developed to accelerate the infusion of NASA Earth science observations, data assimilation and modeling research into NWS forecast operations and decision-making. The SPoRT Center research focus is to improve forecasts through new observation capability and the regional prediction objectives of the US Weather Research Program dealing with 0-1 day forecast issues such as convective initiation and 24-hr quantitative precipitation forecasting. The near real-time availability of high-resolution experimental products of the atmosphere, land, and ocean from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Infrared Spectroradiometer (AIRS), and lightning mapping systems provide an opportunity for science and algorithm risk reduction, and for application assessment prior to planned observations from the next generation of operational low Earth orbiting and geostationary Earth orbiting satellites. This paper describes the process for the transition of experimental products into forecast operations, current products undergoing assessment by forecasters, and plans for the future. The SPoRT Web page is at (http://www.ghcc.msfc.nasa.gov/sport).

The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season.

PubMed

Pavlovic, Radenko; Chen, Jack; Anderson, Kerry; Moran, Michael D; Beaulieu, Paul-André; Davignon, Didier; Cousineau, Sophie

2016-09-01

Environment and Climate Change Canada's FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2-July 15, and August 15-31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of -7.3 µg m(-3) and 3.1 µg m(-3)), it showed better forecast skill than the RAQDPS (MB of -11.7 µg m(-3) and -5.8 µg m(-3)) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m(-3) also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR). Smoke from wildfires can have a large impact on regional air quality (AQ) and can expose populations to elevated pollution levels. Environment and Climate Change Canada has been producing operational air quality forecasts for all of Canada since 2009 and is now working to include near-real-time wildfire emissions (NRTWE) in its operational AQ forecasting system. An experimental forecast system named FireWork, which includes NRTWE, has been undergoing testing and evaluation since 2013. A performance analysis of FireWork forecasts for the 2015 wildfire season shows that FireWork provides significant improvements to surface PM2.5 forecasts and valuable guidance to regional forecasters and first responders.

Should we use seasonnal meteorological ensemble forecasts for hydrological forecasting? A case study for nordic watersheds in Canada.

NASA Astrophysics Data System (ADS)

Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert; Guay, Catherine

2017-04-01

Hydro-electricity is a major source of energy for many countries throughout the world, including Canada. Long lead-time streamflow forecasts are all the more valuable as they help decision making and dam management. Different techniques exist for long-term hydrological forecasting. Perhaps the most well-known is 'Extended Streamflow Prediction' (ESP), which considers past meteorological scenarios as possible, often equiprobable, future scenarios. In the ESP framework, those past-observed meteorological scenarios (climatology) are used in turn as the inputs of a chosen hydrological model to produce ensemble forecasts (one member corresponding to each year in the available database). Many hydropower companies, including Hydro-Québec (province of Quebec, Canada) use variants of the above described ESP system operationally for long-term operation planning. The ESP system accounts for the hydrological initial conditions and for the natural variability of the meteorological variables. However, it cannot consider the current initial state of the atmosphere. Climate models can help remedy this drawback. In the context of a changing climate, dynamical forecasts issued from climate models seem to be an interesting avenue to improve upon the ESP method and could help hydropower companies to adapt their management practices to an evolving climate. Long-range forecasts from climate models can also be helpful for water management at locations where records of past meteorological conditions are short or nonexistent. In this study, we compare 7-month hydrological forecasts obtained from climate model outputs to an ESP system. The ESP system mimics the one used operationally at Hydro-Québec. The dynamical climate forecasts are produced by the European Center for Medium range Weather Forecasts (ECMWF) System4. Forecasts quality is assessed using numerical scores such as the Continuous Ranked Probability Score (CRPS) and the Ignorance score and also graphical tools such as the reliability diagram. This study covers 10 nordic watersheds. We show that forecast performance according to the CRPS varies with lead-time but also with the period of the year. The raw forecasts from the ECMWF System4 display important biases for both temperature and precipitation, which need to be corrected. The linear scaling method is used for this purpose and is found effective. Bias correction improves forecasts performance, especially during the summer when the precipitations are over-estimated. According to the CRPS, bias corrected forecasts from System4 show performances comparable to those of the ESP system. However, the Ignorance score, which penalizes the lack of calibration (under-dispersive forecasts in this case) more severely than the CRPS, provides a different outlook for the comparison of the two systems. In fact, according to the Ignorance score, the ESP system outperforms forecasts based on System4 in most cases. This illustrates that the joint use of several metrics is crucial to assess the quality of a forecasts system thoroughly. Globally, ESP provide reliable forecasts which can be over-dispersed whereas bias corrected ECMWF System4 forecasts are sharper but at the risk of missing events.

A Satellite Frost Forecasting System for Florida

NASA Technical Reports Server (NTRS)

Martsolf, J. D.

1981-01-01

Since the first of two minicomputers that are the main components of the satellite frost forecast system was delivered in 1977, the system has evolved appreciably. A geostationary operational environmental satellite (GOES) system provides the satellite data. The freeze of January 12-14, 1981, was documented with increasing interest in potential of such systems. Satellite data is now acquired digitally rather than by redigitizing the GOES-Tap transmissions. Data acquisition is now automated, i.e., the computers are programmed to operate the system with little, if any, operation intervention.

On the reliability of seasonal climate forecasts.

PubMed

Weisheimer, A; Palmer, T N

2014-07-06

Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1-5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that 'goodness' should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a '5' should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of 'goodness' rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching '5' across all regions and variables in 30 years time.

Forecasting the ocean optical environment in support of Navy mine warfare operations

NASA Astrophysics Data System (ADS)

Ladner, S. D.; Arnone, R.; Jolliff, J.; Casey, B.; Matulewski, K.

2012-06-01

A 3D ocean optical forecast system called TODS (Tactical Ocean Data System) has been developed to determine the performance of underwater LIDAR detection/identification systems. TODS fuses optical measurements from gliders, surface satellite optical properties, and 3D ocean forecast circulation models to extend the 2-dimensional surface satellite optics into a 3-dimensional optical volume including subsurface optical layers of beam attenuation coefficient (c) and diver visibility. Optical 3D nowcast and forecasts are combined with electro-optical identification (EOID) models to determine the underwater LIDAR imaging performance field used to identify subsurface mine threats in rapidly changing coastal regions. TODS was validated during a recent mine warfare exercise with Helicopter Mine Countermeasures Squadron (HM-14). Results include the uncertainties in the optical forecast and lidar performance and sensor tow height predictions that are based on visual detection and identification metrics using actual mine target images from the EOID system. TODS is a new capability of coupling the 3D optical environment and EOID system performance and is proving important for the MIW community as both a tactical decision aid and for use in operational planning, improving timeliness and efficiency in clearance operations.

Application of global weather and climate model output to the design and operation of wind-energy systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Curry, Judith

This project addressed the challenge of providing weather and climate information to support the operation, management and planning for wind-energy systems. The need for forecast information is extending to longer projection windows with increasing penetration of wind power into the grid and also with diminishing reserve margins to meet peak loads during significant weather events. Maintenance planning and natural gas trading is being influenced increasingly by anticipation of wind generation on timescales of weeks to months. Future scenarios on decadal time scales are needed to support assessment of wind farm siting, government planning, long-term wind purchase agreements and the regulatorymore » environment. The challenge of making wind forecasts on these longer time scales is associated with a wide range of uncertainties in general circulation and regional climate models that make them unsuitable for direct use in the design and planning of wind-energy systems. To address this challenge, CFAN has developed a hybrid statistical/dynamical forecasting scheme for delivering probabilistic forecasts on time scales from one day to seven months using what is arguably the best forecasting system in the world (European Centre for Medium Range Weather Forecasting, ECMWF). The project also provided a framework to assess future wind power through developing scenarios of interannual to decadal climate variability and change. The Phase II research has successfully developed an operational wind power forecasting system for the U.S., which is being extended to Europe and possibly Asia.« less

The case for probabilistic forecasting in hydrology

NASA Astrophysics Data System (ADS)

Krzysztofowicz, Roman

2001-08-01

That forecasts should be stated in probabilistic, rather than deterministic, terms has been argued from common sense and decision-theoretic perspectives for almost a century. Yet most operational hydrological forecasting systems produce deterministic forecasts and most research in operational hydrology has been devoted to finding the 'best' estimates rather than quantifying the predictive uncertainty. This essay presents a compendium of reasons for probabilistic forecasting of hydrological variates. Probabilistic forecasts are scientifically more honest, enable risk-based warnings of floods, enable rational decision making, and offer additional economic benefits. The growing demand for information about risk and the rising capability to quantify predictive uncertainties create an unparalleled opportunity for the hydrological profession to dramatically enhance the forecasting paradigm.

Hydrological Forecasting Practices in Brazil

NASA Astrophysics Data System (ADS)

Fan, Fernando; Paiva, Rodrigo; Collischonn, Walter; Ramos, Maria-Helena

2016-04-01

This work brings a review on current hydrological and flood forecasting practices in Brazil, including the main forecasts applications, the different kinds of techniques that are currently being employed and the institutions involved on forecasts generation. A brief overview of Brazil is provided, including aspects related to its geography, climate, hydrology and flood hazards. A general discussion about the Brazilian practices on hydrological short and medium range forecasting is presented. Detailed examples of some hydrological forecasting systems that are operational or in a research/pre-operational phase using the large scale hydrological model MGB-IPH are also presented. Finally, some suggestions are given about how the forecasting practices in Brazil can be understood nowadays, and what are the perspectives for the future.

Battery Energy Storage State-of-Charge Forecasting: Models, Optimization, and Accuracy

DOE PAGES

Rosewater, David; Ferreira, Summer; Schoenwald, David; ...

2018-01-25

Battery energy storage systems (BESS) are a critical technology for integrating high penetration renewable power on an intelligent electrical grid. As limited energy restricts the steady-state operational state-of-charge (SoC) of storage systems, SoC forecasting models are used to determine feasible charge and discharge schedules that supply grid services. Smart grid controllers use SoC forecasts to optimize BESS schedules to make grid operation more efficient and resilient. This study presents three advances in BESS state-of-charge forecasting. First, two forecasting models are reformulated to be conducive to parameter optimization. Second, a new method for selecting optimal parameter values based on operational datamore » is presented. Last, a new framework for quantifying model accuracy is developed that enables a comparison between models, systems, and parameter selection methods. The accuracies achieved by both models, on two example battery systems, with each method of parameter selection are then compared in detail. The results of this analysis suggest variation in the suitability of these models for different battery types and applications. Finally, the proposed model formulations, optimization methods, and accuracy assessment framework can be used to improve the accuracy of SoC forecasts enabling better control over BESS charge/discharge schedules.« less

Battery Energy Storage State-of-Charge Forecasting: Models, Optimization, and Accuracy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rosewater, David; Ferreira, Summer; Schoenwald, David

Battery energy storage systems (BESS) are a critical technology for integrating high penetration renewable power on an intelligent electrical grid. As limited energy restricts the steady-state operational state-of-charge (SoC) of storage systems, SoC forecasting models are used to determine feasible charge and discharge schedules that supply grid services. Smart grid controllers use SoC forecasts to optimize BESS schedules to make grid operation more efficient and resilient. This study presents three advances in BESS state-of-charge forecasting. First, two forecasting models are reformulated to be conducive to parameter optimization. Second, a new method for selecting optimal parameter values based on operational datamore » is presented. Last, a new framework for quantifying model accuracy is developed that enables a comparison between models, systems, and parameter selection methods. The accuracies achieved by both models, on two example battery systems, with each method of parameter selection are then compared in detail. The results of this analysis suggest variation in the suitability of these models for different battery types and applications. Finally, the proposed model formulations, optimization methods, and accuracy assessment framework can be used to improve the accuracy of SoC forecasts enabling better control over BESS charge/discharge schedules.« less

Probabilistic empirical prediction of seasonal climate: evaluation and potential applications

NASA Astrophysics Data System (ADS)

Dieppois, B.; Eden, J.; van Oldenborgh, G. J.

2017-12-01

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a new evaluation of an established empirical system used to predict seasonal climate across the globe. Forecasts for surface air temperature, precipitation and sea level pressure are produced by the KNMI Probabilistic Empirical Prediction (K-PREP) system every month and disseminated via the KNMI Climate Explorer (climexp.knmi.nl). K-PREP is based on multiple linear regression and built on physical principles to the fullest extent with predictive information taken from the global CO2-equivalent concentration, large-scale modes of variability in the climate system and regional-scale information. K-PREP seasonal forecasts for the period 1981-2016 will be compared with corresponding dynamically generated forecasts produced by operational forecast systems. While there are many regions of the world where empirical forecast skill is extremely limited, several areas are identified where K-PREP offers comparable skill to dynamical systems. We discuss two key points in the future development and application of the K-PREP system: (a) the potential for K-PREP to provide a more useful basis for reference forecasts than those based on persistence or climatology, and (b) the added value of including K-PREP forecast information in multi-model forecast products, at least for known regions of good skill. We also discuss the potential development of stakeholder-driven applications of the K-PREP system, including empirical forecasts for circumboreal fire activity.

Validation and Inter-comparison Against Observations of GODAE Ocean View Ocean Prediction Systems

NASA Astrophysics Data System (ADS)

Xu, J.; Davidson, F. J. M.; Smith, G. C.; Lu, Y.; Hernandez, F.; Regnier, C.; Drevillon, M.; Ryan, A.; Martin, M.; Spindler, T. D.; Brassington, G. B.; Oke, P. R.

2016-02-01

For weather forecasts, validation of forecast performance is done at the end user level as well as by the meteorological forecast centers. In the development of Ocean Prediction Capacity, the same level of care for ocean forecast performance and validation is needed. Herein we present results from a validation against observations of 6 Global Ocean Forecast Systems under the GODAE OceanView International Collaboration Network. These systems include the Global Ocean Ice Forecast System (GIOPS) developed by the Government of Canada, two systems PSY3 and PSY4 from the French Mercator-Ocean Ocean Forecasting Group, the FOAM system from UK met office, HYCOM-RTOFS from NOAA/NCEP/NWA of USA, and the Australian Bluelink-OceanMAPS system from the CSIRO, the Australian Meteorological Bureau and the Australian Navy.The observation data used in the comparison are sea surface temperature, sub-surface temperature, sub-surface salinity, sea level anomaly, and sea ice total concentration data. Results of the inter-comparison demonstrate forecast performance limits, strengths and weaknesses of each of the six systems. This work establishes validation protocols and routines by which all new prediction systems developed under the CONCEPTS Collaborative Network will be benchmarked prior to approval for operations. This includes anticipated delivery of CONCEPTS regional prediction systems over the next two years including a pan Canadian 1/12th degree resolution ice ocean prediction system and limited area 1/36th degree resolution prediction systems. The validation approach of comparing forecasts to observations at the time and location of the observation is called Class 4 metrics. It has been adopted by major international ocean prediction centers, and will be recommended to JCOMM-WMO as routine validation approach for operational oceanography worldwide.

Spatial Ensemble Postprocessing of Precipitation Forecasts Using High Resolution Analyses

NASA Astrophysics Data System (ADS)

Lang, Moritz N.; Schicker, Irene; Kann, Alexander; Wang, Yong

2017-04-01

Ensemble prediction systems are designed to account for errors or uncertainties in the initial and boundary conditions, imperfect parameterizations, etc. However, due to sampling errors and underestimation of the model errors, these ensemble forecasts tend to be underdispersive, and to lack both reliability and sharpness. To overcome such limitations, statistical postprocessing methods are commonly applied to these forecasts. In this study, a full-distributional spatial post-processing method is applied to short-range precipitation forecasts over Austria using Standardized Anomaly Model Output Statistics (SAMOS). Following Stauffer et al. (2016), observation and forecast fields are transformed into standardized anomalies by subtracting a site-specific climatological mean and dividing by the climatological standard deviation. Due to the need of fitting only a single regression model for the whole domain, the SAMOS framework provides a computationally inexpensive method to create operationally calibrated probabilistic forecasts for any arbitrary location or for all grid points in the domain simultaneously. Taking advantage of the INCA system (Integrated Nowcasting through Comprehensive Analysis), high resolution analyses are used for the computation of the observed climatology and for model training. The INCA system operationally combines station measurements and remote sensing data into real-time objective analysis fields at 1 km-horizontal resolution and 1 h-temporal resolution. The precipitation forecast used in this study is obtained from a limited area model ensemble prediction system also operated by ZAMG. The so called ALADIN-LAEF provides, by applying a multi-physics approach, a 17-member forecast at a horizontal resolution of 10.9 km and a temporal resolution of 1 hour. The performed SAMOS approach statistically combines the in-house developed high resolution analysis and ensemble prediction system. The station-based validation of 6 hour precipitation sums shows a mean improvement of more than 40% in CRPS when compared to bilinearly interpolated uncalibrated ensemble forecasts. The validation on randomly selected grid points, representing the true height distribution over Austria, still indicates a mean improvement of 35%. The applied statistical model is currently set up for 6-hourly and daily accumulation periods, but will be extended to a temporal resolution of 1-3 hours within a new probabilistic nowcasting system operated by ZAMG.

The Mauna Kea Weather Center: Custom Atmospheric Forecasting Support for Mauna Kea

NASA Astrophysics Data System (ADS)

Businger, Steven

2011-03-01

The success of operations at Mauna Kea Observatories is strongly influenced by weather conditions. The Mauna Kea Weather Center, an interdisciplinary research program, was established in 1999 to develop and provide custom weather support for Mauna Kea Observatories. The operational forecasting goals of the program are to facilitate the best possible use of favorable atmospheric conditions for scientific benefit and to ensure operational safety. During persistent clear periods, astronomical observing quality varies substantially due to changes in the vertical profiles of temperature, wind, moisture, and turbulence. Cloud and storm systems occasionally cause adverse or even hazardous conditions. A dedicated, daily, real-time mesoscale numerical modeling effort provides crucial forecast guidance in both cases. Several key atmospheric variables are forecast with sufficient skill to be of operational and scientific benefit to the telescopes on Mauna Kea. Summit temperature forecasts allow mirrors to be set to the ambient temperature to reduce image distortion. Precipitable water forecasts allow infrared observations to be prioritized according to atmospheric opacity. Forecasts of adverse and hazardous conditions protect the safety of personnel and allow for scheduling of maintenance when observing is impaired by cloud. The research component of the project continues to improve the accuracy and content of the forecasts. In particular, case studies have resulted in operational forecasts of astronomical observing quality, or seeing.

Operational coupled atmosphere - ocean - ice forecast system for the Gulf of St. Lawrence, Canada

NASA Astrophysics Data System (ADS)

Faucher, M.; Roy, F.; Desjardins, S.; Fogarty, C.; Pellerin, P.; Ritchie, H.; Denis, B.

2009-09-01

A fully interactive coupled atmosphere-ocean-ice forecasting system for the Gulf of St. Lawrence (GSL) has been running in experimental mode at the Canadian Meteorological Centre (CMC) for the last two winter seasons. The goal of this project is to provide more accurate weather and sea ice forecasts over the GSL and adjacent coastal areas by including atmosphere-oceanice interactions in the CMC operational forecast system using a formal coupling strategy between two independent modeling components. The atmospheric component is the Canadian operational GEM model (Côté et al. 1998) and the oceanic component is the ocean-ice model for the Gulf of St. Lawrence developed at the Maurice Lamontagne Institute (IML) (Saucier et al. 2003, 2004). The coupling between those two models is achieved by exchanging surface fluxes and variables through MPI communication. The re-gridding of the variables is done with a package developed at the Recherche en Prevision Numerique centre (RPN, Canada). Coupled atmosphere - ocean - ice forecasts are issued once a day based on 00GMT data. Results for the past two years have demonstrated that the coupled system produces improved forecasts in and around the GSL during all seasons, proving that atmosphere-ocean-ice interactions are indeed important even for short-term Canadian weather forecasts. This has important implications for other coupled modeling and data assimilation partnerships that are in progress involving EC, the Department of Fisheries and Oceans (DFO) and the National Defense (DND). Following this experimental phase, it is anticipated that this GSL system will be the first fully interactive coupled system to be implemented at CMC.

Forecasting the Short-Term Passenger Flow on High-Speed Railway with Neural Networks

PubMed Central

Xie, Mei-Quan; Li, Xia-Miao; Zhou, Wen-Liang; Fu, Yan-Bing

2014-01-01

Short-term passenger flow forecasting is an important component of transportation systems. The forecasting result can be applied to support transportation system operation and management such as operation planning and revenue management. In this paper, a divide-and-conquer method based on neural network and origin-destination (OD) matrix estimation is developed to forecast the short-term passenger flow in high-speed railway system. There are three steps in the forecasting method. Firstly, the numbers of passengers who arrive at each station or depart from each station are obtained from historical passenger flow data, which are OD matrices in this paper. Secondly, short-term passenger flow forecasting of the numbers of passengers who arrive at each station or depart from each station based on neural network is realized. At last, the OD matrices in short-term time are obtained with an OD matrix estimation method. The experimental results indicate that the proposed divide-and-conquer method performs well in forecasting the short-term passenger flow on high-speed railway. PMID:25544838

Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan

2015-08-05

Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reductionmore » in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.« less

NOMADS-NOAA Operational Model Archive and Distribution System

Science.gov Websites

Forecast Maps Climate Climate Prediction Climate Archives Weather Safety Storm Ready NOAA Central Library (16km) 6 hours grib filter http OpenDAP-alt URMA hourly - http - Climate Models Climate Forecast System Flux Products 6 hours grib filter http - Climate Forecast System 3D Pressure Products 6 hours grib

Potential for malaria seasonal forecasting in Africa

NASA Astrophysics Data System (ADS)

Tompkins, Adrian; Di Giuseppe, Francesca; Colon-Gonzalez, Felipe; Namanya, Didas; Friday, Agabe

2014-05-01

As monthly and seasonal dynamical prediction systems have improved their skill in the tropics over recent years, there is now the potential to use these forecasts to drive dynamical malaria modelling systems to provide early warnings in epidemic and meso-endemic regions. We outline a new pilot operational system that has been developed at ECMWF and ICTP. It uses a precipitation bias correction methodology to seamlessly join the monthly ensemble prediction system (EPS) and seasonal (system 4) forecast systems of ECMWF together. The resulting temperature and rainfall forecasts for Africa are then used to drive the recently developed ICTP malaria model known as VECTRI. The resulting coupled system of ECMWF climate forecasts and VECTRI thus produces predictions of malaria prevalence rates and transmission intensity across Africa. The forecasts are filtered to highlight the regions and months in which the system has particular value due to high year to year variability. In addition to epidemic areas, these also include meso and hyper-endemic regions which undergo considerable variability in the onset months. We demonstrate the limits of the forecast skill as a function of lead-time, showing that for many areas the dynamical system can add one to two months additional warning time to a system based on environmental monitoring. We then evaluate the past forecasts against district level case data in Uganda and show that when interventions can be discounted, the system can show significant skill at predicting interannual variability in transmission intensity up to 3 or 4 months ahead at the district scale. The prospects for a operational implementation will be briefly discussed.

Forecasting sea fog on the coast of southern China

NASA Astrophysics Data System (ADS)

Huang, H.; Huang, B.; Liu, C.; Tu, J.; Wen, G.; Mao, W.

2016-12-01

Forecast sea fog is still full of challenges. We have performed the numerical forecasting of sea fog on the coast of southern China by using the operational meso-scale regional model GRAPES (Global/Regional assimilation and prediction system). The GRAPES model horizontal resolution was 3km and with 66 vertical levels. A total of 72 hours forecasting of sea fog was conducted with hourly outputs over the sea fog event. The results show that the model system can predict reasonable characteristics of typical sea fog events on the coast of southern China. The scope of sea fog coincides with the observations of meteorological stations, the observations of the Marine Meteorological Science Experiment Base (MMSEB) at Bohe, Maoming and satellite products of sea fog. The goal of this study is to establish an operational numerical forecasting model system of sea fog on the coast of southern China.

Statistical Analysis of Atmospheric Forecast Model Accuracy - A Focus on Multiple Atmospheric Variables and Location-Based Analysis

DTIC Science & Technology

2014-04-01

WRF ) model is a numerical weather prediction system designed for operational forecasting and atmospheric research. This report examined WRF model... WRF , weather research and forecasting, atmospheric effects 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT SAR 18. NUMBER OF...and Forecasting ( WRF ) model. The authors would also like to thank Ms. Sherry Larson, STS Systems Integration, LLC, ARL Technical Publishing Branch

Operational Impact of Data Collected from the Global Hawk Unmanned Aircraft During SHOUT

NASA Astrophysics Data System (ADS)

Wick, G. A.; Dunion, J. P.; Sippel, J.; Cucurull, L.; Aksoy, A.; Kren, A.; Christophersen, H.; Black, P.

2017-12-01

The primary scientific goal of the Sensing Hazards with Operational Unmanned Technology (SHOUT) Project was to determine the potential utility of observations from high-altitude, long-endurance unmanned aircraft systems such as the Global Hawk (GH) aircraft to improve operational forecasts of high-impact weather events or mitigate potential degradation of forecasts in the event of a future gap in satellite coverage. Hurricanes and tropical cyclones are among the most potentially destructive high-impact weather events and pose a major forecasting challenge to NOAA. Major winter storms over the Pacific Ocean, including atmospheric river events, which make landfall and bring strong winds and extreme precipitation to the West Coast and Alaska are also important to forecast accurately because of their societal impact in those parts of the country. In response, the SHOUT project supported three field campaigns with the GH aircraft and dedicated data impact studies exploring the potential for the real-time data from the aircraft to improve the forecasting of both tropical cyclones and landfalling Pacific storms. Dropsonde observations from the GH aircraft were assimilated into the operational Hurricane Weather Research and Forecasting (HWRF) and Global Forecast System (GFS) models. The results from several diverse but complementary studies consistently demonstrated significant positive forecast benefits spanning the regional and global models. Forecast skill improvements within HWRF reached up to about 9% for track and 14% for intensity. Within GFS, track skill improvements for multi-storm averages exceeded 10% and improvements for individual storms reached over 20% depending on forecast lead time. Forecasted precipitation was also improved. Impacts for Pacific winter storms were smaller but still positive. The results are highly encouraging and support the potential for operational utilization of data from a platform like the GH. This presentation summarizes the observations collected and highlights the multiple impact studies completed.

Maintaining a Local Data Integration System in Support of Weather Forecast Operations

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian

2010-01-01

Since 2000, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) at Johnson Space Center in Houston, TX have used a local data integration system (LDIS) as part of their forecast and warning operations. The original LDIS was developed by NASA's Applied Meteorology Unit (AMU; Bauman et ai, 2004) in 1998 (Manobianco and Case 1998) and has undergone subsequent improvements. Each has benefited from three-dimensional (3-D) analyses that are delivered to forecasters every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather forecasts issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national- or regional-scale analysis packages. As such, it provides local forecasters with a more comprehensive understanding of evolving fine-scale weather features

Improving medium-range and seasonal hydroclimate forecasts in the southeast USA

NASA Astrophysics Data System (ADS)

Tian, Di

Accurate hydro-climate forecasts are important for decision making by water managers, agricultural producers, and other stake holders. Numerical weather prediction models and general circulation models may have potential for improving hydro-climate forecasts at different scales. In this study, forecast analogs of the Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) based on different approaches were evaluated for medium-range reference evapotranspiration (ETo), irrigation scheduling, and urban water demand forecasts in the southeast United States; the Climate Forecast System version 2 (CFSv2) and the North American national multi-model ensemble (NMME) were statistically downscaled for seasonal forecasts of ETo, precipitation (P) and 2-m temperature (T2M) at the regional level. The GFS mean temperature (Tmean), relative humidity, and wind speed (Wind) reforecasts combined with the climatology of Reanalysis 2 solar radiation (Rs) produced higher skill than using the direct GFS output only. Constructed analogs showed slightly higher skill than natural analogs for deterministic forecasts. Both irrigation scheduling driven by the GEFS-based ETo forecasts and GEFS-based ETo forecast skill were generally positive up to one week throughout the year. The GEFS improved ETo forecast skill compared to the GFS. The GEFS-based analog forecasts for the input variables of an operational urban water demand model were skillful when applied in the Tampa Bay area. The modified operational models driven by GEFS analog forecasts showed higher forecast skill than the operational model based on persistence. The results for CFSv2 seasonal forecasts showed maximum temperature (Tmax) and Rs had the greatest influence on ETo. The downscaled Tmax showed the highest predictability, followed by Tmean, Tmin, Rs, and Wind. The CFSv2 model could better predict ETo in cold seasons during El Nino Southern Oscillation (ENSO) events only when the forecast initial condition was in ENSO. Downscaled P and T2M forecasts were produced by directly downscaling the NMME P and T2M output or indirectly using the NMME forecasts of Nino3.4 sea surface temperatures to predict local-scale P and T2M. The indirect method generally showed the highest forecast skill which occurs in cold seasons. The bias-corrected NMME ensemble forecast skill did not outperform the best single model.

Systematic Evaluation of Stochastic Methods in Power System Scheduling and Dispatch with Renewable Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Yishen; Zhou, Zhi; Liu, Cong

2016-08-01

As more wind power and other renewable resources are being integrated into the electric power grid, the forecast uncertainty brings operational challenges for the power system operators. In this report, different operational strategies for uncertainty management are presented and evaluated. A comprehensive and consistent simulation framework is developed to analyze the performance of different reserve policies and scheduling techniques under uncertainty in wind power. Numerical simulations are conducted on a modified version of the IEEE 118-bus system with a 20% wind penetration level, comparing deterministic, interval, and stochastic unit commitment strategies. The results show that stochastic unit commitment provides amore » reliable schedule without large increases in operational costs. Moreover, decomposition techniques, such as load shift factor and Benders decomposition, can help in overcoming the computational obstacles to stochastic unit commitment and enable the use of a larger scenario set to represent forecast uncertainty. In contrast, deterministic and interval unit commitment tend to give higher system costs as more reserves are being scheduled to address forecast uncertainty. However, these approaches require a much lower computational effort Choosing a proper lower bound for the forecast uncertainty is important for balancing reliability and system operational cost in deterministic and interval unit commitment. Finally, we find that the introduction of zonal reserve requirements improves reliability, but at the expense of higher operational costs.« less

Evaluation of CMAQ and CAMx Ensemble Air Quality Forecasts during the 2015 MAPS-Seoul Field Campaign

NASA Astrophysics Data System (ADS)

Kim, E.; Kim, S.; Bae, C.; Kim, H. C.; Kim, B. U.

2015-12-01

The performance of Air quality forecasts during the 2015 MAPS-Seoul Field Campaign was evaluated. An forecast system has been operated to support the campaign's daily aircraft route decisions for airborne measurements to observe long-range transporting plume. We utilized two real-time ensemble systems based on the Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Comprehensive Air quality Model with extensions (CAMx) modeling framework and WRF-SMOKE- Community Multi_scale Air Quality (CMAQ) framework over northeastern Asia to simulate PM10 concentrations. Global Forecast System (GFS) from National Centers for Environmental Prediction (NCEP) was used to provide meteorological inputs for the forecasts. For an additional set of retrospective simulations, ERA Interim Reanalysis from European Centre for Medium-Range Weather Forecasts (ECMWF) was also utilized to access forecast uncertainties from the meteorological data used. Model Inter-Comparison Study for Asia (MICS-Asia) and National Institute of Environment Research (NIER) Clean Air Policy Support System (CAPSS) emission inventories are used for foreign and domestic emissions, respectively. In the study, we evaluate the CMAQ and CAMx model performance during the campaign by comparing the results to the airborne and surface measurements. Contributions of foreign and domestic emissions are estimated using a brute force method. Analyses on model performance and emissions will be utilized to improve air quality forecasts for the upcoming KORUS-AQ field campaign planned in 2016.

The Role of a Lid in the 31 May 1985 Tornado Outbreak

DTIC Science & Technology

1988-05-01

severe convection. Subjective analysis necessary to use this mode], however, is much too time consuming for operational forecasting . Therefore, a...analysis necessary to use this conceptual model, however, is much too time consuming for operational forecasting . Therefore, a computer application was...the forecasting of these systems continue to be desirable. Most severe convective storms in mid-latitudes are mesoscale phenomena. The term mesoscale

Operational specification and forecasting advances for Dst, LEO thermospheric densities, and aviation radiation dose and dose rate

NASA Astrophysics Data System (ADS)

Tobiska, W. Kent

Space weather’s effects upon the near-Earth environment are due to dynamic changes in the energy transfer processes from the Sun’s photons, particles, and fields. Of the space environment domains that are affected by space weather, the magnetosphere, thermosphere, and even troposphere are key regions that are affected. Space Environment Technologies (SET) has developed and is producing innovative space weather applications. Key operational systems for providing timely information about the effects of space weather on these domains are SET’s Magnetosphere Alert and Prediction System (MAPS), LEO Alert and Prediction System (LAPS), and Automated Radiation Measurements for Aviation Safety (ARMAS) system. MAPS provides a forecast Dst index out to 6 days through the data-driven, redundant data stream Anemomilos algorithm. Anemomilos uses observational proxies for the magnitude, location, and velocity of solar ejecta events. This forecast index is used by satellite operations to characterize upcoming geomagnetic storms, for example. In addition, an ENLIL/Rice Dst prediction out to several days has also been developed and will be described. LAPS is the SET fully redundant operational system providing recent history, current epoch, and forecast solar and geomagnetic indices for use in operational versions of the JB2008 thermospheric density model. The thermospheric densities produced by that system, driven by the LAPS data, are forecast to 72-hours to provide the global mass densities for satellite operators. ARMAS is a project that has successfully demonstrated the operation of a micro dosimeter on aircraft to capture the real-time radiation environment due to Galactic Cosmic Rays and Solar Energetic Particles. The dose and dose-rates are captured on aircraft, downlinked in real-time via the Iridium satellites, processed on the ground, incorporated into the most recent NAIRAS global radiation climatology data runs, and made available to end users via the web and smart phone apps. ARMAS provides the “weather” of the radiation environment to improve air-crew and passenger safety. Many of the data products from MAPS, LAPS, and ARMAS are available on the SpaceWx smartphone app for iPhone, iPad, iPod, and Android professional users and public space weather education. We describe recent forecasting advances for moving the space weather information from these automated systems into operational, derivative products for communications, aviation, and satellite operations uses.

AN OPERATIONAL EVALUATION OF THE ETA - CMAQ AIR QUALITY FORECAST MODEL

EPA Science Inventory

The National Oceanic and Atmospheric Administration (NOAA), in partnership with the United States Environmental Protection Agency (EPA), are developing an operational, nationwide Air Quality Forecasting (AQF) system. An experimental phase of this program, which couples NOAA's Et...

Distributed HUC-based modeling with SUMMA for ensemble streamflow forecasting over large regional domains.

NASA Astrophysics Data System (ADS)

Saharia, M.; Wood, A.; Clark, M. P.; Bennett, A.; Nijssen, B.; Clark, E.; Newman, A. J.

2017-12-01

Most operational streamflow forecasting systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow require an experienced human forecaster. But this approach faces challenges surrounding process reproducibility, hindcasting capability, and extension to large domains. The operational hydrologic community is increasingly moving towards `over-the-loop' (completely automated) large-domain simulations yet recent developments indicate a widespread lack of community knowledge about the strengths and weaknesses of such systems for forecasting. A realistic representation of land surface hydrologic processes is a critical element for improving forecasts, but often comes at the substantial cost of forecast system agility and efficiency. While popular grid-based models support the distributed representation of land surface processes, intermediate-scale Hydrologic Unit Code (HUC)-based modeling could provide a more efficient and process-aligned spatial discretization, reducing the need for tradeoffs between model complexity and critical forecasting requirements such as ensemble methods and comprehensive model calibration. The National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the USACE to implement, assess, and demonstrate real-time, over-the-loop distributed streamflow forecasting for several large western US river basins and regions. In this presentation, we present early results from short to medium range hydrologic and streamflow forecasts for the Pacific Northwest (PNW). We employ a real-time 1/16th degree daily ensemble model forcings as well as downscaled Global Ensemble Forecasting System (GEFS) meteorological forecasts. These datasets drive an intermediate-scale configuration of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model, which represents the PNW using over 11,700 HUCs. The system produces not only streamflow forecasts (using the MizuRoute channel routing tool) but also distributed model states such as soil moisture and snow water equivalent. We also describe challenges in distributed model-based forecasting, including the application and early results of real-time hydrologic data assimilation.

AIRS Impact on the Analysis and Forecast Track of Tropical Cyclone Nargis in a Global Data Assimilation and Forecasting System

NASA Technical Reports Server (NTRS)

Reale, O.; Lau, W.K.; Susskind, J.; Brin, E.; Liu, E.; Riishojgaard, L. P.; Rosenburg, R.; Fuentes, M.

2009-01-01

Tropical cyclones in the northern Indian Ocean pose serious challenges to operational weather forecasting systems, partly due to their shorter lifespan and more erratic track, compared to those in the Atlantic and the Pacific. Moreover, the automated analyses of cyclones over the northern Indian Ocean, produced by operational global data assimilation systems (DASs), are generally of inferior quality than in other basins. In this work it is shown that the assimilation of Atmospheric Infrared Sounder (AIRS) temperature retrievals under partial cloudy conditions can significantly impact the representation of the cyclone Nargis (which caused devastating loss of life in Myanmar in May 2008) in a global DAS. Forecasts produced from these improved analyses by a global model produce substantially smaller track errors. The impact of the assimilation of clear-sky radiances on the same DAS and forecasting system is positive, but smaller than the one obtained by ingestion of AIRS retrievals, possibly due to poorer coverage.

Resolution of Probabilistic Weather Forecasts with Application in Disease Management.

PubMed

Hughes, G; McRoberts, N; Burnett, F J

2017-02-01

Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.

Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

DOE PAGES

Zhang, Jie; Hodge, Bri -Mathias; Lu, Siyuan; ...

2015-11-10

Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based onmore » state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.« less

Nationwide validation of ensemble streamflow forecasts from the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service

NASA Astrophysics Data System (ADS)

Lee, H. S.; Liu, Y.; Ward, J.; Brown, J.; Maestre, A.; Herr, H.; Fresch, M. A.; Wells, E.; Reed, S. M.; Jones, E.

2017-12-01

The National Weather Service's (NWS) Office of Water Prediction (OWP) recently launched a nationwide effort to verify streamflow forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for a majority of forecast locations across the 13 River Forecast Centers (RFCs). Known as the HEFS Baseline Validation (BV), the project involves a joint effort between the OWP and the RFCs. It aims to provide a geographically consistent, statistically robust validation, and a benchmark to guide the operational implementation of the HEFS, inform practical applications, such as impact-based decision support services, and to provide an objective framework for evaluating strategic investments in the HEFS. For the BV, HEFS hindcasts are issued once per day on a 12Z cycle for the period of 1985-2015 with a forecast horizon of 30 days. For the first two weeks, the hindcasts are forced with precipitation and temperature ensemble forecasts from the Global Ensemble Forecast System of the National Centers for Environmental Prediction, and by resampled climatology for the remaining period. The HEFS-generated ensemble streamflow hindcasts are verified using the Ensemble Verification System. Skill is assessed relative to streamflow hindcasts generated from NWS' current operational system, namely climatology-based Ensemble Streamflow Prediction. In this presentation, we summarize the results and findings to date.

Integration of Weather Data into Airspace and Traffic Operations Simulation (ATOS) for Trajectory- Based Operations Research

NASA Technical Reports Server (NTRS)

Peters, Mark; Boisvert, Ben; Escala, Diego

2009-01-01

Explicit integration of aviation weather forecasts with the National Airspace System (NAS) structure is needed to improve the development and execution of operationally effective weather impact mitigation plans and has become increasingly important due to NAS congestion and associated increases in delay. This article considers several contemporary weather-air traffic management (ATM) integration applications: the use of probabilistic forecasts of visibility at San Francisco, the Route Availability Planning Tool to facilitate departures from the New York airports during thunderstorms, the estimation of en route capacity in convective weather, and the application of mixed-integer optimization techniques to air traffic management when the en route and terminal capacities are varying with time because of convective weather impacts. Our operational experience at San Francisco and New York coupled with very promising initial results of traffic flow optimizations suggests that weather-ATM integrated systems warrant significant research and development investment. However, they will need to be refined through rapid prototyping at facilities with supportive operational users We have discussed key elements of an emerging aviation weather research area: the explicit integration of aviation weather forecasts with NAS structure to improve the effectiveness and timeliness of weather impact mitigation plans. Our insights are based on operational experiences with Lincoln Laboratory-developed integrated weather sensing and processing systems, and derivative early prototypes of explicit ATM decision support tools such as the RAPT in New York City. The technical components of this effort involve improving meteorological forecast skill, tailoring the forecast outputs to the problem of estimating airspace impacts, developing models to quantify airspace impacts, and prototyping automated tools that assist in the development of objective broad-area ATM strategies, given probabilistic weather forecasts. Lincoln Laboratory studies and prototype demonstrations in this area are helping to define the weather-assimilated decision-making system that is envisioned as a key capability for the multi-agency Next Generation Air Transportation System [1]. The Laboratory's work in this area has involved continuing, operations-based evolution of both weather forecasts and models for weather impacts on the NAS. Our experience has been that the development of usable ATM technologies that address weather impacts must proceed via rapid prototyping at facilities whose users are highly motivated to participate in system evolution.

On the reliability of seasonal climate forecasts

PubMed Central

Weisheimer, A.; Palmer, T. N.

2014-01-01

Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1–5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that ‘goodness’ should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a ‘5’ should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of ‘goodness’ rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching ‘5’ across all regions and variables in 30 years time. PMID:24789559

The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season

PubMed Central

Pavlovic, Radenko; Chen, Jack; Anderson, Kerry; Moran, Michael D.; Beaulieu, Paul-André; Davignon, Didier; Cousineau, Sophie

2016-01-01

ABSTRACT Environment and Climate Change Canada’s FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2–July 15, and August 15–31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of –7.3 µg m−3 and 3.1 µg m−3), it showed better forecast skill than the RAQDPS (MB of –11.7 µg m−3 and –5.8 µg m−3) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m−3 also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR). Implications: Smoke from wildfires can have a large impact on regional air quality (AQ) and can expose populations to elevated pollution levels. Environment and Climate Change Canada has been producing operational air quality forecasts for all of Canada since 2009 and is now working to include near-real-time wildfire emissions (NRTWE) in its operational AQ forecasting system. An experimental forecast system named FireWork, which includes NRTWE, has been undergoing testing and evaluation since 2013. A performance analysis of FireWork forecasts for the 2015 wildfire season shows that FireWork provides significant improvements to surface PM2.5 forecasts and valuable guidance to regional forecasters and first responders. PMID:26934496

Improving global flood risk awareness through collaborative research: Id-Lab

NASA Astrophysics Data System (ADS)

Weerts, A.; Zijderveld, A.; Cumiskey, L.; Buckman, L.; Verlaan, M.; Baart, F.

2015-12-01

Scientific and end-user collaboration on operational flood risk modelling and forecasting requires an environment where scientists and end-users can physically work together and demonstrate, enhance and learn about new tools, methods and models for forecasting and warning purposes. Therefore, Deltares has built a real-time demonstration, training and research infrastructure ('operational' room and ICT backend). This research infrastructure supports various functions like (1) Real time response and disaster management, (2) Training, (3) Collaborative Research, (4) Demonstration. The research infrastructure will be used for a mixture of these functions on a regular basis by Deltares and a multitude of both scientists as well as end users such as universities, research institutes, consultants, governments and aid agencies. This infrastructure facilitates emergency advice and support during international and national disasters caused by rainfall, tropical cyclones or tsunamis. It hosts research flood and storm surge forecasting systems for global/continental/regional scale. It facilitates training for emergency & disaster management (along with hosting forecasting system user trainings in for instance the forecasting platform Delft-FEWS) both internally and externally. The facility is expected to inspire and initiate creative innovations by bringing together different experts from various organizations. The room hosts interactive modelling developments, participatory workshops and stakeholder meetings. State of the art tools, models and software, being applied across the globe are available and on display within the facility. We will present the Id-Lab in detail and we will put particular focus on the global operational forecasting systems GLOFFIS (Global Flood Forecasting Information System) and GLOSSIS (Global Storm Surge Information System).

Recent developments of DMI's operational system: Coupled Ecosystem-Circulation-and SPM model.

NASA Astrophysics Data System (ADS)

Murawski, Jens; Tian, Tian; Dobrynin, Mikhail

2010-05-01

ECOOP is a pan- European project with 72 partners from 29 countries around the Baltic Sea, the North Sea, the Iberia-Biscay-Ireland region, the Mediterranean Sea and the Black Sea. The project aims at the development and the integration of the different coastal and regional observation and forecasting systems. The Danish Meteorological Institute DMI coordinates the project and is responsible for the Baltic Sea regional forecasting System. Over the project period, the Baltic Sea system was developed from a purely hydro dynamical model (version V1), running operationally since summer 2009, to a coupled model platform (version V2), including model components for the simulation of suspended particles, data assimilation and ecosystem variables. The ECOOP V2 model is currently tested and validated, and will replace the V1 version soon. The coupled biogeochemical- and circulation model runs operationally since November 2009. The daily forecasts are presented at DMI's homepage http:/ocean.dmi.dk. The presentation includes a short description of the ECOOP forecasting system, discusses the model results and shows the outcome of the model validation.

Predictability of Solar Radiation for Photovoltaics systems over Europe: from short-term to seasonal time-scales

NASA Astrophysics Data System (ADS)

De Felice, Matteo; Petitta, Marcello; Ruti, Paolo

2014-05-01

Photovoltaic diffusion is steadily growing on Europe, passing from a capacity of almost 14 GWp in 2011 to 21.5 GWp in 2012 [1]. Having accurate forecast is needed for planning and operational purposes, with the possibility to model and predict solar variability at different time-scales. This study examines the predictability of daily surface solar radiation comparing ECMWF operational forecasts with CM-SAF satellite measurements on the Meteosat (MSG) full disk domain. Operational forecasts used are the IFS system up to 10 days and the System4 seasonal forecast up to three months. Forecast are analysed considering average and variance of errors, showing error maps and average on specific domains with respect to prediction lead times. In all the cases, forecasts are compared with predictions obtained using persistence and state-of-art time-series models. We can observe a wide range of errors, with the performance of forecasts dramatically affected by orography and season. Lower errors are on southern Italy and Spain, with errors on some areas consistently under 10% up to ten days during summer (JJA). Finally, we conclude the study with some insight on how to "translate" the error on solar radiation to error on solar power production using available production data from solar power plants. [1] EurObserver, "Baromètre Photovoltaïque, Le journal des énergies renouvables, April 2012."

Drought Monitoring and Forecasting Using the Princeton/U Washington National Hydrologic Forecasting System

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Roundy, J. K.; Lettenmaier, D. P.; Mo, K. C.; Xia, Y.; Ek, M. B.

2011-12-01

Extreme hydrologic events in the form of droughts or floods are a significant source of social and economic damage in many parts of the world. Having sufficient warning of extreme events allows managers to prepare for and reduce the severity of their impacts. A hydrologic forecast system can give seasonal predictions that can be used by mangers to make better decisions; however there is still much uncertainty associated with such a system. Therefore it is important to understand the forecast skill of the system before transitioning to operational usage. Seasonal reforecasts (1982 - 2010) from the NCEP Climate Forecast System (both version 1 (CFS) and version 2 (CFSv2), Climate Prediction Center (CPC) outlooks and the European Seasonal Interannual Prediction (EUROSIP) system, are assessed for forecasting skill in drought prediction across the U.S., both singularly and as a multi-model system The Princeton/U Washington national hydrologic monitoring and forecast system is being implemented at NCEP/EMC via their Climate Test Bed as the experimental hydrological forecast system to support U.S. operational drought prediction. Using our system, the seasonal forecasts are biased corrected, downscaled and used to drive the Variable Infiltration Capacity (VIC) land surface model to give seasonal forecasts of hydrologic variables with lead times of up to six months. Results are presented for a number of events, with particular focus on the Apalachicola-Chattahoochee-Flint (ACF) River Basin in the South Eastern United States, which has experienced a number of severe droughts in recent years and is a pilot study basin for the National Integrated Drought Information System (NIDIS). The performance of the VIC land surface model is evaluated using observational forcing when compared to observed streamflow. The effectiveness of the forecast system to predict streamflow and soil moisture is evaluated when compared with observed streamflow and modeled soil moisture driven by observed atmospheric forcing. The forecast skills from the dynamical seasonal models (CFSv1, CFSv2, EUROSIP) and CPC are also compared with forecasts based on the Ensemble Streamflow Prediction (ESP) method, which uses initial conditions and historical forcings to generate seasonal forecasts. The skill of the system to predict drought, drought recovery and related hydrological conditions such as low-flows is assessed, along with quantified uncertainty.

Satellite freeze forecast system. Operating/troubleshooting manual

NASA Technical Reports Server (NTRS)

Martsolf, J. D. (Principal Investigator)

1983-01-01

Examples of operational procedures are given to assist users of the satellites freeze forecasting system (SFFS) in logging in on to the computer, executing the programs in the menu, logging off the computer, and setting up the automatic system. Directions are also given for displaying, acquiring, and listing satellite maps; for communicating via terminal and monitor displays; and for what to do when the SFFS doesn't work. Administrative procedures are included.

Economic analysis for transmission operation and planning

NASA Astrophysics Data System (ADS)

Zhou, Qun

2011-12-01

Restructuring of the electric power industry has caused dramatic changes in the use of transmission system. The increasing congestion conditions as well as the necessity of integrating renewable energy introduce new challenges and uncertainties to transmission operation and planning. Accurate short-term congestion forecasting facilitates market traders in bidding and trading activities. Cost sharing and recovery issue is a major impediment for long-term transmission investment to integrate renewable energy. In this research, a new short-term forecasting algorithm is proposed for predicting congestion, LMPs, and other power system variables based on the concept of system patterns. The advantage of this algorithm relative to standard statistical forecasting methods is that structural aspects underlying power market operations are exploited to reduce the forecasting error. The advantage relative to previously proposed structural forecasting methods is that data requirements are substantially reduced. Forecasting results based on a NYISO case study demonstrate the feasibility and accuracy of the proposed algorithm. Moreover, a negotiation methodology is developed to guide transmission investment for integrating renewable energy. Built on Nash Bargaining theory, the negotiation of investment plans and payment rate can proceed between renewable generation and transmission companies for cost sharing and recovery. The proposed approach is applied to Garver's six bus system. The numerical results demonstrate fairness and efficiency of the approach, and hence can be used as guidelines for renewable energy investors. The results also shed light on policy-making of renewable energy subsidies.

Development of an Adaptable Display and Diagnostic System for the Evaluation of Tropical Cyclone Forecasts

NASA Astrophysics Data System (ADS)

Kucera, P. A.; Burek, T.; Halley-Gotway, J.

2015-12-01

NCAR's Joint Numerical Testbed Program (JNTP) focuses on the evaluation of experimental forecasts of tropical cyclones (TCs) with the goal of developing new research tools and diagnostic evaluation methods that can be transitioned to operations. Recent activities include the development of new TC forecast verification methods and the development of an adaptable TC display and diagnostic system. The next generation display and diagnostic system is being developed to support evaluation needs of the U.S. National Hurricane Center (NHC) and broader TC research community. The new hurricane display and diagnostic capabilities allow forecasters and research scientists to more deeply examine the performance of operational and experimental models. The system is built upon modern and flexible technology that includes OpenLayers Mapping tools that are platform independent. The forecast track and intensity along with associated observed track information are stored in an efficient MySQL database. The system provides easy-to-use interactive display system, and provides diagnostic tools to examine forecast track stratified by intensity. Consensus forecasts can be computed and displayed interactively. The system is designed to display information for both real-time and for historical TC cyclones. The display configurations are easily adaptable to meet the needs of the end-user preferences. Ongoing enhancements include improving capabilities for stratification and evaluation of historical best tracks, development and implementation of additional methods to stratify and compute consensus hurricane track and intensity forecasts, and improved graphical display tools. The display is also being enhanced to incorporate gridded forecast, satellite, and sea surface temperature fields. The presentation will provide an overview of the display and diagnostic system development and demonstration of the current capabilities.

Characteristics of Operational Space Weather Forecasting: Observations and Models

NASA Astrophysics Data System (ADS)

Berger, Thomas; Viereck, Rodney; Singer, Howard; Onsager, Terry; Biesecker, Doug; Rutledge, Robert; Hill, Steven; Akmaev, Rashid; Milward, George; Fuller-Rowell, Tim

2015-04-01

In contrast to research observations, models and ground support systems, operational systems are characterized by real-time data streams and run schedules, with redundant backup systems for most elements of the system. We review the characteristics of operational space weather forecasting, concentrating on the key aspects of ground- and space-based observations that feed models of the coupled Sun-Earth system at the NOAA/Space Weather Prediction Center (SWPC). Building on the infrastructure of the National Weather Service, SWPC is working toward a fully operational system based on the GOES weather satellite system (constant real-time operation with back-up satellites), the newly launched DSCOVR satellite at L1 (constant real-time data network with AFSCN backup), and operational models of the heliosphere, magnetosphere, and ionosphere/thermosphere/mesophere systems run on the Weather and Climate Operational Super-computing System (WCOSS), one of the worlds largest and fastest operational computer systems that will be upgraded to a dual 2.5 Pflop system in 2016. We review plans for further operational space weather observing platforms being developed in the context of the Space Weather Operations Research and Mitigation (SWORM) task force in the Office of Science and Technology Policy (OSTP) at the White House. We also review the current operational model developments at SWPC, concentrating on the differences between the research codes and the modified real-time versions that must run with zero fault tolerance on the WCOSS systems. Understanding the characteristics and needs of the operational forecasting community is key to producing research into the coupled Sun-Earth system with maximal societal benefit.

Solar and Wind Forecasting | Grid Modernization | NREL

Science.gov Websites

and Wind Forecasting Solar and Wind Forecasting As solar and wind power become more common system operators. An aerial photo of the National Wind Technology Center's PV arrays. Capabilities value of accurate forecasting Wind power visualization to direct questions and feedback during industry

An online tool for Operational Probabilistic Drought Forecasting System (OPDFS): a Statistical-Dynamical Framework

NASA Astrophysics Data System (ADS)

Zarekarizi, M.; Moradkhani, H.; Yan, H.

2017-12-01

The Operational Probabilistic Drought Forecasting System (OPDFS) is an online tool recently developed at Portland State University for operational agricultural drought forecasting. This is an integrated statistical-dynamical framework issuing probabilistic drought forecasts monthly for the lead times of 1, 2, and 3 months. The statistical drought forecasting method utilizes copula functions in order to condition the future soil moisture values on the antecedent states. Due to stochastic nature of land surface properties, the antecedent soil moisture states are uncertain; therefore, data assimilation system based on Particle Filtering (PF) is employed to quantify the uncertainties associated with the initial condition of the land state, i.e. soil moisture. PF assimilates the satellite soil moisture data to Variable Infiltration Capacity (VIC) land surface model and ultimately updates the simulated soil moisture. The OPDFS builds on the NOAA's seasonal drought outlook by offering drought probabilities instead of qualitative ordinal categories and provides the user with the probability maps associated with a particular drought category. A retrospective assessment of the OPDFS showed that the forecasting of the 2012 Great Plains and 2014 California droughts were possible at least one month in advance. The OPDFS offers a timely assistance to water managers, stakeholders and decision-makers to develop resilience against uncertain upcoming droughts.

Advanced solar irradiances applied to satellite and ionospheric operational systems

NASA Astrophysics Data System (ADS)

Tobiska, W. Kent; Schunk, Robert; Eccles, Vince; Bouwer, Dave

Satellite and ionospheric operational systems require solar irradiances in a variety of time scales and spectral formats. We describe the development of a system using operational grade solar irradiances that are applied to empirical thermospheric density models and physics-based ionospheric models used by operational systems that require a space weather characterization. The SOLAR2000 (S2K) and SOLARFLARE (SFLR) models developed by Space Environment Technologies (SET) provide solar irradiances from the soft X-rays (XUV) through the Far Ultraviolet (FUV) spectrum. The irradiances are provided as integrated indices for the JB2006 empirical atmosphere density models and as line/band spectral irradiances for the physics-based Ionosphere Forecast Model (IFM) developed by the Space Environment Corporation (SEC). We describe the integration of these irradiances in historical, current epoch, and forecast modes through the Communication Alert and Prediction System (CAPS). CAPS provides real-time and forecast HF radio availability for global and regional users and global total electron content (TEC) conditions.

Short-Term Prediction Research and Transition (SPoRT) Center: Transitioning Satellite Data to Operations

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley

2012-01-01

The Short-term Prediction Research and Transition (SPoRT) Center located at NASA Marshall Space Flight Center has been conducting testbed activities aimed at transitioning satellite products to National Weather Service operational end users for the last 10 years. SPoRT is a NASA/NOAA funded project that has set the bar for transition of products to operational end users through a paradigm of understanding forecast challenges and forecaster needs, displaying products in end users decision support systems, actively assessing the operational impact of these products, and improving products based on forecaster feedback. Aiming for quality partnerships rather than a large quantity of data users, SPoRT has become a community leader in training operational forecasters on the use of up-and-coming satellite data through the use of legacy instruments and proxy data. Traditionally, SPoRT has supplied satellite imagery and products from NASA instruments such as the Moderate-resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS). However, recently, SPoRT has been funded by the GOES-R and Joint Polar Satellite System (JPSS) Proving Grounds to accelerate the transition of selected imagery and products to help improve forecaster awareness of upcoming operational data from the Visible Infrared Imager Radiometer Suite (VIIRS), Cross-track Infrared Sounder (CrIS), Advanced Baseline Imager (ABI), and Geostationary Lightning Mapper (GLM). This presentation provides background on the SPoRT Center, the SPoRT paradigm, and some example products that SPoRT is excited to work with forecasters to evaluate.

Nowcasting system MeteoExpert at Irkutsk airport

NASA Astrophysics Data System (ADS)

Bazlova, Tatiana; Bocharnikov, Nikolai; Solonin, Alexander

2016-04-01

Airport operations are significantly impacted by low visibility concerned with fog. Generation of accurate and timely nowcast products is a basis of early warning automated system providing information about significant weather conditions for decision-makers. Nowcasting system MeteoExpert has been developed that provides aviation forecasters with 0-6 hour nowcasts of the weather conditions including fog and low visibility. The system has been put into operation at the airport Irkutsk since August 2014. Aim is to increase an accuracy of fog forecasts, contributing to the airport safety, efficiency and capacity improvement. Designed for operational use numerical model of atmospheric boundary layer runs with a 10-minute update cycle. An important component of the system is the use of AWOS at the airdrome and three additional automatic weather stations at fogging sites in the vicinity of the airdrome. Nowcasts are visualized on a screen of forecaster's workstation and dedicated website. Nowcasts have been verified against actual observations.

Skill of real-time operational forecasts with the APCC multi-model ensemble prediction system during the period 2008-2015

NASA Astrophysics Data System (ADS)

Min, Young-Mi; Kryjov, Vladimir N.; Oh, Sang Myeong; Lee, Hyun-Ju

2017-12-01

This paper assesses the real-time 1-month lead forecasts of 3-month (seasonal) mean temperature and precipitation on a monthly basis issued by the Asia-Pacific Economic Cooperation Climate Center (APCC) for 2008-2015 (8 years, 96 forecasts). It shows the current level of the APCC operational multi-model prediction system performance. The skill of the APCC forecasts strongly depends on seasons and regions that it is higher for the tropics and boreal winter than for the extratropics and boreal summer due to direct effects and remote teleconnections from boundary forcings. There is a negative relationship between the forecast skill and its interseasonal variability for both variables and the forecast skill for precipitation is more seasonally and regionally dependent than that for temperature. The APCC operational probabilistic forecasts during this period show a cold bias (underforecasting of above-normal temperature and overforecasting of below-normal temperature) underestimating a long-term warming trend. A wet bias is evident for precipitation, particularly in the extratropical regions. The skill of both temperature and precipitation forecasts strongly depends upon the ENSO strength. Particularly, the highest forecast skill noted in 2015/2016 boreal winter is associated with the strong forcing of an extreme El Nino event. Meanwhile, the relatively low skill is associated with the transition and/or continuous ENSO-neutral phases of 2012-2014. As a result the skill of real-time forecast for boreal winter season is higher than that of hindcast. However, on average, the level of forecast skill during the period 2008-2015 is similar to that of hindcast.

Spatio-temporal pattern clustering for skill assessment of the Korea Operational Oceanographic System

NASA Astrophysics Data System (ADS)

Kim, J.; Park, K.

2016-12-01

In order to evaluate the performance of operational forecast models in the Korea operational oceanographic system (KOOS) which has been developed by Korea Institute of Ocean Science and Technology (KIOST), a skill assessment (SA) tool has developed and provided multiple skill metrics including not only correlation and error skills by comparing predictions and observation but also pattern clustering with numerical models, satellite, and observation. The KOOS has produced 72 hours forecast information on atmospheric and hydrodynamic forecast variables of wind, pressure, current, tide, wave, temperature, and salinity at every 12 hours per day produced by operating numerical models such as WRF, ROMS, MOM5, WW-III, and SWAN and the SA has conducted to evaluate the forecasts. We have been operationally operated several kinds of numerical models such as WRF, ROMS, MOM5, MOHID, WW-III. Quantitative assessment of operational ocean forecast model is very important to provide accurate ocean forecast information not only to general public but also to support ocean-related problems. In this work, we propose a method of pattern clustering using machine learning method and GIS-based spatial analytics to evaluate spatial distribution of numerical models and spatial observation data such as satellite and HF radar. For the clustering, we use 10 or 15 years-long reanalysis data which was computed by the KOOS, ECMWF, and HYCOM to make best matching clusters which are classified physical meaning with time variation and then we compare it with forecast data. Moreover, for evaluating current, we develop extraction method of dominant flow and apply it to hydrodynamic models and HF radar's sea surface current data. By applying pattern clustering method, it allows more accurate and effective assessment of ocean forecast models' performance by comparing not only specific observation positions which are determined by observation stations but also spatio-temporal distribution of whole model areas. We believe that our proposed method will be very useful to examine and evaluate large amount of numerical modeling data as well as satellite data.

Development of the GEM-MACH-FireWork System: An Air Quality Model with On-line Wildfire Emissions within the Canadian Operational Air Quality Forecast System

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Chen, Jack; Beaulieu, Paul-Andre; Anselmp, David; Gravel, Sylvie; Moran, Mike; Menard, Sylvain; Davignon, Didier

2014-05-01

A wildfire emissions processing system has been developed to incorporate near-real-time emissions from wildfires and large prescribed burns into Environment Canada's real-time GEM-MACH air quality (AQ) forecast system. Since the GEM-MACH forecast domain covers Canada and most of the U.S.A., including Alaska, fire location information is needed for both of these large countries. During AQ model runs, emissions from individual fire sources are injected into elevated model layers based on plume-rise calculations and then transport and chemistry calculations are performed. This "on the fly" approach to the insertion of the fire emissions provides flexibility and efficiency since on-line meteorology is used and computational overhead in emissions pre-processing is reduced. GEM-MACH-FireWork, an experimental wildfire version of GEM-MACH, was run in real-time mode for the summers of 2012 and 2013 in parallel with the normal operational version. 48-hour forecasts were generated every 12 hours (at 00 and 12 UTC). Noticeable improvements in the AQ forecasts for PM2.5 were seen in numerous regions where fire activity was high. Case studies evaluating model performance for specific regions and computed objective scores will be included in this presentation. Using the lessons learned from the last two summers, Environment Canada will continue to work towards the goal of incorporating near-real-time intermittent wildfire emissions into the operational air quality forecast system.

A Real-Time California Coastal Ocean Nowcast/Forecast System: Skill Assessment, User Products, and Transition from Research to Operations

NASA Astrophysics Data System (ADS)

Farrara, J. D.; Chao, Y.; Chai, F.; Zhang, H.

2016-02-01

The real-time California coastal ocean nowcast/forecast system is described. The model is based on the Regional Ocean Modeling System (ROMS) and covers the entire California coastal ocean with a horizontal resolution of 3 km and 40 vertical layers. The atmospheric forcing is derived from the operational regional atmospheric model forecasts. The lateral boundary conditions are provided by the operational ocean model forecasts. A multi-scale 3-dimensional variational (3DVAR) data assimilation scheme is used to assimilate both in situ (e.g., vertical profiles of temperature and salinity) and remotely sensed data from both satellite (e.g., sea surface temperature and sea surface height) and land-based platforms (e.g., surface current). The performance of our nowcast/forecast system is evaluated in real-time by a number of metrics that are published as soon as they become available. User tools and products have been developed for both general users and super-users (e.g., NOAA Office of Response and Restoration and USCG). Recent results comparing the 3DVAR with the ensemble Kalman Filter (EnKF) using Data Assimilation Research Testbed (DART) will be presented. Preliminary results coupling the ROMS circulation model with a biogeochemistry/ecosystem model (i.e., CoSiNE) will also discussed. Cloud computing services (e.g., Microsoft, Google) are now being tested to increase the reliability and timeliness in order to be accepted as a truly operational system in the near future.

Testing a Coupled Global-limited-area Data Assimilation System using Observations from the 2004 Pacific Typhoon Season

NASA Astrophysics Data System (ADS)

Holt, C. R.; Szunyogh, I.; Gyarmati, G.; Hoffman, R. N.; Leidner, M.

2011-12-01

Tropical cyclone (TC) track and intensity forecasts have improved in recent years due to increased model resolution, improved data assimilation, and the rapid increase in the number of routinely assimilated observations over oceans. The data assimilation approach that has received the most attention in recent years is Ensemble Kalman Filtering (EnKF). The most attractive feature of the EnKF is that it uses a fully flow-dependent estimate of the error statistics, which can have important benefits for the analysis of rapidly developing TCs. We implement the Local Ensemble Transform Kalman Filter algorithm, a vari- ation of the EnKF, on a reduced-resolution version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model and the NCEP Regional Spectral Model (RSM) to build a coupled global-limited area anal- ysis/forecast system. This is the first time, to our knowledge, that such a system is used for the analysis and forecast of tropical cyclones. We use data from summer 2004 to study eight tropical cyclones in the Northwest Pacific. The benchmark data sets that we use to assess the performance of our system are the NCEP Reanalysis and the NCEP Operational GFS analyses from 2004. These benchmark analyses were both obtained by the Statistical Spectral Interpolation, which was the operational data assimilation system of NCEP in 2004. The GFS Operational analysis assimilated a large number of satellite radiance observations in addition to the observations assimilated in our system. All analyses are verified against the Joint Typhoon Warning Center Best Track data set. The errors are calculated for the position and intensity of the TCs. The global component of the ensemble-based system shows improvement in po- sition analysis over the NCEP Reanalysis, but shows no significant difference from the NCEP operational analysis for most of the storm tracks. The regional com- ponent of our system improves position analysis over all the global analyses. The intensity analyses, measured by the minimum sea level pressure, are of similar quality in all of the analyses. Regional deterministic forecasts started from our analyses are generally not significantly different from those started from the GFS operational analysis. On average, the regional experiments performed better for longer than 48 h sea level pressure forecasts, while the global forecast performed better in predicting the position for longer than 48 h.

HEPS4Power - Extended-range Hydrometeorological Ensemble Predictions for Improved Hydropower Operations and Revenues

NASA Astrophysics Data System (ADS)

Bogner, Konrad; Monhart, Samuel; Liniger, Mark; Spririg, Christoph; Jordan, Fred; Zappa, Massimiliano

2015-04-01

In recent years large progresses have been achieved in the operational prediction of floods and hydrological drought with up to ten days lead time. Both the public and the private sectors are currently using probabilistic runoff forecast in order to monitoring water resources and take actions when critical conditions are to be expected. The use of extended-range predictions with lead times exceeding 10 days is not yet established. The hydropower sector in particular might have large benefits from using hydro meteorological forecasts for the next 15 to 60 days in order to optimize the operations and the revenues from their watersheds, dams, captions, turbines and pumps. The new Swiss Competence Centers in Energy Research (SCCER) targets at boosting research related to energy issues in Switzerland. The objective of HEPS4POWER is to demonstrate that operational extended-range hydro meteorological forecasts have the potential to become very valuable tools for fine tuning the production of energy from hydropower systems. The project team covers a specific system-oriented value chain starting from the collection and forecast of meteorological data (MeteoSwiss), leading to the operational application of state-of-the-art hydrological models (WSL) and terminating with the experience in data presentation and power production forecasts for end-users (e-dric.ch). The first task of the HEPS4POWER will be the downscaling and post-processing of ensemble extended-range meteorological forecasts (EPS). The goal is to provide well-tailored forecasts of probabilistic nature that should be reliable in statistical and localized at catchment or even station level. The hydrology related task will consist in feeding the post-processed meteorological forecasts into a HEPS using a multi-model approach by implementing models with different complexity. Also in the case of the hydrological ensemble predictions, post-processing techniques need to be tested in order to improve the quality of the forecasts against observed discharge. Analysis should be specifically oriented to the maximisation of hydroelectricity production. Thus, verification metrics should include economic measures like cost loss approaches. The final step will include the transfer of the HEPS system to several hydropower systems, the connection with the energy market prices and the development of probabilistic multi-reservoir production and management optimizations guidelines. The baseline model chain yielding three-days forecasts established for a hydropower system in southern-Switzerland will be presented alongside with the work-plan to achieve seasonal ensemble predictions.

An Approach to Assess Observation Impact Based on Observation-Minus-Forecast Residuals

NASA Technical Reports Server (NTRS)

Todling, Ricardo

2009-01-01

Langland and Baker (2004) introduced an approach to assess the impact of observations on the forecasts. In that, a state-space aspect of the forecast is defined and a procedure is derived that relates changes in the aspect with changes in the initial conditions associated with the assimilation of observations) ultimately providing information about the impact of individual observations on the forecast. Some features of the approach are to be noted. The typical choice of forecast aspect employed in related works is rather arbitrary and leads to an incomplete assessment of the observing system. Furthermore, the state-space forecast aspect requires availability of a verification state that should ideally be uncorrelated with the forecast but in practice is not. Lastly, the approach involves the adjoint operator of the entire data assimilation system and as such it is constrained by the validity of this operator. In this presentation, an observation-space metric is used that, for a relatively time-homogeneous observing system, allows inferring observation impact on the forecast without some of the limitations above. Specifically, using observation-minus-forecast residuals leads to an approach with the following features: (i) it suggests a rather natural choice of forecast aspect, directly linked to the analysis system and providing full assessment of the observations; (ii) it naturally avoids introducing undesirable correlations in the forecast aspect by verifying against the observations; and (iii) it does not involve linearization and use of adjoints; therefore being applicable to any length of forecast. The state and observation-space approaches might be complementary to some degree, and involve different limitations and complexities. Illustrations are given using the NASA GEOS-5 data.

