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.

On The Usage Of Fire Smoke Emissions In An Air Quality Forecasting System To Reduce Particular Matter Forecasting Error

NASA Astrophysics Data System (ADS)

Huang, H. C.; Pan, L.; McQueen, J.; Lee, P.; ONeill, S. M.; Ruminski, M.; Shafran, P.; DiMego, G.; Huang, J.; Stajner, I.; Upadhayay, S.; Larkin, N. K.

2016-12-01

Wildfires contribute to air quality problems not only towards primary emissions of particular matters (PM) but also emitted ozone precursor gases that can lead to elevated ozone concentration. Wildfires are unpredictable and can be ignited by natural causes such as lightning or accidently by human negligent behavior such as live cigarette. Although wildfire impacts on the air quality can be studied by collecting fire information after events, it is extremely difficult to predict future occurrence and behavior of wildfires for real-time air quality forecasts. Because of the time constraints of operational air quality forecasting, assumption of future day's fire behavior often have to be made based on observed fire information in the past. The United States (U.S.) NOAA/NWS built the National Air Quality Forecast Capability (NAQFC) based on the U.S. EPA CMAQ to provide air quality forecast guidance (prediction) publicly. State and local forecasters use the forecast guidance to issue air quality alerts in their area. The NAQFC fine particulates (PM2.5) prediction includes emissions from anthropogenic and biogenic sources, as well as natural sources such as dust storms and fires. The fire emission input to the NAQFC is derived from the NOAA NESDIS HMS fire and smoke detection product and the emission module of the US Forest Service BlueSky Smoke Modeling Framework. This study focuses on the error estimation of NAQFC PM2.5 predictions resulting from fire emissions. The comparisons between the NAQFC modeled PM2.5 and the EPA AirNow surface observation show that present operational NAQFC fire emissions assumption can lead to a huge error in PM2.5 prediction as fire emissions are sometimes placed at wrong location and time. This PM2.5 prediction error can be propagated from the fire source in the Northwest U.S. to downstream areas as far as the Southeast U.S. From this study, a new procedure has been identified to minimize the aforementioned error. An additional 24 hours

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

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

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.

Forecasting Global Point Rainfall using ECMWF's Ensemble Forecasting System

NASA Astrophysics Data System (ADS)

Pillosu, Fatima; Hewson, Timothy; Zsoter, Ervin; Baugh, Calum

2017-04-01

.e. convective precipitation ratio, speed of steering winds, CAPE - Convective Available Potential Energy - and solar radiation), alongside the rainfall forecasts themselves, to define the "weather types" that in turn define the expected sub-grid variability. The calibration and computational strategy intrinsic to the system will be illustrated. The quality of the global point rainfall forecasts is also illustrated by analysing recent case studies in which extreme totals and a greatly elevated flash flood risk could be foreseen some days in advance but especially by a longer-term verification that arises out of retrospective global point rainfall forecasting for 2016. The second phase, currently in development, is focussing on the relationships with other relevant geographical aspects, for instance, orography and coastlines. Preliminary results will be presented. These are promising but need further study to fully understand their impact on the spatial distribution of point rainfall totals.

The air quality forecast in Beijing with Community Multi-scale Air Quality Modeling (CMAQ) System: model evaluation and improvement

NASA Astrophysics Data System (ADS)

Wu, Q.

2013-12-01

The MM5-SMOKE-CMAQ model system, which is developed by the United States Environmental Protection Agency(U.S. EPA) as the Models-3 system, has been used for the daily air quality forecast in the Beijing Municipal Environmental Monitoring Center(Beijing MEMC), as a part of the Ensemble Air Quality Forecast System for Beijing(EMS-Beijing) since the Olympic Games year 2008. In this study, we collect the daily forecast results of the CMAQ model in the whole year 2010 for the model evaluation. The results show that the model play a good model performance in most days but underestimate obviously in some air pollution episode. A typical air pollution episode from 11st - 20th January 2010 was chosen, which the air pollution index(API) of particulate matter (PM10) observed by Beijing MEMC reaches to 180 while the prediction of PM10-API is about 100. Taking in account all stations in Beijing, including urban and suburban stations, three numerical methods are used for model improvement: firstly, enhance the inner domain with 4km grids, the coverage from only Beijing to the area including its surrounding cities; secondly, update the Beijing stationary area emission inventory, from statistical county-level to village-town level, that would provide more detail spatial informance for area emissions; thirdly, add some industrial points emission in Beijing's surrounding cities, the latter two are both the improvement of emission. As the result, the peak of the nine national standard stations averaged PM10-API, which is simulated by CMAQ as daily hindcast PM10-API, reach to 160 and much near to the observation. The new results show better model performance, which the correlation coefficent is 0.93 in national standard stations average and 0.84 in all stations, the relative error is 15.7% in national standard stations averaged and 27% in all stations. The time series of 9 national standard in Beijing urban The scatter diagram of all stations in Beijing, the red is the forecast and

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.

Performance and Quality Assessment of the Forthcoming Copernicus Marine Service Global Ocean Monitoring and Forecasting Real-Time System

NASA Astrophysics Data System (ADS)

Lellouche, J. M.; Le Galloudec, O.; Greiner, E.; Garric, G.; Regnier, C.; Drillet, Y.

2016-02-01

Mercator Ocean currently delivers in real-time daily services (weekly analyses and daily forecast) with a global 1/12° high resolution system. The model component is the NEMO platform driven at the surface by the IFS ECMWF atmospheric analyses and forecasts. Observations are assimilated by means of a reduced-order Kalman filter with a 3D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. Along track altimeter data, satellite Sea Surface Temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. A 3D-Var scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.Since May 2015, Mercator Ocean opened the Copernicus Marine Service (CMS) and is in charge of the global ocean analyses and forecast, at eddy resolving resolution. In this context, R&D activities have been conducted at Mercator Ocean these last years in order to improve the real-time 1/12° global system for the next CMS version in 2016. The ocean/sea-ice model and the assimilation scheme benefit among others from the following improvements: large-scale and objective correction of atmospheric quantities with satellite data, new Mean Dynamic Topography taking into account the last version of GOCE geoid, new adaptive tuning of some observational errors, new Quality Control on the assimilated temperature and salinity vertical profiles based on dynamic height criteria, assimilation of satellite sea-ice concentration, new freshwater runoff from ice sheets melting …This presentation doesn't focus on the impact of each update, but rather on the overall behavior of the system integrating all updates. This assessment reports on the products quality improvements, highlighting the level of performance and the reliability of the new system.

Metrics for the Evaluation the Utility of Air Quality Forecasting

NASA Astrophysics Data System (ADS)

Sumo, T. M.; Stockwell, W. R.

2013-12-01

Global warming is expected to lead to higher levels of air pollution and therefore the forecasting of both long-term and daily air quality is an important component for the assessment of the costs of climate change and its impact on human health. Some of the risks associated with poor air quality days (where the Air Pollution Index is greater than 100), include hospital visits and mortality. Accurate air quality forecasting has the potential to allow sensitive groups to take appropriate precautions. This research builds metrics for evaluating the utility of air quality forecasting in terms of its potential impacts. Our analysis of air quality models focuses on the Washington, DC/Baltimore, MD region over the summertime ozone seasons between 2010 and 2012. The metrics that are relevant to our analysis include: (1) The number of times that a high ozone or particulate matter (PM) episode is correctly forecasted, (2) the number of times that high ozone or PM episode is forecasted when it does not occur and (3) the number of times when the air quality forecast predicts a cleaner air episode when the air was observed to have high ozone or PM. Our evaluation of the performance of air quality forecasts include those forecasts of ozone and particulate matter and data available from the U.S. Environmental Protection Agency (EPA)'s AIRNOW. We also examined observational ozone and particulate matter data available from Clean Air Partners. Overall the forecast models perform well for our region and time interval.

The Betting Odds Rating System: Using soccer forecasts to forecast soccer.

PubMed

Wunderlich, Fabian; Memmert, Daniel

2018-01-01

Betting odds are frequently found to outperform mathematical models in sports related forecasting tasks, however the factors contributing to betting odds are not fully traceable and in contrast to rating-based forecasts no straightforward measure of team-specific quality is deducible from the betting odds. The present study investigates the approach of combining the methods of mathematical models and the information included in betting odds. A soccer forecasting model based on the well-known ELO rating system and taking advantage of betting odds as a source of information is presented. Data from almost 15.000 soccer matches (seasons 2007/2008 until 2016/2017) are used, including both domestic matches (English Premier League, German Bundesliga, Spanish Primera Division and Italian Serie A) and international matches (UEFA Champions League, UEFA Europe League). The novel betting odds based ELO model is shown to outperform classic ELO models, thus demonstrating that betting odds prior to a match contain more relevant information than the result of the match itself. It is shown how the novel model can help to gain valuable insights into the quality of soccer teams and its development over time, thus having a practical benefit in performance analysis. Moreover, it is argued that network based approaches might help in further improving rating and forecasting methods.

The Betting Odds Rating System: Using soccer forecasts to forecast soccer

PubMed Central

Memmert, Daniel

2018-01-01

Betting odds are frequently found to outperform mathematical models in sports related forecasting tasks, however the factors contributing to betting odds are not fully traceable and in contrast to rating-based forecasts no straightforward measure of team-specific quality is deducible from the betting odds. The present study investigates the approach of combining the methods of mathematical models and the information included in betting odds. A soccer forecasting model based on the well-known ELO rating system and taking advantage of betting odds as a source of information is presented. Data from almost 15.000 soccer matches (seasons 2007/2008 until 2016/2017) are used, including both domestic matches (English Premier League, German Bundesliga, Spanish Primera Division and Italian Serie A) and international matches (UEFA Champions League, UEFA Europe League). The novel betting odds based ELO model is shown to outperform classic ELO models, thus demonstrating that betting odds prior to a match contain more relevant information than the result of the match itself. It is shown how the novel model can help to gain valuable insights into the quality of soccer teams and its development over time, thus having a practical benefit in performance analysis. Moreover, it is argued that network based approaches might help in further improving rating and forecasting methods. PMID:29870554

Methodology for Air Quality Forecast Downscaling from Regional- to Street-Scale

NASA Astrophysics Data System (ADS)

Baklanov, Alexander; Nuterman, Roman; Mahura, Alexander; Amstrup, Bjarne; Hansen Saas, Bent; Havskov Sørensen, Jens; Lorenzen, Thomas; Weismann, Jakob

2010-05-01

The most serious air pollution events occur in cities where there is a combination of high population density and air pollution, e.g. from vehicles. The pollutants can lead to serious human health problems, including asthma, irritation of the lungs, bronchitis, pneumonia, decreased resistance to respiratory infections, and premature death. In particular air pollution is associated with increase in cardiovascular disease and lung cancer. In 2000 WHO estimated that between 2.5 % and 11 % of total annual deaths are caused by exposure to air pollution. However, European-scale air quality models are not suited for local forecasts, as their grid-cell is typically of the order of 5 to 10km and they generally lack detailed representation of urban effects. Two suites are used in the framework of the EC FP7 project MACC (Monitoring of Atmosphere Composition and Climate) to demonstrate how downscaling from the European MACC ensemble to local-scale air quality forecast will be carried out: one will illustrate capabilities for the city of Copenhagen (Denmark); the second will focus on the city of Bucharest (Romania). This work is devoted to the first suite, where methodological aspects of downscaling from regional (European/ Denmark) to urban scale (Copenhagen), and from the urban down to street scale. The first results of downscaling according to the proposed methodology are presented. The potential for downscaling of European air quality forecasts by operating urban and street-level forecast models is evaluated. This will bring a strong support for continuous improvement of the regional forecast modelling systems for air quality in Europe, and underline clear perspectives for the future regional air quality core and downstream services for end-users. At the end of the MACC project, requirements on "how-to-do" downscaling of European air-quality forecasts to the city and street levels with different approaches will be formulated.

An enhanced PM 2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations

NASA Astrophysics Data System (ADS)

Cobourn, W. Geoffrey

2010-08-01

An enhanced PM 2.5 air quality forecast model based on nonlinear regression (NLR) and back-trajectory concentrations has been developed for use in the Louisville, Kentucky metropolitan area. The PM 2.5 air quality forecast model is designed for use in the warm season, from May through September, when PM 2.5 air quality is more likely to be critical for human health. The enhanced PM 2.5 model consists of a basic NLR model, developed for use with an automated air quality forecast system, and an additional parameter based on upwind PM 2.5 concentration, called PM24. The PM24 parameter is designed to be determined manually, by synthesizing backward air trajectory and regional air quality information to compute 24-h back-trajectory concentrations. The PM24 parameter may be used by air quality forecasters to adjust the forecast provided by the automated forecast system. In this study of the 2007 and 2008 forecast seasons, the enhanced model performed well using forecasted meteorological data and PM24 as input. The enhanced PM 2.5 model was compared with three alternative models, including the basic NLR model, the basic NLR model with a persistence parameter added, and the NLR model with persistence and PM24. The two models that included PM24 were of comparable accuracy. The two models incorporating back-trajectory concentrations had lower mean absolute errors and higher rates of detecting unhealthy PM2.5 concentrations compared to the other models.

THE NEW ENGLAND AIR QUALITY FORECASTING PILOT PROGRAM: DEVELOPMENT OF AN EVALUATION PROTOCOL AND PERFORMANCE BENCHMARK

EPA Science Inventory

The National Oceanic and Atmospheric Administration recently sponsored the New England Forecasting Pilot Program to serve as a "test bed" for chemical forecasting by providing all of the elements of a National Air Quality Forecasting System, including the development and implemen...

Air quality real-time forecast before and during the G-20 ...

EPA Pesticide Factsheets

The 2016 G-20 Hangzhou summit, the eleventh annual meeting of the G-20 heads of government, will be held during September 3-5, 2016 in Hangzhou, China. For a successful summit, it is important to ensure good air quality. To achieve this goal, governments of Hangzhou and its surrounding provinces will enforce a series of emission reductions, such as a forced closure of major highly-polluting industries and also limiting car and construction emissions in the cities and surroundings during the 2016 G-20 Hangzhou summit. Air quality forecast systems consisting of the two-way coupled WRF-CMAQ and online-coupled WRF-Chem have been applied to forecast air quality in Hangzhou regularly. This study will present the results of real-time forecasts of air quality over eastern China using 12-km grid spacing and for Hangzhou area using 4-km grid spacing with these two modeling systems using emission inventories for base and 2016 G-20 scenarios before and during the 2016 G-20 Hangzhou summit. Evaluations of models’ performance for both cases for PM2.5, PM10, O3, SO2, NO2, CO, air quality index (AQI), and aerosol optical depth (AOD) are carried out by comparing them with observations obtained from satellites, such as MODIS, and surface monitoring networks. The effects of the emission reduction efforts on expected air quality improvements during the2016 G-20 Hangzhou summit will be studied in depth. This study provides insights on how air quality will be improved by a plan

Performance of stochastic approaches for forecasting river water quality.

PubMed

Ahmad, S; Khan, I H; Parida, B P

2001-12-01

This study analysed water quality data collected from the river Ganges in India from 1981 to 1990 for forecasting using stochastic models. Initially the box and whisker plots and Kendall's tau test were used to identify the trends during the study period. For detecting the possible intervention in the data the time series plots and cusum charts were used. The three approaches of stochastic modelling which account for the effect of seasonality in different ways. i.e. multiplicative autoregressive integrated moving average (ARIMA) model. deseasonalised model and Thomas-Fiering model were used to model the observed pattern in water quality. The multiplicative ARIMA model having both nonseasonal and seasonal components were, in general, identified as appropriate models. In the deseasonalised modelling approach, the lower order ARIMA models were found appropriate for the stochastic component. The set of Thomas-Fiering models were formed for each month for all water quality parameters. These models were then used to forecast the future values. The error estimates of forecasts from the three approaches were compared to identify the most suitable approach for the reliable forecast. The deseasonalised modelling approach was recommended for forecasting of water quality parameters of a river.

AIR QUALITY FORECAST DATABASE AND ANALYSIS

EPA Science Inventory

In 2003, NOAA and EPA signed a Memorandum of Agreement to collaborate on the design and implementation of a capability to produce daily air quality modeling forecast information for the U.S. NOAA's ETA meteorological model and EPA's Community Multiscale Air Quality (CMAQ) model ...

Performance and quality assessment of the recent updated CMEMS global ocean monitoring and forecasting real-time system

NASA Astrophysics Data System (ADS)

Le Galloudec, Olivier; Lellouche, Jean-Michel; Greiner, Eric; Garric, Gilles; Régnier, Charly; Drévillon, Marie; Drillet, Yann

2017-04-01

Since May 2015, Mercator Ocean opened the Copernicus Marine Environment and Monitoring Service (CMEMS) and is in charge of the global eddy resolving ocean analyses and forecast. In this context, Mercator Ocean currently delivers in real-time daily services (weekly analyses and daily forecast) with a global 1/12° high resolution system. The model component is the NEMO platform driven at the surface by the IFS ECMWF atmospheric analyses and forecasts. Observations are assimilated by means of a reduced-order Kalman filter with a 3D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. Along track altimeter data, satellite Sea Surface Temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. A 3D-Var scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity. R&D activities have been conducted at Mercator Ocean these last years to improve the real-time 1/12° global system for recent updated CMEMS version in 2016. The ocean/sea-ice model and the assimilation scheme benefited of the following improvements: large-scale and objective correction of atmospheric quantities with satellite data, new Mean Dynamic Topography taking into account the last version of GOCE geoid, new adaptive tuning of some observational errors, new Quality Control on the assimilated temperature and salinity vertical profiles based on dynamic height criteria, assimilation of satellite sea-ice concentration, new freshwater runoff from ice sheets melting, … This presentation will show the impact of some updates separately, with a particular focus on adaptive tuning experiments of satellite Sea Level Anomaly (SLA) and Sea Surface Temperature (SST) observations errors. For the SLA, the a priori prescribed observation error is globally greatly reduced. The median value of the error changed

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

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

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...

APPLICATION OF BIAS AND ADJUSTMENT TECHNIQUES TO THE ETA-CMAQ AIR QUALITY FORECAST

EPA Science Inventory

The current air quality forecast system, based on linking NOAA's Eta meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, consistently overpredicts surface ozone concentrations, but simulates its day-to-day variability quite well. The ability of bias cor...

Weather forecasting expert system study

NASA Technical Reports Server (NTRS)

1985-01-01

Weather forecasting is critical to both the Space Transportation System (STS) ground operations and the launch/landing activities at NASA Kennedy Space Center (KSC). The current launch frequency places significant demands on the USAF weather forecasters at the Cape Canaveral Forecasting Facility (CCFF), who currently provide the weather forecasting for all STS operations. As launch frequency increases, KSC's weather forecasting problems will be great magnified. The single most important problem is the shortage of highly skilled forecasting personnel. The development of forecasting expertise is difficult and requires several years of experience. Frequent personnel changes within the forecasting staff jeopardize the accumulation and retention of experience-based weather forecasting expertise. The primary purpose of this project was to assess the feasibility of using Artificial Intelligence (AI) techniques to ameliorate this shortage of experts by capturing aria incorporating the forecasting knowledge of current expert forecasters into a Weather Forecasting Expert System (WFES) which would then be made available to less experienced duty forecasters.

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.

Quality Assessment of the Cobel-Isba Numerical Forecast System of Fog and Low Clouds

NASA Astrophysics Data System (ADS)

Bergot, Thierry

2007-06-01

Short-term forecasting of fog is a difficult issue which can have a large societal impact. Fog appears in the surface boundary layer and is driven by the interactions between land surface and the lower layers of the atmosphere. These interactions are still not well parameterized in current operational NWP models, and a new methodology based on local observations, an adaptive assimilation scheme and a local numerical model is tested. The proposed numerical forecast method of foggy conditions has been run during three years at Paris-CdG international airport. This test over a long-time period allows an in-depth evaluation of the forecast quality. This study demonstrates that detailed 1-D models, including detailed physical parameterizations and high vertical resolution, can reasonably represent the major features of the life cycle of fog (onset, development and dissipation) up to +6 h. The error on the forecast onset and burn-off time is typically 1 h. The major weakness of the methodology is related to the evolution of low clouds (stratus lowering). Even if the occurrence of fog is well forecasted, the value of the horizontal visibility is only crudely forecasted. Improvements in the microphysical parameterization and in the translation algorithm converting NWP prognostic variables into a corresponding horizontal visibility seems necessary to accurately forecast the value of the visibility.

A regional air quality forecasting system over Europe: the MACC-II daily ensemble production

NASA Astrophysics Data System (ADS)

Marécal, V.; Peuch, V.-H.; Andersson, C.; Andersson, S.; Arteta, J.; Beekmann, M.; Benedictow, A.; Bergström, R.; Bessagnet, B.; Cansado, A.; Chéroux, F.; Colette, A.; Coman, A.; Curier, R. L.; Denier van der Gon, H. A. C.; Drouin, A.; Elbern, H.; Emili, E.; Engelen, R. J.; Eskes, H. J.; Foret, G.; Friese, E.; Gauss, M.; Giannaros, C.; Guth, J.; Joly, M.; Jaumouillé, E.; Josse, B.; Kadygrov, N.; Kaiser, J. W.; Krajsek, K.; Kuenen, J.; Kumar, U.; Liora, N.; Lopez, E.; Malherbe, L.; Martinez, I.; Melas, D.; Meleux, F.; Menut, L.; Moinat, P.; Morales, T.; Parmentier, J.; Piacentini, A.; Plu, M.; Poupkou, A.; Queguiner, S.; Robertson, L.; Rouïl, L.; Schaap, M.; Segers, A.; Sofiev, M.; Thomas, M.; Timmermans, R.; Valdebenito, Á.; van Velthoven, P.; van Versendaal, R.; Vira, J.; Ung, A.

2015-03-01

This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. The paper gives an overall picture of its status at the end of MACC-II (summer 2014). This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs and PAN + PAN precursors) over 8 vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performances of the system are assessed daily, weekly and 3 monthly (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the median ensemble to forecast regional ozone pollution events. The time period of this case study is also used to illustrate that the median ensemble generally outperforms each of the individual models and that it is still robust even if two of the seven models are missing. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10 and show an overall improvement over time. The change of the skills of the ensemble over the past two summers for ozone and the past two winters for PM10 are discussed in the paper. While the evolution of the ozone scores is not significant, there are improvements of PM10 over the past two winters

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).

Ensemble Statistical Post-Processing of the National Air Quality Forecast Capability: Enhancing Ozone Forecasts in Baltimore, Maryland

NASA Technical Reports Server (NTRS)

Garner, Gregory G.; Thompson, Anne M.

2013-01-01

An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone. The user is provided the freedom to tailor the forecast to the decision at hand by using decision-specific probability thresholds that define a forecast for an ozone exceedance. Taking advantage of the ESP, the user not only receives an increase in value over the NAQFC, but also receives value for An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone

Forecasting forecast skill

NASA Technical Reports Server (NTRS)

Kalnay, Eugenia; Dalcher, Amnon

1987-01-01

It is shown that it is possible to predict the skill of numerical weather forecasts - a quantity which is variable from day to day and region to region. This has been accomplished using as predictor the dispersion (measured by the average correlation) between members of an ensemble of forecasts started from five different analyses. The analyses had been previously derived for satellite-data-impact studies and included, in the Northern Hemisphere, moderate perturbations associated with the use of different observing systems. When the Northern Hemisphere was used as a verification region, the prediction of skill was rather poor. This is due to the fact that such a large area usually contains regions with excellent forecasts as well as regions with poor forecasts, and does not allow for discrimination between them. However, when regional verifications were used, the ensemble forecast dispersion provided a very good prediction of the quality of the individual forecasts.

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

Performance assessment of a Bayesian Forecasting System (BFS) for real-time flood forecasting

NASA Astrophysics Data System (ADS)

Biondi, D.; De Luca, D. L.

2013-02-01

SummaryThe paper evaluates, for a number of flood events, the performance of a Bayesian Forecasting System (BFS), with the aim of evaluating total uncertainty in real-time flood forecasting. The predictive uncertainty of future streamflow is estimated through the Bayesian integration of two separate processors. The former evaluates the propagation of input uncertainty on simulated river discharge, the latter computes the hydrological uncertainty of actual river discharge associated with all other possible sources of error. A stochastic model and a distributed rainfall-runoff model were assumed, respectively, for rainfall and hydrological response simulations. A case study was carried out for a small basin in the Calabria region (southern Italy). The performance assessment of the BFS was performed with adequate verification tools suited for probabilistic forecasts of continuous variables such as streamflow. Graphical tools and scalar metrics were used to evaluate several attributes of the forecast quality of the entire time-varying predictive distributions: calibration, sharpness, accuracy, and continuous ranked probability score (CRPS). Besides the overall system, which incorporates both sources of uncertainty, other hypotheses resulting from the BFS properties were examined, corresponding to (i) a perfect hydrological model; (ii) a non-informative rainfall forecast for predicting streamflow; and (iii) a perfect input forecast. The results emphasize the importance of using different diagnostic approaches to perform comprehensive analyses of predictive distributions, to arrive at a multifaceted view of the attributes of the prediction. For the case study, the selected criteria revealed the interaction of the different sources of error, in particular the crucial role of the hydrological uncertainty processor when compensating, at the cost of wider forecast intervals, for the unreliable and biased predictive distribution resulting from the Precipitation Uncertainty

IMPROVING NATIONAL AIR QUALITY FORECASTS WITH SATELLITE AEROSOL OBSERVATIONS

EPA Science Inventory

Air quality forecasts for major US metropolitan areas have been provided to the public through a partnership between the US Environmental Protection Agency and state and local air agencies since 1997. Recent years have witnessed improvement in forecast skill and expansion of fore...

FORECASTING AIR QUALITY OVER THE UNITED STATES

EPA Science Inventory

Increased awareness of national air quality issues on the part of the media and the general public have recently led to more demand for short-term (1-2 day) air quality forecasts for use in assessing potential health impacts (e.g., on children, the elderly, and asthmatics) and po...

PREMAQ: A NEW PRE-PROCESSOR TO CMAQ FOR AIR-QUALITY FORECASTING

EPA Science Inventory

A new pre-processor to CMAQ (PREMAQ) has been developed as part of the national air-quality forecasting system. PREMAQ combines the functionality of MCIP and parts of SMOKE in a single real-time processor. PREMAQ was specifically designed to link NCEP's Eta model with CMAQ, and...

Towards an operational high-resolution air quality forecasting system at ECCC

NASA Astrophysics Data System (ADS)

Munoz-Alpizar, Rodrigo; Stroud, Craig; Ren, Shuzhan; Belair, Stephane; Leroyer, Sylvie; Souvanlasy, Vanh; Spacek, Lubos; Pavlovic, Radenko; Davignon, Didier; Moran, Moran

2017-04-01

Urban environments are particularly sensitive to weather, air quality (AQ), and climatic conditions. Despite the efforts made in Canada to reduce pollution in urban areas, AQ continues to be a concern for the population, especially during short-term episodes that could lead to exceedances of daily air quality standards. Furthermore, urban air pollution has long been associated with significant adverse health effects. In Canada, the large percentage of the population living in urban areas ( 81%, according to the Canada's 2011 census) is exposed to elevated air pollution due to local emissions sources. Thus, in order to improve the services offered to the Canadian public, Environment and Climate Change Canada has launched an initiative to develop a high-resolution air quality prediction capacity for urban areas in Canada. This presentation will show observed pollution trends (2010-2016) for Canadian mega-cities along with some preliminary high-resolution air quality modelling results. Short-term and long-term plans for urban AQ forecasting in Canada will also be described.

Evaluating the impact of AMDAR data quality control in China on the short-range convection forecasts using the WRF model

NASA Astrophysics Data System (ADS)

Wang, Xiaofeng; Jiang, Qin; Zhang, Lei

2016-04-01

A quality control system for the Aircraft Meteorological Data Relay (AMDAR) data has been implemented in China. This system is an extension to the AMDAR quality control system used at the US National Centers for Environmental Prediction. We present a study in which the characteristics of each AMDAR data quality type were examined and the impact of the AMDAR data quality system on short-range convective weather forecasts using the WRF model was investigated. The main results obtained from this study are as follows. (1) The hourly rejection rate of AMDAR data during 2014 was 5.79%, and most of the rejections happened in Near Duplicate Check. (2) There was a significant diurnal variation for both quantity and quality of AMDAR data. Duplicated reports increased with the increase of data quantity, while suspicious and disorderly reports decreased with the increase of data quantity. (3) The characteristics of the data quality were different in each model layer, with the quality problems occurring mainly at the surface as well as at the height where the power or the flight mode of the aircraft underwent adjustment. (4) Assimilating the AMDAR data improved the forecast accuracy, particularly over the region where strong convection occurred. (5) Significant improvements made by assimilating AMDAR data were found after six hours into the model forecast. The conclusion from this study is that the newly implemented AMDAR data quality system can help improve the accuracy of short-range convection forecasts using the WRF model.

Goals of Quality in Doctoral Studies and Forecasted Outcomes

ERIC Educational Resources Information Center

Zelvys, Rimantas

2007-01-01

This article discusses the quality assurance policy of doctoral studies implemented in Lithuania and its probable outcomes are forecasted. In scientific literature the quality of education is commonly defined as a holistic phenomenon composed of the quality of initial conditions, quality of process and quality of outputs. The accomplished document…

Road weather forecast quality analysis : project summary

DOT National Transportation Integrated Search

2006-03-01

The purpose of this research is to enhance the use of KDOTs Roadway Weather : Information System by improving the weather forecasts themselves and raising the level of : confidence in these forecasts.

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

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

Toward a US National Air Quality Forecast Capability: Current and Planned Capabilities

EPA Science Inventory

As mandated by Congress, NOAA is establishing a US national air quality forecast capability. This capability is being built with EPA, to provide air quality forecast information with enough accuracy and lead-time so that people can take actions to limit harmful effects of poor a...

A global flash flood forecasting system

NASA Astrophysics Data System (ADS)

Baugh, Calum; Pappenberger, Florian; Wetterhall, Fredrik; Hewson, Tim; Zsoter, Ervin

2016-04-01

The sudden and devastating nature of flash flood events means it is imperative to provide early warnings such as those derived from Numerical Weather Prediction (NWP) forecasts. Currently such systems exist on basin, national and continental scales in Europe, North America and Australia but rely on high resolution NWP forecasts or rainfall-radar nowcasting, neither of which have global coverage. To produce global flash flood forecasts this work investigates the possibility of using forecasts from a global NWP system. In particular we: (i) discuss how global NWP can be used for flash flood forecasting and discuss strengths and weaknesses; (ii) demonstrate how a robust evaluation can be performed given the rarity of the event; (iii) highlight the challenges and opportunities in communicating flash flood uncertainty to decision makers; and (iv) explore future developments which would significantly improve global flash flood forecasting. The proposed forecast system uses ensemble surface runoff forecasts from the ECMWF H-TESSEL land surface scheme. A flash flood index is generated using the ERIC (Enhanced Runoff Index based on Climatology) methodology [Raynaud et al., 2014]. This global methodology is applied to a series of flash floods across southern Europe. Results from the system are compared against warnings produced using the higher resolution COSMO-LEPS limited area model. The global system is evaluated by comparing forecasted warning locations against a flash flood database of media reports created in partnership with floodlist.com. To deal with the lack of objectivity in media reports we carefully assess the suitability of different skill scores and apply spatial uncertainty thresholds to the observations. To communicate the uncertainties of the flash flood system output we experiment with a dynamic region-growing algorithm. This automatically clusters regions of similar return period exceedence probabilities, thus presenting the at-risk areas at a spatial

Air Quality Forecasts Using the NASA GEOS Model: A Unified Tool from Local to Global Scales

NASA Technical Reports Server (NTRS)

Knowland, E. Emma; Keller, Christoph; Nielsen, J. Eric; Orbe, Clara; Ott, Lesley; Pawson, Steven; Saunders, Emily; Duncan, Bryan; Cook, Melanie; Liu, Junhua;

The Mediterranean Forecasting System: recent developments

NASA Astrophysics Data System (ADS)

Tonani, Marina; Oddo, Paolo; Korres, Gerasimos; Clementi, Emanuela; Dobricic, Srdjan; Drudi, Massimiliano; Pistoia, Jenny; Guarnieri, Antonio; Romaniello, Vito; Girardi, Giacomo; Grandi, Alessandro; Bonaduce, Antonio; Pinardi, Nadia

2014-05-01

Recent developments of the Mediterranean Monitoring and Forecasting Centre of the EU-Copernicus marine service, the Mediterranean Forecasting System (MFS), are presented. MFS provides forecast, analysis and reanalysis for the physical and biogeochemical parameters of the Mediterranean Sea. The different components of the system are continuously updated in order to provide to the users the best available product. This work is focus on the physical component of the system. The physical core of MFS is composed by an ocean general circulation model (NEMO) coupled with a spectral wave model (Wave Watch-III). The NEMO model provides to WW-III surface currents and SST fields, while WW-III returns back to NEMO the neutral component of the surface drag coefficient. Satellite Sea Level Anomaly observations and in-situ T & S vertical profiles are assimilated into this system using a variational assimilation scheme based on 3DVAR (Dobricic, 2008) . Sensitive experiments have been performed in order to assess the impact of the assimilation of the latest available SLA missions, Altika and Cryosat together with the long term available mission of Jason2. The results show a significant improvement of the MFS skill due to the multi-mission along track assimilation. The primitive equations module has been recently upgraded with the introduction of the atmospheric pressure term and a new, explicit, numerical scheme has been adopted to solve the barotropic component of the equations of motion. The SLA satellite observations for data assimilation have been consequently modified in order to account for the new atmospheric pressure term introduced in the equations. This new system has been evaluated using tide gauge coastal buoys and the satellite along track data. The quality of the SSH has improved significantly while a minor impact has been observed on the other state variables (temperature, salinity and currents). Experiments with a higher resolution NWP (numerical weather prediction

Daily air quality index forecasting with hybrid models: A case in China.