Self-Organizing Maps-based ocean currents forecasting system.

PubMed

Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir

2016-03-16

An ocean surface currents forecasting system, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training.

Self-Organizing Maps-based ocean currents forecasting system

PubMed Central

Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir

2016-01-01

An ocean surface currents forecasting system, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training. PMID:26979129

Seasonal Forecast Skill And Teleconnections Over East Africa

NASA Astrophysics Data System (ADS)

MacLeod, D.; Palmer, T.

2017-12-01

Many people living in East Africa are significantly exposed to risks arising from climate variability. The region experiences two rainy seasons and poor performance of either or both of these (such as seen recently in 2016/17) reduces agricultural productivity and threatens food security. In combination with other factors this can lead to famine. By utilizing seasonal climate forecasts, preparatory actions can be taken in order to mitigate the risks arising from such climate variability. As part of the project ForPAc: "Towards forecast-based preparedness action", we are working with humanitarian agencies in Kenya to build such early warning systems on subseasonal-to-seasonal timescales. Here, the seasonal predictability and forecast skill of the two East African rainy seasons will be presented. Results from the new ECMWF operational forecasting system SEAS5 will be shown and compared to the previous System 4. Analysis of a new 110 year long atmosphere-only simulation will also be discussed, demonstrating impacts of atmosphere-ocean coupling as well as putting operational forecast skill in a long-term context. Particular focus will be given to the model representation of teleconnections of seasonal climate with global sea surface temperatures; highlighting sources of forecast error and informing future model development.

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution systemmore » operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.« less

Ocean state and uncertainty forecasts using HYCOM with Local Ensemble Transfer Kalman Filter (LETKF)

NASA Astrophysics Data System (ADS)

Wei, Mozheng; Hogan, Pat; Rowley, Clark; Smedstad, Ole-Martin; Wallcraft, Alan; Penny, Steve

2017-04-01

An ensemble forecast system based on the US Navy's operational HYCOM using Local Ensemble Transfer Kalman Filter (LETKF) technology has been developed for ocean state and uncertainty forecasts. One of the advantages is that the best possible initial analysis states for the HYCOM forecasts are provided by the LETKF which assimilates the operational observations using ensemble method. The background covariance during this assimilation process is supplied with the ensemble, thus it avoids the difficulty of developing tangent linear and adjoint models for 4D-VAR from the complicated hybrid isopycnal vertical coordinate in HYCOM. Another advantage is that the ensemble system provides the valuable uncertainty estimate corresponding to every state forecast from HYCOM. Uncertainty forecasts have been proven to be critical for the downstream users and managers to make more scientifically sound decisions in numerical prediction community. In addition, ensemble mean is generally more accurate and skilful than the single traditional deterministic forecast with the same resolution. We will introduce the ensemble system design and setup, present some results from 30-member ensemble experiment, and discuss scientific, technical and computational issues and challenges, such as covariance localization, inflation, model related uncertainties and sensitivity to the ensemble size.

Comparison of Observation Impacts in Two Forecast Systems using Adjoint Methods

NASA Technical Reports Server (NTRS)

Gelaro, Ronald; Langland, Rolf; Todling, Ricardo

2009-01-01

An experiment is being conducted to compare directly the impact of all assimilated observations on short-range forecast errors in different operational forecast systems. We use the adjoint-based method developed by Langland and Baker (2004), which allows these impacts to be efficiently calculated. This presentation describes preliminary results for a "baseline" set of observations, including both satellite radiances and conventional observations, used by the Navy/NOGAPS and NASA/GEOS-5 forecast systems for the month of January 2007. In each system, about 65% of the total reduction in 24-h forecast error is provided by satellite observations, although the impact of rawinsonde, aircraft, land, and ship-based observations remains significant. Only a small majority (50- 55%) of all observations assimilated improves the forecast, while the rest degrade it. It is found that most of the total forecast error reduction comes from observations with moderate-size innovations providing small to moderate impacts, not from outliers with very large positive or negative innovations. In a global context, the relative impacts of the major observation types are fairly similar in each system, although regional differences in observation impact can be significant. Of particular interest is the fact that while satellite radiances have a large positive impact overall, they degrade the forecast in certain locations common to both systems, especially over land and ice surfaces. Ongoing comparisons of this type, with results expected from other operational centers, should lead to more robust conclusions about the impacts of the various components of the observing system as well as about the strengths and weaknesses of the methodologies used to assimilate them.

An operational ensemble prediction system for catchment rainfall over eastern Africa spanning multiple temporal and spatial scales

NASA Astrophysics Data System (ADS)

Riddle, E. E.; Hopson, T. M.; Gebremichael, M.; Boehnert, J.; Broman, D.; Sampson, K. M.; Rostkier-Edelstein, D.; Collins, D. C.; Harshadeep, N. R.; Burke, E.; Havens, K.

2017-12-01

While it is not yet certain how precipitation patterns will change over Africa in the future, it is clear that effectively managing the available water resources is going to be crucial in order to mitigate the effects of water shortages and floods that are likely to occur in a changing climate. One component of effective water management is the availability of state-of-the-art and easy to use rainfall forecasts across multiple spatial and temporal scales. We present a web-based system for displaying and disseminating ensemble forecast and observed precipitation data over central and eastern Africa. The system provides multi-model rainfall forecasts integrated to relevant hydrological catchments for timescales ranging from one day to three months. A zoom-in features is available to access high resolution forecasts for small-scale catchments. Time series plots and data downloads with forecasts, recent rainfall observations and climatological data are available by clicking on individual catchments. The forecasts are calibrated using a quantile regression technique and an optimal multi-model forecast is provided at each timescale. The forecast skill at the various spatial and temporal scales will discussed, as will current applications of this tool for managing water resources in Sudan and optimizing hydropower operations in Ethiopia and Tanzania.

Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting.

PubMed

Alamaniotis, Miltiadis; Bargiotas, Dimitrios; Tsoukalas, Lefteri H

2016-01-01

Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.

Towards uncertainty estimation for operational forecast products - a multi-model-ensemble approach for the North Sea and the Baltic Sea

NASA Astrophysics Data System (ADS)

Golbeck, Inga; Li, Xin; Janssen, Frank

2014-05-01

Several independent operational ocean models provide forecasts of the ocean state (e.g. sea level, temperature, salinity and ice cover) in the North Sea and the Baltic Sea on a daily basis. These forecasts are the primary source of information for a variety of information and emergency response systems used e.g. to issue sea level warnings or carry out oil drift forecast. The forecasts are of course highly valuable as such, but often suffer from a lack of information on their uncertainty. With the aim of augmenting the existing operational ocean forecasts in the North Sea and the Baltic Sea by a measure of uncertainty a multi-model-ensemble (MME) system for sea surface temperature (SST), sea surface salinity (SSS) and water transports has been set up in the framework of the MyOcean-2 project. Members of MyOcean-2, the NOOS² and HIROMB/BOOS³ communities provide 48h-forecasts serving as inputs. Different variables are processed separately due to their different physical characteristics. Based on the so far collected daily MME products of SST and SSS, a statistical method, Empirical Orthogonal Function (EOF) analysis is applied to assess their spatial and temporal variability. For sea surface currents, progressive vector diagrams at specific points are consulted to estimate the performance of the circulation models especially in hydrodynamic important areas, e.g. inflow/outflow of the Baltic Sea, Norwegian trench and English Channel. For further versions of the MME system, it is planned to extend the MME to other variables like e.g. sea level, ocean currents or ice cover based on the needs of the model providers and their customers. It is also planned to include in-situ data to augment the uncertainty information and for validation purposes. Additionally, weighting methods will be implemented into the MME system to develop more complex uncertainty measures. The methodology used to create the MME will be outlined and different ensemble products will be presented. In addition, some preliminary results based on the statistical analysis of the uncertainty measures provide first estimates of the regional and temporal performance of the ocean models for each parameter. ²Northwest European Shelf Operational Oceanography System ³High-resolution Operational Model of the Baltic / Baltic Operational Oceanographic System

Design and skill assessment of an Operational Forecasting System for currents and sea level variability to the Santos Estuarine System - Brazil

NASA Astrophysics Data System (ADS)

Godoi Rezende Costa, C.; Castro, B. M.; Blumberg, A. F.; Leite, J. R. B., Sr.

2017-12-01

Santos City is subject to an average of 12 storm tide events per year. Such events bring coastal flooding able to threat human life and damage coastal infrastructure. Severe events have forced the interruption of ferry boat services and ship traffic through Santos Harbor, causing great impacts to Santos Port, the largest in South America, activities. Several studies have focused on the hydrodynamics of storm tide events but only a few of those studies have pursued an operational initiative to predict short term (< 3 days) sea level variability. The goals of this study are (i) to describe the design of an operational forecasting system built to predict sea surface elevation and currents in the Santos Estuarine System and (ii) to evaluate model performance in simulating observed sea surface elevation. The Santos Operational Forecasting System (SOFS) hydrodynamic module is based on the Stevens Institute Estuarine and Coastal Ocean Model (sECOM). The fully automated SOFS is designed to provide up to 71 h forecast of sea surface elevations and currents every day. The system automatically collects results from global models to run the SOFS nested into another sECOM based model for the South Brazil Bight (SBB). Global forecasting results used to force both models come from Mercator Ocean, released by Copernicus Marine Service, and from the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) stablished by the Center for Weather Forecasts and Climate Studies (with Portuguese acronym CPTEC). The complete routines task take about 8 hours of run time to finish. SOFS was able to hindcast a severe storm tide event that took place in Santos on August 21-22, 2016. Comparisons with observed sea level provided skills of 0.92 and maximum root mean square errors of 25 cm. The good agreement with observed data shows the potential of the designed system to predict storm tides and to support both human and assets protection.

Wind Power Forecasting Error Frequency Analyses for Operational Power System Studies: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Florita, A.; Hodge, B. M.; Milligan, M.

2012-08-01

The examination of wind power forecasting errors is crucial for optimal unit commitment and economic dispatch of power systems with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power forecasts will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power forecasts and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites andmore » for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power forecasting error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level forecasts than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power forecasting has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.« less

New Developments in Wildfire Pollution Forecasting at the Canadian Meteorological Centre

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Chen, Jack; Munoz-Alpizar, Rodrigo; Davignon, Didier; Beaulieu, Paul-Andre; Landry, Hugo; Menard, Sylvain; Gravel, Sylvie; Moran, Michael

2017-04-01

Environment and Climate Change Canada's air quality forecast system with near-real-time wildfire emissions, named FireWork, was developed in 2012 and has been run by the Canadian Meteorological Centre Operations division (CMCO) since 2013. In June 2016 this system was upgraded to operational status and wildfire smoke forecasts for North America are now available to the general public. FireWork's ability to model the transport and diffusion of wildfire smoke plumes has proved to be valuable to regional air quality forecasters and emergency first responders. Some of the most challenging issues with wildfire pollution modelling concern the production of wildfire emission estimates and near-source dispersion within the air quality model. As a consequence, FireWork is undergoing constant development. During the massive Fort McMurray wildfire event in western Canada in May 2016, for example, different wildfire emissions processing approaches and wildfire emissions injection and dispersion schemes were tested within the air quality model. Work on various FireWork components will continue in order to deliver a new operational version of the forecasting system for the 2017 wildfire season. Some of the proposed improvements will be shown in this presentation along with current and planned FireWork post-processing products.

Wave ensemble forecast system for tropical cyclones in the Australian region

NASA Astrophysics Data System (ADS)

Zieger, Stefan; Greenslade, Diana; Kepert, Jeffrey D.

2018-05-01

Forecasting of waves under extreme conditions such as tropical cyclones is vitally important for many offshore industries, but there remain many challenges. For Northwest Western Australia (NW WA), wave forecasts issued by the Australian Bureau of Meteorology have previously been limited to products from deterministic operational wave models forced by deterministic atmospheric models. The wave models are run over global (resolution 1/4∘) and regional (resolution 1/10∘) domains with forecast ranges of + 7 and + 3 day respectively. Because of this relatively coarse resolution (both in the wave models and in the forcing fields), the accuracy of these products is limited under tropical cyclone conditions. Given this limited accuracy, a new ensemble-based wave forecasting system for the NW WA region has been developed. To achieve this, a new dedicated 8-km resolution grid was nested in the global wave model. Over this grid, the wave model is forced with winds from a bias-corrected European Centre for Medium Range Weather Forecast atmospheric ensemble that comprises 51 ensemble members to take into account the uncertainties in location, intensity and structure of a tropical cyclone system. A unique technique is used to select restart files for each wave ensemble member. The system is designed to operate in real time during the cyclone season providing + 10-day forecasts. This paper will describe the wave forecast components of this system and present the verification metrics and skill for specific events.

THE EMISSION PROCESSING SYSTEM FOR THE ETA/CMAQ AIR QUALITY FORECAST SYSTEM

EPA Science Inventory

NOAA and EPA have created an Air Quality Forecast (AQF) system. This AQF system links an adaptation of the EPA's Community Multiscale Air Quality Model with the 12 kilometer ETA model running operationally at NOAA's National Center for Environmental Predication (NCEP). One of th...

Improved Use of Satellite Imagery to Forecast Hurricanes

NASA Technical Reports Server (NTRS)

Louis, Jean-Francois

2001-01-01

This project tested a novel method that uses satellite imagery to correct phase errors in the initial state for numerical weather prediction, applied to hurricane forecasts. The system was tested on hurricanes Guillermo (1997), Felicia (1997) and Iniki (1992). We compared the performance of the system with and without phase correction to a procedure that uses bogus data in the initial state, similar to current operational procedures. The phase correction keeps the hurricane on track in the analysis and is far superior to a system without phase correction. Compared to operational procedure, phase correction generates somewhat worse 3-day forecast of the hurricane track, but better forecast of intensity. It is believed that the phase correction module would work best in the context of 4-dimensional variational data assimilation. Very little modification to 4DVar would be required.

The development and evaluation of a hydrological seasonal forecast system prototype for predicting spring flood volumes in Swedish rivers

NASA Astrophysics Data System (ADS)

Foster, Kean; Bertacchi Uvo, Cintia; Olsson, Jonas

2018-05-01

Hydropower makes up nearly half of Sweden's electrical energy production. However, the distribution of the water resources is not aligned with demand, as most of the inflows to the reservoirs occur during the spring flood period. This means that carefully planned reservoir management is required to help redistribute water resources to ensure optimal production and accurate forecasts of the spring flood volume (SFV) is essential for this. The current operational SFV forecasts use a historical ensemble approach where the HBV model is forced with historical observations of precipitation and temperature. In this work we develop and test a multi-model prototype, building on previous work, and evaluate its ability to forecast the SFV in 84 sub-basins in northern Sweden. The hypothesis explored in this work is that a multi-model seasonal forecast system incorporating different modelling approaches is generally more skilful at forecasting the SFV in snow dominated regions than a forecast system that utilises only one approach. The testing is done using cross-validated hindcasts for the period 1981-2015 and the results are evaluated against both climatology and the current system to determine skill. Both the multi-model methods considered showed skill over the reference forecasts. The version that combined the historical modelling chain, dynamical modelling chain, and statistical modelling chain performed better than the other and was chosen for the prototype. The prototype was able to outperform the current operational system 57 % of the time on average and reduce the error in the SFV by ˜ 6 % across all sub-basins and forecast dates.

Use of wind data in global modelling

NASA Technical Reports Server (NTRS)

Pailleux, J.

1985-01-01

The European Centre for Medium Range Weather Forecasts (ECMWF) is producing operational global analyses every 6 hours and operational global forecasts every day from the 12Z analysis. How the wind data are used in the ECMWF golbal analysis is described. For each current wind observing system, its ability to provide initial conditions for the forecast model is discussed as well as its weaknesses. An assessment of the impact of each individual system on the quality of the analysis and the forecast is given each time it is possible. Sometimes the deficiencies which are pointed out are related not only to the observing system itself but also to the optimum interpolation (OI) analysis scheme; then some improvements are generally possible through ad hoc modifications of the analysis scheme and especially tunings of the structure functions. Examples are given. The future observing network over the North Atlantic is examined. Several countries, coordinated by WMO, are working to set up an 'Operational WWW System Evaluation' (OWSE), in order to evaluate the operational aspects of the deployment of new systems (ASDAR, ASAP). Most of the new systems are expected to be deployed before January 1987, and in order to make the best use of the available resources during the deployment phase, some network studies are carried out at the present time, by using simulated data for ASDAR and ASAP systems. They are summarized.

Intra-Hour Dispatch and Automatic Generator Control Demonstration with Solar Forecasting - Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coimbra, Carlos F. M.

2016-02-25

In this project we address multiple resource integration challenges associated with increasing levels of solar penetration that arise from the variability and uncertainty in solar irradiance. We will model the SMUD service region as its own balancing region, and develop an integrated, real-time operational tool that takes solar-load forecast uncertainties into consideration and commits optimal energy resources and reserves for intra-hour and intra-day decisions. The primary objectives of this effort are to reduce power system operation cost by committing appropriate amount of energy resources and reserves, as well as to provide operators a prediction of the generation fleet’s behavior inmore » real time for realistic PV penetration scenarios. The proposed methodology includes the following steps: clustering analysis on the expected solar variability per region for the SMUD system, Day-ahead (DA) and real-time (RT) load forecasts for the entire service areas, 1-year of intra-hour CPR forecasts for cluster centers, 1-year of smart re-forecasting CPR forecasts in real-time for determination of irreducible errors, and uncertainty quantification for integrated solar-load for both distributed and central stations (selected locations within service region) PV generation.« less

Evaluations of Extended-Range tropical Cyclone Forecasts in the Western North Pacific by using the Ensemble Reforecasts: Preliminary Results

NASA Astrophysics Data System (ADS)

Tsai, Hsiao-Chung; Chen, Pang-Cheng; Elsberry, Russell L.

2017-04-01

The objective of this study is to evaluate the predictability of the extended-range forecasts of tropical cyclone (TC) in the western North Pacific using reforecasts from National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) during 1996-2015, and from the Climate Forecast System (CFS) during 1999-2010. Tsai and Elsberry have demonstrated that an opportunity exists to support hydrological operations by using the extended-range TC formation and track forecasts in the western North Pacific from the ECMWF 32-day ensemble. To demonstrate this potential for the decision-making processes regarding water resource management and hydrological operation in Taiwan reservoir watershed areas, special attention is given to the skill of the NCEP GEFS and CFS models in predicting the TCs affecting the Taiwan area. The first objective of this study is to analyze the skill of NCEP GEFS and CFS TC forecasts and quantify the forecast uncertainties via verifications of categorical binary forecasts and probabilistic forecasts. The second objective is to investigate the relationships among the large-scale environmental factors [e.g., El Niño Southern Oscillation (ENSO), Madden-Julian Oscillation (MJO), etc.] and the model forecast errors by using the reforecasts. Preliminary results are indicating that the skill of the TC activity forecasts based on the raw forecasts can be further improved if the model biases are minimized by utilizing these reforecasts.

Operational hydrological forecasting in Bavaria. Part I: Forecast uncertainty

NASA Astrophysics Data System (ADS)

Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

2009-04-01

In Bavaria, operational flood forecasting has been established since the disastrous flood of 1999. Nowadays, forecasts based on rainfall information from about 700 raingauges and 600 rivergauges are calculated and issued for nearly 100 rivergauges. With the added experience of the 2002 and 2005 floods, awareness grew that the standard deterministic forecast, neglecting the uncertainty associated with each forecast is misleading, creating a false feeling of unambiguousness. As a consequence, a system to identify, quantify and communicate the sources and magnitude of forecast uncertainty has been developed, which will be presented in part I of this study. In this system, the use of ensemble meteorological forecasts plays a key role which will be presented in part II. Developing the system, several constraints stemming from the range of hydrological regimes and operational requirements had to be met: Firstly, operational time constraints obviate the variation of all components of the modeling chain as would be done in a full Monte Carlo simulation. Therefore, an approach was chosen where only the most relevant sources of uncertainty were dynamically considered while the others were jointly accounted for by static error distributions from offline analysis. Secondly, the dominant sources of uncertainty vary over the wide range of forecasted catchments: In alpine headwater catchments, typically of a few hundred square kilometers in size, rainfall forecast uncertainty is the key factor for forecast uncertainty, with a magnitude dynamically changing with the prevailing predictability of the atmosphere. In lowland catchments encompassing several thousands of square kilometers, forecast uncertainty in the desired range (usually up to two days) is mainly dependent on upstream gauge observation quality, routing and unpredictable human impact such as reservoir operation. The determination of forecast uncertainty comprised the following steps: a) From comparison of gauge observations and several years of archived forecasts, overall empirical error distributions termed 'overall error' were for each gauge derived for a range of relevant forecast lead times. b) The error distributions vary strongly with the hydrometeorological situation, therefore a subdivision into the hydrological cases 'low flow, 'rising flood', 'flood', flood recession' was introduced. c) For the sake of numerical compression, theoretical distributions were fitted to the empirical distributions using the method of moments. Here, the normal distribution was generally best suited. d) Further data compression was achieved by representing the distribution parameters as a function (second-order polynome) of lead time. In general, the 'overall error' obtained from the above procedure is most useful in regions where large human impact occurs and where the influence of the meteorological forecast is limited. In upstream regions however, forecast uncertainty is strongly dependent on the current predictability of the atmosphere, which is contained in the spread of an ensemble forecast. Including this dynamically in the hydrological forecast uncertainty estimation requires prior elimination of the contribution of the weather forecast to the 'overall error'. This was achieved by calculating long series of hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The resulting error distribution is termed 'model error' and can be applied on hydrological ensemble forecasts, where ensemble rainfall forecasts are used as forcing. The concept will be illustrated by examples (good and bad ones) covering a wide range of catchment sizes, hydrometeorological regimes and quality of hydrological model calibration. The methodology to combine the static and dynamic shares of uncertainty will be presented in part II of this study.

Verification of space weather forecasts at the UK Met Office

NASA Astrophysics Data System (ADS)

Bingham, S.; Sharpe, M.; Jackson, D.; Murray, S.

2017-12-01

The UK Met Office Space Weather Operations Centre (MOSWOC) has produced space weather guidance twice a day since its official opening in 2014. Guidance includes 4-day probabilistic forecasts of X-ray flares, geomagnetic storms, high-energy electron events and high-energy proton events. Evaluation of such forecasts is important to forecasters, stakeholders, model developers and users to understand the performance of these forecasts and also strengths and weaknesses to enable further development. Met Office terrestrial near real-time verification systems have been adapted to provide verification of X-ray flare and geomagnetic storm forecasts. Verification is updated daily to produce Relative Operating Characteristic (ROC) curves and Reliability diagrams, and rolling Ranked Probability Skill Scores (RPSSs) thus providing understanding of forecast performance and skill. Results suggest that the MOSWOC issued X-ray flare forecasts are usually not statistically significantly better than a benchmark climatological forecast (where the climatology is based on observations from the previous few months). By contrast, the issued geomagnetic storm activity forecast typically performs better against this climatological benchmark.

Tropopause sharpening by data assimilation

NASA Astrophysics Data System (ADS)

Pilch Kedzierski, R.; Neef, L.; Matthes, K.

2016-08-01

Data assimilation was recently suggested to smooth out the sharp gradients that characterize the tropopause inversion layer (TIL) in systems that did not assimilate TIL-resolving observations. We investigate whether this effect is present in the ERA-Interim reanalysis and the European Centre for Medium-Range Weather Forecasts (ECMWF) operational forecast system (which assimilate high-resolution observations) by analyzing the 4D-Var increments and how the TIL is represented in their data assimilation systems. For comparison, we also diagnose the TIL from high-resolution GPS radio occultation temperature profiles from the COSMIC satellite mission, degraded to the same vertical resolution as ERA-Interim and ECMWF operational analyses. Our results show that more recent reanalysis and forecast systems improve the representation of the TIL, updating the earlier hypothesis. However, the TIL in ERA-Interim and ECMWF operational analyses is still weaker and farther away from the tropopause than GPS radio occultation observations of the same vertical resolution.

Science and Engineering of an Operational Tsunami Forecasting System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gonzalez, Frank

2009-04-06

After a review of tsunami statistics and the destruction caused by tsunamis, a means of forecasting tsunamis is discussed as part of an overall program of reducing fatalities through hazard assessment, education, training, mitigation, and a tsunami warning system. The forecast is accomplished via a concept called Deep Ocean Assessment and Reporting of Tsunamis (DART). Small changes of pressure at the sea floor are measured and relayed to warning centers. Under development is an international modeling network to transfer, maintain, and improve tsunami forecast models.

Science and Engineering of an Operational Tsunami Forecasting System

ScienceCinema

Gonzalez, Frank

2017-12-09

After a review of tsunami statistics and the destruction caused by tsunamis, a means of forecasting tsunamis is discussed as part of an overall program of reducing fatalities through hazard assessment, education, training, mitigation, and a tsunami warning system. The forecast is accomplished via a concept called Deep Ocean Assessment and Reporting of Tsunamis (DART). Small changes of pressure at the sea floor are measured and relayed to warning centers. Under development is an international modeling network to transfer, maintain, and improve tsunami forecast models.

EMISSIONS PROCESSING FOR THE ETA/ CMAQ AIR QUALITY FORECAST SYSTEM

EPA Science Inventory

NOAA and EPA have created an Air Quality Forecast (AQF) system. This AQF system links an adaptation of the EPA's Community Multiscale Air Quality Model with the 12 kilometer ETA model running operationally at NOAA's National Center for Environmental Predication (NCEP). One of the...

Use of observational and model-derived fields and regime model output statistics in mesoscale forecasting

NASA Technical Reports Server (NTRS)

Forbes, G. S.; Pielke, R. A.

1985-01-01

Various empirical and statistical weather-forecasting studies which utilize stratification by weather regime are described. Objective classification was used to determine weather regime in some studies. In other cases the weather pattern was determined on the basis of a parameter representing the physical and dynamical processes relevant to the anticipated mesoscale phenomena, such as low level moisture convergence and convective precipitation, or the Froude number and the occurrence of cold-air damming. For mesoscale phenomena already in existence, new forecasting techniques were developed. The use of cloud models in operational forecasting is discussed. Models to calculate the spatial scales of forcings and resultant response for mesoscale systems are presented. The use of these models to represent the climatologically most prevalent systems, and to perform case-by-case simulations is reviewed. Operational implementation of mesoscale data into weather forecasts, using both actual simulation output and method-output statistics is discussed.

Skill of a global seasonal ensemble streamflow forecasting system

NASA Astrophysics Data System (ADS)

Candogan Yossef, Naze; Winsemius, Hessel; Weerts, Albrecht; van Beek, Rens; Bierkens, Marc

2013-04-01

Forecasting of water availability and scarcity is a prerequisite for managing the risks and opportunities caused by the inter-annual variability of streamflow. Reliable seasonal streamflow forecasts are necessary to prepare for an appropriate response in disaster relief, management of hydropower reservoirs, water supply, agriculture and navigation. Seasonal hydrological forecasting on a global scale could be valuable especially for developing regions of the world, where effective hydrological forecasting systems are scarce. In this study, we investigate the forecasting skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCR-GLOBWB. FEWS-World has been setup within the European Commission 7th Framework Programme project Global Water Scarcity Information Service (GLOWASIS). Skill is assessed in historical simulation mode as well as retroactive forecasting mode. The assessment in historical simulation mode used a meteorological forcing based on observations from the Climate Research Unit of the University of East Anglia and the ERA-40 reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF). We assessed the skill of the global hydrological model PCR-GLOBWB in reproducing past discharge extremes in 20 large rivers of the world. This preliminary assessment concluded that the prospects for seasonal forecasting with PCR-GLOBWB or comparable models are positive. However this assessment did not include actual meteorological forecasts. Thus the meteorological forcing errors were not assessed. Yet, in a forecasting setup, the predictive skill of a hydrological forecasting system is affected by errors due to uncertainty from numerical weather prediction models. For the assessment in retroactive forecasting mode, the model is forced with actual ensemble forecasts from the seasonal forecast archives of ECMWF. Skill is assessed at 78 stations on large river basins across the globe, for all the months of the year and for lead times up to 6 months. The forecasted discharges are compared with observed monthly streamflow records using the ensemble verification measures Brier Skill Score (BSS) and Continuous Ranked Probability Score (CRPS). The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts from ECMWF. The results will be disseminated on the internet, and hopefully provide information that is valuable for users in data and model-poor regions of the world.

Application of the CloudSat and NEXRAD Radars Toward Improvements in High Resolution Operational Forecasts

NASA Technical Reports Server (NTRS)

Molthan, A. L.; Haynes, J. A.; Case, J. L.; Jedlovec, G. L.; Lapenta, W. M.

2008-01-01

As computational power increases, operational forecast models are performing simulations with higher spatial resolution allowing for the transition from sub-grid scale cloud parameterizations to an explicit forecast of cloud characteristics and precipitation through the use of single- or multi-moment bulk water microphysics schemes. investments in space-borne and terrestrial remote sensing have developed the NASA CloudSat Cloud Profiling Radar and the NOAA National Weather Service NEXRAD system, each providing observations related to the bulk properties of clouds and precipitation through measurements of reflectivity. CloudSat and NEXRAD system radars observed light to moderate snowfall in association with a cold-season, midlatitude cyclone traversing the Central United States in February 2007. These systems are responsible for widespread cloud cover and various types of precipitation, are of economic consequence, and pose a challenge to operational forecasters. This event is simulated with the Weather Research and Forecast (WRF) Model, utilizing the NASA Goddard Cumulus Ensemble microphysics scheme. Comparisons are made between WRF-simulated and observed reflectivity available from the CloudSat and NEXRAD systems. The application of CloudSat reflectivity is made possible through the QuickBeam radiative transfer model, with cautious application applied in light of single scattering characteristics and spherical target assumptions. Significant differences are noted within modeled and observed cloud profiles, based upon simulated reflectivity, and modifications to the single-moment scheme are tested through a supplemental WRF forecast that incorporates a temperature dependent snow crystal size distribution.

Operational Monitoring and Forecasting in Regional Seas: the Aegean Sea example

NASA Astrophysics Data System (ADS)

Nittis, K.; Perivoliotis, L.; Zervakis, V.; Papadopoulos, A.; Tziavos, C.

2003-04-01

The increasing economic activities in the coastal zone and the associated pressure on the marine environment have raised the interest on monitoring systems able to provide supporting information for its effective management and protection. Such an integrated monitoring, forecasting and information system is being developed during the past years in the Aegean Sea. Its main component is the POSEIDON network that provides real-time data for meteorological and surface oceanographic parameters (waves, currents, hydrological and biochemical data) from 11 fixed oceanographic buoys. The numerical forecasting system is composed by an ETA atmospheric model, a WAM wave model and a POM hydrodynamic model that provide every day 72 hours forecasts. The system is operational since May 2000 and its products are published through Internet while a sub-set is also available through cellular telephony. New type of observing platforms will be available in the near future through a number of EU funded research projects. The Mediterranean Moored Multi-sensor Array (M3A) that was developed for the needs of the Mediterranean Forecasting System and was tested during 2000-2001 will be operational in 2004 during the MFSTEP project. The M3A system incorporates sensors for optical and chemical measurements (Oxygen, Turbidity, Chlorophyll-a, Nutrients and PAR) in the euphotic zone (0-100m) together with sensors for physical parameters (Temperature, Salinity, Current speed and direction) at the 0-500m layer. A Ferry-Box system will also operate during 2004 in the southern Aegean Sea, providing surface data for physical and bio-chemical properties. The ongoing modeling efforts include coupling with larger scale circulation models of the Mediterranean, high-resolution downscaling to coastal areas of the Aegean Sea and development of multi-variate data assimilation methods.

Improved water allocation utilizing probabilistic climate forecasts: Short-term water contracts in a risk management framework

NASA Astrophysics Data System (ADS)

Sankarasubramanian, A.; Lall, Upmanu; Souza Filho, Francisco Assis; Sharma, Ashish

2009-11-01

Probabilistic, seasonal to interannual streamflow forecasts are becoming increasingly available as the ability to model climate teleconnections is improving. However, water managers and practitioners have been slow to adopt such products, citing concerns with forecast skill. Essentially, a management risk is perceived in "gambling" with operations using a probabilistic forecast, while a system failure upon following existing operating policies is "protected" by the official rules or guidebook. In the presence of a prescribed system of prior allocation of releases under different storage or water availability conditions, the manager has little incentive to change. Innovation in allocation and operation is hence key to improved risk management using such forecasts. A participatory water allocation process that can effectively use probabilistic forecasts as part of an adaptive management strategy is introduced here. Users can express their demand for water through statements that cover the quantity needed at a particular reliability, the temporal distribution of the "allocation," the associated willingness to pay, and compensation in the event of contract nonperformance. The water manager then assesses feasible allocations using the probabilistic forecast that try to meet these criteria across all users. An iterative process between users and water manager could be used to formalize a set of short-term contracts that represent the resulting prioritized water allocation strategy over the operating period for which the forecast was issued. These contracts can be used to allocate water each year/season beyond long-term contracts that may have precedence. Thus, integrated supply and demand management can be achieved. In this paper, a single period multiuser optimization model that can support such an allocation process is presented. The application of this conceptual model is explored using data for the Jaguaribe Metropolitan Hydro System in Ceara, Brazil. The performance relative to the current allocation process is assessed in the context of whether such a model could support the proposed short-term contract based participatory process. A synthetic forecasting example is also used to explore the relative roles of forecast skill and reservoir storage in this framework.

The FAST-T approach for operational, real time, short term hydrological forecasting: Results from the Betania Hydropower Reservoir case study

NASA Astrophysics Data System (ADS)

Domínguez, Efraín; Angarita, Hector; Rosmann, Thomas; Mendez, Zulma; Angulo, Gustavo

2013-04-01

A viable quantitative hydrological forecasting service is a combination of technological elements, personnel and knowledge, working together to establish a stable operational cycle of forecasts emission, dissemination and assimilation; hence, the process for establishing such system usually requires significant resources and time to reach an adequate development and integration in order to produce forecasts with acceptable levels of performance. Here are presented the results of this process for the recently implemented Operational Forecast Service for the Betania's Hydropower Reservoir - or SPHEB, located at the Upper-Magdalena River Basin (Colombia). The current scope of the SPHEB includes forecasting of water levels and discharge for the three main streams affluent to the reservoir, for lead times between +1 to +57 hours, and +1 to +10 days. The core of the SPHEB is the Flexible, Adaptive, Simple and Transient Time forecasting approach, namely FAST-T. This comprises of a set of data structures, mathematical kernel, distributed computing and network infrastructure designed to provide seamless real-time operational forecast and automatic model adjustment in case of failures in data transmission or assimilation. Among FAST-T main features are: an autonomous evaluation and detection of the most relevant information for the later configuration of forecasting models; an adaptively linearized mathematical kernel, the optimal adaptive linear combination or OALC, which provides a computationally simple and efficient algorithm for real-time applications; and finally, a meta-model catalog, containing prioritized forecast models at given stream conditions. The SPHEB is at present feed by the fraction of hydrological monitoring network installed at the basin that has telemetric capabilities via NOAA-GOES satellites (8 stages, approximately 47%) with data availability of about a 90% at one hour intervals. However, there is a dense network of 'conventional' hydro-meteorological stages -read manually once or twice per day - that, despite not ideal in the context of real-time system, improve model performance significantly, and therefore are entered into the system by manual input. At its current configuration, the SPHEB performance objectives are fulfilled for 90% of the forecasts with lead times up to +2 days and +15 hours (using the predictability criteria of the Russian Hydrometeorological Center S/?Δ) and the average accuracy is in the range 70-99% ( r2 criteria). However, longer lead times are at present not satisfactory in terms of forecasts accuracy.

Integrated Urban Flood Analysis considering Optimal Operation of Flood Control Facilities in Urban Drainage Networks

NASA Astrophysics Data System (ADS)

Moon, Y. I.; Kim, M. S.; Choi, J. H.; Yuk, G. M.

2017-12-01

eavy rainfall has become a recent major cause of urban area flooding due to the climate change and urbanization. To prevent property damage along with casualties, a system which can alert and forecast urban flooding must be developed. Optimal performance of reducing flood damage can be expected of urban drainage facilities when operated in smaller rainfall events over extreme ones. Thus, the purpose of this study is to execute: A) flood forecasting system using runoff analysis based on short term rainfall; and B) flood warning system which operates based on the data from pump stations and rainwater storage in urban basins. In result of the analysis, it is shown that urban drainage facilities using short term rainfall forecasting data by radar will be more effective to reduce urban flood damage than using only the inflow data of the facility. Keywords: Heavy Rainfall, Urban Flood, Short-term Rainfall Forecasting, Optimal operating of urban drainage facilities. AcknowledgmentsThis research was supported by a grant (17AWMP-B066744-05) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport of Korean government.

An operational real-time flood forecasting system in Southern Italy

NASA Astrophysics Data System (ADS)

Ortiz, Enrique; Coccia, Gabriele; Todini, Ezio

2015-04-01

A real-time flood forecasting system has been operating since year 2012 as a non-structural measure for mitigating the flood risk in Campania Region (Southern Italy), within the Sele river basin (3.240 km2). The Sele Flood Forecasting System (SFFS) has been built within the FEWS (Flood Early Warning System) platform developed by Deltares and it assimilates the numerical weather predictions of the COSMO LAM family: the deterministic COSMO-LAMI I2, the deterministic COSMO-LAMI I7 and the ensemble numerical weather predictions COSMO-LEPS (16 members). Sele FFS is composed by a cascade of three main models. The first model is a fully continuous physically based distributed hydrological model, named TOPKAPI-eXtended (Idrologia&Ambiente s.r.l., Naples, Italy), simulating the dominant processes controlling the soil water dynamics, runoff generation and discharge with a spatial resolution of 250 m. The second module is a set of Neural-Networks (ANN) built for forecasting the river stages at a set of monitored cross-sections. The third component is a Model Conditional Processor (MCP), which provides the predictive uncertainty (i.e., the probability of occurrence of a future flood event) within the framework of a multi-temporal forecast, according to the most recent advancements on this topic (Coccia and Todini, HESS, 2011). The MCP provides information about the probability of exceedance of a maximum river stage within the forecast lead time, by means of a discrete time function representing the variation of cumulative probability of exceeding a river stage during the forecast lead time and the distribution of the time occurrence of the flood peak, starting from one or more model forecasts. This work shows the Sele FFS performance after two years of operation, evidencing the added-values that can provide to a flood early warning and emergency management system.

An Integrated Ensemble-Based Operational Framework to Predict Urban Flooding: A Case Study of Hurricane Sandy in the Passaic and Hackensack River Basins

NASA Astrophysics Data System (ADS)

Saleh, F.; Ramaswamy, V.; Georgas, N.; Blumberg, A. F.; Wang, Y.

2016-12-01

Advances in computational resources and modeling techniques are opening the path to effectively integrate existing complex models. In the context of flood prediction, recent extreme events have demonstrated the importance of integrating components of the hydrosystem to better represent the interactions amongst different physical processes and phenomena. As such, there is a pressing need to develop holistic and cross-disciplinary modeling frameworks that effectively integrate existing models and better represent the operative dynamics. This work presents a novel Hydrologic-Hydraulic-Hydrodynamic Ensemble (H3E) flood prediction framework that operationally integrates existing predictive models representing coastal (New York Harbor Observing and Prediction System, NYHOPS), hydrologic (US Army Corps of Engineers Hydrologic Modeling System, HEC-HMS) and hydraulic (2-dimensional River Analysis System, HEC-RAS) components. The state-of-the-art framework is forced with 125 ensemble meteorological inputs from numerical weather prediction models including the Global Ensemble Forecast System, the European Centre for Medium-Range Weather Forecasts (ECMWF), the Canadian Meteorological Centre (CMC), the Short Range Ensemble Forecast (SREF) and the North American Mesoscale Forecast System (NAM). The framework produces, within a 96-hour forecast horizon, on-the-fly Google Earth flood maps that provide critical information for decision makers and emergency preparedness managers. The utility of the framework was demonstrated by retrospectively forecasting an extreme flood event, hurricane Sandy in the Passaic and Hackensack watersheds (New Jersey, USA). Hurricane Sandy caused significant damage to a number of critical facilities in this area including the New Jersey Transit's main storage and maintenance facility. The results of this work demonstrate that ensemble based frameworks provide improved flood predictions and useful information about associated uncertainties, thus improving the assessment of risks as when compared to a deterministic forecast. The work offers perspectives for short-term flood forecasts, flood mitigation strategies and best management practices for climate change scenarios.

Fews-Risk: A step towards risk-based flood forecasting

NASA Astrophysics Data System (ADS)

Bachmann, Daniel; Eilander, Dirk; de Leeuw, Annemargreet; Diermanse, Ferdinand; Weerts, Albrecht; de Bruijn, Karin; Beckers, Joost; Boelee, Leonore; Brown, Emma; Hazlewood, Caroline

2015-04-01

Operational flood prediction and the assessment of flood risk are important components of flood management. Currently, the model-based prediction of discharge and/or water level in a river is common practice for operational flood forecasting. Based on the prediction of these values decisions about specific emergency measures are made within operational flood management. However, the information provided for decision support is restricted to pure hydrological or hydraulic aspects of a flood. Information about weak sections within the flood defences, flood prone areas and assets at risk in the protected areas are rarely used in a model-based flood forecasting system. This information is often available for strategic planning, but is not in an appropriate format for operational purposes. The idea of FEWS-Risk is the extension of existing flood forecasting systems with elements of strategic flood risk analysis, such as probabilistic failure analysis, two dimensional flood spreading simulation and the analysis of flood impacts and consequences. Thus, additional information is provided to the decision makers, such as: • Location, timing and probability of failure of defined sections of the flood defence line; • Flood spreading, extent and hydraulic values in the hinterland caused by an overflow or a breach flow • Impacts and consequences in case of flooding in the protected areas, such as injuries or casualties and/or damages to critical infrastructure or economy. In contrast with purely hydraulic-based operational information, these additional data focus upon decision support for answering crucial questions within an operational flood forecasting framework, such as: • Where should I reinforce my flood defence system? • What type of action can I take to mend a weak spot in my flood defences? • What are the consequences of a breach? • Which areas should I evacuate first? This presentation outlines the additional required workflows towards risk-based flood forecasting systems. In a cooperation between HR Wallingford and Deltares, the extended workflows are being integrated into the Delft-FEWS software system. Delft-FEWS provides modules for managing the data handling and forecasting process. Results of a pilot study that demonstrates the new tools are presented. The value of the newly generated information for decision support during a flood event is discussed.

Influence of Forecast Accuracy of Photovoltaic Power Output on Facility Planning and Operation of Microgrid under 30 min Power Balancing Control

NASA Astrophysics Data System (ADS)

Kato, Takeyoshi; Sone, Akihito; Shimakage, Toyonari; Suzuoki, Yasuo

A microgrid (MG) is one of the measures for enhancing the high penetration of renewable energy (RE)-based distributed generators (DGs). For constructing a MG economically, the capacity optimization of controllable DGs against RE-based DGs is essential. By using a numerical simulation model developed based on the demonstrative studies on a MG using PAFC and NaS battery as controllable DGs and photovoltaic power generation system (PVS) as a RE-based DG, this study discusses the influence of forecast accuracy of PVS output on the capacity optimization and daily operation evaluated with the cost. The main results are as follows. The required capacity of NaS battery must be increased by 10-40% against the ideal situation without the forecast error of PVS power output. The influence of forecast error on the received grid electricity would not be so significant on annual basis because the positive and negative forecast error varies with days. The annual total cost of facility and operation increases by 2-7% due to the forecast error applied in this study. The impact of forecast error on the facility optimization and operation optimization is almost the same each other at a few percentages, implying that the forecast accuracy should be improved in terms of both the number of times with large forecast error and the average error.

A study of application of remote sensing to river forecasting. Volume 1: Executive summary

NASA Technical Reports Server (NTRS)

1975-01-01

A project is described whose goal was to define, implement and evaluate a pilot demonstration test to show the practicability of applying remotely sensed data to operational river forecasting in gaged or previously ungaged watersheds. A secondary objective was to provide NASA with documentation describing the computer programs that comprise the streamflow forecasting simulation model used. A computer-based simulation model was adapted to a streamflow forecasting application and implemented in an IBM System/360 Model 44 computer, operating in a dedicated mode, with operator interactive control through a Model 2250 keyboard/graphic CRT terminal. The test site whose hydrologic behavior was simulated is a small basin (365 square kilometers) designated Town Creek near Geraldine, Alabama.

Verifying and Postprocesing the Ensemble Spread-Error Relationship

NASA Astrophysics Data System (ADS)

Hopson, Tom; Knievel, Jason; Liu, Yubao; Roux, Gregory; Wu, Wanli

2013-04-01

With the increased utilization of ensemble forecasts in weather and hydrologic applications, there is a need to verify their benefit over less expensive deterministic forecasts. One such potential benefit of ensemble systems is their capacity to forecast their own forecast error through the ensemble spread-error relationship. The paper begins by revisiting the limitations of the Pearson correlation alone in assessing this relationship. Next, we introduce two new metrics to consider in assessing the utility an ensemble's varying dispersion. We argue there are two aspects of an ensemble's dispersion that should be assessed. First, and perhaps more fundamentally: is there enough variability in the ensembles dispersion to justify the maintenance of an expensive ensemble prediction system (EPS), irrespective of whether the EPS is well-calibrated or not? To diagnose this, the factor that controls the theoretical upper limit of the spread-error correlation can be useful. Secondly, does the variable dispersion of an ensemble relate to variable expectation of forecast error? Representing the spread-error correlation in relation to its theoretical limit can provide a simple diagnostic of this attribute. A context for these concepts is provided by assessing two operational ensembles: 30-member Western US temperature forecasts for the U.S. Army Test and Evaluation Command and 51-member Brahmaputra River flow forecasts of the Climate Forecast and Applications Project for Bangladesh. Both of these systems utilize a postprocessing technique based on quantile regression (QR) under a step-wise forward selection framework leading to ensemble forecasts with both good reliability and sharpness. In addition, the methodology utilizes the ensemble's ability to self-diagnose forecast instability to produce calibrated forecasts with informative skill-spread relationships. We will describe both ensemble systems briefly, review the steps used to calibrate the ensemble forecast, and present verification statistics using error-spread metrics, along with figures from operational ensemble forecasts before and after calibration.

Towards the Operational Ensemble-based Data Assimilation System for the Wave Field at the National Weather Service

NASA Astrophysics Data System (ADS)

Flampouris, Stylianos; Penny, Steve; Alves, Henrique

2017-04-01

The National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA) provides the operational wave forecast for the US National Weather Service (NWS). Given the continuous efforts to improve forecast, NCEP is developing an ensemble-based data assimilation system, based on the local ensemble transform Kalman filter (LETKF), the existing operational global wave ensemble system (GWES) and on satellite and in-situ observations. While the LETKF was designed for atmospheric applications (Hunt et al 2007), and has been adapted for several ocean models (e.g. Penny 2016), this is the first time applied for oceanic waves assimilation. This new wave assimilation system provides a global estimation of the surface sea state and its approximate uncertainty. It achieves this by analyzing the 21-member ensemble of the significant wave height provided by GWES every 6h. Observations from four altimeters and all the available in-situ measurements are used in this analysis. The analysis of the significant wave height is used for initializing the next forecasting cycle; the data assimilation system is currently being tested for operational use.

Comparing Two Approaches for Assessing Observation Impact

NASA Technical Reports Server (NTRS)

Todling, Ricardo

2013-01-01

Langland and Baker introduced an approach to assess the impact of observations on the forecasts. In that approach, a state-space aspect of the forecast is defined and a procedure is derived ultimately relating changes in the aspect with changes in the observing system. Some features of the state-space approach are to be noted: the typical choice of forecast aspect is rather subjective and leads to incomplete assessment of the observing system, it requires availability of a verification state that is in practice correlated with the forecast, and it involves the adjoint operator of the entire data assimilation system and is thus constrained by the validity of this operator. This article revisits the topic of observation impacts from the perspective of estimation theory. An observation-space metric is used to allow inferring observation impact on the forecasts without the limitations just mentioned. Using differences of observation-minus-forecast residuals obtained from consecutive forecasts leads to the following advantages: (i) it suggests a rather natural choice of forecast aspect that directly links to the data assimilation procedure, (ii) it avoids introducing undesirable correlations in the forecast aspect since verification is done against the observations, and (iii) it does not involve linearization and use of adjoints. The observation-space approach has the additional advantage of being nearly cost free and very simple to implement. In its simplest form it reduces to evaluating the statistics of observationminus- background and observation-minus-analysis residuals with traditional methods. Illustrations comparing the approaches are given using the NASA Goddard Earth Observing System.

Discharge data assimilation in a distributed hydrologic model for flood forecasting purposes

NASA Astrophysics Data System (ADS)

Ercolani, G.; Castelli, F.

2017-12-01

Flood early warning systems benefit from accurate river flow forecasts, and data assimilation may improve their reliability. However, the actual enhancement that can be obtained in the operational practice should be investigated in detail and quantified. In this work we assess the benefits that the simultaneous assimilation of discharge observations at multiple locations can bring to flow forecasting through a distributed hydrologic model. The distributed model, MOBIDIC, is part of the operational flood forecasting chain of Tuscany Region in Central Italy. The assimilation system adopts a mixed variational-Monte Carlo approach to update efficiently initial river flow, soil moisture, and a parameter related to runoff production. The evaluation of the system is based on numerous hindcast experiments of real events. The events are characterized by significant rainfall that resulted in both high and relatively low flow in the river network. The area of study is the main basin of Tuscany Region, i.e. Arno river basin, which extends over about 8300 km2 and whose mean annual precipitation is around 800 mm. Arno's mainstream, with its nearly 240 km length, passes through major Tuscan cities, as Florence and Pisa, that are vulnerable to floods (e.g. flood of November 1966). The assimilation tests follow the usage of the model in the forecasting chain, employing the operational resolution in both space and time (500 m and 15 minutes respectively) and releasing new flow forecasts every 6 hours. The assimilation strategy is evaluated in respect to open loop simulations, i.e. runs that do not exploit discharge observations through data assimilation. We compare hydrographs in their entirety, as well as classical performance indexes, as error on peak flow and Nash-Sutcliffe efficiency. The dependence of performances on lead time and location is assessed. Results indicate that the operational forecasting chain can benefit from the developed assimilation system, although with a significant variability due to the specific characteristics of any single event, and with downstream locations more sensitive to observations than upstream sites.

Advances in air quality prediction with the use of integrated systems

NASA Astrophysics Data System (ADS)

Dragani, R.; Benedetti, A.; Engelen, R. J.; Peuch, V. H.

2017-12-01

Recent years have seen the rise of global operational atmospheric composition forecasting systems for several applications including climate monitoring, provision of boundary conditions for regional air quality forecasting, energy sector applications, to mention a few. Typically, global forecasts are provided in the medium-range up to five days ahead and are initialized with an analysis based on satellite data. In this work we present the latest advances in data assimilation using the ECMWF's 4D-Var system extended to atmospheric composition which is currently operational under the Copernicus Atmosphere Monitoring Service of the European Commission. The service is based on acquisition of all relevant data available in near-real-time, the processing of these datasets in the assimilation and the subsequent dissemination of global forecasts at ECMWF. The global forecasts are used by the CAMS regional models as boundary conditions for the European forecasts based on a multi-model ensemble. The global forecasts are also used to provide boundary conditions for other parts of the world (e.g., China) and are freely available to all interested entities. Some of the regional models also perform assimilation of satellite and ground-based observations. All products are assessed, validated and made publicly available on https://atmosphere.copernicus.eu/.

Operational value of ensemble streamflow forecasts for hydropower production: A Canadian case study

NASA Astrophysics Data System (ADS)

Boucher, Marie-Amélie; Tremblay, Denis; Luc, Perreault; François, Anctil

2010-05-01

Ensemble and probabilistic forecasts have many advantages over deterministic ones, both in meteorology and hydrology (e.g. Krzysztofowicz, 2001). Mainly, they inform the user on the uncertainty linked to the forecast. It has been brought to attention that such additional information could lead to improved decision making (e.g. Wilks and Hamill, 1995; Mylne, 2002; Roulin, 2007), but very few studies concentrate on operational situations involving the use of such forecasts. In addition, many authors have demonstrated that ensemble forecasts outperform deterministic forecasts in terms of performance (e.g. Jaun et al., 2005; Velazquez et al., 2009; Laio and Tamea, 2007). However, such performance is mostly assessed on the basis of numerical scoring rules, which compare the forecasts to the observations, and seldom in terms of management gains. The proposed case study adopts an operational point of view, on the basis that a novel forecasting system has value only if it leads to increase monetary and societal gains (e.g. Murphy, 1994; Laio and Tamea, 2007). More specifically, Environment Canada operational ensemble precipitation forecasts are used to drive the HYDROTEL distributed hydrological model (Fortin et al., 1995), calibrated on the Gatineau watershed located in Québec, Canada. The resulting hydrological ensemble forecasts are then incorporated into Hydro-Québec SOHO stochastic management optimization tool that automatically search for optimal operation decisions for the all reservoirs and hydropower plants located on the basin. The timeline of the study is the fall season of year 2003. This period is especially relevant because of high precipitations that nearly caused a major spill, and forced the preventive evacuation of a portion of the population located near one of the dams. We show that the use of the ensemble forecasts would have reduced the occurrence of spills and flooding, which is of particular importance for dams located in populous area, and increased hydropower production. The ensemble precipitation forecasts extend from March 1st of 2002 to December 31st of 2003. They were obtained using two atmospheric models, SEF (8 members plus the control deterministic forecast) and GEM (8 members). The corresponding deterministic precipitation forecast issued by SEF model is also used within HYDROTEL in order to compare ensemble streamflow forecasts with their deterministic counterparts. Although this study does not incorporate all the sources of uncertainty, precipitation is certainly the most important input for hydrological modeling and conveys a great portion of the total uncertainty. References: Fortin, J.P., Moussa, R., Bocquillon, C. and Villeneuve, J.P. 1995: HYDROTEL, un modèle hydrologique distribué pouvant bénéficier des données fournies par la télédétection et les systèmes d'information géographique, Revue des Sciences de l'Eau, 8(1), 94-124. Jaun, S., Ahrens, B., Walser, A., Ewen, T. and Schaer, C. 2008: A probabilistic view on the August 2005 floods in the upper Rhine catchment, Natural Hazards and Earth System Sciences, 8 (2), 281-291. Krzysztofowicz, R. 2001: The case for probabilistic forecasting in hydrology, Journal of Hydrology, 249, 2-9. Murphy, A.H. 1994: Assessing the economic value of weather forecasts: An overview of methods, results and issues, Meteorological Applications, 1, 69-73. Mylne, K.R. 2002: Decision-Making from probability forecasts based on forecast value, Meteorological Applications, 9, 307-315. Laio, F. and Tamea, S. 2007: Verification tools for probabilistic forecasts of continuous hydrological variables, Hydrology and Earth System Sciences, 11, 1267-1277. Roulin, E. 2007: Skill and relative economic value of medium-range hydrological ensemble predictions, Hydrology and Earth System Sciences, 11, 725-737. Velazquez, J.-A., Petit, T., Lavoie, A., Boucher, M.-A., Turcotte, R., Fortin, V. and Anctil, F. 2009: An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting, Hydrology and Earth System Sciences, 13(11), 2221-2231. Wilks, D.S. and Hamill, T.M. 1995: Potential economic value of ensemble-based surface weather forecasts, Monthly Weather Review, 123(12), 3565-3575.

Operational Earthquake Forecasting of Aftershocks for New England

NASA Astrophysics Data System (ADS)

Ebel, J.; Fadugba, O. I.

2015-12-01

Although the forecasting of mainshocks is not possible, recent research demonstrates that probabilistic forecasts of expected aftershock activity following moderate and strong earthquakes is possible. Previous work has shown that aftershock sequences in intraplate regions behave similarly to those in California, and thus the operational aftershocks forecasting methods that are currently employed in California can be adopted for use in areas of the eastern U.S. such as New England. In our application, immediately after a felt earthquake in New England, a forecast of expected aftershock activity for the next 7 days will be generated based on a generic aftershock activity model. Approximately 24 hours after the mainshock, the parameters of the aftershock model will be updated using the observed aftershock activity observed to that point in time, and a new forecast of expected aftershock activity for the next 7 days will be issued. The forecast will estimate the average number of weak, felt aftershocks and the average expected number of aftershocks based on the aftershock statistics of past New England earthquakes. The forecast also will estimate the probability that an earthquake that is stronger than the mainshock will take place during the next 7 days. The aftershock forecast will specify the expected aftershocks locations as well as the areas over which aftershocks of different magnitudes could be felt. The system will use web pages, email and text messages to distribute the aftershock forecasts. For protracted aftershock sequences, new forecasts will be issued on a regular basis, such as weekly. Initially, the distribution system of the aftershock forecasts will be limited, but later it will be expanded as experience with and confidence in the system grows.

Adaptive Regulation of the Northern California Reservoir System for Water, Energy, and Environmental Management

NASA Astrophysics Data System (ADS)

Georgakakos, A. P.; Kistenmacher, M.; Yao, H.; Georgakakos, K. P.

2014-12-01

The 2014 National Climate Assessment of the US Global Change Research Program emphasizes that water resources managers and planners in most US regions will have to cope with new risks, vulnerabilities, and opportunities, and recommends the development of adaptive capacity to effectively respond to the new water resources planning and management challenges. In the face of these challenges, adaptive reservoir regulation is becoming all the more ncessary. Water resources management in Northern California relies on the coordinated operation of several multi-objective reservoirs on the Trinity, Sacramento, American, Feather, and San Joaquin Rivers. To be effective, reservoir regulation must be able to (a) account for forecast uncertainty; (b) assess changing tradeoffs among water uses and regions; and (c) adjust management policies as conditions change; and (d) evaluate the socio-economic and environmental benefits and risks of forecasts and policies for each region and for the system as a whole. The Integrated Forecast and Reservoir Management (INFORM) prototype demonstration project operated in Northern California through the collaboration of several forecast and management agencies has shown that decision support systems (DSS) with these attributes add value to stakeholder decision processes compared to current, less flexible management practices. Key features of the INFORM DSS include: (a) dynamically downscaled operational forecasts and climate projections that maintain the spatio-temporal coherence of the downscaled land surface forcing fields within synoptic scales; (b) use of ensemble forecast methodologies for reservoir inflows; (c) assessment of relevant tradeoffs among water uses on regional and local scales; (d) development and evaluation of dynamic reservoir policies with explicit consideration of hydro-climatic forecast uncertainties; and (e) focus on stakeholder information needs.This article discusses the INFORM integrated design concept, underlying methodologies, and selected applications with the California water resources system.

Quantitative precipitation forecasts in the Alps - an assessment from the Forecast Demonstration Project MAP D-PHASE

NASA Astrophysics Data System (ADS)

Ament, F.; Weusthoff, T.; Arpagaus, M.; Rotach, M.

2009-04-01

The main aim of the WWRP Forecast Demonstration Project MAP D-PHASE is to demonstrate the performance of today's models to forecast heavy precipitation and flood events in the Alpine region. Therefore an end-to-end, real-time forecasting system was installed and operated during the D PHASE Operations Period from June to November 2007. Part of this system are 30 numerical weather prediction models (deterministic as well as ensemble systems) operated by weather services and research institutes, which issue alerts if predicted precipitation accumulations exceed critical thresholds. Additionally to the real-time alerts, all relevant model fields of these simulations are stored in a central data archive. This comprehensive data set allows a detailed assessment of today's quantitative precipitation forecast (QPF) performance in the Alpine region. We will present results of QPF verifications against Swiss radar and rain gauge data both from a qualitative point of view, in terms of alerts, as well as from a quantitative perspective, in terms of precipitation rate. Various influencing factors like lead time, accumulation time, selection of warning thresholds, or bias corrections will be discussed. Additional to traditional verifications of area average precipitation amounts, the performance of the models to predict the correct precipitation statistics without requiring a point-to-point match will be described by using modern Fuzzy verification techniques. Both analyses reveal significant advantages of deep convection resolving models compared to coarser models with parameterized convection. An intercomparison of the model forecasts themselves reveals a remarkably high variability between different models, and makes it worthwhile to evaluate the potential of a multi-model ensemble. Various multi-model ensemble strategies will be tested by combining D-PHASE models to virtual ensemble systems.

A Prototype Regional GSI-based EnKF-Variational Hybrid Data Assimilation System for the Rapid Refresh Forecasting System: Dual-Resolution Implementation and Testing Results

NASA Astrophysics Data System (ADS)

Pan, Yujie; Xue, Ming; Zhu, Kefeng; Wang, Mingjun

2018-05-01

A dual-resolution (DR) version of a regional ensemble Kalman filter (EnKF)-3D ensemble variational (3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution (HR) deterministic background forecast with lower-resolution (LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/˜13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation (GSI) 3D variational (3DVar) analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar. Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.

Verification of Advances in a Coupled Snow-runoff Modeling Framework for Operational Streamflow Forecasts

NASA Astrophysics Data System (ADS)

Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

2011-12-01

The National Oceanic and Atmospheric Administration's (NOAA's) River Forecast Centers (RFCs) issue hydrologic forecasts related to flood events, reservoir operations for water supply, streamflow regulation, and recreation on the nation's streams and rivers. The RFCs use the National Weather Service River Forecast System (NWSRFS) for streamflow forecasting which relies on a coupled snow model (i.e. SNOW17) and rainfall-runoff model (i.e. SAC-SMA) in snow-dominated regions of the US. Errors arise in various steps of the forecasting system from input data, model structure, model parameters, and initial states. The goal of the current study is to undertake verification of potential improvements in the SNOW17-SAC-SMA modeling framework developed for operational streamflow forecasts. We undertake verification for a range of parameters sets (i.e. RFC, DREAM (Differential Evolution Adaptive Metropolis)) as well as a data assimilation (DA) framework developed for the coupled models. Verification is also undertaken for various initial conditions to observe the influence of variability in initial conditions on the forecast. The study basin is the North Fork America River Basin (NFARB) located on the western side of the Sierra Nevada Mountains in northern California. Hindcasts are verified using both deterministic (i.e. Nash Sutcliffe efficiency, root mean square error, and joint distribution) and probabilistic (i.e. reliability diagram, discrimination diagram, containing ratio, and Quantile plots) statistics. Our presentation includes comparison of the performance of different optimized parameters and the DA framework as well as assessment of the impact associated with the initial conditions used for streamflow forecasts for the NFARB.

Towards a coastal ocean forecasting system in Southern Adriatic Northern Ionian seas based on unstructured-grid model

NASA Astrophysics Data System (ADS)

Federico, Ivan; Oddo, Paolo; Pinardi, Nadia; Coppini, Giovanni

2014-05-01

The Southern Adriatic Northern Ionian Forecasting System (SANIFS) operational chain is based on a nesting approach. The large scale model for the entire Mediterranean basin (MFS, Mediterranean Forecasting system, operated by INGV, e.g. Tonani et al. 2008, Oddo et al. 2009) provides lateral open boundary conditions to the regional model for Adriatic and Ionian seas (AIFS, Adriatic Ionian Forecasting System) which provides the open-sea fields (initial conditions and lateral open boundary conditions) to SANIFS. The latter, here presented, is a coastal ocean model based on SHYFEM (Shallow HYdrodynamics Finite Element Model) code, which is an unstructured grid, finite element three-dimensional hydrodynamic model (e.g. Umgiesser et al., 2004, Ferrarin et al., 2013). The SANIFS hydrodynamic model component has been designed to provide accurate information of hydrodynamics and active tracer fields in the coastal waters of Southern Eastern Italy (Apulia, Basilicata and Calabria regions), where the model is characterized by a resolution of about of 200-500 m. The horizontal resolution is also accurate in open-sea areas, where the elements size is approximately 3 km. During the development phase the model has been initialized and forced at the lateral open boundaries through a full nesting strategy directly with the MFS fields. The heat fluxes has been computed by bulk formulae using as input data the operational analyses of European Centre for Medium-Range Weather Forecasts. Short range pre-operational forecast tests have been performed in different seasons to evaluate the robustness of the implemented model in different oceanographic conditions. Model results are validated by means of comparison with MFS operational results and observations. The model is able to reproduce the large-scale oceanographic structures of the area (keeping similar structures of MFS in open sea), while in the coastal area significant improvements in terms of reproduced structures and dynamics are evident.

Monthly forecasting of agricultural pests in Switzerland

NASA Astrophysics Data System (ADS)

Hirschi, M.; Dubrovsky, M.; Spirig, C.; Samietz, J.; Calanca, P.; Weigel, A. P.; Fischer, A. M.; Rotach, M. W.

2012-04-01

Given the repercussions of pests and diseases on agricultural production, detailed forecasting tools have been developed to simulate the degree of infestation depending on actual weather conditions. The life cycle of pests is most successfully predicted if the micro-climate of the immediate environment (habitat) of the causative organisms can be simulated. Sub-seasonal pest forecasts therefore require weather information for the relevant habitats and the appropriate time scale. The pest forecasting system SOPRA (www.sopra.info) currently in operation in Switzerland relies on such detailed weather information, using hourly weather observations up to the day the forecast is issued, but only a climatology for the forecasting period. Here, we aim at improving the skill of SOPRA forecasts by transforming the weekly information provided by ECMWF monthly forecasts (MOFCs) into hourly weather series as required for the prediction of upcoming life phases of the codling moth, the major insect pest in apple orchards worldwide. Due to the probabilistic nature of operational monthly forecasts and the limited spatial and temporal resolution, their information needs to be post-processed for use in a pest model. In this study, we developed a statistical downscaling approach for MOFCs that includes the following steps: (i) application of a stochastic weather generator to generate a large pool of daily weather series consistent with the climate at a specific location, (ii) a subsequent re-sampling of weather series from this pool to optimally represent the evolution of the weekly MOFC anomalies, and (iii) a final extension to hourly weather series suitable for the pest forecasting model. Results show a clear improvement in the forecast skill of occurrences of upcoming codling moth life phases when incorporating MOFCs as compared to the operational pest forecasting system. This is true both in terms of root mean squared errors and of the continuous rank probability scores of the probabilistic forecasts vs. the mean absolute errors of the deterministic system. Also, the application of the climate conserving recalibration (CCR, Weigel et al. 2009) technique allows for successful correction of the under-confidence in the forecasted occurrences of codling moth life phases. Reference: Weigel, A. P.; Liniger, M. A. & Appenzeller, C. (2009). Seasonal Ensemble Forecasts: Are Recalibrated Single Models Better than Multimodels? Mon. Wea. Rev., 137, 1460-1479.

SONARC: A Sea Ice Monitoring and Forecasting System to Support Safe Operations and Navigation in Arctic Seas

NASA Astrophysics Data System (ADS)

Stephenson, S. R.; Babiker, M.; Sandven, S.; Muckenhuber, S.; Korosov, A.; Bobylev, L.; Vesman, A.; Mushta, A.; Demchev, D.; Volkov, V.; Smirnov, K.; Hamre, T.

2015-12-01

Sea ice monitoring and forecasting systems are important tools for minimizing accident risk and environmental impacts of Arctic maritime operations. Satellite data such as synthetic aperture radar (SAR), combined with atmosphere-ice-ocean forecasting models, navigation models and automatic identification system (AIS) transponder data from ships are essential components of such systems. Here we present first results from the SONARC project (project term: 2015-2017), an international multidisciplinary effort to develop novel and complementary ice monitoring and forecasting systems for vessels and offshore platforms in the Arctic. Automated classification methods (Zakhvatkina et al., 2012) are applied to Sentinel-1 dual-polarization SAR images from the Barents and Kara Sea region to identify ice types (e.g. multi-year ice, level first-year ice, deformed first-year ice, new/young ice, open water) and ridges. Short-term (1-3 days) ice drift forecasts are computed from SAR images using feature tracking and pattern tracking methods (Berg & Eriksson, 2014). Ice classification and drift forecast products are combined with ship positions based on AIS data from a selected period of 3-4 weeks to determine optimal vessel speed and routing in ice. Results illustrate the potential of high-resolution SAR data for near-real-time monitoring and forecasting of Arctic ice conditions. Over the next 3 years, SONARC findings will contribute new knowledge about sea ice in the Arctic while promoting safe and cost-effective shipping, domain awareness, resource management, and environmental protection.

Maintaining a Local Data Integration System in Support of Weather Forecast Operations

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian

2010-01-01

Since 2000, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) have used a local data integration system (LDIS) as part of their forecast and warning operations. Each has benefited from 3-dimensional analyses that are delivered to forecasters every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather forecasts issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national- or regional-scale analysis packages. As such, it provides local forecasters with a more comprehensive and complete understanding of evolving fine-scale weather features. Recent efforts have been undertaken to update the LDIS through the formal tasking process of NASA's Applied Meteorology Unit. The goals include upgrading LDIS with the latest version of ADAS, incorporating new sources of observational data, and making adjustments to shell scripts written to govern the system. A series of scripts run a complete modeling system consisting of the preprocessing step, the main model integration, and the post-processing step. The preprocessing step prepares the terrain, surface characteristics data sets, and the objective analysis for model initialization. Data ingested through ADAS include (but are not limited to) Level II Weather Surveillance Radar- 1988 Doppler (WSR-88D) data from six Florida radars, Geostationary Operational Environmental Satellites (GOES) visible and infrared satellite imagery, surface and upper air observations throughout Florida from NOAA's Earth System Research Laboratory/Global Systems Division/Meteorological Assimilation Data Ingest System (MADIS), as well as the Kennedy Space Center ICape Canaveral Air Force Station wind tower network. The scripts provide NWS MLB and SMG with several options for setting a desirable runtime configuration of the LDIS to account for adjustments in grid spacing, domain location, choice of observational data sources, and selection of background model fields, among others. The utility of an improved LDIS will be demonstrated through postanalysis warm and cool season case studies that compare high-resolution model output with and without the ADAS analyses. Operationally, these upgrades will result in more accurate depictions of the current local environment to help with short-range weather forecasting applications, while also offering an improved initialization for local versions of the Weather Research and Forecasting model.

Flood Forecast Accuracy and Decision Support System Approach: the Venice Case

NASA Astrophysics Data System (ADS)

Canestrelli, A.; Di Donato, M.