PubMed

Zhu, Suling; Lian, Xiuyuan; Liu, Haixia; Hu, Jianming; Wang, Yuanyuan; Che, Jinxing

2017-12-01

Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the

Water quality in the Schuylkill River, Pennsylvania: the potential for long-lead forecasts

NASA Astrophysics Data System (ADS)

Block, P. J.; Peralez, J.

2012-12-01

Prior analysis of pathogen levels in the Schuylkill River has led to a categorical daily forecast of water quality (denoted as red, yellow, or green flag days.) The forecast, available to the public online through the Philadelphia Water Department, is predominantly based on the local precipitation forecast. In this study, we explore the feasibility of extending the forecast to the seasonal scale by associating large-scale climate drivers with local precipitation and water quality parameter levels. This advance information is relevant for recreational activities, ecosystem health, and water treatment (energy, chemicals), as the Schuylkill provides 40% of Philadelphia's water supply. Preliminary results indicate skillful prediction of average summertime water quality parameters and characteristics, including chloride, coliform, turbidity, alkalinity, and others, using season-ahead oceanic and atmospheric variables, predominantly from the North Atlantic. Water quality parameter trends, including historic land use changes along the river, association with climatic variables, and prediction models will be presented.

Simple statistical bias correction techniques greatly improve moderate resolution air quality forecast at station level

NASA Astrophysics Data System (ADS)

Curci, Gabriele; Falasca, Serena

2017-04-01

Deterministic air quality forecast is routinely carried out at many local Environmental Agencies in Europe and throughout the world by means of eulerian chemistry-transport models. The skill of these models in predicting the ground-level concentrations of relevant pollutants (ozone, nitrogen dioxide, particulate matter) a few days ahead has greatly improved in recent years, but it is not yet always compliant with the required quality level for decision making (e.g. the European Commission has set a maximum uncertainty of 50% on daily values of relevant pollutants). Post-processing of deterministic model output is thus still regarded as a useful tool to make the forecast more reliable. In this work, we test several bias correction techniques applied to a long-term dataset of air quality forecasts over Europe and Italy. We used the WRF-CHIMERE modelling system, which provides operational experimental chemical weather forecast at CETEMPS (http://pumpkin.aquila.infn.it/forechem/), to simulate the years 2008-2012 at low resolution over Europe (0.5° x 0.5°) and moderate resolution over Italy (0.15° x 0.15°). We compared the simulated dataset with available observation from the European Environmental Agency database (AirBase) and characterized model skill and compliance with EU legislation using the Delta tool from FAIRMODE project (http://fairmode.jrc.ec.europa.eu/). The bias correction techniques adopted are, in order of complexity: (1) application of multiplicative factors calculated as the ratio of model-to-observed concentrations averaged over the previous days; (2) correction of the statistical distribution of model forecasts, in order to make it similar to that of the observations; (3) development and application of Model Output Statistics (MOS) regression equations. We illustrate differences and advantages/disadvantages of the three approaches. All the methods are relatively easy to implement for other modelling systems.

Development of Water Quality Forecasting Models Based on the SOM-ANN on TMDL Unit Watershed in Nakdong River

NASA Astrophysics Data System (ADS)

KIM, M.; Kim, J.; Baek, J.; Kim, C.; Shin, H.

2013-12-01

It has being happened as flush flood or red/green tide in various natural phenomena due to climate change and indiscreet development of river or land. Especially, water being very important to man should be protected and managed from water quality pollution, and in water resources management, real-time watershed monitoring system is being operated with the purpose of keeping watch and managing on rivers. It is especially important to monitor and forecast water quality in watershed. A study area selected Nak_K as one site among TMDL unit watershed in Nakdong River. This study is to develop a water quality forecasting model connected with making full use of observed data of 8 day interval from Nakdong River Environment Research Center. When forecasting models for each of the BOD, DO, COD, and chlorophyll-a are established considering correlation of various water quality factors, it is needed to select water quality factors showing highly considerable correlation with each water quality factor which is BOD, DO, COD, and chlorophyll-a. For analyzing the correlation of the factors (reservoir discharge, precipitation, air temperature, DO, BOD, COD, Tw, TN, TP, chlorophyll-a), in this study, self-organizing map was used and cross correlation analysis method was also used for comparing results drawn. Based on the results, each forecasting model for BOD, DO, COD, and chlorophyll-a was developed during the short period as 8, 16, 24, 32 days at 8 day interval. The each forecasting model is based on neural network with back propagation algorithm. That is, the study is connected with self-organizing map for analyzing correlation among various factors and neural network model for forecasting of water quality. It is considerably effective to manage the water quality in plenty of rivers, then, it specially is possible to monitor a variety of accidents in water quality. It will work well to protect water quality and to prevent destruction of the environment becoming more and more

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.

Performance and diagnostic evaluation of ozone predictions by the Eta-Community Multiscale Air Quality Forecast System during the 2002 New England Air Quality Study.

PubMed

Yu, Shaocai; Mathur, Rohit; Kang, Daiwen; Schere, Kenneth; Eder, Brian; Pleim, Jonathan

2006-10-01

A real-time air quality forecasting system (Eta-Community Multiscale Air Quality [CMAQ] model suite) has been developed by linking the National Centers for Environmental Estimation Eta model to the U.S. Environmental Protection Agency (EPA) CMAQ model. This work presents results from the application of the Eta-CMAQ modeling system for forecasting ozone (O3) over the Northeastern United States during the 2002 New England Air Quality Study (NEAQS). Spatial and temporal performance of the Eta-CMAQ model for O3 was evaluated by comparison with observations from the EPA Air Quality System (AQS) network. This study also examines the ability of the model to simulate the processes governing the distributions of tropospheric O3 on the basis of the intensive datasets obtained at the four Atmospheric Investigation, Regional Modeling, Analysis, and Estimation (AIRMAP) and Harvard Forest (HF) surface sites. The episode analysis reveals that the model captured the buildup of O3 concentrations over the northeastern domain from August 11 and reproduced the spatial distributions of observed O3 very well for the daytime (8:00 p.m.) of both August 8 and 12 with most of normalized mean bias (NMB) within +/- 20%. The model reproduced 53.3% of the observed hourly O3 within a factor of 1.5 with NMB of 29.7% and normalized mean error of 46.9% at the 342 AQS sites. The comparison of modeled and observed lidar O3 vertical profiles shows that whereas the model reproduced the observed vertical structure, it tended to overestimate at higher altitude. The model reproduced 64-77% of observed NO2 photolysis rate values within a factor of 1.5 at the AIRMAP sites. At the HF site, comparison of modeled and observed O3/nitrogen oxide (NOx) ratios suggests that the site is mainly under strongly NOx-sensitive conditions (>53%). It was found that the modeled lower limits of the O3 production efficiency values (inferred from O3-CO correlation) are close to the observations.

Operational air quality forecast guidance for the United States

NASA Astrophysics Data System (ADS)

Stajner, Ivanka; Lee, Pius; Tong, Daniel; Pan, Li; McQueen, Jeff; Huang, Jinaping; Djalalova, Irina; Wilczak, James; Huang, Ho-Chun; Wang, Jun; Stein, Ariel; Upadhayay, Sikchya

2016-04-01

NOAA provides operational air quality predictions for ozone and wildfire smoke over the United States (U.S.) and predictions of airborne dust over the contiguous 48 states at http://airquality.weather.gov. These predictions are produced using U.S. Environmental Protection Agency (EPA) Community Model for Air Quality (CMAQ) and NOAA's HYSPLIT model (Stein et al., 2015) with meteorological inputs from the North American Mesoscale Forecast System (NAM). The current efforts focus on improving test predictions of fine particulate matter (PM2.5) from CMAQ. Emission inputs for ozone and PM2.5 predictions include inventory information from the U.S. EPA and recently added contributions of particulate matter from intermittent wildfires and windblown dust that rely on near real-time information. Current testing includes refinement of the vertical grid structure in CMAQ and inclusion of contributions of dust transport from global sources into the U.S. domain using the NEMS Global Aerosol Capability (NGAC). The addition of wildfire smoke and dust contributions in CMAQ reduced model underestimation of PM2.5 in summertime. Wintertime overestimation of PM2.5 was reduced by suppressing emissions of soil particles when the terrain is covered by snow or ice. Nevertheless, seasonal biases and biases in the diurnal cycle of PM2.5 are still substantial. Therefore, a new bias correction procedure based on an analog ensemble approach was introduced (Djalalova et al., 2015). It virtually eliminates biases in monthly means or in the diurnal cycle, but it also reduces day-to-day variability in PM2.5 predictions. Refinements to the bias correction procedure are being developed. Upgrades for the representation of wildfire smoke emissions within the domain and from global sources are in testing. Another area of active development includes approaches to scale emission inventories for nitrogen oxides in order to reproduce recent changes observed by the AirNow surface monitoring network and by

Forecasting in Complex Systems

NASA Astrophysics Data System (ADS)

Rundle, J. B.; Holliday, J. R.; Graves, W. R.; Turcotte, D. L.; Donnellan, A.

2014-12-01

Complex nonlinear systems are typically characterized by many degrees of freedom, as well as interactions between the elements. Interesting examples can be found in the areas of earthquakes and finance. In these two systems, fat tails play an important role in the statistical dynamics. For earthquake systems, the Gutenberg-Richter magnitude-frequency is applicable, whereas for daily returns for the securities in the financial markets are known to be characterized by leptokurtotic statistics in which the tails are power law. Very large fluctuations are present in both systems. In earthquake systems, one has the example of great earthquakes such as the M9.1, March 11, 2011 Tohoku event. In financial systems, one has the example of the market crash of October 19, 1987. Both were largely unexpected events that severely impacted the earth and financial systems systemically. Other examples include the M9.3 Andaman earthquake of December 26, 2004, and the Great Recession which began with the fall of Lehman Brothers investment bank on September 12, 2013. Forecasting the occurrence of these damaging events has great societal importance. In recent years, national funding agencies in a variety of countries have emphasized the importance of societal relevance in research, and in particular, the goal of improved forecasting technology. Previous work has shown that both earthquakes and financial crashes can be described by a common Landau-Ginzburg-type free energy model. These metastable systems are characterized by fat tail statistics near the classical spinodal. Correlations in these systems can grow and recede, but do not imply causation, a common source of misunderstanding. In both systems, a common set of techniques can be used to compute the probabilities of future earthquakes or crashes. In this talk, we describe the basic phenomenology of these systems and emphasize their similarities and differences. We also consider the problem of forecast validation and verification

Air quality early-warning system for cities in China

NASA Astrophysics Data System (ADS)

Xu, Yunzhen; Yang, Wendong; Wang, Jianzhou

2017-01-01

Air pollution has become a serious issue in many developing countries, especially in China, and could generate adverse effects on human beings. Air quality early-warning systems play an increasingly significant role in regulatory plans that reduce and control emissions of air pollutants and inform the public in advance when harmful air pollution is foreseen. However, building a robust early-warning system that will improve the ability of early-warning is not only a challenge but also a critical issue for the entire society. Relevant research is still poor in China and cannot always satisfy the growing requirements of regulatory planning, despite the issue's significance. Therefore, in this paper, a hybrid air quality early-warning system was successfully developed, composed of forecasting and evaluation. First, a hybrid forecasting model was proposed as an important part of this system based on the theory of "decomposition and ensemble" and combined with the advanced data processing technique, support vector machine, the latest bio-inspired optimization algorithm and the leave-one-out strategy for deciding weights. Afterwards, to intensify the research, fuzzy evaluation was performed, which also plays an indispensable role in the early-warning system. The forecasting model and fuzzy evaluation approaches are complementary. Case studies using daily air pollution concentrations of six air pollutants from three cities in China (i.e., Taiyuan, Harbin and Chongqing) are used as examples to evaluate the efficiency and effectiveness of the developed air quality early-warning system. Experimental results demonstrate that both the accuracy and the effectiveness of the developed system are greatly superior for air quality early warning. Furthermore, the application of forecasting and evaluation enables the informative and effective quantification of future air quality, offering a significant advantage, and can be employed to develop rapid air quality early-warning systems.

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

Improving Forecast Skill by Assimilation of Quality Controlled AIRS Version 5 Temperature Soundings

NASA Technical Reports Server (NTRS)

Susskind, Joel; Reale, Oreste

2009-01-01

The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains two significant improvements over Version 4: 1) Improved physics allows for use of AIRS observations in the entire 4.3 micron CO2 absorption band in the retrieval of temperature profile T(p) during both day and night. Tropospheric sounding 15 micron CO2 observations are now used primarily in the generation of cloud cleared radiances R(sub i). This approach allows for the generation of accurate values of R(sub i) and T(p) under most cloud conditions. 2) Another very significant improvement in Version 5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by-channel error estimates for R(sub i). These error estimates are used for Quality Control of the retrieved products. We have conducted forecast impact experiments assimilating AIRS temperature profiles with different levels of Quality Control using the NASA GEOS-5 data assimilation system. Assimilation of Quality Controlled T(p) resulted in significantly improved forecast skill compared to that obtained from analyses obtained when all data used operationally by NCEP, except for AIRS data, is assimilated. We also conducted an experiment assimilating AIRS radiances uncontaminated by clouds, as done operationally by ECMWF and NCEP. Forecast resulting from assimilated AIRS radiances were of poorer quality than those obtained assimilating AIRS temperatures.

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.

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.

Hybrid Intrusion Forecasting Framework for Early Warning System

NASA Astrophysics Data System (ADS)

Kim, Sehun; Shin, Seong-Jun; Kim, Hyunwoo; Kwon, Ki Hoon; Han, Younggoo

Recently, cyber attacks have become a serious hindrance to the stability of Internet. These attacks exploit interconnectivity of networks, propagate in an instant, and have become more sophisticated and evolutionary. Traditional Internet security systems such as firewalls, IDS and IPS are limited in terms of detecting recent cyber attacks in advance as these systems respond to Internet attacks only after the attacks inflict serious damage. In this paper, we propose a hybrid intrusion forecasting system framework for an early warning system. The proposed system utilizes three types of forecasting methods: time-series analysis, probabilistic modeling, and data mining method. By combining these methods, it is possible to take advantage of the forecasting technique of each while overcoming their drawbacks. Experimental results show that the hybrid intrusion forecasting method outperforms each of three forecasting methods.

Utilizing Climate Forecasts for Improving Water and Power Systems Coordination

NASA Astrophysics Data System (ADS)

Arumugam, S.; Queiroz, A.; Patskoski, J.; Mahinthakumar, K.; DeCarolis, J.