2016-02-01

In the recent years numerical models for weather predictions have experienced continuous advances in technology. As a result, all the disciplines making use of weather forecasts have made significant steps forward. In the case of the Safeguard of Venice, a large effort has been put in order to improve the forecast of tidal levels. In this context, the Istituzione Centro Previsioni e Segnalazioni Maree (ICPSM) of the Venice Municipality has developed and tested many different forecast models, both of the statistical and deterministic type, and has shown to produce very accurate forecasts. For Venice, the maximum admissible forecast error should be (ideally) of the order of ten centimeters at 24 hours. The entity of the forecast error clearly affects the decisional process, which mainly consists of alerting the population, activating the movable barriers installed at the three tidal inlets and contacting the port authority. This process becomes more challenging whenever the weather predictions, and therefore the water level forecasts, suddenly change. These new forecasts have to be quickly transformed into operational tasks. Therefore, it is of the utter importance to set up scheduled alerts and emergency plans by means of easy-to-follow procedures. On this direction, Technital has set up a Decision Support System based on expert procedures that minimizes the human mistakes and, as a consequence, reduces the risk of flooding of the historical center. Moreover, the Decision Support System can communicate predefined alerts to all the interested subjects. The System uses the water levels forecasts produced by the ICPSM by taking into account the accuracy at different leading times. The Decision Support System has been successfully tested with 8 years of data, 6 of them in real time. Venice experience shows that the Decision Support System is an essential tool which assesses the risks associated with a particular event, provides clear operational procedures and minimizes the impact of natural floods on human lives, private properties and historical monuments.

A maritime decision support system to assess risk in the presence of environmental uncertainties: the REP10 experiment

NASA Astrophysics Data System (ADS)

Grasso, Raffaele; Cococcioni, Marco; Mourre, Baptiste; Chiggiato, Jacopo; Rixen, Michel

2012-03-01

The aim of this work is to report on an activity carried out during the 2010 Recognized Environmental Picture experiment, held in the Ligurian Sea during summer 2010. The activity was the first at-sea test of the recently developed decision support system (DSS) for operation planning, which had previously been tested in an artificial experiment. The DSS assesses the impact of both environmental conditions (meteorological and oceanographic) and non-environmental conditions (such as traffic density maps) on people and assets involved in the operation and helps in deciding a course of action that allows safer operation. More precisely, the environmental variables (such as wind speed, current speed and significant wave height) taken as input by the DSS are the ones forecasted by a super-ensemble model, which fuses the forecasts provided by multiple forecasting centres. The uncertainties associated with the DSS's inputs (generally due to disagreement between forecasts) are propagated through the DSS's output by using the unscented transform. In this way, the system is not only able to provide a traffic light map ( run/ not run the operation), but also to specify the confidence level associated with each action. This feature was tested on a particular type of operation with underwater gliders: the glider surfacing for data transmission. It is also shown how the availability of a glider path prediction tool provides surfacing options along the predicted path. The applicability to different operations is demonstrated by applying the same system to support diver operations.

Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

DOE PAGES

Staid, Andrea; Watson, Jean -Paul; Wets, Roger J. -B.; ...

2017-07-11

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less

Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Staid, Andrea; Watson, Jean -Paul; Wets, Roger J. -B.

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less

A temporal-spatial postprocessing model for probabilistic run-off forecast. With a case study from Ulla-Førre with five catchments and ten lead times

NASA Astrophysics Data System (ADS)

Engeland, K.; Steinsland, I.

2012-04-01

This work is driven by the needs of next generation short term optimization methodology for hydro power production. Stochastic optimization are about to be introduced; i.e. optimizing when available resources (water) and utility (prices) are uncertain. In this paper we focus on the available resources, i.e. water, where uncertainty mainly comes from uncertainty in future runoff. When optimizing a water system all catchments and several lead times have to be considered simultaneously. Depending on the system of hydropower reservoirs, it might be a set of headwater catchments, a system of upstream /downstream reservoirs where water used from one catchment /dam arrives in a lower catchment maybe days later, or a combination of both. The aim of this paper is therefore to construct a simultaneous probabilistic forecast for several catchments and lead times, i.e. to provide a predictive distribution for the forecasts. Stochastic optimization methods need samples/ensembles of run-off forecasts as input. Hence, it should also be possible to sample from our probabilistic forecast. A post-processing approach is taken, and an error model based on Box- Cox transformation, power transform and a temporal-spatial copula model is used. It accounts for both between catchment and between lead time dependencies. In operational use it is strait forward to sample run-off ensembles from this models that inherits the catchment and lead time dependencies. The methodology is tested and demonstrated in the Ulla-Førre river system, and simultaneous probabilistic forecasts for five catchments and ten lead times are constructed. The methodology has enough flexibility to model operationally important features in this case study such as hetroscadasety, lead-time varying temporal dependency and lead-time varying inter-catchment dependency. Our model is evaluated using CRPS for marginal predictive distributions and energy score for joint predictive distribution. It is tested against deterministic run-off forecast, climatology forecast and a persistent forecast, and is found to be the better probabilistic forecast for lead time grater then two. From an operational point of view the results are interesting as the between catchment dependency gets stronger with longer lead-times.

Transitioning a Chesapeake Bay Ecological Prediction System to Operations

NASA Astrophysics Data System (ADS)

Brown, C.; Green, D. S.; Eco Forecasters

2011-12-01

Ecological prediction of the impacts of physical, chemical, biological, and human-induced change on ecosystems and their components, encompass a wide range of space and time scales, and subject matter. They vary from predicting the occurrence and/or transport of certain species, such harmful algal blooms, or biogeochemical constituents, such as dissolved oxygen concentrations, to large-scale ecosystem responses and higher trophic levels. The timescales of ecological prediction, including guidance and forecasts, range from nowcasts and short-term forecasts (days), to intraseasonal and interannual outlooks (weeks to months), to decadal and century projections in climate change scenarios. The spatial scales range from small coastal inlets to basin and global scale biogeochemical and ecological forecasts. The types of models that have been used include conceptual, empirical, mechanistic, and hybrid approaches. This presentation will identify the challenges and progress toward transitioning experimental model-based ecological prediction into operational guidance and forecasting. Recent efforts are targeting integration of regional ocean, hydrodynamic and hydrological models and leveraging weather and water service infrastructure to enable the prototyping of an operational ecological forecast capability for the Chesapeake Bay and its tidal tributaries. A path finder demonstration predicts the probability of encountering sea nettles (Chrysaora quinquecirrha), a stinging jellyfish. These jellyfish can negatively impact safety and economic activities in the bay and an impact-based forecast that predicts where and when this biotic nuisance occurs may help management effects. The issuance of bay-wide nowcasts and three-day forecasts of sea nettle probability are generated daily by forcing an empirical habitat model (that predicts the probability of sea nettles) with real-time and 3-day forecasts of sea-surface temperature (SST) and salinity (SSS). In the first demonstration phase, the sea surface temperature (SST) and sea surface salinity (SSS) fields are generated by the Chesapeake Bay Operational Forecast System (CBOFS2), a 3-dimensional hydrodynamic model developed and operated by NOAA's National Ocean Service and run operationally at the National Weather Service National Centers for Environmental Prediction (NCEP). Importantly, this system is readily modified to predict the probability of other important target organisms, such as harmful algal blooms, biogeochemical constituents, such as dissolved oxygen concentration, and water-borne pathogens. Extending this initial effort includes advancement of a regional coastal ocean modeling testbed and proving ground. Such formal collaboration is intended to accelerate transition to operations and increase confidence and use of forecast guidance. The outcome will be improved decision making by emergency and resource managers, scientific researchers and the general public. The presentation will describe partnership plans for this testbed as well as the potential implications for the services and research community.

Improved Weather and Power Forecasts for Energy Operations - the German Research Project EWeLiNE

NASA Astrophysics Data System (ADS)

Lundgren, Kristina; Siefert, Malte; Hagedorn, Renate; Majewski, Detlev

2014-05-01

The German energy system is going through a fundamental change. Based on the energy plans of the German federal government, the share of electrical power production from renewables should increase to 35% by 2020. This means that, in the near future at certain times renewable energies will provide a major part of Germany's power production. Operating a power supply system with a large share of weather-dependent power sources in a secure way requires improved power forecasts. One of the most promising strategies to improve the existing wind power and PV power forecasts is to optimize the underlying weather forecasts and to enhance the collaboration between the meteorology and energy sectors. Deutscher Wetterdienst addresses these challenges in collaboration with Fraunhofer IWES within the research project EWeLiNE. The overarching goal of the project is to improve the wind and PV power forecasts by combining improved power forecast models and optimized weather forecasts. During the project, the numerical weather prediction models COSMO-DE and COSMO-DE-EPS (Ensemble Prediction System) by Deutscher Wetterdienst will be generally optimized towards improved wind power and PV forecasts. For instance, it will be investigated whether the assimilation of new types of data, e.g. power production data, can lead to improved weather forecasts. With regard to the probabilistic forecasts, the focus is on the generation of ensembles and ensemble calibration. One important aspect of the project is to integrate the probabilistic information into decision making processes by developing user-specified products. In this paper we give an overview of the project and present first results.

Final report of the operation and demonstration test of short-range weather forecasting decision support within an advanced transportation weather information system (#Safe)

DOT National Transportation Integrated Search

2006-04-01

The purpose of the Advanced Transportation Weather Information System (ATWIS) was to provide en-route weather forecasts and road condition information to the traveling public across North Dakota and South Dakota. ATWIS was the first system to develop...

Winter wheat quality monitoring and forecasting system based on remote sensing and environmental factors

NASA Astrophysics Data System (ADS)

Haiyang, Yu; Yanmei, Liu; Guijun, Yang; Xiaodong, Yang; Dong, Ren; Chenwei, Nie

2014-03-01

To achieve dynamic winter wheat quality monitoring and forecasting in larger scale regions, the objective of this study was to design and develop a winter wheat quality monitoring and forecasting system by using a remote sensing index and environmental factors. The winter wheat quality trend was forecasted before the harvest and quality was monitored after the harvest, respectively. The traditional quality-vegetation index from remote sensing monitoring and forecasting models were improved. Combining with latitude information, the vegetation index was used to estimate agronomy parameters which were related with winter wheat quality in the early stages for forecasting the quality trend. A combination of rainfall in May, temperature in May, illumination at later May, the soil available nitrogen content and other environmental factors established the quality monitoring model. Compared with a simple quality-vegetation index, the remote sensing monitoring and forecasting model used in this system get greatly improved accuracy. Winter wheat quality was monitored and forecasted based on the above models, and this system was completed based on WebGIS technology. Finally, in 2010 the operation process of winter wheat quality monitoring system was presented in Beijing, the monitoring and forecasting results was outputted as thematic maps.

GIS model-based real-time hydrological forecasting and operation management system for the Lake Balaton and its watershed

NASA Astrophysics Data System (ADS)

Adolf Szabó, János; Zoltán Réti, Gábor; Tóth, Tünde

2017-04-01

Today, the most significant mission of the decision makers on integrated water management issues is to carry out sustainable management for sharing the resources between a variety of users and the environment under conditions of considerable uncertainty (such as climate/land-use/population/etc. change) conditions. In light of this increasing water management complexity, we consider that the most pressing needs is to develop and implement up-to-date GIS model-based real-time hydrological forecasting and operation management systems for aiding decision-making processes to improve water management. After years of researches and developments the HYDROInform Ltd. has developed an integrated, on-line IT system (DIWA-HFMS: DIstributed WAtershed - Hydrologyc Forecasting & Modelling System) which is able to support a wide-ranging of the operational tasks in water resources management such as: forecasting, operation of lakes and reservoirs, water-control and management, etc. Following a test period, the DIWA-HFMS has been implemented for the Lake Balaton and its watershed (in 500 m resolution) at Central-Transdanubian Water Directorate (KDTVIZIG). The significant pillars of the system are: - The DIWA (DIstributed WAtershed) hydrologic model, which is a 3D dynamic water-balance model that distributed both in space and its parameters, and which was developed along combined principles but its mostly based on physical foundations. The DIWA integrates 3D soil-, 2D surface-, and 1D channel-hydraulic components as well. - Lakes and reservoir-operating component; - Radar-data integration module; - fully online data collection tools; - scenario manager tool to create alternative scenarios, - interactive, intuitive, highly graphical user interface. In Vienna, the main functions, operations and results-management of the system will be presented.

Unmanned Aircraft Systems Demand Forecast Study

NASA Technical Reports Server (NTRS)

Hackenberg, Davis L.

2017-01-01

UAS demand slides discuss the purpose, scope, and assumptions of the UAS Demand Forecast Study. It discusses some operational environments and market research study, this information is broad knowledge in the UAS community.

Error discrimination of an operational hydrological forecasting system at a national scale

NASA Astrophysics Data System (ADS)

Jordan, F.; Brauchli, T.

2010-09-01

The use of operational hydrological forecasting systems is recommended for hydropower production as well as flood management. However, the forecast uncertainties can be important and lead to bad decisions such as false alarms and inappropriate reservoir management of hydropower plants. In order to improve the forecasting systems, it is important to discriminate the different sources of uncertainties. To achieve this task, reanalysis of past predictions can be realized and provide information about the structure of the global uncertainty. In order to discriminate between uncertainty due to the weather numerical model and uncertainty due to the rainfall-runoff model, simulations assuming perfect weather forecast must be realized. This contribution presents the spatial analysis of the weather uncertainties and their influence on the river discharge prediction of a few different river basins where an operational forecasting system exists. The forecast is based on the RS 3.0 system [1], [2], which is also running the open Internet platform www.swissrivers.ch [3]. The uncertainty related to the hydrological model is compared to the uncertainty related to the weather prediction. A comparison between numerous weather prediction models [4] at different lead times is also presented. The results highlight an important improving potential of both forecasting components: the hydrological rainfall-runoff model and the numerical weather prediction models. The hydrological processes must be accurately represented during the model calibration procedure, while weather prediction models suffer from a systematic spatial bias. REFERENCES [1] Garcia, J., Jordan, F., Dubois, J. & Boillat, J.-L. 2007. "Routing System II, Modélisation d'écoulements dans des systèmes hydrauliques", Communication LCH n° 32, Ed. Prof. A. Schleiss, Lausanne [2] Jordan, F. 2007. Modèle de prévision et de gestion des crues - optimisation des opérations des aménagements hydroélectriques à accumulation pour la réduction des débits de crue, thèse de doctorat n° 3711, Ecole Polytechnique Fédérale, Lausanne [3] Keller, R. 2009. "Le débit des rivières au peigne fin", Revue Technique Suisse, N°7/8 2009, Swiss engineering RTS, UTS SA, Lausanne, p. 11 [4] Kaufmann, P., Schubiger, F. & Binder, P. 2003. Precipitation forecasting by a mesoscale numerical weather prediction (NWP) model : eight years of experience, Hydrology and Earth System

Short-term load and wind power forecasting using neural network-based prediction intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2014-02-01

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

Forecasting skills of the ensemble hydro-meteorological system for the Po river floods

NASA Astrophysics Data System (ADS)

Ricciardi, Giuseppe; Montani, Andrea; Paccagnella, Tiziana; Pecora, Silvano; Tonelli, Fabrizio

2013-04-01

The Po basin is the largest and most economically important river-basin in Italy. Extreme hydrological events, including floods, flash floods and droughts, are expected to become more severe in the next future due to climate change, and related ground effects are linked both with environmental and social resilience. A Warning Operational Center (WOC) for hydrological event management was created in Emilia Romagna region. In the last years, the WOC faced challenges in legislation, organization, technology and economics, achieving improvements in forecasting skill and information dissemination. Since 2005, an operational forecasting and modelling system for flood modelling and forecasting has been implemented, aimed at supporting and coordinating flood control and emergency management on the whole Po basin. This system, referred to as FEWSPo, has also taken care of environmental aspects of flood forecast. The FEWSPo system has reached a very high level of complexity, due to the combination of three different hydrological-hydraulic chains (HEC-HMS/RAS - MIKE11 NAM/HD, Topkapi/Sobek), with several meteorological inputs (forecasted - COSMOI2, COSMOI7, COSMO-LEPS among others - and observed). In this hydrological and meteorological ensemble the management of the relative predictive uncertainties, which have to be established and communicated to decision makers, is a debated scientific and social challenge. Real time activities face professional, modelling and technological aspects but are also strongly interrelated with organization and human aspects. The authors will report a case study using the operational flood forecast hydro-meteorological ensemble, provided by the MIKE11 chain fed by COSMO_LEPS EQPF. The basic aim of the proposed approach is to analyse limits and opportunities of the long term forecast (with a lead time ranging from 3 to 5 days), for the implementation of low cost actions, also looking for a well informed decision making and the improvement of flood preparedness and crisis management for basins greater than 1.000 km2.

The Ensemble Space Weather Modeling System (eSWMS): Status, Capabilities and Challenges

NASA Astrophysics Data System (ADS)

Fry, C. D.; Eccles, J. V.; Reich, J. P.

2010-12-01

Marking a milestone in space weather forecasting, the Space Weather Modeling System (SWMS) successfully completed validation testing in advance of operational testing at Air Force Weather Agency’s primary space weather production center. This is the first coupling of stand-alone, physics-based space weather models that are currently in operations at AFWA supporting the warfighter. Significant development effort went into ensuring the component models were portable and scalable while maintaining consistent results across diverse high performance computing platforms. Coupling was accomplished under the Earth System Modeling Framework (ESMF). The coupled space weather models are the Hakamada-Akasofu-Fry version 2 (HAFv2) solar wind model and GAIM1, the ionospheric forecast component of the Global Assimilation of Ionospheric Measurements (GAIM) model. The SWMS was developed by team members from AFWA, Explorations Physics International, Inc. (EXPI) and Space Environment Corporation (SEC). The successful development of the SWMS provides new capabilities beyond enabling extended lead-time, data-driven ionospheric forecasts. These include ingesting diverse data sets at higher resolution, incorporating denser computational grids at finer time steps, and performing probability-based ensemble forecasts. Work of the SWMS development team now focuses on implementing the ensemble-based probability forecast capability by feeding multiple scenarios of 5 days of solar wind forecasts to the GAIM1 model based on the variation of the input fields to the HAFv2 model. The ensemble SWMS (eSWMS) will provide the most-likely space weather scenario with uncertainty estimates for important forecast fields. The eSWMS will allow DoD mission planners to consider the effects of space weather on their systems with more advance warning than is currently possible. The payoff is enhanced, tailored support to the warfighter with improved capabilities, such as point-to-point HF propagation forecasts, single-frequency GPS error corrections, and high cadence, high-resolution Space Situational Awareness (SSA) products. We present the current status of eSWMS, its capabilities, limitations and path of transition to operational use.

Assessment of a new seasonal to inter-annual operational Great Lakes water supply, water levels, and connecting channel flow forecasting system

NASA Astrophysics Data System (ADS)

Gronewold, A.; Fry, L. M.; Hunter, T.; Pei, L.; Smith, J.; Lucier, H.; Mueller, R.

2017-12-01

The U.S. Army Corps of Engineers (USACE) has recently operationalized a suite of ensemble forecasts of Net Basin Supply (NBS), water levels, and connecting channel flows that was developed through a collaboration among USACE, NOAA's Great Lakes Environmental Research Laboratory, Ontario Power Generation (OPG), New York Power Authority (NYPA), and the Niagara River Control Center (NRCC). These forecasts are meant to provide reliable projections of potential extremes in daily discharge in the Niagara and St. Lawrence Rivers over a long time horizon (5 years). The suite of forecasts includes eight configurations that vary by (a) NBS model configuration, (b) meteorological forcings, and (c) incorporation of seasonal climate projections through the use of weighting. Forecasts are updated on a weekly basis, and represent the first operational forecasts of Great Lakes water levels and flows that span daily to inter-annual horizons and employ realistic regulation logic and lake-to-lake routing. We will present results from a hindcast assessment conducted during the transition from research to operation, as well as early indications of success rates determined through operational verification of forecasts. Assessment will include an exploration of the relative skill of various forecast configurations at different time horizons and the potential for application to hydropower decision making and Great Lakes water management.

Effect of Streamflow Forecast Uncertainty on Real-Time Reservoir Operation

NASA Astrophysics Data System (ADS)

Zhao, T.; Cai, X.; Yang, D.

2010-12-01

Various hydrological forecast products have been applied to real-time reservoir operation, including deterministic streamflow forecast (DSF), DSF-based probabilistic streamflow forecast (DPSF), and ensemble streamflow forecast (ESF), which represent forecast uncertainty in the form of deterministic forecast error, deterministic forecast error-based uncertainty distribution, and ensemble forecast errors, respectively. Compared to previous studies that treat these forecast products as ad hoc inputs for reservoir operation models, this paper attempts to model the uncertainties involved in the various forecast products and explores their effect on real-time reservoir operation decisions. In hydrology, there are various indices reflecting the magnitude of streamflow forecast uncertainty; meanwhile, few models illustrate the forecast uncertainty evolution process. This research introduces Martingale Model of Forecast Evolution (MMFE) from supply chain management and justifies its assumptions for quantifying the evolution of uncertainty in streamflow forecast as time progresses. Based on MMFE, this research simulates the evolution of forecast uncertainty in DSF, DPSF, and ESF, and applies the reservoir operation models (dynamic programming, DP; stochastic dynamic programming, SDP; and standard operation policy, SOP) to assess the effect of different forms of forecast uncertainty on real-time reservoir operation. Through a hypothetical single-objective real-time reservoir operation model, the results illustrate that forecast uncertainty exerts significant effects. Reservoir operation efficiency, as measured by a utility function, decreases as the forecast uncertainty increases. Meanwhile, these effects also depend on the type of forecast product being used. In general, the utility of reservoir operation with ESF is nearly as high as the utility obtained with a perfect forecast; the utilities of DSF and DPSF are similar to each other but not as efficient as ESF. Moreover, streamflow variability and reservoir capacity can change the magnitude of the effects of forecast uncertainty, but not the relative merit of DSF, DPSF, and ESF. Schematic diagram of the increase in forecast uncertainty with forecast lead-time and the dynamic updating property of real-time streamflow forecast

How to improve an un-alterable model forecast? A sequential data assimilation based error updating approach

NASA Astrophysics Data System (ADS)

Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K. T.

2012-12-01

Accuracy of reservoir inflow forecasts is instrumental for maximizing value of water resources and influences operation of hydropower reservoirs significantly. Improving hourly reservoir inflow forecasts over a 24 hours lead-time is considered with the day-ahead (Elspot) market of the Nordic exchange market in perspectives. The procedure presented comprises of an error model added on top of an un-alterable constant parameter conceptual model, and a sequential data assimilation routine. The structure of the error model was investigated using freely available software for detecting mathematical relationships in a given dataset (EUREQA) and adopted to contain minimum complexity for computational reasons. As new streamflow data become available the extra information manifested in the discrepancies between measurements and conceptual model outputs are extracted and assimilated into the forecasting system recursively using Sequential Monte Carlo technique. Besides improving forecast skills significantly, the probabilistic inflow forecasts provided by the present approach entrains suitable information for reducing uncertainty in decision making processes related to hydropower systems operation. The potential of the current procedure for improving accuracy of inflow forecasts at lead-times unto 24 hours and its reliability in different seasons of the year will be illustrated and discussed thoroughly.

An operational procedure for rapid flood risk assessment in Europe

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Kalas, Milan; Salamon, Peter; Bianchi, Alessandra; Alfieri, Lorenzo; Feyen, Luc

2017-07-01

The development of methods for rapid flood mapping and risk assessment is a key step to increase the usefulness of flood early warning systems and is crucial for effective emergency response and flood impact mitigation. Currently, flood early warning systems rarely include real-time components to assess potential impacts generated by forecasted flood events. To overcome this limitation, this study describes the benchmarking of an operational procedure for rapid flood risk assessment based on predictions issued by the European Flood Awareness System (EFAS). Daily streamflow forecasts produced for major European river networks are translated into event-based flood hazard maps using a large map catalogue derived from high-resolution hydrodynamic simulations. Flood hazard maps are then combined with exposure and vulnerability information, and the impacts of the forecasted flood events are evaluated in terms of flood-prone areas, economic damage and affected population, infrastructures and cities.An extensive testing of the operational procedure has been carried out by analysing the catastrophic floods of May 2014 in Bosnia-Herzegovina, Croatia and Serbia. The reliability of the flood mapping methodology is tested against satellite-based and report-based flood extent data, while modelled estimates of economic damage and affected population are compared against ground-based estimations. Finally, we evaluate the skill of risk estimates derived from EFAS flood forecasts with different lead times and combinations of probabilistic forecasts. Results highlight the potential of the real-time operational procedure in helping emergency response and management.

Probabilistic Weather Information Tailored to the Needs of Transmission System Operators

NASA Astrophysics Data System (ADS)

Alberts, I.; Stauch, V.; Lee, D.; Hagedorn, R.

2014-12-01

Reliable and accurate forecasts for wind and photovoltaic (PV) power production are essential for stable transmission systems. A high potential for improving the wind and PV power forecasts lies in optimizing the weather forecasts, since these energy sources are highly weather dependent. For this reason the main objective of the German research project EWeLiNE is to improve the quality the underlying numerical weather predictions towards energy operations. In this project, the German Meteorological Service (DWD), the Fraunhofer Institute for Wind Energy and Energy System Technology, and three of the German transmission system operators (TSOs) are working together to improve the weather and power forecasts. Probabilistic predictions are of particular interest, as the quantification of uncertainties provides an important tool for risk management. Theoretical considerations suggest that it can be advantageous to use probabilistic information to represent and respond to the remaining uncertainties in the forecasts. However, it remains a challenge to integrate this information into the decision making processes related to market participation and power systems operations. The project is planned and carried out in close cooperation with the involved TSOs in order to ensure the usability of the products developed. It will conclude with a demonstration phase, in which the improved models and newly developed products are combined into a process chain and used to provide information to TSOs in a real-time decision support tool. The use of a web-based development platform enables short development cycles and agile adaptation to evolving user needs. This contribution will present the EWeLiNE project and discuss ideas on how to incorporate probabilistic information into the users' current decision making processes.

A national framework for flood forecasting model assessment for use in operations and investment planning over England and Wales

NASA Astrophysics Data System (ADS)

Moore, Robert J.; Wells, Steven C.; Cole, Steven J.

2016-04-01

It has been common for flood forecasting systems to be commissioned at a catchment or regional level in response to local priorities and hydrological conditions, leading to variety in system design and model choice. As systems mature and efficiencies of national management are sought, there can be a drive towards system rationalisation, gaining an overview of model performance and consideration of simplification through model-type convergence. Flood forecasting model assessments, whilst overseen at a national level, may be commissioned and managed at a catchment and regional level, take a variety of forms and be large in number. This presents a challenge when an integrated national assessment is required to guide operational use of flood forecasts and plan future investment in flood forecasting models and supporting hydrometric monitoring. This contribution reports on how a nationally consistent framework for flood forecasting model performance has been developed to embrace many past, ongoing and future assessments for local river systems by engineering consultants across England & Wales. The outcome is a Performance Summary for every site model assessed which, on a single page, contains relevant catchment information for context, a selection of overlain forecast and observed hydrographs and a set of performance statistics with associated displays of novel condensed form. One display provides performance comparison with other models that may exist for the site. The performance statistics include skill scores for forecasting events (flow/level threshold crossings) of differing severity/rarity, indicating their probability and likely timing, which have real value in an operational setting. The local models assessed can be of any type and span rainfall-runoff (conceptual and transfer function) and flow routing (hydrological and hydrodynamic) forms. Also accommodated by the framework is the national G2G (Grid-to-Grid) distributed hydrological model, providing area-wide coverage across the fluvial rivers of England and Wales, which can be assessed at gauged sites. Thus the performance of the national G2G model forecasts can be directly compared with that from the local models. The Performance Summary for each site model is complemented by a national spatial analysis of model performance stratified by model-type, geographical region and forecast lead-time. The map displays provide an extensive evidence-base that can be interrogated, through a Flood Forecasting Model Performance web portal, to reveal fresh insights into comparative performance across locations, lead-times and models. This work was commissioned by the Environment Agency in partnership with Natural Resources Wales and the Flood Forecasting Centre for England and Wales.

Regional Air Quality forecAST (RAQAST) Over the U.S

NASA Astrophysics Data System (ADS)

Yoshida, Y.; Choi, Y.; Zeng, T.; Wang, Y.

2005-12-01

A regional chemistry and transport modeling system is used to provide 48-hour forecast of the concentrations of ozone and its precursors over the United States. Meteorological forecast is conducted using the NCAR/Penn State MM5 model. The regional chemistry and transport model simulates the sources, transport, chemistry, and deposition of 24 chemical tracers. The lateral and upper boundary conditions of trace gas concentrations are specified using the monthly mean output from the global GEOS-CHEM model. The initial and boundary conditions for meteorological fields are taken from the NOAA AVN forecast. The forecast has been operational since August, 2003. Model simulations are evaluated using surface, aircraft, and satellite measurements in the A'hindcast' mode. The next step is an automated forecast evaluation system.

Development and Implementation of Dynamic Scripts to Execute Cycled GSI/WRF Forecasts

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi; Berndt, Emily; Li, Xuanli; Watson, Leela

2014-01-01

The Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model and Gridpoint Statistical Interpolation (GSI) data assimilation (DA) are the operational systems that make up the North American Mesoscale (NAM) model and the NAM Data Assimilation System (NDAS) analysis used by National Weather Service forecasters. The Developmental Testbed Center (DTC) manages and distributes the code for the WRF and GSI, but it is up to individual researchers to link the systems together and write scripts to run the systems, which can take considerable time for those not familiar with the code. The objective of this project is to develop and disseminate a set of dynamic scripts that mimic the unique cycling configuration of the operational NAM to enable researchers to develop new modeling and data assimilation techniques that can be easily transferred to operations. The current version of the SPoRT GSI/WRF Scripts (v3.0.1) is compatible with WRF v3.3 and GSI v3.0.

Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

NASA Astrophysics Data System (ADS)

Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

2009-04-01

In part I of this study, the operational flood forecasting system in Bavaria and an approach to identify and quantify forecast uncertainty was introduced. The approach is split into the calculation of an empirical 'overall error' from archived forecasts and the calculation of an empirical 'model error' based on hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The 'model error' can especially in upstream catchments where forecast uncertainty is strongly dependent on the current predictability of the atrmosphere be superimposed on the spread of a hydrometeorological ensemble forecast. In Bavaria, two meteorological ensemble prediction systems are currently tested for operational use: the 16-member COSMO-LEPS forecast and a poor man's ensemble composed of DWD GME, DWD Cosmo-EU, NCEP GFS, Aladin-Austria, MeteoSwiss Cosmo-7. The determination of the overall forecast uncertainty is dependent on the catchment characteristics: 1. Upstream catchment with high influence of weather forecast a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. b) Corresponding to the characteristics of the meteorological ensemble forecast, each resulting forecast hydrograph can be regarded as equally likely. c) The 'model error' distribution, with parameters dependent on hydrological case and lead time, is added to each forecast timestep of each ensemble member d) For each forecast timestep, the overall (i.e. over all 'model error' distribution of each ensemble member) error distribution is calculated e) From this distribution, the uncertainty range on a desired level (here: the 10% and 90% percentile) is extracted and drawn as forecast envelope. f) As the mean or median of an ensemble forecast does not necessarily exhibit meteorologically sound temporal evolution, a single hydrological forecast termed 'lead forecast' is chosen and shown in addition to the uncertainty bounds. This can be either an intermediate forecast between the extremes of the ensemble spread or a manually selected forecast based on a meteorologists advice. 2. Downstream catchments with low influence of weather forecast In downstream catchments with strong human impact on discharge (e.g. by reservoir operation) and large influence of upstream gauge observation quality on forecast quality, the 'overall error' may in most cases be larger than the combination of the 'model error' and an ensemble spread. Therefore, the overall forecast uncertainty bounds are calculated differently: a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. Here, additionally the corresponding inflow hydrograph from all upstream catchments must be used. b) As for an upstream catchment, the uncertainty range is determined by combination of 'model error' and the ensemble member forecasts c) In addition, the 'overall error' is superimposed on the 'lead forecast'. For reasons of consistency, the lead forecast must be based on the same meteorological forecast in the downstream and all upstream catchments. d) From the resulting two uncertainty ranges (one from the ensemble forecast and 'model error', one from the 'lead forecast' and 'overall error'), the envelope is taken as the most prudent uncertainty range. In sum, the uncertainty associated with each forecast run is calculated and communicated to the public in the form of 10% and 90% percentiles. As in part I of this study, the methodology as well as the useful- or uselessness of the resulting uncertainty ranges will be presented and discussed by typical examples.

Balancing Flood Risk and Water Supply in California: Policy Search Combining Short-Term Forecast Ensembles and Groundwater Recharge

NASA Astrophysics Data System (ADS)

Herman, J. D.; Steinschneider, S.; Nayak, M. A.

2017-12-01

Short-term weather forecasts are not codified into the operating policies of federal, multi-purpose reservoirs, despite their potential to improve service provision. This is particularly true for facilities that provide flood protection and water supply, since the potential flood damages are often too severe to accept the risk of inaccurate forecasts. Instead, operators must maintain empty storage capacity to mitigate flood risk, even if the system is currently in drought, as occurred in California from 2012-2016. This study investigates the potential for forecast-informed operating rules to improve water supply efficiency while maintaining flood protection, combining state-of-the-art weather hindcasts with a novel tree-based policy optimization framework. We hypothesize that forecasts need only accurately predict the occurrence of a storm, rather than its intensity, to be effective in regions like California where wintertime, synoptic-scale storms dominate the flood regime. We also investigate the potential for downstream groundwater injection to improve the utility of forecasts. These hypotheses are tested in a case study of Folsom Reservoir on the American River. Because available weather hindcasts are relatively short (10-20 years), we propose a new statistical framework to develop synthetic forecasts to assess the risk associated with inaccurate forecasts. The efficiency of operating policies is tested across a range of scenarios that include varying forecast skill and additional groundwater pumping capacity. Results suggest that the combined use of groundwater storage and short-term weather forecasts can substantially improve the tradeoff between water supply and flood control objectives in large, multi-purpose reservoirs in California.

Assessment of GNSS-based height data of multiple ships for measuring and forecasting great tsunamis

NASA Astrophysics Data System (ADS)

Inazu, Daisuke; Waseda, Takuji; Hibiya, Toshiyuki; Ohta, Yusaku

2016-12-01

Ship height positioning by the Global Navigation Satellite System (GNSS) was investigated for measuring and forecasting great tsunamis. We first examined GNSS height-positioning data of a navigating vessel. If we use the kinematic precise point positioning (PPP) method, tsunamis greater than 10-1 m will be detected by ship height positioning. Based on Automatic Identification System (AIS) data, we found that tens of cargo ships and tankers are usually identified to navigate over the Nankai Trough, southwest Japan. We assumed that a future Nankai Trough great earthquake tsunami will be observed by the kinematic PPP height positioning of an AIS-derived ship distribution, and examined the tsunami forecast capability of the offshore tsunami measurements based on the PPP-based ship height. A method to estimate the initial tsunami height distribution using offshore tsunami observations was used for forecasting. Tsunami forecast tests were carried out using simulated tsunami data by the PPP-based ship height of 92 cargo ships/tankers, and by currently operating deep-sea pressure and Global Positioning System (GPS) buoy observations at 71 stations over the Nankai Trough. The forecast capability using the PPP-based height of the 92 ships was shown to be comparable to or better than that using the operating offshore observatories at the 71 stations. We suppose that, immediately after the occurrence of a great earthquake, stations receiving successive ship information (AIS data) along certain areas of the coast would fail to acquire ship data due to strong ground shaking, especially near the epicenter. Such a situation would significantly deteriorate the tsunami-forecast capability using ship data. On the other hand, operational real-time analysis of seismic/geodetic data would be carried out for estimating a tsunamigenic fault model. Incorporating the seismic/geodetic fault model estimation into the tsunami forecast above possibly compensates for the deteriorated forecast capability.

The Value, Protocols, and Scientific Ethics of Earthquake Forecasting

NASA Astrophysics Data System (ADS)

Jordan, Thomas H.

2013-04-01

Earthquakes are different from other common natural hazards because precursory signals diagnostic of the magnitude, location, and time of impending seismic events have not yet been found. Consequently, the short-term, localized prediction of large earthquakes at high probabilities with low error rates (false alarms and failures-to-predict) is not yet feasible. An alternative is short-term probabilistic forecasting based on empirical statistical models of seismic clustering. During periods of high seismic activity, short-term earthquake forecasts can attain prospective probability gains up to 1000 relative to long-term forecasts. The value of such information is by no means clear, however, because even with hundredfold increases, the probabilities of large earthquakes typically remain small, rarely exceeding a few percent over forecasting intervals of days or weeks. Civil protection agencies have been understandably cautious in implementing operational forecasting protocols in this sort of "low-probability environment." This paper will explore the complex interrelations among the valuation of low-probability earthquake forecasting, which must account for social intangibles; the protocols of operational forecasting, which must factor in large uncertainties; and the ethics that guide scientists as participants in the forecasting process, who must honor scientific principles without doing harm. Earthquake forecasts possess no intrinsic societal value; rather, they acquire value through their ability to influence decisions made by users seeking to mitigate seismic risk and improve community resilience to earthquake disasters. According to the recommendations of the International Commission on Earthquake Forecasting (www.annalsofgeophysics.eu/index.php/annals/article/view/5350), operational forecasting systems should appropriately separate the hazard-estimation role of scientists from the decision-making role of civil protection authorities and individuals. They should provide public sources of information on short-term probabilities that are authoritative, scientific, open, and timely. Alert procedures should be negotiated with end-users to facilitate decisions at different levels of society, based in part on objective analysis of costs and benefits but also on less tangible aspects of value-of-information, such as gains in psychological preparedness and resilience. Unfortunately, in most countries, operational forecasting systems do not conform to such high standards, and earthquake scientists are often called upon to advise the public in roles that exceed their civic authority, expertise in risk communication, and situational knowledge. Certain ethical principles are well established; e.g., announcing unreliable predictions in public forums should be avoided, because bad information can be dangerous. But what are the professional responsibilities of earthquake scientists during seismic crises, especially when the public information through official channels is thought to be inadequate or incorrect? How much should these responsibilities be discounted in the face of personal liability? How should scientists contend with highly uncertain forecasts? To what degree should the public be involved in controversies about forecasting results? No simple answers to these questions can be offered, but the need for answers can be reduced by improving operational forecasting systems. This will require more substantial, and more trustful, collaborations between scientists, civil authorities, and public stakeholders.

Satellite freeze forecast system

NASA Technical Reports Server (NTRS)

Martsolf, J. D. (Principal Investigator)

1983-01-01

Provisions for back-up operations for the satellite freeze forecast system are discussed including software and hardware maintenance and DS/1000-1V linkage; troubleshooting; and digitized radar usage. The documentation developed; dissemination of data products via television and the IFAS computer network; data base management; predictive models; the installation of and progress towards the operational status of key stations; and digital data acquisition are also considered. The d addition of dew point temperature into the P-model is outlined.

OceanNOMADS: Real-time and retrospective access to operational U.S. ocean prediction products

NASA Astrophysics Data System (ADS)

Harding, J. M.; Cross, S. L.; Bub, F.; Ji, M.

2011-12-01

The National Oceanic and Atmospheric Administration (NOAA) National Operational Model Archive and Distribution System (NOMADS) provides both real-time and archived atmospheric model output from servers at the National Centers for Environmental Prediction (NCEP) and National Climatic Data Center (NCDC) respectively (http://nomads.ncep.noaa.gov/txt_descriptions/marRutledge-1.pdf). The NOAA National Ocean Data Center (NODC) with NCEP is developing a complementary capability called OceanNOMADS for operational ocean prediction models. An NCEP ftp server currently provides real-time ocean forecast output (http://www.opc.ncep.noaa.gov/newNCOM/NCOM_currents.shtml) with retrospective access through NODC. A joint effort between the Northern Gulf Institute (NGI; a NOAA Cooperative Institute) and the NOAA National Coastal Data Development Center (NCDDC; a division of NODC) created the developmental version of the retrospective OceanNOMADS capability (http://www.northerngulfinstitute.org/edac/ocean_nomads.php) under the NGI Ecosystem Data Assembly Center (EDAC) project (http://www.northerngulfinstitute.org/edac/). Complementary funding support for the developmental OceanNOMADS from U.S. Integrated Ocean Observing System (IOOS) through the Southeastern University Research Association (SURA) Model Testbed (http://testbed.sura.org/) this past year provided NODC the analogue that facilitated the creation of an NCDDC production version of OceanNOMADS (http://www.ncddc.noaa.gov/ocean-nomads/). Access tool development and storage of initial archival data sets occur on the NGI/NCDDC developmental servers with transition to NODC/NCCDC production servers as the model archives mature and operational space and distribution capability grow. Navy operational global ocean forecast subsets for U.S waters comprise the initial ocean prediction fields resident on the NCDDC production server. The NGI/NCDDC developmental server currently includes the Naval Research Laboratory Inter-America Seas Nowcast/Forecast System over the Gulf of Mexico from 2004-Mar 2011, the operational Naval Oceanographic Office (NAVOCEANO) regional USEast ocean nowcast/forecast system from early 2009 to present, and the NAVOCEANO operational regional AMSEAS (Gulf of Mexico/Caribbean) ocean nowcast/forecast system from its inception 25 June 2010 to present. AMSEAS provided one of the real-time ocean forecast products accessed by NOAA's Office of Response and Restoration from the NGI/NCDDC developmental OceanNOMADS during the Deep Water Horizon oil spill last year. The developmental server also includes archived, real-time Navy coastal forecast products off coastal Japan in support of U.S./Japanese joint efforts following the 2011 tsunami. Real-time NAVOCEANO output from regional prediction systems off Southern California and around Hawaii, currently available on the NCEP ftp server, are scheduled for archival on the developmental OceanNOMADS by late 2011 along with the next generation Navy/NOAA global ocean prediction output. Accession and archival of additional regions is planned as server capacities increase.

On the reliable use of satellite-derived surface water products for global flood monitoring

NASA Astrophysics Data System (ADS)

Hirpa, F. A.; Revilla-Romero, B.; Thielen, J.; Salamon, P.; Brakenridge, R.; Pappenberger, F.; de Groeve, T.

2015-12-01

Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response management. To this end, real-time flood forecasting and satellite-based detection systems have been developed at global scale. However, due to the limited availability of up-to-date ground observations, the reliability of these systems for real-time applications have not been assessed in large parts of the globe. In this study, we performed comparative evaluations of the commonly used satellite-based global flood detections and operational flood forecasting system using 10 major flood cases reported over three years (2012-2014). Specially, we assessed the flood detection capabilities of the near real-time global flood maps from the Global Flood Detection System (GFDS), and from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the operational forecasts from the Global Flood Awareness System (GloFAS) for the major flood events recorded in global flood databases. We present the evaluation results of the global flood detection and forecasting systems in terms of correctly indicating the reported flood events and highlight the exiting limitations of each system. Finally, we propose possible ways forward to improve the reliability of large scale flood monitoring tools.

The Experimental Regional Ensemble Forecast System (ExREF): Its Use in NWS Forecast Operations and Preliminary Verification

NASA Technical Reports Server (NTRS)

Reynolds, David; Rasch, William; Kozlowski, Daniel; Burks, Jason; Zavodsky, Bradley; Bernardet, Ligia; Jankov, Isidora; Albers, Steve

2014-01-01

The Experimental Regional Ensemble Forecast (ExREF) system is a tool for the development and testing of new Numerical Weather Prediction (NWP) methodologies. ExREF is run in near-realtime by the Global Systems Division (GSD) of the NOAA Earth System Research Laboratory (ESRL) and its products are made available through a website, an ftp site, and via the Unidata Local Data Manager (LDM). The ExREF domain covers most of North America and has 9-km horizontal grid spacing. The ensemble has eight members, all employing WRF-ARW. The ensemble uses a variety of initial conditions from LAPS and the Global Forecasting System (GFS) and multiple boundary conditions from the GFS ensemble. Additionally, a diversity of physical parameterizations is used to increase ensemble spread and to account for the uncertainty in forecasting extreme precipitation events. ExREF has been a component of the Hydrometeorology Testbed (HMT) NWP suite in the 2012-2013 and 2013-2014 winters. A smaller domain covering just the West Coast was created to minimize band-width consumption for the NWS. This smaller domain has and is being distributed to the National Weather Service (NWS) Weather Forecast Office and California Nevada River Forecast Center in Sacramento, California, where it is ingested into the Advanced Weather Interactive Processing System (AWIPS I and II) to provide guidance on the forecasting of extreme precipitation events. This paper will review the cooperative effort employed by NOAA ESRL, NASA SPoRT (Short-term Prediction Research and Transition Center), and the NWS to facilitate the ingest and display of ExREF data utilizing the AWIPS I and II D2D and GFE (Graphical Software Editor) software. Within GFE is a very useful verification software package called BoiVer that allows the NWS to utilize the River Forecast Center's 4 km gridded QPE to compare with all operational NWP models 6-hr QPF along with the ExREF mean 6-hr QPF so the forecasters can build confidence in the use of the ExREF in preparing their rainfall forecasts. Preliminary results will be presented.

Demonstrating the Alaska Ocean Observing System in Prince William Sound

NASA Astrophysics Data System (ADS)

Schoch, G. Carl; McCammon, Molly

2013-07-01

The Alaska Ocean Observing System and the Oil Spill Recovery Institute developed a demonstration project over a 5 year period in Prince William Sound. The primary goal was to develop a quasi-operational system that delivers weather and ocean information in near real time to diverse user communities. This observing system now consists of atmospheric and oceanic sensors, and a new generation of computer models to numerically simulate and forecast weather, waves, and ocean circulation. A state of the art data management system provides access to these products from one internet portal at http://www.aoos.org. The project culminated in a 2009 field experiment that evaluated the observing system and performance of the model forecasts. Observations from terrestrial weather stations and weather buoys validated atmospheric circulation forecasts. Observations from wave gages on weather buoys validated forecasts of significant wave heights and periods. There was an emphasis on validation of surface currents forecasted by the ocean circulation model for oil spill response and search and rescue applications. During the 18 day field experiment a radar array mapped surface currents and drifting buoys were deployed. Hydrographic profiles at fixed stations, and by autonomous vehicles along transects, were made to acquire measurements through the water column. Terrestrial weather stations were the most reliable and least costly to operate, and in situ ocean sensors were more costly and considerably less reliable. The radar surface current mappers were the least reliable and most costly but provided the assimilation and validation data that most improved ocean circulation forecasts. We describe the setting of Prince William Sound and the various observational platforms and forecast models of the observing system, and discuss recommendations for future development.

A non-parametric postprocessor for bias-correcting multi-model ensemble forecasts of hydrometeorological and hydrologic variables

NASA Astrophysics Data System (ADS)

Brown, James; Seo, Dong-Jun

2010-05-01

Operational forecasts of hydrometeorological and hydrologic variables often contain large uncertainties, for which ensemble techniques are increasingly used. However, the utility of ensemble forecasts depends on the unbiasedness of the forecast probabilities. We describe a technique for quantifying and removing biases from ensemble forecasts of hydrometeorological and hydrologic variables, intended for use in operational forecasting. The technique makes no a priori assumptions about the distributional form of the variables, which is often unknown or difficult to model parametrically. The aim is to estimate the conditional cumulative distribution function (ccdf) of the observed variable given a (possibly biased) real-time ensemble forecast from one or several forecasting systems (multi-model ensembles). The technique is based on Bayesian optimal linear estimation of indicator variables, and is analogous to indicator cokriging (ICK) in geostatistics. By developing linear estimators for the conditional expectation of the observed variable at many thresholds, ICK provides a discrete approximation of the full ccdf. Since ICK minimizes the conditional error variance of the indicator expectation at each threshold, it effectively minimizes the Continuous Ranked Probability Score (CRPS) when infinitely many thresholds are employed. However, the ensemble members used as predictors in ICK, and other bias-correction techniques, are often highly cross-correlated, both within and between models. Thus, we propose an orthogonal transform of the predictors used in ICK, which is analogous to using their principal components in the linear system of equations. This leads to a well-posed problem in which a minimum number of predictors are used to provide maximum information content in terms of the total variance explained. The technique is used to bias-correct precipitation ensemble forecasts from the NCEP Global Ensemble Forecast System (GEFS), for which independent validation results are presented. Extension to multimodel ensembles from the NCEP GFS and Short Range Ensemble Forecast (SREF) systems is also proposed.

Exploring the calibration of a wind forecast ensemble for energy applications

NASA Astrophysics Data System (ADS)

Heppelmann, Tobias; Ben Bouallegue, Zied; Theis, Susanne

2015-04-01

In the German research project EWeLiNE, Deutscher Wetterdienst (DWD) and Fraunhofer Institute for Wind Energy and Energy System Technology (IWES) are collaborating with three German Transmission System Operators (TSO) in order to provide the TSOs with improved probabilistic power forecasts. Probabilistic power forecasts are derived from probabilistic weather forecasts, themselves derived from ensemble prediction systems (EPS). Since the considered raw ensemble wind forecasts suffer from underdispersiveness and bias, calibration methods are developed for the correction of the model bias and the ensemble spread bias. The overall aim is to improve the ensemble forecasts such that the uncertainty of the possible weather deployment is depicted by the ensemble spread from the first forecast hours. Additionally, the ensemble members after calibration should remain physically consistent scenarios. We focus on probabilistic hourly wind forecasts with horizon of 21 h delivered by the convection permitting high-resolution ensemble system COSMO-DE-EPS which has become operational in 2012 at DWD. The ensemble consists of 20 ensemble members driven by four different global models. The model area includes whole Germany and parts of Central Europe with a horizontal resolution of 2.8 km and a vertical resolution of 50 model levels. For verification we use wind mast measurements around 100 m height that corresponds to the hub height of wind energy plants that belong to wind farms within the model area. Calibration of the ensemble forecasts can be performed by different statistical methods applied to the raw ensemble output. Here, we explore local bivariate Ensemble Model Output Statistics at individual sites and quantile regression with different predictors. Applying different methods, we already show an improvement of ensemble wind forecasts from COSMO-DE-EPS for energy applications. In addition, an ensemble copula coupling approach transfers the time-dependencies of the raw ensemble to the calibrated ensemble. The calibrated wind forecasts are evaluated first with univariate probabilistic scores and additionally with diagnostics of wind ramps in order to assess the time-consistency of the calibrated ensemble members.

ENSURF: multi-model sea level forecast - implementation and validation results for the IBIROOS and Western Mediterranean regions

NASA Astrophysics Data System (ADS)

Pérez, B.; Brower, R.; Beckers, J.; Paradis, D.; Balseiro, C.; Lyons, K.; Cure, M.; Sotillo, M. G.; Hacket, B.; Verlaan, M.; Alvarez Fanjul, E.

2011-04-01

ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast that makes use of existing storm surge or circulation models today operational in Europe, as well as near-real time tide gauge data in the region, with the following main goals: - providing an easy access to existing forecasts, as well as to its performance and model validation, by means of an adequate visualization tool - generation of better forecasts of sea level, including confidence intervals, by means of the Bayesian Model Average Technique (BMA) The system was developed and implemented within ECOOP (C.No. 036355) European Project for the NOOS and the IBIROOS regions, based on MATROOS visualization tool developed by Deltares. Both systems are today operational at Deltares and Puertos del Estado respectively. The Bayesian Modelling Average technique generates an overall forecast probability density function (PDF) by making a weighted average of the individual forecasts PDF's; the weights represent the probability that a model will give the correct forecast PDF and are determined and updated operationally based on the performance of the models during a recent training period. This implies the technique needs the availability of sea level data from tide gauges in near-real time. Results of validation of the different models and BMA implementation for the main harbours will be presented for the IBIROOS and Western Mediterranean regions, where this kind of activity is performed for the first time. The work has proved to be useful to detect problems in some of the circulation models not previously well calibrated with sea level data, to identify the differences on baroclinic and barotropic models for sea level applications and to confirm the general improvement of the BMA forecasts.

An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): assessing the added value of probabilistic forecasts

NASA Astrophysics Data System (ADS)

Addor, N.; Jaun, S.; Fundel, F.; Zappa, M.

2012-04-01

The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This model chain relies on deterministic (COSMO-7) and probabilistic (COSMO-LEPS) atmospheric forecasts, which are used to force a semi-distributed hydrological model (PREVAH) coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework with which we assessed the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added value conveyed by the probability information, a 31-month reforecast was produced for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain is of up to 2 days lead time for the catchment considered. Brier skill scores show that probabilistic hydrological forecasts outperform their deterministic counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. We finally highlight challenges for making decisions on the basis of hydrological predictions, and discuss the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment.

Assessment of Folsom Lake Watershed response to historical and potential future climate scenarios

USGS Publications Warehouse

Carpenter, Theresa M.; Georgakakos, Konstantine P.

2000-01-01

An integrated forecast-control system was designed to allow the profitable use of ensemble forecasts for the operational management of multi-purpose reservoirs. The system ingests large-scale climate model monthly precipitation through the adjustment of the marginal distribution of reservoir-catchment precipitation to reflect occurrence of monthly climate precipitation amounts in the extreme terciles of their distribution. Generation of ensemble reservoir inflow forecasts is then accomplished with due account for atmospheric- forcing and hydrologic- model uncertainties. These ensemble forecasts are ingested by the decision component of the integrated system, which generates non- inferior trade-off surfaces and, given management preferences, estimates of reservoir- management benefits over given periods. In collaboration with the Bureau of Reclamation and the California Nevada River Forecast Center, the integrated system is applied to Folsom Lake in California to evaluate the benefits for flood control, hydroelectric energy production, and low flow augmentation. In addition to retrospective studies involving the historical period 1964-1993, system simulations were performed for the future period 2001-2030, under a control (constant future greenhouse-gas concentrations assumed at the present levels) and a greenhouse-gas- increase (1-% per annum increase assumed) scenario. The present paper presents and validates ensemble 30-day reservoir- inflow forecasts under a variety of situations. Corresponding reservoir management results are presented in Yao and Georgakakos, A., this issue. Principle conclusions of this paper are that the integrated system provides reliable ensemble inflow volume forecasts at the 5-% confidence level for the majority of the deciles of forecast frequency, and that the use of climate model simulations is beneficial mainly during high flow periods. It is also found that, for future periods with potential sharp climatic increases of precipitation amount and to maintain good reliability levels, operational ensemble inflow forecasting should involve atmospheric forcing from appropriate climatic periods.

Promoting Interests in Atmospheric Science at a Liberal Arts Institution

NASA Astrophysics Data System (ADS)

Roussev, S.; Sherengos, P. M.; Limpasuvan, V.; Xue, M.

2007-12-01

Coastal Carolina University (CCU) students in Computer Science participated in a project to set up an operational weather forecast for the local community. The project involved the construction of two computing clusters and the automation of daily forecasting. Funded by NSF-MRI, two high-performance clusters were successfully established to run the University of Oklahoma's Advance Regional Prediction System (ARPS). Daily weather predictions are made over South Carolina and North Carolina at 3-km horizontal resolution (roughly 1.9 miles) using initial and boundary condition data provided by UNIDATA. At this high resolution, the model is cloud- resolving, thus providing detailed picture of heavy thunderstorms and precipitation. Forecast results are displayed on CCU's website (https://marc.coastal.edu/HPC) to complement observations at the National Weather Service in Wilmington N.C. Present efforts include providing forecasts at 1-km resolution (or finer), comparisons with other models like Weather Research and Forecasting (WRF) model, and the examination of local phenomena (like water spouts and tornadoes). Through these activities the students learn about shell scripting, cluster operating systems, and web design. More importantly, students are introduced to Atmospheric Science, the processes involved in making weather forecasts, and the interpretation of their forecasts. Simulations generated by the forecasts will be integrated into the contents of CCU's course like Fluid Dynamics, Atmospheric Sciences, Atmospheric Physics, and Remote Sensing. Operated jointly between the departments of Applied Physics and Computer Science, the clusters are expected to be used by CCU faculty and students for future research and inquiry-based projects in Computer Science, Applied Physics, and Marine Science.

A hybrid spatiotemporal drought forecasting model for operational use

NASA Astrophysics Data System (ADS)

Vasiliades, L.; Loukas, A.

2010-09-01

Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. This study develops a hybrid spatiotemporal scheme for integrated spatial and temporal forecasting. Temporal forecasting is achieved using feed-forward neural networks and the temporal forecasts are extended to the spatial dimension using a spatial recurrent neural network model. The methodology is demonstrated for an operational meteorological drought index the Standardized Precipitation Index (SPI) calculated at multiple timescales. 48 precipitation stations and 18 independent precipitation stations, located at Pinios river basin in Thessaly region, Greece, were used for the development and spatiotemporal validation of the hybrid spatiotemporal scheme. Several quantitative temporal and spatial statistical indices were considered for the performance evaluation of the models. Furthermore, qualitative statistical criteria based on contingency tables between observed and forecasted drought episodes were calculated. The results show that the lead time of forecasting for operational use depends on the SPI timescale. The hybrid spatiotemporal drought forecasting model could be operationally used for forecasting up to three months ahead for SPI short timescales (e.g. 3-6 months) up to six months ahead for large SPI timescales (e.g. 24 months). The above findings could be useful in developing a drought preparedness plan in the region.

Towards Operational Meteotsunami Early Warning System: the Adriatic Project MESSI

NASA Astrophysics Data System (ADS)

Vilibic, I.; Sepic, J.; Denamiel, C. L.; Mihanovic, H.; Muslim, S.; Tudor, M.; Ivankovic, D.; Jelavic, D.; Kovacevic, V.; Masce, T.; Dadic, V.; Gacic, M.; Horvath, K.; Monserrat, S.; Rabinovich, A.; Telisman-Prtenjak, M.

2017-12-01

A number of destructive meteotsunamis - atmospherically-driven long ocean waves in a tsunami frequency band - occurred during the last decade through the world oceans. Owing to significant damage caused by these meteotsunamis, several scientific groups (occasionally in collaboration with public offices) have started developing meteotsunami warning systems. Creation of one such system has been initialized in the late 2015 within the MESSI (Meteotsunamis, destructive long ocean waves in the tsunami frequency band: from observations and simulations towards a warning system) project. Main goal of this project is to build a prototype of a meteotsunami warning system for the eastern Adriatic coast. The system will be based on real-time measurements, operational atmosphere and ocean modeling and real time decision-making process. Envisioned MESSI meteotsunami warning system consists of three modules: (1) synoptic warning module, which will use established correlation between forecasted synoptic fields and high-frequency sea level oscillations to provide qualitative meteotsunami forecasts for up to a week in advance, (2) probabilistic premodeling prediction module, which will use operational WRF-ROMS-ADCIRC modeling system and compare the forecast with an atlas of presimulations to get the probabilistic meteotsunami forecast for up to three days in advance, and (3) real-time module, which is based on real time tracking of properties of air pressure disturbance (amplitude, speed, direction, period, ...) and their real-time comparison with the atlas of meteotsunami simulations. System will be tested on recent meteotsunami events which were recorded in the MESSI area shortly after the operational meteotsunami network installation. Albeit complex, such a multilevel warning system has a potential to be adapted to most meteotsunami hot spots, simply by tuning the system parameters to the available atmospheric and ocean data.

A probabilistic drought forecasting framework: A combined dynamical and statistical approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Hongxiang; Moradkhani, Hamid; Zarekarizi, Mahkameh

In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is to seek the use of data assimilation to quantify initial condition uncertainty with the Monte Carlo ensemble members, rather than relying entirely on the hydrologic model or land surface model to generate a single deterministic initial condition, as currently implemented in the operational drought forecasting systems. Next, the initial condition uncertainty is quantified through data assimilation and coupled with a newly developed probabilistic drought forecasting model using a copula function. The initialmore » condition at each forecast start date are sampled from the data assimilation ensembles for forecast initialization. Finally, seasonal drought forecasting products are generated with the updated initial conditions. This study introduces the theory behind the proposed drought forecasting system, with an application in Columbia River Basin, Pacific Northwest, United States. Results from both synthetic and real case studies suggest that the proposed drought forecasting system significantly improves the seasonal drought forecasting skills and can facilitate the state drought preparation and declaration, at least three months before the official state drought declaration.« less

Scientific assessment of accuracy, skill and reliability of ocean probabilistic forecast products.

NASA Astrophysics Data System (ADS)

Wei, M.; Rowley, C. D.; Barron, C. N.; Hogan, P. J.

2016-02-01

As ocean operational centers are increasingly adopting and generating probabilistic forecast products for their customers with valuable forecast uncertainties, how to assess and measure these complicated probabilistic forecast products objectively is challenging. The first challenge is how to deal with the huge amount of the data from the ensemble forecasts. The second one is how to describe the scientific quality of probabilistic products. In fact, probabilistic forecast accuracy, skills, reliability, resolutions are different attributes of a forecast system. We briefly introduce some of the fundamental metrics such as the Reliability Diagram, Reliability, Resolution, Brier Score (BS), Brier Skill Score (BSS), Ranked Probability Score (RPS), Ranked Probability Skill Score (RPSS), Continuous Ranked Probability Score (CRPS), and Continuous Ranked Probability Skill Score (CRPSS). The values and significance of these metrics are demonstrated for the forecasts from the US Navy's regional ensemble system with different ensemble members. The advantages and differences of these metrics are studied and clarified.

Projected Applications of a ``Climate in a Box'' Computing System at the NASA Short-term Prediction Research and Transition (SPoRT) Center

NASA Astrophysics Data System (ADS)

Jedlovec, G.; Molthan, A.; Zavodsky, B.; Case, J.; Lafontaine, F.

2010-12-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique observations and research capabilities to the operational weather community, with a goal of improving short-term forecasts on a regional scale. Advances in research computing have lead to “Climate in a Box” systems, with hardware configurations capable of producing high resolution, near real-time weather forecasts, but with footprints, power, and cooling requirements that are comparable to desktop systems. The SPoRT Center has developed several capabilities for incorporating unique NASA research capabilities and observations with real-time weather forecasts. Planned utilization includes the development of a fully-cycled data assimilation system used to drive 36-48 hour forecasts produced by the NASA Unified version of the Weather Research and Forecasting (WRF) model (NU-WRF). The horsepower provided by the “Climate in a Box” system is expected to facilitate the assimilation of vertical profiles of temperature and moisture provided by the Atmospheric Infrared Sounder (AIRS) aboard the NASA Aqua satellite. In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA’s Aqua and Terra satellites provide high-resolution sea surface temperatures and vegetation characteristics. The development of MODIS normalized difference vegetation index (NVDI) composites for use within the NASA Land Information System (LIS) will assist in the characterization of vegetation, and subsequently the surface albedo and processes related to soil moisture. Through application of satellite simulators, NASA satellite instruments can be used to examine forecast model errors in cloud cover and other characteristics. Through the aforementioned application of the “Climate in a Box” system and NU-WRF capabilities, an end goal is the establishment of a real-time forecast system that fully integrates modeling and analysis capabilities developed within the NASA SPoRT Center, with benefits provided to the operational forecasting community.

Projected Applications of a "Climate in a Box" Computing System at the NASA Short-Term Prediction Research and Transition (SPoRT) Center

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Molthan, Andrew L.; Zavodsky, Bradley; Case, Jonathan L.; LaFontaine, Frank J.

2010-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique observations and research capabilities to the operational weather community, with a goal of improving short-term forecasts on a regional scale. Advances in research computing have lead to "Climate in a Box" systems, with hardware configurations capable of producing high resolution, near real-time weather forecasts, but with footprints, power, and cooling requirements that are comparable to desktop systems. The SPoRT Center has developed several capabilities for incorporating unique NASA research capabilities and observations with real-time weather forecasts. Planned utilization includes the development of a fully-cycled data assimilation system used to drive 36-48 hour forecasts produced by the NASA Unified version of the Weather Research and Forecasting (WRF) model (NU-WRF). The horsepower provided by the "Climate in a Box" system is expected to facilitate the assimilation of vertical profiles of temperature and moisture provided by the Atmospheric Infrared Sounder (AIRS) aboard the NASA Aqua satellite. In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA s Aqua and Terra satellites provide high-resolution sea surface temperatures and vegetation characteristics. The development of MODIS normalized difference vegetation index (NVDI) composites for use within the NASA Land Information System (LIS) will assist in the characterization of vegetation, and subsequently the surface albedo and processes related to soil moisture. Through application of satellite simulators, NASA satellite instruments can be used to examine forecast model errors in cloud cover and other characteristics. Through the aforementioned application of the "Climate in a Box" system and NU-WRF capabilities, an end goal is the establishment of a real-time forecast system that fully integrates modeling and analysis capabilities developed within the NASA SPoRT Center, with benefits provided to the operational forecasting community.

Validation of reactive gases and aerosols in the MACC global analysis and forecast system

NASA Astrophysics Data System (ADS)

Eskes, H.; Huijnen, V.; Arola, A.; Benedictow, A.; Blechschmidt, A.-M.; Botek, E.; Boucher, O.; Bouarar, I.; Chabrillat, S.; Cuevas, E.; Engelen, R.; Flentje, H.; Gaudel, A.; Griesfeller, J.; Jones, L.; Kapsomenakis, J.; Katragkou, E.; Kinne, S.; Langerock, B.; Razinger, M.; Richter, A.; Schultz, M.; Schulz, M.; Sudarchikova, N.; Thouret, V.; Vrekoussis, M.; Wagner, A.; Zerefos, C.

2015-11-01

The European MACC (Monitoring Atmospheric Composition and Climate) project is preparing the operational Copernicus Atmosphere Monitoring Service (CAMS), one of the services of the European Copernicus Programme on Earth observation and environmental services. MACC uses data assimilation to combine in situ and remote sensing observations with global and regional models of atmospheric reactive gases, aerosols, and greenhouse gases, and is based on the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF). The global component of the MACC service has a dedicated validation activity to document the quality of the atmospheric composition products. In this paper we discuss the approach to validation that has been developed over the past 3 years. Topics discussed are the validation requirements, the operational aspects, the measurement data sets used, the structure of the validation reports, the models and assimilation systems validated, the procedure to introduce new upgrades, and the scoring methods. One specific target of the MACC system concerns forecasting special events with high-pollution concentrations. Such events receive extra attention in the validation process. Finally, a summary is provided of the results from the validation of the latest set of daily global analysis and forecast products from the MACC system reported in November 2014.

Global Turbulence Decision Support for Aviation

NASA Astrophysics Data System (ADS)

Williams, J.; Sharman, R.; Kessinger, C.; Feltz, W.; Wimmers, A.

2009-09-01

Turbulence is widely recognized as the leading cause of injuries to flight attendants and passengers on commercial air carriers, yet legacy decision support products such as SIGMETs and SIGWX charts provide relatively low spatial- and temporal-resolution assessments and forecasts of turbulence, with limited usefulness for strategic planning and tactical turbulence avoidance. A new effort is underway to develop an automated, rapid-update, gridded global turbulence diagnosis and forecast system that addresses upper-level clear-air turbulence, mountain-wave turbulence, and convectively-induced turbulence. This NASA-funded effort, modeled on the U.S. Federal Aviation Administration's Graphical Turbulence Guidance (GTG) and GTG Nowcast systems, employs NCEP Global Forecast System (GFS) model output and data from NASA and operational satellites to produce quantitative turbulence nowcasts and forecasts. A convective nowcast element based on GFS forecasts and satellite data provides a basis for diagnosing convective turbulence. An operational prototype "Global GTG” system has been running in real-time at the U.S. National Center for Atmospheric Research since the spring of 2009. Initial verification based on data from TRMM, Cloudsat and MODIS (for the convection nowcasting) and AIREPs and AMDAR data (for turbulence) are presented. This product aims to provide the "single authoritative source” for global turbulence information for the U.S. Next Generation Air Transportation System.

A global empirical system for probabilistic seasonal climate prediction

NASA Astrophysics Data System (ADS)

Eden, J. M.; van Oldenborgh, G. J.; Hawkins, E.; Suckling, E. B.

2015-12-01

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961-2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño-Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

An empirical system for probabilistic seasonal climate prediction

NASA Astrophysics Data System (ADS)

Eden, Jonathan; van Oldenborgh, Geert Jan; Hawkins, Ed; Suckling, Emma

2016-04-01

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961-2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño-Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

Evaluation of Flood Forecast and Warning in Elbe river basin - Impact of Forecaster's Strategy

NASA Astrophysics Data System (ADS)

Danhelka, Jan; Vlasak, Tomas

2010-05-01

Czech Hydrometeorological Institute (CHMI) is responsible for flood forecasting and warning in the Czech Republic. To meet that issue CHMI operates hydrological forecasting systems and publish flow forecast in selected profiles. Flood forecast and warning is an output of system that links observation (flow and atmosphere), data processing, weather forecast (especially NWP's QPF), hydrological modeling and modeled outputs evaluation and interpretation by forecaster. Forecast users are interested in final output without separating uncertainties of separate steps of described process. Therefore an evaluation of final operational forecasts was done for profiles within Elbe river basin produced by AquaLog forecasting system during period 2002 to 2008. Effects of uncertainties of observation, data processing and especially meteorological forecasts were not accounted separately. Forecast of flood levels exceedance (peak over the threshold) during forecasting period was the main criterion as flow increase forecast is of the highest importance. Other evaluation criteria included peak flow and volume difference. In addition Nash-Sutcliffe was computed separately for each time step (1 to 48 h) of forecasting period to identify its change with the lead time. Textual flood warnings are issued for administrative regions to initiate flood protection actions in danger of flood. Flood warning hit rate was evaluated at regions level and national level. Evaluation found significant differences of model forecast skill between forecasting profiles, particularly less skill was evaluated at small headwater basins due to domination of QPF uncertainty in these basins. The average hit rate was 0.34 (miss rate = 0.33, false alarm rate = 0.32). However its explored spatial difference is likely to be influenced also by different fit of parameters sets (due to different basin characteristics) and importantly by different impact of human factor. Results suggest that the practice of interactive model operation, experience and forecasting strategy differs between responsible forecasting offices. Warning is based on model outputs interpretation by hydrologists-forecaster. Warning hit rate reached 0.60 for threshold set to lowest flood stage of which 0.11 was underestimation of flood degree (miss 0.22, false alarm 0.28). Critical success index of model forecast was 0.34, while the same criteria for warning reached 0.55. We assume that the increase accounts not only to change of scale from single forecasting point to region for warning, but partly also to forecaster's added value. There is no official warning strategy preferred in the Czech Republic (f.e. tolerance towards higher false alarm rate). Therefore forecaster decision and personal strategy is of great importance. Results show quite successful warning for 1st flood level exceedance, over-warning for 2nd flood level, but under-warning for 3rd (highest) flood level. That suggests general forecaster's preference of medium level warning (2nd flood level is legally determined to be the start of the flood and flood protection activities). In conclusion human forecaster's experience and analysis skill increases flood warning performance notably. However society preference should be specifically addressed in the warning strategy definition to support forecaster's decision making.

EVALUATION OF ETA- CMAQ O3 FORECAST OVER DIFFERENT REGIONS OF THE CONTINENTAL US AND USING NEW CATEGORICAL EVALUATION METRICS

EPA Science Inventory

Developmental forecasts simulations with the Eta-CMAQ modeling system over the continental U.S. were initiated in 2005. This paper presents an evaluation of surface O₃ forecast over different regions of the continental U.S. In addition, to the traditional operational e...