2016-12-01

Climate forecasts, typically monthly-to-seasonal precipitation forecasts, are commonly used to develop streamflow forecasts for improving reservoir management. Irrespective of their high skill in forecasting, temperature forecasts in developing power demand forecasts are not often considered along with streamflow forecasts for improving water and power systems coordination. In this study, we consider a prototype system to analyze the utility of climate forecasts, both precipitation and temperature, for improving water and power systems coordination. The prototype system, a unit-commitment model that schedules power generation from various sources, is considered and its performance is compared with an energy system model having an equivalent reservoir representation. Different skill sets of streamflow forecasts and power demand forecasts are forced on both water and power systems representations for understanding the level of model complexity required for utilizing monthly-to-seasonal climate forecasts to improve coordination between these two systems. The analyses also identify various decision-making strategies - forward purchasing of fuel stocks, scheduled maintenance of various power systems and tradeoff on water appropriation between hydropower and other uses - in the context of various water and power systems configurations. Potential application of such analyses for integrating large power systems with multiple river basins is also discussed.

Development and validation of a regional coupled forecasting system for S2S forecasts

NASA Astrophysics Data System (ADS)

Sun, R.; Subramanian, A. C.; Hoteit, I.; Miller, A. J.; Ralph, M.; Cornuelle, B. D.

2017-12-01

Accurate and efficient forecasting of oceanic and atmospheric circulation is essential for a wide variety of high-impact societal needs, including: weather extremes; environmental protection and coastal management; management of fisheries, marine conservation; water resources; and renewable energy. Effective forecasting relies on high model fidelity and accurate initialization of the models with observed state of the ocean-atmosphere-land coupled system. A regional coupled ocean-atmosphere model with the Weather Research and Forecasting (WRF) model and the MITGCM ocean model coupled using the ESMF (Earth System Modeling Framework) coupling framework is developed to resolve mesoscale air-sea feedbacks. The regional coupled model allows oceanic mixed layer heat and momentum to interact with the atmospheric boundary layer dynamics at the mesoscale and submesoscale spatiotemporal regimes, thus leading to feedbacks which are otherwise not resolved in coarse resolution global coupled forecasting systems or regional uncoupled forecasting systems. The model is tested in two scenarios in the mesoscale eddy rich Red Sea and Western Indian Ocean region as well as mesoscale eddies and fronts of the California Current System. Recent studies show evidence for air-sea interactions involving the oceanic mesoscale in these two regions which can enhance predictability on sub seasonal timescale. We will present results from this newly developed regional coupled ocean-atmosphere model for forecasts over the Red Sea region as well as the California Current region. The forecasts will be validated against insitu observations in the region as well as reanalysis fields.

CALIPSO Satellite Lidar Identification Of Elevated Dust Over Australia Compared With Air Quality Model PM60 Forecasts

NASA Technical Reports Server (NTRS)

Young, Stuart A.; Vaughan, Mark; Omar, Ali; Liu, Zhaoyan; Lee, Sunhee; Hu, Youngxiang; Cope, Martin

2008-01-01

Global measurements of the vertical distribution of clouds and aerosols have been recorded by the lidar on board the CALIPSO (Cloud Aerosol Lidar Infrared Pathfinder Satellite Observations) satellite since June 2006. Such extensive, height-resolved measurements provide a rare and valuable opportunity for developing, testing and validating various atmospheric models, including global climate, numerical weather prediction, chemical transport and air quality models. Here we report on the initial results of an investigation into the performance of the Australian Air Quality Forecast System (AAQFS) model in forecasting the distribution of elevated dust over the Australian region. The model forecasts of PM60 dust distribution are compared with the CALIPSO lidar Vertical Feature Mask (VFM) data product. The VFM classifies contiguous atmospheric regions of enhanced backscatter as either cloud or aerosols. Aerosols are further classified into six subtypes. By comparing forecast PM60 concentration profiles to the spatial distribution of dust reported in the CALIPSO VFM, we can assess the model s ability to predict the occurrence and the vertical and horizontal extents of dust events within the study area.

Skill assessment of the coupled physical-biogeochemical operational Mediterranean Forecasting System

NASA Astrophysics Data System (ADS)

Cossarini, Gianpiero; Clementi, Emanuela; Salon, Stefano; Grandi, Alessandro; Bolzon, Giorgio; Solidoro, Cosimo

2016-04-01

The Mediterranean Monitoring and Forecasting Centre (Med-MFC) is one of the regional production centres of the European Marine Environment Monitoring Service (CMEMS-Copernicus). Med-MFC operatively manages a suite of numerical model systems (3DVAR-NEMO-WW3 and 3DVAR-OGSTM-BFM) that provides gridded datasets of physical and biogeochemical variables for the Mediterranean marine environment with a horizontal resolution of about 6.5 km. At the present stage, the operational Med-MFC produces ten-day forecast: daily for physical parameters and bi-weekly for biogeochemical variables. The validation of the coupled model system and the estimate of the accuracy of model products are key issues to ensure reliable information to the users and the downstream services. Product quality activities at Med-MFC consist of two levels of validation and skill analysis procedures. Pre-operational qualification activities focus on testing the improvement of the quality of a new release of the model system and relays on past simulation and historical data. Then, near real time (NRT) validation activities aim at the routinely and on-line skill assessment of the model forecast and relays on the NRT available observations. Med-MFC validation framework uses both independent (i.e. Bio-Argo float data, in-situ mooring and vessel data of oxygen, nutrients and chlorophyll, moored buoys, tide-gauges and ADCP of temperature, salinity, sea level and velocity) and semi-independent data (i.e. data already used for assimilation, such as satellite chlorophyll, Satellite SLA and SST and in situ vertical profiles of temperature and salinity from XBT, Argo and Gliders) We give evidence that different variables (e.g. CMEMS-products) can be validated at different levels (i.e. at the forecast level or at the level of model consistency) and at different spatial and temporal scales. The fundamental physical parameters temperature, salinity and sea level are routinely validated on daily, weekly and quarterly base

Weather Forecasting Systems and Methods

NASA Technical Reports Server (NTRS)

Mecikalski, John (Inventor); MacKenzie, Wayne M., Jr. (Inventor); Walker, John Robert (Inventor)

2014-01-01

A weather forecasting system has weather forecasting logic that receives raw image data from a satellite. The raw image data has values indicative of light and radiance data from the Earth as measured by the satellite, and the weather forecasting logic processes such data to identify cumulus clouds within the satellite images. For each identified cumulus cloud, the weather forecasting logic applies interest field tests to determine a score indicating the likelihood of the cumulus cloud forming precipitation and/or lightning in the future within a certain time period. Based on such scores, the weather forecasting logic predicts in which geographic regions the identified cumulus clouds will produce precipitation and/or lighting within during the time period. Such predictions may then be used to provide a weather map thereby providing users with a graphical illustration of the areas predicted to be affected by precipitation within the time period.

A seasonal agricultural drought forecast system for food-insecure regions of East Africa

USGS Publications Warehouse

Shukla, Shraddhanand; McNally, Amy; Husak, Gregory; Funk, Christopher C.

2014-01-01

 The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993–2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall

Earthquake Forecasting System in Italy

NASA Astrophysics Data System (ADS)

Falcone, G.; Marzocchi, W.; Murru, M.; Taroni, M.; Faenza, L.

2017-12-01

In Italy, after the 2009 L'Aquila earthquake, a procedure was developed for gathering and disseminating authoritative information about the time dependence of seismic hazard to help communities prepare for a potentially destructive earthquake. The most striking time dependency of the earthquake occurrence process is the time clustering, which is particularly pronounced in time windows of days and weeks. The Operational Earthquake Forecasting (OEF) system that is developed at the Seismic Hazard Center (Centro di Pericolosità Sismica, CPS) of the Istituto Nazionale di Geofisica e Vulcanologia (INGV) is the authoritative source of seismic hazard information for Italian Civil Protection. The philosophy of the system rests on a few basic concepts: transparency, reproducibility, and testability. In particular, the transparent, reproducible, and testable earthquake forecasting system developed at CPS is based on ensemble modeling and on a rigorous testing phase. Such phase is carried out according to the guidance proposed by the Collaboratory for the Study of Earthquake Predictability (CSEP, international infrastructure aimed at evaluating quantitatively earthquake prediction and forecast models through purely prospective and reproducible experiments). In the OEF system, the two most popular short-term models were used: the Epidemic-Type Aftershock Sequences (ETAS) and the Short-Term Earthquake Probabilities (STEP). Here, we report the results from OEF's 24hour earthquake forecasting during the main phases of the 2016-2017 sequence occurred in Central Apennines (Italy).

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

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

AIRS Impact on Analysis and Forecast of an Extreme Rainfall Event (Indus River Valley 2010) with a Global Data Assimilation and Forecast System

NASA Technical Reports Server (NTRS)

Reale, O.; Lau, W. K.; Susskind, J.; Rosenberg, R.

2011-01-01

A set of data assimilation and forecast experiments are performed with the NASA Global data assimilation and forecast system GEOS-5, to compare the impact of different approaches towards assimilation of Advanced Infrared Spectrometer (AIRS) data on the precipitation analysis and forecast skill. The event chosen is an extreme rainfall episode which occurred in late July 11 2010 in Pakistan, causing massive floods along the Indus River Valley. Results show that the assimilation of quality-controlled AIRS temperature retrievals obtained under partly cloudy conditions produce better precipitation analyses, and substantially better 7-day forecasts, than assimilation of clear-sky radiances. The improvement of precipitation forecast skill up to 7 day is very significant in the tropics, and is caused by an improved representation, attributed to cloudy retrieval assimilation, of two contributing mechanisms: the low-level moisture advection, and the concentration of moisture over the area in the days preceding the precipitation peak.

Chesapeake Bay Forecast System: Oxygen Prediction for the Sustainable Ecosystem Management

NASA Astrophysics Data System (ADS)

Mathukumalli, B.; Long, W.; Zhang, X.; Wood, R.; Murtugudde, R. G.

2010-12-01

The Chesapeake Bay Forecast System (CBFS) is a flexible, end-to-end expert prediction tool for decision makers that will provide customizable, user-specified predictions and projections of the region’s climate, air and water quality, local chemistry, and ecosystems at days to decades. As a part of CBFS, the long-term water quality data were collected and assembled to develop ecological models for the sustainable management of the Chesapeake Bay. Cultural eutrophication depletes oxygen levels in this ecosystem particularly in summer which has several negative implications on the structure and function of ecosystem. In order to understand dynamics and prediction of spatially-explicit oxygen levels in the Bay, an empirical process based ecological model is developed with long-term control variables (water temperature, salinity, nitrogen and phosphorus). Statistical validation methods were employed to demonstrate usability of predictions for management purposes and the predicted oxygen levels are quite faithful to observations. The predicted oxygen values and other physical outputs from downscaling of regional weather and climate predictions, or forecasts from hydrodynamic models can be used to forecast various ecological components. Such forecasts would be useful for both recreational and commercial users of the bay (for example, bass fishing). Furthermore, this work can also be used to predict extent of hypoxia/anoxia not only from anthropogenic nutrient pollution, but also from global warming. Some hindcasts and forecasts are discussed along with the ongoing efforts at a mechanistic ecosystem model to provide prognostic oxygen predictions and projections and upper trophic modeling using an energetics approach.

The Eruption Forecasting Information System: Volcanic Eruption Forecasting Using Databases

NASA Astrophysics Data System (ADS)

Ogburn, S. E.; Harpel, C. J.; Pesicek, J. D.; Wellik, J.

2016-12-01

Forecasting eruptions, including the onset size, duration, location, and impacts, is vital for hazard assessment and risk mitigation. The Eruption Forecasting Information System (EFIS) project is a new initiative of the US Geological Survey-USAID Volcano Disaster Assistance Program (VDAP) and will advance VDAP's ability to forecast the outcome of volcanic unrest. The project supports probability estimation for eruption forecasting by creating databases useful for pattern recognition, identifying monitoring data thresholds beyond which eruptive probabilities increase, and for answering common forecasting questions. A major component of the project is a global relational database, which contains multiple modules designed to aid in the construction of probabilistic event trees and to answer common questions that arise during volcanic crises. The primary module contains chronologies of volcanic unrest. This module allows us to query eruption chronologies, monitoring data, descriptive information, operational data, and eruptive phases alongside other global databases, such as WOVOdat and the Global Volcanism Program. The EFIS database is in the early stages of development and population; thus, this contribution also is a request for feedback from the community. Preliminary data are already benefitting several research areas. For example, VDAP provided a forecast of the likely remaining eruption duration for Sinabung volcano, Indonesia, using global data taken from similar volcanoes in the DomeHaz database module, in combination with local monitoring time-series data. In addition, EFIS seismologists used a beta-statistic test and empirically-derived thresholds to identify distal volcano-tectonic earthquake anomalies preceding Alaska volcanic eruptions during 1990-2015 to retrospectively evaluate Alaska Volcano Observatory eruption precursors. This has identified important considerations for selecting analog volcanoes for global data analysis, such as differences between

Hydrodynamics and Water Quality forecasting over a Cloud Computing environment: INDIGO-DataCloud

NASA Astrophysics Data System (ADS)

Aguilar Gómez, Fernando; de Lucas, Jesús Marco; García, Daniel; Monteoliva, Agustín

2017-04-01

Algae Bloom due to eutrophication is an extended problem for water reservoirs and lakes that impacts directly in water quality. It can create a dead zone that lacks enough oxygen to support life and it can also be human harmful, so it must be controlled in water masses for supplying, bathing or other uses. Hydrodynamic and Water Quality modelling can contribute to forecast the status of the water system in order to alert authorities before an algae bloom event occurs. It can be used to predict scenarios and find solutions to reduce the harmful impact of the blooms. High resolution models need to process a big amount of data using a robust enough computing infrastructure. INDIGO-DataCloud (https://www.indigo-datacloud.eu/) is an European Commission funded project that aims at developing a data and computing platform targeting scientific communities, deployable on multiple hardware and provisioned over hybrid (private or public) e-infrastructures. The project addresses the development of solutions for different Case Studies using different Cloud-based alternatives. In the first INDIGO software release, a set of components are ready to manage the deployment of services to perform N number of Delft3D simulations (for calibrating or scenario definition) over a Cloud Computing environment, using the Docker technology: TOSCA requirement description, Docker repository, Orchestrator, AAI (Authorization, Authentication) and OneData (Distributed Storage System). Moreover, the Future Gateway portal based on Liferay, provides an user-friendly interface where the user can configure the simulations. Due to the data approach of INDIGO, the developed solutions can contribute to manage the full data life cycle of a project, thanks to different tools to manage datasets or even metadata. Furthermore, the cloud environment contributes to provide a dynamic, scalable and easy-to-use framework for non-IT experts users. This framework is potentially capable to automatize the processing of

An Evaluation of the NOAA Climate Forecast System Subseasonal Forecasts

NASA Astrophysics Data System (ADS)

Mass, C.; Weber, N.

2016-12-01

This talk will describe a multi-year evaluation of the 1-5 week forecasts of the NOAA Climate Forecasting System (CFS) over the globe, North America, and the western U.S. Forecasts are evaluated for both specific times and for a variety of time-averaging periods. Initial results show a loss of predictability at approximately three weeks, with sea surface temperature retaining predictability longer than atmospheric variables. It is shown that a major CFS problem is an inability to realistically simulate propagating convection in the tropics, with substantial implications for midlatitude teleconnections and subseasonal predictability. The inability of CFS to deal with tropical convection will be discussed in connection with the prediction of extreme climatic events over the midlatitudes.

Forecasting, Forecasting

Treesearch

Michael A. Fosberg

1987-01-01

Future improvements in the meteorological forecasts used in fire management will come from improvements in three areas: observational systems, forecast techniques, and postprocessing of forecasts and better integration of this information into the fire management process.

Short-term load forecasting of power system

NASA Astrophysics Data System (ADS)

Xu, Xiaobin

2017-05-01

In order to ensure the scientific nature of optimization about power system, it is necessary to improve the load forecasting accuracy. Power system load forecasting is based on accurate statistical data and survey data, starting from the history and current situation of electricity consumption, with a scientific method to predict the future development trend of power load and change the law of science. Short-term load forecasting is the basis of power system operation and analysis, which is of great significance to unit combination, economic dispatch and safety check. Therefore, the load forecasting of the power system is explained in detail in this paper. First, we use the data from 2012 to 2014 to establish the partial least squares model to regression analysis the relationship between daily maximum load, daily minimum load, daily average load and each meteorological factor, and select the highest peak by observing the regression coefficient histogram Day maximum temperature, daily minimum temperature and daily average temperature as the meteorological factors to improve the accuracy of load forecasting indicators. Secondly, in the case of uncertain climate impact, we use the time series model to predict the load data for 2015, respectively, the 2009-2014 load data were sorted out, through the previous six years of the data to forecast the data for this time in 2015. The criterion for the accuracy of the prediction is the average of the standard deviations for the prediction results and average load for the previous six years. Finally, considering the climate effect, we use the BP neural network model to predict the data in 2015, and optimize the forecast results on the basis of the time series model.