Integrating Solar PV in Utility System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mills, A.; Botterud, A.; Wu, J.

2013-10-31

This study develops a systematic framework for estimating the increase in operating costs due to uncertainty and variability in renewable resources, uses the framework to quantify the integration costs associated with sub-hourly solar power variability and uncertainty, and shows how changes in system operations may affect these costs. Toward this end, we present a statistical method for estimating the required balancing reserves to maintain system reliability along with a model for commitment and dispatch of the portfolio of thermal and renewable resources at different stages of system operations. We estimate the costs of sub-hourly solar variability, short-term forecast errors, andmore » day-ahead (DA) forecast errors as the difference in production costs between a case with “realistic” PV (i.e., subhourly solar variability and uncertainty are fully included in the modeling) and a case with “well behaved” PV (i.e., PV is assumed to have no sub-hourly variability and can be perfectly forecasted). In addition, we highlight current practices that allow utilities to compensate for the issues encountered at the sub-hourly time frame with increased levels of PV penetration. In this analysis we use the analytical framework to simulate utility operations with increasing deployment of PV in a case study of Arizona Public Service Company (APS), a utility in the southwestern United States. In our analysis, we focus on three processes that are important in understanding the management of PV variability and uncertainty in power system operations. First, we represent the decisions made the day before the operating day through a DA commitment model that relies on imperfect DA forecasts of load and wind as well as PV generation. Second, we represent the decisions made by schedulers in the operating day through hour-ahead (HA) scheduling. Peaking units can be committed or decommitted in the HA schedules and online units can be redispatched using forecasts that are improved relative to DA forecasts, but still imperfect. Finally, we represent decisions within the operating hour by schedulers and transmission system operators as real-time (RT) balancing. We simulate the DA and HA scheduling processes with a detailed unit-commitment (UC) and economic dispatch (ED) optimization model. This model creates a least-cost dispatch and commitment plan for the conventional generating units using forecasts and reserve requirements as inputs. We consider only the generation units and load of the utility in this analysis; we do not consider opportunities to trade power with neighboring utilities. We also do not consider provision of reserves from renewables or from demand-side options. We estimate dynamic reserve requirements in order to meet reliability requirements in the RT operations, considering the uncertainty and variability in load, solar PV, and wind resources. Balancing reserve requirements are based on the 2.5th and 97.5th percentile of 1-min deviations from the HA schedule in a previous year. We then simulate RT deployment of balancing reserves using a separate minute-by-minute simulation of deviations from the HA schedules in the operating year. In the simulations we assume that balancing reserves can be fully deployed in 10 min. The minute-by-minute deviations account for HA forecasting errors and the actual variability of the load, wind, and solar generation. Using these minute-by-minute deviations and deployment of balancing reserves, we evaluate the impact of PV on system reliability through the calculation of the standard reliability metric called Control Performance Standard 2 (CPS2). Broadly speaking, the CPS2 score measures the percentage of 10-min periods in which a balancing area is able to balance supply and demand within a specific threshold. Compliance with the North American Electric Reliability Corporation (NERC) reliability standards requires that the CPS2 score must exceed 90% (i.e., the balancing area must maintain adequate balance for 90% of the 10-min periods). The combination of representing DA forecast errors in the DA commitments, using 1-min PV data to simulate RT balancing, and estimates of reliability performance through the CPS2 metric, all factors that are important to operating systems with increasing amounts of PV, makes this study unique in its scope.« less

The ALADIN System and its canonical model configurations AROME CY41T1 and ALARO CY40T1

NASA Astrophysics Data System (ADS)

Termonia, Piet; Fischer, Claude; Bazile, Eric; Bouyssel, François; Brožková, Radmila; Bénard, Pierre; Bochenek, Bogdan; Degrauwe, Daan; Derková, Mariá; El Khatib, Ryad; Hamdi, Rafiq; Mašek, Ján; Pottier, Patricia; Pristov, Neva; Seity, Yann; Smolíková, Petra; Španiel, Oldřich; Tudor, Martina; Wang, Yong; Wittmann, Christoph; Joly, Alain

2018-01-01

The ALADIN System is a numerical weather prediction (NWP) system developed by the international ALADIN consortium for operational weather forecasting and research purposes. It is based on a code that is shared with the global model IFS of the ECMWF and the ARPEGE model of Météo-France. Today, this system can be used to provide a multitude of high-resolution limited-area model (LAM) configurations. A few configurations are thoroughly validated and prepared to be used for the operational weather forecasting in the 16 partner institutes of this consortium. These configurations are called the ALADIN canonical model configurations (CMCs). There are currently three CMCs: the ALADIN baseline CMC, the AROME CMC and the ALARO CMC. Other configurations are possible for research, such as process studies and climate simulations. The purpose of this paper is (i) to define the ALADIN System in relation to the global counterparts IFS and ARPEGE, (ii) to explain the notion of the CMCs, (iii) to document their most recent versions, and (iv) to illustrate the process of the validation and the porting of these configurations to the operational forecast suites of the partner institutes of the ALADIN consortium. This paper is restricted to the forecast model only; data assimilation techniques and postprocessing techniques are part of the ALADIN System but they are not discussed here.

Intercomparison of Operational Ocean Forecasting Systems in the framework of GODAE

NASA Astrophysics Data System (ADS)

Hernandez, F.

2009-04-01

One of the main benefits of the GODAE 10-year activity is the implementation of ocean forecasting systems in several countries. In 2008, several systems are operated routinely, at global or basin scale. Among them, the BLUElink (Australia), HYCOM (USA), MOVE/MRI.COM (Japan), Mercator (France), FOAM (United Kingdom), TOPAZ (Norway) and C-NOOFS (Canada) systems offered to demonstrate their operational feasibility by performing an intercomparison exercise during a three months period (February to April 2008). The objectives were: a) to show that operational ocean forecasting systems are operated routinely in different countries, and that they can interact; b) to perform in a similar way a scientific validation aimed to assess the quality of the ocean estimates, the performance, and forecasting capabilities of each system; and c) to learn from this intercomparison exercise to increase inter-operability and collaboration in real time. The intercomparison relies on the assessment strategy developed for the EU MERSEA project, where diagnostics over the global ocean have been revisited by the GODAE contributors. This approach, based on metrics, allow for each system: a) to verify if ocean estimates are consistent with the current general knowledge of the dynamics; and b) to evaluate the accuracy of delivered products, compared to space and in-situ observations. Using the same diagnostics also allows one to intercompare the results from each system consistently. Water masses and general circulation description by the different systems are consistent with WOA05 Levitus climatology. The large scale dynamics (tropical, subtropical and subpolar gyres ) are also correctly reproduced. At short scales, benefit of high resolution systems can be evidenced on the turbulent eddy field, in particular when compared to eddy kinetic energy deduced from satellite altimetry of drifter observations. Comparisons to high resolution SST products show some discrepancies on ocean surface representation, either due to model and forcing fields errors, or assimilation scheme efficiency. Comparisons to sea-ice satellite products also evidence discrepancies linked to model, forcing and assimilation strategies of each forecasting system. Key words: Intercomparison, ocean analysis, operational oceanography, system assessment, metrics, validation GODAE Intercomparison Team: L. Bertino (NERSC/Norway), G. Brassington (BMRC/Australia), E. Chassignet (FSU/USA), J. Cummings (NRL/USA), F. Davidson (DFO/Canda), M. Drévillon (CERFACS/France), P. Hacker (IPRC/USA), M. Kamachi (MRI/Japan), J.-M. Lellouche (CERFACS/France), K. A. Lisæter (NERSC/Norway), R. Mahdon (UKMO/UK), M. Martin (UKMO/UK), A. Ratsimandresy (DFO/Canada), and C. Regnier (Mercator Ocean/France)

Net-zero Building Cluster Simulations and On-line Energy Forecasting for Adaptive and Real-Time Control and Decisions

NASA Astrophysics Data System (ADS)

Li, Xiwang

Buildings consume about 41.1% of primary energy and 74% of the electricity in the U.S. Moreover, it is estimated by the National Energy Technology Laboratory that more than 1/4 of the 713 GW of U.S. electricity demand in 2010 could be dispatchable if only buildings could respond to that dispatch through advanced building energy control and operation strategies and smart grid infrastructure. In this study, it is envisioned that neighboring buildings will have the tendency to form a cluster, an open cyber-physical system to exploit the economic opportunities provided by a smart grid, distributed power generation, and storage devices. Through optimized demand management, these building clusters will then reduce overall primary energy consumption and peak time electricity consumption, and be more resilient to power disruptions. Therefore, this project seeks to develop a Net-zero building cluster simulation testbed and high fidelity energy forecasting models for adaptive and real-time control and decision making strategy development that can be used in a Net-zero building cluster. The following research activities are summarized in this thesis: 1) Development of a building cluster emulator for building cluster control and operation strategy assessment. 2) Development of a novel building energy forecasting methodology using active system identification and data fusion techniques. In this methodology, a systematic approach for building energy system characteristic evaluation, system excitation and model adaptation is included. The developed methodology is compared with other literature-reported building energy forecasting methods; 3) Development of the high fidelity on-line building cluster energy forecasting models, which includes energy forecasting models for buildings, PV panels, batteries and ice tank thermal storage systems 4) Small scale real building validation study to verify the performance of the developed building energy forecasting methodology. The outcomes of this thesis can be used for building cluster energy forecasting model development and model based control and operation optimization. The thesis concludes with a summary of the key outcomes of this research, as well as a list of recommendations for future work.

Hydrologic Modeling at the National Water Center: Operational Implementation of the WRF-Hydro Model to support National Weather Service Hydrology

NASA Astrophysics Data System (ADS)

Cosgrove, B.; Gochis, D.; Clark, E. P.; Cui, Z.; Dugger, A. L.; Fall, G. M.; Feng, X.; Fresch, M. A.; Gourley, J. J.; Khan, S.; Kitzmiller, D.; Lee, H. S.; Liu, Y.; McCreight, J. L.; Newman, A. J.; Oubeidillah, A.; Pan, L.; Pham, C.; Salas, F.; Sampson, K. M.; Smith, M.; Sood, G.; Wood, A.; Yates, D. N.; Yu, W.; Zhang, Y.

2015-12-01

The National Weather Service (NWS) National Water Center(NWC) is collaborating with the NWS National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) to implement a first-of-its-kind operational instance of the Weather Research and Forecasting (WRF)-Hydro model over the Continental United States (CONUS) and contributing drainage areas on the NWS Weather and Climate Operational Supercomputing System (WCOSS) supercomputer. The system will provide seamless, high-resolution, continuously cycling forecasts of streamflow and other hydrologic outputs of value from both deterministic- and ensemble-type runs. WRF-Hydro will form the core of the NWC national water modeling strategy, supporting NWS hydrologic forecast operations along with emergency response and water management efforts of partner agencies. Input and output from the system will be comprehensively verified via the NWC Water Resource Evaluation Service. Hydrologic events occur on a wide range of temporal scales, from fast acting flash floods, to long-term flow events impacting water supply. In order to capture this range of events, the initial operational WRF-Hydro configuration will feature 1) hourly analysis runs, 2) short-and medium-range deterministic forecasts out to two day and ten day horizons and 3) long-range ensemble forecasts out to 30 days. All three of these configurations are underpinned by a 1km execution of the NoahMP land surface model, with channel routing taking place on 2.67 million NHDPlusV2 catchments covering the CONUS and contributing areas. Additionally, the short- and medium-range forecasts runs will feature surface and sub-surface routing on a 250m grid, while the hourly analyses will feature this same 250m routing in addition to nudging-based assimilation of US Geological Survey (USGS) streamflow observations. A limited number of major reservoirs will be configured within the model to begin to represent the first-order impacts of streamflow regulation.

A framework for improving a seasonal hydrological forecasting system using sensitivity analysis

NASA Astrophysics Data System (ADS)

Arnal, Louise; Pappenberger, Florian; Smith, Paul; Cloke, Hannah

2017-04-01

Seasonal streamflow forecasts are of great value for the socio-economic sector, for applications such as navigation, flood and drought mitigation and reservoir management for hydropower generation and water allocation to agriculture and drinking water. However, as we speak, the performance of dynamical seasonal hydrological forecasting systems (systems based on running seasonal meteorological forecasts through a hydrological model to produce seasonal hydrological forecasts) is still limited in space and time. In this context, the ESP (Ensemble Streamflow Prediction) remains an attractive forecasting method for seasonal streamflow forecasting as it relies on forcing a hydrological model (starting from the latest observed or simulated initial hydrological conditions) with historical meteorological observations. This makes it cheaper to run than a standard dynamical seasonal hydrological forecasting system, for which the seasonal meteorological forecasts will first have to be produced, while still producing skilful forecasts. There is thus the need to focus resources and time towards improvements in dynamical seasonal hydrological forecasting systems which will eventually lead to significant improvements in the skill of the streamflow forecasts generated. Sensitivity analyses are a powerful tool that can be used to disentangle the relative contributions of the two main sources of errors in seasonal streamflow forecasts, namely the initial hydrological conditions (IHC; e.g., soil moisture, snow cover, initial streamflow, among others) and the meteorological forcing (MF; i.e., seasonal meteorological forecasts of precipitation and temperature, input to the hydrological model). Sensitivity analyses are however most useful if they inform and change current operational practices. To this end, we propose a method to improve the design of a seasonal hydrological forecasting system. This method is based on sensitivity analyses, informing the forecasters as to which element of the forecasting chain (i.e., IHC or MF) could potentially lead to the highest increase in seasonal hydrological forecasting performance, after each forecast update.

The Implementation of NEMS GFS Aerosol Component (NGAC) Version 1.0 for Global Dust Forecasting at NOAA NCEP

NASA Technical Reports Server (NTRS)

Lu, Cheng-Hsuan; Da Silva, Arlindo M.; Wang, Jun; Moorthi, Shrinivas; Chin, Mian; Colarco, Peter; Tang, Youhua; Bhattacharjee, Partha S.; Chen, Shen-Po; Chuang, Hui-Ya;

The NRL relocatable ocean/acoustic ensemble forecast system

NASA Astrophysics Data System (ADS)

Rowley, C.; Martin, P.; Cummings, J.; Jacobs, G.; Coelho, E.; Bishop, C.; Hong, X.; Peggion, G.; Fabre, J.

2009-04-01

A globally relocatable regional ocean nowcast/forecast system has been developed to support rapid implementation of new regional forecast domains. The system is in operational use at the Naval Oceanographic Office for a growing number of regional and coastal implementations. The new system is the basis for an ocean acoustic ensemble forecast and adaptive sampling capability. We present an overview of the forecast system and the ocean ensemble and adaptive sampling methods. The forecast system consists of core ocean data analysis and forecast modules, software for domain configuration, surface and boundary condition forcing processing, and job control, and global databases for ocean climatology, bathymetry, tides, and river locations and transports. The analysis component is the Navy Coupled Ocean Data Assimilation (NCODA) system, a 3D multivariate optimum interpolation system that produces simultaneous analyses of temperature, salinity, geopotential, and vector velocity using remotely-sensed SST, SSH, and sea ice concentration, plus in situ observations of temperature, salinity, and currents from ships, buoys, XBTs, CTDs, profiling floats, and autonomous gliders. The forecast component is the Navy Coastal Ocean Model (NCOM). The system supports one-way nesting and multiple assimilation methods. The ensemble system uses the ensemble transform technique with error variance estimates from the NCODA analysis to represent initial condition error. Perturbed surface forcing or an atmospheric ensemble is used to represent errors in surface forcing. The ensemble transform Kalman filter is used to assess the impact of adaptive observations on future analysis and forecast uncertainty for both ocean and acoustic properties.

Remote Sensing and River Discharge Forecasting for Major Rivers in South Asia (Invited)

NASA Astrophysics Data System (ADS)

Webster, P. J.; Hopson, T. M.; Hirpa, F. A.; Brakenridge, G. R.; De-Groeve, T.; Shrestha, K.; Gebremichael, M.; Restrepo, P. J.

2013-12-01

The South Asia is a flashpoint for natural disasters particularly flooding of the Indus, Ganges, and Brahmaputra has profound societal impacts for the region and globally. The 2007 Brahmaputra floods affecting India and Bangladesh, the 2008 avulsion of the Kosi River in India, the 2010 flooding of the Indus River in Pakistan and the 2013 Uttarakhand exemplify disasters on scales almost inconceivable elsewhere. Their frequent occurrence of floods combined with large and rapidly growing populations, high levels of poverty and low resilience, exacerbate the impact of the hazards. Mitigation of these devastating hazards are compounded by limited flood forecast capability, lack of rain/gauge measuring stations and forecast use within and outside the country, and transboundary data sharing on natural hazards. Here, we demonstrate the utility of remotely-derived hydrologic and weather products in producing skillful flood forecasting information without reliance on vulnerable in situ data sources. Over the last decade a forecast system has been providing operational probabilistic forecasts of severe flooding of the Brahmaputra and Ganges Rivers in Bangldesh was developed (Hopson and Webster 2010). The system utilizes ECMWF weather forecast uncertainty information and ensemble weather forecasts, rain gauge and satellite-derived precipitation estimates, together with the limited near-real-time river stage observations from Bangladesh. This system has been expanded to Pakistan and has successfully forecast the 2010-2012 flooding (Shrestha and Webster 2013). To overcome the in situ hydrological data problem, recent efforts in parallel with the numerical modeling have utilized microwave satellite remote sensing of river widths to generate operational discharge advective-based forecasts for the Ganges and Brahmaputra. More than twenty remotely locations upstream of Bangldesh were used to produce stand-alone river flow nowcasts and forecasts at 1-15 days lead time. showing that satellite-based flow estimates are a useful source of dynamical surface water information in data-scarce regions and that they could be used for model calibration and data assimilation purposes in near-time hydrologic forecast applications (Hirpa et al. 2013). More recent efforts during this year's monsoon season are optimally combining these different independent sources of river forecast information along with archived flood inundation imagery of the Dartmouth Flood Observatory to improve the visualization and overall skill of the ongoing CFAB ensemble weather forecast-based flood forecasting system within the unique context of the ongoing flood forecasting efforts for Bangladesh.

Applications of a shadow camera system for energy meteorology

NASA Astrophysics Data System (ADS)

Kuhn, Pascal; Wilbert, Stefan; Prahl, Christoph; Garsche, Dominik; Schüler, David; Haase, Thomas; Ramirez, Lourdes; Zarzalejo, Luis; Meyer, Angela; Blanc, Philippe; Pitz-Paal, Robert

2018-02-01

Downward-facing shadow cameras might play a major role in future energy meteorology. Shadow cameras directly image shadows on the ground from an elevated position. They are used to validate other systems (e.g. all-sky imager based nowcasting systems, cloud speed sensors or satellite forecasts) and can potentially provide short term forecasts for solar power plants. Such forecasts are needed for electricity grids with high penetrations of renewable energy and can help to optimize plant operations. In this publication, two key applications of shadow cameras are briefly presented.

Applications systems verification and transfer project. Volume 1: Operational applications of satellite snow cover observations: Executive summary. [usefulness of satellite snow-cover data for water yield prediction

NASA Technical Reports Server (NTRS)

Rango, A.

1981-01-01

Both LANDSAT and NOAA satellite data were used in improving snowmelt runoff forecasts. When the satellite snow cover data were tested in both empirical seasonal runoff estimation and short term modeling approaches, a definite potential for reducing forecast error was evident. A cost benefit analysis run in conjunction with the snow mapping indicated a $36.5 million annual benefit accruing from a one percent improvement in forecast accuracy using the snow cover data for the western United States. The annual cost of employing the system would be $505,000. The snow mapping has proven that satellite snow cover data can be used to reduce snowmelt runoff forecast error in a cost effective manner once all operational satellite data are available within 72 hours after acquisition. Executive summaries of the individual snow mapping projects are presented.

Did we see the 2011 summer heat wave coming?

NASA Astrophysics Data System (ADS)

Luo, Lifeng; Zhang, Yan

2012-05-01

A series of climate extreme events affected many parts of the US during 2011, including the severe drought in Texas, the spring tornado outbreak in the southern states, and the weeklong summer heat wave in the Central Plains. Successful prediction of these events can better inform and prepare the general public to cope with these extremes. In this study, we investigate the operational capability of the new NCEP Climate Forecast System (CFSv2) in predicting the 2011 summer heat wave. We found that starting from April 2011, the operational CFSv2 forecast consistently suggested an elevated probability of extremely hot days during the forthcoming summer over the Central Plains, and as the summer was approaching the forecast became more certain about the summer heat wave in its geographic location, intensity and timing. This study demonstrates the capability of the new seasonal forecast system and its potential usefulness in decision making process.

Development of On-line Wildfire Emissions for the Operational Canadian Air Quality Forecast System

NASA Astrophysics Data System (ADS)

Pavlovic, R.; Menard, S.; Chen, J.; Anselmo, D.; Paul-Andre, B.; Gravel, S.; Moran, M. D.; Davignon, D.

2013-12-01

An emissions processing system has been developed to incorporate near-real-time emissions from wildfires and large prescribed burns into Environment Canada's real-time GEM-MACH air quality (AQ) forecast system. Since the GEM-MACH forecast domain covers Canada and most of the USA, including Alaska, fire location information is needed for both of these large countries. Near-real-time satellite data are obtained and processed separately for the two countries for organizational reasons. Fire location and fuel consumption data for Canada are provided by the Canadian Forest Service's Canadian Wild Fire Information System (CWFIS) while fire location and emissions data for the U.S. are provided by the SMARTFIRE (Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation) system via the on-line BlueSky Gateway. During AQ model runs, emissions from individual fire sources are injected into elevated model layers based on plume-rise calculations and then transport and chemistry calculations are performed. This 'on the fly' approach to the insertion of emissions provides greater flexibility since on-line meteorology is used and reduces computational overhead in emission pre-processing. An experimental wildfire version of GEM-MACH was run in real-time mode for the summers of 2012 and 2013. 48-hour forecasts were generated every 12 hours (at 00 and 12 UTC). Noticeable improvements in the AQ forecasts for PM2.5 were seen in numerous regions where fire activity was high. Case studies evaluating model performance for specific regions, computed objective scores, and subjective evaluations by AQ forecasters will be included in this presentation. Using the lessons learned from the last two summers, Environment Canada will continue to work towards the goal of incorporating near-real-time intermittent wildfire emissions within the operational air quality forecast system.

International Cooperative for Aerosol Prediction Workshop on Aerosol Forecast Verification

NASA Technical Reports Server (NTRS)

Benedetti, Angela; Reid, Jeffrey S.; Colarco, Peter R.

2011-01-01

The purpose of this workshop was to reinforce the working partnership between centers who are actively involved in global aerosol forecasting, and to discuss issues related to forecast verification. Participants included representatives from operational centers with global aerosol forecasting requirements, a panel of experts on Numerical Weather Prediction and Air Quality forecast verification, data providers, and several observers from the research community. The presentations centered on a review of current NWP and AQ practices with subsequent discussion focused on the challenges in defining appropriate verification measures for the next generation of aerosol forecast systems.

Workshop Summary: International Cooperative for Aerosol Prediction Workshop On Aerosol Forecast Verification

NASA Technical Reports Server (NTRS)

Benedetti, Angela; Reid, Jeffrey S.; Colarco, Peter R.

2011-01-01

The purpose of this workshop was to reinforce the working partnership between centers who are actively involved in global aerosol forecasting, and to discuss issues related to forecast verification. Participants included representatives from operational centers with global aerosol forecasting requirements, a panel of experts on Numerical Weather Prediction and Air Quality forecast verification, data providers, and several observers from the research community. The presentations centered on a review of current NWP and AQ practices with subsequent discussion focused on the challenges in defining appropriate verification measures for the next generation of aerosol forecast systems.

Precipitable water vapour forecasting: a tool for optimizing IR observations at Roque de los Muchachos Observatory

NASA Astrophysics Data System (ADS)

Pérez-Jordán, wG; Castro-Almazán, J. A.; Muñoz-Tuñón, C.

2018-07-01

We validate the Weather Research and Forecasting (WRF) model for precipitable water vapour (PWV) forecasting as a fully operational tool for optimizing astronomical infrared observations at Roque de los Muchachos Observatory (ORM). For the model validation, we used GNSS-based (Global Navigation Satellite System) data from the PWV monitor located at the ORM. We have run WRF every 24 h for near two months, with a horizon of 48 h (hourly forecasts), from 2016 January 11 to March 04. These runs represent 1296 hourly forecast points. The validation is carried out using different approaches: performance as a function of the forecast range, time horizon accuracy, performance as a function of the PWV value, and performance of the operational WRF time series with 24- and 48-h horizons. Excellent agreement was found between the model forecasts and observations, with R = 0.951 and 0.904 for the 24- and 48-h forecast time series, respectively. The 48-h forecast was further improved by correcting a time lag of 2 h found in the predictions. The final errors, taking into account all the uncertainties involved, are 1.75 mm for the 24-h forecasts and 1.99 mm for 48 h. We found linear trends in both the correlation and root-mean-square error of the residuals (measurements - forecasts) as a function of the forecast range within the horizons analysed (up to 48 h). In summary, the WRF performance is excellent and accurate, thus allowing it to be implemented as an operational tool at the ORM.

Precipitable water vapour forecasting: a tool for optimizing IR observations at Roque de los Muchachos Observatory.

NASA Astrophysics Data System (ADS)

Pérez-Jordán, G.; Castro-Almazán, J. A.; Muñoz-Tuñón, C.

2018-04-01

We validate the Weather Research and Forecasting (WRF) model for precipitable water vapour (PWV) forecasting as a fully operational tool for optimizing astronomical infrared (IR) observations at Roque de los Muchachos Observatory (ORM). For the model validation we used GNSS-based (Global Navigation Satellite System) data from the PWV monitor located at the ORM. We have run WRF every 24 h for near two months, with a horizon of 48 hours (hourly forecasts), from 2016 January 11 to 2016 March 4. These runs represent 1296 hourly forecast points. The validation is carried out using different approaches: performance as a function of the forecast range, time horizon accuracy, performance as a function of the PWV value, and performance of the operational WRF time series with 24- and 48-hour horizons. Excellent agreement was found between the model forecasts and observations, with R =0.951 and R =0.904 for the 24- and 48-h forecast time series respectively. The 48-h forecast was further improved by correcting a time lag of 2 h found in the predictions. The final errors, taking into account all the uncertainties involved, are 1.75 mm for the 24-h forecasts and 1.99 mm for 48 h. We found linear trends in both the correlation and RMSE of the residuals (measurements - forecasts) as a function of the forecast range within the horizons analysed (up to 48 h). In summary, the WRF performance is excellent and accurate, thus allowing it to be implemented as an operational tool at the ORM.

ADAS Update and Maintainability

NASA Technical Reports Server (NTRS)

Watson, Leela R.

2010-01-01

Since 2000, both the National Weather Service Melbourne (NWS MLB) and the Spaceflight Meteorology Group (SMG) have used a local data integration system (LOIS) as part of their forecast and warning operations. The original LOIS was developed by the Applied Meteorology Unit (AMU) in 1998 (Manobianco and Case 1998) and has undergone subsequent improvements. Each has benefited from three-dimensional (3-D) analyses that are delivered to forecasters every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather forecasts issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction System (ARPS) Data Analysis System (AD AS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national or regional-scale analysis packages. As such, it provides local forecasters with a more comprehensive understanding of evolving fine-scale weather features. Over the years, the LDIS has become problematic to maintain since it depends on AMU-developed shell scripts that were written for an earlier version of the ADAS software. The goals of this task were to update the NWS MLB/SMG LDIS with the latest version of ADAS, incorporate new sources of observational data, and upgrade and modify the AMU-developed shell scripts written to govern the system. In addition, the previously developed ADAS graphical user interface (GUI) was updated. Operationally, these upgrades will result in more accurate depictions of the current local environment to help with short-range weather forecasting applications, while also offering an improved initialization for local versions of the Weather Research and Forecasting (WRF) model used by both groups.

MOSE: A Demonstrator for an Automatic Operational System for the Optical Turbulence Forecast for ESO Sites

NASA Astrophysics Data System (ADS)

Masciadri, Elena; Lascaux, F.; Turchi, A.; Fini, L.

2017-09-01

"Most of the observations performed with new-generation ground-based telescopes are employing the Service Mode. To optimize the flexible-scheduling of scientific programs and instruments, the optical turbulence (OT) forecast is a must, particularly when observations are supported by adaptive optics (AO) and Interferometry. Reliable OT forecast are crucial to optimize the usage of AO and interferometric facilities which is not possible when using only optical measurements. Numerical techniques are the best placed to achieve such a goal. The MOSE project (MOdeling ESO Sites), co-funded by ESO, aimed at proving the feasibility of the forecast of (1) all the classical atmospheric parameters (such as temperature, wind speed and direction, relative humidity) and (2) the optical turbulence i.e. the CN 2 profiles and all the main integrated astro-climatic parameters derived from the CN 2 (the seeing, the isoplanatic angle, the wavefront coherence time) above the two ESO sites of Cerro Paranal and Cerro Armazones. The proposed technique is based on the use of a non-hydrostatic atmospheric meso-scale model and a dedicated code for the optical turbulence. The final goal of the project aimed at implementing an automatic system for the operational forecasts of the aforementioned parameters to support the astronomical observations above the two sites. MOSE Phase A and B have been completed and a set of dedicated papers have been published on the topic. Model performances have been extensively quantified with several dedicated figures of merit and we proved that our tool is able to provide reliable forecasts of optical turbulence and atmospheric parameters with very satisfactory score of success. This should guarantee us to make a step ahead in the framework of the Service Mode of new generation telescopes. A conceptual design as well as an operational plan of the automatic system has been submitted to ESO as integral part of the feasibility study. We completed a negotiation with ESO for the implementation of the demonstrator of system on March 2016. In this seminar I will review the principles on which the proposed technique is based on; I will briefly review the most important challenges associated to the optical turbulence forecast for ground-based observations, I will summarize the most important results we achieved at conclusion of the feasibility study, how our results open new scenarios for the operation of the most sophisticated AO systems (WFAO), the next steps for the implementation of a demonstrator and plans for the forecast of further parameters. I will conclude showing a few outputs of the operational system we implemented for the LBT in the context of a similar project (ALTA Project). "

A quality assessment of the MARS crop yield forecasting system for the European Union

NASA Astrophysics Data System (ADS)

van der Velde, Marijn; Bareuth, Bettina

2015-04-01

Timely information on crop production forecasts can become of increasing importance as commodity markets are more and more interconnected. Impacts across large crop production areas due to (e.g.) extreme weather and pest outbreaks can create ripple effects that may affect food prices and availability elsewhere. The MARS Unit (Monitoring Agricultural ResourceS), DG Joint Research Centre, European Commission, has been providing forecasts of European crop production levels since 1993. The operational crop production forecasting is carried out with the MARS Crop Yield Forecasting System (M-CYFS). The M-CYFS is used to monitor crop growth development, evaluate short-term effects of anomalous meteorological events, and provide monthly forecasts of crop yield at national and European Union level. The crop production forecasts are published in the so-called MARS bulletins. Forecasting crop yield over large areas in the operational context requires quality benchmarks. Here we present an analysis of the accuracy and skill of past crop yield forecasts of the main crops (e.g. soft wheat, grain maize), throughout the growing season, and specifically for the final forecast before harvest. Two simple benchmarks to assess the skill of the forecasts were defined as comparing the forecasts to 1) a forecast equal to the average yield and 2) a forecast using a linear trend established through the crop yield time-series. These reveal a variability in performance as a function of crop and Member State. In terms of production, the yield forecasts of 67% of the EU-28 soft wheat production and 80% of the EU-28 maize production have been forecast superior to both benchmarks during the 1993-2013 period. In a changing and increasingly variable climate crop yield forecasts can become increasingly valuable - provided they are used wisely. We end our presentation by discussing research activities that could contribute to this goal.

Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan

2015-10-05

Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reductionmore » in the amount of reserves that must be held to accommodate the uncertainty of solar power output.« less

Rapid wave and storm surge warning system for tropical cyclones in Mexico

NASA Astrophysics Data System (ADS)

Appendini, C. M.; Rosengaus, M.; Meza, R.; Camacho, V.

2015-12-01

The National Hurricane Center (NHC) in Miami, is responsible for the forecast of tropical cyclones in the North Atlantic and Eastern North Pacific basins. As such, Mexico, Central America and Caribbean countries depend on the information issued by the NHC related to the characteristics of a particular tropical cyclone and associated watch and warning areas. Despite waves and storm surge are important hazards for marine operations and coastal dwellings, their forecast is not part of the NHC responsibilities. This work presents a rapid wave and storm surge warning system based on 3100 synthetic tropical cyclones doing landfall in Mexico. Hydrodynamic and wave models were driven by the synthetic events to create a robust database composed of maximum envelops of wind speed, significant wave height and storm surge for each event. The results were incorporated into a forecast system that uses the NHC advisory to locate the synthetic events passing inside specified radiuses for the present and forecast position of the real event. Using limited computer resources, the system displays the information meeting the search criteria, and the forecaster can select specific events to generate the desired hazard map (i.e. wind, waves, and storm surge) based on the maximum envelop maps. This system was developed in a limited time frame to be operational in 2015 by the National Hurricane and Severe Storms Unit of the Mexican National Weather Service, and represents a pilot project for other countries in the region not covered by detailed storm surge and waves forecasts.

Solar power satellite system definition study. Volume 1, phase 1: Executive summary

NASA Technical Reports Server (NTRS)

1979-01-01

A systems definition study of the solar satellite system (SPS) is presented. The technical feasibility of solar power satellites based on forecasts of technical capability in the various applicable technologies is assessed. The performance, cost, operational characteristics, reliability, and the suitability of SPS's as power generators for typical commercial electricity grids are discussed. The uncertainties inherent in the system characteristics forecasts are assessed.

The MST radar technique: Requirements for operational weather forecasting

NASA Technical Reports Server (NTRS)

Larsen, M. F.

1983-01-01

There is a feeling that the accuracy of mesoscale forecasts for spatial scales of less than 1000 km and time scales of less than 12 hours can be improved significantly if resources are applied to the problem in an intensive effort over the next decade. Since the most dangerous and damaging types of weather occur at these scales, there are major advantages to be gained if such a program is successful. The interest in improving short term forecasting is evident. The technology at the present time is sufficiently developed, both in terms of new observing systems and the computing power to handle the observations, to warrant an intensive effort to improve stormscale forecasting. An assessment of the extent to which the so-called MST radar technique fulfills the requirements for an operational mesoscale observing network is reviewed and the extent to which improvements in various types of forecasting could be expected if such a network is put into operation are delineated.

Ocean modelling aspects for drift applications

NASA Astrophysics Data System (ADS)

Stephane, L.; Pierre, D.

2010-12-01

Nowadays, many authorities in charge of rescue-at-sea operations lean on operational oceanography products to outline research perimeters. Moreover, current fields estimated with sophisticated ocean forecasting systems can be used as input data for oil spill/ adrift object fate models. This emphasises the necessity of an accurate sea state forecast, with a mastered level of reliability. This work focuses on several problems inherent to drift modeling, dealing in the first place with the efficiency of the oceanic current field representation. As we want to discriminate the relevance of a particular physical process or modeling option, the idea is to generate series of current fields of different characteristics and then qualify them in term of drift prediction efficiency. Benchmarked drift scenarios were set up from real surface drifters data, collected in the Mediterranean sea and off the coasts of Angola. The time and space scales that we are interested in are about 72 hr forecasts (typical timescale communicated in case of crisis), for distance errors that we hope about a few dozen of km around the forecast (acceptable for reconnaissance by aircrafts) For the ocean prediction, we used some regional oceanic configurations based on the NEMO 2.3 code, nested into Mercator 1/12° operational system. Drift forecasts were computed offline with Mothy (Météo France oil spill modeling system) and Ariane (B. Blanke, 1997), a Lagrangian diagnostic tool. We were particularly interested in the importance of the horizontal resolution, vertical mixing schemes, and any processes that may impact the surface layer. The aim of the study is to ultimately point at the most suitable set of parameters for drift forecast use inside operational oceanic systems. We are also motivated in assessing the relevancy of ensemble forecasts regarding determinist predictions. Several tests showed that mis-described observed trajectories can finally be modelled statistically by using uncertainties over the initial position of the drifting material. Works in the near future will explore that concept with ensemble of currents obtained with different initial conditions, phase shifted boundary forcings or perturbed atmospheric surface forcings.

Tropical-Cyclone Formation: Theory and Idealized Modelling

DTIC Science & Technology

2010-11-01

to saturation at the sea-surface temperature and the positive entropy flux from the ocean surface...and Atmospheric Administration; IFEX = Intensity Forecasting Experiment. 15GFS = NOAA Global Forecasting System ; NOGAPS = Navy Operational Global... Atmospheric Prediction System ; UKMET = United Kingdom Meteorological Office. 16 http://www.met.nps.edu/~mtmontgo/storms2010.html 18 overcomes

Practical implementation of a particle filter data assimilation approach to estimate initial hydrologic conditions and initialize medium-range streamflow forecasts

NASA Astrophysics Data System (ADS)

Clark, Elizabeth; Wood, Andy; Nijssen, Bart; Mendoza, Pablo; Newman, Andy; Nowak, Kenneth; Arnold, Jeffrey

2017-04-01

In an automated forecast system, hydrologic data assimilation (DA) performs the valuable function of correcting raw simulated watershed model states to better represent external observations, including measurements of streamflow, snow, soil moisture, and the like. Yet the incorporation of automated DA into operational forecasting systems has been a long-standing challenge due to the complexities of the hydrologic system, which include numerous lags between state and output variations. To help demonstrate that such methods can succeed in operational automated implementations, we present results from the real-time application of an ensemble particle filter (PF) for short-range (7 day lead) ensemble flow forecasts in western US river basins. We use the System for Hydromet Applications, Research and Prediction (SHARP), developed by the National Center for Atmospheric Research (NCAR) in collaboration with the University of Washington, U.S. Army Corps of Engineers, and U.S. Bureau of Reclamation. SHARP is a fully automated platform for short-term to seasonal hydrologic forecasting applications, incorporating uncertainty in initial hydrologic conditions (IHCs) and in hydrometeorological predictions through ensemble methods. In this implementation, IHC uncertainty is estimated by propagating an ensemble of 100 temperature and precipitation time series through conceptual and physically-oriented models. The resulting ensemble of derived IHCs exhibits a broad range of possible soil moisture and snow water equivalent (SWE) states. The PF selects and/or weights and resamples the IHCs that are most consistent with external streamflow observations, and uses the particles to initialize a streamflow forecast ensemble driven by ensemble precipitation and temperature forecasts downscaled from the Global Ensemble Forecast System (GEFS). We apply this method in real-time for several basins in the western US that are important for water resources management, and perform a hindcast experiment to evaluate the utility of PF-based data assimilation on streamflow forecasts skill. This presentation describes findings, including a comparison of sequential and non-sequential particle weighting methods.

Next Generation Community Based Unified Global Modeling System Development and Operational Implementation Strategies at NCEP

NASA Astrophysics Data System (ADS)

Tallapragada, V.

2017-12-01

NOAA's Next Generation Global Prediction System (NGGPS) has provided the unique opportunity to develop and implement a non-hydrostatic global model based on Geophysical Fluid Dynamics Laboratory (GFDL) Finite Volume Cubed Sphere (FV3) Dynamic Core at National Centers for Environmental Prediction (NCEP), making a leap-step advancement in seamless prediction capabilities across all spatial and temporal scales. Model development efforts are centralized with unified model development in the NOAA Environmental Modeling System (NEMS) infrastructure based on Earth System Modeling Framework (ESMF). A more sophisticated coupling among various earth system components is being enabled within NEMS following National Unified Operational Prediction Capability (NUOPC) standards. The eventual goal of unifying global and regional models will enable operational global models operating at convective resolving scales. Apart from the advanced non-hydrostatic dynamic core and coupling to various earth system components, advanced physics and data assimilation techniques are essential for improved forecast skill. NGGPS is spearheading ambitious physics and data assimilation strategies, concentrating on creation of a Common Community Physics Package (CCPP) and Joint Effort for Data Assimilation Integration (JEDI). Both initiatives are expected to be community developed, with emphasis on research transitioning to operations (R2O). The unified modeling system is being built to support the needs of both operations and research. Different layers of community partners are also established with specific roles/responsibilities for researchers, core development partners, trusted super-users, and operations. Stakeholders are engaged at all stages to help drive the direction of development, resources allocations and prioritization. This talk presents the current and future plans of unified model development at NCEP for weather, sub-seasonal, and seasonal climate prediction applications with special emphasis on implementation of NCEP FV3 Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) into operations by 2019.

Tsunami Forecast Progress Five Years After Indonesian Disaster

NASA Astrophysics Data System (ADS)

Titov, Vasily V.; Bernard, Eddie N.; Weinstein, Stuart A.; Kanoglu, Utku; Synolakis, Costas E.

2010-05-01

Almost five years after the 26 December 2004 Indian Ocean tragedy, tsunami warnings are finally benefiting from decades of research toward effective model-based forecasts. Since the 2004 tsunami, two seminal advances have been (i) deep-ocean tsunami measurements with tsunameters and (ii) their use in accurately forecasting tsunamis after the tsunami has been generated. Using direct measurements of deep-ocean tsunami heights, assimilated into numerical models for specific locations, greatly improves the real-time forecast accuracy over earthquake-derived magnitude estimates of tsunami impact. Since 2003, this method has been used to forecast tsunamis at specific harbors for different events in the Pacific and Indian Oceans. Recent tsunamis illustrated how this technology is being adopted in global tsunami warning operations. The U.S. forecasting system was used by both research and operations to evaluate the tsunami hazard. Tests demonstrated the effectiveness of operational tsunami forecasting using real-time deep-ocean data assimilated into forecast models. Several examples also showed potential of distributed forecast tools. With IOC and USAID funding, NOAA researchers at PMEL developed the Community Model Interface for Tsunami (ComMIT) tool and distributed it through extensive capacity-building sessions in the Indian Ocean. Over hundred scientists have been trained in tsunami inundation mapping, leading to the first generation of inundation models for many Indian Ocean shorelines. These same inundation models can also be used for real-time tsunami forecasts as was demonstrated during several events. Contact Information Vasily V. Titov, Seattle, Washington, USA, 98115

Improving inflow forecasting into hydropower reservoirs through a complementary modelling framework

NASA Astrophysics Data System (ADS)

Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K.

2014-10-01

Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead-time is considered within the day-ahead (Elspot) market of the Nordic exchange market. We present here a new approach for issuing hourly reservoir inflow forecasts that aims to improve on existing forecasting models that are in place operationally, without needing to modify the pre-existing approach, but instead formulating an additive or complementary model that is independent and captures the structure the existing model may be missing. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. The procedure presented comprises an error model added on top of an un-alterable constant parameter conceptual model, the models being demonstrated with reference to the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead-times up to 17 h. Season based evaluations indicated that the improvement in inflow forecasts varies across seasons and inflow forecasts in autumn and spring are less successful with the 95% prediction interval bracketing less than 95% of the observations for lead-times beyond 17 h.

Climate Change Adaptation in the Western U.S.: the Case for Dynamic Rule Curves in Water Resources Management

NASA Astrophysics Data System (ADS)

Lee, S.; Hamlet, A. F.; Burges, S. J.

2008-12-01

Climate change in the Western U.S. will bring systematic hydrologic changes affecting many water resources systems. Successful adaptation to these changes, which will be ongoing through the 21st century, will require the 'rebalancing' of competing system objectives such as water supply, flood control, hydropower production, and environmental services in response to hydrologic (and other) changes. Although fixed operating policies for the operation of reservoirs has been a traditional approach to water management in the 20th century, the rapid pace of projected climate shifts (~0.5 F per decade), and the prohibitive costs of recursive policy intervention to mitigate impacts, suggest that more sophisticated approaches will be needed to cope with climate change on a long term basis. The use of 'dynamic rule curves' is an approach that maintains some of the key characteristics of current water management practice (reservoir rule curves) while avoiding many of the fundamental drawbacks of traditional water resources management strategies in a non-stationary climate. In this approach, water resources systems are optimized for each operational period using ensemble streamflow and/or water demand forecasts. The ensemble of optimized reservoir storage traces are then analyzed to produce a set of unique reservoir rule curves for each operational period reflecting the current state of the system. The potential advantage of this approach is that hydrologic changes associated with climate change (such as systematically warmer temperatures) can be captured explicitly in operational hydrologic forecasts, which would in turn inform the optimized reservoir management solutions, creating water resources systems that are largely 'self tending' as the climate system evolves. Furthermore, as hydrologic forecasting systems improve (e.g. in response to improved ENSO forecasting or other scientific advances), so does the performance of reservoir operations. An example of the approach is given for flood control in the Columbia River basin.

A VVWBO-BVO-based GM (1,1) and its parameter optimization by GRA-IGSA integration algorithm for annual power load forecasting

PubMed Central

Wang, Hongguang

2018-01-01

Annual power load forecasting is not only the premise of formulating reasonable macro power planning, but also an important guarantee for the safety and economic operation of power system. In view of the characteristics of annual power load forecasting, the grey model of GM (1,1) are widely applied. Introducing buffer operator into GM (1,1) to pre-process the historical annual power load data is an approach to improve the forecasting accuracy. To solve the problem of nonadjustable action intensity of traditional weakening buffer operator, variable-weight weakening buffer operator (VWWBO) and background value optimization (BVO) are used to dynamically pre-process the historical annual power load data and a VWWBO-BVO-based GM (1,1) is proposed. To find the optimal value of variable-weight buffer coefficient and background value weight generating coefficient of the proposed model, grey relational analysis (GRA) and improved gravitational search algorithm (IGSA) are integrated and a GRA-IGSA integration algorithm is constructed aiming to maximize the grey relativity between simulating value sequence and actual value sequence. By the adjustable action intensity of buffer operator, the proposed model optimized by GRA-IGSA integration algorithm can obtain a better forecasting accuracy which is demonstrated by the case studies and can provide an optimized solution for annual power load forecasting. PMID:29768450

Ensemble Models

EPA Science Inventory

Ensemble forecasting has been used for operational numerical weather prediction in the United States and Europe since the early 1990s. An ensemble of weather or climate forecasts is used to characterize the two main sources of uncertainty in computer models of physical systems: ...

An integrated weather and sea-state forecasting system for the Arabian Peninsula (WASSF)

NASA Astrophysics Data System (ADS)

Kallos, George; Galanis, George; Spyrou, Christos; Mitsakou, Christina; Solomos, Stavros; Bartsotas, Nikolaos; Kalogrei, Christina; Athanaselis, Ioannis; Sofianos, Sarantis; Vervatis, Vassios; Axaopoulos, Panagiotis; Papapostolou, Alexandros; Qahtani, Jumaan Al; Alaa, Elyas; Alexiou, Ioannis; Beard, Daniel

2013-04-01

Nowadays, large industrial conglomerates such as the Saudi ARAMCO, require a series of weather and sea state forecasting products that cannot be found in state meteorological offices or even commercial data providers. The two major objectives of the system is prevention and mitigation of environmental problems and of course early warning of local conditions associated with extreme weather events. The management and operations part is related to early warning of weather and sea-state events that affect operations of various facilities. The environmental part is related to air quality and especially the desert dust levels in the atmosphere. The components of the integrated system include: (i) a weather and desert dust prediction system with forecasting horizon of 5 days, (ii) a wave analysis and prediction component for Red Sea and Arabian Gulf, (iii) an ocean circulation and tidal analysis and prediction of both Red Sea and Arabian Gulf and (iv) an Aviation part specializing in the vertical structure of the atmosphere and extreme events that affect air transport and other operations. Specialized data sets required for on/offshore operations are provided ate regular basis. State of the art modeling components are integrated to a unique system that distributes the produced analysis and forecasts to each department. The weather and dust prediction system is SKIRON/Dust, the wave analysis and prediction system is based on WAM cycle 4 model from ECMWF, the ocean circulation model is MICOM while the tidal analysis and prediction is a development of the Ocean Physics and Modeling Group of University of Athens, incorporating the Tidal Model Driver. A nowcasting subsystem is included. An interactive system based on Google Maps gives the capability to extract and display the necessary information for any location of the Arabian Peninsula, the Red Sea and Arabian Gulf.

Ocean Predictability and Uncertainty Forecasts Using Local Ensemble Transfer Kalman Filter (LETKF)

NASA Astrophysics Data System (ADS)

Wei, M.; Hogan, P. J.; Rowley, C. D.; Smedstad, O. M.; Wallcraft, A. J.; Penny, S. G.

2017-12-01

Ocean predictability and uncertainty are studied with an ensemble system that has been developed based on the US Navy's operational HYCOM using the Local Ensemble Transfer Kalman Filter (LETKF) technology. One of the advantages of this method is that the best possible initial analysis states for the HYCOM forecasts are provided by the LETKF which assimilates operational observations using ensemble method. The background covariance during this assimilation process is implicitly supplied with the ensemble avoiding the difficult task of developing tangent linear and adjoint models out of HYCOM with the complicated hybrid isopycnal vertical coordinate for 4D-VAR. The flow-dependent background covariance from the ensemble will be an indispensable part in the next generation hybrid 4D-Var/ensemble data assimilation system. The predictability and uncertainty for the ocean forecasts are studied initially for the Gulf of Mexico. The results are compared with another ensemble system using Ensemble Transfer (ET) method which has been used in the Navy's operational center. The advantages and disadvantages are discussed.

Enhancing Community Based Early Warning Systems in Nepal with Flood Forecasting Using Local and Global Models

NASA Astrophysics Data System (ADS)

Dugar, Sumit; Smith, Paul; Parajuli, Binod; Khanal, Sonu; Brown, Sarah; Gautam, Dilip; Bhandari, Dinanath; Gurung, Gehendra; Shakya, Puja; Kharbuja, RamGopal; Uprety, Madhab

2017-04-01

Operationalising effective Flood Early Warning Systems (EWS) in developing countries like Nepal poses numerous challenges, with complex topography and geology, sparse network of river and rainfall gauging stations and diverse socio-economic conditions. Despite these challenges, simple real-time monitoring based EWSs have been in place for the past decade. A key constraint of these simple systems is the very limited lead time for response - as little as 2-3 hours, especially for rivers originating from steep mountainous catchments. Efforts to increase lead time for early warning are focusing on imbedding forecasts into the existing early warning systems. In 2016, the Nepal Department of Hydrology and Meteorology (DHM) piloted an operational Probabilistic Flood Forecasting Model in major river basins across Nepal. This comprised a low data approach to forecast water levels, developed jointly through a research/practitioner partnership with Lancaster University and WaterNumbers (UK) and the International NGO Practical Action. Using Data-Based Mechanistic Modelling (DBM) techniques, the model assimilated rainfall and water levels to generate localised hourly flood predictions, which are presented as probabilistic forecasts, increasing lead times from 2-3 hours to 7-8 hours. The Nepal DHM has simultaneously started utilizing forecasts from the Global Flood Awareness System (GLoFAS) that provides streamflow predictions at the global scale based upon distributed hydrological simulations using numerical ensemble weather forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts). The aforementioned global and local models have already affected the approach to early warning in Nepal, being operational during the 2016 monsoon in the West Rapti basin in Western Nepal. On 24 July 2016, GLoFAS hydrological forecasts for the West Rapti indicated a sharp rise in river discharge above 1500 m3/sec (equivalent to the river warning level at 5 meters) with 53% probability of exceeding the Medium Level Alert in two days. Rainfall stations upstream of the West Rapti catchment recorded heavy rainfall on 26 July, and localized forecasts from the probabilistic model at 8 am suggested that the water level would cross a pre-determined warning level in the next 3 hours. The Flood Forecasting Section at DHM issued a flood advisory, and disseminated SMS flood alerts to more than 13,000 at-risk people residing along the floodplains. Water levels crossed the danger threshold (5.4 meters) at 11 am, peaking at 8.15 meters at 10 pm. Extension of the warning lead time from probabilistic forecasts was significant in minimising the risk to lives and livelihoods as communities gained extra time to prepare, evacuate and respond. Likewise, longer timescale forecasts from GLoFAS could be potentially linked with no-regret early actions leading to improved preparedness and emergency response. These forecasting tools have contributed to enhance the effectiveness and efficiency of existing community based systems, increasing the lead time for response. Nevertheless, extensive work is required on appropriate ways to interpret and disseminate probabilistic forecasts having longer (2-14 days) and shorter (3-5 hours) time horizon for operational deployment as there are numerous uncertainties associated with predictions.

Establishing NWP capabilities in African Small Island States (SIDs)

NASA Astrophysics Data System (ADS)

Rögnvaldsson, Ólafur

2017-04-01

Íslenskar orkurannsóknir (ÍSOR), in collaboration with Belgingur Ltd. and the United Nations Economic Commission for Africa (UNECA) signed a Letter of Agreement in 2015 regarding collaboration in the "Establishing Operational Capacity for Building, Deploying and Using Numerical Weather and Seasonal Prediction Systems in Small Island States in Africa (SIDs)" project. The specific objectives of the collaboration were the following: - Build capacity of National Meteorological and Hydrology Services (NMHS) staff on the use of the WRF atmospheric model for weather and seasonal forecasting, interpretation of model results, and the use of observations to verify and improve model simulations. - Establish a platform for integrating short to medium range weather forecasts, as well as seasonal forecasts, into already existing infrastructure at NMHS and Regional Climate Centres. - Improve understanding of existing model results and forecast verification, for improving decision-making on the time scale of days to weeks. To meet these challenges the operational Weather On Demand (WOD) forecasting system, developed by Belgingur, is being installed in a number of SIDs countries (Cabo Verde, Guinea-Bissau, and Seychelles), as well as being deployed for the Pan-Africa region, with forecasts being disseminated to collaborating NMHSs.

An Assessment of the Subseasonal Forecast Performance in the Extended Global Ensemble Forecast System (GEFS)

NASA Astrophysics Data System (ADS)

Sinsky, E.; Zhu, Y.; Li, W.; Guan, H.; Melhauser, C.

2017-12-01

Optimal forecast quality is crucial for the preservation of life and property. Improving monthly forecast performance over both the tropics and extra-tropics requires attention to various physical aspects such as the representation of the underlying SST, model physics and the representation of the model physics uncertainty for an ensemble forecast system. This work focuses on the impact of stochastic physics, SST and the convection scheme on forecast performance for the sub-seasonal scale over the tropics and extra-tropics with emphasis on the Madden-Julian Oscillation (MJO). A 2-year period is evaluated using the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS). Three experiments with different configurations than the operational GEFS were performed to illustrate the impact of the stochastic physics, SST and convection scheme. These experiments are compared against a control experiment (CTL) which consists of the operational GEFS but its integration is extended from 16 to 35 days. The three configurations are: 1) SPs, which uses a Stochastically Perturbed Physics Tendencies (SPPT), Stochastic Perturbed Humidity (SHUM) and Stochastic Kinetic Energy Backscatter (SKEB); 2) SPs+SST_bc, which uses a combination of SPs and a bias-corrected forecast SST from the NCEP Climate Forecast System Version 2 (CFSv2); and 3) SPs+SST_bc+SA_CV, which combines SPs, a bias-corrected forecast SST and a scale aware convection scheme. When comparing to the CTL experiment, SPs shows substantial improvement. The MJO skill has improved by about 4 lead days during the 2-year period. Improvement is also seen over the extra-tropics due to the updated stochastic physics, where there is a 3.1% and a 4.2% improvement during weeks 3 and 4 over the northern hemisphere and southern hemisphere, respectively. Improvement is also seen when the bias-corrected CFSv2 SST is combined with SPs. Additionally, forecast performance enhances when the scale aware convection scheme (SPs+SST_bc+SA_CV) is added, especially over the tropics. Among the three experiments, the SPs+SST_bc+SA_CV is the best configuration in MJO forecast skill.

OpenDA-WFLOW framework for improving hydrologic predictions using distributed hydrologic models

NASA Astrophysics Data System (ADS)

Weerts, Albrecht; Schellekens, Jaap; Kockx, Arno; Hummel, Stef

2017-04-01

Data assimilation (DA) holds considerable potential for improving hydrologic predictions (Liu et al., 2012) and increase the potential for early warning and/or smart water management. However, advances in hydrologic DA research have not yet been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. The objective of this work is to highlight the development of a generic linkage of the open source OpenDA package and the open source community hydrologic modeling framework Openstreams/WFLOW and its application in operational hydrological forecasting on various spatial scales. The coupling between OpenDA and Openstreams/wflow framework is based on the emerging standard Basic Model Interface (BMI) as advocated by CSDMS using cross-platform webservices (i.e. Apache Thrift) developed by Hut et al. (2016). The potential application of the OpenDA-WFLOW for operational hydrologic forecasting including its integration with Delft-FEWS (used by more than 40 operational forecast centers around the world (Werner et al., 2013)) is demonstrated by the presented case studies. We will also highlight the possibility to give real-time insight into the working of the DA methods applied for supporting the forecaster as mentioned as one of the burning issues by Liu et al., (2012).

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

DOE PAGES

Bessa, Ricardo; Möhrlen, Corinna; Fundel, Vanessa; ...

2017-09-14

Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding ofmore » its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. Our paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. Furthermore, a set of recommendations for standardization and improved training of operators are provided along with examples of best practices.« less

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bessa, Ricardo; Möhrlen, Corinna; Fundel, Vanessa

Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding ofmore » its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. Our paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. Furthermore, a set of recommendations for standardization and improved training of operators are provided along with examples of best practices.« less

Environmental Regulations and Changes in Petroleum Refining Operations (Short-Term Energy Outlook Supplement June 1998)

EIA Publications

1998-01-01

Changes in domestic refining operations are identified and related to the summer Reid vapor pressure (RVP) restrictions and oxygenate blending requirements. This analysis uses published Energy Information Administration survey data and linear regression equations from the Short-Term Integrated Forecasting System (STIFS). The STIFS model is used for producing forecasts appearing in the Short-Term Energy Outlook.

Meteorological support for space operations: Review and recommendations

NASA Technical Reports Server (NTRS)

1988-01-01

The current meteorological support provided to NASA by NOAA, Air Weather Service, and other contractors is reviewed and suggestions are offered for its improvement. These recommendations include improvement in NASA's internal management organizational structure that would accommodate continued improvement in operational weather support, installation of new observing systems, improvement in analysis and forecasting procedures, and the establishment of an Applied Research and Forecasting Facility.

Worldwide satellite market demand forecast

NASA Technical Reports Server (NTRS)

Bowyer, J. M.; Frankfort, M.; Steinnagel, K. M.

1981-01-01

The forecast is for the years 1981 - 2000 with benchmark years at 1985, 1990 and 2000. Two typs of markets are considered for this study: Hardware (worldwide total) - satellites, earth stations and control facilities (includes replacements and spares); and non-hardware (addressable by U.S. industry) - planning, launch, turnkey systems and operations. These markets were examined for the INTELSAT System (international systems and domestic and regional systems using leased transponders) and domestic and regional systems. Forecasts were determined for six worldwide regions encompassing 185 countries using actual costs for existing equipment and engineering estimates of costs for advanced systems. Most likely (conservative growth rate estimates) and optimistic (mid range growth rate estimates) scenarios were employed for arriving at the forecasts which are presented in constant 1980 U.S. dollars. The worldwide satellite market demand forecast predicts that the market between 181 and 2000 will range from $35 to $50 billion. Approximately one-half of the world market, $16 to $20 billion, will be generated in the United States.

Worldwide satellite market demand forecast

NASA Astrophysics Data System (ADS)

Bowyer, J. M.; Frankfort, M.; Steinnagel, K. M.

1981-06-01

The forecast is for the years 1981 - 2000 with benchmark years at 1985, 1990 and 2000. Two typs of markets are considered for this study: Hardware (worldwide total) - satellites, earth stations and control facilities (includes replacements and spares); and non-hardware (addressable by U.S. industry) - planning, launch, turnkey systems and operations. These markets were examined for the INTELSAT System (international systems and domestic and regional systems using leased transponders) and domestic and regional systems. Forecasts were determined for six worldwide regions encompassing 185 countries using actual costs for existing equipment and engineering estimates of costs for advanced systems. Most likely (conservative growth rate estimates) and optimistic (mid range growth rate estimates) scenarios were employed for arriving at the forecasts which are presented in constant 1980 U.S. dollars. The worldwide satellite market demand forecast predicts that the market between 181 and 2000 will range from $35 to $50 billion. Approximately one-half of the world market, $16 to $20 billion, will be generated in the United States.

Development of an Operation Control System for Photovoltaics and Electric Storage Heaters for Houses Based on Information in Weather Forecasts

NASA Astrophysics Data System (ADS)

Obara, Shin'ya

An all-electric home using an electric storage heater with safety and cleaning is expanded. However, the general electric storage heater leads to an unpleasant room temperature and energy loss by the overs and shorts of the amount of heat radiation when the climate condition changes greatly. Consequently, the operation of the electric storage heater introduced into an all-electric home, a storage type electric water heater, and photovoltaics was planned using weather forecast information distributed by a communication line. The comfortable evaluation (the difference between a room-temperature target and a room-temperature result) when the proposed system was employed based on the operation planning, purchase electric energy, and capacity of photovoltaics was investigated. As a result, comfortable heating operation was realized by using weather forecast data; furthermore, it is expected that the purchase cost of the commercial power in daytime can be reduced by introducing photovoltaics. Moreover, when the capacity of the photovoltaics was increased, the surplus power was stored in the electric storage heater, but an extremely unpleasant room temperature was not shown in the investigation ranges of this paper. By obtaining weather information from the forecast of the day from an external service using a communication line, the heating system of the all-electric home with low energy loss and comfort temperature is realizable.

Final Technical Report for Contract No. DE-EE0006332, "Integrated Simulation Development and Decision Support Tool-Set for Utility Market and Distributed Solar Power Generation"

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cormier, Dallas; Edra, Sherwin; Espinoza, Michael

This project will enable utilities to develop long-term strategic plans that integrate high levels of renewable energy generation, and to better plan power system operations under high renewable penetration. The program developed forecast data streams for decision support and effective integration of centralized and distributed solar power generation in utility operations. This toolset focused on real time simulation of distributed power generation within utility grids with the emphasis on potential applications in day ahead (market) and real time (reliability) utility operations. The project team developed and demonstrated methodologies for quantifying the impact of distributed solar generation on core utility operations,more » identified protocols for internal data communication requirements, and worked with utility personnel to adapt the new distributed generation (DG) forecasts seamlessly within existing Load and Generation procedures through a sophisticated DMS. This project supported the objectives of the SunShot Initiative and SUNRISE by enabling core utility operations to enhance their simulation capability to analyze and prepare for the impacts of high penetrations of solar on the power grid. The impact of high penetration solar PV on utility operations is not only limited to control centers, but across many core operations. Benefits of an enhanced DMS using state-of-the-art solar forecast data were demonstrated within this project and have had an immediate direct operational cost savings for Energy Marketing for Day Ahead generation commitments, Real Time Operations, Load Forecasting (at an aggregate system level for Day Ahead), Demand Response, Long term Planning (asset management), Distribution Operations, and core ancillary services as required for balancing and reliability. This provided power system operators with the necessary tools and processes to operate the grid in a reliable manner under high renewable penetration.« less

The GOES-R Geostationary Lightning Mapper (GLM) and the Global Observing System for Total Lightning

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, R. J.; Koshak, W.; Buechler, D.; Carey, L.; Chronis, T.; Mach, D.; Bateman, M.; Peterson, H.; McCaul, E. W., Jr.;

Weather Prediction Improvement Using Advanced Satellite Technology

NASA Technical Reports Server (NTRS)

Einaudi, Franco; Uccellini, L.; Purdom, J.; Rogers, D.; Gelaro, R.; Dodge, J.; Atlas, R.; Lord, S.

2001-01-01

We discuss in this paper some of the problems that exist today in the fall utilization of satellite data to improve weather forecasts and we propose specific recommendations to solve them. This discussion can be viewed as an aspect of the general debate on how best to organize the transition from research to operational satellites and how to evaluate the impact of a research instrument on numerical weather predictions. A method for providing this transition is offered by the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP). This mission will bridge the time between the present NOAA and Department of Defense (DOD) polar orbiting missions and the initiation of the converged NPOESS series and will evaluate some of the Earth Observing System (EOS) instruments as appropriate for operational missions. Thus, this mission can be viewed as an effort to meet the operational requirements of NOAA and DOD and the research requirements of NASA. More generally, however, it can be said that the process of going from the conception of new, more advanced instruments to their operational implementation and full utilization by the weather forecast communities is not optimal. Instruments developed for research purposes may have insufficient funding to explore their potential operational capabilities. Furthermore, instrument development programs designed for operational satellites typically have insufficient funding for assimilation algorithms needed to transform the satellite observations into data that can be used by sophisticated global weather forecast models. As a result, years often go by before satellite data are efficiently used for operational forecasts. NASA and NOAA each have unique expertise in the design of satellite instruments, their use for basic and applied research and their utilization in weather and climate research. At a time of limited resources, the two agencies must combine their efforts to work toward common goals of full utilization of satellite data. This is a challenge that requires the assimilation of myriad new data into increasingly sophisticated numerical forecast models that run on increasingly sophisticated computer systems. In section II, we briefly outline the impact of satellite data on the quality of the National Centers for Environmental Prediction (NCEP) forecasts. In section III, we describe the present status of the utilization of satellite data in NCEP models and the challenges that lie ahead. In section IV, we propose solutions whose goals are summarized in section V.

Satellite based Ocean Forecasting, the SOFT project

NASA Astrophysics Data System (ADS)

Stemmann, L.; Tintoré, J.; Moneris, S.

2003-04-01

The knowledge of future oceanic conditions would have enormous impact on human marine related areas. For such reasons, a number of international efforts are being carried out to obtain reliable and manageable ocean forecasting systems. Among the possible techniques that can be used to estimate the near future states of the ocean, an ocean forecasting system based on satellite imagery is developped through the Satelitte based Ocean ForecasTing project (SOFT). SOFT, established by the European Commission, considers the development of a forecasting system of the ocean space-time variability based on satellite data by using Artificial Intelligence techniques. This system will be merged with numerical simulation approaches, via assimilation techniques, to get a hybrid SOFT-numerical forecasting system of improved performance. The results of the project will provide efficient forecasting of sea-surface temperature structures, currents, dynamic height, and biological activity associated to chlorophyll fields. All these quantities could give valuable information on the planning and management of human activities in marine environments such as navigation, fisheries, pollution control, or coastal management. A detailed identification of present or new needs and potential end-users concerned by such an operational tool is being performed. The project would study solutions adapted to these specific needs.

How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction

NASA Astrophysics Data System (ADS)

Pappenberger, F.; Ramos, M. H.; Cloke, H. L.; Wetterhall, F.; Alfieri, L.; Bogner, K.; Mueller, A.; Salamon, P.

2015-03-01

The skill of a forecast can be assessed by comparing the relative proximity of both the forecast and a benchmark to the observations. Example benchmarks include climatology or a naïve forecast. Hydrological ensemble prediction systems (HEPS) are currently transforming the hydrological forecasting environment but in this new field there is little information to guide researchers and operational forecasters on how benchmarks can be best used to evaluate their probabilistic forecasts. In this study, it is identified that the forecast skill calculated can vary depending on the benchmark selected and that the selection of a benchmark for determining forecasting system skill is sensitive to a number of hydrological and system factors. A benchmark intercomparison experiment is then undertaken using the continuous ranked probability score (CRPS), a reference forecasting system and a suite of 23 different methods to derive benchmarks. The benchmarks are assessed within the operational set-up of the European Flood Awareness System (EFAS) to determine those that are 'toughest to beat' and so give the most robust discrimination of forecast skill, particularly for the spatial average fields that EFAS relies upon. Evaluating against an observed discharge proxy the benchmark that has most utility for EFAS and avoids the most naïve skill across different hydrological situations is found to be meteorological persistency. This benchmark uses the latest meteorological observations of precipitation and temperature to drive the hydrological model. Hydrological long term average benchmarks, which are currently used in EFAS, are very easily beaten by the forecasting system and the use of these produces much naïve skill. When decomposed into seasons, the advanced meteorological benchmarks, which make use of meteorological observations from the past 20 years at the same calendar date, have the most skill discrimination. They are also good at discriminating skill in low flows and for all catchment sizes. Simpler meteorological benchmarks are particularly useful for high flows. Recommendations for EFAS are to move to routine use of meteorological persistency, an advanced meteorological benchmark and a simple meteorological benchmark in order to provide a robust evaluation of forecast skill. This work provides the first comprehensive evidence on how benchmarks can be used in evaluation of skill in probabilistic hydrological forecasts and which benchmarks are most useful for skill discrimination and avoidance of naïve skill in a large scale HEPS. It is recommended that all HEPS use the evidence and methodology provided here to evaluate which benchmarks to employ; so forecasters can have trust in their skill evaluation and will have confidence that their forecasts are indeed better.

Validation of reactive gases and aerosols in the MACC global analysis and forecast system

NASA Astrophysics Data System (ADS)

Eskes, H.; Huijnen, V.; Arola, A.; Benedictow, A.; Blechschmidt, A.-M.; Botek, E.; Boucher, O.; Bouarar, I.; Chabrillat, S.; Cuevas, E.; Engelen, R.; Flentje, H.; Gaudel, A.; Griesfeller, J.; Jones, L.; Kapsomenakis, J.; Katragkou, E.; Kinne, S.; Langerock, B.; Razinger, M.; Richter, A.; Schultz, M.; Schulz, M.; Sudarchikova, N.; Thouret, V.; Vrekoussis, M.; Wagner, A.; Zerefos, C.

2015-02-01

The European MACC (Monitoring Atmospheric Composition and Climate) project is preparing the operational Copernicus Atmosphere Monitoring Service (CAMS), one of the services of the European Copernicus Programme on Earth observation and environmental services. MACC uses data assimilation to combine in-situ and remote sensing observations with global and regional models of atmospheric reactive gases, aerosols and greenhouse gases, and is based on the Integrated Forecast System of the ECMWF. The global component of the MACC service has a dedicated validation activity to document the quality of the atmospheric composition products. In this paper we discuss the approach to validation that has been developed over the past three years. Topics discussed are the validation requirements, the operational aspects, the measurement data sets used, the structure of the validation reports, the models and assimilation systems validated, the procedure to introduce new upgrades, and the scoring methods. One specific target of the MACC system concerns forecasting special events with high pollution concentrations. Such events receive extra attention in the validation process. Finally, a summary is provided of the results from the validation of the latest set of daily global analysis and forecast products from the MACC system reported in November 2014.

Flare forecasting at the Met Office Space Weather Operations Centre

NASA Astrophysics Data System (ADS)

Murray, S. A.; Bingham, S.; Sharpe, M.; Jackson, D. R.