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

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

Forecasting air quality time series using deep learning.

PubMed

Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse

2018-04-13

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution

Evaluation of Pollen Apps Forecasts: The Need for Quality Control in an eHealth Service

PubMed Central

Berger, Uwe; Kmenta, Maximilian

2017-01-01

Background Pollen forecasts are highly valuable for allergen avoidance and thus raising the quality of life of persons concerned by pollen allergies. They are considered as valuable free services for the public. Careful scientific evaluation of pollen forecasts in terms of accurateness and reliability has not been available till date. Objective The aim of this study was to analyze 9 mobile apps, which deliver pollen information and pollen forecasts, with a focus on their accurateness regarding the prediction of the pollen load in the grass pollen season 2016 to assess their usefulness for pollen allergy sufferers. Methods The following number of apps was evaluated for each location: 3 apps for Vienna (Austria), 4 apps for Berlin (Germany), and 1 app each for Basel (Switzerland) and London (United Kingdom). All mobile apps were freely available. Today’s grass pollen forecast was compared throughout the defined grass pollen season at each respective location with measured grass pollen concentrations. Hit rates were calculated for the exact performance and for a tolerance in a range of ±2 and ±4 pollen per cubic meter. Results In general, for most apps, hit rates score around 50% (6 apps). It was found that 1 app showed better results, whereas 3 apps performed less well. Hit rates increased when calculated with tolerances for most apps. In contrast, the forecast for the “readiness to flower” for grasses was performed at a sufficiently accurate level, although only two apps provided such a forecast. The last of those forecasts coincided with the first moderate grass pollen load on the predicted day or 3 days after and performed even from about a month before well within the range of 3 days. Advertisement was present in 3 of the 9 analyzed apps, whereas an imprint mentioning institutions with experience in pollen forecasting was present in only three other apps. Conclusions The quality of pollen forecasts is in need of improvement, and quality control for pollen

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

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.

Utilizing Operational and Improved Remote Sensing Measurements to Assess Air Quality Monitoring Model Forecasts

NASA Astrophysics Data System (ADS)

Gan, Chuen-Meei

Air quality model forecasts from Weather Research and Forecast (WRF) and Community Multiscale Air Quality (CMAQ) are often used to support air quality applications such as regulatory issues and scientific inquiries on atmospheric science processes. In urban environments, these models become more complex due to the inherent complexity of the land surface coupling and the enhanced pollutants emissions. This makes it very difficult to diagnose the model, if the surface parameter forecasts such as PM2.5 (particulate matter with aerodynamic diameter less than 2.5 microm) are not accurate. For this reason, getting accurate boundary layer dynamic forecasts is as essential as quantifying realistic pollutants emissions. In this thesis, we explore the usefulness of vertical sounding measurements on assessing meteorological and air quality forecast models. In particular, we focus on assessing the WRF model (12km x 12km) coupled with the CMAQ model for the urban New York City (NYC) area using multiple vertical profiling and column integrated remote sensing measurements. This assessment is helpful in probing the root causes for WRF-CMAQ overestimates of surface PM2.5 occurring both predawn and post-sunset in the NYC area during the summer. In particular, we find that the significant underestimates in the WRF PBL height forecast is a key factor in explaining this anomaly. On the other hand, the model predictions of the PBL height during daytime when convective heating dominates were found to be highly correlated to lidar derived PBL height with minimal bias. Additional topics covered in this thesis include mathematical method using direct Mie scattering approach to convert aerosol microphysical properties from CMAQ into optical parameters making direct comparisons with lidar and multispectral radiometers feasible. Finally, we explore some tentative ideas on combining visible (VIS) and mid-infrared (MIR) sensors to better separate aerosols into fine and coarse modes.

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

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...

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

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.

COMPUTATIONAL ASPECTS OF THE AIR QUALITY FORECASTING VERSION OF CMAQ (CMAQ-F)

EPA Science Inventory

The air quality forecast version of the Community Modeling Air Quality (CMAQ) model (CMAQ-F) was developed from the public release version of CMAQ (available from http://www.cmascenter.org), and is running operationally at the National Weather Service's National Centers for Envir...

Real-time emergency forecasting technique for situation management systems

NASA Astrophysics Data System (ADS)

Kopytov, V. V.; Kharechkin, P. V.; Naumenko, V. V.; Tretyak, R. S.; Tebueva, F. B.

2018-05-01

The article describes the real-time emergency forecasting technique that allows increasing accuracy and reliability of forecasting results of any emergency computational model applied for decision making in situation management systems. Computational models are improved by the Improved Brown’s method applying fractal dimension to forecast short time series data being received from sensors and control systems. Reliability of emergency forecasting results is ensured by the invalid sensed data filtering according to the methods of correlation analysis.

Climate Forecast System

Science.gov Websites

Weather Service NWS logo - Click to go to the NWS home page Climate Forecast System Home News Organization Web portal to all Federal, state and local government Web resources and services. The NCEP Climate when using the CFS Reanalysis (CFSR) data. Saha, Suranjana, and Coauthors, 2010: The NCEP Climate

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.

Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

NASA Astrophysics Data System (ADS)

Pasternack, Alexander; Bhend, Jonas; Liniger, Mark A.; Rust, Henning W.; Müller, Wolfgang A.; Ulbrich, Uwe

2018-01-01

Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

The Influence of Observation Errors on Analysis Error and Forecast Skill Investigated with an Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, N. C.; Errico, R. M.; Tai, K.-S.

2013-01-01

The Global Modeling and Assimilation Office (GMAO) observing system simulation experiment (OSSE) framework is used to explore the response of analysis error and forecast skill to observation quality. In an OSSE, synthetic observations may be created that have much smaller error than real observations, and precisely quantified error may be applied to these synthetic observations. Three experiments are performed in which synthetic observations with magnitudes of applied observation error that vary from zero to twice the estimated realistic error are ingested into the Goddard Earth Observing System Model (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation for a one-month period representing July. The analysis increment and observation innovation are strongly impacted by observation error, with much larger variances for increased observation error. The analysis quality is degraded by increased observation error, but the change in root-mean-square error of the analysis state is small relative to the total analysis error. Surprisingly, in the 120 hour forecast increased observation error only yields a slight decline in forecast skill in the extratropics, and no discernable degradation of forecast skill in the tropics.

Evaluation of Pollen Apps Forecasts: The Need for Quality Control in an eHealth Service.

PubMed

Bastl, Katharina; Berger, Uwe; Kmenta, Maximilian

2017-05-08

Pollen forecasts are highly valuable for allergen avoidance and thus raising the quality of life of persons concerned by pollen allergies. They are considered as valuable free services for the public. Careful scientific evaluation of pollen forecasts in terms of accurateness and reliability has not been available till date. The aim of this study was to analyze 9 mobile apps, which deliver pollen information and pollen forecasts, with a focus on their accurateness regarding the prediction of the pollen load in the grass pollen season 2016 to assess their usefulness for pollen allergy sufferers. The following number of apps was evaluated for each location: 3 apps for Vienna (Austria), 4 apps for Berlin (Germany), and 1 app each for Basel (Switzerland) and London (United Kingdom). All mobile apps were freely available. Today's grass pollen forecast was compared throughout the defined grass pollen season at each respective location with measured grass pollen concentrations. Hit rates were calculated for the exact performance and for a tolerance in a range of ±2 and ±4 pollen per cubic meter. In general, for most apps, hit rates score around 50% (6 apps). It was found that 1 app showed better results, whereas 3 apps performed less well. Hit rates increased when calculated with tolerances for most apps. In contrast, the forecast for the "readiness to flower" for grasses was performed at a sufficiently accurate level, although only two apps provided such a forecast. The last of those forecasts coincided with the first moderate grass pollen load on the predicted day or 3 days after and performed even from about a month before well within the range of 3 days. Advertisement was present in 3 of the 9 analyzed apps, whereas an imprint mentioning institutions with experience in pollen forecasting was present in only three other apps. The quality of pollen forecasts is in need of improvement, and quality control for pollen forecasts is recommended to avoid potential harm to

Evaluation of ensemble forecast uncertainty using a new proper score: application to medium-range and seasonal forecasts

NASA Astrophysics Data System (ADS)

Christensen, Hannah; Moroz, Irene; Palmer, Tim

2015-04-01

Forecast verification is important across scientific disciplines as it provides a framework for evaluating the performance of a forecasting system. In the atmospheric sciences, probabilistic skill scores are often used for verification as they provide a way of unambiguously ranking the performance of different probabilistic forecasts. In order to be useful, a skill score must be proper -- it must encourage honesty in the forecaster, and reward forecasts which are reliable and which have good resolution. A new score, the Error-spread Score (ES), is proposed which is particularly suitable for evaluation of ensemble forecasts. It is formulated with respect to the moments of the forecast. The ES is confirmed to be a proper score, and is therefore sensitive to both resolution and reliability. The ES is tested on forecasts made using the Lorenz '96 system, and found to be useful for summarising the skill of the forecasts. The European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) is evaluated using the ES. Its performance is compared to a perfect statistical probabilistic forecast -- the ECMWF high resolution deterministic forecast dressed with the observed error distribution. This generates a forecast that is perfectly reliable if considered over all time, but which does not vary from day to day with the predictability of the atmospheric flow. The ES distinguishes between the dynamically reliable EPS forecasts and the statically reliable dressed deterministic forecasts. Other skill scores are tested and found to be comparatively insensitive to this desirable forecast quality. The ES is used to evaluate seasonal range ensemble forecasts made with the ECMWF System 4. The ensemble forecasts are found to be skilful when compared with climatological or persistence forecasts, though this skill is dependent on region and time of year.

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.

Enhanced road weather forecasting : Clarus regional demonstrations.

DOT National Transportation Integrated Search

2011-01-01

The quality of road weather forecasts has major impacts on users of surface transportation systems and managers of those systems. Improving the quality involves the ability to provide accurate, route-specific road weather information (e.g., timing an...

Signature-forecasting and early outbreak detection system

PubMed Central

Naumova, Elena N.; MacNeill, Ian B.

2008-01-01

SUMMARY Daily disease monitoring via a public health surveillance system provides valuable information on population risks. Efficient statistical tools for early detection of rapid changes in the disease incidence are a must for modern surveillance. The need for statistical tools for early detection of outbreaks that are not based on historical information is apparent. A system is discussed for monitoring cases of infections with a view to early detection of outbreaks and to forecasting the extent of detected outbreaks. We propose a set of adaptive algorithms for early outbreak detection that does not rely on extensive historical recording. We also include knowledge of infection disease epidemiology into forecasts. To demonstrate this system we use data from the largest water-borne outbreak of cryptosporidiosis, which occurred in Milwaukee in 1993. Historical data are smoothed using a loess-type smoother. Upon receipt of a new datum, the smoothing is updated and estimates are made of the first two derivatives of the smooth curve, and these are used for near-term forecasting. Recent data and the near-term forecasts are used to compute a color-coded warning index, which quantify the level of concern. The algorithms for computing the warning index have been designed to balance Type I errors (false prediction of an epidemic) and Type II errors (failure to correctly predict an epidemic). If the warning index signals a sufficiently high probability of an epidemic, then a forecast of the possible size of the outbreak is made. This longer term forecast is made by fitting a ‘signature’ curve to the available data. The effectiveness of the forecast depends upon the extent to which the signature curve captures the shape of outbreaks of the infection under consideration. PMID:18716671

Climate Forecast System

Science.gov Websites

Skip Navigation Links www.nws.noaa.gov NOAA logo - Click to go to the NOAA home page National Weather Service NWS logo - Click to go to the NWS home page Climate Forecast System Home News Organization Search : Go Search Go CFS Home CFS version 2 News Documentation Downloads Reanalysis CFSv2 at CPC CFS

Analyzing Effect of System Inertia on Grid Frequency Forecasting Usnig Two Stage Neuro-Fuzzy System

NASA Astrophysics Data System (ADS)

Chourey, Divyansh R.; Gupta, Himanshu; Kumar, Amit; Kumar, Jitesh; Kumar, Anand; Mishra, Anup

2018-04-01

Frequency forecasting is an important aspect of power system operation. The system frequency varies with load-generation imbalance. Frequency variation depends upon various parameters including system inertia. System inertia determines the rate of fall of frequency after the disturbance in the grid. Though, inertia of the system is not considered while forecasting the frequency of power system during planning and operation. This leads to significant errors in forecasting. In this paper, the effect of inertia on frequency forecasting is analysed for a particular grid system. In this paper, a parameter equivalent to system inertia is introduced. This parameter is used to forecast the frequency of a typical power grid for any instant of time. The system gives appreciable result with reduced error.

Real-time drought forecasting system for irrigation management

NASA Astrophysics Data System (ADS)

Ceppi, A.; Ravazzani, G.; Corbari, C.; Salerno, R.; Meucci, S.; Mancini, M.

2014-09-01

In recent years frequent periods of water scarcity have enhanced the need to use water more carefully, even in European areas which traditionally have an abundant supply of water, such as the Po Valley in northern Italy. In dry periods, water shortage problems can be enhanced by conflicting uses of water, such as irrigation, industry and power production (hydroelectric and thermoelectric). Furthermore, in the last decade the social perspective in relation to this issue has been increasing due to the possible impact of climate change and global warming scenarios which emerge from the IPCC Fifth Assessment Report (IPCC, 2013). Hence, the increased frequency of drought periods has stimulated the improvement of irrigation and water management. In this study we show the development and implementation of the PREGI real-time drought forecasting system; PREGI is an Italian acronym that means "hydro-meteorological forecast for irrigation management". The system, planned as a tool for irrigation optimization, is based on meteorological ensemble forecasts (20 members) at medium range (30 days) coupled with hydrological simulations of water balance to forecast the soil water content on a maize field in the Muzza Bassa Lodigiana (MBL) consortium in northern Italy. The hydrological model was validated against measurements of latent heat flux acquired by an eddy-covariance station, and soil moisture measured by TDR (time domain reflectivity) probes; the reliability of this forecasting system and its benefits were assessed in the 2012 growing season. The results obtained show how the proposed drought forecasting system is able to have a high reliability of forecast at least for 7-10 days ahead of time.

Linked Hydrologic-Hydrodynamic Model Framework to Forecast Impacts of Rivers on Beach Water Quality

NASA Astrophysics Data System (ADS)

Anderson, E. J.; Fry, L. M.; Kramer, E.; Ritzenthaler, A.

2014-12-01

The goal of NOAA's beach quality forecasting program is to use a multi-faceted approach to aid in detection and prediction of bacteria in recreational waters. In particular, our focus has been on the connection between tributary loads and bacteria concentrations at nearby beaches. While there is a clear link between stormwater runoff and beach water quality, quantifying the contribution of river loadings to nearshore bacterial concentrations is complicated due to multiple processes that drive bacterial concentrations in rivers as well as those processes affecting the fate and transport of bacteria upon exiting the rivers. In order to forecast potential impacts of rivers on beach water quality, we developed a linked hydrologic-hydrodynamic water quality framework that simulates accumulation and washoff of bacteria from the landscape, and then predicts the fate and transport of washed off bacteria from the watershed to the coastal zone. The framework includes a watershed model (IHACRES) to predict fecal indicator bacteria (FIB) loadings to the coastal environment (accumulation, wash-off, die-off) as a function of effective rainfall. These loadings are input into a coastal hydrodynamic model (FVCOM), including a bacteria transport model (Lagrangian particle), to simulate 3D bacteria transport within the coastal environment. This modeling system provides predictive tools to assist local managers in decision-making to reduce human health threats.

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

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

Operational skill assessment of the IBI-MFC Ocean Forecasting System within the frame of the CMEMS.