2017-04-01

The Met Office Space Weather Operations Centre produces 24/7/365 space weather guidance, alerts, and forecasts to a wide range of government and commercial end-users across the United Kingdom. Solar flare forecasts are one of its products, which are issued multiple times a day in two forms: forecasts for each active region on the solar disk over the next 24 h and full-disk forecasts for the next 4 days. Here the forecasting process is described in detail, as well as first verification of archived forecasts using methods commonly used in operational weather prediction. Real-time verification available for operational flare forecasting use is also described. The influence of human forecasters is highlighted, with human-edited forecasts outperforming original model results and forecasting skill decreasing over longer forecast lead times.

IASI Radiance Data Assimilation in Local Ensemble Transform Kalman Filter

NASA Astrophysics Data System (ADS)

Cho, K.; Hyoung-Wook, C.; Jo, Y.

2016-12-01

Korea institute of Atmospheric Prediction Systems (KIAPS) is developing NWP model with data assimilation systems. Local Ensemble Transform Kalman Filter (LETKF) system, one of the data assimilation systems, has been developed for KIAPS Integrated Model (KIM) based on cubed-sphere grid and has successfully assimilated real data. LETKF data assimilation system has been extended to 4D- LETKF which considers time-evolving error covariance within assimilation window and IASI radiance data assimilation using KPOP (KIAPS package for observation processing) with RTTOV (Radiative Transfer for TOVS). The LETKF system is implementing semi operational prediction including conventional (sonde, aircraft) observation and AMSU-A (Advanced Microwave Sounding Unit-A) radiance data from April. Recently, the semi operational prediction system updated radiance observations including GPS-RO, AMV, IASI (Infrared Atmospheric Sounding Interferometer) data at July. A set of simulation of KIM with ne30np4 and 50 vertical levels (of top 0.3hPa) were carried out for short range forecast (10days) within semi operation prediction LETKF system with ensemble forecast 50 members. In order to only IASI impact, our experiments used only conventional and IAIS radiance data to same semi operational prediction set. We carried out sensitivity test for IAIS thinning method (3D and 4D). IASI observation number was increased by temporal (4D) thinning and the improvement of IASI radiance data impact on the forecast skill of model will expect.

Atlantic Real-Time Ocean Forecast System (Discontinued)

Science.gov Websites

RTOFS is described in the following paper (PDF): "A Real Time Ocean Forecast System for the North options The following selections are available from the main menu at the top of the page. Compare with Obs email by selecting from the following list of contacts: Outreach: Liyan Liu Operations: Avichal Mehra

Operational Hydrological Forecasting During the Iphex-iop Campaign - Meet the Challenge

NASA Technical Reports Server (NTRS)

Tao, Jing; Wu, Di; Gourley, Jonathan; Zhang, Sara Q.; Crow, Wade; Peters-Lidard, Christa D.; Barros, Ana P.

2016-01-01

An operational streamflow forecasting testbed was implemented during the Intense Observing Period (IOP) of the Integrated Precipitation and Hydrology Experiment (IPHEx-IOP) in May-June 2014 to characterize flood predictability in complex terrain. Specifically, hydrological forecasts were issued daily for 12 headwater catchments in the Southern Appalachians using the Duke Coupled surface-groundwater Hydrology Model (DCHM) forced by hourly atmospheric fields and QPFs (Quantitative Precipitation Forecasts) produced by the NASA-Unified Weather Research and Forecasting (NU-WRF) model. Previous day hindcasts forced by radar-based QPEs (Quantitative Precipitation Estimates) were used to provide initial conditions for present day forecasts. This manuscript first describes the operational testbed framework and workflow during the IPHEx-IOP including a synthesis of results. Second, various data assimilation approaches are explored a posteriori (post-IOP) to improve operational (flash) flood forecasting. Although all flood events during the IOP were predicted by the IPHEx operational testbed with lead times of up to 6 h, significant errors of over- and, or under-prediction were identified that could be traced back to the QPFs and subgrid-scale variability of radar QPEs. To improve operational flood prediction, three data-merging strategies were pursued post-IOP: (1) the spatial patterns of QPFs were improved through assimilation of satellite-based microwave radiances into NU-WRF; (2) QPEs were improved by merging raingauge observations with ground-based radar observations using bias-correction methods to produce streamflow hindcasts and associated uncertainty envelope capturing the streamflow observations, and (3) river discharge observations were assimilated into the DCHM to improve streamflow forecasts using the Ensemble Kalman Filter (EnKF), the fixed-lag Ensemble Kalman Smoother (EnKS), and the Asynchronous EnKF (i.e. AEnKF) methods. Both flood hindcasts and forecasts were significantly improved by assimilating discharge observations into the DCHM. Specifically, Nash-Sutcliff Efficiency (NSE) values as high as 0.98, 0.71 and 0.99 at 15-min time-scales were attained for three headwater catchments in the inner mountain region demonstrating that the assimilation of discharge observations at the basins outlet can reduce the errors and uncertainties in soil moisture at very small scales. Success in operational flood forecasting at lead times of 6, 9, 12 and 15 h was also achieved through discharge assimilation with NSEs of 0.87, 0.78, 0.72 and 0.51, respectively. Analysis of experiments using various data assimilation system configurations indicates that the optimal assimilation time window depends both on basin properties and storm-specific space-time-structure of rainfall, and therefore adaptive, context-aware configurations of the data assimilation system are recommended to address the challenges of flood prediction in headwater basins.

Operational hydrological forecasting during the IPHEx-IOP campaign - Meet the challenge

NASA Astrophysics Data System (ADS)

Tao, Jing; Wu, Di; Gourley, Jonathan; Zhang, Sara Q.; Crow, Wade; Peters-Lidard, Christa; Barros, Ana P.

2016-10-01

An operational streamflow forecasting testbed was implemented during the Intense Observing Period (IOP) of the Integrated Precipitation and Hydrology Experiment (IPHEx-IOP) in May-June 2014 to characterize flood predictability in complex terrain. Specifically, hydrological forecasts were issued daily for 12 headwater catchments in the Southern Appalachians using the Duke Coupled surface-groundwater Hydrology Model (DCHM) forced by hourly atmospheric fields and QPFs (Quantitative Precipitation Forecasts) produced by the NASA-Unified Weather Research and Forecasting (NU-WRF) model. Previous day hindcasts forced by radar-based QPEs (Quantitative Precipitation Estimates) were used to provide initial conditions for present day forecasts. This manuscript first describes the operational testbed framework and workflow during the IPHEx-IOP including a synthesis of results. Second, various data assimilation approaches are explored a posteriori (post-IOP) to improve operational (flash) flood forecasting. Although all flood events during the IOP were predicted by the IPHEx operational testbed with lead times of up to 6 h, significant errors of over- and, or under-prediction were identified that could be traced back to the QPFs and subgrid-scale variability of radar QPEs. To improve operational flood prediction, three data-merging strategies were pursued post-IOP: (1) the spatial patterns of QPFs were improved through assimilation of satellite-based microwave radiances into NU-WRF; (2) QPEs were improved by merging raingauge observations with ground-based radar observations using bias-correction methods to produce streamflow hindcasts and associated uncertainty envelope capturing the streamflow observations, and (3) river discharge observations were assimilated into the DCHM to improve streamflow forecasts using the Ensemble Kalman Filter (EnKF), the fixed-lag Ensemble Kalman Smoother (EnKS), and the Asynchronous EnKF (i.e. AEnKF) methods. Both flood hindcasts and forecasts were significantly improved by assimilating discharge observations into the DCHM. Specifically, Nash-Sutcliff Efficiency (NSE) values as high as 0.98, 0.71 and 0.99 at 15-min time-scales were attained for three headwater catchments in the inner mountain region demonstrating that the assimilation of discharge observations at the basin's outlet can reduce the errors and uncertainties in soil moisture at very small scales. Success in operational flood forecasting at lead times of 6, 9, 12 and 15 h was also achieved through discharge assimilation with NSEs of 0.87, 0.78, 0.72 and 0.51, respectively. Analysis of experiments using various data assimilation system configurations indicates that the optimal assimilation time window depends both on basin properties and storm-specific space-time-structure of rainfall, and therefore adaptive, context-aware configurations of the data assimilation system are recommended to address the challenges of flood prediction in headwater basins.

Assimilation of Quality Controlled AIRS Temperature Profiles using the NCEP GFS

NASA Technical Reports Server (NTRS)

Susskind, Joel; Reale, Oreste; Iredell, Lena; Rosenberg, Robert

2013-01-01

We have previously conducted a number of data assimilation experiments using AIRS Version-5 quality controlled temperature profiles as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The data assimilation and forecast system we used was the Goddard Earth Observing System Model , Version-5 (GEOS-5) Data Assimilation System (DAS), which represents a combination of the NASA GEOS-5 forecast model with the National Centers for Environmental Prediction (NCEP) operational Grid Point Statistical Interpolation (GSI) global analysis scheme. All analyses and forecasts were run at a 0.5deg x 0.625deg spatial resolution. Data assimilation experiments were conducted in four different seasons, each in a different year. Three different sets of data assimilation experiments were run during each time period: Control; AIRS T(p); and AIRS Radiance. In the "Control" analysis, all the data used operationally by NCEP was assimilated, but no AIRS data was assimilated. Radiances from the Aqua AMSU-A instrument were also assimilated operationally by NCEP and are included in the "Control". The AIRS Radiance assimilation adds AIRS observed radiance observations for a select set of channels to the data set being assimilated, as done operationally by NCEP. In the AIRS T(p) assimilation, all information used in the Control was assimilated as well as Quality Controlled AIRS Version-5 temperature profiles, i.e., AIRS T(p) information was substituted for AIRS radiance information. The AIRS Version-5 temperature profiles were presented to the GSI analysis as rawinsonde profiles, assimilated down to a case-by-case appropriate pressure level p(sub best) determined using the Quality Control procedure. Version-5 also determines case-by-case, level-by-level error estimates of the temperature profiles, which were used as the uncertainty of each temperature measurement. These experiments using GEOS-5 have shown that forecasts resulting from analyses using the AIRS T(p) assimilation system were superior to those from the Radiance assimilation system, both with regard to global 7 day forecast skill and also the ability to predict storm tracks and intensity.

Effects of the uncertainty of energy price and water availability forecasts on the operation of Alpine hydropower reservoir systems

NASA Astrophysics Data System (ADS)

Anghileri, D.; Castelletti, A.; Burlando, P.

2016-12-01

European energy markets have experienced dramatic changes in the last years because of the massive introduction of Variable Renewable Sources (VRSs), such as wind and solar power sources, in the generation portfolios in many countries. VRSs i) are intermittent, i.e., their production is highly variable and only partially predictable, ii) are characterized by no correlation between production and demand, iii) have negligible costs of production, and iv) have been largely subsidized. These features result in lower energy prices, but, at the same time, in increased price volatility, and in network stability issues, which pose a threat to traditional power sources because of smaller incomes and higher maintenance costs associated to a more flexible operation of power systems. Storage hydropower systems play an important role in compensating production peaks, both in term of excess and shortage of energy. Traditionally, most of the research effort in hydropower reservoir operation has focused on modeling and forecasting reservoir inflow as well as designing reservoir operation accordingly. Nowadays, price variability may be the largest source of uncertainty in the context of hydropower systems, especially when considering medium-to-large reservoirs, whose storage can easily buffer small inflow fluctuations. In this work, we compare the effects of uncertain inflow and energy price forecasts on hydropower production and profitability. By adding noise to historic inflow and price trajectories, we build a set of synthetic forecasts corresponding to different levels of predictability and assess their impact on reservoir operating policies and performances. The study is conducted on different hydropower systems, including storage systems and pumped-storage systems, with different characteristics, e.g., different inflow-capacity ratios. The analysis focuses on Alpine hydropower systems where the hydrological regime ranges from purely ice and snow-melt dominated to mixed snow-melt and rain-dominated regimes.

Routine High-Resolution Forecasts/Analyses for the Pacific Disaster Center: User Manual

NASA Technical Reports Server (NTRS)

Roads, John; Han, J.; Chen, S.; Burgan, R.; Fujioka, F.; Stevens, D.; Funayama, D.; Chambers, C.; Bingaman, B.; McCord, C.;

Operational Space Weather Needs - Perspectives from SEASONS 2014

NASA Astrophysics Data System (ADS)

Comberiate, J.; Kelly, M. A.; Paxton, L. J.; Schaefer, R. K.; Bust, G. S.; Sotirelis, T.; Fox, N. J.

2014-12-01

A key challenge for the operational space weather community is the gap between the latest scientific data, models, methods, and indices and those that are currently used in operational systems. The November 2014 SEASONS (Space Environment Applications, Systems, and Operations for National Security) Workshop at JHU/APL in Laurel, Maryland, brings together representatives from the operational and scientific communities. The theme of SEASONS 2014 is "Beyond Climatology," with a focus on how space weather events threaten operational assets and disrupt missions. Here we present perspectives from SEASONS 2014 on new observations, models in development, and forecasting methods that are of interest to the operational space weather community. Highlighted topics include ionospheric data assimilation and forecasting models, HF propagation models, radiation belt observations, and energetic particle modeling. The SEASONS 2014 web site can be found at https://secwww.jhuapl.edu/SEASONS/

Visualization of ocean forecast in BYTHOS

NASA Astrophysics Data System (ADS)

Zhuk, E.; Zodiatis, G.; Nikolaidis, A.; Stylianou, S.; Karaolia, A.

2016-08-01

The Cyprus Oceanography Center has been constantly searching for new ideas for developing and implementing innovative methods and new developments concerning the use of Information Systems in Oceanography, to suit both the Center's monitoring and forecasting products. Within the frame of this scope two major online managing and visualizing data systems have been developed and utilized, those of CYCOFOS and BYTHOS. The Cyprus Coastal Ocean Forecasting and Observing System - CYCOFOS provides a variety of operational predictions such as ultra high, high and medium resolution ocean forecasts in the Levantine Basin, offshore and coastal sea state forecasts in the Mediterranean and Black Sea, tide forecasting in the Mediterranean, ocean remote sensing in the Eastern Mediterranean and coastal and offshore monitoring. As a rich internet application, BYTHOS enables scientists to search, visualize and download oceanographic data online and in real time. The recent improving of BYTHOS system is the extension with access and visualization of CYCOFOS data and overlay forecast fields and observing data. The CYCOFOS data are stored at OPENDAP Server in netCDF format. To search, process and visualize it the php and python scripts were developed. Data visualization is achieved through Mapserver. The BYTHOS forecast access interface allows to search necessary forecasting field by recognizing type, parameter, region, level and time. Also it provides opportunity to overlay different forecast and observing data that can be used for complex analyze of sea basin aspects.

Sub-seasonal-to-seasonal Reservoir Inflow Forecast using Bayesian Hierarchical Hidden Markov Model

NASA Astrophysics Data System (ADS)

Mukhopadhyay, S.; Arumugam, S.

2017-12-01

Sub-seasonal-to-seasonal (S2S) (15-90 days) streamflow forecasting is an emerging area of research that provides seamless information for reservoir operation from weather time scales to seasonal time scales. From an operational perspective, sub-seasonal inflow forecasts are highly valuable as these enable water managers to decide short-term releases (15-30 days), while holding water for seasonal needs (e.g., irrigation and municipal supply) and to meet end-of-the-season target storage at a desired level. We propose a Bayesian Hierarchical Hidden Markov Model (BHHMM) to develop S2S inflow forecasts for the Tennessee Valley Area (TVA) reservoir system. Here, the hidden states are predicted by relevant indices that influence the inflows at S2S time scale. The hidden Markov model also captures the both spatial and temporal hierarchy in predictors that operate at S2S time scale with model parameters being estimated as a posterior distribution using a Bayesian framework. We present our work in two steps, namely single site model and multi-site model. For proof of concept, we consider inflows to Douglas Dam, Tennessee, in the single site model. For multisite model we consider reservoirs in the upper Tennessee valley. Streamflow forecasts are issued and updated continuously every day at S2S time scale. We considered precipitation forecasts obtained from NOAA Climate Forecast System (CFSv2) GCM as predictors for developing S2S streamflow forecasts along with relevant indices for predicting hidden states. Spatial dependence of the inflow series of reservoirs are also preserved in the multi-site model. To circumvent the non-normality of the data, we consider the HMM in a Generalized Linear Model setting. Skill of the proposed approach is tested using split sample validation against a traditional multi-site canonical correlation model developed using the same set of predictors. From the posterior distribution of the inflow forecasts, we also highlight different system behavior under varied global and local scale climatic influences from the developed BHMM.

The Nature and Variability of Ensemble Sensitivity Fields that Diagnose Severe Convection

NASA Astrophysics Data System (ADS)

Ancell, B. C.

2017-12-01

Ensemble sensitivity analysis (ESA) is a statistical technique that uses information from an ensemble of forecasts to reveal relationships between chosen forecast metrics and the larger atmospheric state at various forecast times. A number of studies have employed ESA from the perspectives of dynamical interpretation, observation targeting, and ensemble subsetting toward improved probabilistic prediction of high-impact events, mostly at synoptic scales. We tested ESA using convective forecast metrics at the 2016 HWT Spring Forecast Experiment to understand the utility of convective ensemble sensitivity fields in improving forecasts of severe convection and its individual hazards. The main purpose of this evaluation was to understand the temporal coherence and general characteristics of convective sensitivity fields toward future use in improving ensemble predictability within an operational framework.The magnitude and coverage of simulated reflectivity, updraft helicity, and surface wind speed were used as response functions, and the sensitivity of these functions to winds, temperatures, geopotential heights, and dew points at different atmospheric levels and at different forecast times were evaluated on a daily basis throughout the HWT Spring Forecast experiment. These sensitivities were calculated within the Texas Tech real-time ensemble system, which possesses 42 members that run twice daily to 48-hr forecast time. Here we summarize both the findings regarding the nature of the sensitivity fields and the evaluation of the participants that reflects their opinions of the utility of operational ESA. The future direction of ESA for operational use will also be discussed.

Advancing Data assimilation for Baltic Monitoring and Forecasting Center: implementation and evaluation of HBP-PDAF system

NASA Astrophysics Data System (ADS)

Korabel, Vasily; She, Jun; Huess, Vibeke; Woge Nielsen, Jacob; Murawsky, Jens; Nerger, Lars

2017-04-01

The potential of an efficient data assimilation (DA) scheme to improve model forecast skill was successfully demonstrated by many operational centres around the world. The Baltic-North Sea region is one of the most heavily monitored seas. Ferryboxes, buoys, ADCP moorings, shallow water Argo floats, and research vessels are providing more and more near-real time observations. Coastal altimetry has now providing increasing amount of high resolution sea level observations, which will be significantly expanded by the launch of SWOT satellite in next years. This will turn operational DA into a valuable tool for improving forecast quality in the region. This motivated us to focus on advancing DA for the Baltic Monitoring and Forecasting Centre (BAL MFC) in order to create a common framework for operational data assimilation in the Baltic Sea. We have implemented HBM-PDAF system based on the Parallel Data Assimilation Framework (PDAF), a highly versatile and optimised parallel suit with a choice of sequential schemes originally developed at AWI, and a hydrodynamic HIROMB-BOOS Model (HBM). At initial phase, only the satellite Sea Surface Temperature (SST) Level 3 data has been assimilated. Several related aspects are discussed, including improvements of the forecast quality for both surface and subsurface fields, the estimation of ensemble-based forecast error covariance, as well as possibilities of assimilating new types of observations, such as in-situ salinity and temperature profiles, coastal altimetry, and ice concentration.

Timetable of an operational flood forecasting system

NASA Astrophysics Data System (ADS)

Liechti, Katharina; Jaun, Simon; Zappa, Massimiliano

2010-05-01

At present a new underground part of Zurich main station is under construction. For this purpose the runoff capacity of river Sihl, which is passing beneath the main station, is reduced by 40%. If a flood is to occur the construction site is evacuated and gates can be opened for full runoff capacity to prevent bigger damages. However, flooding the construction site, even if it is controlled, is coupled with costs and retardation. The evacuation of the construction site at Zurich main station takes about 2 to 4 hours and opening the gates takes another 1 to 2 hours each. In the upper part of the 336 km2 Sihl catchment the Sihl lake, a reservoir lake, is situated. It belongs and is used by the Swiss Railway Company for hydropower production. This lake can act as a retention basin for about 46% of the Sihl catchment. Lowering the lake level to gain retention capacity, and therewith safety, is coupled with direct loss for the Railway Company. To calculate the needed retention volume and the water to be released facing unfavourable weather conditions, forecasts with a minimum lead time of 2 to 3 days are needed. Since the catchment is rather small, this can only be realised by the use of meteorological forecast data. Thus the management of the construction site depends on accurate forecasts to base their decisions on. Therefore an operational hydrological ensemble prediction system (HEPS) was introduced in September 2008 by the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). It delivers daily discharge forecasts with a time horizon of 5 days. The meteorological forecasts are provided by MeteoSwiss and stem from the operational limited-area COSMO-LEPS which downscales the ECMWF ensemble prediction system to a spatial resolution of 7 km. Additional meteorological data for model calibration and initialisation (air temperature, precipitation, water vapour pressure, global radiation, wind speed and sunshine duration) and radar data are also provided by MeteoSwiss. Additional meteorological and hydrological observations are provided by a hydropower company, the Canton of Zurich and the Federal Office for the Environment (FOEN). The hydrological forecasting is calculated by the semi-distributed hydrological model PREVAH (Precipitation-Runoff-EVapotranspiration-HRU-related Model) and is further processed by the hydraulic model FLORIS. Finally the forecasts and alerts along with additional meteorological and hydrological observations and forecasts from collaborating institution are sent to a webserver accessible for decision makers. We will document the setup of our operational flood forecasting system, evaluate its performance and show how the collaboration and communication between science and practice, including all the different interests, works for this particular example.

On the forecasting the unfavorable periods in the technosphere by the space weather factors

NASA Astrophysics Data System (ADS)

Lyakhov, N. N.

2002-12-01

There is the considerable progress in development of geomagnetic disturbances forecast technique, in the necessary time, by solar activity phenomena last years. The possible relationship between violations of the traffic safety terms (VTS) in East Siberian Railway during 1986-1999 and the space weather factors was investigated. The overall number of cases under consideration is equal to 11575. By methods of correlation and spectral analysis it was shown, that statistics of VTS has not a random and it's character is probably caused by space weather factors. The principal difference between rhythmic of VTS by purely technical reasons (MECH) (failures in mechanical systems) and, that of VTS caused by wrong operations of a personnel (MAN), is noted. Increase of sudden storm commencements number results in increase of probability of mistakable actions of an operator. Probability of violations in mechanical systems increases with increase of number of quiet geomagnetic conditions. This, in its turn, dictate different approach to the ordered rows of MECH and MAN data when forecasting the unfavourable periods as the priods of increased risk in working out a wrong decision by technological process participants. The advances in forecasting of geomagnetic environment technique made possible to start construction of systems of the operative informing about unfavourable factors of space weather for the interested organizations.

Streamflow Forecasting Using Nuero-Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Nanduri, U. V.; Swain, P. C.

2005-12-01

The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A Neuro-Fuzzy model is developed to forecast ten-daily flows into the Hirakud reservoir on River Mahanadi in the state of Orissa in India. Correlation analysis is carried out to find out the most influential variables on the ten daily flow at Hirakud. Based on this analysis, four variables, namely, flow during the previous time period, ql1, rainfall during the previous two time periods, rl1 and rl2, and flow during the same period in previous year, qpy, are identified as the most influential variables to forecast the ten daily flow. Performance measures such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and coefficient of efficiency R2 are computed for training and testing phases of the model to evaluate its performance. The results indicate that the ten-daily forecasting model is efficient in predicting the high and medium flows with reasonable accuracy. The forecast of low flows is associated with less efficiency. REFERENCES Jang, J.S.R. (1993). "ANFIS: Adaptive - network- based fuzzy inference system." IEEE Trans. on Systems, Man and Cybernetics, 23 (3), 665-685. Shamseldin, A.Y. (1997). "Application of a neural network technique to rainfall-runoff modeling." Journal of Hydrology, 199, 272-294. World Meteorological Organization (1975). Intercomparison of conceptual models used in operational hydrological forecasting. World Meteorological Organization, Technical Report No.429, Geneva, Switzerland.

How do I know if I’ve improved my continental scale flood early warning system?

NASA Astrophysics Data System (ADS)

Cloke, Hannah L.; Pappenberger, Florian; Smith, Paul J.; Wetterhall, Fredrik

2017-04-01

Flood early warning systems mitigate damages and loss of life and are an economically efficient way of enhancing disaster resilience. The use of continental scale flood early warning systems is rapidly growing. The European Flood Awareness System (EFAS) is a pan-European flood early warning system forced by a multi-model ensemble of numerical weather predictions. Responses to scientific and technical changes can be complex in these computationally expensive continental scale systems, and improvements need to be tested by evaluating runs of the whole system. It is demonstrated here that forecast skill is not correlated with the value of warnings. In order to tell if the system has been improved an evaluation strategy is required that considers both forecast skill and warning value. The combination of a multi-forcing ensemble of EFAS flood forecasts is evaluated with a new skill-value strategy. The full multi-forcing ensemble is recommended for operational forecasting, but, there are spatial variations in the optimal forecast combination. Results indicate that optimizing forecasts based on value rather than skill alters the optimal forcing combination and the forecast performance. Also indicated is that model diversity and ensemble size are both important in achieving best overall performance. The use of several evaluation measures that consider both skill and value is strongly recommended when considering improvements to early warning systems.

Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework

NASA Astrophysics Data System (ADS)

Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K.

2015-08-01

Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead time is considered within the day-ahead (Elspot) market of the Nordic exchange market. A complementary modelling framework presents an approach for improving real-time forecasting without needing to modify the pre-existing forecasting model, but instead formulating an independent additive or complementary model that captures the structure the existing operational model may be missing. We present here the application of this principle for issuing improved hourly inflow forecasts into hydropower reservoirs over extended lead times, and the parameter estimation procedure reformulated to deal with bias, persistence and heteroscedasticity. The procedure presented comprises an error model added on top of an unalterable constant parameter conceptual model. This procedure is applied in the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead times up to 17 h. Evaluation of the percentage of observations bracketed in the forecasted 95 % confidence interval indicated that the degree of success in containing 95 % of the observations varies across seasons and hydrologic years.

The use of seasonal forecasts in a crop failure early warning system for West Africa

NASA Astrophysics Data System (ADS)

Nicklin, K. J.; Challinor, A.; Tompkins, A.

2011-12-01

Seasonal rainfall in semi-arid West Africa is highly variable. Farming systems in the region are heavily dependent on the monsoon rains leading to large variability in crop yields and a population that is vulnerable to drought. The existing crop yield forecasting system uses observed weather to calculate a water satisfaction index, which is then related to expected crop yield (Traore et al, 2006). Seasonal climate forecasts may be able to increase the lead-time of yield forecasts and reduce the humanitarian impact of drought. This study assesses the potential for a crop failure early warning system, which uses dynamic seasonal forecasts and a process-based crop model. Two sets of simulations are presented. In the first, the crop model is driven with observed weather as a control run. Observed rainfall is provided by the GPCP 1DD data set, whilst observed temperature and solar radiation data are given by the ERA-Interim reanalysis. The crop model used is the groundnut version of the General Large Area Model for annual crops (GLAM), which has been designed to operate on the grids used by seasonal weather forecasts (Challinor et al, 2004). GLAM is modified for use in West Africa by allowing multiple planting dates each season, replanting failed crops and producing parameter sets for Spanish- and Virginia- type West African groundnut. Crop yields are simulated for three different assumptions concerning the distribution and relative abundance of Spanish- and Virginia- type groundnut. Model performance varies with location, but overall shows positive skill in reproducing observed crop failure. The results for the three assumptions are similar, suggesting that the performance of the system is limited by something other than information on the type of groundnut grown. In the second set of simulations the crop model is driven with observed weather up to the forecast date, followed by ECMWF system 3 seasonal forecasts until harvest. The variation of skill with forecast date is assessed along with the extent to which forecasts can be improved by bias correction of the rainfall data. Two forms of bias correction are applied: a novel method of spatially bias correcting daily data, and statistical bias correction of the frequency and intensity distribution. Results are presented using both observed yields and the control run as the reference for verification. The potential for current dynamic seasonal forecasts to form part of an operational system giving timely and accurate warnings of crop failure is discussed. Traore S.B. et al., 2006. A Review of Agrometeorological Monitoring Tools and Methods Used in the West African Sahel. In: Motha R.P. et al., Strengthening Operational Agrometeorological Services at the National Level. Technical Bulletin WAOB-2006-1 and AGM-9, WMO/TD No. 1277. Pages 209-220. www.wamis.org/agm/pubs/agm9/WMO-TD1277.pdf Challinor A.J. et al., 2004. Design and optimisation of a large-area process based model for annual crops. Agric. For. Meteorol. 124, 99-120.

Operation of a real-time warning system for debris flows in the San Francisco bay area, California

USGS Publications Warehouse

Wilson, Raymond C.; Mark, Robert K.; Barbato, Gary; ,

1993-01-01

The United States Geological Survey (USGS) and the National Weather Service (NWS) have developed an operational warning system for debris flows during severe rainstorms in the San Francisco Bay region. The NWS makes quantitative forecasts of precipitation from storm systems approaching the Bay area and coordinates a regional network of radio-telemetered rain gages. The USGS has formulated thresholds for the intensity and duration of rainfall required to initiate debris flows. The first successful public warnings were issued during a severe storm sequence in February 1986. Continued operation of the warning system since 1986 has provided valuable working experience in rainfall forecasting and monitoring, refined rainfall thresholds, and streamlined procedures for issuing public warnings. Advisory statements issued since 1986 are summarized.

Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system.

PubMed

Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic

2014-06-28

The finite resolution of general circulation models of the coupled atmosphere-ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere-ocean climate system in operational forecast mode, and the latest seasonal forecasting system--System 4--has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981-2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden-Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid-latitude circulation regimes over the Pacific-North America region.

Benchmark analysis of forecasted seasonal temperature over different climatic areas

NASA Astrophysics Data System (ADS)

Giunta, G.; Salerno, R.; Ceppi, A.; Ercolani, G.; Mancini, M.

2015-12-01

From a long-term perspective, an improvement of seasonal forecasting, which is often exclusively based on climatology, could provide a new capability for the management of energy resources in a time scale of just a few months. This paper regards a benchmark analysis in relation to long-term temperature forecasts over Italy in the year 2010, comparing the eni-kassandra meteo forecast (e-kmf®) model, the Climate Forecast System-National Centers for Environmental Prediction (CFS-NCEP) model, and the climatological reference (based on 25-year data) with observations. Statistical indexes are used to understand the reliability of the prediction of 2-m monthly air temperatures with a perspective of 12 weeks ahead. The results show how the best performance is achieved by the e-kmf® system which improves the reliability for long-term forecasts compared to climatology and the CFS-NCEP model. By using the reliable high-performance forecast system, it is possible to optimize the natural gas portfolio and management operations, thereby obtaining a competitive advantage in the European energy market.

An operational coupled wave-current forecasting system for the northern Adriatic Sea

NASA Astrophysics Data System (ADS)

Russo, A.; Coluccelli, A.; Deserti, M.; Valentini, A.; Benetazzo, A.; Carniel, S.

2012-04-01

Since 2005 an Adriatic implementation of the Regional Ocean Modeling System (AdriaROMS) is being producing operational short-term forecasts (72 hours) of some hydrodynamic properties (currents, sea level, temperature, salinity) of the Adriatic Sea at 2 km horizontal resolution and 20 vertical s-levels, on a daily basis. The main objective of AdriaROMS, which is managed by the Hydro-Meteo-Clima Service (SIMC) of ARPA Emilia Romagna, is to provide useful products for civil protection purposes (sea level forecasts, outputs to run other forecasting models as for saline wedge, oil spills and coastal erosion). In order to improve the forecasts in the coastal area, where most of the attention is focused, a higher resolution model (0.5 km, again with 20 vertical s-levels) has been implemented for the northern Adriatic domain. The new implementation is based on the Coupled-Ocean-Atmosphere-Wave-Sediment Transport Modeling System (COAWST)and adopts ROMS for the hydrodynamic and Simulating WAve Nearshore (SWAN) for the wave module, respectively. Air-sea fluxes are computed using forecasts produced by the COSMO-I7 operational atmospheric model. At the open boundary of the high resolution model, temperature, salinity and velocity fields are provided by AdriaROMS while the wave characteristics are provided by an operational SWAN implementation (also managed by SIMC). Main tidal components are imposed as well, derived from a tidal model. Work in progress is oriented now on the validation of model results by means of extensive comparisons with acquired hydrographic measurements (such as CTDs or XBTs from sea-truth campaigns), currents and waves acquired at observational sites (including those of SIMC, CNR-ISMAR network and its oceanographic tower, located off the Venice littoral) and satellite-derived wave-heights data. Preliminary results on the forecast waves denote how, especially during intense storms, the effect of coupling can lead to significant variations in the wave heights. Part of the activity has been funded by the EU FP VII program (project "MICORE", contract n. 202798) and by the Regione Veneto regional law 15/2007 (Progetto "MARINA").

The Impact of STTP on the GEFS Forecast of Week-2 and Beyond in the Presence of Stochastic Physics

NASA Astrophysics Data System (ADS)

Hou, D.

2015-12-01

The Stochastic Total Tendency Perturbation (STTP) scheme was designed to represent the model related uncertainties not considered in the numerical model itself and the physics based stochastic schemes. It has been applied in NCEP's Global Ensemble Forecast System (GEFS) since 2010, showing significant positive impacts on the forecast with improved spread-error ratio and probabilistic forecast skills. The scheme is robust and it went well with the resolution increases and model improvements in 2012 and 2015 with minimum changes. Recently, a set of stochastic physics schemes are coded in the Global Forecast System model and tested in the GEFS package. With these schemes turned on and STTP off, the forecast performance is comparable or even superior to the operational GEFS, in which STTP is the only contributor to the model related uncertainties. This is true especially in week one. However, over the second week and beyond, both the experimental and the operational GEFS has insufficient spread, especially over the warmer seasons. This is a major challenge when the GEFS is extended to sub-seasonal (week 4-6) time scales. The impact of STTP on the GEFS forecast in the presence of stochastic physics is investigated by turning both the stochastic physics schemes and STTP on and carefully tuning their amplitudes. Analysis will be focused on the forecast of extended range, especially week 2. Its impacts on week 3-4 will also be addressed.

Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.

2010-01-01

The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the loadmore » and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. Currently, uncertainties associated with wind and load forecasts, as well as uncertainties associated with random generator outages and unexpected disconnection of supply lines, are not taken into account in power grid operation. Thus, operators have little means to weigh the likelihood and magnitude of upcoming events of power imbalance. In this project, funded by the U.S. Department of Energy (DOE), a framework has been developed for incorporating uncertainties associated with wind and load forecast errors, unpredicted ramps, and forced generation disconnections into the energy management system (EMS) as well as generation dispatch and commitment applications. A new approach to evaluate the uncertainty ranges for the required generation performance envelope including balancing capacity, ramping capability, and ramp duration has been proposed. The approach includes three stages: forecast and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence levels. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis, incorporating all sources of uncertainties of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the “flying brick” technique has been developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation algorithm has been developed to validate the accuracy of the confidence intervals.« less

Stochastic Forcing for High-Resolution Regional and Global Ocean and Atmosphere-Ocean Coupled Ensemble Forecast System

NASA Astrophysics Data System (ADS)

Rowley, C. D.; Hogan, P. J.; Martin, P.; Thoppil, P.; Wei, M.

2017-12-01

An extended range ensemble forecast system is being developed in the US Navy Earth System Prediction Capability (ESPC), and a global ocean ensemble generation capability to represent uncertainty in the ocean initial conditions has been developed. At extended forecast times, the uncertainty due to the model error overtakes the initial condition as the primary source of forecast uncertainty. Recently, stochastic parameterization or stochastic forcing techniques have been applied to represent the model error in research and operational atmospheric, ocean, and coupled ensemble forecasts. A simple stochastic forcing technique has been developed for application to US Navy high resolution regional and global ocean models, for use in ocean-only and coupled atmosphere-ocean-ice-wave ensemble forecast systems. Perturbation forcing is added to the tendency equations for state variables, with the forcing defined by random 3- or 4-dimensional fields with horizontal, vertical, and temporal correlations specified to characterize different possible kinds of error. Here, we demonstrate the stochastic forcing in regional and global ensemble forecasts with varying perturbation amplitudes and length and time scales, and assess the change in ensemble skill measured by a range of deterministic and probabilistic metrics.

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operatormore » can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.« less

Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operatormore » can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.« less

Moving beyond the cost-loss ratio: economic assessment of streamflow forecasts for a risk-averse decision maker

NASA Astrophysics Data System (ADS)

Matte, Simon; Boucher, Marie-Amélie; Boucher, Vincent; Fortier Filion, Thomas-Charles

2017-06-01

A large effort has been made over the past 10 years to promote the operational use of probabilistic or ensemble streamflow forecasts. Numerous studies have shown that ensemble forecasts are of higher quality than deterministic ones. Many studies also conclude that decisions based on ensemble rather than deterministic forecasts lead to better decisions in the context of flood mitigation. Hence, it is believed that ensemble forecasts possess a greater economic and social value for both decision makers and the general population. However, the vast majority of, if not all, existing hydro-economic studies rely on a cost-loss ratio framework that assumes a risk-neutral decision maker. To overcome this important flaw, this study borrows from economics and evaluates the economic value of early warning flood systems using the well-known Constant Absolute Risk Aversion (CARA) utility function, which explicitly accounts for the level of risk aversion of the decision maker. This new framework allows for the full exploitation of the information related to a forecasts' uncertainty, making it especially suited for the economic assessment of ensemble or probabilistic forecasts. Rather than comparing deterministic and ensemble forecasts, this study focuses on comparing different types of ensemble forecasts. There are multiple ways of assessing and representing forecast uncertainty. Consequently, there exist many different means of building an ensemble forecasting system for future streamflow. One such possibility is to dress deterministic forecasts using the statistics of past error forecasts. Such dressing methods are popular among operational agencies because of their simplicity and intuitiveness. Another approach is the use of ensemble meteorological forecasts for precipitation and temperature, which are then provided as inputs to one or many hydrological model(s). In this study, three concurrent ensemble streamflow forecasting systems are compared: simple statistically dressed deterministic forecasts, forecasts based on meteorological ensembles, and a variant of the latter that also includes an estimation of state variable uncertainty. This comparison takes place for the Montmorency River, a small flood-prone watershed in southern central Quebec, Canada. The assessment of forecasts is performed for lead times of 1 to 5 days, both in terms of forecasts' quality (relative to the corresponding record of observations) and in terms of economic value, using the new proposed framework based on the CARA utility function. It is found that the economic value of a forecast for a risk-averse decision maker is closely linked to the forecast reliability in predicting the upper tail of the streamflow distribution. Hence, post-processing forecasts to avoid over-forecasting could help improve both the quality and the value of forecasts.

Improving the Forecast Accuracy of an Ocean Observation and Prediction System by Adaptive Control of the Sensor Network

NASA Astrophysics Data System (ADS)

Talukder, A.; Panangadan, A. V.; Blumberg, A. F.; Herrington, T.; Georgas, N.

2008-12-01

The New York Harbor Observation and Prediction System (NYHOPS) is a real-time, estuarine and coastal ocean observing and modeling system for the New York Harbor and surrounding waters. Real-time measurements from in-situ mobile and stationary sensors in the NYHOPS networks are assimilated into marine forecasts in order to reduce the discrepancy with ground truth. The forecasts are obtained from the ECOMSED hydrodynamic model, a shallow water derivative of the Princeton Ocean Model. Currently, all sensors in the NYHOPS system are operated in a fixed mode with uniform sampling rates. This technology infusion effort demonstrates the use of Model Predictive Control (MPC) to autonomously adapt the operation of both mobile and stationary sensors in response to changing events that are -automatically detected from the ECOMSED forecasts. The controller focuses sensing resources on those regions that are expected to be impacted by the detected events. The MPC approach involves formulating the problem of calculating the optimal sensor parameters as a constrained multi-objective optimization problem. We have developed an objective function that takes into account the spatiotemporal relationship of the in-situ sensor locations and the locations of events detected by the model. Experiments in simulation were carried out using data collected during a freshwater flooding event. The location of the resulting freshwater plume was calculated from the corresponding model forecasts and was used by the MPC controller to derive control parameters for the sensing assets. The operational parameters that are controlled include the sampling rates of stationary sensors, paths of unmanned underwater vehicles (UUVs), and data transfer routes between sensors and the central modeling computer. The simulation experiments show that MPC-based sensor control reduces the RMS error in the forecast by a factor of 380% as compared to uniform sampling. The paths of multiple UUVs were simultaneously calculated such that measurements from on-board sensors would lead to maximal reduction in the forecast error after data assimilation. The MPC controller also reduces the consumption of system resources such as energy expended in sampling and wireless communication. The MPC-based control approach can be generalized to accept data from remote sensing satellites. This will enable in-situ sensors to be regulated using forecasts generated by assimilating local high resolution in-situ measurements with wide-area observations from remote sensing satellites.

Smart Irrigation From Soil Moisture Forecast Using Satellite And Hydro -Meteorological Modelling

NASA Astrophysics Data System (ADS)

Corbari, Chiara; Mancini, Marco; Ravazzani, Giovanni; Ceppi, Alessandro; Salerno, Raffaele; Sobrino, Josè

2017-04-01

Increased water demand and climate change impacts have recently enhanced the need to improve water resources management, even in those areas which traditionally have an abundant supply of water. The highest consumption of water is devoted to irrigation for agricultural production, and so it is in this area that efforts have to be focused to study possible interventions. The SIM project funded by EU in the framework of the WaterWorks2014 - Water Joint Programming Initiative aims at developing an operational tool for real-time forecast of crops irrigation water requirements to support parsimonious water management and to optimize irrigation scheduling providing real-time and forecasted soil moisture behavior at high spatial and temporal resolutions with forecast horizons from few up to thirty days. This study discusses advances in coupling satellite driven soil water balance model and meteorological forecast as support for precision irrigation use comparing different case studies in Italy, in the Netherlands, in China and Spain, characterized by different climatic conditions, water availability, crop types and irrigation techniques and water distribution rules. Herein, the applications in two operative farms in vegetables production in the South of Italy where semi-arid climatic conditions holds, two maize fields in Northern Italy in a more water reach environment with flood irrigation will be presented. This system combines state of the art mathematical models and new technologies for environmental monitoring, merging ground observed data with Earth observations. Discussion on the methodology approach is presented, comparing for a reanalysis periods the forecast system outputs with observed soil moisture and crop water needs proving the reliability of the forecasting system and its benefits. The real-time visualization of the implemented system is also presented through web-dashboards.

Global Real-Time Ocean Forecast System

Science.gov Websites

services. Marine Modeling and Analysis Branch Logo Click here to go to the MMAB home page Global Real-Time 17 Oct 2017 at 0Z, the Global RTOFS model has been upgraded to version 1.1.2. Changes include: The ). The global operational Real-Time Ocean Forecast System (Global RTOFS) at the National Centers for

Determining effective forecast horizons for multi-purpose reservoirs with short- and long-term operating objectives

NASA Astrophysics Data System (ADS)

Luchner, Jakob; Anghileri, Daniela; Castelletti, Andrea

2017-04-01

Real-time control of multi-purpose reservoirs can benefit significantly from hydro-meteorological forecast products. Because of their reliability, the most used forecasts range on time scales from hours to few days and are suitable for short-term operation targets such as flood control. In recent years, hydro-meteorological forecasts have become more accurate and reliable on longer time scales, which are more relevant to long-term reservoir operation targets such as water supply. While the forecast quality of such products has been studied extensively, the forecast value, i.e. the operational effectiveness of using forecasts to support water management, has been only relatively explored. It is comparatively easy to identify the most effective forecasting information needed to design reservoir operation rules for flood control but it is not straightforward to identify which forecast variable and lead time is needed to define effective hedging rules for operational targets with slow dynamics such as water supply. The task is even more complex when multiple targets, with diverse slow and fast dynamics, are considered at the same time. In these cases, the relative importance of different pieces of information, e.g. magnitude and timing of peak flow rate and accumulated inflow on different time lags, may vary depending on the season or the hydrological conditions. In this work, we analyze the relationship between operational forecast value and streamflow forecast horizon for different multi-purpose reservoir trade-offs. We use the Information Selection and Assessment (ISA) framework to identify the most effective forecast variables and horizons for informing multi-objective reservoir operation over short- and long-term temporal scales. The ISA framework is an automatic iterative procedure to discriminate the information with the highest potential to improve multi-objective reservoir operating performance. Forecast variables and horizons are selected using a feature selection technique. The technique determines the most informative combination in a multi-variate regression model to the optimal reservoir releases based on perfect information at a fixed objective trade-off. The improved reservoir operation is evaluated against optimal reservoir operation conditioned upon perfect information on future disturbances and basic reservoir operation using only the day of the year and the reservoir level. Different objective trade-offs are selected for analyzing resulting differences in improved reservoir operation and selected forecast variables and horizons. For comparison, the effective streamflow forecast horizon determined by the ISA framework is benchmarked against the performances obtained with a deterministic model predictive control (MPC) optimization scheme. Both the ISA framework and the MPC optimization scheme are applied to the real-world case study of Lake Como, Italy, using perfect streamflow forecast information. The principal operation targets for Lake Como are flood control and downstream water supply which makes its operation a suitable case study. Results provide critical feedback to reservoir operators on the use of long-term streamflow forecasts and to the hydro-meteorological forecasting community with respect to the forecast horizon needed from reliable streamflow forecasts.

A Gaussian Processes Technique for Short-term Load Forecasting with Considerations of Uncertainty

NASA Astrophysics Data System (ADS)

Ohmi, Masataro; Mori, Hiroyuki

In this paper, an efficient method is proposed to deal with short-term load forecasting with the Gaussian Processes. Short-term load forecasting plays a key role to smooth power system operation such as economic load dispatching, unit commitment, etc. Recently, the deregulated and competitive power market increases the degree of uncertainty. As a result, it is more important to obtain better prediction results to save the cost. One of the most important aspects is that power system operator needs the upper and lower bounds of the predicted load to deal with the uncertainty while they require more accurate predicted values. The proposed method is based on the Bayes model in which output is expressed in a distribution rather than a point. To realize the model efficiently, this paper proposes the Gaussian Processes that consists of the Bayes linear model and kernel machine to obtain the distribution of the predicted value. The proposed method is successively applied to real data of daily maximum load forecasting.

Development of visibility forecasting modeling framework for the Lower Fraser Valley of British Columbia using Canada's Regional Air Quality Deterministic Prediction System.

PubMed

So, Rita; Teakles, Andrew; Baik, Jonathan; Vingarzan, Roxanne; Jones, Keith

2018-05-01

Visibility degradation, one of the most noticeable indicators of poor air quality, can occur despite relatively low levels of particulate matter when the risk to human health is low. The availability of timely and reliable visibility forecasts can provide a more comprehensive understanding of the anticipated air quality conditions to better inform local jurisdictions and the public. This paper describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada's operational Regional Air Quality Deterministic Prediction System (RAQDPS) for the Lower Fraser Valley of British Columbia. A baseline model (GM-IMPROVE) was constructed using the revised IMPROVE algorithm based on unprocessed forecasts from the RAQDPS. Three additional prototypes (UMOS-HYB, GM-MLR, GM-RF) were also developed and assessed for forecast performance of up to 48 hr lead time during various air quality and meteorological conditions. Forecast performance was assessed by examining their ability to provide both numerical and categorical forecasts in the form of 1-hr total extinction and Visual Air Quality Ratings (VAQR), respectively. While GM-IMPROVE generally overestimated extinction more than twofold, it had skill in forecasting the relative species contribution to visibility impairment, including ammonium sulfate and ammonium nitrate. Both statistical prototypes, GM-MLR and GM-RF, performed well in forecasting 1-hr extinction during daylight hours, with correlation coefficients (R) ranging from 0.59 to 0.77. UMOS-HYB, a prototype based on postprocessed air quality forecasts without additional statistical modeling, provided reasonable forecasts during most daylight hours. In terms of categorical forecasts, the best prototype was approximately 75 to 87% correct, when forecasting for a condensed three-category VAQR. A case study, focusing on a poor visual air quality yet low Air Quality Health Index episode, illustrated that the statistical prototypes were able to provide timely and skillful visibility forecasts with lead time up to 48 hr. This study describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada's operational Regional Air Quality Deterministic Prediction System. The main applications include tourism and recreation planning, input into air quality management programs, and educational outreach. Visibility forecasts, when supplemented with the existing air quality and health based forecasts, can assist jurisdictions to anticipate the visual air quality impacts as perceived by the public, which can potentially assist in formulating the appropriate air quality bulletins and recommendations.

Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2015-09-01

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

Test operation of a real-time tsunami inundation forecast system using actual data observed by S-net

NASA Astrophysics Data System (ADS)

Suzuki, W.; Yamamoto, N.; Miyoshi, T.; Aoi, S.

2017-12-01

If the tsunami inundation information can be rapidly and stably forecast before the large tsunami attacks, the information would have effectively people realize the impeding danger and necessity of evacuation. Toward that goal, we have developed a prototype system to perform the real-time tsunami inundation forecast for Chiba prefecture, eastern Japan, using off-shore ocean bottom pressure data observed by the seafloor observation network for earthquakes and tsunamis along the Japan Trench (S-net) (Aoi et al., 2015, AGU). Because tsunami inundation simulation requires a large computation cost, we employ a database approach searching the pre-calculated tsunami scenarios that reasonably explain the observed S-net pressure data based on the multi-index method (Yamamoto et al., 2016, EPS). The scenario search is regularly repeated, not triggered by the occurrence of the tsunami event, and the forecast information is generated from the selected scenarios that meet the criterion. Test operation of the prototype system using the actual observation data started in April, 2017 and the performance and behavior of the system during non-tsunami event periods have been examined. It is found that the treatment of the noises affecting the observed data is the main issue to be solved toward the improvement of the system. Even if the observed pressure data are filtered to extract the tsunami signals, the noises in ordinary times or unusually large noises like high ocean waves due to storm affect the comparison between the observed and scenario data. Due to the noises, the tsunami scenarios are selected and the tsunami is forecast although any tsunami event does not actually occur. In most cases, the selected scenarios due to the noises have the fault models in the region along the Kurile or Izu-Bonin Trenches, far from the S-net region, or the fault models below the land. Based on the parallel operation of the forecast system with a different scenario search condition and examination of the fault models, we improve the stability and performance of the forecast system.This work was supported by Council for Science, Technology and Innovation(CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), "Enhancement of societal resiliency against natural disasters"(Funding agency: JST).

Assimilation and High Resolution Forecasts of Surface and Near Surface Conditions for the 2010 Vancouver Winter Olympic and Paralympic Games

NASA Astrophysics Data System (ADS)

Bernier, Natacha B.; Bélair, Stéphane; Bilodeau, Bernard; Tong, Linying

2014-01-01

A dynamical model was experimentally implemented to provide high resolution forecasts at points of interests in the 2010 Vancouver Olympics and Paralympics Region. In a first experiment, GEM-Surf, the near surface and land surface modeling system, is driven by operational atmospheric forecasts and used to refine the surface forecasts according to local surface conditions such as elevation and vegetation type. In this simple form, temperature and snow depth forecasts are improved mainly as a result of the better representation of real elevation. In a second experiment, screen level observations and operational atmospheric forecasts are blended to drive a continuous cycle of near surface and land surface hindcasts. Hindcasts of the previous day conditions are then regarded as today's optimized initial conditions. Hence, in this experiment, given observations are available, observation driven hindcasts continuously ensure that daily forecasts are issued from improved initial conditions. GEM-Surf forecasts obtained from improved short-range hindcasts produced using these better conditions result in improved snow depth forecasts. In a third experiment, assimilation of snow depth data is applied to further optimize GEM-Surf's initial conditions, in addition to the use of blended observations and forecasts for forcing. Results show that snow depth and summer temperature forecasts are further improved by the addition of snow depth data assimilation.

Ecological Forecasting of Vibrio sp. in U.S. Coastal Waters Using an Operational Platform, a Pilot Project of the NOAA Ecological Forecasting Roadmap. Development of Web based Tools and Forecasts to Help the Public Avoid Exposure to Vibrio vulnificus and Shell Fish Harvesters Avoid Dangerous Concentrations of Vibrio parahaemolyticus.

NASA Astrophysics Data System (ADS)

Daniels, R. M.; Jacobs, J. M.; Paranjpye, R.; Lanerolle, L. W.

2016-02-01

The Pathogens group of the NOAA Ecological Forecasting Roadmap has begun a range of efforts to monitor and predict potential pathogen occurrences in shellfish and in U.S. Coastal waters. NOAA/NCOSS along with NMFS/NWFSC have led the Pathogens group and the development of web based tools and forecasts for both Vibrio vulnificus and Vibrio parahaemolyticus. A strong relationship with FDA has allowed the team to develop forecasts that will serve U.S. shellfish harvesters and consumers. NOAA/NOS/CSDL has provided modeling expertise to help the group use the hydrodynamic models and their forecasts of physical variables that drive the ecological predictions. The NOAA/NWS/Ocean Prediction Center has enabled these ecological forecasting efforts by providing the infrastructure, computing knowledge and experience in an operational culture. Daily forecasts have been demonstrated and are available from the web for the Chesapeake Bay, Delaware Bay, Northern Gulf of Mexico, Tampa Bay, Puget Sound and Long Island Sound. The forecast systems run on a daily basis being fed by NOS model data from the NWS/NCEP super computers. New forecast tools including V. parahaemolyticus post harvest growth and doubling time in ambient air temperature will be described.

Application and verification of ECMWF seasonal forecast for wind energy

NASA Astrophysics Data System (ADS)

Žagar, Mark; Marić, Tomislav; Qvist, Martin; Gulstad, Line

2015-04-01

A good understanding of long-term annual energy production (AEP) is crucial when assessing the business case of investing in green energy like wind power. The art of wind-resource assessment has emerged into a scientific discipline on its own, which has advanced at high pace over the last decade. This has resulted in continuous improvement of the AEP accuracy and, therefore, increase in business case certainty. Harvesting the full potential output of a wind farm or a portfolio of wind farms depends heavily on optimizing operation and management strategy. The necessary information for short-term planning (up to 14 days) is provided by standard weather and power forecasting services, and the long-term plans are based on climatology. However, the wind-power industry is lacking quality information on intermediate scales of the expected variability in seasonal and intra-annual variations and their geographical distribution. The seasonal power forecast presented here is designed to bridge this gap. The seasonal power production forecast is based on the ECMWF seasonal weather forecast and the Vestas' high-resolution, mesoscale weather library. The seasonal weather forecast is enriched through a layer of statistical post-processing added to relate large-scale wind speed anomalies to mesoscale climatology. The resulting predicted energy production anomalies, thus, include mesoscale effects not captured by the global forecasting systems. The turbine power output is non-linearly related to the wind speed, which has important implications for the wind power forecast. In theory, the wind power is proportional to the cube of wind speed. However, due to the nature of turbine design, this exponent is close to 3 only at low wind speeds, becomes smaller as the wind speed increases, and above 11-13 m/s the power output remains constant, called the rated power. The non-linear relationship between wind speed and the power output generally increases sensitivity of the forecasted power to the wind speed anomalies. On the other hand, in some cases and areas where turbines operate close to, or above the rated power, the sensitivity of power forecast is reduced. Thus, the seasonal power forecasting system requires good knowledge of the changes in frequency of events with sufficient wind speeds to have acceptable skill. The scientific background for the Vestas seasonal power forecasting system is described and the relationship between predicted monthly wind speed anomalies and observed wind energy production are investigated for a number of operating wind farms in different climate zones. Current challenges will be discussed and some future research and development areas identified.

A multiscale forecasting method for power plant fleet management

NASA Astrophysics Data System (ADS)

Chen, Hongmei

In recent years the electric power industry has been challenged by a high level of uncertainty and volatility brought on by deregulation and globalization. A power producer must minimize the life cycle cost while meeting stringent safety and regulatory requirements and fulfilling customer demand for high reliability. Therefore, to achieve true system excellence, a more sophisticated system-level decision-making process with a more accurate forecasting support system to manage diverse and often widely dispersed generation units as a single, easily scaled and deployed fleet system in order to fully utilize the critical assets of a power producer has been created as a response. The process takes into account the time horizon for each of the major decision actions taken in a power plant and develops methods for information sharing between them. These decisions are highly interrelated and no optimal operation can be achieved without sharing information in the overall process. The process includes a forecasting system to provide information for planning for uncertainty. A new forecasting method is proposed, which utilizes a synergy of several modeling techniques properly combined at different time-scales of the forecasting objects. It can not only take advantages of the abundant historical data but also take into account the impact of pertinent driving forces from the external business environment to achieve more accurate forecasting results. Then block bootstrap is utilized to measure the bias in the estimate of the expected life cycle cost which will actually be needed to drive the business for a power plant in the long run. Finally, scenario analysis is used to provide a composite picture of future developments for decision making or strategic planning. The decision-making process is applied to a typical power producer chosen to represent challenging customer demand during high-demand periods. The process enhances system excellence by providing more accurate market information, evaluating the impact of external business environment, and considering cross-scale interactions between decision actions. Along with this process, system operation strategies, maintenance schedules, and capacity expansion plans that guide the operation of the power plant are optimally identified, and the total life cycle costs are estimated.

Evaluating the improvements of the BOLAM meteorological model operational at ISPRA: A case study approach - preliminary results

NASA Astrophysics Data System (ADS)

Mariani, S.; Casaioli, M.; Lastoria, B.; Accadia, C.; Flavoni, S.

2009-04-01

The Institute for Environmental Protection and Research - ISPRA (former Agency for Environmental Protection and Technical Services - APAT) runs operationally since 2000 an integrated meteo-marine forecasting chain, named the Hydro-Meteo-Marine Forecasting System (Sistema Idro-Meteo-Mare - SIMM), formed by a cascade of four numerical models, telescoping from the Mediterranean basin to the Venice Lagoon, and initialized by means of analyses and forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). The operational integrated system consists of a meteorological model, the parallel verision of BOlogna Limited Area Model (BOLAM), coupled over the Mediterranean sea with a WAve Model (WAM), a high-resolution shallow-water model of the Adriatic and Ionian Sea, namely the Princeton Ocean Model (POM), and a finite-element version of the same model (VL-FEM) on the Venice Lagoon, aimed to forecast the acqua alta events. Recently, the physically based, fully distributed, rainfall-runoff TOPographic Kinematic APproximation and Integration (TOPKAPI) model has been integrated into the system, coupled to BOLAM, over two river basins, located in the central and northeastern part of Italy, respectively. However, at the present time, this latter part of the forecasting chain is not operational and it is used in a research configuration. BOLAM was originally implemented in 2000 onto the Quadrics parallel supercomputer (and for this reason referred to as QBOLAM, as well) and only at the end of 2006 it was ported (together with the other operational marine models of the forecasting chain) onto the Silicon Graphics Inc. (SGI) Altix 8-processor machine. In particular, due to the Quadrics implementation, the Kuo scheme was formerly implemented into QBOLAM for the cumulus convection parameterization. On the contrary, when porting SIMM onto the Altix Linux cluster, it was achievable to implement into QBOLAM the more advanced convection parameterization by Kain and Fritsch. A fully updated serial version of the BOLAM code has been recently acquired. Code improvements include a more precise advection scheme (Weighted Average Flux); explicit advection of five hydrometeors, and state-of-the-art parameterization schemes for radiation, convection, boundary layer turbulence and soil processes (also with possible choice among different available schemes). The operational implementation of the new code into the SIMM model chain, which requires the development of a parallel version, will be achieved during 2009. In view of this goal, the comparative verification of the different model versions' skill represents a fundamental task. On this purpose, it has been decided to evaluate the performance improvement of the new BOLAM code (in the available serial version, hereinafter BOLAM 2007) with respect to the version with the Kain-Fritsch scheme (hereinafter KF version) and to the older one employing the Kuo scheme (hereinafter Kuo version). In the present work, verification of precipitation forecasts from the three BOLAM versions is carried on in a case study approach. The intense rainfall episode occurred on 10th - 17th December 2008 over Italy has been considered. This event produced indeed severe damages in Rome and its surrounding areas. Objective and subjective verification methods have been employed in order to evaluate model performance against an observational dataset including rain gauge observations and satellite imagery. Subjective comparison of observed and forecast precipitation fields is suitable to give an overall description of the forecast quality. Spatial errors (e.g., shifting and pattern errors) and rainfall volume error can be assessed quantitatively by means of object-oriented methods. By comparing satellite images with model forecast fields, it is possible to investigate the differences between the evolution of the observed weather system and the predicted ones, and its sensitivity to the improvements in the model code. Finally, the error in forecasting the cyclone evolution can be tentatively related with the precipitation forecast error.

Integrated Drought Monitoring and Forecasts for Decision Making in Water and Agricultural Sectors over the Southeastern US under Changing Climate

NASA Astrophysics Data System (ADS)

Arumugam, S.; Mazrooei, A.; Ward, R.

2017-12-01

Changing climate arising from structured oscillations such as ENSO and rising temperature poses challenging issues in meeting the increasing water demand (due to population growth) for public supply and agriculture over the Southeast US. This together with infrastructural (e.g., most reservoirs being within-year systems) and operational (e.g., static rule curves) constraints requires an integrated approach that seamlessly monitors and forecasts water and soil moisture conditions to support adaptive decision making in water and agricultural sectors. In this talk, we discuss the utility of an integrated drought management portal that both monitors and forecasts streamflow and soil moisture over the southeast US. The forecasts are continuously developed and updated by forcing monthly-to-seasonal climate forecasts with a land surface model for various target basins. The portal also houses a reservoir allocation model that allows water managers to explore different release policies in meeting the system constraints and target storages conditioned on the forecasts. The talk will also demonstrate how past events (e.g., 2007-2008 drought) could be proactively monitored and managed to improve decision making in water and agricultural sectors over the Southeast US. Challenges in utilizing the portal information from institutional and operational perspectives will also be presented.

Two-stage seasonal streamflow forecasts to guide water resources decisions and water rights allocation

NASA Astrophysics Data System (ADS)

Block, P. J.; Gonzalez, E.; Bonnafous, L.

2011-12-01

Decision-making in water resources is inherently uncertain producing copious risks, ranging from operational (present) to planning (season-ahead) to design/adaptation (decadal) time-scales. These risks include human activity and climate variability/change. As the risks in designing and operating water systems and allocating available supplies vary systematically in time, prospects for predicting and managing such risks become increasingly attractive. Considerable effort has been undertaken to improve seasonal forecast skill and advocate for integration to reduce risk, however only minimal adoption is evident. Impediments are well defined, yet tailoring forecast products and allowing for flexible adoption assist in overcoming some obstacles. The semi-arid Elqui River basin in Chile is contending with increasing levels of water stress and demand coupled with insufficient investment in infrastructure, taxing its ability to meet agriculture, hydropower, and environmental requirements. The basin is fed from a retreating glacier, with allocation principles founded on a system of water rights and markets. A two-stage seasonal streamflow forecast at leads of one and two seasons prescribes the probability of reductions in the value of each water right, allowing water managers to inform their constituents in advance. A tool linking the streamflow forecast to a simple reservoir decision model also allows water managers to select a level of confidence in the forecast information.

Assimilation of the seabird and ship drift data in the north-eastern sea of Japan into an operational ocean nowcast/forecast system

PubMed Central

Miyazawa, Yasumasa; Guo, Xinyu; Varlamov, Sergey M.; Miyama, Toru; Yoda, Ken; Sato, Katsufumi; Kano, Toshiyuki; Sato, Keiji

2015-01-01

At the present time, ocean current is being operationally monitored mainly by combined use of numerical ocean nowcast/forecast models and satellite remote sensing data. Improvement in the accuracy of the ocean current nowcast/forecast requires additional measurements with higher spatial and temporal resolution as expected from the current observation network. Here we show feasibility of assimilating high-resolution seabird and ship drift data into an operational ocean forecast system. Data assimilation of geostrophic current contained in the observed drift leads to refinement in the gyre mode events of the Tsugaru warm current in the north-eastern sea of Japan represented by the model. Fitting the observed drift to the model depends on ability of the drift representing geostrophic current compared to that representing directly wind driven components. A preferable horizontal scale of 50 km indicated for the seabird drift data assimilation implies their capability of capturing eddies with smaller horizontal scale than the minimum scale of 100 km resolved by the satellite altimetry. The present study actually demonstrates that transdisciplinary approaches combining bio-/ship- logging and numerical modeling could be effective for enhancement in monitoring the ocean current. PMID:26633309

Analysis and Synthesis of Load Forecasting Data for Renewable Integration Studies: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steckler, N.; Florita, A.; Zhang, J.

2013-11-01

As renewable energy constitutes greater portions of the generation fleet, the importance of modeling uncertainty as part of integration studies also increases. In pursuit of optimal system operations, it is important to capture not only the definitive behavior of power plants, but also the risks associated with systemwide interactions. This research examines the dependence of load forecast errors on external predictor variables such as temperature, day type, and time of day. The analysis was utilized to create statistically relevant instances of sequential load forecasts with only a time series of historic, measured load available. The creation of such load forecastsmore » relies on Bayesian techniques for informing and updating the model, thus providing a basis for networked and adaptive load forecast models in future operational applications.« less

Study on Battery Capacity for Grid-connection Power Planning with Forecasts in Clustered Photovoltaic Systems

NASA Astrophysics Data System (ADS)

Shimada, Takae; Kawasaki, Norihiro; Ueda, Yuzuru; Sugihara, Hiroyuki; Kurokawa, Kosuke

This paper aims to clarify the battery capacity required by a residential area with densely grid-connected photovoltaic (PV) systems. This paper proposes a planning method of tomorrow's grid-connection power from/to the external electric power system by using demand power forecasting and insolation forecasting for PV power predictions, and defines a operation method of the electricity storage device to control the grid-connection power as planned. A residential area consisting of 389 houses consuming 2390 MWh/year of electricity with 2390kW PV systems is simulated based on measured data and actual forecasts. The simulation results show that 8.3MWh of battery capacity is required in the conditions of half-hour planning and 1% or less of planning error ratio and PV output limiting loss ratio. The results also show that existing technologies of forecasting reduce required battery capacity to 49%, and increase the allowable installing PV amount to 210%.

Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system

PubMed Central

Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic

2014-01-01

The finite resolution of general circulation models of the coupled atmosphere–ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere–ocean climate system in operational forecast mode, and the latest seasonal forecasting system—System 4—has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981–2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden–Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid-latitude circulation regimes over the Pacific–North America region. PMID:24842026

Integrated Forecast-Decision Systems For River Basin Planning and Management

NASA Astrophysics Data System (ADS)

Georgakakos, A. P.

2005-12-01

A central application of climatology, meteorology, and hydrology is the generation of reliable forecasts for water resources management. In principle, effective use of forecasts could improve water resources management by providing extra protection against floods, mitigating the adverse effects of droughts, generating more hydropower, facilitating recreational activities, and minimizing the impacts of extreme events on the environment and the ecosystems. In practice, however, realization of these benefits depends on three requisite elements. First is the skill and reliability of forecasts. Second is the existence of decision support methods/systems with the ability to properly utilize forecast information. And third is the capacity of the institutional infrastructure to incorporate the information provided by the decision support systems into the decision making processes. This presentation discusses several decision support systems (DSS) using ensemble forecasting that have been developed by the Georgia Water Resources Institute for river basin management. These DSS are currently operational in Africa, Europe, and the US and address integrated water resources and energy planning and management in river basins with multiple water uses, multiple relevant temporal and spatial scales, and multiple decision makers. The article discusses the methods used and advocates that the design, development, and implementation of effective forecast-decision support systems must bring together disciplines, people, and institutions necessary to address today's complex water resources challenges.

Implementation and Research on the Operational Use of the Mesoscale Prediction Model COAMPS in Poland

DTIC Science & Technology

2007-09-30

COAMPS model. Bogumil Jakubiak, University of Warsaw – participated in EGU General Assembly , Vienna Austria 15-20 April 2007 giving one oral and two...conditional forecast (background) error probability density function using an ensemble of the model forecast to generate background error statistics...COAMPS system on ICM machines at Warsaw University for the purpose of providing operational support to the general public using the ICM meteorological

Operational Earthquake Forecasting and Earthquake Early Warning: The Challenges of Introducing Scientific Innovations for Public Safety

NASA Astrophysics Data System (ADS)

Goltz, J. D.

2016-12-01

Although variants of both earthquake early warning and short-term operational earthquake forecasting systems have been implemented or are now being implemented in some regions and nations, they have been slow to gain acceptance within the disciplines that produced them as well as among those for whom they were intended to assist. To accelerate the development and implementation of these technologies will require the cooperation and collaboration of multiple disciplines, some inside and others outside of academia. Seismologists, social scientists, emergency managers, elected officials and key opinion leaders from the media and public must be the participants in this process. Representatives of these groups come from both inside and outside of academia and represent very different organizational cultures, backgrounds and expectations for these systems, sometimes leading to serious disagreements and impediments to further development and implementation. This presentation will focus on examples of the emergence of earthquake early warning and operational earthquake forecasting systems in California, Japan and other regions and document the challenges confronted in the ongoing effort to improve seismic safety.

Methodology for Designing Operational Banking Risks Monitoring System

NASA Astrophysics Data System (ADS)

Kostjunina, T. N.

2018-05-01

The research looks at principles of designing an information system for monitoring operational banking risks. A proposed design methodology enables one to automate processes of collecting data on information security incidents in the banking network, serving as the basis for an integrated approach to the creation of an operational risk management system. The system can operate remotely ensuring tracking and forecasting of various operational events in the bank network. A structure of a content management system is described.

Wind Energy Management System Integration Project Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.

2010-09-01

The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation) and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and windmore » forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. In order to improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively, by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. In this report, a new methodology to predict the uncertainty ranges for the required balancing capacity, ramping capability and ramp duration is presented. Uncertainties created by system load forecast errors, wind and solar forecast errors, generation forced outages are taken into account. The uncertainty ranges are evaluated for different confidence levels of having the actual generation requirements within the corresponding limits. The methodology helps to identify system balancing reserve requirement based on a desired system performance levels, identify system “breaking points”, where the generation system becomes unable to follow the generation requirement curve with the user-specified probability level, and determine the time remaining to these potential events. The approach includes three stages: statistical and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence intervals. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis incorporating all sources of uncertainty and parameters of a continuous (wind forecast and load forecast errors) and discrete (forced generator outages and failures to start up) nature. Preliminary simulations using California Independent System Operator (California ISO) real life data have shown the effectiveness of the proposed approach. A tool developed based on the new methodology described in this report will be integrated with the California ISO systems. Contractual work is currently in place to integrate the tool with the AREVA EMS system.« less

Towards uncertainty estimates in global operational forecasts of trace gases in the Copernicus Atmosphere Monitoring System

NASA Astrophysics Data System (ADS)

Huijnen, V.; Bouarar, I.; Chabrillat, S. H.; Christophe, Y.; Thierno, D.; Karydis, V.; Marecal, V.; Pozzer, A.; Flemming, J.