NASA Astrophysics Data System (ADS)

Lorente Jimenez, Pablo; Garcia-Sotillo, Marcos; Amo-Balandron, Arancha; Aznar Lecocq, Roland; Perez Gomez, Begoña; Levier, Bruno; Alvarez-Fanjul, Enrique

2016-04-01

Since operational ocean forecasting systems (OOFSs) are increasingly used as tools to support high-stakes decision-making for coastal management, a rigorous skill assessment of model performance becomes essential. In this context, the IBI-MFC (Iberia-Biscay-Ireland Monitoring & Forecasting Centre) has been providing daily ocean model estimates and forecasts 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). A comprehensive web validation tool named NARVAL (North Atlantic Regional VALidation) has been developed to routinely monitor IBI performance and to evaluate model's veracity and prognostic capabilities. Three-dimensional comparisons are carried out on a different time basis ('online mode' - daily verifications - and 'delayed mode' - for longer time periods -) using a broad variety of in-situ (buoys, tide-gauges, ARGO-floats, drifters and gliders) and remote-sensing (satellite and HF radars) observational sources as reference fields to validate against the NEMO model solution. Product quality indicators and meaningful skill metrics are automatically computed not only averaged over the entire IBI domain but also over specific sub-regions of particular interest from a user perspective (i.e. coastal or shelf areas) in order to determine IBI spatial and temporal uncertainty levels. A complementary aspect of NARVAL web tool is the intercomparison of different CMEMS forecast model solutions in overlapping areas. Noticeable efforts are in progress in order to quantitatively assess the quality and consistency of nested system outputs by setting up specific intercomparison exercises on different temporal and spatial scales, encompassing global configurations (CMEMS Global system), regional applications (NWS and MED ones) and local high-resolution coastal models (i.e. the PdE SAMPA system in the Gibraltar Strait). NARVAL constitutes a powerful approach to increase

Louisiana Airport System Plan aviation activity forecasts 1990-2010.

DOT National Transportation Integrated Search

1991-07-01

This report documents the methodology used to develop the aviation activity forecasts prepared as a part of the update to the Louisiana Airport System Plan and provides Louisiana aviation forecasts for the years 1990 to 2010. In general, the forecast...

A short-term ensemble wind speed forecasting system for wind power applications

NASA Astrophysics Data System (ADS)

Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

2011-12-01

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

A new air quality monitoring and early warning system: Air quality assessment and air pollutant concentration prediction.

PubMed

Yang, Zhongshan; Wang, Jian

2017-10-01

Air pollution in many countries is worsening with industrialization and urbanization, resulting in climate change and affecting people's health, thus, making the work of policymakers more difficult. It is therefore both urgent and necessary to establish amore scientific air quality monitoring and early warning system to evaluate the degree of air pollution objectively, and predict pollutant concentrations accurately. However, the integration of air quality assessment and air pollutant concentration prediction to establish an air quality system is not common. In this paper, we propose a new air quality monitoring and early warning system, including an assessment module and forecasting module. In the air quality assessment module, fuzzy comprehensive evaluation is used to determine the main pollutants and evaluate the degree of air pollution more scientifically. In the air pollutant concentration prediction module, a novel hybridization model combining complementary ensemble empirical mode decomposition, a modified cuckoo search and differential evolution algorithm, and an Elman neural network, is proposed to improve the forecasting accuracy of six main air pollutant concentrations. To verify the effectiveness of this system, pollutant data for two cities in China are used. The result of the fuzzy comprehensive evaluation shows that the major air pollutants in Xi'an and Jinan are PM 10 and PM 2.5 respectively, and that the air quality of Xi'an is better than that of Jinan. The forecasting results indicate that the proposed hybrid model is remarkably superior to all benchmark models on account of its higher prediction accuracy and stability. Copyright © 2017 Elsevier Inc. All rights reserved.

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...

Evaluation of a Wildfire Smoke Forecasting System as a Tool for Public Health Protection

PubMed Central

Brauer, Michael; Henderson, Sarah B.

2013-01-01

Background: Exposure to wildfire smoke has been associated with cardiopulmonary health impacts. Climate change will increase the severity and frequency of smoke events, suggesting a need for enhanced public health protection. Forecasts of smoke exposure can facilitate public health responses. Objectives: We evaluated the utility of a wildfire smoke forecasting system (BlueSky) for public health protection by comparing its forecasts with observations and assessing their associations with population-level indicators of respiratory health in British Columbia, Canada. Methods: We compared BlueSky PM2.5 forecasts with PM2.5 measurements from air quality monitors, and BlueSky smoke plume forecasts with plume tracings from National Oceanic and Atmospheric Administration Hazard Mapping System remote sensing data. Daily counts of the asthma drug salbutamol sulfate dispensations and asthma-related physician visits were aggregated for each geographic local health area (LHA). Daily continuous measures of PM2.5 and binary measures of smoke plume presence, either forecasted or observed, were assigned to each LHA. Poisson regression was used to estimate the association between exposure measures and health indicators. Results: We found modest agreement between forecasts and observations, which was improved during intense fire periods. A 30-μg/m3 increase in BlueSky PM2.5 was associated with an 8% increase in salbutamol dispensations and a 5% increase in asthma-related physician visits. BlueSky plume coverage was associated with 5% and 6% increases in the two health indicators, respectively. The effects were similar for observed smoke, and generally stronger in very smoky areas. Conclusions: BlueSky forecasts showed modest agreement with retrospective measures of smoke and were predictive of respiratory health indicators, suggesting they can provide useful information for public health protection. Citation: Yao J, Brauer M, Henderson SB. 2013. Evaluation of a wildfire smoke

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.

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

Evaluation of a wildfire smoke forecasting system as a tool for public health protection.

PubMed

Yao, Jiayun; Brauer, Michael; Henderson, Sarah B

2013-10-01

Exposure to wildfire smoke has been associated with cardiopulmonary health impacts. Climate change will increase the severity and frequency of smoke events, suggesting a need for enhanced public health protection. Forecasts of smoke exposure can facilitate public health responses. We evaluated the utility of a wildfire smoke forecasting system (BlueSky) for public health protection by comparing its forecasts with observations and assessing their associations with population-level indicators of respiratory health in British Columbia, Canada. We compared BlueSky PM2.5 forecasts with PM2.5 measurements from air quality monitors, and BlueSky smoke plume forecasts with plume tracings from National Oceanic and Atmospheric Administration Hazard Mapping System remote sensing data. Daily counts of the asthma drug salbutamol sulfate dispensations and asthma-related physician visits were aggregated for each geographic local health area (LHA). Daily continuous measures of PM2.5 and binary measures of smoke plume presence, either forecasted or observed, were assigned to each LHA. Poisson regression was used to estimate the association between exposure measures and health indicators. We found modest agreement between forecasts and observations, which was improved during intense fire periods. A 30-μg/m3 increase in BlueSky PM2.5 was associated with an 8% increase in salbutamol dispensations and a 5% increase in asthma-related physician visits. BlueSky plume coverage was associated with 5% and 6% increases in the two health indicators, respectively. The effects were similar for observed smoke, and generally stronger in very smoky areas. BlueSky forecasts showed modest agreement with retrospective measures of smoke and were predictive of respiratory health indicators, suggesting they can provide useful information for public health protection.

New smoke predictions for Alaska in NOAA’s National Air Quality Forecast Capability

NASA Astrophysics Data System (ADS)

Davidson, P. M.; Ruminski, M.; Draxler, R.; Kondragunta, S.; Zeng, J.; Rolph, G.; Stajner, I.; Manikin, G.

2009-12-01

Smoke from wildfire is an important component of fine particle pollution, which is responsible for tens of thousands of premature deaths each year in the US. In Alaska, wildfire smoke is the leading cause of poor air quality in summer. Smoke forecast guidance helps air quality forecasters and the public take steps to limit exposure to airborne particulate matter. A new smoke forecast guidance tool, built by a cross-NOAA team, leverages efforts of NOAA’s partners at the USFS on wildfire emissions information, and with EPA, in coordinating with state/local air quality forecasters. Required operational deployment criteria, in categories of objective verification, subjective feedback, and production readiness, have been demonstrated in experimental testing during 2008-2009, for addition to the operational products in NOAA's National Air Quality Forecast Capability. The Alaska smoke forecast tool is an adaptation of NOAA’s smoke predictions implemented operationally for the lower 48 states (CONUS) in 2007. The tool integrates satellite information on location of wildfires with weather (North American mesoscale model) and smoke dispersion (HYSPLIT) models to produce daily predictions of smoke transport for Alaska, in binary and graphical formats. Hour-by hour predictions at 12km grid resolution of smoke at the surface and in the column are provided each day by 13 UTC, extending through midnight next day. Forecast accuracy and reliability are monitored against benchmark criteria for accuracy and reliability. While wildfire activity in the CONUS is year-round, the intense wildfire activity in AK is limited to the summer. Initial experimental testing during summer 2008 was hindered by unusually limited wildfire activity and very cloudy conditions. In contrast, heavier than average wildfire activity during summer 2009 provided a representative basis (more than 60 days of wildfire smoke) for demonstrating required prediction accuracy. A new satellite observation product

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

Update on the NASA GEOS-5 Aerosol Forecasting and Data Assimilation System

NASA Technical Reports Server (NTRS)

Colarco, Peter; da Silva, Arlindo; Aquila, Valentina; Bian, Huisheng; Buchard, Virginie; Castellanos, Patricia; Darmenov, Anton; Follette-Cook, Melanie; Govindaraju, Ravi; Keller, Christoph;

Observation impact studies with the Mercator Ocean analysis and forecasting systems

NASA Astrophysics Data System (ADS)

Remy, E. D.; Le Traon, P. Y.; Lellouche, J. M.; Drevillon, M.; Turpin, V.; Benkiran, M.

2016-02-01

Mercator Ocean produces and delivers in real-time ocean analysis and forecasts on a daily basis. The quality of the analysis highly relies on the availability and quality of the assimilated observations.Tools are developed to estimate the impact of the present network and to help designing the future evolutions of the observing systems in the context of near real time production of ocean analysis and forecasts. OSE and OSSE are the main approaches used in this context. They allow the assessment of the efficiency of a given data set to constrain the ocean model circulation through the data assimilation process. Illustrations will mainly focus on the present and future evolution of the Argo observation network and altimetry constellation, including the potential impact of future SWOT data. Our systems show clear sensitivities to observation array changes, mainly depending on the specified observation error and regional dynamic. Impact on non observed variables can be important and are important to evaluate. Dedicated diagnostics has to be define to measure the improvements bring by each data set. Alternative approaches to OSE and OSSE are also explored: approximate computation of DFS will be presented and discussed. Limitations of each approach will be discussed in the context of real time operation.

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.

Road icing forecasting and detecting system

NASA Astrophysics Data System (ADS)

Xu, Hongke; Zheng, Jinnan; Li, Peiqi; Wang, Qiucai

2017-05-01

Regard for the facts that the low accuracy and low real-time of the artificial observation to determine the road icing condition, and it is difficult to forecast icing situation, according to the main factors influencing the road-icing, and the electrical characteristics reflected by the pavement ice layer, this paper presents an innovative system, that is, ice-forecasting of the highway's dangerous section. The system bases on road surface water salinity measurements and pavement temperature measurement to calculate the freezing point of water and temperature change trend, and then predicts the occurrence time of road icing; using capacitance measurements to verdict the road surface is frozen or not; This paper expounds the method of using single chip microcomputer as the core of the control system and described the business process of the system.

Impact of Ozone Radiative Feedbacks on Global Weather Forecasting

NASA Astrophysics Data System (ADS)

Ivanova, I.; de Grandpré, J.; Rochon, Y. J.; Sitwell, M.

2017-12-01

A coupled Chemical Data Assimilation system for ozone is being developed at Environment and Climate Change Canada (ECCC) with the goals to improve the forecasting of UV index and the forecasting of air quality with the Global Environmental Multi-scale (GEM) Model for Air quality and Chemistry (MACH). Furthermore, this system provides an opportunity to evaluate the benefit of ozone assimilation for improving weather forecasting with the ECCC Global Deterministic Prediction System (GDPS) for Numerical Weather Prediction (NWP). The present UV index forecasting system uses a statistical approach for evaluating the impact of ozone in clear-sky and cloudy conditions, and the use of real-time ozone analysis and ozone forecasts is highly desirable. Improving air quality forecasting with GEM-MACH further necessitates the development of integrated dynamical-chemical assimilation system. Upon its completion, real-time ozone analysis and ozone forecasts will also be available for piloting the regional air quality system, and for the computation of ozone heating rates, in replacement of the monthly mean ozone distribution currently used in the GDPS. Experiments with ozone radiative feedbacks were run with the GDPS at 25km resolution and 84 levels with a lid at 0.1 hPa and were initialized with ozone analysis that has assimilated total ozone column from OMI, OMPS, and GOME satellite instruments. The results show that the use of prognostic ozone for the computation of the heating/cooling rates has a significant impact on the temperature distribution throughout the stratosphere and upper troposphere regions. The impact of ozone assimilation is especially significant in the tropopause region, where ozone heating in the infrared wavelengths is important and ozone lifetime is relatively long. The implementation of the ozone radiative feedback in the GDPS requires addressing various issues related to model biases (temperature and humidity) and biases in equilibrium state (ozone mixing

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.

Evaluation of a new microphysical aerosol module in the ECMWF Integrated Forecasting System

NASA Astrophysics Data System (ADS)

Woodhouse, Matthew; Mann, Graham; Carslaw, Ken; Morcrette, Jean-Jacques; Schulz, Michael; Kinne, Stefan; Boucher, Olivier

2013-04-01

The Monitoring Atmospheric Composition and Climate II (MACC-II) project will provide a system for monitoring and predicting atmospheric composition. As part of the first phase of MACC, the GLOMAP-mode microphysical aerosol scheme (Mann et al., 2010, GMD) was incorporated within the ECMWF Integrated Forecasting System (IFS). The two-moment modal GLOMAP-mode scheme includes new particle formation, condensation, coagulation, cloud-processing, and wet and dry deposition. GLOMAP-mode is already incorporated as a module within the TOMCAT chemistry transport model and within the UK Met Office HadGEM3 general circulation model. The microphysical, process-based GLOMAP-mode scheme allows an improved representation of aerosol size and composition and can simulate aerosol evolution in the troposphere and stratosphere. The new aerosol forecasting and re-analysis system (known as IFS-GLOMAP) will also provide improved boundary conditions for regional air quality forecasts, and will benefit from assimilation of observed aerosol optical depths in near real time. Presented here is an evaluation of the performance of the IFS-GLOMAP system in comparison to in situ aerosol mass and number measurements, and remotely-sensed aerosol optical depth measurements. Future development will provide a fully-coupled chemistry-aerosol scheme, and the capability to resolve nitrate aerosol.

Forecasting in the presence of expectations

NASA Astrophysics Data System (ADS)

Allen, R.; Zivin, J. G.; Shrader, J.

2016-05-01

Physical processes routinely influence economic outcomes, and actions by economic agents can, in turn, influence physical processes. This feedback creates challenges for forecasting and inference, creating the potential for complementarity between models from different academic disciplines. Using the example of prediction of water availability during a drought, we illustrate the potential biases in forecasts that only take part of a coupled system into account. In particular, we show that forecasts can alter the feedbacks between supply and demand, leading to inaccurate prediction about future states of the system. Although the example is specific to drought, the problem of feedback between expectations and forecast quality is not isolated to the particular model-it is relevant to areas as diverse as population assessments for conservation, balancing the electrical grid, and setting macroeconomic policy.

Improving of local ozone forecasting by integrated models.

PubMed

Gradišar, Dejan; Grašič, Boštjan; Božnar, Marija Zlata; Mlakar, Primož; Kocijan, Juš

2016-09-01

This paper discuss the problem of forecasting the maximum ozone concentrations in urban microlocations, where reliable alerting of the local population when thresholds have been surpassed is necessary. To improve the forecast, the methodology of integrated models is proposed. The model is based on multilayer perceptron neural networks that use as inputs all available information from QualeAria air-quality model, WRF numerical weather prediction model and onsite measurements of meteorology and air pollution. While air-quality and meteorological models cover large geographical 3-dimensional space, their local resolution is often not satisfactory. On the other hand, empirical methods have the advantage of good local forecasts. In this paper, integrated models are used for improved 1-day-ahead forecasting of the maximum hourly value of ozone within each day for representative locations in Slovenia. The WRF meteorological model is used for forecasting meteorological variables and the QualeAria air-quality model for gas concentrations. Their predictions, together with measurements from ground stations, are used as inputs to a neural network. The model validation results show that integrated models noticeably improve ozone forecasts and provide better alert systems.

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

The Role of Model and Initial Condition Error in Numerical Weather Forecasting Investigated with an Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, Nikki C.; Errico, Ronald M.

2013-01-01

A series of experiments that explore the roles of model and initial condition error in numerical weather prediction are performed using an observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The use of an OSSE allows the analysis and forecast errors to be explicitly calculated, and different hypothetical observing networks can be tested with ease. In these experiments, both a full global OSSE framework and an 'identical twin' OSSE setup are utilized to compare the behavior of the data assimilation system and evolution of forecast skill with and without model error. The initial condition error is manipulated by varying the distribution and quality of the observing network and the magnitude of observation errors. The results show that model error has a strong impact on both the quality of the analysis field and the evolution of forecast skill, including both systematic and unsystematic model error components. With a realistic observing network, the analysis state retains a significant quantity of error due to systematic model error. If errors of the analysis state are minimized, model error acts to rapidly degrade forecast skill during the first 24-48 hours of forward integration. In the presence of model error, the impact of observation errors on forecast skill is small, but in the absence of model error, observation errors cause a substantial degradation of the skill of medium range forecasts.

Development of a method for comprehensive water quality forecasting and its application in Miyun reservoir of Beijing, China.

PubMed

Zhang, Lei; Zou, Zhihong; Shan, Wei

2017-06-01

Water quality forecasting is an essential part of water resource management. Spatiotemporal variations of water quality and their inherent constraints make it very complex. This study explored a data-based method for short-term water quality forecasting. Prediction of water quality indicators including dissolved oxygen, chemical oxygen demand by KMnO 4 and ammonia nitrogen using support vector machine was taken as inputs of the particle swarm algorithm based optimal wavelet neural network to forecast the whole status index of water quality. Gubeikou monitoring section of Miyun reservoir in Beijing, China was taken as the study case to examine effectiveness of this approach. The experiment results also revealed that the proposed model has advantages of stability and time reduction in comparison with other data-driven models including traditional BP neural network model, wavelet neural network model and Gradient Boosting Decision Tree model. It can be used as an effective approach to perform short-term comprehensive water quality prediction. Copyright © 2016. Published by Elsevier B.V.

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.

DEA-I: A Globally Configurable Open Source Software Package in Support of Air Quality Forecasts

NASA Astrophysics Data System (ADS)

Davies, J.; Strabala, K.; Pierce, R.; Huang, H.; Schiffer, E.

2012-12-01

During September 2003, a team of NASA, NOAA, and EPA researchers demonstrated a prototype for using Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth retrievals in daily air quality forecasts; this became known as IDEA (Infusing satellite Data into Environmental Applications). IDEA was part of the NASA Applied Sciences Program strategy to demonstrate practical uses of NASA-sponsored observations from space and predictions. Following its successful demonstration an export version of IDEA, known as IDEA International (IDEA-I), has now been released. IDEA-I supports the Global Earth Observation Systems of Systems (GEOSS) Group on Earth Observations (GEO) Health Societal Benefit Area (SBA) and is being developed within the framework of the GEO Earth Observations in Decision Support Call for Proposals. The vehicle for IDEA-I release is the International MODIS and AIRS (Atmospheric Infrared Sounder) Processing Package (IMAPP), developed at the Space Science and Engineering Center, University of Wisconsin-Madison (SSEC/UW-Madison). IMAPP is a NASA-funded and freely-distributed software package which allows any ground station capable of receiving direct broadcast from Terra or Aqua to produce calibrated and geolocated radiances, and a suite of environmental products, of which the IDEA-I 48-hour forward trajectory prediction of high aerosol events is now a part. IDEA-I provides a tool for linking ground-based and satellite capabilities to support international air quality forecasting activities and is to be demonstrated internationally through user training and impact evaluation via a series of IMAPP workshops. This presentation describes the IMAPP implementation of IDEA-I in terms of its simple installation and configuration, and through examples of its operation in several regions known for periodic high aerosol events.; Screen capture of the University of Wisconsin implementation of the real-time direct broadcast IDEA-I Air Quality monitoring

NCEP Air Quality Forecast(AQF) Graphics

Science.gov Websites

NAM-CMAQ Experimental Run predictions 00 03 06 09 12 15 18 21 24 27 30 33 36 39 42 45 48 Select experimental bias correction predictions NAM vs Nest forecasts Change Variable Type: Hourly CMAQ Forecasts

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.

Impact of Lidar Wind Sounding on Mesoscale Forecast

NASA Technical Reports Server (NTRS)

Miller, Timothy L.; Chou, Shih-Hung; Goodman, H. Michael (Technical Monitor)

2001-01-01

An Observing System Simulation Experiment (OSSE) was conducted to study the impact of airborne lidar wind sounding on mesoscale weather forecast. A wind retrieval scheme, which interpolates wind data from a grid data system, simulates the retrieval of wind profile from a satellite lidar system. A mesoscale forecast system based on the PSU/NCAR MM5 model is developed and incorporated the assimilation of the retrieved line-of-sight wind. To avoid the "identical twin" problem, the NCEP reanalysis data is used as our reference "nature" atmosphere. The simulated space-based lidar wind observations were retrieved by interpolating the NCEP values to the observation locations. A modified dataset obtained by smoothing the NCEP dataset was used as the initial state whose forecast was sought to be improved by assimilating the retrieved lidar observations. Forecasts using wind profiles with various lidar instrument parameters has been conducted. The results show that to significantly improve the mesoscale forecast the satellite should fly near the storm center with large scanning radius. Increasing lidar firing rate also improves the forecast. Cloud cover and lack of aerosol degrade the quality of the lidar wind data and, subsequently, the forecast.

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.

Assessment of an ensemble seasonal streamflow forecasting system for Australia

NASA Astrophysics Data System (ADS)

Bennett, James C.; Wang, Quan J.; Robertson, David E.; Schepen, Andrew; Li, Ming; Michael, Kelvin

2017-11-01

Despite an increasing availability of skilful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skilful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean-land-atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall-runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skilful forecasts at shorter lead times ( < 4 months), and transits to climatology-like forecasts at longer lead times. Forecasts are generally reliable and unbiased. However, FoGSS does not perform well in very dry catchments (catchments that experience zero flows more than half the time in some months), sometimes producing strongly negative forecast skill and poor reliability. We attempt to improve forecasts through the use of (i) ESP rainfall forcings, (ii) different rainfall-runoff models, and (iii) a Bayesian prior to encourage the error model to return climatology forecasts in months when the rainfall-runoff model performs poorly. Of these, the use of the prior offers the clearest benefit in very dry catchments, where it moderates strongly negative forecast skill and reduces bias in some instances. However, the prior does

Evaluation and economic value of winter weather forecasts

NASA Astrophysics Data System (ADS)

Snyder, Derrick W.

State and local highway agencies spend millions of dollars each year to deploy winter operation teams to plow snow and de-ice roadways. Accurate and timely weather forecast information is critical for effective decision making. Students from Purdue University partnered with the Indiana Department of Transportation to create an experimental winter weather forecast service for the 2012-2013 winter season in Indiana to assist in achieving these goals. One forecast product, an hourly timeline of winter weather hazards produced daily, was evaluated for quality and economic value. Verification of the forecasts was performed with data from the Rapid Refresh numerical weather model. Two objective verification criteria were developed to evaluate the performance of the timeline forecasts. Using both criteria, the timeline forecasts had issues with reliability and discrimination, systematically over-forecasting the amount of winter weather that was observed while also missing significant winter weather events. Despite these quality issues, the forecasts still showed significant, but varied, economic value compared to climatology. Economic value of the forecasts was estimated to be 29.5 million or 4.1 million, depending on the verification criteria used. Limitations of this valuation system are discussed and a framework is developed for more thorough studies in the future.

Research and application of a hybrid model based on dynamic fuzzy synthetic evaluation for establishing air quality forecasting and early warning system: A case study in China.

PubMed

Xu, Yunzhen; Du, Pei; Wang, Jianzhou

2017-04-01

As the atmospheric environment pollution has been becoming more and more serious in China, it is highly desirable to develop a scientific and effective early warning system that plays a great significant role in analyzing and monitoring air quality. However, establishing a robust early warning system for warning the public in advance and ameliorating air quality is not only an extremely challenging task but also a public concerned problem for human health. Most previous studies are focused on improving the prediction accuracy, which usually ignore the significance of uncertainty information and comprehensive evaluation concerning air pollutants. Therefore, in this paper a novel robust early warning system was successfully developed, which consists of three modules: evaluation module, forecasting module and characteristics estimating module. In this system, a new dynamic fuzzy synthetic evaluation is proposed and applied to determine air quality levels and primary pollutants, which can be regarded as the research objectives; Moreover, to further mine and analyze the characteristics of air pollutants, four different distribution functions and interval forecasting method are also employed that can not only provide predictive range, confidence level and the other uncertain information of the pollutants future values, but also assist decision-makers in reducing and controlling the emissions of atmospheric pollutants. Case studies utilizing hourly PM 2.5 , PM 10 and SO 2 data collected from Tianjin and Shanghai in China are applied as illustrative examples to estimate the effectiveness and efficiency of the proposed system. Experimental results obviously indicated that the developed novel early warning system is much suitable for analyzing and monitoring air pollution, which can also add a novel viable option for decision-makers. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Comparison Study of Two Numerical Tsunami Forecasting Systems

NASA Astrophysics Data System (ADS)

Greenslade, Diana J. M.; Titov, Vasily V.

2008-12-01

This paper presents a comparison of two tsunami forecasting systems: the NOAA/PMEL system (SIFT) and the Australian Bureau of Meteorology system (T1). Both of these systems are based on a tsunami scenario database and both use the same numerical model. However, there are some major differences in the way in which the scenarios are constructed and in the implementation of the systems. Two tsunami events are considered here: Tonga 2006 and Sumatra 2007. The results show that there are some differences in the distribution of maximum wave amplitude, particularly for the Tonga event, however both systems compare well to the available tsunameter observations. To assess differences in the forecasts for coastal amplitude predictions, the offshore forecast results from both systems were used as boundary conditions for a high-resolution model for Hilo, Hawaii. The minor differences seen between the two systems in deep water become considerably smaller at the tide gauge and both systems compare very well with the observations.

The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region

NASA Astrophysics Data System (ADS)

Song, Yiliao; Qin, Shanshan; Qu, Jiansheng; Liu, Feng

2015-10-01

The issue of air quality regarding PM pollution levels in China is a focus of public attention. To address that issue, to date, a series of studies is in progress, including PM monitoring programs, PM source apportionment, and the enactment of new ambient air quality index standards. However, related research concerning computer modeling for PM future trends estimation is rare, despite its significance to forecasting and early warning systems. Thereby, a study regarding deterministic and interval forecasts of PM is performed. In this study, data on hourly and 12 h-averaged air pollutants are applied to forecast PM concentrations within the Yangtze River Delta (YRD) region of China. The characteristics of PM emissions have been primarily examined and analyzed using different distribution functions. To improve the distribution fitting that is crucial for estimating PM levels, an artificial intelligence algorithm is incorporated to select the optimal parameters. Following that step, an ANF model is used to conduct deterministic forecasts of PM. With the identified distributions and deterministic forecasts, different levels of PM intervals are estimated. The results indicate that the lognormal or gamma distributions are highly representative of the recorded PM data with a goodness-of-fit R2 of approximately 0.998. Furthermore, the results of the evaluation metrics (MSE, MAPE and CP, AW) also show high accuracy within the deterministic and interval forecasts of PM, indicating that this method enables the informative and effective quantification of future PM trends.

Improving Forecast Skill by Assimilation of Quality-controlled AIRS Temperature Retrievals under Partially Cloudy Conditions

NASA Technical Reports Server (NTRS)

Reale, O.; Susskind, J.; Rosenberg, R.; Brin, E.; Riishojgaard, L.; Liu, E.; Terry, J.; Jusem, J. C.

2007-01-01

The National Aeronautics and Space Administration (NASA) Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite has been long recognized as an important contributor towards the improvement of weather forecasts. At this time only a small fraction of the total data produced by AIRS is being used by operational weather systems. In fact, in addition to effects of thinning and quality control, the only AIRS data assimilated are radiance observations of channels unaffected by clouds. Observations in mid-lower tropospheric sounding AIRS channels are assimilated primarily under completely clear-sky conditions, thus imposing a very severe limitation on the horizontal distribution of the AIRS-derived information. In this work it is shown that the ability to derive accurate temperature profiles from AIRS observations in partially cloud-contaminated areas can be utilized to further improve the impact of AIRS observations in a global model and forecasting system. The analyses produced by assimilating AIRS temperature profiles obtained under partial cloud cover result in a substantially colder representation of the northern hemisphere lower midtroposphere at higher latitudes. This temperature difference has a strong impact, through hydrostatic adjustment, in the midtropospheric geopotential heights, which causes a different representation of the polar vortex especially over northeastern Siberia and Alaska. The AIRS-induced anomaly propagates through the model's dynamics producing improved 5-day forecasts.

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

Uncertainty forecasts improve weather-related decisions and attenuate the effects of forecast error.

PubMed

Joslyn, Susan L; LeClerc, Jared E

2012-03-01

Although uncertainty is inherent in weather forecasts, explicit numeric uncertainty estimates are rarely included in public forecasts for fear that they will be misunderstood. Of particular concern are situations in which precautionary action is required at low probabilities, often the case with severe events. At present, a categorical weather warning system is used. The work reported here tested the relative benefits of several forecast formats, comparing decisions made with and without uncertainty forecasts. In three experiments, participants assumed the role of a manager of a road maintenance company in charge of deciding whether to pay to salt the roads and avoid a potential penalty associated with icy conditions. Participants used overnight low temperature forecasts accompanied in some conditions by uncertainty estimates and in others by decision advice comparable to categorical warnings. Results suggested that uncertainty information improved decision quality overall and increased trust in the forecast. Participants with uncertainty forecasts took appropriate precautionary action and withheld unnecessary action more often than did participants using deterministic forecasts. When error in the forecast increased, participants with conventional forecasts were reluctant to act. However, this effect was attenuated by uncertainty forecasts. Providing categorical decision advice alone did not improve decisions. However, combining decision advice with uncertainty estimates resulted in the best performance overall. The results reported here have important implications for the development of forecast formats to increase compliance with severe weather warnings as well as other domains in which one must act in the face of uncertainty. PsycINFO Database Record (c) 2012 APA, all rights reserved.

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.

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.

Power Grid Maintenance Scheduling Intelligence Arrangement Supporting System Based on Power Flow Forecasting

NASA Astrophysics Data System (ADS)

Xie, Chang; Wen, Jing; Liu, Wenying; Wang, Jiaming

With the development of intelligent dispatching, the intelligence level of network control center full-service urgent need to raise. As an important daily work of network control center, the application of maintenance scheduling intelligent arrangement to achieve high-quality and safety operation of power grid is very important. By analyzing the shortages of the traditional maintenance scheduling software, this paper designs a power grid maintenance scheduling intelligence arrangement supporting system based on power flow forecasting, which uses the advanced technologies in maintenance scheduling, such as artificial intelligence, online security checking, intelligent visualization techniques. It implements the online security checking of maintenance scheduling based on power flow forecasting and power flow adjusting based on visualization, in order to make the maintenance scheduling arrangement moreintelligent and visual.

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.

Forecasting in an integrated surface water-ground water system: The Big Cypress Basin, South Florida

NASA Astrophysics Data System (ADS)

Butts, M. B.; Feng, K.; Klinting, A.; Stewart, K.; Nath, A.; Manning, P.; Hazlett, T.; Jacobsen, T.

2009-04-01

The South Florida Water Management District (SFWMD) manages and protects the state's water resources on behalf of 7.5 million South Floridians and is the lead agency in restoring America's Everglades - the largest environmental restoration project in US history. Many of the projects to restore and protect the Everglades ecosystem are part of the Comprehensive Everglades Restoration Plan (CERP). The region has a unique hydrological regime, with close connection between surface water and groundwater, and a complex managed drainage network with many structures. Added to the physical complexity are the conflicting needs of the ecosystem for protection and restoration, versus the substantial urban development with the accompanying water supply, water quality and flood control issues. In this paper a novel forecasting and real-time modelling system is presented for the Big Cypress Basin. The Big Cypress Basin includes 272 km of primary canals and 46 water control structures throughout the area that provide limited levels of flood protection, as well as water supply and environmental quality management. This system is linked to the South Florida Water Management District's extensive real-time (SCADA) data monitoring and collection system. Novel aspects of this system include the use of a fully distributed and integrated modeling approach and a new filter-based updating approach for accurately forecasting river levels. Because of the interaction between surface- and groundwater a fully integrated forecast modeling approach is required. Indeed, results for the Tropical Storm Fay in 2008, the groundwater levels show an extremely rapid response to heavy rainfall. Analysis of this storm also shows that updating levels in the river system can have a direct impact on groundwater levels.