2017-12-01

Operational atmospheric composition analyses and forecasts such as developed in the Copernicus Atmosphere Monitoring Service (CAMS) rely on modules describing emissions, chemical conversion, transport and removal processing, as well as data assimilation methods. The CAMS forecasts can be used to drive regional air quality models across the world. Critical analyses of uncertainties in any of these processes are continuously needed to advance the quality of such systems on a global scale, ranging from the surface up to the stratosphere. With regard to the atmospheric chemistry to describe the fate of trace gases, the operational system currently relies on a modified version of the CB05 chemistry scheme for the troposphere combined with the Cariolle scheme to describe stratospheric ozone, as integrated in ECMWF's Integrated Forecasting System (IFS). It is further constrained by assimilation of satellite observations of CO, O3 and NO2. As part of CAMS we have recently developed three fully independent schemes to describe the chemical conversion throughout the atmosphere. These parameterizations originate from parent model codes in MOZART, MOCAGE and a combination of TM5/BASCOE. In this contribution we evaluate the correspondence and elemental differences in the performance of the three schemes in an otherwise identical model configuration (excluding data-assimilation) against a large range of in-situ and satellite-based observations of ozone, CO, VOC's and chlorine-containing trace gases for both troposphere and stratosphere. This analysis aims to provide a measure of model uncertainty in the operational system for tracers that are not, or poorly, constrained by data assimilation. It aims also to provide guidance on the directions for further model improvement with regard to the chemical conversion module.

Impact of Moist Physics Complexity on Tropical Cyclone Simulations from the Hurricane Weather Research and Forecast System

NASA Astrophysics Data System (ADS)

Kalina, E. A.; Biswas, M.; Newman, K.; Grell, E. D.; Bernardet, L.; Frimel, J.; Carson, L.

2017-12-01

The parameterization of moist physics in numerical weather prediction models plays an important role in modulating tropical cyclone structure, intensity, and evolution. The Hurricane Weather Research and Forecast system (HWRF), the National Oceanic and Atmospheric Administration's operational model for tropical cyclone prediction, uses the Scale-Aware Simplified Arakawa-Schubert (SASAS) cumulus scheme and a modified version of the Ferrier-Aligo (FA) microphysics scheme to parameterize moist physics. The FA scheme contains a number of simplifications that allow it to run efficiently in an operational setting, which includes prescribing values for hydrometeor number concentrations (i.e., single-moment microphysics) and advecting the total condensate rather than the individual hydrometeor species. To investigate the impact of these simplifying assumptions on the HWRF forecast, the FA scheme was replaced with the more complex double-moment Thompson microphysics scheme, which individually advects cloud ice, cloud water, rain, snow, and graupel. Retrospective HWRF forecasts of tropical cyclones that occurred in the Atlantic and eastern Pacific ocean basins from 2015-2017 were then simulated and compared to those produced by the operational HWRF configuration. Both traditional model verification metrics (i.e., tropical cyclone track and intensity) and process-oriented metrics (e.g., storm size, precipitation structure, and heating rates from the microphysics scheme) will be presented and compared. The sensitivity of these results to the cumulus scheme used (i.e., the operational SASAS versus the Grell-Freitas scheme) also will be examined. Finally, the merits of replacing the moist physics schemes that are used operationally with the alternatives tested here will be discussed from a standpoint of forecast accuracy versus computational resources.

A Wind Forecasting System for Energy Application

NASA Astrophysics Data System (ADS)

Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

2010-05-01

Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated probabilistic wind forecasts which will be invaluable in wind energy management. In brief, this method turns the ensemble forecasts into a calibrated predictive probability distribution. Each ensemble member is provided with a 'weight' determined by its relative predictive skill over a training period of around 30 days. Verification of data is carried out using observed wind data from operational wind farms. These are then compared to existing forecasts produced by ECMWF and Met Eireann in relation to skill scores. We are developing decision-making models to show the benefits achieved using the data produced by our wind energy forecasting system. An energy trading model will be developed, based on the rules currently used by the Single Electricity Market Operator for energy trading in Ireland. This trading model will illustrate the potential for financial savings by using the forecast data generated by this research.

The Navy's First Seasonal Ice Forecasts using the Navy's Arctic Cap Nowcast/Forecast System

NASA Astrophysics Data System (ADS)

Preller, Ruth

2013-04-01

As conditions in the Arctic continue to change, the Naval Research Laboratory (NRL) has developed an interest in longer-term seasonal ice extent forecasts. The Arctic Cap Nowcast/Forecast System (ACNFS), developed by the Oceanography Division of NRL, was run in forward model mode, without assimilation, to estimate the minimum sea ice extent for September 2012. The model was initialized with varying assimilative ACNFS analysis fields (June 1, July 1, August 1 and September 1, 2012) and run forward for nine simulations using the archived Navy Operational Global Atmospheric Prediction System (NOGAPS) atmospheric forcing fields from 2003-2011. The mean ice extent in September, averaged across all ensemble members was the projected summer ice extent. These results were submitted to the Study of Environmental Arctic Change (SEARCH) Sea Ice Outlook project (http://www.arcus.org/search/seaiceoutlook). The ACNFS is a ~3.5 km coupled ice-ocean model that produces 5 day forecasts of the Arctic sea ice state in all ice covered areas in the northern hemisphere (poleward of 40° N). The ocean component is the HYbrid Coordinate Ocean Model (HYCOM) and is coupled to the Los Alamos National Laboratory Community Ice CodE (CICE) via the Earth System Modeling Framework (ESMF). The ocean and ice models are run in an assimilative cycle with the Navy's Coupled Ocean Data Assimilation (NCODA) system. Currently the ACNFS is being transitioned to operations at the Naval Oceanographic Office.

Analysis of data systems requirements for global crop production forecasting in the 1985 time frame

NASA Technical Reports Server (NTRS)

Downs, S. W.; Larsen, P. A.; Gerstner, D. A.

1978-01-01

Data systems concepts that would be needed to implement the objective of the global crop production forecasting in an orderly transition from experimental to operational status in the 1985 time frame were examined. Information needs of users were converted into data system requirements, and the influence of these requirements on the formulation of a conceptual data system was analyzed. Any potential problem areas in meeting these data system requirements were identified in an iterative process.

Use of MODIS Cloud Top Pressure to Improve Assimilation Yields of AIRS Radiances in GSI

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi

2014-01-01

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA's Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Previously, it has been shown that cloud top designation associated with quality control procedures within the Gridpoint Statistical Interpolation (GSI) system used operationally by a number of Joint Center for Satellite Data Assimilation (JCSDA) partners may not provide the best representation of cloud top pressure (CTP). Because this designated CTP determines which channels are cloud-free and, thus, available for assimilation, ensuring the most accurate representation of this value is imperative to obtaining the greatest impact from satellite radiances. This paper examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing analysis increments and numerical forecasts generated using operational techniques with a research technique that swaps CTP from the Moderate-resolution Imaging Spectroradiometer (MODIS) for the value of CTP calculated from the radiances within GSI.

Initializing numerical weather prediction models with satellite-derived surface soil moisture: Data assimilation experiments with ECMWF's Integrated Forecast System and the TMI soil moisture data set

NASA Astrophysics Data System (ADS)

Drusch, M.

2007-02-01

Satellite-derived surface soil moisture data sets are readily available and have been used successfully in hydrological applications. In many operational numerical weather prediction systems the initial soil moisture conditions are analyzed from the modeled background and 2 m temperature and relative humidity. This approach has proven its efficiency to improve surface latent and sensible heat fluxes and consequently the forecast on large geographical domains. However, since soil moisture is not always related to screen level variables, model errors and uncertainties in the forcing data can accumulate in root zone soil moisture. Remotely sensed surface soil moisture is directly linked to the model's uppermost soil layer and therefore is a stronger constraint for the soil moisture analysis. For this study, three data assimilation experiments with the Integrated Forecast System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) have been performed for the 2-month period of June and July 2002: a control run based on the operational soil moisture analysis, an open loop run with freely evolving soil moisture, and an experimental run incorporating TMI (TRMM Microwave Imager) derived soil moisture over the southern United States. In this experimental run the satellite-derived soil moisture product is introduced through a nudging scheme using 6-hourly increments. Apart from the soil moisture analysis, the system setup reflects the operational forecast configuration including the atmospheric 4D-Var analysis. Soil moisture analyzed in the nudging experiment is the most accurate estimate when compared against in situ observations from the Oklahoma Mesonet. The corresponding forecast for 2 m temperature and relative humidity is almost as accurate as in the control experiment. Furthermore, it is shown that the soil moisture analysis influences local weather parameters including the planetary boundary layer height and cloud coverage.

Multivariate time series modeling of short-term system scale irrigation demand

NASA Astrophysics Data System (ADS)

Perera, Kushan C.; Western, Andrew W.; George, Biju; Nawarathna, Bandara

2015-12-01

Travel time limits the ability of irrigation system operators to react to short-term irrigation demand fluctuations that result from variations in weather, including very hot periods and rainfall events, as well as the various other pressures and opportunities that farmers face. Short-term system-wide irrigation demand forecasts can assist in system operation. Here we developed a multivariate time series (ARMAX) model to forecast irrigation demands with respect to aggregated service points flows (IDCGi, ASP) and off take regulator flows (IDCGi, OTR) based across 5 command areas, which included area covered under four irrigation channels and the study area. These command area specific ARMAX models forecast 1-5 days ahead daily IDCGi, ASP and IDCGi, OTR using the real time flow data recorded at the service points and the uppermost regulators and observed meteorological data collected from automatic weather stations. The model efficiency and the predictive performance were quantified using the root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), anomaly correlation coefficient (ACC) and mean square skill score (MSSS). During the evaluation period, NSE for IDCGi, ASP and IDCGi, OTR across 5 command areas were ranged 0.98-0.78. These models were capable of generating skillful forecasts (MSSS ⩾ 0.5 and ACC ⩾ 0.6) of IDCGi, ASP and IDCGi, OTR for all 5 lead days and IDCGi, ASP and IDCGi, OTR forecasts were better than using the long term monthly mean irrigation demand. Overall these predictive performance from the ARMAX time series models were higher than almost all the previous studies we are aware. Further, IDCGi, ASP and IDCGi, OTR forecasts have improved the operators' ability to react for near future irrigation demand fluctuations as the developed ARMAX time series models were self-adaptive to reflect the short-term changes in the irrigation demand with respect to various pressures and opportunities that farmers' face, such as changing water policy, continued development of water markets, drought and changing technology.

Developments of the European Flood Awareness System (EFAS)

NASA Astrophysics Data System (ADS)

Thiemig, Vera; Olav Skøien, Jon; Salamon, Peter; Pappenberger, Florian; Wetterhall, Fredrik; Holst, Bo; Asp, Sara-Sophia; Garcia Padilla, Mercedes; Garcia, Rafael J.; Schweim, Christoph; Ziese, Markus

2017-04-01

EFAS (http://www.efas.eu) is an operational system for flood forecasting and early warning for the entire Europe, which is fully operational as part of the Copernicus Emergency Management Service since 2012. The prime aim of EFAS is to gain time for preparedness measures before major flood events - particularly in trans-national river basins - strike. This is achieved by providing complementary, added value information to the national and regional services holding the mandate for flood warning as well as to the ERCC (European Response and Coordination Centre). Using a coherent model for all of Europe forced with a range of deterministic and ensemble weather forecasts, the system can give a probabilistic flood forecast for a medium range lead time (up to 10 days) independent of country borders. The system is under continuous development, and we will present the basic set up, some prominent examples of recent and ongoing developments (such as the rapid impact assessment, seasonal outlook and the extended domain) and the future challenges.

Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 2. Data assimilation

NASA Astrophysics Data System (ADS)

Benedetti, A.; Morcrette, J.-J.; Boucher, O.; Dethof, A.; Engelen, R. J.; Fisher, M.; Flentje, H.; Huneeus, N.; Jones, L.; Kaiser, J. W.; Kinne, S.; Mangold, A.; Razinger, M.; Simmons, A. J.; Suttie, M.

2009-07-01

This study presents the new aerosol assimilation system, developed at the European Centre for Medium-Range Weather Forecasts, for the Global and regional Earth-system Monitoring using Satellite and in-situ data (GEMS) project. The aerosol modeling and analysis system is fully integrated in the operational four-dimensional assimilation apparatus. Its purpose is to produce aerosol forecasts and reanalyses of aerosol fields using optical depth data from satellite sensors. This paper is the second of a series which describes the GEMS aerosol effort. It focuses on the theoretical architecture and practical implementation of the aerosol assimilation system. It also provides a discussion of the background errors and observations errors for the aerosol fields, and presents a subset of results from the 2-year reanalysis which has been run for 2003 and 2004 using data from the Moderate Resolution Imaging Spectroradiometer on the Aqua and Terra satellites. Independent data sets are used to show that despite some compromises that have been made for feasibility reasons in regards to the choice of control variable and error characteristics, the analysis is very skillful in drawing to the observations and in improving the forecasts of aerosol optical depth.

Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of offline modeling errors

NASA Astrophysics Data System (ADS)

Marti, Alejandro; Folch, Arnau

2018-03-01

Volcanic ash modeling systems are used to simulate the atmospheric dispersion of volcanic ash and to generate forecasts that quantify the impacts from volcanic eruptions on infrastructures, air quality, aviation, and climate. The efficiency of response and mitigation actions is directly associated with the accuracy of the volcanic ash cloud detection and modeling systems. Operational forecasts build on offline coupled modeling systems in which meteorological variables are updated at the specified coupling intervals. Despite the concerns from other communities regarding the accuracy of this strategy, the quantification of the systematic errors and shortcomings associated with the offline modeling systems has received no attention. This paper employs the NMMB-MONARCH-ASH model to quantify these errors by employing different quantitative and categorical evaluation scores. The skills of the offline coupling strategy are compared against those from an online forecast considered to be the best estimate of the true outcome. Case studies are considered for a synthetic eruption with constant eruption source parameters and for two historical events, which suitably illustrate the severe aviation disruptive effects of European (2010 Eyjafjallajökull) and South American (2011 Cordón Caulle) volcanic eruptions. Evaluation scores indicate that systematic errors due to the offline modeling are of the same order of magnitude as those associated with the source term uncertainties. In particular, traditional offline forecasts employed in operational model setups can result in significant uncertainties, failing to reproduce, in the worst cases, up to 45-70 % of the ash cloud of an online forecast. These inconsistencies are anticipated to be even more relevant in scenarios in which the meteorological conditions change rapidly in time. The outcome of this paper encourages operational groups responsible for real-time advisories for aviation to consider employing computationally efficient online dispersal models.

Advanced Intelligent System Application to Load Forecasting and Control for Hybrid Electric Bus

NASA Technical Reports Server (NTRS)

Momoh, James; Chattopadhyay, Deb; Elfayoumy, Mahmoud

1996-01-01

The primary motivation for this research emanates from providing a decision support system to the electric bus operators in the municipal and urban localities which will guide the operators to maintain an optimal compromise among the noise level, pollution level, fuel usage etc. This study is backed up by our previous studies on study of battery characteristics, permanent magnet DC motor studies and electric traction motor size studies completed in the first year. The operator of the Hybrid Electric Car must determine optimal power management schedule to meet a given load demand for different weather and road conditions. The decision support system for the bus operator comprises three sub-tasks viz. forecast of the electrical load for the route to be traversed divided into specified time periods (few minutes); deriving an optimal 'plan' or 'preschedule' based on the load forecast for the entire time-horizon (i.e., for all time periods) ahead of time; and finally employing corrective control action to monitor and modify the optimal plan in real-time. A fully connected artificial neural network (ANN) model is developed for forecasting the kW requirement for hybrid electric bus based on inputs like climatic conditions, passenger load, road inclination, etc. The ANN model is trained using back-propagation algorithm employing improved optimization techniques like projected Lagrangian technique. The pre-scheduler is based on a Goal-Programming (GP) optimization model with noise, pollution and fuel usage as the three objectives. GP has the capability of analyzing the trade-off among the conflicting objectives and arriving at the optimal activity levels, e.g., throttle settings. The corrective control action or the third sub-task is formulated as an optimal control model with inputs from the real-time data base as well as the GP model to minimize the error (or deviation) from the optimal plan. These three activities linked with the ANN forecaster proving the output to the GP model which in turn produces the pre-schedule of the optimal control model. Some preliminary results based on a hypothetical test case will be presented for the load forecasting module. The computer codes for the three modules will be made available fe adoption by bus operating agencies. Sample results will be provided using these models. The software will be a useful tool for supporting the control systems for the Electric Bus project of NASA.

An operational wave forecasting system for the east coast of India

NASA Astrophysics Data System (ADS)

Sandhya, K. G.; Murty, P. L. N.; Deshmukh, Aditya N.; Balakrishnan Nair, T. M.; Shenoi, S. S. C.

2018-03-01

Demand for operational ocean state forecasting is increasing, owing to the ever-increasing marine activities in the context of blue economy. In the present study, an operational wave forecasting system for the east coast of India is proposed using unstructured Simulating WAves Nearshore model (UNSWAN). This modelling system uses very high resolution mesh near the Indian east coast and coarse resolution offshore, and thus avoids the necessity of nesting with a global wave model. The model is forced with European Centre for Medium-Range Weather Forecasts (ECMWF) winds and simulates wave parameters and wave spectra for the next 3 days. The spatial pictures of satellite data overlaid on simulated wave height show that the model is capable of simulating the significant wave heights and their gradients realistically. Spectral validation has been done using the available data to prove the reliability of the model. To further evaluate the model performance, the wave forecast for the entire year 2014 is evaluated against buoy measurements over the region at 4 waverider buoy locations. Seasonal analysis of significant wave height (Hs) at the four locations showed that the correlation between the modelled and observed was the highest (in the range 0.78-0.96) during the post-monsoon season. The variability of Hs was also the highest during this season at all locations. The error statistics showed clear seasonal and geographical location dependence. The root mean square error at Visakhapatnam was the same (0.25) for all seasons, but it was the smallest for pre-monsoon season (0.12 m and 0.17 m) for Puducherry and Gopalpur. The wind sea component showed higher variability compared to the corresponding swell component in all locations and for all seasons. The variability was picked by the model to a reasonable level in most of the cases. The results of statistical analysis show that the modelling system is suitable for use in the operational scenario.

Benefits of Sharing Information: Supermodel Ensemble and Applications in South America

NASA Astrophysics Data System (ADS)

Dias, P. L.

2006-05-01

A model intercomparison program involving a large number of academic and operational institutions has been implemented in South America since 2003, motivated by the SALLJEX Intercomparison Program in 2003 (a research program focused on the identification of the role of the Andes low level jet moisture transport from the Amazon to the Plata basin) and the WMO/THORPEX (www.wmo.int/thorpex) goals to improve predictability through the proper combination of numerical weather forecasts. This program also explores the potential predictability associated with the combination of a large number of possible scenarios in the time scale of a few days to up to 15 days. Five academic institutions and five operational forecasting centers in several countries in South America, 1 academic institution in the USA, and the main global forecasting centers (NCEP, UKMO, ECMWF) agreed to provide numerical products based on operational and experimental models. The metric for model validation is concentrated on the fit of the forecast to surface observations. Meteorological data from airports, synoptic stations operated by national weather services, automatic data platforms maintained by different institutions, the PIRATA buoys etc are all collected through LDM/NCAR or direct transmission. Approximately 40 models outputs are available on a daily basis, twice a day. A simple procedure based on data assimilation principles was quite successful in combining the available forecasts in order to produce temperature, dew point, wind, pressure and precipitation forecasts at station points in S. America. The procedure is based on removing each model bias at the observational point and a weighted average based on the mean square error of the forecasts. The base period for estimating the bias and mean square error is of the order of 15 to 30 days. Products of the intercomparison model program and the optimal statistical combination of the available forecasts are public and available in real time (www.master.iag.usp.br/). Monitoring of the use of the products reveal a growing trend in the last year (reaching about 10.000 accesses per day in recent months). The intercomparison program provides a rich data set for educational products (real time use in Synoptic Meteorology and Numerical Weather Forecasting lectures), operational weather forecasts in national or regional weather centers and for research purposes. During the first phase of the program it was difficult to convince potential participants to share the information in the public homepage. However, as the system evolved, more and more institutions became associated with the program. The general opinion of the participants is that the system provides an unified metric for evaluation, a forum for discussion of the physical origin of the model forecast differences and therefore improvement of the quality of the numerical guidance.

An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): skill, case studies and scenarios

NASA Astrophysics Data System (ADS)

Addor, N.; Jaun, S.; Zappa, M.

2011-01-01

The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This models chain relies on limited-area atmospheric forecasts provided by the deterministic model COSMO-7 and the probabilistic model COSMO-LEPS. These atmospheric forecasts are used to force a semi-distributed hydrological model (PREVAH), coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework to compare the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added-value conveyed by the probability information, a reforecast was made for the period June 2007 to December 2009 for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain can be of up to 2 days lead time for the catchment considered. Brier skill scores show that COSMO-LEPS-based hydrological forecasts overall outperform their COSMO-7 based counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts, as shown by comparisons with a reference run driven by observed meteorological parameters. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. The two most intense events of the study period are investigated utilising a novel graphical representation of probability forecasts and used to generate high discharge scenarios. They highlight challenges for making decisions on the basis of hydrological predictions, and indicate the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment.

Community Coordinated Modeling Center: Paving the Way for Progress in Space Science Research to Operational Space Weather Forecasting

NASA Astrophysics Data System (ADS)

Kuznetsova, M. M.; Maddox, M. M.; Mays, M. L.; Mullinix, R.; MacNeice, P. J.; Pulkkinen, A. A.; Rastaetter, L.; Shim, J.; Taktakishvili, A.; Zheng, Y.; Wiegand, C.

2013-12-01

Community Coordinated Modeling Center (CCMC) was established at the dawn of the millennium as an essential element on the National Space Weather Program. One of the CCMC goals was to pave the way for progress in space science research to operational space weather forecasting. Over the years the CCMC acquired the unique experience in preparing complex models and model chains for operational environment, in developing and maintaining powerful web-based tools and systems ready to be used by space weather service providers and decision makers as well as in space weather prediction capabilities assessments. The presentation will showcase latest innovative solutions for space weather research, analysis, forecasting and validation and review on-going community-wide initiatives enabled by CCMC applications.

A coupled human-natural system to assess the operational value of weather and climate services for agriculture

NASA Astrophysics Data System (ADS)

Li, Yu; Giuliani, Matteo; Castelletti, Andrea

2017-09-01

Recent advances in weather and climate (W&C) services are showing increasing forecast skills over seasonal and longer timescales, potentially providing valuable support in informing decisions in a variety of economic sectors. Quantifying this value, however, might not be straightforward as better forecast quality does not necessarily imply better decisions by the end users, especially when forecasts do not reach their final users, when providers are not trusted, or when forecasts are not appropriately understood. In this study, we contribute an assessment framework to evaluate the operational value of W&C services for informing agricultural practices by complementing traditional forecast quality assessments with a coupled human-natural system behavioural model which reproduces farmers' decisions. This allows a more critical assessment of the forecast value mediated by the end users' perspective, including farmers' risk attitudes and behavioural factors. The application to an agricultural area in northern Italy shows that the quality of state-of-the-art W&C services is still limited in predicting the weather and the crop yield of the incoming agricultural season, with ECMWF annual products simulated by the IFS/HOPE model resulting in the most skillful product in the study area. However, we also show that the accuracy of estimating crop yield and the probability of making optimal decisions are not necessarily linearly correlated, with the overall assessment procedure being strongly impacted by the behavioural attitudes of farmers, which can produce rank reversals in the quantification of the W&C services operational value depending on the different perceptions of risk and uncertainty.

Improving River Flow Predictions from the NOAA NCRFC Forecasting Model by Incorporating Satellite Observations

NASA Astrophysics Data System (ADS)

Tuttle, S. E.; Jacobs, J. M.; Restrepo, P. J.; Deweese, M. M.; Connelly, B.; Buan, S.

2016-12-01

The NOAA National Weather Service North Central River Forecast Center (NCRFC) is responsible for issuing river flow forecasts for parts of the Upper Mississippi, Great Lakes, and Hudson Bay drainages, including the Red River of the North basin (RRB). The NCRFC uses an operational hydrologic modeling infrastructure called the Community Hydrologic Prediction System (CHPS) for its operational forecasts, which currently links the SNOW-17 snow accumulation and ablation model, to the Sacramento-Soil Moisture Accounting (SAC-SMA) rainfall-runoff model, to a number of hydrologic and hydraulic flow routing models. The operational model is lumped and requires only area-averaged precipitation and air temperature as inputs. NCRFC forecasters use observational data of hydrological state variables as a source of supplemental information during forecasting, and can use professional judgment to modify the model states in real time. In a few recent years (e.g. 2009, 2013), the RRB exhibited unexpected anomalous hydrologic behavior, resulting in overestimation of peak flood discharge by up to 70% and highlighting the need for observations with high temporal and spatial coverage. Unfortunately, observations of hydrological states (e.g. soil moisture, snow water equivalent (SWE)) are relatively scarce in the RRB. Satellite remote sensing can fill this need. We use Minnesota's Buffalo River watershed within the RRB as a test case and update the operational CHPS model using modifications based on satellite observations, including AMSR-E SWE and SMOS soil moisture estimates. We evaluate the added forecasting skill of the satellite-enhanced model compared to measured streamflow using hindcasts from 2010-2013.

A Comparative Verification of Forecasts from Two Operational Solar Wind Models

DTIC Science & Technology

2010-12-16

knowing how much confidence to place on predicted parameters. Cost /benefit information is provided to administrators who decide to sustain or...components of the magnetic field vector in the geocentric solar magnetospheric (GSM) coordinate system at each hour of forecast time. For an example of a

National Centers for Environmental Prediction

Science.gov Websites

Operational Forecast Graphics Experimental Forecast Graphics Verification and Diagnostics Model Configuration consists of the following components: - The NOAA Environmental Modeling System (NEMS) version of the Non updates for the 12 km parent domain and the 3 km CONUS/Alaska nests. The non-cycled nests (Hawaii, Puerto

Operational Earthquake Forecasting: Proposed Guidelines for Implementation (Invited)

NASA Astrophysics Data System (ADS)

Jordan, T. H.

2010-12-01

The goal of operational earthquake forecasting (OEF) is to provide the public with authoritative information about how seismic hazards are changing with time. During periods of high seismic activity, short-term earthquake forecasts based on empirical statistical models can attain nominal probability gains in excess of 100 relative to the long-term forecasts used in probabilistic seismic hazard analysis (PSHA). Prospective experiments are underway by the Collaboratory for the Study of Earthquake Predictability (CSEP) to evaluate the reliability and skill of these seismicity-based forecasts in a variety of tectonic environments. How such information should be used for civil protection is by no means clear, because even with hundredfold increases, the probabilities of large earthquakes typically remain small, rarely exceeding a few percent over forecasting intervals of days or weeks. Civil protection agencies have been understandably cautious in implementing formal procedures for OEF in this sort of “low-probability environment.” Nevertheless, the need to move more quickly towards OEF has been underscored by recent experiences, such as the 2009 L’Aquila earthquake sequence and other seismic crises in which an anxious public has been confused by informal, inconsistent earthquake forecasts. Whether scientists like it or not, rising public expectations for real-time information, accelerated by the use of social media, will require civil protection agencies to develop sources of authoritative information about the short-term earthquake probabilities. In this presentation, I will discuss guidelines for the implementation of OEF informed by my experience on the California Earthquake Prediction Evaluation Council, convened by CalEMA, and the International Commission on Earthquake Forecasting, convened by the Italian government following the L’Aquila disaster. (a) Public sources of information on short-term probabilities should be authoritative, scientific, open, and timely, and they need to convey the epistemic uncertainties in the operational forecasts. (b) Earthquake probabilities should be based on operationally qualified, regularly updated forecasting systems. All operational procedures should be rigorously reviewed by experts in the creation, delivery, and utility of earthquake forecasts. (c) The quality of all operational models should be evaluated for reliability and skill by retrospective testing, and the models should be under continuous prospective testing in a CSEP-type environment against established long-term forecasts and a wide variety of alternative, time-dependent models. (d) Short-term models used in operational forecasting should be consistent with the long-term forecasts used in PSHA. (e) Alert procedures should be standardized to facilitate decisions at different levels of government and among the public, based in part on objective analysis of costs and benefits. (f) In establishing alert procedures, consideration should also be made of the less tangible aspects of value-of-information, such as gains in psychological preparedness and resilience. Authoritative statements of increased risk, even when the absolute probability is low, can provide a psychological benefit to the public by filling information vacuums that can lead to informal predictions and misinformation.

Evaluation of flash-flood discharge forecasts in complex terrain using precipitation

USGS Publications Warehouse

Yates, D.; Warner, T.T.; Brandes, E.A.; Leavesley, G.H.; Sun, Jielun; Mueller, C.K.

2001-01-01

Operational prediction of flash floods produced by thunderstorm (convective) precipitation in mountainous areas requires accurate estimates or predictions of the precipitation distribution in space and time. The details of the spatial distribution are especially critical in complex terrain because the watersheds are generally small in size, and small position errors in the forecast or observed placement of the precipitation can distribute the rain over the wrong watershed. In addition to the need for good precipitation estimates and predictions, accurate flood prediction requires a surface-hydrologic model that is capable of predicting stream or river discharge based on the precipitation-rate input data. Different techniques for the estimation and prediction of convective precipitation will be applied to the Buffalo Creek, Colorado flash flood of July 1996, where over 75 mm of rain from a thunderstorm fell on the watershed in less than 1 h. The hydrologic impact of the precipitation was exacerbated by the fact that a significant fraction of the watershed experienced a wildfire approximately two months prior to the rain event. Precipitation estimates from the National Weather Service's operational Weather Surveillance Radar-Doppler 1988 and the National Center for Atmospheric Research S-band, research, dual-polarization radar, colocated to the east of Denver, are compared. In addition, very short range forecasts from a convection-resolving dynamic model, which is initialized variationally using the radar reflectivity and Doppler winds, are compared with forecasts from an automated-algorithmic forecast system that also employs the radar data. The radar estimates of rain rate, and the two forecasting systems that employ the radar data, have degraded accuracy by virtue of the fact that they are applied in complex terrain. Nevertheless, the radar data and forecasts from the dynamic model and the automated algorithm could be operationally useful for input to surface-hydrologic models employed for flood warning. Precipitation data provided by these various techniques at short time scales and at fine spatial resolutions are employed as detailed input to a distributed-parameter hydrologic model for flash-flood prediction and analysis. With the radar-based precipitation estimates employed as input, the simulated flood discharge was similar to that observed. The dynamic-model precipitation forecast showed the most promise in providing a significant discharge-forecast lead time. The algorithmic system's precipitation forecast did not demonstrate as much skill, but the associated discharge forecast would still have been sufficient to have provided an alert of impending flood danger.

A cross impact methodology for the assessment of US telecommunications system with application to fiber optics development: Executive summary

NASA Technical Reports Server (NTRS)

Martino, J. P.; Lenz, R. C., Jr.; Chen, K. L.

1979-01-01

A cross impact model of the U.S. telecommunications system was developed. For this model, it was necessary to prepare forecasts of the major segments of the telecommunications system, such as satellites, telephone, TV, CATV, radio broadcasting, etc. In addition, forecasts were prepared of the traffic generated by a variety of new or expanded services, such as electronic check clearing and point of sale electronic funds transfer. Finally, the interactions among the forecasts were estimated (the cross impacts). Both the forecasts and the cross impacts were used as inputs to the cross impact model, which could then be used to stimulate the future growth of the entire U.S. telecommunications system. By varying the inputs, technology changes or policy decisions with regard to any segment of the system could be evaluated in the context of the remainder of the system. To illustrate the operation of the model, a specific study was made of the deployment of fiber optics, throughout the telecommunications system.

A cross impact methodology for the assessment of US telecommunications system with application to fiber optics development, volume 1

NASA Technical Reports Server (NTRS)

Martino, J. P.; Lenz, R. C., Jr.; Chen, K. L.; Kahut, P.; Sekely, R.; Weiler, J.

1979-01-01

A cross impact model of the U.S. telecommunications system was developed. It was necessary to prepare forecasts of the major segments of the telecommunications system, such as satellites, telephone, TV, CATV, radio broadcasting, etc. In addition, forecasts were prepared of the traffic generated by a variety of new or expanded services, such as electronic check clearing and point of sale electronic funds transfer. Finally, the interactions among the forecasts were estimated (the cross impact). Both the forecasts and the cross impacts were used as inputs to the cross impact model, which could then be used to stimulate the future growth of the entire U.S. telecommunications system. By varying the inputs, technology changes or policy decisions with regard to any segment of the system could be evaluated in the context of the remainder of the system. To illustrate the operation of the model, a specific study was made of the deployment of fiber optics throughout the telecommunications system.

Ensemble Bayesian forecasting system Part I: Theory and algorithms

NASA Astrophysics Data System (ADS)

Herr, Henry D.; Krzysztofowicz, Roman

2015-05-01

The ensemble Bayesian forecasting system (EBFS), whose theory was published in 2001, is developed for the purpose of quantifying the total uncertainty about a discrete-time, continuous-state, non-stationary stochastic process such as a time series of stages, discharges, or volumes at a river gauge. The EBFS is built of three components: an input ensemble forecaster (IEF), which simulates the uncertainty associated with random inputs; a deterministic hydrologic model (of any complexity), which simulates physical processes within a river basin; and a hydrologic uncertainty processor (HUP), which simulates the hydrologic uncertainty (an aggregate of all uncertainties except input). It works as a Monte Carlo simulator: an ensemble of time series of inputs (e.g., precipitation amounts) generated by the IEF is transformed deterministically through a hydrologic model into an ensemble of time series of outputs, which is next transformed stochastically by the HUP into an ensemble of time series of predictands (e.g., river stages). Previous research indicated that in order to attain an acceptable sampling error, the ensemble size must be on the order of hundreds (for probabilistic river stage forecasts and probabilistic flood forecasts) or even thousands (for probabilistic stage transition forecasts). The computing time needed to run the hydrologic model this many times renders the straightforward simulations operationally infeasible. This motivates the development of the ensemble Bayesian forecasting system with randomization (EBFSR), which takes full advantage of the analytic meta-Gaussian HUP and generates multiple ensemble members after each run of the hydrologic model; this auxiliary randomization reduces the required size of the meteorological input ensemble and makes it operationally feasible to generate a Bayesian ensemble forecast of large size. Such a forecast quantifies the total uncertainty, is well calibrated against the prior (climatic) distribution of predictand, possesses a Bayesian coherence property, constitutes a random sample of the predictand, and has an acceptable sampling error-which makes it suitable for rational decision making under uncertainty.

Operational 0-3 h probabilistic quantitative precipitation forecasts: Recent performance and potential enhancements

NASA Astrophysics Data System (ADS)

Sokol, Z.; Kitzmiller, D.; Pešice, P.; Guan, S.

2009-05-01

The NOAA National Weather Service has maintained an automated, centralized 0-3 h prediction system for probabilistic quantitative precipitation forecasts since 2001. This advective-statistical system (ADSTAT) produces probabilities that rainfall will exceed multiple threshold values up to 50 mm at some location within a 40-km grid box. Operational characteristics and development methods for the system are described. Although development data were stratified by season and time of day, ADSTAT utilizes only a single set of nation-wide equations that relate predictor variables derived from radar reflectivity, lightning, satellite infrared temperatures, and numerical prediction model output to rainfall occurrence. A verification study documented herein showed that the operational ADSTAT reliably models regional variations in the relative frequency of heavy rain events. This was true even in the western United States, where no regional-scale, gridded hourly precipitation data were available during the development period in the 1990s. An effort was recently launched to improve the quality of ADSTAT forecasts by regionalizing the prediction equations and to adapt the model for application in the Czech Republic. We have experimented with incorporating various levels of regional specificity in the probability equations. The geographic localization study showed that in the warm season, regional climate differences and variations in the diurnal temperature cycle have a marked effect on the predictor-predictand relationships, and thus regionalization would lead to better statistical reliability in the forecasts.

AROME-Arctic: New operational NWP model for the Arctic region

NASA Astrophysics Data System (ADS)

Süld, Jakob; Dale, Knut S.; Myrland, Espen; Batrak, Yurii; Homleid, Mariken; Valkonen, Teresa; Seierstad, Ivar A.; Randriamampianina, Roger

2016-04-01

In the frame of the EU-funded project ACCESS (Arctic Climate Change, Economy and Society), MET Norway aimed 1) to describe the present monitoring and forecasting capabilities in the Arctic; and 2) to identify the key factors limiting the forecasting capabilities and to give recommendations on key areas to improve the forecasting capabilities in the Arctic. We have observed that the NWP forecast quality is lower in the Arctic than in the regions further south. Earlier research indicated that one of the factors behind this is the composition of the observing system in the Arctic, in particular the scarceness of conventional observations. To further assess possible strategies for alleviating the situation and propose scenarios for a future Arctic observing system, we have performed a set of experiments to gain a more detailed insight in the contribution of the components of the present observing system in a regional state-of-the-art non-hydrostatic NWP model using the AROME physics (Seity et al, 2011) at 2.5 km horizontal resolution - AROME-Arctic. Our observing system experiment studies showed that conventional observations (Synop, Buoys) can play an important role in correcting the surface state of the model, but prove that the present upper-air conventional (Radiosondes, Aircraft) observations in the area are too scarce to have a significant effect on forecasts. We demonstrate that satellite sounding data play an important role in improving forecast quality. This is the case with satellite temperature sounding data (AMSU-A, IASI), as well as with the satellite moisture sounding data (AMSU-B/MHS, IASI). With these sets of observations, the AROME-Arctic clearly performs better in forecasting extreme events, like for example polar lows. For more details see presentation by Randriamampianina et al. in this session. The encouraging performance of AROME-Arctic lead us to implement it with more observations and improved settings into daily runs with the objective to substitute our actual operational Arctic mesoscale HIRLAM (High Resolution Limited Area Model) NWP model. This presentation will discuss in detail the operational implementation of the AROME-Arctic model together with post-processing methods. Aimed services in the Arctic region covered by the model, such as online weather forecasting (yr.no) and tracking of polar lows (barentswatch.no), is also included.

The Tracking Meteogram, an AWIPS II Tool for Time-Series Analysis

NASA Technical Reports Server (NTRS)

Burks, Jason Eric; Sperow, Ken

2015-01-01

A new tool has been developed for the National Weather Service (NWS) Advanced Weather Interactive Processing System (AWIPS) II through collaboration between NASA's Short-term Prediction Research and Transition (SPoRT) and the NWS Meteorological Development Laboratory (MDL). Referred to as the "Tracking Meteogram", the tool aids NWS forecasters in assessing meteorological parameters associated with moving phenomena. The tool aids forecasters in severe weather situations by providing valuable satellite and radar derived trends such as cloud top cooling rates, radial velocity couplets, reflectivity, and information from ground-based lightning networks. The Tracking Meteogram tool also aids in synoptic and mesoscale analysis by tracking parameters such as the deepening of surface low pressure systems, changes in surface or upper air temperature, and other properties. The tool provides a valuable new functionality and demonstrates the flexibility and extensibility of the NWS AWIPS II architecture. In 2014, the operational impact of the tool was formally evaluated through participation in the NOAA/NWS Operations Proving Ground (OPG), a risk reduction activity to assess performance and operational impact of new forecasting concepts, tools, and applications. Performance of the Tracking Meteogram Tool during the OPG assessment confirmed that it will be a valuable asset to the operational forecasters. This presentation reviews development of the Tracking Meteogram tool, performance and feedback acquired during the OPG activity, and future goals for continued support and extension to other application areas.

Coastal ocean forecasting with an unstructured grid model in the southern Adriatic and northern Ionian seas

NASA Astrophysics Data System (ADS)

Federico, Ivan; Pinardi, Nadia; Coppini, Giovanni; Oddo, Paolo; Lecci, Rita; Mossa, Michele

2017-01-01

SANIFS (Southern Adriatic Northern Ionian coastal Forecasting System) is a coastal-ocean operational system based on the unstructured grid finite-element three-dimensional hydrodynamic SHYFEM model, providing short-term forecasts. The operational chain is based on a downscaling approach starting from the large-scale system for the entire Mediterranean Basin (MFS, Mediterranean Forecasting System), which provides initial and boundary condition fields to the nested system. The model is configured to provide hydrodynamics and active tracer forecasts both in open ocean and coastal waters of southeastern Italy using a variable horizontal resolution from the open sea (3-4 km) to coastal areas (50-500 m). Given that the coastal fields are driven by a combination of both local (also known as coastal) and deep-ocean forcings propagating along the shelf, the performance of SANIFS was verified both in forecast and simulation mode, first (i) on the large and shelf-coastal scales by comparing with a large-scale survey CTD (conductivity-temperature-depth) in the Gulf of Taranto and then (ii) on the coastal-harbour scale (Mar Grande of Taranto) by comparison with CTD, ADCP (acoustic doppler current profiler) and tide gauge data. Sensitivity tests were performed on initialization conditions (mainly focused on spin-up procedures) and on surface boundary conditions by assessing the reliability of two alternative datasets at different horizontal resolution (12.5 and 6.5 km). The SANIFS forecasts at a lead time of 1 day were compared with the MFS forecasts, highlighting that SANIFS is able to retain the large-scale dynamics of MFS. The large-scale dynamics of MFS are correctly propagated to the shelf-coastal scale, improving the forecast accuracy (+17 % for temperature and +6 % for salinity compared to MFS). Moreover, the added value of SANIFS was assessed on the coastal-harbour scale, which is not covered by the coarse resolution of MFS, where the fields forecasted by SANIFS reproduced the observations well (temperature RMSE equal to 0.11 °C). Furthermore, SANIFS simulations were compared with hourly time series of temperature, sea level and velocity measured on the coastal-harbour scale, showing a good agreement. Simulations in the Gulf of Taranto described a circulation mainly characterized by an anticyclonic gyre with the presence of cyclonic vortexes in shelf-coastal areas. A surface water inflow from the open sea to Mar Grande characterizes the coastal-harbour scale.

Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States.

PubMed

Yamana, Teresa K; Kandula, Sasikiran; Shaman, Jeffrey

2017-11-01

Recent research has produced a number of methods for forecasting seasonal influenza outbreaks. However, differences among the predicted outcomes of competing forecast methods can limit their use in decision-making. Here, we present a method for reconciling these differences using Bayesian model averaging. We generated retrospective forecasts of peak timing, peak incidence, and total incidence for seasonal influenza outbreaks in 48 states and 95 cities using 21 distinct forecast methods, and combined these individual forecasts to create weighted-average superensemble forecasts. We compared the relative performance of these individual and superensemble forecast methods by geographic location, timing of forecast, and influenza season. We find that, overall, the superensemble forecasts are more accurate than any individual forecast method and less prone to producing a poor forecast. Furthermore, we find that these advantages increase when the superensemble weights are stratified according to the characteristics of the forecast or geographic location. These findings indicate that different competing influenza prediction systems can be combined into a single more accurate forecast product for operational delivery in real time.

Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States

PubMed Central

Kandula, Sasikiran; Shaman, Jeffrey

2017-01-01

Recent research has produced a number of methods for forecasting seasonal influenza outbreaks. However, differences among the predicted outcomes of competing forecast methods can limit their use in decision-making. Here, we present a method for reconciling these differences using Bayesian model averaging. We generated retrospective forecasts of peak timing, peak incidence, and total incidence for seasonal influenza outbreaks in 48 states and 95 cities using 21 distinct forecast methods, and combined these individual forecasts to create weighted-average superensemble forecasts. We compared the relative performance of these individual and superensemble forecast methods by geographic location, timing of forecast, and influenza season. We find that, overall, the superensemble forecasts are more accurate than any individual forecast method and less prone to producing a poor forecast. Furthermore, we find that these advantages increase when the superensemble weights are stratified according to the characteristics of the forecast or geographic location. These findings indicate that different competing influenza prediction systems can be combined into a single more accurate forecast product for operational delivery in real time. PMID:29107987

Evaluation of streamflow forecast for the National Water Model of U.S. National Weather Service

NASA Astrophysics Data System (ADS)

Rafieeinasab, A.; McCreight, J. L.; Dugger, A. L.; Gochis, D.; Karsten, L. R.; Zhang, Y.; Cosgrove, B.; Liu, Y.

2016-12-01

The National Water Model (NWM), an implementation of the community WRF-Hydro modeling system, is an operational hydrologic forecasting model for the contiguous United States. The model forecasts distributed hydrologic states and fluxes, including soil moisture, snowpack, ET, and ponded water. In particular, the NWM provides streamflow forecasts at more than 2.7 million river reaches for three forecast ranges: short (15 hr), medium (10 days), and long (30 days). In this study, we verify short and medium range streamflow forecasts in the context of the verification of their respective quantitative precipitation forecasts/forcing (QPF), the High Resolution Rapid Refresh (HRRR) and the Global Forecast System (GFS). The streamflow evaluation is performed for summer of 2016 at more than 6,000 USGS gauges. Both individual forecasts and forecast lead times are examined. Selected case studies of extreme events aim to provide insight into the quality of the NWM streamflow forecasts. A goal of this comparison is to address how much streamflow bias originates from precipitation forcing bias. To this end, precipitation verification is performed over the contributing areas above (and between assimilated) USGS gauge locations. Precipitation verification is based on the aggregated, blended StageIV/StageII data as the "reference truth". We summarize the skill of the streamflow forecasts, their skill relative to the QPF, and make recommendations for improving NWM forecast skill.

Forecast of jet engine exhaust emissions for future high altitude commercial aircraft

NASA Technical Reports Server (NTRS)

Grobman, J.; Ingebo, R. D.

1974-01-01

Projected minimum levels of engine exhaust emissions that may be practicably achievable for future commercial aircraft operating at high altitude cruise conditions are presented. The forecasts are based on: (1) current knowledge of emission characteristics of combustors and augmentors; (2) the current status of combustion research in emission reduction technology; (3) predictable trends in combustion systems and operating conditions as required for projected engine designs that are candidates for advanced subsonic or supersonic commercial aircraft. Results are presented for cruise conditions in terms of an emission index, g pollutant/kg fuel. Two sets of engine exhaust emission predictions are presented: the first, based on an independent NASA study and the second, based on the consensus of an ad hoc committee composed of industry, university, and government representatives. The consensus forecasts are in general agreement with the NASA forecasts.

Forecast of jet engine exhaust emissions for future high altitude commercial aircraft

NASA Technical Reports Server (NTRS)

Grobman, J.; Ingebo, R. D.

1974-01-01

Projected minimum levels of engine exhaust emissions that may be practicably achievable for future commercial aircraft operating at high altitude cruise conditions are presented. The forecasts are based on: (1) current knowledge of emission characteristics of combustors and augmentors; (2) the current status of combustion research in emission reduction technology; and (3) predictable trends in combustion systems and operating conditions as required for projected engine designs that are candidates for advanced subsonic or supersonic commercial aircraft. Results are presented for cruise conditions in terms of an emission index, g pollutant/kg fuel. Two sets of engine exhaust emission predictions are presented: the first, based on an independent NASA study and the second, based on the consensus of an ad hoc committee composed of industry, university, and government representatives. The consensus forecasts are in general agreement with the NASA forecasts.

Towards seasonal forecasting of malaria in India.

PubMed

Lauderdale, Jonathan M; Caminade, Cyril; Heath, Andrew E; Jones, Anne E; MacLeod, David A; Gouda, Krushna C; Murty, Upadhyayula Suryanarayana; Goswami, Prashant; Mutheneni, Srinivasa R; Morse, Andrew P

2014-08-10

Malaria presents public health challenge despite extensive intervention campaigns. A 30-year hindcast of the climatic suitability for malaria transmission in India is presented, using meteorological variables from a state of the art seasonal forecast model to drive a process-based, dynamic disease model. The spatial distribution and seasonal cycles of temperature and precipitation from the forecast model are compared to three observationally-based meteorological datasets. These time series are then used to drive the disease model, producing a simulated forecast of malaria and three synthetic malaria time series that are qualitatively compared to contemporary and pre-intervention malaria estimates. The area under the Relative Operator Characteristic (ROC) curve is calculated as a quantitative metric of forecast skill, comparing the forecast to the meteorologically-driven synthetic malaria time series. The forecast shows probabilistic skill in predicting the spatial distribution of Plasmodium falciparum incidence when compared to the simulated meteorologically-driven malaria time series, particularly where modelled incidence shows high seasonal and interannual variability such as in Orissa, West Bengal, and Jharkhand (North-east India), and Gujarat, Rajastan, Madhya Pradesh and Maharashtra (North-west India). Focusing on these two regions, the malaria forecast is able to distinguish between years of "high", "above average" and "low" malaria incidence in the peak malaria transmission seasons, with more than 70% sensitivity and a statistically significant area under the ROC curve. These results are encouraging given that the three month forecast lead time used is well in excess of the target for early warning systems adopted by the World Health Organization. This approach could form the basis of an operational system to identify the probability of regional malaria epidemics, allowing advanced and targeted allocation of resources for combatting malaria in India.

Short-term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

NASA Astrophysics Data System (ADS)

Hebert, David A.; Allard, Richard A.; Metzger, E. Joseph; Posey, Pamela G.; Preller, Ruth H.; Wallcraft, Alan J.; Phelps, Michael W.; Smedstad, Ole Martin

2015-12-01

In this study the forecast skill of the U.S. Navy operational Arctic sea ice forecast system, the Arctic Cap Nowcast/Forecast System (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1-7 day forecasts of Arctic sea ice and ocean conditions. Many quantities are forecast by ACNFS; the most commonly used include ice concentration, ice thickness, ice velocity, sea surface temperature, sea surface salinity, and sea surface velocities. Ice concentration forecast skill is compared to a persistent ice state and historical sea ice climatology. Skill scores are focused on areas where ice concentration changes by ±5% or more, and are therefore limited to primarily the marginal ice zone. We demonstrate that ACNFS forecasts are skilful compared to assuming a persistent ice state, especially beyond 24 h. ACNFS is also shown to be particularly skilful compared to a climatologic state for forecasts up to 102 h. Modeled ice drift velocity is compared to observed buoy data from the International Arctic Buoy Programme. A seasonal bias is shown where ACNFS is slower than IABP velocity in the summer months and faster in the winter months. In February 2015, ACNFS began to assimilate a blended ice concentration derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Interactive Multisensor Snow and Ice Mapping System (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short-term forecast skill and ice edge location compared to the independently derived National Ice Center Ice Edge product.

4-D Cloud Water Content Fields Derived from Operational Satellite Data

NASA Technical Reports Server (NTRS)

Smith, William L., Jr.; Minnis, Patrick

2010-01-01

In order to improve operational safety and efficiency, the transportation industry, including aviation, has an urgent need for accurate diagnoses and predictions of clouds and associated weather conditions. Adverse weather accounts for 70% of all air traffic delays within the U.S. National Airspace System. The Federal Aviation Administration has determined that as much as two thirds of weather-related delays are potentially avoidable with better weather information and roughly 20% of all aviation accidents are weather related. Thus, it is recognized that an important factor in meeting the goals of the Next Generation Transportation System (NexGen) vision is the improved integration of weather information. The concept of a 4-D weather cube is being developed to address that need by integrating observed and forecasted weather information into a shared 4-D database, providing an integrated and nationally consistent weather picture for a variety of users and to support operational decision support systems. Weather analyses and forecasts derived using Numerical Weather Prediction (NWP) models are a critical tool that forecasters rely on for guidance and also an important element in current and future decision support systems. For example, the Rapid Update Cycle (RUC) and the recently implemented Rapid Refresh (RR) Weather Research and Forecast (WRF) models provide high frequency forecasts and are key elements of the FAA Aviation Weather Research Program. Because clouds play a crucial role in the dynamics and thermodynamics of the atmosphere, they must be adequately accounted for in NWP models. The RUC, for example, cycles at full resolution five cloud microphysical species (cloud water, cloud ice, rain, snow, and graupel) and has the capability of updating these fields from observations. In order to improve the models initial state and subsequent forecasts, cloud top altitude (or temperature, T(sub c)) derived from operational satellite data, surface observations of cloud base altitude, radar reflectivity, and lightning data are used to help build and remove clouds in the models assimilation system. Despite this advance and the many recent advances made in our understanding of cloud physical processes and radiative effects, many problems remain in adequately representing clouds in models. While the assimilation of cloud top information derived from operational satellite data has merit, other information is available that has not yet been exploited. For example, the vertically integrated cloud water content (CWC) or cloud water path (CWP) and cloud geometric thickness (delta Z) are standard products being derived routinely from operational satellite data. These and other cloud products have been validated under a variety of conditions. Since the uncertainties have generally been found to be less than those found in model analyses and forecasts, the satellite products should be suitable for data assimilation, provided an appropriate strategy can be developed that links the satellite-derived cloud parameters with cloud parameters specified in the model. In this paper, we briefly outline such a strategy and describe a methodology to retrieve cloud water content profiles from operational satellite data. Initial results and future plans are presented. It is expected that the direct assimilation of this new product will provide the most accurate depiction of the vertical distribution of cloud water ever produced at the high spatial and temporal resolution needed for short term weather analyses and forecasts.

Objective Lightning Forecasting at Kennedy Space Center and Cape Canaveral Air Force Station using Cloud-to-Ground Lightning Surveillance System Data

NASA Technical Reports Server (NTRS)

Lambert, Winfred; Wheeler, Mark; Roeder, William

2005-01-01

The 45th Weather Squadron (45 WS) at Cape Canaveral Air-Force Station (CCAFS)ln Florida issues a probability of lightning occurrence in their daily 24-hour and weekly planning forecasts. This information is used for general planning of operations at CCAFS and Kennedy Space Center (KSC). These facilities are located in east-central Florida at the east end of a corridor known as 'Lightning Alley', an indication that lightning has a large impact on space-lift operations. Much of the current lightning probability forecast is based on a subjective analysis of model and observational data and an objective forecast tool developed over 30 years ago. The 45 WS requested that a new lightning probability forecast tool based on statistical analysis of more recent historical warm season (May-September) data be developed in order to increase the objectivity of the daily thunderstorm probability forecast. The resulting tool is a set of statistical lightning forecast equations, one for each month of the warm season, that provide a lightning occurrence probability for the day by 1100 UTC (0700 EDT) during the warm season.

An experimental system for flood risk forecasting at global scale

NASA Astrophysics Data System (ADS)

Alfieri, L.; Dottori, F.; Kalas, M.; Lorini, V.; Bianchi, A.; Hirpa, F. A.; Feyen, L.; Salamon, P.

2016-12-01

Global flood forecasting and monitoring systems are nowadays a reality and are being applied by an increasing range of users and practitioners in disaster risk management. Furthermore, there is an increasing demand from users to integrate flood early warning systems with risk based forecasts, combining streamflow estimations with expected inundated areas and flood impacts. To this end, we have developed an experimental procedure for near-real time flood mapping and impact assessment based on the daily forecasts issued by the Global Flood Awareness System (GloFAS). The methodology translates GloFAS streamflow forecasts into event-based flood hazard maps based on the predicted flow magnitude and the forecast lead time and a database of flood hazard maps with global coverage. Flood hazard maps are then combined with exposure and vulnerability information to derive flood risk. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To further increase the reliability of the proposed methodology we integrated model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification of impact forecasts. The preliminary tests provided good results and showed the potential of the developed real-time operational procedure in helping emergency response and management. In particular, the link with social media is crucial for improving the accuracy of impact predictions.

Advanced inflow forecasting for a hydropower plant in an Alpine hydropower regulated catchment - coupling of operational and hydrological forecasts

NASA Astrophysics Data System (ADS)

Tilg, Anna-Maria; Schöber, Johannes; Huttenlau, Matthias; Messner, Jakob; Achleitner, Stefan

2017-04-01

Hydropower is a renewable energy source which can help to stabilize fluctuations in the volatile energy market. Especially pumped-storage infrastructures in the European Alps play an important role within the European energy grid system. Today, the runoff of rivers in the Alps is often influenced by cascades of hydropower infrastructures where the operational procedures are triggered by energy market demands, water deliveries and flood control aspects rather than by hydro-meteorological variables. An example for such a highly hydropower regulated river is the catchment of the river Inn in the Eastern European Alps, originating in the Engadin (Switzerland). A new hydropower plant is going to be built as transboundary project at the boarder of Switzerland and Austria using the water of the Inn River. For the operation, a runoff forecast to the plant is required. The challenge in this case is that a high proportion of runoff is turbine water from an upstream situated hydropower cascade. The newly developed physically based hydrological forecasting system is mainly capable to cover natural hydrological runoff processes caused by storms and snow melt but can model only a small degree of human impact. These discontinuous parts of the runoff downstream of the pumped storage are described by means of an additional statistical model which has been developed. The main goal of the statistical model is to forecast the turbine water up to five days in advance. The lead time of the data driven model exceeds the lead time of the used energy production forecast. Additionally, the amount of turbine water is linked to the need of electricity production and the electricity price. It has been shown that especially the parameters day-ahead prognosis of the energy production and turbine inflow of the previous week are good predictors and are therefore used as input parameters for the model. As the data is restricted due to technical conditions, so-called Tobit models have been used to develop a linear regression for the runoff forecast. Although the day-ahead prognosis cannot always be kept, the regression model delivers, especially during office hours, very reasonable results. In the remaining hours the error between measurement and the forecast increases. Overall, the inflow forecast can be substantially improved by the implementation of the developed regression in the hydrological modelling system.

Energy Storage Sizing Taking Into Account Forecast Uncertainties and Receding Horizon Operation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, Kyri; Hug, Gabriela; Li, Xin

Energy storage systems (ESS) have the potential to be very beneficial for applications such as reducing the ramping of generators, peak shaving, and balancing not only the variability introduced by renewable energy sources, but also the uncertainty introduced by errors in their forecasts. Optimal usage of storage may result in reduced generation costs and an increased use of renewable energy. However, optimally sizing these devices is a challenging problem. This paper aims to provide the tools to optimally size an ESS under the assumption that it will be operated under a model predictive control scheme and that the forecast ofmore » the renewable energy resources include prediction errors. A two-stage stochastic model predictive control is formulated and solved, where the optimal usage of the storage is simultaneously determined along with the optimal generation outputs and size of the storage. Wind forecast errors are taken into account in the optimization problem via probabilistic constraints for which an analytical form is derived. This allows for the stochastic optimization problem to be solved directly, without using sampling-based approaches, and sizing the storage to account not only for a wide range of potential scenarios, but also for a wide range of potential forecast errors. In the proposed formulation, we account for the fact that errors in the forecast affect how the device is operated later in the horizon and that a receding horizon scheme is used in operation to optimally use the available storage.« less

Flood Warning and Forecasting System in Slovakia

NASA Astrophysics Data System (ADS)

Leskova, Danica

2016-04-01

In 2015, it finished project Flood Warning and Forecasting System (POVAPSYS) as part of the flood protection in Slovakia till 2010. The aim was to build POVAPSYS integrated computerized flood forecasting and warning system. It took a qualitatively higher level of output meteorological and hydrological services in case of floods affecting large territorial units, as well as local flood events. It is further unfolding demands on performance and coordination of meteorological and hydrological services, troubleshooting observation, evaluation of data, fast communication, modeling and forecasting of meteorological and hydrological processes. Integration of all information entering and exiting to and from the project POVAPSYS provides Hydrological Flood Forecasting System (HYPOS). The system provides information on the current hydrometeorological situation and its evolution with the generation of alerts and notifications in case of exceeding predefined thresholds. HYPOS's functioning of the system requires flawless operability in critical situations while minimizing the loss of its key parts. HYPOS is a core part of the project POVAPSYS, it is a comprehensive software solutions based on a modular principle, providing data and processed information including alarms, in real time. In order to achieve full functionality of the system, in proposal, we have put emphasis on reliability, robustness, availability and security.

Conditional Monthly Weather Resampling Procedure for Operational Seasonal Water Resources Forecasting

NASA Astrophysics Data System (ADS)

Beckers, J.; Weerts, A.; Tijdeman, E.; Welles, E.; McManamon, A.

2013-12-01

To provide reliable and accurate seasonal streamflow forecasts for water resources management several operational hydrologic agencies and hydropower companies around the world use the Extended Streamflow Prediction (ESP) procedure. The ESP in its original implementation does not accommodate for any additional information that the forecaster may have about expected deviations from climatology in the near future. Several attempts have been conducted to improve the skill of the ESP forecast, especially for areas which are affected by teleconnetions (e,g. ENSO, PDO) via selection (Hamlet and Lettenmaier, 1999) or weighting schemes (Werner et al., 2004; Wood and Lettenmaier, 2006; Najafi et al., 2012). A disadvantage of such schemes is that they lead to a reduction of the signal to noise ratio of the probabilistic forecast. To overcome this, we propose a resampling method conditional on climate indices to generate meteorological time series to be used in the ESP. The method can be used to generate a large number of meteorological ensemble members in order to improve the statistical properties of the ensemble. The effectiveness of the method was demonstrated in a real-time operational hydrologic seasonal forecasts system for the Columbia River basin operated by the Bonneville Power Administration. The forecast skill of the k-nn resampler was tested against the original ESP for three basins at the long-range seasonal time scale. The BSS and CRPSS were used to compare the results to those of the original ESP method. Positive forecast skill scores were found for the resampler method conditioned on different indices for the prediction of spring peak flows in the Dworshak and Hungry Horse basin. For the Libby Dam basin however, no improvement of skill was found. The proposed resampling method is a promising practical approach that can add skill to ESP forecasts at the seasonal time scale. Further improvement is possible by fine tuning the method and selecting the most informative climate indices for the region of interest.

Integrating Satellite Measurements from Polar-orbiting instruments into Smoke Disperson Forecasts

NASA Astrophysics Data System (ADS)

Smith, N.; Pierce, R. B.; Barnet, C.; Gambacorta, A.; Davies, J. E.; Strabala, K.

2015-12-01

The IDEA-I (Infusion of Satellite Data into Environmental Applications-International) is a real-time system that currently generates trajectory-based forecasts of aerosol dispersion and stratospheric intrusions. Here we demonstrate new capabilities that use satellite measurements from the Joint Polar Satellite System (JPSS) Suomi-NPP (S-NPP) instruments (operational since 2012) in the generation of trajectory-based predictions of smoke dispersion from North American wildfires. Two such data products are used, namely the Visible Infrared Imaging Radiometer Suite (VIIRS) Aerosol Optical Depth (AOD) and the combined Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) NOAA-Unique CrIS-ATMS Processing System (NUCAPS) carbon monoxide (CO) retrievals. The latter is a new data product made possible by the release of full spectral-resolution CrIS measurements since December 2014. Once NUCAPS CO becomes operationally available it will be used in real-time applications such as IDEA-I along with VIIRS AOD and meteorological forecast fields to support National Weather Service (NWS) Incident Meteorologist (IMET) and air quality management decision making. By combining different measurements, the information content of the IDEA-I transport and dispersion forecast is improved within the complex terrain features that dominate the Western US and Alaska. The primary user community of smoke forecasts is the Western regions of the National Weather Service (NWS) and US Environmental Protection Agency (EPA) due to the significant impacts of wildfires in these regions. With this we demonstrate the quality of the smoke dispersion forecasts that can be achieved by integrating polar-orbiting satellite measurements with forecast models to enable on-site decision support services for fire incident management teams and other real-time air quality agencies.

Global scale predictability of floods

NASA Astrophysics Data System (ADS)

Weerts, Albrecht; Gijsbers, Peter; Sperna Weiland, Frederiek

2016-04-01

Flood (and storm surge) forecasting at the continental and global scale has only become possible in recent years (Emmerton et al., 2016; Verlaan et al., 2015) due to the availability of meteorological forecast, global scale precipitation products and global scale hydrologic and hydrodynamic models. Deltares has setup GLOFFIS a research-oriented multi model operational flood forecasting system based on Delft-FEWS in an open experimental ICT facility called Id-Lab. In GLOFFIS both the W3RA and PCRGLOB-WB model are run in ensemble mode using GEFS and ECMWF-EPS (latency 2 days). GLOFFIS will be used for experiments into predictability of floods (and droughts) and their dependency on initial state estimation, meteorological forcing and the hydrologic model used. Here we present initial results of verification of the ensemble flood forecasts derived with the GLOFFIS system. Emmerton, R., Stephens, L., Pappenberger, F., Pagano, T., Weerts, A., Wood, A. Salamon, P., Brown, J., Hjerdt, N., Donnelly, C., Cloke, H. Continental and Global Scale Flood Forecasting Systems, WIREs Water (accepted), 2016 Verlaan M, De Kleermaeker S, Buckman L. GLOSSIS: Global storm surge forecasting and information system 2015, Australasian Coasts & Ports Conference, 15-18 September 2015,Auckland, New Zealand.

Assessing the value of post-processed state-of-the-art long-term weather forecast ensembles for agricultural water management mediated by farmers' behaviours

NASA Astrophysics Data System (ADS)

Li, Yu; Giuliani, Matteo; Castelletti, Andrea

2016-04-01

Recent advances in modelling of coupled ocean-atmosphere dynamics significantly improved skills of long-term climate forecast from global circulation models (GCMs). These more accurate weather predictions are supposed to be a valuable support to farmers in optimizing farming operations (e.g. crop choice, cropping and watering time) and for more effectively coping with the adverse impacts of climate variability. Yet, assessing how actually valuable this information can be to a farmer is not straightforward and farmers' response must be taken into consideration. Indeed, in the context of agricultural systems potentially useful forecast information should alter stakeholders' expectation, modify their decisions, and ultimately produce an impact on their performance. Nevertheless, long-term forecast are mostly evaluated in terms of accuracy (i.e., forecast quality) by comparing hindcast and observed values and only few studies investigated the operational value of forecast looking at the gain of utility within the decision-making context, e.g. by considering the derivative of forecast information, such as simulated crop yields or simulated soil moisture, which are essential to farmers' decision-making process. In this study, we contribute a step further in the assessment of the operational value of long-term weather forecasts products by embedding these latter into farmers' behavioral models. This allows a more critical assessment of the forecast value mediated by the end-users' perspective, including farmers' risk attitudes and behavioral patterns. Specifically, we evaluate the operational value of thirteen state-of-the-art long-range forecast products against climatology forecast and empirical prediction (i.e. past year climate and historical average) within an integrated agronomic modeling framework embedding an implicit model of the farmers' decision-making process. Raw ensemble datasets are bias-corrected and downscaled using a stochastic weather generator, in order to address the mismatch of the spatio-temporal scale between forecast data from GCMs and our model. For each product, the experiment is composed by two cascade simulations: 1) an ex-ante simulation using forecast data, and 2) an ex-post simulation with observations. Multi-year simulations are performed to account for climate variability, and the operational value of the different forecast products is evaluated against the perfect foresight on the basis of expected crop productivity as well as the final decisions under different decision-making criterions. Our results show that not all products generate beneficial effects to farmers' performance, and the forecast errors might be amplified due to farmers' decision-making process and risk attitudes, yielding little or even worse performance compared with the empirical approaches.

How can monthly to seasonal forecasts help to better manage power systems? (Invited)

NASA Astrophysics Data System (ADS)

Dubus, L.; Troccoli, A.

2013-12-01

The energy industry increasingly depends on weather and climate, at all space and time scales. This is especially true in countries with volunteer renewable energies development policies. There is no doubt that Energy and Meteorology is a burgeoning inter-sectoral discipline. It is also clear that the catalyst for the stronger interaction between these two sectors is the renewed and fervent interest in renewable energies, especially wind and solar power. Recent progress in meteorology has led to a marked increase in the knowledge of the climate system and in the ability to forecast climate on monthly to seasonal time scales. Several studies have already demonstrated the effectiveness of using these forecasts for energy operations, for instance for hydro-power applications. However, it is also obvious that scientific progress on its own is not sufficient to increase the value of weather forecasts. The process of integration of new meteorological products into operational tools and decision making processes is not straightforward but it is at least as important as the scientific discovery. In turn, such integration requires effective communication between users and providers of these products. We will present some important aspects of energy systems in which monthly to seasonal forecasts can bring useful, if not vital, information, and we will give some examples of encouraging energy/meteorology collaborations. We will also provide some suggestions for a strengthened collaboration into the future.

National Centers for Environmental Prediction

Science.gov Websites

/ VISION | About EMC EMC > NAM > EXPERIMENTAL DATA Home NAM Operational Products HIRESW Operational Products Operational Forecast Graphics Experimental Forecast Graphics Verification and Diagnostics Model PARALLEL/EXPERIMENTAL MODEL FORECAST GRAPHICS OPERATIONAL VERIFICATION / DIAGNOSTICS PARALLEL VERIFICATION

Long-Range Atmosphere-Ocean Forecasting in Support of Undersea Warfare Operations in the Western North Pacific

DTIC Science & Technology

2009-09-01

Forecasts ECS East China Sea ESRL Earth Systems Research Laboratory FA False alarm FARate False alarm rate xviii GDEM Generalized Digital...uses a LTM based, global ocean climatology database called Generalized Digital Environment Model ( GDEM ), in tactical decision aid (TDA) software, such...environment for USW planning. GDEM climatology is derived using temperature and salinity profiles from the Modular Ocean Data Assimilation System

Impact of assimilating GOES imager clear-sky radiance with a rapid refresh assimilation system for convection-permitting forecast over Mexico

NASA Astrophysics Data System (ADS)

Yang, Chun; Liu, Zhiquan; Gao, Feng; Childs, Peter P.; Min, Jinzhong

2017-05-01

The Geostationary Operational Environmental Satellite (GOES) imager data could provide a continuous image of the evolutionary pattern of severe weather phenomena with its high spatial and temporal resolution. The capability to assimilate the GOES imager radiances has been developed within the Weather Research and Forecasting model's data assimilation system. Compared to the benchmark experiment with no GOES imager data, the impact of assimilating GOES imager radiances on the analysis and forecast of convective process over Mexico in 7-10 March 2016 was assessed through analysis/forecast cycling experiments using rapid refresh assimilation system with hybrid-3DEnVar scheme. With GOES imager radiance assimilation, better analyses were obtained in terms of the humidity, temperature, and simulated water vapor channel brightness temperature distribution. Positive forecast impacts from assimilating GOES imager radiance were seen when verified against the Tropospheric Airborne Meteorological Data Reporting observation, GOES imager observation, and Mexico station precipitation data.

Biomass burning source characterization requirements in air quality models with and without data assimilation: challenges and opportunities

NASA Astrophysics Data System (ADS)

Hyer, E. J.; Zhang, J. L.; Reid, J. S.; Curtis, C. A.; Westphal, D. L.

2007-12-01

Quantitative models of the transport and evolution of atmospheric pollution have graduated from the laboratory to become a part of the operational activity of forecast centers. Scientists studying the composition and variability of the atmosphere put great efforts into developing methods for accurately specifying sources of pollution, including natural and anthropogenic biomass burning. These methods must be adapted for use in operational contexts, which impose additional strictures on input data and methods. First, only input data sources available in near real-time are suitable for use in operational applications. Second, operational applications must make use of redundant data sources whenever possible. This is a shift in philosophy: in a research context, the most accurate and complete data set will be used, whereas in an operational context, the system must be designed with maximum redundancy. The goal in an operational context is to produce, to the extent possible, consistent and timely output, given sometimes inconsistent inputs. The Naval Aerosol Analysis and Prediction System (NAAPS), a global operational aerosol analysis and forecast system, recently began incorporating assimilation of satellite-derived aerosol optical depth. Assimilation of satellite AOD retrievals has dramatically improved aerosol analyses and forecasts from this system. The use of aerosol data assimilation also changes the strategy for improving the smoke source function. The absolute magnitude of emissions events can be refined through feedback from the data assimilation system, both in real- time operations and in post-processing analysis of data assimilation results. In terms of the aerosol source functions, the largest gains in model performance are now to be gained by reducing data latency and minimizing missed detections. In this presentation, recent model development work on the Fire Locating and Monitoring of Burning Emissions (FLAMBE) system that provides smoke aerosol boundary conditions for NAAPS is described, including redundant integration of multiple satellite platforms and development of feedback loops between the data assimilation system and smoke source.

Flood monitoring for ungauged rivers: the power of combining space-based monitoring and global forecasting models

NASA Astrophysics Data System (ADS)

Revilla-Romero, Beatriz; Netgeka, Victor; Raynaud, Damien; Thielen, Jutta

2013-04-01

Flood warning systems typically rely on forecasts from national meteorological services and in-situ observations from hydrological gauging stations. This capacity is not equally developed in flood-prone developing countries. Low-cost satellite monitoring systems and global flood forecasting systems can be an alternative source of information for national flood authorities. The Global Flood Awareness System (GloFAS) has been develop jointly with the European Centre for Medium-Range Weather Forecast (ECMWF) and the Joint Research Centre, and it is running quasi operational now since June 2011. The system couples state-of-the art weather forecasts with a hydrological model driven at a continental scale. The system provides downstream countries with information on upstream river conditions as well as continental and global overviews. In its test phase, this global forecast system provides probabilities for large transnational river flooding at the global scale up to 30 days in advance. It has shown its real-life potential for the first time during the flood in Southeast Asia in 2011, and more recently during the floods in Australia in March 2012, India (Assam, September-October 2012) and Chad Floods (August-October 2012).The Joint Research Centre is working on further research and development, rigorous testing and adaptations of the system to create an operational tool for decision makers, including national and regional water authorities, water resource managers, hydropower companies, civil protection and first line responders, and international humanitarian aid organizations. Currently efforts are being made to link GloFAS to the Global Flood Detection System (GFDS). GFDS is a Space-based river gauging and flood monitoring system using passive microwave remote sensing which was developed by a collaboration between the JRC and Dartmouth Flood Observatory. GFDS provides flood alerts based on daily water surface change measurements from space. Alerts are shown on a world map, with detailed reports for individual gauging sites. A comparison of discharge estimates from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS) with observations for representative climatic zones is presented. Both systems have demonstrated strong potential in forecasting and detecting recent catastrophic floods. The usefulness of their combined information on global scale for decision makers at different levels is discussed. Combining space-based monitoring and global forecasting models is an innovative approach and has significant benefits for international river commissions as well as international aid organisations. This is in line with the objectives of the Hyogo and the Post-2015 Framework that aim at the development of systems which involve trans-boundary collaboration, space-based earth observation, flood forecasting and early warning.

Verification of Space Weather Forecasts using Terrestrial Weather Approaches

NASA Astrophysics Data System (ADS)

Henley, E.; Murray, S.; Pope, E.; Stephenson, D.; Sharpe, M.; Bingham, S.; Jackson, D.

2015-12-01

The Met Office Space Weather Operations Centre (MOSWOC) provides a range of 24/7 operational space weather forecasts, alerts, and warnings, which provide valuable information on space weather that can degrade electricity grids, radio communications, and satellite electronics. Forecasts issued include arrival times of coronal mass ejections (CMEs), and probabilistic forecasts for flares, geomagnetic storm indices, and energetic particle fluxes and fluences. These forecasts are produced twice daily using a combination of output from models such as Enlil, near-real-time observations, and forecaster experience. Verification of forecasts is crucial for users, researchers, and forecasters to understand the strengths and limitations of forecasters, and to assess forecaster added value. To this end, the Met Office (in collaboration with Exeter University) has been adapting verification techniques from terrestrial weather, and has been working closely with the International Space Environment Service (ISES) to standardise verification procedures. We will present the results of part of this work, analysing forecast and observed CME arrival times, assessing skill using 2x2 contingency tables. These MOSWOC forecasts can be objectively compared to those produced by the NASA Community Coordinated Modelling Center - a useful benchmark. This approach cannot be taken for the other forecasts, as they are probabilistic and categorical (e.g., geomagnetic storm forecasts give probabilities of exceeding levels from minor to extreme). We will present appropriate verification techniques being developed to address these forecasts, such as rank probability skill score, and comparing forecasts against climatology and persistence benchmarks. As part of this, we will outline the use of discrete time Markov chains to assess and improve the performance of our geomagnetic storm forecasts. We will also discuss work to adapt a terrestrial verification visualisation system to space weather, to help MOSWOC forecasters view verification results in near real-time; plans to objectively assess flare forecasts under the EU Horizon 2020 FLARECAST project; and summarise ISES efforts to achieve consensus on verification.

Targeted observations to improve tropical cyclone track forecasts in the Atlantic and eastern Pacific basins

NASA Astrophysics Data System (ADS)

Aberson, Sim David

In 1997, the National Hurricane Center and the Hurricane Research Division began conducting operational synoptic surveillance missions with the Gulfstream IV-SP jet aircraft to improve operational forecast models. During the first two years, twenty-four missions were conducted around tropical cyclones threatening the continental United States, Puerto Rico, and the Virgin Islands. Global Positioning System dropwindsondes were released from the aircraft at 150--200 km intervals along the flight track in the tropical cyclone environment to obtain wind, temperature, and humidity profiles from flight level (around 150 hPa) to the surface. The observations were processed and formatted aboard the aircraft and transmitted to the National Centers for Environmental Prediction (NCEP). There, they were ingested into the Global Data Assimilation System that subsequently provides initial and time-dependent boundary conditions for numerical models that forecast tropical cyclone track and intensity. Three dynamical models were employed in testing the targeting and sampling strategies. With the assimilation into the numerical guidance of all the observations gathered during the surveillance missions, only the 12-h Geophysical Fluid Dynamics Laboratory Hurricane Model forecast showed statistically significant improvement. Neither the forecasts from the Aviation run of the Global Spectral Model nor the shallow-water VICBAR model were improved with the assimilation of the dropwindsonde data. This mediocre result is found to be due mainly to the difficulty in operationally quantifying the storm-motion vector used to create accurate synthetic data to represent the tropical cyclone vortex in the models. A secondary limit on forecast improvements from the surveillance missions is the limited amount of data provided by the one surveillance aircraft in regular missions. The inability of some surveillance missions to surround the tropical cyclone with dropwindsonde observations is a possible third limit, though the results are inconclusive. Due to limited aircraft resources, optimal observing strategies for these missions must be developed. Since observations in areas of decaying error modes are unlikely to have large impact on subsequent forecasts, such strategies should be based on taking observations in those geographic locations corresponding to the most rapidly growing error modes in the numerical models and on known deficiencies in current data assimilation systems. Here, the most rapidly growing modes are represented by areas of large forecast spread in the NCEP bred-mode global ensemble forecasting system. The sampling strategy requires sampling the entire target region at approximately the same resolution as the North American rawinsonde network to limit the possibly spurious spread of information from dropwindsonde observations into data-sparse regions where errors are likely to grow. When only the subset of data in these fully-sampled target regions is assimilated into the numerical models, statistically significant reduction of the track forecast errors of up to 25% within the critical first two days of the forecast are seen. These model improvements are comparable with the cumulative business-as-usual track forecast model improvements expected over eighteen years.

An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): skill, case studies and scenarios

NASA Astrophysics Data System (ADS)

Addor, N.; Jaun, S.; Fundel, F.; Zappa, M.

2011-07-01

The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This model chain relies on limited-area atmospheric forecasts provided by the deterministic model COSMO-7 and the probabilistic model COSMO-LEPS. These atmospheric forecasts are used to force a semi-distributed hydrological model (PREVAH), coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework with which to compare the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added-value conveyed by the probability information, a reforecast was made for the period June 2007 to December 2009 for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain can be of up to 2 days lead time for the catchment considered. Brier skill scores show that overall COSMO-LEPS-based hydrological forecasts outperforms their COSMO-7-based counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts, as shown by comparisons with a reference run driven by observed meteorological parameters. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. The two most intense events of the study period are investigated utilising a novel graphical representation of probability forecasts, and are used to generate high discharge scenarios. They highlight challenges for making decisions on the basis of hydrological predictions, and indicate the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment. No definitive conclusion on the model chain capacity to forecast flooding events endangering the city of Zurich could be drawn because of the under-sampling of extreme events. Further research on the form of the reforecasts needed to infer on floods associated to return periods of several decades, centuries, is encouraged.

Flood Forecasting in Wales: Challenges and Solutions

NASA Astrophysics Data System (ADS)

How, Andrew; Williams, Christopher

2015-04-01

With steep, fast-responding river catchments, exposed coastal reaches with large tidal ranges and large population densities in some of the most at-risk areas; flood forecasting in Wales presents many varied challenges. Utilising advances in computing power and learning from best practice within the United Kingdom and abroad have seen significant improvements in recent years - however, many challenges still remain. Developments in computing and increased processing power comes with a significant price tag; greater numbers of data sources and ensemble feeds brings a better understanding of uncertainty but the wealth of data needs careful management to ensure a clear message of risk is disseminated; new modelling techniques utilise better and faster computation, but lack the history of record and experience gained from the continued use of more established forecasting models. As a flood forecasting team we work to develop coastal and fluvial forecasting models, set them up for operational use and manage the duty role that runs the models in real time. An overview of our current operational flood forecasting system will be presented, along with a discussion on some of the solutions we have in place to address the challenges we face. These include: • real-time updating of fluvial models • rainfall forecasting verification • ensemble forecast data • longer range forecast data • contingency models • offshore to nearshore wave transformation • calculation of wave overtopping

A NASPAC-Based Analysis of the Delay and Cost Effects of the Dallas/Fort Worth Metroplex Plan

DTIC Science & Technology

1992-10-01

Federal Aviation Administration Operations Research Service 14. Sponsoring Agency Code Washington, DC 20590 AOR-100 15. Supplementary Notes 16 . Abstract...LIST OF ILLUSTRATIONS Figure Page 1 DFW Area Airports 3 2 Forecasted Number of Daily Operations at DFW 12 3 Annual Savings at DFW with the Plan 16 4...Increase in System Operational Delay Without Plan 24 15 Average System Operational Delay 26 16 System Operational Delay Savings with Plan 26 17 System

Regional Precipitation Forecast with Atmospheric InfraRed Sounder (AIRS) Profile Assimilation

NASA Technical Reports Server (NTRS)

Chou, S.-H.; Zavodsky, B. T.; Jedloved, G. J.

2010-01-01

Advanced technology in hyperspectral sensors such as the Atmospheric InfraRed Sounder (AIRS; Aumann et al. 2003) on NASA's polar orbiting Aqua satellite retrieve higher vertical resolution thermodynamic profiles than their predecessors due to increased spectral resolution. Although these capabilities do not replace the robust vertical resolution provided by radiosondes, they can serve as a complement to radiosondes in both space and time. These retrieved soundings can have a significant impact on weather forecasts if properly assimilated into prediction models. Several recent studies have evaluated the performance of specific operational weather forecast models when AIRS data are included in the assimilation process. LeMarshall et al. (2006) concluded that AIRS radiances significantly improved 500 hPa anomaly correlations in medium-range forecasts of the Global Forecast System (GFS) model. McCarty et al. (2009) demonstrated similar forecast improvement in 0-48 hour forecasts in an offline version of the operational North American Mesoscale (NAM) model when AIRS radiances were assimilated at the regional scale. Reale et al. (2008) showed improvements to Northern Hemisphere 500 hPa height anomaly correlations in NASA's Goddard Earth Observing System Model, Version 5 (GEOS-5) global system with the inclusion of partly cloudy AIRS temperature profiles. Singh et al. (2008) assimilated AIRS temperature and moisture profiles into a regional modeling system for a study of a heavy rainfall event during the summer monsoon season in Mumbai, India. This paper describes an approach to assimilate AIRS temperature and moisture profiles into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimensional variational (3DVAR) assimilation system (WRF-Var; Barker et al. 2004). Section 2 describes the AIRS instrument and how the quality indicators are used to intelligently select the highest-quality data for assimilation. Section 3 presents an overall precipitation improvement with AIRS assimilation during a 37-day case study period, and Section 4 focuses on a single case study to further investigate the meteorological impact of AIRS profiles on synoptic scale models. Finally, Section 5 provides a summary of the paper.

USDA Foreign Agricultural Service overview for operational monitoring of current crop conditions and production forecasts.

NASA Astrophysics Data System (ADS)

Crutchfield, J.

2016-12-01

The presentation will discuss the current status of the International Production Assessment Division of the USDA ForeignAgricultural Service for operational monitoring and forecasting of current crop conditions, and anticipated productionchanges to produce monthly, multi-source consensus reports on global crop conditions including the use of Earthobservations (EO) from satellite and in situ sources.United States Department of Agriculture (USDA) Foreign Agricultural Service (FAS) International Production AssessmentDivision (IPAD) deals exclusively with global crop production forecasting and agricultural analysis in support of the USDAWorld Agricultural Outlook Board (WAOB) lockup process and contributions to the World Agricultural Supply DemandEstimates (WASE) report. Analysts are responsible for discrete regions or countries and conduct in-depth long-termresearch into national agricultural statistics, farming systems, climatic, environmental, and economic factors affectingcrop production. IPAD analysts become highly valued cross-commodity specialists over time, and are routinely soughtout for specialized analyses to support governmental studies. IPAD is responsible for grain, oilseed, and cotton analysison a global basis. IPAD is unique in the tools it uses to analyze crop conditions around the world, including customweather analysis software and databases, satellite imagery and value-added image interpretation products. It alsoincorporates all traditional agricultural intelligence resources into its forecasting program, to make the fullest use ofavailable information in its operational commodity forecasts and analysis. International travel and training play animportant role in learning about foreign agricultural production systems and in developing analyst knowledge andcapabilities.

The New Era in Operational Forecasting

NASA Astrophysics Data System (ADS)

Tobiska, W.; Schunk, R. W.; Sojka, J. J.; Carlson, H. C.; Gardner, L. C.; Scherliess, L.; Zhu, L.; Eccles, J. V.; Rice, D. D.; Bouwer, D.; Bailey, J. J.; Knipp, D. J.; Blake, J. B.; Rex, J.; Fuschino, R.; Mertens, C. J.; Gersey, B.; Wilkins, R.; Atwell, W.

2012-12-01

Space weather's effects upon the near-Earth environment are due to dynamic changes in the energy transfer processes from the Sun's photons, particles, and fields. Of the space environment domains that are affected by space weather, the ionosphere, thermosphere, and even troposphere are key regions that are affected. The Utah State University (USU) Space Weather Center (SWC) and Space Environment Technologies (SET) are developing and producing commercial space weather applications. Key systems for providing timely information about the effects of space weather are SWC's Global Assimilation of Ionospheric Measurements (GAIM) system, SET's Magnetosphere Alert and Prediction System (MAPS), and SET's Automated Radiation Measurements for Aviation Safety (ARMAS) system. GAIM, operated by SWC, improves real-time communication and navigation systems by continuously ingesting up to 10,000 slant TEC measurements every 15-minutes from approximately 500 stations. Ionosonde data from several dozen global stations is ingested every 15 minutes to improve the vertical profiles within GAIM. These operational runs enable the reporting of global radio high frequency (HF) signal strengths and near vertical incidence skywave (NVIS) maps used by amateur radio operators and emergency responders via the http://q-upnow.com website. MAPS provides a forecast Dst index out to 6 days through the data-driven Anemomilos algorithm. Anemomilos uses observational proxies for the magnitude, location, and velocity of solar ejecta events. This forecast index is used by satellite operations to characterize upcoming geomagnetic storms, for example. ARMAS is demonstrating a prototype flight of microdosimeters on aircraft to capture the "weather" of the radiation environment for air-crew and passenger safety. It assimilates real-time radiation dose and dose rate data into the global NAIRAS radiation system to correct the global climatology for more accurate radiation fields along flight tracks. This team also provides the space weather smartphone app called SpaceWx for iPhone, iPad, iPod, and Android for professional users and public space weather education. SpaceWx displays the real-time solar, heliosphere, magnetosphere, thermosphere, and ionosphere drivers to changes in the total electron content, for example, as well as global NVIS maps. We describe recent forecasting advances for moving space weather information through automated systems into operational, derivative products for communications, aviation, and satellite operations uses.

Cable Overheating Risk Warning Method Based on Impedance Parameter Estimation in Distribution Network

NASA Astrophysics Data System (ADS)

Yu, Zhang; Xiaohui, Song; Jianfang, Li; Fei, Gao

2017-05-01

Cable overheating will lead to the cable insulation level reducing, speed up the cable insulation aging, even easy to cause short circuit faults. Cable overheating risk identification and warning is nessesary for distribution network operators. Cable overheating risk warning method based on impedance parameter estimation is proposed in the paper to improve the safty and reliability operation of distribution network. Firstly, cable impedance estimation model is established by using least square method based on the data from distribiton SCADA system to improve the impedance parameter estimation accuracy. Secondly, calculate the threshold value of cable impedance based on the historical data and the forecast value of cable impedance based on the forecasting data in future from distribiton SCADA system. Thirdly, establish risks warning rules library of cable overheating, calculate the cable impedance forecast value and analysis the change rate of impedance, and then warn the overheating risk of cable line based on the overheating risk warning rules library according to the variation relationship between impedance and line temperature rise. Overheating risk warning method is simulated in the paper. The simulation results shows that the method can identify the imedance and forecast the temperature rise of cable line in distribution network accurately. The result of overheating risk warning can provide decision basis for operation maintenance and repair.

Space and ground segment performance of the FORMOSAT-3/COSMIC mission: four years in orbit

NASA Astrophysics Data System (ADS)

Fong, C.-J.; Whiteley, D.; Yang, E.; Cook, K.; Chu, V.; Schreiner, B.; Ector, D.; Wilczynski, P.; Liu, T.-Y.; Yen, N.

2011-01-01

The FORMOSAT-3/COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) mission consisting of six Low-Earth-Orbit (LEO) satellites is the world's first demonstration constellation using radio occultation signals from Global Positioning System (GPS) satellites. The radio occultation signals are retrieved in near real-time for global weather/climate monitoring, numerical weather prediction, and space weather research. The mission has processed on average 1400 to 1800 high-quality atmospheric sounding profiles per day. The atmospheric radio occultation soundings data are assimilated into operational numerical weather prediction models for global weather prediction, including typhoon/hurricane/cyclone forecasts. The radio occultation data has shown a positive impact on weather predictions at many national weather forecast centers. A proposed follow-on mission transitions the program from the current experimental research system to a significantly improved real-time operational system, which will reliably provide 8000 radio occultation soundings per day. The follow-on mission as planned will consist of 12 satellites with a data latency of 45 min, which will provide greatly enhanced opportunities for operational forecasts and scientific research. This paper will address the FORMOSAT-3/COSMIC system and mission overview, the spacecraft and ground system performance after four years in orbit, the lessons learned from the encountered technical challenges and observations, and the expected design improvements for the new spacecraft and ground system.

Risk Based Reservoir Operations Using Ensemble Streamflow Predictions for Lake Mendocino in Mendocino County, California

NASA Astrophysics Data System (ADS)

Delaney, C.; Mendoza, J.; Whitin, B.; Hartman, R. K.

2017-12-01

Ensemble Forecast Operations (EFO) is a risk based approach of reservoir flood operations that incorporates ensemble streamflow predictions (ESPs) made by NOAA's California-Nevada River Forecast Center (CNRFC). With the EFO approach, each member of an ESP is individually modeled to forecast system conditions and calculate risk of reaching critical operational thresholds. Reservoir release decisions are computed which seek to manage forecasted risk to established risk tolerance levels. A water management model was developed for Lake Mendocino, a 111,000 acre-foot reservoir located near Ukiah, California, to evaluate the viability of the EFO alternative to improve water supply reliability but not increase downstream flood risk. Lake Mendocino is a dual use reservoir, which is owned and operated for flood control by the United States Army Corps of Engineers and is operated for water supply by the Sonoma County Water Agency. Due to recent changes in the operations of an upstream hydroelectric facility, this reservoir has suffered from water supply reliability issues since 2007. The EFO alternative was simulated using a 26-year (1985-2010) ESP hindcast generated by the CNRFC, which approximates flow forecasts for 61 ensemble members for a 15-day horizon. Model simulation results of the EFO alternative demonstrate a 36% increase in median end of water year (September 30) storage levels over existing operations. Additionally, model results show no increase in occurrence of flows above flood stage for points downstream of Lake Mendocino. This investigation demonstrates that the EFO alternative may be a viable approach for managing Lake Mendocino for multiple purposes (water supply, flood mitigation, ecosystems) and warrants further investigation through additional modeling and analysis.

Implementation of Real-Time Bias-Adjusted O3 and PM2.5 Air Quality Forecasts and their Performance Evaluations during 2008 over the Continental United States

EPA Science Inventory

The National Oceanic and Atmospheric Administration (NOAA), in partnership with the United States Environmental Protection Agency (EPA), is operationally implementing an Air Quality Forecast (AQF) system. This program, which couples NOAA's North American Mesoscale (NAM) weather p...

Real-Time Bias-Adjusted O3 and PM2.5 Air Quality Index Forecasts and their Performance Evaluations over the Continental United States

EPA Science Inventory

The National Air Quality Forecast Capacity (NAQFC) system, which links NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, provided operational ozone (O₃) and experimental fine particular matter (PM_2...

Weather monitoring and forecasting over eastern Attica (Greece) in the frame of FLIRE project

NASA Astrophysics Data System (ADS)

Kotroni, Vassiliki; Lagouvardos, Konstantinos; Chrysoulakis, Nektarios; Makropoulos, Christtos; Mimikou, Maria; Papathanasiou, Chrysoula; Poursanidis, Dimitris

2015-04-01

In the frame of FLIRE project a Decision Support System has been built with the aim to support decision making of Civil Protection Agencies and local stakeholders in the area of east Attica (Greece), in the cases of forest fires and floods. In this presentation we focus on a specific action that focuses on the provision of high resolution short-term weather forecasting data as well as of dense meteorological observations over the study area. Both weather forecasts and observations serve as an input in the Weather Information Management Tool (WIMT) of the Decision Support System. We focus on: (a) the description of the adopted strategy for setting-up the operational weather forecasting chain that provides the weather forecasts for the FLIRE project needs, (b) the presentation of the surface network station that provides real-time weather monitoring of the study area and (c) the strategy adopted for issuing smart alerts for thunderstorm forecasting based of real-time lightning observations as well as satellite observations.

Towards a More Accurate Solar Power Forecast By Improving NWP Model Physics

NASA Astrophysics Data System (ADS)

Köhler, C.; Lee, D.; Steiner, A.; Ritter, B.

2014-12-01

The growing importance and successive expansion of renewable energies raise new challenges for decision makers, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the uncertainties associated with the large share of weather-dependent power sources. Precise power forecast, well-timed energy trading on the stock market, and electrical grid stability can be maintained. The research project EWeLiNE is a collaboration of the German Weather Service (DWD), the Fraunhofer Institute (IWES) and three German transmission system operators (TSOs). Together, wind and photovoltaic (PV) power forecasts shall be improved by combining optimized NWP and enhanced power forecast models. The conducted work focuses on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. Not only the representation of the model cloud characteristics, but also special events like Sahara dust over Germany and the solar eclipse in 2015 are treated and their effect on solar power accounted for. An overview of the EWeLiNE project and results of the ongoing research will be presented.

The NWRA Classification Infrastructure: description and extension to the Discriminant Analysis Flare Forecasting System (DAFFS)

NASA Astrophysics Data System (ADS)

Leka, K. D.; Barnes, Graham; Wagner, Eric

2018-04-01

A classification infrastructure built upon Discriminant Analysis (DA) has been developed at NorthWest Research Associates for examining the statistical differences between samples of two known populations. Originating to examine the physical differences between flare-quiet and flare-imminent solar active regions, we describe herein some details of the infrastructure including: parametrization of large datasets, schemes for handling "null" and "bad" data in multi-parameter analysis, application of non-parametric multi-dimensional DA, an extension through Bayes' theorem to probabilistic classification, and methods invoked for evaluating classifier success. The classifier infrastructure is applicable to a wide range of scientific questions in solar physics. We demonstrate its application to the question of distinguishing flare-imminent from flare-quiet solar active regions, updating results from the original publications that were based on different data and much smaller sample sizes. Finally, as a demonstration of "Research to Operations" efforts in the space-weather forecasting context, we present the Discriminant Analysis Flare Forecasting System (DAFFS), a near-real-time operationally-running solar flare forecasting tool that was developed from the research-directed infrastructure.

Chance-Constrained Day-Ahead Hourly Scheduling in Distribution System Operation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Zhang, Yingchen; Muljadi, Eduard

This paper aims to propose a two-step approach for day-ahead hourly scheduling in a distribution system operation, which contains two operation costs, the operation cost at substation level and feeder level. In the first step, the objective is to minimize the electric power purchase from the day-ahead market with the stochastic optimization. The historical data of day-ahead hourly electric power consumption is used to provide the forecast results with the forecasting error, which is presented by a chance constraint and formulated into a deterministic form by Gaussian mixture model (GMM). In the second step, the objective is to minimize themore » system loss. Considering the nonconvexity of the three-phase balanced AC optimal power flow problem in distribution systems, the second-order cone program (SOCP) is used to relax the problem. Then, a distributed optimization approach is built based on the alternating direction method of multiplier (ADMM). The results shows that the validity and effectiveness method.« less

Operational prediction of rip currents using numerical model and nearshore bathymetry from video images

NASA Astrophysics Data System (ADS)

Sembiring, L.; Van Ormondt, M.; Van Dongeren, A. R.; Roelvink, J. A.

2017-07-01

Rip currents are one of the most dangerous coastal hazards for swimmers. In order to minimize the risk, a coastal operational-process based-model system can be utilized in order to provide forecast of nearshore waves and currents that may endanger beach goers. In this paper, an operational model for rip current prediction by utilizing nearshore bathymetry obtained from video image technique is demonstrated. For the nearshore scale model, XBeach1 is used with which tidal currents, wave induced currents (including the effect of the wave groups) can be simulated simultaneously. Up-to-date bathymetry will be obtained using video images technique, cBathy 2. The system will be tested for the Egmond aan Zee beach, located in the northern part of the Dutch coastline. This paper will test the applicability of bathymetry obtained from video technique to be used as input for the numerical modelling system by comparing simulation results using surveyed bathymetry and model results using video bathymetry. Results show that the video technique is able to produce bathymetry converging towards the ground truth observations. This bathymetry validation will be followed by an example of operational forecasting type of simulation on predicting rip currents. Rip currents flow fields simulated over measured and modeled bathymetries are compared in order to assess the performance of the proposed forecast system.

Severe rainfall prediction systems for civil protection purposes

NASA Astrophysics Data System (ADS)

Comellas, A.; Llasat, M. C.; Molini, L.; Parodi, A.; Siccardi, F.

2010-09-01

One of the most common natural hazards impending on Mediterranean regions is the occurrence of severe weather structures able to produce heavy rainfall. Floods have killed about 1000 people across all Europe in last 10 years. With the aim of mitigating this kind of risk, quantitative precipitation forecasts (QPF) and rain probability forecasts are two tools nowadays available for national meteorological services and institutions responsible for weather forecasting in order to and predict rainfall, by using either the deterministic or the probabilistic approach. This study provides an insight of the different approaches used by Italian (DPC) and Catalonian (SMC) Civil Protection and the results they achieved with their peculiar issuing-system for early warnings. For the former, the analysis considers the period between 2006-2009 in which the predictive ability of the forecasting system, based on the numerical weather prediction model COSMO-I7, has been put into comparison with ground based observations (composed by more than 2000 raingauge stations, Molini et al., 2009). Italian system is mainly focused on regional-scale warnings providing forecasts for periods never shorter than 18 hours and very often have a 36-hour maximum duration . The information contained in severe weather bulletins is not quantitative and usually is referred to a specific meteorological phenomena (thunderstorms, wind gales et c.). Updates and refining have a usual refresh time of 24 hours. SMC operates within the Catalonian boundaries and uses a warning system that mixes both quantitative and probabilistic information. For each administrative region ("comarca") Catalonia is divided into, forecasters give an approximate value of the average predicted rainfall and the probability of overcoming that threshold. Usually warnings are re-issued every 6 hours and their duration depends on the predicted time extent of the storm. In order to provide a comprehensive QPF verification, the rainfall predicted by Mesoscale Model 5 (MM5), the SMC forecast operational model, is compared with the local rain gauge network for year 2008 (Comellas et al., 2010). This study presents benefits and drawbacks of both Italian and Catalonian systems. Moreover, a particular attention is paid on the link between system's predictive ability and the predicted severe weather type as a function of its space-time development.

Validation of the CME Geomagnetic Forecast Alerts Under the COMESEP Alert System

NASA Astrophysics Data System (ADS)

Dumbović, Mateja; Srivastava, Nandita; Rao, Yamini K.; Vršnak, Bojan; Devos, Andy; Rodriguez, Luciano

2017-08-01

Under the European Union 7th Framework Programme (EU FP7) project Coronal Mass Ejections and Solar Energetic Particles (COMESEP, http://comesep.aeronomy.be), an automated space weather alert system has been developed to forecast solar energetic particles (SEP) and coronal mass ejection (CME) risk levels at Earth. The COMESEP alert system uses the automated detection tool called Computer Aided CME Tracking (CACTus) to detect potentially threatening CMEs, a drag-based model (DBM) to predict their arrival, and a CME geoeffectiveness tool (CGFT) to predict their geomagnetic impact. Whenever CACTus detects a halo or partial halo CME and issues an alert, the DBM calculates its arrival time at Earth and the CGFT calculates its geomagnetic risk level. The geomagnetic risk level is calculated based on an estimation of the CME arrival probability and its likely geoeffectiveness, as well as an estimate of the geomagnetic storm duration. We present the evaluation of the CME risk level forecast with the COMESEP alert system based on a study of geoeffective CMEs observed during 2014. The validation of the forecast tool is made by comparing the forecasts with observations. In addition, we test the success rate of the automatic forecasts (without human intervention) against the forecasts with human intervention using advanced versions of the DBM and CGFT (independent tools available at the Hvar Observatory website, http://oh.geof.unizg.hr). The results indicate that the success rate of the forecast in its current form is unacceptably low for a realistic operation system. Human intervention improves the forecast, but the false-alarm rate remains unacceptably high. We discuss these results and their implications for possible improvement of the COMESEP alert system.

Operational wind shear detection and warning - The 'CLAWS' experience at Denver and future objectives

NASA Technical Reports Server (NTRS)

Mccarthy, John; Wilson, James W.; Hjelmfelt, Mark R.

1986-01-01

An operational wind shear detection and warning experiment was conducted at Denver's Stapleton International Airport in summer 1984. Based on meteorological interpretation of scope displays from a Doppler weather radar, warnings were transmitted to the air traffic control tower via voice radio. Analyses of results indicated real skill in daily microburst forecasts and very short-term (less than 5-min) warnings. Wind shift advisories with 15-30 min forecasts, permitted more efficient runway reconfigurations. Potential fuel savings were estimated at $875,000/yr at Stapleton. The philosophy of future development toward an automated, operational system is discussed.

Skilful seasonal forecasts of streamflow over Europe?

NASA Astrophysics Data System (ADS)

Arnal, Louise; Cloke, Hannah L.; Stephens, Elisabeth; Wetterhall, Fredrik; Prudhomme, Christel; Neumann, Jessica; Krzeminski, Blazej; Pappenberger, Florian

2018-04-01

This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate-model-based seasonal streamflow forecasting.

Urban flood early warning systems: approaches to hydrometeorological forecasting and communicating risk

NASA Astrophysics Data System (ADS)

Cranston, Michael; Speight, Linda; Maxey, Richard; Tavendale, Amy; Buchanan, Peter

2015-04-01

One of the main challenges for the flood forecasting community remains the provision of reliable early warnings of surface (or pluvial) flooding. The Scottish Flood Forecasting Service has been developing approaches for forecasting the risk of surface water flooding including capitalising on the latest developments in quantitative precipitation forecasting from the Met Office. A probabilistic Heavy Rainfall Alert decision support tool helps operational forecasters assess the likelihood of surface water flooding against regional rainfall depth-duration estimates from MOGREPS-UK linked to historical short-duration flooding in Scotland. The surface water flood risk is communicated through the daily Flood Guidance Statement to emergency responders. A more recent development is an innovative risk-based hydrometeorological approach that links 24-hour ensemble rainfall forecasts through a hydrological model (Grid-to-Grid) to a library of impact assessments (Speight et al., 2015). The early warning tool - FEWS Glasgow - presents the risk of flooding to people, property and transport across a 1km grid over the city of Glasgow with a lead time of 24 hours. Communication of the risk was presented in a bespoke surface water flood forecast product designed based on emergency responder requirements and trialled during the 2014 Commonwealth Games in Glasgow. The development of new approaches to surface water flood forecasting are leading to improved methods of communicating the risk and better performance in early warning with a reduction in false alarm rates with summer flood guidance in 2014 (67%) compared to 2013 (81%) - although verification of instances of surface water flooding remains difficult. However the introduction of more demanding hydrometeorological capabilities with associated greater levels of uncertainty does lead to an increased demand on operational flood forecasting skills and resources. Speight, L., Cole, S.J., Moore, R.J., Pierce, C., Wright, B., Golding, B., Cranston, M., Tavendale, A., Ghimire, S., and Dhondia, J. (2015) Developing surface water flood forecasting capabilities in Scotland: an operational pilot for the 2014 Commonwealth Games in Glasgow. Journal of Flood Risk Management, In Press.

Innovative Tools for Water Quality/Quantity Management: New York City's Operations Support Tool

NASA Astrophysics Data System (ADS)

Wang, L.; Schaake, J. C.; Day, G. N.; Porter, J.; Sheer, D. P.; Pyke, G.

2011-12-01

The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies more than 1 billion gallons of water per day to over 9 million customers. Recently, DEP has initiated design of an Operations Support Tool (OST), a state-of-the-art decision support system to provide computational and predictive support for water supply operations and planning. This presentation describes the technical structure of OST, including the underlying water supply and water quality models, data sources and database management, reservoir inflow forecasts, and the functionalities required to meet the needs of a diverse group of end users. OST is a major upgrade of DEP's current water supply - water quality model, developed to evaluate alternatives for controlling turbidity in NYC's Catskill reservoirs. While the current model relies on historical hydrologic and meteorological data, OST can be driven by forecasted future conditions. It will receive a variety of near-real-time data from a number of sources. OST will support two major types of simulations: long-term, for evaluating policy or infrastructure changes over an extended period of time; and short-term "position analysis" (PA) simulations, consisting of multiple short simulations, all starting from the same initial conditions. Typically, the starting conditions for a PA run will represent those for the current day and traces of forecasted hydrology will drive the model for the duration of the simulation period. The result of these simulations will be a distribution of future system states based on system operating rules and the range of input ensemble streamflow predictions. DEP managers will analyze the output distributions and make operation decisions using risk-based metrics such as probability of refill. Currently, in the developmental stages of OST, forecasts are based on antecedent hydrologic conditions and are statistical in nature. The statistical algorithm is a relatively simple and versatile, but lacks short-term skill critical for water quality and spill management. To improve short-term skill, OST will ultimately operate with meteorologically driven hydrologic forecasts provided by the National Weather Service (NWS). OST functionalities will support a wide range of DEP uses, including short term operational projections, outage planning and emergency management, operating rule development, and water supply planning. A core use of OST will be to inform reservoir management strategies to control and mitigate turbidity events while ensuring water supply reliability. OST will also allow DEP to manage its complex reservoir system to meet multiple objectives, including ecological flows, tailwater fisheries and recreational releases, and peak flow mitigation for downstream communities.

Valuing year-to-go hydrologic forecast improvements for a peaking hydropower system in the Sierra Nevada

NASA Astrophysics Data System (ADS)

Rheinheimer, David E.; Bales, Roger C.; Oroza, Carlos A.; Lund, Jay R.; Viers, Joshua H.

2016-05-01

We assessed the potential value of hydrologic forecasting improvements for a snow-dominated high-elevation hydropower system in the Sierra Nevada of California, using a hydropower optimization model. To mimic different forecasting skill levels for inflow time series, rest-of-year inflows from regression-based forecasts were blended in different proportions with representative inflows from a spatially distributed hydrologic model. The statistical approach mimics the simpler, historical forecasting approach that is still widely used. Revenue was calculated using historical electricity prices, with perfect price foresight assumed. With current infrastructure and operations, perfect hydrologic forecasts increased annual hydropower revenue by 0.14 to 1.6 million, with lower values in dry years and higher values in wet years, or about $0.8 million (1.2%) on average, representing overall willingness-to-pay for perfect information. A second sensitivity analysis found a wider range of annual revenue gain or loss using different skill levels in snow measurement in the regression-based forecast, mimicking expected declines in skill as the climate warms and historical snow measurements no longer represent current conditions. The value of perfect forecasts was insensitive to storage capacity for small and large reservoirs, relative to average inflow, and modestly sensitive to storage capacity with medium (current) reservoir storage. The value of forecasts was highly sensitive to powerhouse capacity, particularly for the range of capacities in the northern Sierra Nevada. The approach can be extended to multireservoir, multipurpose systems to help guide investments in forecasting.

An Integrated Uncertainty Analysis and Ensemble-based Data Assimilation Framework for Operational Snow Predictions

NASA Astrophysics Data System (ADS)

He, M.; Hogue, T. S.; Franz, K.; Margulis, S. A.; Vrugt, J. A.

2009-12-01

The National Weather Service (NWS), the agency responsible for short- and long-term streamflow predictions across the nation, primarily applies the SNOW17 model for operational forecasting of snow accumulation and melt. The SNOW17-forecasted snowmelt serves as an input to a rainfall-runoff model for streamflow forecasts in snow-dominated areas. The accuracy of streamflow predictions in these areas largely relies on the accuracy of snowmelt. However, no direct snowmelt measurements are available to validate the SNOW17 predictions. Instead, indirect measurements such as snow water equivalent (SWE) measurements or discharge are typically used to calibrate SNOW17 parameters. In addition, the forecast practice is inherently deterministic, lacking tools to systematically address forecasting uncertainties (e.g., uncertainties in parameters, forcing, SWE and discharge observations, etc.). The current research presents an Integrated Uncertainty analysis and Ensemble-based data Assimilation (IUEA) framework to improve predictions of snowmelt and discharge while simultaneously providing meaningful estimates of the associated uncertainty. The IUEA approach uses the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) to simultaneously estimate uncertainties in model parameters, forcing, and observations. The robustness and usefulness of the IUEA-SNOW17 framework is evaluated for snow-dominated watersheds in the northern Sierra Mountains, using the coupled IUEA-SNOW17 and an operational soil moisture accounting model (SAC-SMA). Preliminary results are promising and indicate successful performance of the coupled IUEA-SNOW17 framework. Implementation of the SNOW17 with the IUEA is straightforward and requires no major modification to the SNOW17 model structure. The IUEA-SNOW17 framework is intended to be modular and transferable and should assist the NWS in advancing the current forecasting system and reinforcing current operational forecasting skill.

U.S. Electric System Operating Data

EIA Publications

EIA provides hourly electricity operating data, including actual and forecast demand, net generation, and the power flowing between electric systems. EIA's new U.S. Electric System Operating Data tool provides nearly real-time demand data, plus analysis and visualizations of hourly, daily, and weekly electricity supply and demand on a national and regional level for all of the 66 electric system balancing authorities that make up the U.S. electric grid.

Quasi-most unstable modes: a window to 'À la carte' ensemble diversity?

NASA Astrophysics Data System (ADS)

Homar Santaner, Victor; Stensrud, David J.

2010-05-01

The atmospheric scientific community is nowadays facing the ambitious challenge of providing useful forecasts of atmospheric events that produce high societal impact. The low level of social resilience to false alarms creates tremendous pressure on forecasting offices to issue accurate, timely and reliable warnings.Currently, no operational numerical forecasting system is able to respond to the societal demand for high-resolution (in time and space) predictions in the 12-72h time span. The main reasons for such deficiencies are the lack of adequate observations and the high non-linearity of the numerical models that are currently used. The whole weather forecasting problem is intrinsically probabilistic and current methods aim at coping with the various sources of uncertainties and the error propagation throughout the forecasting system. This probabilistic perspective is often created by generating ensembles of deterministic predictions that are aimed at sampling the most important sources of uncertainty in the forecasting system. The ensemble generation/sampling strategy is a crucial aspect of their performance and various methods have been proposed. Although global forecasting offices have been using ensembles of perturbed initial conditions for medium-range operational forecasts since 1994, no consensus exists regarding the optimum sampling strategy for high resolution short-range ensemble forecasts. Bred vectors, however, have been hypothesized to better capture the growing modes in the highly nonlinear mesoscale dynamics of severe episodes than singular vectors or observation perturbations. Yet even this technique is not able to produce enough diversity in the ensembles to accurately and routinely predict extreme phenomena such as severe weather. Thus, we propose a new method to generate ensembles of initial conditions perturbations that is based on the breeding technique. Given a standard bred mode, a set of customized perturbations is derived with specified amplitudes and horizontal scales. This allows the ensemble to excite growing modes across a wider range of scales. Results show that this approach produces significantly more spread in the ensemble prediction than standard bred modes alone. Several examples that illustrate the benefits from this approach for severe weather forecasts will be provided.

Operational early warning of shallow landslides in Norway: Evaluation of landslide forecasts and associated challenges

NASA Astrophysics Data System (ADS)

Dahl, Mads-Peter; Colleuille, Hervé; Boje, Søren; Sund, Monica; Krøgli, Ingeborg; Devoli, Graziella

2015-04-01

The Norwegian Water Resources and Energy Directorate (NVE) runs a national early warning system (EWS) for shallow landslides in Norway. Slope failures included in the EWS are debris slides, debris flows, debris avalanches and slush flows. The EWS has been operational on national scale since 2013 and consists of (a) quantitative landslide thresholds and daily hydro-meteorological prognosis; (b) daily qualitative expert evaluation of prognosis / additional data in decision to determine warning levels; (c) publication of warning levels through various custom build internet platforms. The effectiveness of an EWS depends on both the quality of forecasts being issued, and the communication of forecasts to the public. In this analysis a preliminary evaluation of landslide forecasts from the Norwegian EWS within the period 2012-2014 is presented. Criteria for categorizing forecasts as correct, missed events or false alarms are discussed and concrete examples of forecasts falling into the latter two categories are presented. The evaluation show a rate of correct forecasts exceeding 90%. However correct forecast categorization is sometimes difficult, particularly due to poorly documented landslide events. Several challenges has to be met in the process of further lowering rates of missed events of false alarms in the EWS. Among others these include better implementation of susceptibility maps in landslide forecasting, more detailed regionalization of hydro-meteorological landslide thresholds, improved prognosis on precipitation, snowmelt and soil water content as well as the build-up of more experience among the people performing landslide forecasting.

Using NMME in Region-Specific Operational Seasonal Climate Forecasts

NASA Astrophysics Data System (ADS)

Gronewold, A.; Bolinger, R. A.; Fry, L. M.; Kompoltowicz, K.

2015-12-01

The National Oceanic and Atmospheric Administration's Climate Prediction Center (NOAA/CPC) provides access to a suite of real-time monthly climate forecasts that comprise the North American Multi-Model Ensemble (NMME) in an attempt to meet increasing demands for monthly to seasonal climate prediction. While the graphical map forecasts of the NMME are informative, there is a need to provide decision-makers with probabilistic forecasts specific to their region of interest. Here, we demonstrate the potential application of the NMME to address regional climate projection needs by developing new forecasts of temperature and precipitation for the North American Great Lakes, the largest system of lakes on Earth. Regional opertional water budget forecasts rely on these outlooks to initiate monthly forecasts not only of the water budget, but of monthly lake water levels as well. More specifically, we present an alternative for improving existing operational protocols that currently involve a relatively time-consuming and subjective procedure based on interpreting the maps of the NMME. In addition, all forecasts are currently presented in the NMME in a probabilistic format, with equal weighting given to each member of the ensemble. In our new evolution of this product, we provide historical context for the forecasts by superimposing them (in an on-line graphical user interface) with the historical range of observations. Implementation of this new tool has already led to noticeable advantages in regional water budget forecasting, and has the potential to be transferred to other regional decision-making authorities as well.

1/32° real-time global ocean prediction and value-added over 1/16° resolution

NASA Astrophysics Data System (ADS)

Shriver, J. F.; Hurlburt, H. E.; Smedstad, O. M.; Wallcraft, A. J.; Rhodes, R. C.

2007-03-01

A 1/32° global ocean nowcast/forecast system has been developed by the Naval Research Laboratory at the Stennis Space Center. It started running at the Naval Oceanographic Office in near real-time on 1 Nov. 2003 and has been running daily in real-time since 1 Mar. 2005. It became an operational system on 6 March 2006, replacing the existing 1/16° system which ceased operation on 12 March 2006. Both systems use the NRL Layered Ocean Model (NLOM) with assimilation of sea surface height from satellite altimeters and sea surface temperature from multi-channel satellite infrared radiometers. Real-time and archived results are available online at http://www.ocean.nrlssc.navy.mil/global_nlom. The 1/32° system has improvements over the earlier system that can be grouped into two categories: (1) better resolution and representation of dynamical processes and (2) design modifications. The design modifications are the result of accrued knowledge since the development of the earlier 1/16° system. The improved horizontal resolution of the 1/32° system has significant dynamical benefits which increase the ability of the model to accurately nowcast and skillfully forecast. At the finer resolution, current pathways and their transports become more accurate, the sea surface height (SSH) variability increases and becomes more realistic and even the global ocean circulation experiences some changes (including inter-basin exchange). These improvements make the 1/32° system a better dynamical interpolator of assimilated satellite altimeter track data, using a one-day model forecast as the first guess. The result is quantitatively more accurate nowcasts, as is illustrated by several model-data comparisons. Based on comparisons with ocean color imagery in the northwestern Arabian Sea and the Gulf of Oman, the 1/32° system has even demonstrated the ability to map small eddies, 25-75 km in diameter, with 70% reliability and a median eddy center location error of 22.5 km, a surprising and unanticipated result from assimilation of altimeter track data. For all of the eddies (50% small eddies), the reliability was 80% and the median eddy center location error was 29 km. The 1/32° system also exhibits improved forecast skill in relation to the 1/16° system. This is due to ( a) a more accurate initial condition for the forecast and ( b) better resolution and representation of critical dynamical processes (such as upper ocean - topographic coupling via mesoscale flow instabilities) which allow the model to more accurately evolve these features in time while running in forecast mode (forecast atmospheric forcing for the first 5 days, then gradually reverting toward climatology for the remainder of the 30-day forecast period). At 1/32° resolution, forecast SSH generally compares better with unassimilated observations and the anomaly correlation of the forecast SSH exceeds that from persistence by a larger amount than found in the 1/16° system.

Operational Space Weather Activities in the US

NASA Astrophysics Data System (ADS)

Berger, Thomas; Singer, Howard; Onsager, Terrance; Viereck, Rodney; Murtagh, William; Rutledge, Robert

2016-07-01

We review the current activities in the civil operational space weather forecasting enterprise of the United States. The NOAA/Space Weather Prediction Center is the nation's official source of space weather watches, warnings, and alerts, working with partners in the Air Force as well as international operational forecast services to provide predictions, data, and products on a large variety of space weather phenomena and impacts. In October 2015, the White House Office of Science and Technology Policy released the National Space Weather Strategy (NSWS) and associated Space Weather Action Plan (SWAP) that define how the nation will better forecast, mitigate, and respond to an extreme space weather event. The SWAP defines actions involving multiple federal agencies and mandates coordination and collaboration with academia, the private sector, and international bodies to, among other things, develop and sustain an operational space weather observing system; develop and deploy new models of space weather impacts to critical infrastructure systems; define new mechanisms for the transition of research models to operations and to ensure that the research community is supported for, and has access to, operational model upgrade paths; and to enhance fundamental understanding of space weather through support of research models and observations. The SWAP will guide significant aspects of space weather operational and research activities for the next decade, with opportunities to revisit the strategy in the coming years through the auspices of the National Science and Technology Council.

On the assimilation of satellite derived soil moisture in numerical weather prediction models

NASA Astrophysics Data System (ADS)

Drusch, M.

2006-12-01

Satellite derived surface soil moisture data sets are readily available and have been used successfully in hydrological applications. In many operational numerical weather prediction systems the initial soil moisture conditions are analysed from the modelled background and 2 m temperature and relative humidity. This approach has proven its efficiency to improve surface latent and sensible heat fluxes and consequently the forecast on large geographical domains. However, since soil moisture is not always related to screen level variables, model errors and uncertainties in the forcing data can accumulate in root zone soil moisture. Remotely sensed surface soil moisture is directly linked to the model's uppermost soil layer and therefore is a stronger constraint for the soil moisture analysis. Three data assimilation experiments with the Integrated Forecast System (IFS) of the European Centre for Medium-range Weather Forecasts (ECMWF) have been performed for the two months period of June and July 2002: A control run based on the operational soil moisture analysis, an open loop run with freely evolving soil moisture, and an experimental run incorporating bias corrected TMI (TRMM Microwave Imager) derived soil moisture over the southern United States through a nudging scheme using 6-hourly departures. Apart from the soil moisture analysis, the system setup reflects the operational forecast configuration including the atmospheric 4D-Var analysis. Soil moisture analysed in the nudging experiment is the most accurate estimate when compared against in-situ observations from the Oklahoma Mesonet. The corresponding forecast for 2 m temperature and relative humidity is almost as accurate as in the control experiment. Furthermore, it is shown that the soil moisture analysis influences local weather parameters including the planetary boundary layer height and cloud coverage. The transferability of the results to other satellite derived soil moisture data sets will be discussed.

Application of polar orbiter products in weather forecasting using open source tools and open standards

NASA Astrophysics Data System (ADS)

Plieger, Maarten; de Vreede, Ernst

2015-04-01

EUMETSAT disseminates data for a number of polar satellites. At KNMI these data are not fully used for operational weather forecasting mainly because of the irregular coverage and lack of tools for handling these different types of data and products. For weather forecasting there is a lot of interest in the application of products from these polar orbiters. One of the key aspects is the high-resolution of these products, which can complement the information provided by numerical weather forecasts. Another advantage over geostationary satellites is the high coverage at higher latitudes and lack of parallax. Products like the VIIRS day-night band offer many possibilities for this application. This presentation will describe a project that aims to make available a number of products from polar satellites to the forecasting operation. The goal of the project is to enable easy and timely access to polar orbiter products and enable combined presentations of satellite imagery with model data. The system will be able to generate RGB composites (“false colour images”) for operational use. The system will be built using open source components and open standards. Pytroll components are used for data handling, reprojection and derived product generation. For interactive presentation of imagery the browser based ADAGUC WMS viewer component is used. Image generation is done by ADAGUC server components, which provide OGC WMS services. Polar satellite products are stored as true color RGBA data in the NetCDF file format, the satellite swaths are stored as regular grids with their own custom geographical projection. The ADAGUC WMS system is able to reproject, render and combine these data in a webbrowser interactively. Results and lessons learned will be presented at the conference.

Sensing, Measurement, and Forecasting | Grid Modernization | NREL

Science.gov Websites

into operational intelligence to support grid operations and planning. Photo of solar resource monitoring equipment Grid operations involve assessing the grid's health in real time, predicting its to hours and days-to support advances in power system operations and planning. Capabilities Solar

JPSS Preparations at the Satellite Proving Ground for Marine, Precipitation, and Satellite Analysis

NASA Astrophysics Data System (ADS)

Folmer, M. J.; Berndt, E.; Clark, J.; Orrison, A.; Kibler, J.; Sienkiewicz, J. M.; Nelson, J. A., Jr.; Goldberg, M.

2016-12-01

The National Oceanic and Atmospheric Administration (NOAA) Satellite Proving Ground (PG) for Marine, Precipitation, and Satellite Analysis (MPS) has been demonstrating and evaluating Suomi National Polar-orbiting Partnership (S-NPP) products along with other polar-orbiting satellite platforms in preparation for the Joint Polar Satellite System - 1 (JPSS-1) launch in March 2017. The first S-NPP imagery was made available to the MPS PG during the evolution of Hurricane Sandy in October 2012 and has since been popular in operations. Since this event the MPS PG Satellite Liaison has been working with forecasters on ways to integrate single-channel and multispectral imagery from the Visible Infrared Imaging Radiometer Suite (VIIRS), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Advanced Very High Resolution Radiometer (AVHRR)into operations to complement numerical weather prediction and geostationary satellite savvy National Weather Service (NWS) National Centers. Additional unique products have been introduced to operations to address specific forecast challenges, including the Cooperative Institute for Research in the Atmosphere (CIRA) Layered Precipitable Water, the National Environmental Satellite, Data, and Information Service (NESDIS) Snowfall Rate product, NOAA Unique Combined Atmospheric Processing System (NUCAPS) Soundings, ozone products from the Atmospheric Infrared Sounder (AIRS), Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS), and Infrared Atmospheric Sounding Interferometer (IASI). In addition, new satellite domains have been created to provide forecasters at the NWS Ocean Prediction Center and Weather Prediction Center with better quality imagery at high latitudes. This has led to research projects that are addressing forecast challenges such as tropical to extratropical transition and explosive cyclogenesis. This presentation will provide examples of how the MPS PG has been introducing and integrating these products into operations to help solve these forecast challenges.

Client-Side Data Processing and Training for Multispectral Imagery Applications in the GOES-R Era

NASA Technical Reports Server (NTRS)

Fuell, Kevin; Gravelle, Chad; Burks, Jason; Berndt, Emily; Schultz, Lori; Molthan, Andrew; Leroy, Anita

2016-01-01

RGB imagery can be created locally (i.e. client-side) from single band imagery already on the system with little impact given recommended change to texture cache in AWIPS II. Training/Reference material accessible to forecasters within their operational display system improves RGB interpretation and application as demonstrated at OPG. Application examples from experienced forecasters are needed to support the larger community use of RGB imagery and these can be integrated into the user's display system.

Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

PubMed

Abbasi, Maryam; El Hanandeh, Ali

2016-10-01

Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg. Copyright © 2016 Elsevier Ltd. All rights reserved.

Probabilistic Nowcasting of Low-Visibility Procedure States at Vienna International Airport During Cold Season

NASA Astrophysics Data System (ADS)

Kneringer, Philipp; Dietz, Sebastian J.; Mayr, Georg J.; Zeileis, Achim

2018-04-01

Airport operations are sensitive to visibility conditions. Low-visibility events may lead to capacity reduction, delays and economic losses. Different levels of low-visibility procedures (lvp) are enacted to ensure aviation safety. A nowcast of the probabilities for each of the lvp categories helps decision makers to optimally schedule their operations. An ordered logistic regression (OLR) model is used to forecast these probabilities directly. It is applied to cold season forecasts at Vienna International Airport for lead times of 30-min out to 2 h. Model inputs are standard meteorological measurements. The skill of the forecasts is accessed by the ranked probability score. OLR outperforms persistence, which is a strong contender at the shortest lead times. The ranked probability score of the OLR is even better than the one of nowcasts from human forecasters. The OLR-based nowcasting system is computationally fast and can be updated instantaneously when new data become available.

Upper Rio Grande water operations model: A tool for enhanced system management

Treesearch

Gail Stockton; D. Michael Roark

1999-01-01

The Upper Rio Grande Water Operations Model (URGWOM) under development through a multi-agency effort has demonstrated capability to represent the physical river/reservoir system, to track and account for Rio Grande flows and imported San Juan flows, and to forecast flows at various points in the system. Testing of the Rio Chama portion of the water operations model was...

Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

NASA Technical Reports Server (NTRS)

Mazrooei, Amirhossein; Sinah, Tusshar; Sankarasubramanian, A.; Kumar, Sujay V.; Peters-Lidard, Christa D.

2015-01-01

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

Model Error Estimation for the CPTEC Eta Model

NASA Technical Reports Server (NTRS)

Tippett, Michael K.; daSilva, Arlindo

1999-01-01

Statistical data assimilation systems require the specification of forecast and observation error statistics. Forecast error is due to model imperfections and differences between the initial condition and the actual state of the atmosphere. Practical four-dimensional variational (4D-Var) methods try to fit the forecast state to the observations and assume that the model error is negligible. Here with a number of simplifying assumption, a framework is developed for isolating the model error given the forecast error at two lead-times. Two definitions are proposed for the Talagrand ratio tau, the fraction of the forecast error due to model error rather than initial condition error. Data from the CPTEC Eta Model running operationally over South America are used to calculate forecast error statistics and lower bounds for tau.

Koopman Operator Framework for Time Series Modeling and Analysis

NASA Astrophysics Data System (ADS)

Surana, Amit

2018-01-01

We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.

Skillful seasonal predictions of winter precipitation over southern China

NASA Astrophysics Data System (ADS)

Lu, Bo; Scaife, Adam A.; Dunstone, Nick; Smith, Doug; Ren, Hong-Li; Liu, Ying; Eade, Rosie

2017-07-01

Southern China experiences large year-to-year variability in the amount of winter precipitation, which can result in severe social and economic impacts. In this study, we demonstrate prediction skill of southern China winter precipitation by three operational seasonal prediction models: the operational Global seasonal forecasting system version 5 (GloSea5), the NCEP Climate Forecast System (CFSv2) and the Beijing Climate Center Climate System Model (BCC-CSM1.1m). The correlation scores reach 0.76 and 0.67 in GloSea5 and CFSv2, respectively; and the amplitude of the ensemble mean forecast signal is comparable to the observed variations. The skilful predictions in GloSea5 and CFSv2 mainly benefit from the successful representation of the observed ENSO teleconnection. El Niño weakens the Walker circulation and leads to the strengthening of the subtropical high over the northwestern Pacific. The anti-cyclone then induces anomalous northward flow over the South China Sea and brings water vapor to southern China, resulting in more precipitation. This teleconnection pattern is too weak in BCC-CSM1.1m, which explains its low skill (0.13). Whereas the most skilful forecast system is also able to simulate the influence of the Indian Ocean on southern China precipitation via changes in southwesterly winds over the Bay of Bengal. Finally, we examine the real-time forecast for 2015/16 winter when a strong El Niño event led to the highest rainfall over southern China in recent decades. We find that the GloSea5 system gave good advice as it produced the third wettest southern China in the hindcast, but underestimated the observed amplitude. This is likely due to the underestimation of the Siberian High strength in 2015/2016 winter, which has driven strong convergence over southern China. We conclude that some current seasonal forecast systems can give useful warning of impending extremes. However, there is still need for further model improvement to fully represent the complex dynamics of the region.

Forecasting drought risks for a water supply storage system using bootstrap position analysis

USGS Publications Warehouse

Tasker, Gary; Dunne, Paul

1997-01-01

Forecasting the likelihood of drought conditions is an integral part of managing a water supply storage and delivery system. Position analysis uses a large number of possible flow sequences as inputs to a simulation of a water supply storage and delivery system. For a given set of operating rules and water use requirements, water managers can use such a model to forecast the likelihood of specified outcomes such as reservoir levels falling below a specified level or streamflows falling below statutory passing flows a few months ahead conditioned on the current reservoir levels and streamflows. The large number of possible flow sequences are generated using a stochastic streamflow model with a random resampling of innovations. The advantages of this resampling scheme, called bootstrap position analysis, are that it does not rely on the unverifiable assumption of normality and it allows incorporation of long-range weather forecasts into the analysis.

How much are you prepared to PAY for a forecast?

NASA Astrophysics Data System (ADS)

Arnal, Louise; Coughlan, Erin; Ramos, Maria-Helena; Pappenberger, Florian; Wetterhall, Fredrik; Bachofen, Carina; van Andel, Schalk Jan

2015-04-01

Probabilistic hydro-meteorological forecasts are a crucial element of the decision-making chain in the field of flood prevention. The operational use of probabilistic forecasts is increasingly promoted through the development of new novel state-of-the-art forecast methods and numerical skill is continuously increasing. However, the value of such forecasts for flood early-warning systems is a topic of diverging opinions. Indeed, the word value, when applied to flood forecasting, is multifaceted. It refers, not only to the raw cost of acquiring and maintaining a probabilistic forecasting system (in terms of human and financial resources, data volume and computational time), but also and most importantly perhaps, to the use of such products. This game aims at investigating this point. It is a willingness to pay game, embedded in a risk-based decision-making experiment. Based on a ``Red Cross/Red Crescent, Climate Centre'' game, it is a contribution to the international Hydrologic Ensemble Prediction Experiment (HEPEX). A limited number of probabilistic forecasts will be auctioned to the participants; the price of these forecasts being market driven. All participants (irrespective of having bought or not a forecast set) will then be taken through a decision-making process to issue warnings for extreme rainfall. This game will promote discussions around the topic of the value of forecasts for decision-making in the field of flood prevention.

An operational mesoscale ensemble data assimilation and prediction system: E-RTFDDA

NASA Astrophysics Data System (ADS)

Liu, Y.; Hopson, T.; Roux, G.; Hacker, J.; Xu, M.; Warner, T.; Swerdlin, S.

2009-04-01

Mesoscale (2-2000 km) meteorological processes differ from synoptic circulations in that mesoscale weather changes rapidly in space and time, and physics processes that are parameterized in NWP models play a great role. Complex interactions of synoptic circulations, regional and local terrain, land-surface heterogeneity, and associated physical properties, and the physical processes of radiative transfer, cloud and precipitation and boundary layer mixing, are crucial in shaping regional weather and climate. Mesoscale ensemble analysis and prediction should sample the uncertainties of mesoscale modeling systems in representing these factors. An innovative mesoscale Ensemble Real-Time Four Dimensional Data Assimilation (E-RTFDDA) and forecasting system has been developed at NCAR. E-RTFDDA contains diverse ensemble perturbation approaches that consider uncertainties in all major system components to produce multi-scale continuously-cycling probabilistic data assimilation and forecasting. A 30-member E-RTFDDA system with three nested domains with grid sizes of 30, 10 and 3.33 km has been running on a Department of Defense high-performance computing platform since September 2007. It has been applied at two very different US geographical locations; one in the western inter-mountain area and the other in the northeastern states, producing 6 hour analyses and 48 hour forecasts, with 4 forecast cycles a day. The operational model outputs are analyzed to a) assess overall ensemble performance and properties, b) study terrain effect on mesoscale predictability, c) quantify the contribution of different ensemble perturbation approaches to the overall forecast skill, and d) assess the additional contributed skill from an ensemble calibration process based on a quantile-regression algorithm. The system and the results will be reported at the meeting.

A domain analysis approach to clear-air turbulence forecasting using high-density in-situ measurements

NASA Astrophysics Data System (ADS)

Abernethy, Jennifer A.

Pilots' ability to avoid clear-air turbulence (CAT) during flight affects the safety of the millions of people who fly commercial airlines and other aircraft, and turbulence costs millions in injuries and aircraft maintenance every year. Forecasting CAT is not straightforward, however; microscale features like the turbulence eddies that affect aircraft (100m) are below the current resolution of operational numerical weather prediction (NWP) models, and the only evidence of CAT episodes, until recently, has been sparse, subjective reports from pilots known as PIREPs. To forecast CAT, researchers use a simple weighted sum of top-performing turbulence indicators derived from NWP model outputs---termed diagnostics---based on their agreement with current PIREPs. However, a new, quantitative source of observation data---high-density measurements made by sensor equipment and software on aircraft, called in-situ measurements---is now available. The main goal of this thesis is to develop new data analysis and processing techniques to apply to the model and new observation data, in order to improve CAT forecasting accuracy. This thesis shows that using in-situ data improves forecasting accuracy and that automated machine learning algorithms such as support vector machines (SVM), logistic regression, and random forests, can match current performance while eliminating almost all hand-tuning. Feature subset selection is paired with the new algorithms to choose diagnostics that predict well as a group rather than individually. Specializing forecasts and choice of diagnostics by geographic region further improves accuracy because of the geographic variation in turbulence sources. This work uses random forests to find climatologically-relevant regions based on these variations and implements a forecasting system testbed which brings these techniques together to rapidly prototype new, regionalized versions of operational CAT forecasting systems.

Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore.

PubMed

Shi, Yuan; Liu, Xu; Kok, Suet-Yheng; Rajarethinam, Jayanthi; Liang, Shaohong; Yap, Grace; Chong, Chee-Seng; Lee, Kim-Sung; Tan, Sharon S Y; Chin, Christopher Kuan Yew; Lo, Andrew; Kong, Waiming; Ng, Lee Ching; Cook, Alex R

2016-09-01

With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention. We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak. We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore. Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore's dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response. Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue. Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore. Environ Health Perspect 124:1369-1375; http://dx.doi.org/10.1289/ehp.1509981.

A two-stage method of quantitative flood risk analysis for reservoir real-time operation using ensemble-based hydrologic forecasts

NASA Astrophysics Data System (ADS)

Liu, P.

2013-12-01

Quantitative analysis of the risk for reservoir real-time operation is a hard task owing to the difficulty of accurate description of inflow uncertainties. The ensemble-based hydrologic forecasts directly depict the inflows not only the marginal distributions but also their persistence via scenarios. This motivates us to analyze the reservoir real-time operating risk with ensemble-based hydrologic forecasts as inputs. A method is developed by using the forecast horizon point to divide the future time into two stages, the forecast lead-time and the unpredicted time. The risk within the forecast lead-time is computed based on counting the failure number of forecast scenarios, and the risk in the unpredicted time is estimated using reservoir routing with the design floods and the reservoir water levels of forecast horizon point. As a result, a two-stage risk analysis method is set up to quantify the entire flood risks by defining the ratio of the number of scenarios that excessive the critical value to the total number of scenarios. The China's Three Gorges Reservoir (TGR) is selected as a case study, where the parameter and precipitation uncertainties are implemented to produce ensemble-based hydrologic forecasts. The Bayesian inference, Markov Chain Monte Carlo, is used to account for the parameter uncertainty. Two reservoir operation schemes, the real operated and scenario optimization, are evaluated for the flood risks and hydropower profits analysis. With the 2010 flood, it is found that the improvement of the hydrologic forecast accuracy is unnecessary to decrease the reservoir real-time operation risk, and most risks are from the forecast lead-time. It is therefore valuable to decrease the avarice of ensemble-based hydrologic forecasts with less bias for a reservoir operational purpose.

Ensemble flare forecasting: using numerical weather prediction techniques to improve space weather operations

NASA Astrophysics Data System (ADS)

Murray, S.; Guerra, J. A.

2017-12-01

One essential component of operational space weather forecasting is the prediction of solar flares. Early flare forecasting work focused on statistical methods based on historical flaring rates, but more complex machine learning methods have been developed in recent years. A multitude of flare forecasting methods are now available, however it is still unclear which of these methods performs best, and none are substantially better than climatological forecasts. Current operational space weather centres cannot rely on automated methods, and generally use statistical forecasts with a little human intervention. Space weather researchers are increasingly looking towards methods used in terrestrial weather to improve current forecasting techniques. Ensemble forecasting has been used in numerical weather prediction for many years as a way to combine different predictions in order to obtain a more accurate result. It has proved useful in areas such as magnetospheric modelling and coronal mass ejection arrival analysis, however has not yet been implemented in operational flare forecasting. Here we construct ensemble forecasts for major solar flares by linearly combining the full-disk probabilistic forecasts from a group of operational forecasting methods (ASSA, ASAP, MAG4, MOSWOC, NOAA, and Solar Monitor). Forecasts from each method are weighted by a factor that accounts for the method's ability to predict previous events, and several performance metrics (both probabilistic and categorical) are considered. The results provide space weather forecasters with a set of parameters (combination weights, thresholds) that allow them to select the most appropriate values for constructing the 'best' ensemble forecast probability value, according to the performance metric of their choice. In this way different forecasts can be made to fit different end-user needs.

Developments in radar and remote-sensing methods for measuring and forecasting rainfall.

PubMed

Collier, C G

2002-07-15

Over the last 25 years or so, weather-radar networks have become an integral part of operational meteorological observing systems. While measurements of rainfall made using radar systems have been used qualitatively by weather forecasters, and by some operational hydrologists, acceptance has been limited as a consequence of uncertainties in the quality of the data. Nevertheless, new algorithms for improving the accuracy of radar measurements of rainfall have been developed, including the potential to calibrate radars using the measurements of attenuation on microwave telecommunications links. Likewise, ways of assimilating these data into both meteorological and hydrological models are being developed. In this paper we review the current accuracy of radar estimates of rainfall, pointing out those approaches to the improvement of accuracy which are likely to be most successful operationally. Comment is made on the usefulness of satellite data for estimating rainfall in a flood-forecasting context. Finally, problems in coping with the error characteristics of all these data using both simple schemes and more complex four-dimensional variational analysis are being addressed, and are discussed briefly in this paper.

Use of EOS Data in AWIPS for Weather Forecasting

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Haines, Stephanie L.; Suggs, Ron J.; Bradshaw, Tom; Darden, Chris; Burks, Jason

2003-01-01

Operational weather forecasting relies heavily on real time data and modeling products for forecast preparation and dissemination of significant weather information to the public. The synthesis of this information (observations and model products) by the meteorologist is facilitated by a decision support system to display and integrate the information in a useful fashion. For the NWS this system is called Advanced Weather Interactive Processing System (AWIPS). Over the last few years NASA has launched a series of new Earth Observation Satellites (EOS) for climate monitoring that include several instruments that provide high-resolution measurements of atmospheric and surface features important for weather forecasting and analysis. The key to the utilization of these unique new measurements by the NWS is the real time integration of the EOS data into the AWIPS system. This is currently being done in the Huntsville and Birmingham NWS Forecast Offices under the NASA Short-term Prediction Research and Transition (SPORT) Program. This paper describes the use of near real time MODIS and AIRS data in AWIPS to improve the detection of clouds, moisture variations, atmospheric stability, and thermal signatures that can lead to significant weather development. The paper and the conference presentation will focus on several examples where MODIS and AIRS data have made a positive impact on forecast accuracy. The results of an assessment of the utility of these products for weather forecast improvement made at the Huntsville NWS Forecast Office will be presented.

The CMEMS IBI-MFC Forecasting Service in 2017: Evolution and Novelties associated to the CMEMS service release

NASA Astrophysics Data System (ADS)

Lorente, Pablo; Sotillo, Marcos G.; Gutknecht, Elodie; Dabrowski, Tomasz; Aouf, Lotfi; Toledano, Cristina; Amo-Baladron, Arancha; Aznar, Roland; De Pascual, Alvaro; Levier, Bruno; Bowyer, Peter; Rainaud, Romain; Alvarez-Fanjul, Enrique

2017-04-01

The IBI-MFC (Iberia-Biscay-Ireland Monitoring & Forecasting Centre) has been providing daily ocean model estimates and forecasts of diverse physical parameters for the IBI regional seas since 2011, first in the frame of MyOcean projects and later as part of the Copernicus Marine Environment Monitoring Service (CMEMS). By April 2017, coincident with the V3 CMEMS Service Release, the IBI-MFC will extend their near real time (NRT) forecast capabilities. Two new operational IBI forecast systems will be operationally run to generate high resolution biochemical (BIO) and wave (WAV) products on the IBI area. The IBI-NRT-BIO forecast system, based on a 1/36° NEMO-PISCES model application, is run once a week coupled with the IBI physical forecast solution and nested to the CMEMS GLOBAL-BIO solution. On the other hand, the IBI-NRT-WAV system, based on a MeteoFrance-WAM 10km resolution model application, runs twice a day using ECMWF wind forcing. Among other novelties related to the evolution of the IBI physical (PHY) solution, it is worthwhile mentioning the provision, as part of the IBI-NRT-PHY product daily updated, of three-dimensional hourly data on specific areas within the IBI domain. The delivery of these new hourly data along the whole water column has been achieved after the request from IBI users, in order to foster downscaling approaches by providing coherent open boundary conditions to any potential high-resolution coastal model nested to IBI regional solution. An extensive skill assessment of IBI-NRT forecast products has been conducted through the NARVAL (North Atlantic Regional VALidation) web tool, by means of the automatic computation of statistical metrics and quality indicators. By now, this tool has been focused on the validation of the IBI-NRT-PHY system. Nowadays, NARVAL is facing a significant upgrade to validate the aforementioned new biogeochemical and wave IBI products. To this aim, satellite derived observations of chlorophyll and significant wave height will be used, together with in-situ wave parameters measured by mooring buoys. Within this validation framework, special emphasis has been placed on the intercomparison of different forecast model solutions in overlapping areas in order to evaluate models' performances and prognostic capabilities. This common uncertainty estimates of IBI and other model solution is currently performed by NARVAL using both CMEMS forecast model sources (i.e. GLOBAL-MFC, MED-MFC and NWS-MFC) and non-CMEMS operational forecast solutions (mostly downstream application nested to the IBI solution). With respect to the IBI multi-year (MY) products, it is worth mentioning that the actual biogeochemical and physical reanalysis products will be re-run along year 2017, extending its time coverage backwards until 1992. Based on these IBI-MY products, a variety of climatic indicators related to essential oceanographic processes (i.e. western coastal upwelling or the Mediterranean Outflow Water) are currently being computed.

The Impact of British Airways Wind Observations on the Goddard Earth Observing System Analyses and Forecasts

NASA Technical Reports Server (NTRS)

Rukhovets, Leonid; Sienkiewicz, M.; Tenenbaum, J.; Kondratyeva, Y.; Owens, T.; Oztunali, M.; Atlas, Robert (Technical Monitor)

2001-01-01

British Airways flight data recorders can provide valuable meteorological information, but they are not available in real-time on the Global Telecommunication System. Information from the flight recorders was used in the Global Aircraft Data Set (GADS) experiment as independent observations to estimate errors in wind analyses produced by major operational centers. The GADS impact on the Goddard Earth Observing System Data Assimilation System (GEOS DAS) analyses was investigated using GEOS-1 DAS version. Recently, a new Data Assimilation System (fvDAS) has been developed at the Data Assimilation Office, NASA Goddard. Using fvDAS , the, GADS impact on analyses and forecasts was investigated. It was shown the GADS data intensify wind speed analyses of jet streams for some cases. Five-day forecast anomaly correlations and root mean squares were calculated for 300, 500 hPa and SLP for six different areas: Northern and Southern Hemispheres, North America, Europe, Asia, USA These scores were obtained as averages over 21 forecasts from January 1998. Comparisons with scores for control experiments without GADS showed a positive impact of the GADS data on forecasts beyond 2-3 days for all levels at the most areas.

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias

With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by firstmore » layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.« less

Identifying, Tracking, and Prioritizing Parts Unavailability

DTIC Science & Technology

2013-03-01

of RFM with MRP Equivalents in Parentheses ................ 13 Figure 2. Flow Chart of Repair Process...by the Air Force is the Reparability Forecast Model (RFM). The RFM operates similar to a Material Requirements Planning ( MRP ) system, where it...Outputs of RFM with MRP Equivalents in Parentheses (Gaudette et al, 2002: 7) The Reparability Forecast Model does not determine stock levels or

Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate

NASA Astrophysics Data System (ADS)

Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert

2017-11-01

Hydropower production requires optimal dam and reservoir management to prevent flooding damage and avoid operation losses. In a northern climate, where spring freshet constitutes the main inflow volume, seasonal forecasts can help to establish a yearly strategy. Long-term hydrological forecasts often rely on past observations of streamflow or meteorological data. Another alternative is to use ensemble meteorological forecasts produced by climate models. In this paper, those produced by the ECMWF (European Centre for Medium-Range Forecast) System 4 are examined and bias is characterized. Bias correction, through the linear scaling method, improves the performance of the raw ensemble meteorological forecasts in terms of continuous ranked probability score (CRPS). Then, three seasonal ensemble hydrological forecasting systems are compared: (1) the climatology of simulated streamflow, (2) the ensemble hydrological forecasts based on climatology (ESP) and (3) the hydrological forecasts based on bias-corrected ensemble meteorological forecasts from System 4 (corr-DSP). Simulated streamflow computed using observed meteorological data is used as benchmark. Accounting for initial conditions is valuable even for long-term forecasts. ESP and corr-DSP both outperform the climatology of simulated streamflow for lead times from 1 to 5 months depending on the season and watershed. Integrating information about future meteorological conditions also improves monthly volume forecasts. For the 1-month lead time, a gain exists for almost all watersheds during winter, summer and fall. However, volume forecasts performance for spring varies from one watershed to another. For most of them, the performance is close to the performance of ESP. For longer lead times, the CRPS skill score is mostly in favour of ESP, even if for many watersheds, ESP and corr-DSP have comparable skill. Corr-DSP appears quite reliable but, in some cases, under-dispersion or bias is observed. A more complex bias-correction method should be further investigated to remedy this weakness and take more advantage of the ensemble forecasts produced by the climate model. Overall, in this study, bias-corrected ensemble meteorological forecasts appear to be an interesting source of information for hydrological forecasting for lead times up to 1 month. They could also complement ESP for longer lead times.

Evaluating the Impact of AIRS Observations on Regional Forecasts at the SPoRT Center

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley

2011-01-01

NASA Short-term Prediction Research and Transition (SPoRT) Center collaborates with operational partners of different sizes and operational goals to improve forecasts using targeted projects and data sets. Modeling and DA activities focus on demonstrating utility of NASA data sets and capabilities within operational systems. SPoRT has successfully assimilated the Atmospheric Infrared Sounder (AIRS) radiance and profile data. A collaborative project is underway with the Joint Center for Satellite Data Assimilation (JCSDA) to use AIRS profiles to better understand the impact of AIRS radiances assimilated within Gridpoint Statistical Interpolation (GSI) in hopes of engaging the operational DA community in a reassessment of assimilation methodologies to more effectively assimilate hyperspectral radiances.

Value of Seasonal Fuzzy-based Inflow Prediction in the Jucar River Basin

NASA Astrophysics Data System (ADS)

Pulido-Velazquez, M.; Macian-Sorribes, H.

2016-12-01

The development and application of climate services in Integrated Water Resources Management (IWRM) is said to add important benefits in terms of water use efficiency due to an increase ability to foresee future water availability. A method to evaluate the economic impact of these services is presented, based on the use of hydroeconomic modelling techniques (hydroeconomic simulation) to compare the net benefits from water use in the system with and without the inflow forecasting. The Jucar River Basin (Spain) has been used as case study. Operating rules currently applied in the basin were assessed using fuzzy rule-based (FRB) systems via a co-development process involving the system operators. These operating rules use as input variable the hydrological inflows in several sub-basins, which need to be foreseen by the system operators. The inflow forecasting mechanism to preview water availability in the irrigation season (May-September) relied on fuzzy regression in which future inflows were foreseen based on past inflows and rainfall in the basin. This approach was compared with the current use of the two past year inflows for projecting the future inflow. For each irrigation season, the previewed inflows were determined using both methods and their impact on the system operation assessed through a hydroeconomic DSS. Results show that the implementation of the fuzzy inflow forecasting system offers higher economic returns. Another advantage of the fuzzy approach regards to the uncertainty treatment using fuzzy numbers, which allow us to estimate the uncertainty range of the expected benefits. Consequently, we can use the fuzzy approach to estimate the uncertainty associated with both the prediction and the associated benefits.

Impact of Interactive Aerosol on the African Easterly Jet in the NASA GEOS-5 Global Forecasting System

NASA Technical Reports Server (NTRS)

Reale, O.; Lau, K. M.; da Silva, A.

2010-01-01

The real-time treatment of interactive realistically varying aerosol in a global operational forecasting system, as opposed to prescribed (fixed or climatologically varying) aerosols, is a very difficult challenge that only recently begins to be addressed. Experiment results from a recent version of the NASA GEOS-5 forecasting system, inclusive of interactive aerosol treatment, are presented in this work. Four sets of 30 5-day forecasts are initialized from a high quality set of analyses previously produced and documented to cover the period from 15 August to 16 September 2006, which corresponds to the NASA African Monsoon Multidisciplinary Analysis (NAMMA) observing campaign. The four forecast sets are at two different horizontal resolutions and with and without interactive aerosol treatment. The net impact of aerosol, at times in which there is a strong dust outbreak, is a temperature increase at the dust level and decrease in the near-surface levels, in complete agreement with previous observational and modeling studies. Moreover, forecasts in which interactive aerosols are included depict an African Easterly (AEJ) at slightly higher elevation, and slightly displace northward, with respect to the forecasts in which aerosols are not include. The shift in the AEJ position goes in the direction of observations and agrees with previous results.

Experiments with Seasonal Forecasts of ocean conditions for the Northern region of the California Current upwelling system

PubMed Central

Siedlecki, Samantha A.; Kaplan, Isaac C.; Hermann, Albert J.; Nguyen, Thanh Tam; Bond, Nicholas A.; Newton, Jan A.; Williams, Gregory D.; Peterson, William T.; Alin, Simone R.; Feely, Richard A.

2016-01-01

Resource managers at the state, federal, and tribal levels make decisions on a weekly to quarterly basis, and fishers operate on a similar timeframe. To determine the potential of a support tool for these efforts, a seasonal forecast system is experimented with here. JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and forecasts from NOAA’s Climate Forecast System (CFS). Model performance and predictability were examined for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, reforecast, and forecast comparisons with observations. Results indicate J-SCOPE forecasts have measurable skill on seasonal timescales. Experiments suggest that seasonal forecasting of ocean conditions important for fisheries is possible with the right combination of components. Those components include regional predictability on seasonal timescales of the physical environment from a large-scale model, a high-resolution regional model with biogeochemistry that simulates seasonal conditions in hindcasts, a relationship with local stakeholders, and a real-time observational network. Multiple efforts and approaches in different regions would advance knowledge to provide additional tools to fishers and other stakeholders. PMID:27273473

Experiments with Seasonal Forecasts of ocean conditions for the Northern region of the California Current upwelling system

NASA Astrophysics Data System (ADS)

Siedlecki, Samantha A.; Kaplan, Isaac C.; Hermann, Albert J.; Nguyen, Thanh Tam; Bond, Nicholas A.; Newton, Jan A.; Williams, Gregory D.; Peterson, William T.; Alin, Simone R.; Feely, Richard A.

2016-06-01

Resource managers at the state, federal, and tribal levels make decisions on a weekly to quarterly basis, and fishers operate on a similar timeframe. To determine the potential of a support tool for these efforts, a seasonal forecast system is experimented with here. JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and forecasts from NOAA’s Climate Forecast System (CFS). Model performance and predictability were examined for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, reforecast, and forecast comparisons with observations. Results indicate J-SCOPE forecasts have measurable skill on seasonal timescales. Experiments suggest that seasonal forecasting of ocean conditions important for fisheries is possible with the right combination of components. Those components include regional predictability on seasonal timescales of the physical environment from a large-scale model, a high-resolution regional model with biogeochemistry that simulates seasonal conditions in hindcasts, a relationship with local stakeholders, and a real-time observational network. Multiple efforts and approaches in different regions would advance knowledge to provide additional tools to fishers and other stakeholders.

Experiments with Seasonal Forecasts of ocean conditions for the Northern region of the California Current upwelling system.

PubMed

Siedlecki, Samantha A; Kaplan, Isaac C; Hermann, Albert J; Nguyen, Thanh Tam; Bond, Nicholas A; Newton, Jan A; Williams, Gregory D; Peterson, William T; Alin, Simone R; Feely, Richard A

2016-06-07

Resource managers at the state, federal, and tribal levels make decisions on a weekly to quarterly basis, and fishers operate on a similar timeframe. To determine the potential of a support tool for these efforts, a seasonal forecast system is experimented with here. JISAO's Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and forecasts from NOAA's Climate Forecast System (CFS). Model performance and predictability were examined for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, reforecast, and forecast comparisons with observations. Results indicate J-SCOPE forecasts have measurable skill on seasonal timescales. Experiments suggest that seasonal forecasting of ocean conditions important for fisheries is possible with the right combination of components. Those components include regional predictability on seasonal timescales of the physical environment from a large-scale model, a high-resolution regional model with biogeochemistry that simulates seasonal conditions in hindcasts, a relationship with local stakeholders, and a real-time observational network. Multiple efforts and approaches in different regions would advance knowledge to provide additional tools to fishers and other stakeholders.

Replacing climatological potential evapotranspiration estimates with dynamic satellite-based observations in operational hydrologic prediction models

NASA Astrophysics Data System (ADS)

Franz, K. J.; Bowman, A. L.; Hogue, T. S.; Kim, J.; Spies, R.

2011-12-01

In the face of a changing climate, growing populations, and increased human habitation in hydrologically risky locations, both short- and long-range planners increasingly require robust and reliable streamflow forecast information. Current operational forecasting utilizes watershed-scale, conceptual models driven by ground-based (commonly point-scale) observations of precipitation and temperature and climatological potential evapotranspiration (PET) estimates. The PET values are derived from historic pan evaporation observations and remain static from year-to-year. The need for regional dynamic PET values is vital for improved operational forecasting. With the advent of satellite remote sensing and the adoption of a more flexible operational forecast system by the National Weather Service, incorporation of advanced data products is now more feasible than in years past. In this study, we will test a previously developed satellite-derived PET product (UCLA MODIS-PET) in the National Weather Service forecast models and compare the model results to current methods. The UCLA MODIS-PET method is based on the Priestley-Taylor formulation, is driven with MODIS satellite products, and produces a daily, 250m PET estimate. The focus area is eight headwater basins in the upper Midwest U.S. There is a need to develop improved forecasting methods for this region that are able to account for climatic and landscape changes more readily and effectively than current methods. This region is highly flood prone yet sensitive to prolonged dry periods in late summer and early fall, and is characterized by a highly managed landscape, which has drastically altered the natural hydrologic cycle. Our goal is to improve model simulations, and thereby, the initial conditions prior to the start of a forecast through the use of PET values that better reflect actual watershed conditions. The forecast models are being tested in both distributed and lumped mode.

Magnetogram Forecast: An All-Clear Space Weather Forecasting System

NASA Technical Reports Server (NTRS)

Barghouty, Nasser; Falconer, David

2015-01-01

Solar flares and coronal mass ejections (CMEs) are the drivers of severe space weather. Forecasting the probability of their occurrence is critical in improving space weather forecasts. The National Oceanic and Atmospheric Administration (NOAA) currently uses the McIntosh active region category system, in which each active region on the disk is assigned to one of 60 categories, and uses the historical flare rates of that category to make an initial forecast that can then be adjusted by the NOAA forecaster. Flares and CMEs are caused by the sudden release of energy from the coronal magnetic field by magnetic reconnection. It is believed that the rate of flare and CME occurrence in an active region is correlated with the free energy of an active region. While the free energy cannot be measured directly with present observations, proxies of the free energy can instead be used to characterize the relative free energy of an active region. The Magnetogram Forecast (MAG4) (output is available at the Community Coordinated Modeling Center) was conceived and designed to be a databased, all-clear forecasting system to support the operational goals of NASA's Space Radiation Analysis Group. The MAG4 system automatically downloads nearreal- time line-of-sight Helioseismic and Magnetic Imager (HMI) magnetograms on the Solar Dynamics Observatory (SDO) satellite, identifies active regions on the solar disk, measures a free-energy proxy, and then applies forecasting curves to convert the free-energy proxy into predicted event rates for X-class flares, M- and X-class flares, CMEs, fast CMEs, and solar energetic particle events (SPEs). The forecast curves themselves are derived from a sample of 40,000 magnetograms from 1,300 active region samples, observed by the Solar and Heliospheric Observatory Michelson Doppler Imager. Figure 1 is an example of MAG4 visual output

Three-model ensemble wind prediction in southern Italy

NASA Astrophysics Data System (ADS)

Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo

2016-03-01

Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

An Enhanced Convective Forecast (ECF) for the New York TRACON Area

NASA Technical Reports Server (NTRS)

Wheeler, Mark; Stobie, James; Gillen, Robert; Jedlovec, Gary; Sims, Danny

2008-01-01

In an effort to relieve summer-time congestion in the NY Terminal Radar Approach Control (TRACON) area, the FAA is testing an enhanced convective forecast (ECF) product. The test began in June 2008 and is scheduled to run through early September. The ECF is updated every two hours, right before the Air Traffic Control System Command Center (ATCSCC) national planning telcon. It is intended to be used by traffic managers throughout the National Airspace System (NAS) and airlines dispatchers to supplement information from the Collaborative Convective Forecast Product (CCFP) and the Corridor Integrated Weather System (CIWS). The ECF begins where the current CIWS forecast ends at 2 hours and extends out to 12 hours. Unlike the CCFP it is a detailed deterministic forecast with no aerial coverage limits. It is created by an ENSCO forecaster using a variety of guidance products including, the Weather Research and Forecast (WRF) model. This is the same version of the WRF that ENSCO runs over the Florida peninsula in support of launch operations at the Kennedy Space Center. For this project, the WRF model domain has been shifted to the Northeastern US. Several products from the NASA SPoRT group are also used by the ENSCO forecaster. In this paper we will provide examples of the ECF products and discuss individual cases of traffic management actions using ECF guidance.

National Centers for Environmental Prediction

Science.gov Websites

Products Operational Forecast Graphics Experimental Forecast Graphics Verification and Diagnostics Model PARALLEL/EXPERIMENTAL MODEL FORECAST GRAPHICS OPERATIONAL VERIFICATION / DIAGNOSTICS PARALLEL VERIFICATION Developmental Air Quality Forecasts and Verification Back to Table of Contents 2. PARALLEL/EXPERIMENTAL GRAPHICS

National Centers for Environmental Prediction

Science.gov Websites

Operational Forecast Graphics Experimental Forecast Graphics Verification and Diagnostics Model Configuration /EXPERIMENTAL MODEL FORECAST GRAPHICS OPERATIONAL VERIFICATION / DIAGNOSTICS PARALLEL VERIFICATION / DIAGNOSTICS Developmental Air Quality Forecasts and Verification Back to Table of Contents 2. PARALLEL/EXPERIMENTAL GRAPHICS

Sol-Terra - AN Operational Space Weather Forecasting Model Framework

NASA Astrophysics Data System (ADS)

Bisi, M. M.; Lawrence, G.; Pidgeon, A.; Reid, S.; Hapgood, M. A.; Bogdanova, Y.; Byrne, J.; Marsh, M. S.; Jackson, D.; Gibbs, M.

2015-12-01

The SOL-TERRA project is a collaboration between RHEA Tech, the Met Office, and RAL Space funded by the UK Space Agency. The goal of the SOL-TERRA project is to produce a Roadmap for a future coupled Sun-to-Earth operational space weather forecasting system covering domains from the Sun down to the magnetosphere-ionosphere-thermosphere and neutral atmosphere. The first stage of SOL-TERRA is underway and involves reviewing current models that could potentially contribute to such a system. Within a given domain, the various space weather models will be assessed how they could contribute to such a coupled system. This will be done both by reviewing peer reviewed papers, and via direct input from the model developers to provide further insight. Once the models have been reviewed then the optimal set of models for use in support of forecast-based SWE modelling will be selected, and a Roadmap for the implementation of an operational forecast-based SWE modelling framework will be prepared. The Roadmap will address the current modelling capability, knowledge gaps and further work required, and also the implementation and maintenance of the overall architecture and environment that the models will operate within. The SOL-TERRA project will engage with external stakeholders in order to ensure independently that the project remains on track to meet its original objectives. A group of key external stakeholders have been invited to provide their domain-specific expertise in reviewing the SOL-TERRA project at critical stages of Roadmap preparation; namely at the Mid-Term Review, and prior to submission of the Final Report. This stakeholder input will ensure that the SOL-TERRA Roadmap will be enhanced directly through the input of modellers and end-users. The overall goal of the SOL-TERRA project is to develop a Roadmap for an operational forecast-based SWE modelling framework with can be implemented within a larger subsequent activity. The SOL-TERRA project is supported within the UK Space Agency's National Space Technology Programme under contract number RP10G0348A03.

Spatial Pattern Classification for More Accurate Forecasting of Variable Energy Resources

NASA Astrophysics Data System (ADS)

Novakovskaia, E.; Hayes, C.; Collier, C.

2014-12-01

The accuracy of solar and wind forecasts is becoming increasingly essential as grid operators continue to integrate additional renewable generation onto the electric grid. Forecast errors affect rate payers, grid operators, wind and solar plant maintenance crews and energy traders through increases in prices, project down time or lost revenue. While extensive and beneficial efforts were undertaken in recent years to improve physical weather models for a broad spectrum of applications these improvements have generally not been sufficient to meet the accuracy demands of system planners. For renewables, these models are often used in conjunction with additional statistical models utilizing both meteorological observations and the power generation data. Forecast accuracy can be dependent on specific weather regimes for a given location. To account for these dependencies it is important that parameterizations used in statistical models change as the regime changes. An automated tool, based on an artificial neural network model, has been developed to identify different weather regimes as they impact power output forecast accuracy at wind or solar farms. In this study, improvements in forecast accuracy were analyzed for varying time horizons for wind farms and utility-scale PV plants located in different geographical regions.

Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning, volume 4

NASA Technical Reports Server (NTRS)

Keitz, J. F.

1982-01-01

The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This volume of the report discusses the results of Task 4 of the four major tasks included in the study. Task 4 uses flight plan segment wind and temperature differences as indicators of dates and geographic areas for which significant forecast errors may have occurred. An in-depth analysis is then conducted for the days identified. The analysis show that significant errors occur in the operational forecast on 15 of the 33 arbitrarily selected days included in the study. Wind speeds in an area of maximum winds are underestimated by at least 20 to 25 kts. on 14 of these days. The analysis also show that there is a tendency to repeat the same forecast errors from prog to prog. Also, some perceived forecast errors from the flight plan comparisons could not be verified by visual inspection of the corresponding National Meteorological Center forecast and analyses charts, and it is likely that they are the result of weather data interpolation techniques or some other data processing procedure in the airlines' flight planning systems.

Assimilating NOAA SST data into BSH operational circulation model for North and Baltic Seas

NASA Astrophysics Data System (ADS)

Losa, Svetlana; Schroeter, Jens; Nerger, Lars; Janjic, Tijana; Danilov, Sergey; Janssen, Frank

A data assimilation (DA) system is developed for BSH operational circulation model in order to improve forecast of current velocities, sea surface height, temperature and salinity in the North and Baltic Seas. Assimilated data are NOAA sea surface temperature (SST) data for the following period: 01.10.07 -30.09.08. All data assimilation experiments are based on im-plementation of one of the so-called statistical DA methods -Singular Evolutive Interpolated Kalman (SEIK) filter, -with different ways of prescribing assumed model and data errors statis-tics. Results of the experiments will be shown and compared against each other. Hydrographic data from MARNET stations and sea level at series of tide gauges are used as independent information to validate the data assimilation system. Keywords: Operational Oceanography and forecasting

Against all odds -- Probabilistic forecasts and decision making

NASA Astrophysics Data System (ADS)

Liechti, Katharina; Zappa, Massimiliano

2015-04-01

In the city of Zurich (Switzerland) the setting is such that the damage potential due to flooding of the river Sihl is estimated to about 5 billion US dollars. The flood forecasting system that is used by the administration for decision making runs continuously since 2007. It has a time horizon of max. five days and operates at hourly time steps. The flood forecasting system includes three different model chains. Two of those are run by the deterministic NWP models COSMO-2 and COSMO-7 and one is driven by the probabilistic NWP COSMO-Leps. The model chains are consistent since February 2010, so five full years are available for the evaluation for the system. The system was evaluated continuously and is a very nice example to present the added value that lies in probabilistic forecasts. The forecasts are available on an online-platform to the decision makers. Several graphical representations of the forecasts and forecast-history are available to support decision making and to rate the current situation. The communication between forecasters and decision-makers is quite close. To put it short, an ideal situation. However, an event or better put a non-event in summer 2014 showed that the knowledge about the general superiority of probabilistic forecasts doesn't necessarily mean that the decisions taken in a specific situation will be based on that probabilistic forecast. Some years of experience allow gaining confidence in the system, both for the forecasters and for the decision-makers. Even if from the theoretical point of view the handling during crisis situation is well designed, a first event demonstrated that the dialog with the decision-makers still lacks of exercise during such situations. We argue, that a false alarm is a needed experience to consolidate real-time emergency procedures relying on ensemble predictions. A missed event would probably also fit, but, in our case, we are very happy not to report about this option.

Combination of synoptical-analogous and dynamical methods to increase skill score of monthly air temperature forecasts over Northern Eurasia

NASA Astrophysics Data System (ADS)

Khan, Valentina; Tscepelev, Valery; Vilfand, Roman; Kulikova, Irina; Kruglova, Ekaterina; Tischenko, Vladimir

2016-04-01

Long-range forecasts at monthly-seasonal time scale are in great demand of socio-economic sectors for exploiting climate-related risks and opportunities. At the same time, the quality of long-range forecasts is not fully responding to user application necessities. Different approaches, including combination of different prognostic models, are used in forecast centers to increase the prediction skill for specific regions and globally. In the present study, two forecasting methods are considered which are exploited in operational practice of Hydrometeorological Center of Russia. One of them is synoptical-analogous method of forecasting of surface air temperature at monthly scale. Another one is dynamical system based on the global semi-Lagrangian model SL-AV, developed in collaboration of Institute of Numerical Mathematics and Hydrometeorological Centre of Russia. The seasonal version of this model has been used to issue global and regional forecasts at monthly-seasonal time scales. This study presents results of the evaluation of surface air temperature forecasts generated with using above mentioned synoptical-statistical and dynamical models, and their combination to potentially increase skill score over Northern Eurasia. The test sample of operational forecasts is encompassing period from 2010 through 2015. The seasonal and interannual variability of skill scores of these methods has been discussed. It was noticed that the quality of all forecasts is highly dependent on the inertia of macro-circulation processes. The skill scores of forecasts are decreasing during significant alterations of synoptical fields for both dynamical and empirical schemes. Procedure of combination of forecasts from different methods, in some cases, has demonstrated its effectiveness. For this study the support has been provided by Grant of Russian Science Foundation (№14-37-00053).

Cloud Computing Applications in Support of Earth Science Activities at Marshall Space Flight Center

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Limaye, Ashutosh S.; Srikishen, Jayanthi

2011-01-01

Currently, the NASA Nebula Cloud Computing Platform is available to Agency personnel in a pre-release status as the system undergoes a formal operational readiness review. Over the past year, two projects within the Earth Science Office at NASA Marshall Space Flight Center have been investigating the performance and value of Nebula s "Infrastructure as a Service", or "IaaS" concept and applying cloud computing concepts to advance their respective mission goals. The Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique NASA satellite observations and weather forecasting capabilities for use within the operational forecasting community through partnerships with NOAA s National Weather Service (NWS). SPoRT has evaluated the performance of the Weather Research and Forecasting (WRF) model on virtual machines deployed within Nebula and used Nebula instances to simulate local forecasts in support of regional forecast studies of interest to select NWS forecast offices. In addition to weather forecasting applications, rapidly deployable Nebula virtual machines have supported the processing of high resolution NASA satellite imagery to support disaster assessment following the historic severe weather and tornado outbreak of April 27, 2011. Other modeling and satellite analysis activities are underway in support of NASA s SERVIR program, which integrates satellite observations, ground-based data and forecast models to monitor environmental change and improve disaster response in Central America, the Caribbean, Africa, and the Himalayas. Leveraging SPoRT s experience, SERVIR is working to establish a real-time weather forecasting model for Central America. Other modeling efforts include hydrologic forecasts for Kenya, driven by NASA satellite observations and reanalysis data sets provided by the broader meteorological community. Forecast modeling efforts are supplemented by short-term forecasts of convective initiation, determined by geostationary satellite observations processed on virtual machines powered by Nebula.

Optimising seasonal streamflow forecast lead time for operational decision making in Australia

NASA Astrophysics Data System (ADS)

Schepen, Andrew; Zhao, Tongtiegang; Wang, Q. J.; Zhou, Senlin; Feikema, Paul

2016-10-01

Statistical seasonal forecasts of 3-month streamflow totals are released in Australia by the Bureau of Meteorology and updated on a monthly basis. The forecasts are often released in the second week of the forecast period, due to the onerous forecast production process. The current service relies on models built using data for complete calendar months, meaning the forecast production process cannot begin until the first day of the forecast period. Somehow, the bureau needs to transition to a service that provides forecasts before the beginning of the forecast period; timelier forecast release will become critical as sub-seasonal (monthly) forecasts are developed. Increasing the forecast lead time to one month ahead is not considered a viable option for Australian catchments that typically lack any predictability associated with snowmelt. The bureau's forecasts are built around Bayesian joint probability models that have antecedent streamflow, rainfall and climate indices as predictors. In this study, we adapt the modelling approach so that forecasts have any number of days of lead time. Daily streamflow and sea surface temperatures are used to develop predictors based on 28-day sliding windows. Forecasts are produced for 23 forecast locations with 0-14- and 21-day lead time. The forecasts are assessed in terms of continuous ranked probability score (CRPS) skill score and reliability metrics. CRPS skill scores, on average, reduce monotonically with increase in days of lead time, although both positive and negative differences are observed. Considering only skilful forecast locations, CRPS skill scores at 7-day lead time are reduced on average by 4 percentage points, with differences largely contained within +5 to -15 percentage points. A flexible forecasting system that allows for any number of days of lead time could benefit Australian seasonal streamflow forecast users by allowing more time for forecasts to be disseminated, comprehended and made use of prior to the commencement of a forecast season. The system would allow for forecasts to be updated if necessary.

Demand forecasting for automotive sector in Malaysia by system dynamics approach

NASA Astrophysics Data System (ADS)

Zulkepli, Jafri; Fong, Chan Hwa; Abidin, Norhaslinda Zainal

2015-12-01

In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.

Space weather forecasting: Past, Present, Future

NASA Astrophysics Data System (ADS)

Lanzerotti, L. J.

2012-12-01

There have been revolutionary advances in electrical technologies over the last 160 years. The historical record demonstrates that space weather processes have often provided surprises in the implementation and operation of many of these technologies. The historical record also demonstrates that as the complexity of systems increase, including their interconnectedness and interoperability, they can become more susceptible to space weather effects. An engineering goal, beginning during the decades following the 1859 Carrington event, has been to attempt to forecast solar-produced disturbances that could affect technical systems, be they long grounded conductor-based or radio-based or required for exploration, or the increasingly complex systems immersed in the space environment itself. Forecasting of space weather events involves both frontier measurements and models to address engineering requirements, and industrial and governmental policies that encourage and permit creativity and entrepreneurship. While analogies of space weather forecasting to terrestrial weather forecasting are frequently made, and while many of the analogies are valid, there are also important differences. This presentation will provide some historical perspectives on the forecast problem, a personal assessment of current status of several areas including important policy issues, and a look into the not-too-distant future.

Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parks, K.; Wan, Y. H.; Wiener, G.

2011-10-01

The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'),more » or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability. The downside is costs are higher. In organized electricity markets, units that are committed for reliability reasons are paid their offer price even when prevailing market prices are lower. Often, these uplift charges are allocated to market participants that caused the inefficient dispatch in the first place. Thus, wind energy facilities are burdened with their share of costs proportional to their forecast errors. For Xcel Energy, wind energy uncertainty costs manifest depending on specific market structures. In the Public Service of Colorado (PSCo), inefficient commitment and dispatch caused by wind uncertainty increases fuel costs. Wind resources participating in the Midwest Independent System Operator (MISO) footprint make substantial payments in the real-time markets to true-up their day-ahead positions and are additionally burdened with deviation charges called a Revenue Sufficiency Guarantee (RSG) to cover out of market costs associated with operations. Southwest Public Service (SPS) wind plants cause both commitment inefficiencies and are charged Southwest Power Pool (SPP) imbalance payments due to wind uncertainty and variability. Wind energy forecasting helps mitigate these costs. Wind integration studies for the PSCo and Northern States Power (NSP) operating companies have projected increasing costs as more wind is installed on the system due to forecast error. It follows that reducing forecast error would reduce these costs. This is echoed by large scale studies in neighboring regions and states that have recommended adoption of state-of-the-art wind forecasting tools in day-ahead and real-time planning and operations. Further, Xcel Energy concluded reduction of the normalized mean absolute error by one percent would have reduced costs in 2008 by over $1 million annually in PSCo alone. The value of reducing forecast error prompted Xcel Energy to make substantial investments in wind energy forecasting research and development.« less

Towards an Australian ensemble streamflow forecasting system for flood prediction and water management

NASA Astrophysics Data System (ADS)

Bennett, J.; David, R. E.; Wang, Q.; Li, M.; Shrestha, D. L.

2016-12-01

Flood forecasting in Australia has historically relied on deterministic forecasting models run only when floods are imminent, with considerable forecaster input and interpretation. These now co-existed with a continually available 7-day streamflow forecasting service (also deterministic) aimed at operational water management applications such as environmental flow releases. The 7-day service is not optimised for flood prediction. We describe progress on developing a system for ensemble streamflow forecasting that is suitable for both flood prediction and water management applications. Precipitation uncertainty is handled through post-processing of Numerical Weather Prediction (NWP) output with a Bayesian rainfall post-processor (RPP). The RPP corrects biases, downscales NWP output, and produces reliable ensemble spread. Ensemble precipitation forecasts are used to force a semi-distributed conceptual rainfall-runoff model. Uncertainty in precipitation forecasts is insufficient to reliably describe streamflow forecast uncertainty, particularly at shorter lead-times. We characterise hydrological prediction uncertainty separately with a 4-stage error model. The error model relies on data transformation to ensure residuals are homoscedastic and symmetrically distributed. To ensure streamflow forecasts are accurate and reliable, the residuals are modelled using a mixture-Gaussian distribution with distinct parameters for the rising and falling limbs of the forecast hydrograph. In a case study of the Murray River in south-eastern Australia, we show ensemble predictions of floods generally have lower errors than deterministic forecasting methods. We also discuss some of the challenges in operationalising short-term ensemble streamflow forecasts in Australia, including meeting the needs for accurate predictions across all flow ranges and comparing forecasts generated by event and continuous hydrological models.

Operational atmospheric modeling system CARIS for effective emergency response associated with hazardous chemical releases in Korea.

PubMed

Kim, Cheol-Hee; Park, Jin-Ho; Park, Cheol-Jin; Na, Jin-Gyun

2004-03-01

The Chemical Accidents Response Information System (CARIS) was developed at the Center for Chemical Safety Management in South Korea in order to track and predict the dispersion of hazardous chemicals in the case of an accident or terrorist attack involving chemical companies. The main objective of CARIS is to facilitate an efficient emergency response to hazardous chemical accidents by rapidly providing key information in the decision-making process. In particular, the atmospheric modeling system implemented in CARIS, which is composed of a real-time numerical weather forecasting model and an air pollution dispersion model, can be used as a tool to forecast concentrations and to provide a wide range of assessments associated with various hazardous chemicals in real time. This article introduces the components of CARIS and describes its operational modeling system. Some examples of the operational modeling system and its use for emergency preparedness are presented and discussed. Finally, this article evaluates the current numerical weather prediction model for Korea.

Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Service (KMS)

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Mungai, John; Sakwa, Vincent; Kabuchanga, Eric; Zavodsky, Bradley T.; Limaye, Ashutosh S.

2014-01-01

SPoRT/SERVIR/RCMRD/KMS Collaboration: Builds off strengths of each organization. SPoRT: Transition of satellite, modeling and verification capabilities; SERVIR-Africa/RCMRD: International capacity-building expertise; KMS: Operational organization with regional weather forecasting expertise in East Africa. Hypothesis: Improved land-surface initialization over Eastern Africa can lead to better temperature, moisture, and ultimately precipitation forecasts in NWP models. KMS currently initializes Weather Research and Forecasting (WRF) model with NCEP/Global Forecast System (GFS) model 0.5-deg initial / boundary condition data. LIS will provide much higher-resolution land-surface data at a scale more representative to regional WRF configuration. Future implementation of real-time NESDIS/VIIRS vegetation fraction to further improve land surface representativeness.

Nowcasting of rainfall and of combined sewage flow in urban drainage systems.

PubMed

Achleitner, Stefan; Fach, Stefan; Einfalt, Thomas; Rauch, Wolfgang

2009-01-01

Nowcasting of rainfall may be used additionally to online rain measurements to optimize the operation of urban drainage systems. Uncertainties quoted for the rain volume are in the range of 5% to 10% mean square error (MSE), where for rain intensities 45% to 75% MSE are noted. For larger forecast periods up to 3 hours, the uncertainties will increase up to some hundred percents. Combined with the growing number of real time control concepts in sewer systems, rainfall forecast is used more and more in urban drainage systems. Therefore it is of interest how the uncertainties influence the final evaluation of a defined objective function. Uncertainty levels associated with the forecast itself are not necessarily transferable to resulting uncertainties in the catchment's flow dynamics. The aim of this paper is to analyse forecasts of rainfall and specific sewer output variables. For this study the combined sewer system of the city of Linz in the northern part of Austria located on the Danube has been selected. The city itself represents a total area of 96 km2 with 39 municipalities connected. It was found that the available weather radar data leads to large deviations in the forecast for precipitation at forecast horizons larger than 90 minutes. The same is true for sewer variables such a CSO overflow for small sub-catchments. Although the results improve for larger spatial scales, acceptable levels at forecast horizons larger than 90 minutes are not reached.

An Experimental High-Resolution Forecast System During the Vancouver 2010 Winter Olympic and Paralympic Games

NASA Astrophysics Data System (ADS)

Mailhot, J.; Milbrandt, J. A.; Giguère, A.; McTaggart-Cowan, R.; Erfani, A.; Denis, B.; Glazer, A.; Vallée, M.

2014-01-01

Environment Canada ran an experimental numerical weather prediction (NWP) system during the Vancouver 2010 Winter Olympic and Paralympic Games, consisting of nested high-resolution (down to 1-km horizontal grid-spacing) configurations of the GEM-LAM model, with improved geophysical fields, cloud microphysics and radiative transfer schemes, and several new diagnostic products such as density of falling snow, visibility, and peak wind gust strength. The performance of this experimental NWP system has been evaluated in these winter conditions over complex terrain using the enhanced mesoscale observing network in place during the Olympics. As compared to the forecasts from the operational regional 15-km GEM model, objective verification generally indicated significant added value of the higher-resolution models for near-surface meteorological variables (wind speed, air temperature, and dewpoint temperature) with the 1-km model providing the best forecast accuracy. Appreciable errors were noted in all models for the forecasts of wind direction and humidity near the surface. Subjective assessment of several cases also indicated that the experimental Olympic system was skillful at forecasting meteorological phenomena at high-resolution, both spatially and temporally, and provided enhanced guidance to the Olympic forecasters in terms of better timing of precipitation phase change, squall line passage, wind flow channeling, and visibility reduction due to fog and snow.

Fine-grained dengue forecasting using telephone triage services

PubMed Central

Abdur Rehman, Nabeel; Kalyanaraman, Shankar; Ahmad, Talal; Pervaiz, Fahad; Saif, Umar; Subramanian, Lakshminarayanan

2016-01-01

Thousands of lives are lost every year in developing countries for failing to detect epidemics early because of the lack of real-time disease surveillance data. We present results from a large-scale deployment of a telephone triage service as a basis for dengue forecasting in Pakistan. Our system uses statistical analysis of dengue-related phone calls to accurately forecast suspected dengue cases 2 to 3 weeks ahead of time at a subcity level (correlation of up to 0.93). Our system has been operational at scale in Pakistan for the past 3 years and has received more than 300,000 phone calls. The predictions from our system are widely disseminated to public health officials and form a critical part of active government strategies for dengue containment. Our work is the first to demonstrate, with significant empirical evidence, that an accurate, location-specific disease forecasting system can be built using analysis of call volume data from a public health hotline. PMID:27419226

Operational use of the AIRS Total Column Ozone Retrievals along with the RGB Airmass Product as Part of the GOES-R Proving Ground

NASA Technical Reports Server (NTRS)

Folmer, M.; Zavodsky, Bradley; Molthan, Andrew

2012-01-01

The Red, Green, Blue (RGB) Air Mass product has been demonstrated in the GOES ]R Proving Ground as a possible decision aid. Forecasters have been trained on the usefulness of identifying stratospheric intrusions and potential vorticity (PV) anomalies that can lead to explosive cyclogenesis, genesis of mesoscale convective systems (MCSs), or the transition of tropical cyclones to extratropical cyclones. It has also been demonstrated to distinguish different air mass types from warm, low ozone air masses to cool, high ozone air masses and the various interactions with the PV anomalies. To assist the forecasters in understanding the stratospheric contribution to high impact weather systems, the Atmospheric Infrared Sounder (AIRS) Total Column Ozone Retrievals have been made available as an operational tool. These AIRS retrievals provide additional information on the amount of ozone that is associated with the red coloring seen in the RGB Air Mass product. This paper discusses how the AIRS retrievals can be used to quantify the red coloring in RGB Air Mass product. These retrievals can be used to diagnose the depth of the stratospheric intrusions associated with different types of weather systems and provide the forecasters decision aid tools that can improve the quality of forecast products.

The combined value of wind and solar power forecasting improvements and electricity storage

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Bri-Mathias; Brancucci Martinez-Anido, Carlo; Wang, Qin

As the penetration rates of variable renewable energy increase, the value of power systems operation flexibility technology options, such as renewable energy forecasting improvements and electricity storage, is also assumed to increase. In this work, we examine the value of these two technologies, when used independently and concurrently, for two real case studies that represent the generation mixes for the California and Midcontinent Independent System Operators (CAISO and MISO). Since both technologies provide additional system flexibility they reduce operational costs and renewable curtailment for both generation mixes under study. Interestingly, the relative impacts are quite similar when both technologies aremore » used together. Though both flexibility options can solve some of the same issues that arise with high penetration levels of renewables, they do not seem to significantly increase or decrease the economic potential of the other technology.« less

The combined value of wind and solar power forecasting improvements and electricity storage

DOE PAGES

Hodge, Bri-Mathias; Brancucci Martinez-Anido, Carlo; Wang, Qin; ...

2018-02-12

As the penetration rates of variable renewable energy increase, the value of power systems operation flexibility technology options, such as renewable energy forecasting improvements and electricity storage, is also assumed to increase. In this work, we examine the value of these two technologies, when used independently and concurrently, for two real case studies that represent the generation mixes for the California and Midcontinent Independent System Operators (CAISO and MISO). Since both technologies provide additional system flexibility they reduce operational costs and renewable curtailment for both generation mixes under study. Interestingly, the relative impacts are quite similar when both technologies aremore » used together. Though both flexibility options can solve some of the same issues that arise with high penetration levels of renewables, they do not seem to significantly increase or decrease the economic potential of the other technology.« less

Forecasting snowmelt flooding over Britain using the Grid-to-Grid model: a review and assessment of methods

NASA Astrophysics Data System (ADS)

Dey, Seonaid R. A.; Moore, Robert J.; Cole, Steven J.; Wells, Steven C.

2017-04-01

In many regions of high annual snowfall, snowmelt modelling can prove to be a vital component of operational flood forecasting and warning systems. Although Britain as a whole does not experience prolonged periods of lying snow, with the exception of the Scottish Highlands, the inclusion of snowmelt modelling can still have a significant impact on the skill of flood forecasts. Countrywide operational flood forecasts over Britain are produced using the national Grid-to-Grid (G2G) distributed hydrological model. For Scotland, snowmelt is included in these forecasts through a G2G snow hydrology module involving temperature-based snowfall/rainfall partitioning and functions for temperature-excess snowmelt, snowpack storage and drainage. Over England and Wales, the contribution of snowmelt is included by pre-processing the precipitation prior to input into G2G. This removes snowfall diagnosed from weather model outputs and adds snowmelt from an energy budget land surface scheme to form an effective liquid water gridded input to G2G. To review the operational options for including snowmelt modelling in G2G over Britain, a project was commissioned by the Environment Agency through the Flood Forecasting Centre (FFC) for England and Wales and in partnership with the Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW). Results obtained from this snowmelt review project will be reported on here. The operational methods used by the FFC and SEPA are compared on past snowmelt floods, alongside new alternative methods of treating snowmelt. Both case study and longer-term analyses are considered, covering periods selected from the winters 2009-2010, 2012-2013, 2013-2014 and 2014-2015. Over Scotland, both of the snowmelt methods used operationally by FFC and SEPA provided a clear improvement to the river flow simulations. Over England and Wales, fewer and less significant snowfall events occurred, leading to less distinction in the results between the methods. It is noted that, for all methods considered, large uncertainties remain in flood forecasts influenced by snowmelt. Understanding and quantifying these uncertainties should lead to more informed flood forecasts and associated guidance information.

SWIFT2: Software for continuous ensemble short-term streamflow forecasting for use in research and operations

NASA Astrophysics Data System (ADS)

Perraud, Jean-Michel; Bennett, James C.; Bridgart, Robert; Robertson, David E.

2016-04-01

Research undertaken through the Water Information Research and Development Alliance (WIRADA) has laid the foundations for continuous deterministic and ensemble short-term forecasting services. One output of this research is the software Short-term Water Information Forecasting Tools version 2 (SWIFT2). SWIFT2 is developed for use in research on short term streamflow forecasting techniques as well as operational forecasting services at the Australian Bureau of Meteorology. The variety of uses in research and operations requires a modular software system whose components can be arranged in applications that are fit for each particular purpose, without unnecessary software duplication. SWIFT2 modelling structures consist of sub-areas of hydrologic models, nodes and links with in-stream routing and reservoirs. While this modelling structure is customary, SWIFT2 is built from the ground up for computational and data intensive applications such as ensemble forecasts necessary for the estimation of the uncertainty in forecasts. Support for parallel computation on multiple processors or on a compute cluster is a primary use case. A convention is defined to store large multi-dimensional forecasting data and its metadata using the netCDF library. SWIFT2 is written in modern C++ with state of the art software engineering techniques and practices. A salient technical feature is a well-defined application programming interface (API) to facilitate access from different applications and technologies. SWIFT2 is already seamlessly accessible on Windows and Linux via packages in R, Python, Matlab and .NET languages such as C# and F#. Command line or graphical front-end applications are also feasible. This poster gives an overview of the technology stack, and illustrates the resulting features of SWIFT2 for users. Research and operational uses share the same common core C++ modelling shell for consistency, but augmented by different software modules suitable for each context. The accessibility via interactive modelling languages is particularly amenable to using SWIFT2 in exploratory research, with a dynamic and versatile experimental modelling workflow. This does not come at the expense of the stability and reliability required for use in operations, where only mature and stable components are used.

National Water Model assessment for water management needs over the Western United States.

NASA Astrophysics Data System (ADS)

Viterbo, F.; Thorstensen, A.; Cifelli, R.; Hughes, M.; Johnson, L.; Gochis, D.; Wood, A.; Nowak, K.; Dahm, K.

2017-12-01

The NOAA National Water Model (NWM) became operational in August 2016, providing the first ever, real-time distributed high-resolution forecasts for the continental United States. Since the model predictions occur at the CONUS scale, there is a need to evaluate the NWM in different regions to assess the wide variety and heterogeneity of hydrological processes that are included (e.g., snow melting, ice freezing, flash flooding events). In particular, to address water management needs in the western U.S., a collaborative project between the Bureau of Reclamation, NOAA, and NCAR is ongoing to assess the NWM performance for reservoir inflow forecasting needs and water management operations. In this work, the NWM is evaluated using different forecast ranges (short to medium) and retrospective historical runs forced by North American Land Data Assimilation System (NLDAS) analysis to assess the NWM skills over key headwaters watersheds in the western U.S. that are of interest to the Bureau of Reclamation. The streamflow results are analyzed and compared with the available observations at the gauge sites, evaluating different NWM operational versions together with the already existing local River Forecast Center forecasts. The NWM uncertainty is also considered, evaluating the propagation of the precipitation forcing uncertainties in the resulting hydrograph. In addition, the possible advantages of high-resolution distributed output variables (such as soil moisture, evapotranspiration fluxes) are investigated, to determine the utility of such information for water managers in terms of watershed characteristics in areas that traditionally have not had any forecast information. The results highlight the NWM's ability to provide high-resolution forecast information in space and time. As anticipated, the performance is best in regions that are dominated by natural flows and where the model has benefited from efforts toward parameter calibration. In highly regulated basins, the water management operations result in NWM overestimation of the peak flows and too fast recession curves. As a future project goal, some reforecasts will be run on target locations, ingesting water management information into the NWM and comparing the new results with the actual operational forecast.

Valuing hydrological forecasts for a pumped storage assisted hydro facility

NASA Astrophysics Data System (ADS)

Zhao, Guangzhi; Davison, Matt

2009-07-01

SummaryThis paper estimates the value of a perfectly accurate short-term hydrological forecast to the operator of a hydro electricity generating facility which can sell its power at time varying but predictable prices. The expected value of a less accurate forecast will be smaller. We assume a simple random model for water inflows and that the costs of operating the facility, including water charges, will be the same whether or not its operator has inflow forecasts. Thus, the improvement in value from better hydrological prediction results from the increased ability of the forecast using facility to sell its power at high prices. The value of the forecast is therefore the difference between the sales of a facility operated over some time horizon with a perfect forecast, and the sales of a similar facility operated over the same time horizon with similar water inflows which, though governed by the same random model, cannot be forecast. This paper shows that the value of the forecast is an increasing function of the inflow process variance and quantifies how much the value of this perfect forecast increases with the variance of the water inflow process. Because the lifetime of hydroelectric facilities is long, the small increase observed here can lead to an increase in the profitability of hydropower investments.

The SPoRT-WRF: Evaluating the Impact of NASA Datasets on Convective Forecasts

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Case, Jonathan; Kozlowski, Danielle; Molthan, Andrew

2012-01-01

The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting entities, including a number of National Weather Service offices. SPoRT transitions real-time NASA products and capabilities to its partners to address specific operational forecast challenges. One challenge that forecasters face is applying convection-allowing numerical models to predict mesoscale convective weather. In order to address this specific forecast challenge, SPoRT produces real-time mesoscale model forecasts using the Weather Research and Forecasting (WRF) model that includes unique NASA products and capabilities. Currently, the SPoRT configuration of the WRF model (SPoRT-WRF) incorporates the 4-km Land Information System (LIS) land surface data, 1-km SPoRT sea surface temperature analysis and 1-km Moderate resolution Imaging Spectroradiometer (MODIS) greenness vegetation fraction (GVF) analysis, and retrieved thermodynamic profiles from the Atmospheric Infrared Sounder (AIRS). The LIS, SST, and GVF data are all integrated into the SPoRT-WRF through adjustments to the initial and boundary conditions, and the AIRS data are assimilated into a 9-hour SPoRT WRF forecast each day at 0900 UTC. This study dissects the overall impact of the NASA datasets and the individual surface and atmospheric component datasets on daily mesoscale forecasts. A case study covering the super tornado outbreak across the Ce ntral and Southeastern United States during 25-27 April 2011 is examined. Three different forecasts are analyzed including the SPoRT-WRF (NASA surface and atmospheric data), the SPoRT WRF without AIRS (NASA surface data only), and the operational National Severe Storms Laboratory (NSSL) WRF (control with no NASA data). The forecasts are compared qualitatively by examining simulated versus observed radar reflectivity. Differences between the simulated reflectivity are further investigated using convective parameters along with model soundings to determine the impacts of the various NASA datasets. Additionally, quantitative evaluation of select meteorological parameters is performed using the Meteorological Evaluation Tools model verification package to compare forecasts to in situ surface and upper air observations.

The GOES-R Proving Ground: 2012 Update

NASA Astrophysics Data System (ADS)

Gurka, J.; Goodman, S. J.; Schmit, T.; Demaria, M.; Mostek, A.; Siewert, C.; Reed, B.

2011-12-01

The Geostationary Operational Environmental Satellite (GOES)-R will provide a great leap forward in observing capabilities, but will also offer a significant challenge to ensure that users are ready to exploit the vast improvements in spatial, spectral, and temporal resolutions. To ensure user readiness, forecasters and other users must have access to prototype advanced products well before launch, and have the opportunity to provide feedback to product developers and computing and communications managers. The operational assessment is critical to ensure that the end products and NOAA's computing and communications systems truly meet their needs in a rapidly evolving environment. The GOES-R Proving Ground (PG) engages the National Weather Service (NWS) forecast, watch and warning community and other agency users in pre-operational demonstrations of select products with GOES-R attributes (enhanced spectral, spatial, and temporal resolution). In the PG, developers and forecasters test and apply algorithms for new GOES-R satellite data and products using proxy and simulated data sets, including observations from current and future satellite instruments (MODIS, AIRS, IASI, SEVIRI, NAST-I, NPP/VIIRS/CrIS, LIS), lightning networks, and computer simulated products. The complete list of products to be evaluated in 2012 will be determined after evaluating results from experiments in 2011 at the NWS' Storm Prediction Center, National Hurricane Center, Aviation Weather Center, Ocean Prediction Center, Hydrometeorological Prediction Center, and from the six NWS regions. In 2012 and beyond, the PG will test and validate data processing and distribution systems and the applications of these products in operational settings. Additionally developers and forecasters will test and apply display techniques and decision aid tools in operational environments. The PG is both a recipient and a source of training. Training materials are developed using various distance training tools in close collaboration with NWS Training Division and its partners at COMET, CIMSS, CIRA and other offices. The training is used to prepare the participants of PG activities, such as the Hazardous Weather Testbed's Spring Experiment and other locations listed above. A key component of the proving ground is two-way interaction, where researchers introduce new products and techniques to forecasters and other scientists. The forecasters and other users then provide feedback and ideas for improved or new products and how to best incorporate these into NOAA's integrated observing and analysis operations. This presentation will provide examples of GOES-R proxy products and forecaster evaluations from experiments at the Storm Prediction Center (SPC), the National Hurricane Center (NHC), the Aviation Weather Center (AWC), and the Alaska Region.

Real Time Volcanic Cloud Products and Predictions for Aviation Alerts

NASA Technical Reports Server (NTRS)

Krotkov, Nickolay A.; Habib, Shahid; da Silva, Arlindo; Hughes, Eric; Yang, Kai; Brentzel, Kelvin; Seftor, Colin; Li, Jason Y.; Schneider, David; Guffanti, Marianne;

Sub-seasonal Predictability of Heavy Precipitation Events: Implication for Real-time Flood Management in Iran

NASA Astrophysics Data System (ADS)

Najafi, H.; Shahbazi, A.; Zohrabi, N.; Robertson, A. W.; Mofidi, A.; Massah Bavani, A. R.

2016-12-01

Each year, a number of high impact weather events occur worldwide. Since any level of predictability at sub-seasonal to seasonal timescale is highly beneficial to society, international efforts is now on progress to promote reliable Ensemble Prediction Systems for monthly forecasts within the WWRP/WCRP initiative (S2S) project and North American Multi Model Ensemble (NMME). For water resources managers in the face of extreme events, not only can reliable forecasts of high impact weather events prevent catastrophic losses caused by floods but also contribute to benefits gained from hydropower generation and water markets. The aim of this paper is to analyze the predictability of recent severe weather events over Iran. Two recent heavy precipitations are considered as an illustration to examine whether S2S forecasts can be used for developing flood alert systems especially where large cascade of dams are in operation. Both events have caused major damages to cities and infrastructures. The first severe precipitation was is in the early November 2015 when heavy precipitation (more than 50 mm) occurred in 2 days. More recently, up to 300 mm of precipitation is observed within less than a week in April 2016 causing a consequent flash flood. Over some stations, the observed precipitation was even more than the total annual mean precipitation. To analyze the predictive capability, ensemble forecasts from several operational centers including (European Centre for Medium-Range Weather Forecasts (ECMWF) system, Climate Forecast System Version 2 (CFSv2) and Chinese Meteorological Center (CMA) are evaluated. It has been observed that significant changes in precipitation anomalies were likely to be predicted days in advance. The next step will be to conduct thorough analysis based on comparing multi-model outputs over the full hindcast dataset developing real-time high impact weather prediction systems.

Predictive Analytics for Coordinated Optimization in Distribution Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Rui

This talk will present NREL's work on developing predictive analytics that enables the optimal coordination of all the available resources in distribution systems to achieve the control objectives of system operators. Two projects will be presented. One focuses on developing short-term state forecasting-based optimal voltage regulation in distribution systems; and the other one focuses on actively engaging electricity consumers to benefit distribution system operations.

Operational air quality forecasting system for Spain: CALIOPE

NASA Astrophysics Data System (ADS)

Baldasano, J. M.; Piot, M.; Jorba, O.; Goncalves, M.; Pay, M.; Pirez, C.; Lopez, E.; Gasso, S.; Martin, F.; García-Vivanco, M.; Palomino, I.; Querol, X.; Pandolfi, M.; Dieguez, J. J.; Padilla, L.

2009-12-01

The European Commission (EC) and the United States Environmental Protection Agency (US-EPA) have shown great concerns to understand the transport and dynamics of pollutants in the atmosphere. According to the European directives (1996/62/EC, 2002/3/EC, 2008/50/EC), air quality modeling, if accurately applied, is a useful tool to understand the dynamics of air pollutants, to analyze and forecast the air quality, and to develop programs reducing emissions and alert the population when health-related issues occur. The CALIOPE project, funded by the Spanish Ministry of the Environment, has the main objective to establish an air quality forecasting system for Spain. A partnership of four research institutions composes the CALIOPE project: the Barcelona Supercomputing Center (BSC), the center of investigation CIEMAT, the Earth Sciences Institute ‘Jaume Almera’ (IJA-CSIC) and the CEAM Foundation. CALIOPE will become the official Spanish air quality operational system. This contribution focuses on the recent developments and implementation of the integrated modelling system for the Iberian Peninsula (IP) and Canary Islands (CI) with a high spatial and temporal resolution (4x4 sq. km for IP and 2x2 sq. km for CI, 1 hour), namely WRF-ARW/HERMES04/CMAQ/BSC-DREAM. The HERMES04 emission model has been specifically developed as a high-resolution (1x1 sq. km, 1 hour) emission model for Spain. It includes biogenic and anthropogenic emissions such as on-road and paved-road resuspension production, power plant generation, ship and plane traffic, airports and ports activities, industrial and agricultural sectors as well as domestic and commercial emissions. The qualitative and quantitative evaluation of the model was performed for a reference year (2004) using data from ground-based measurement networks. The products of the CALIOPE system will provide 24h and 48h forecasts for O3, NO2, SO2, CO, PM10 and PM2.5 at surface level. An operational evaluation system has been developed to provide near real-time evaluation products for the Spanish territory. For this purpose, more than 130 surface stations, 2 ozonesondes and the OMI satellite retrieval information are introduced to the system on a daily basis. A web-based visualization system allows a straightforward access to all the evaluation products. The present contribution will describe the main characteristics of the operational system and results of the operational evaluation.

The NASA Short-term Prediction and Research Transition (SPoRT) Center: A Research to Operations Test Bed

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.

2005-01-01

Over the last three years, NASA/MSFC scientists have embarked on an effort to transition unique NASA EOS data/products and research technology to selected NWSEOs in the southeast U.S. This activity, called the Short-term Prediction and - Research Transition (SPoRT) program, supports the NASA Science Mission Directorate and its Earth-Sun System Mission to develop a scientific understanding of the Earth System and its response to natural or human-induced changes that will enable improved prediction capability for climate, weather, and natural hazards. The overarching question related to weather prediction is "How well can weather forecasting duration and reliability be improved by new space-based observations, data assimilation, and modeling?" The transition activity has included the real-time delivery of MODIS data and products to several NWS Forecast Offices. Local NWS FOs have used the MODIS data to complement the coarse resolution GOES data for a number of applications. Specialized products have also been developed and made available to local and remote offices for their weather applications. Data from &e Lightning Mapping Array (LMA) network has been used in severe storm forecasts at several offices in the region. At the regional scale and forecast horizons from 0-1 day, the next generation of high-resolution mesoscale forecast and data assimilation models have been used to provide local offices with unique weather forecasts not otherwise available. The continued use of near red-time infusion of NASA science products into high-resolution mesoscale forecast and decision-making models can be expected to improve the model initialization as well as short-term forecasts. A current focus of SPoRT is to expand collaborations to include contributions from the assimilation of AMSR-E data in the ADASIARPS forecast system (OU), inclusion of MODIS SSTs and AIRS thermodynamic profiles in the WRF, and to extend the distribution of real-time MODIS and AMSR-E data and products to the Florida coastal WFOs. A SPoRT Test bed, together with input from other interagency and university partners, will provide a means and a process to effectively transition ESE observations and technology to NWS operations and decision makers at both the globdnational and regional scales. The transition of emerging experimental products into operations through the SPoRT infrastructure will allow NASA to foster and accelerate the progress of this Science Mission Directorate research strategy over the coming years.

Information Services of Maritime Industry

NASA Astrophysics Data System (ADS)

Palazov, Atanas; Stefanov, Asen

2015-04-01

The ultimate goal of modern oceanography is an end user oriented product. Beneficiaries are the governmental services, coast-based enterprises and research institutions that make use of the products generated by operational oceanography. Direct potential users and customers are coastal managers, shipping, offshore industry, ports and harbours, fishing, tourism and recreation industry, and scientific community. Indirect beneficiaries, through climate forecasting based on ocean observations, are food, energy, water and medical suppliers. Five general classes of users for data and information are specified: (1) operational users that analyze the collected data and produce different forecasts serving to impose regulation measures; (2) authorities and managers of large-scale projects needing timely oceanographic information, including statistics and climatic trends; (3) industrial enterprises, safety of structures and avoiding of pollution; (4) tourism and recreation related users aiming protection of human health; (5) scientists, engineers, and economists carrying out special researches, strategic design studies, and other investigations to advance the application of marine data. The analysis of information received during the extensive inquiry among all potential end users reveals variety of data and information needs encompassing physical, chemical, biological and hydrometeorological observation. Nevertheless, the common requirement concerns development of observing and forecasting systems providing accurate real-time or near-real time data and information supporting decision making and environmental management. Availability of updated information on the actual state as well as forecast for the future changes of marine environment are essential for the success and safety of maritime operations in the offshore industry. For this purpose different systems have been developed to collect data and to produce forecasts on the state of the marine environment and to provide them in real-time to the users in applying the latest advances in instrument-building, information and communication technologies. In the Bulgarian sector of the Black Sea have been developed and putted in operation several systems for the collection and presentation of marine data for the needs of different users. The systems are located both along the coast and in the open sea and the information they provide is used by both the maritime industry and the widest range of users. Combining them into a national operational marine observational system is a task that has to be solved, and that will allow to get a more complete and comprehensive picture of the state of the marine environment in the Bulgarian sector of the Black Sea. Such a system will help to support the activities of the offshore industry.

Coastal and Riverine Flood Forecast Model powered by ADCIRC

NASA Astrophysics Data System (ADS)

Khalid, A.; Ferreira, C.

2017-12-01

Coastal flooding is becoming a major threat to increased population in the coastal areas. To protect coastal communities from tropical storms & hurricane damages, early warning systems are being developed. These systems have the capability of real time flood forecasting to identify hazardous coastal areas and aid coastal communities in rescue operations. State of the art hydrodynamic models forced by atmospheric forcing have given modelers the ability to forecast storm surge, water levels and currents. This helps to identify the areas threatened by intense storms. Study on Chesapeake Bay area has gained national importance because of its combined riverine and coastal phenomenon, which leads to greater uncertainty in flood predictions. This study presents an automated flood forecast system developed by following Advanced Circulation (ADCIRC) Surge Guidance System (ASGS) guidelines and tailored to take in riverine and coastal boundary forcing, thus includes all the hydrodynamic processes to forecast total water in the Potomac River. As studies on tidal and riverine flow interaction are very scarce in number, our forecast system would be a scientific tool to examine such area and fill the gaps with precise prediction for Potomac River. Real-time observations from National Oceanic and Atmospheric Administration (NOAA) and field measurements have been used as model boundary feeding. The model performance has been validated by using major historical riverine and coastal flooding events. Hydrodynamic model ADCIRC produced promising predictions for flood inundation areas. As better forecasts can be achieved by using coupled models, this system is developed to take boundary conditions from Global WaveWatchIII for the research purposes. Wave and swell propagation will be fed through Global WavewatchIII model to take into account the effects of swells and currents. This automated forecast system is currently undergoing rigorous testing to include any missing parameters which might provide better and more reliable forecast for the flood affected communities.

A Real-Time Offshore Weather Risk Advisory System

NASA Astrophysics Data System (ADS)

Jolivet, Samuel; Zemskyy, Pavlo; Mynampati, Kalyan; Babovic, Vladan

2015-04-01

Offshore oil and gas operations in South East Asia periodically face extended downtime due to unpredictable weather conditions, including squalls that are accompanied by strong winds, thunder, and heavy rains. This downtime results in financial losses. Hence, a real time weather risk advisory system is developed to provide the offshore Oil and Gas (O&G) industry specific weather warnings in support of safety and environment security. This system provides safe operating windows based on sensitivity of offshore operations to sea state. Information products for safety and security include area of squall occurrence for the next 24 hours, time before squall strike, and heavy sea state warning for the next 3, 6, 12 & 24 hours. These are predicted using radar now-cast, high resolution Numerical Weather Prediction (NWP) and Data Assimilation (DA). Radar based now-casting leverages the radar data to produce short term (up to 3 hours) predictions of severe weather events including squalls/thunderstorms. A sea state approximation is provided through developing a translational model based on these predictions to risk rank the sensitivity of operations. A high resolution Weather Research and Forecasting (WRF, an open source NWP model) is developed for offshore Brunei, Malaysia and the Philippines. This high resolution model is optimized and validated against the adaptation of temperate to tropical met-ocean parameterization. This locally specific parameters are calibrated against federated data to achieve a 24 hour forecast of high resolution Convective Available Potential Energy (CAPE). CAPE is being used as a proxy for the risk of squall occurrence. Spectral decomposition is used to blend the outputs of the now-cast and the forecast in order to assimilate near real time weather observations as an implementation of the integration of data sources. This system uses the now-cast for the first 3 hours and then the forecast prediction horizons of 3, 6, 12 & 24 hours. The output is a 24 hour window of high resolution/accuracy forecasts leveraging available data-model integration and CAPE prediction. The systems includes dissemination of WRF outputs over the World Wide Web. Components of the system (including WRF computational engine and results dissemination modules) are deployed in to computational cloud. This approach tends to increase system robustness and sustainability. The creation of such a system to share information between the public and private sectors and across territorial boundaries is an important step towards the next generation of governance for climate risk and extreme weather offshore. The system benefits offshore operators by reducing downtime related to accidents and incidents; eliminate unnecessary hiring costs related to waiting on weather; and improve the efficiency and planning of transport and logistics by providing a rolling weather risk advisory.

Development of a WRF-RTFDDA-based high-resolution hybrid data-assimilation and forecasting system toward to operation in the Middle East

NASA Astrophysics Data System (ADS)

Liu, Y.; Wu, W.; Zhang, Y.; Kucera, P. A.; Liu, Y.; Pan, L.

2012-12-01

Weather forecasting in the Middle East is challenging because of its complicated geographical nature including massive coastal area and heterogeneous land, and regional spare observational network. Strong air-land-sea interactions form multi-scale weather regimes in the area, which require a numerical weather prediction model capable of properly representing multi-scale atmospheric flow with appropriate initial conditions. The WRF-based Real-Time Four Dimensional Data Assimilation (RTFDDA) system is one of advanced multi-scale weather analysis and forecasting facilities developed at the Research Applications Laboratory (RAL) of NCAR. The forecasting system is applied for the Middle East with careful configuration. To overcome the limitation of the very sparsely available conventional observations in the region, we develop a hybrid data assimilation algorithm combining RTFDDA and WRF-3DVAR, which ingests remote sensing data from satellites and radar. This hybrid data assimilation blends Newtonian nudging FDDA and 3DVAR technology to effectively assimilate both conventional observations and remote sensing measurements and provide improved initial conditions for the forecasting system. For brevity, the forecasting system is called RTF3H (RTFDDA-3DVAR Hybrid). In this presentation, we will discuss the hybrid data assimilation algorithm, and its implementation, and the applications for high-impact weather events in the area. Sensitivity studies are conducted to understand the strength and limitations of this hybrid data assimilation algorithm.