Air Quality Modeling and Forecasting over the United States Using WRF-Chem

NASA Astrophysics Data System (ADS)

Boxe, C.; Hafsa, U.; Blue, S.; Emmanuel, S.; Griffith, E.; Moore, J.; Tam, J.; Khan, I.; Cai, Z.; Bocolod, B.; Zhao, J.; Ahsan, S.; Gurung, D.; Tang, N.; Bartholomew, J.; Rafi, R.; Caltenco, K.; Rivas, M.; Ditta, H.; Alawlaqi, H.; Rowley, N.; Khatim, F.; Ketema, N.; Strothers, J.; Diallo, I.; Owens, C.; Radosavljevic, J.; Austin, S. A.; Johnson, L. P.; Zavala-Gutierrez, R.; Breary, N.; Saint-Hilaire, D.; Skeete, D.; Stock, J.; Salako, O.

2016-12-01

WRF-Chem is the Weather Research and Forecasting (WRF) model coupled with Chemistry. The model simulates the emission, transport, mixing, and chemical transformation of trace gases and aerosols simultaneously with the meteorology. The model is used for investigation of regional-scale air quality, field program analysis, and cloud-scale interactions between clouds and chemistry. The development of WRF-Chem is a collaborative effort among the community led by NOAA/ESRL scientists. The Official WRF-Chem web page is located at the NOAA web site. Our model development is closely linked with both NOAA/ESRL and DOE/PNNL efforts. Description of PNNL WRF-Chem model development is located at the PNNL web site as well as the PNNL Aerosol Modeling Testbed. High school and undergraduate students, representative of academic institutions throughout USA's Tri-State Area (New York, New Jersey, Connecticut), set up WRF-Chem on CUNY CSI's High Performance Computing Center. Students learned the back-end coding that governs WRF-Chems structure and the front-end coding that displays visually specified weather simulations and forecasts. Students also investigated the impact, to select baseline simulations/forecasts, due to the reaction, NO2 + OH + M → HOONO + M (k = 9.2 × 10-12 cm3 molecule-1 s-1, Mollner et al. 2010). The reaction of OH and NO2 to form gaseous nitric acid (HONO2) is among the most influential and in atmospheric chemistry. Till a few years prior, its rate coefficient remained poorly determined under tropospheric conditions because of difficulties in making laboratory measurements at 760 torr. These activities fosters student coding competencies and deep insights into weather forecast and air quality.

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.

Improving Global Forecast System of extreme precipitation events with regional statistical model: Application of quantile-based probabilistic forecasts

NASA Astrophysics Data System (ADS)

Shastri, Hiteshri; Ghosh, Subimal; Karmakar, Subhankar

2017-02-01

Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability. Although significant scientific improvements took place in the global models for weather forecasting, they are still not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecast System are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood-prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/calibration data set used in the study. We find promising results and emphasize to implement such data-driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning.

Evaluation and intercomparison of air quality forecasts over Korea during the KORUS-AQ campaign

NASA Astrophysics Data System (ADS)

Lee, Seungun; Park, Rokjin J.; Kim, Soontae; Song, Chul H.; Kim, Cheol-Hee; Woo, Jung-Hun

2017-04-01

We evaluate and intercompare ozone and aerosol simulations over Korea during the KORUS-AQ campaign, which was conducted in May-June 2016. Four global and regional air quality models participated in the campaign and provided daily air quality forecasts over Korea to guide aircraft flight paths for detecting air pollution events over Korean peninsula and its nearby oceans. We first evaluate the model performance by comparing simulated and observed hourly surface ozone and PM2.5 concentrations at ground sites in Korea and find that the models successfully capture intermittent air pollution events and reproduce the daily variation of ozone and PM2.5 concentrations. However, significant underestimates of peak ozone concentrations in the afternoon are also found in most models. Among chemical constituents of PM2.5, the models typically overestimate observed nitrate aerosol concentrations and underestimate organic aerosol concentrations, although the observed mass concentrations of PM2.5 are seemingly reproduced by the models. In particular, all models used the same anthropogenic emission inventory (KU-CREATE) for daily air quality forecast, but they show a considerable discrepancy for ozone and aerosols. Compared to individual model results, the ensemble mean of all models shows the best performance with correlation coefficients of 0.73 for ozone and 0.57 for PM2.5. We here investigate contributing factors to the discrepancy, which will serve as a guidance to improve the performance of the air quality forecast.

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.

APPLICATION AND EVALUATION OF CMAQ IN THE UNITED STATES: AIR QUALITY FORECASTING AND RETROSPECTIVE MODELING

EPA Science Inventory

Presentation slides provide background on model evaluation techniques. Also included in the presentation is an operational evaluation of 2001 Community Multiscale Air Quality (CMAQ) annual simulation, and an evaluation of PM_2.5 for the CMAQ air quality forecast (AQF) ...

Seasonal Water Balance Forecasts for Drought Early Warning in Ethiopia

NASA Astrophysics Data System (ADS)

Spirig, Christoph; Bhend, Jonas; Liniger, Mark

2016-04-01

Droughts severely impact Ethiopian agricultural production. Successful early warning for drought conditions in the upcoming harvest season therefore contributes to better managing food shortages arising from adverse climatic conditions. So far, however, meteorological seasonal forecasts have not been used in Ethiopia's national food security early warning system (i.e. the LEAP platform). Here we analyse the forecast quality of seasonal forecasts of total rainfall and of the meteorological water balance as a proxy for plant available water. We analyse forecast skill of June to September rainfall and water balance from dynamical seasonal forecast systems, the ECMWF System4 and EC-EARTH global forecasting systems. Rainfall forecasts outperform forecasts assuming a stationary climate mainly in north-eastern Ethiopia - an area that is particularly vulnerable to droughts. Forecasts of the water balance index seem to be even more skilful and thus more useful than pure rainfall forecasts. The results vary though for different lead times and skill measures employed. We further explore the potential added value of dynamically downscaling the forecasts through several dynamical regional climate models made available through the EU FP7 project EUPORIAS. Preliminary results suggest that dynamically downscaled seasonal forecasts are not significantly better compared with seasonal forecasts from the global models. We conclude that seasonal forecasts of a simple climate index such as the water balance have the potential to benefit drought early warning in Ethiopia, both due to its positive predictive skill and higher usefulness than seasonal mean quantities.

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint

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

Zhang, J.; Hodge, B. M.; Florita, A.

2013-10-01

Forecasting solar energy generation is a challenging task due to the variety of solar power systems and weather regimes encountered. Forecast inaccuracies can result in substantial economic losses and power system reliability issues. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, applications, etc.). In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design of experiments methodology, in conjunction with response surface and sensitivity analysis methods. The resultsmore » show that the developed metrics can efficiently evaluate the quality of solar forecasts, and assess the economic and reliability impact of improved solar forecasting.« less

Recent Trends in Variable Generation Forecasting and Its Value to the Power System

DOE PAGES

Orwig, Kirsten D.; Ahlstrom, Mark L.; Banunarayanan, Venkat; ...

2014-12-23

We report that the rapid deployment of wind and solar energy generation systems has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power forecasts is still viewed by the power industry as being quite high, and many barriers to forecast adoption by power system operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power forecasts. Additionally, several utilities and grid operators have recognized the value ofmore » adopting variable generation forecasting and have taken great strides to enhance their usage of forecasting. In parallel, power system markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power forecasting technologies in the U.S., the role of forecasting in an evolving power system framework, and the benefits to intended forecast users.« 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.

Real-time forecasting at weekly timescales of the SST and SLA of the Ligurian Sea with a satellite-based ocean forecasting (SOFT) system

NASA Astrophysics Data System (ADS)

ÁLvarez, A.; Orfila, A.; Tintoré, J.

2004-03-01

Satellites are the only systems able to provide continuous information on the spatiotemporal variability of vast areas of the ocean. Relatively long-term time series of satellite data are nowadays available. These spatiotemporal time series of satellite observations can be employed to build empirical models, called satellite-based ocean forecasting (SOFT) systems, to forecast certain aspects of future ocean states. SOFT systems can predict satellite-observed fields at different timescales. The forecast skill of SOFT systems forecasting the sea surface temperature (SST) at monthly timescales has been extensively explored in previous works. In this work we study the performance of two SOFT systems forecasting, respectively, the SST and sea level anomaly (SLA) at weekly timescales, that is, providing forecasts of the weekly averaged SST and SLA fields with 1 week in advance. The SOFT systems were implemented in the Ligurian Sea (Western Mediterranean Sea). Predictions from the SOFT systems are compared with observations and with the predictions obtained from persistence models. Results indicate that the SOFT system forecasting the SST field is always superior in terms of predictability to persistence. Minimum prediction errors in the SST are obtained during winter and spring seasons. On the other hand, the biggest differences between the performance of SOFT and persistence models are found during summer and autumn. These changes in the predictability are explained on the basis of the particular variability of the SST field in the Ligurian Sea. Concerning the SLA field, no improvements with respect to persistence have been found for the SOFT system forecasting the SLA field.

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

Different types of drifts in two seasonal forecast systems and their dependence on ENSO

NASA Astrophysics Data System (ADS)

Hermanson, L.; Ren, H.-L.; Vellinga, M.; Dunstone, N. D.; Hyder, P.; Ineson, S.; Scaife, A. A.; Smith, D. M.; Thompson, V.; Tian, B.; Williams, K. D.

2017-11-01

Seasonal forecasts using coupled ocean-atmosphere climate models are increasingly employed to provide regional climate predictions. For the quality of forecasts to improve, regional biases in climate models must be diagnosed and reduced. The evolution of biases as initialized forecasts drift away from the observations is poorly understood, making it difficult to diagnose the causes of climate model biases. This study uses two seasonal forecast systems to examine drifts in sea surface temperature (SST) and precipitation, and compares them to the long-term bias in the free-running version of each model. Drifts are considered from daily to multi-annual time scales. We define three types of drift according to their relation with the long-term bias in the free-running model: asymptoting, overshooting and inverse drift. We find that precipitation almost always has an asymptoting drift. SST drifts on the other hand, vary between forecasting systems, where one often overshoots and the other often has an inverse drift. We find that some drifts evolve too slowly to have an impact on seasonal forecasts, even though they are important for climate projections. The bias found over the first few days can be very different from that in the free-running model, so although daily weather predictions can sometimes provide useful information on the causes of climate biases, this is not always the case. We also find that the magnitude of equatorial SST drifts, both in the Pacific and other ocean basins, depends on the El Niño Southern Oscillation (ENSO) phase. Averaging over all hindcast years can therefore hide the details of ENSO state dependent drifts and obscure the underlying physical causes. Our results highlight the need to consider biases across a range of timescales in order to understand their causes and develop improved climate models.

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.

Developing Environmental Scanning/Forecasting Systems To Augment Community College Planning.

ERIC Educational Resources Information Center

Morrison, James L.; Held, William G.

A description is provided of a conference session that was conducted to explore the structure and function of an environmental scanning/forecasting system that could be used in a community college to facilitate planning. Introductory comments argue that a college that establishes an environmental scanning and forecasting system is able to identify…

A Decision Support System for effective use of probability forecasts

NASA Astrophysics Data System (ADS)

De Kleermaeker, Simone; Verkade, Jan

2013-04-01

Often, water management decisions are based on hydrological forecasts. These forecasts, however, are affected by inherent uncertainties. It is increasingly common for forecasting agencies to make explicit estimates of these uncertainties and thus produce probabilistic forecasts. Associated benefits include the decision makers' increased awareness of forecasting uncertainties and the potential for risk-based decision-making. Also, a stricter separation of responsibilities between forecasters and decision maker can be made. However, simply having probabilistic forecasts available is not sufficient to realise the associated benefits. Additional effort is required in areas such as forecast visualisation and communication, decision making in uncertainty and forecast verification. Also, revised separation of responsibilities requires a shift in institutional arrangements and responsibilities. A recent study identified a number of additional issues related to the effective use of probability forecasts. When moving from deterministic to probability forecasting, a dimension is added to an already multi-dimensional problem; this makes it increasingly difficult for forecast users to extract relevant information from a forecast. A second issue is that while probability forecasts provide a necessary ingredient for risk-based decision making, other ingredients may not be present. For example, in many cases no estimates of flood damage, of costs of management measures and of damage reduction are available. This paper presents the results of the study, including some suggestions for resolving these issues and the integration of those solutions in a prototype decision support system (DSS). A pathway for further development of the DSS is outlined.

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.

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

Subseasonal to Seasonal Forecasting at NASA in Support of the National Earth System Prediction Capability

NASA Astrophysics Data System (ADS)

Considine, D. B.; Pawson, S.; Koster, R. D.; Kovach, R. M.; Vernieres, G.; Schubert, S. D.

2016-12-01

NASA has developed and maintains, within the Goddard Modeling and Assimilation Office (GMAO), a seasonal-to-interannual prediction activity in support of the National ESPC, based on the GEOS-5 Atmosphere-Ocean General Circulation Model (AOGCM). This system generates atmospheric, land, and ocean/ice analyses that are used to produce global forecasts. Each month, a 17-member ensemble of forecasts is made, from which various oceanic indices (e.g., El Niño, East Indian Dipole, Atlantic SST anomalies), are computed. Additionally, monthly and seasonal anomalies are computed for several variables from the atmosphere (e.g., 2-meter temperatures, precipitation, geopotential heights), land (drought indices), ocean (subsurface temperature anomalies), and sea ice. These forecasts are provided to the National Multi Model Ensemble (NMME) and the Study of Environmental Arctic Change (SEARCH) sea ice outlook. The quasi-operational nature of this system, with constant generation of products that are shared with the broader community, allows for continual assessment of the impacts of NASA observations on seasonal forecasts - a current example is the altimetry data from the JASON series of satellites. The GMAO's seasonal prediction system is currently being upgraded. Alongside typical enhancements, such as increased spatial resolution and use of more recent model versions with improved representation of physical processes, these developments are designed to enhance the use of NASA observations. One example is the use of aerosol information from NASA's EOS instruments (MODIS). A major motivation is also to include NASA's novel data types, such as soil-moisture from SMAP and other sources of oceanic information (such as salinity). This approach enables NASA to continue contributing to national seasonal forecasting efforts, while simultaneously introducing its novel observing capabilities into the seasonal system in a manner that can demonstrate their systematic impacts on the quality

ENSO Prediction in the NASA GMAO GEOS-5 Seasonal Forecasting System

NASA Astrophysics Data System (ADS)

Kovach, R. M.; Borovikov, A.; Marshak, J.; Pawson, S.; Vernieres, G.

2016-12-01

Seasonal-to-Interannual coupled forecasts are conducted in near-real time with the Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation Model (AOGCM). A 30-year suite of 9-month hindcasts is available, initialized with the MERRA-Ocean, MERRA-Land, and MERRA atmospheric fields. These forecasts are used to predict the timing and magnitude of ENSO and other short-term climate variability. The 2015 El Niño peaked in November 2015 and was considered a "very strong" event with the Equatorial Pacific Ocean sea-surface-temperature (SST) anomalies higher than 2.0 °C. These very strong temperature anomalies began in Sep/Oct/Nov (SON) of 2015 and persisted through Dec/Jan/Feb (DJF) of 2016. The other two very strong El Niño events recently recorded occurred in 1981/82 and 1997/98. The GEOS-5 system began predicting a very strong El Niño for SON starting with the March 2015 forecast. At this time, the GMAO forecast was an outlier in both the NMME and IRI multi-model ensemble prediction plumes. The GMAO May 2015 forecast for the November 2015 peak in temperature anomaly in the Niño3.4 region was in excellent agreement with the real event, but in May this forecast was still one of the outliers in the multi-model forecasts. The GEOS-5 May 2015 forecast also correctly predicted the weakening of the Eastern Pacific (Niño1+2) anomalies for SON. We will present a summary of the NASA GMAO GEOS-5 Seasonal Forecast System skills based on historic hindcasts. Initial conditions, prediction of ocean surface and subsurface evolution for the 2015/16 El Niño will be compared to the 1998/97 event. GEOS-5 capability to predict the precipitation, i.e. to model the teleconnection patterns associated with El Niño will also be shown. To conclude, we will highlight some new developments in the GEOS forecasting system.

The quality and value of seasonal precipitation forecasts for an early warning of large-scale droughts and floods in West Africa

NASA Astrophysics Data System (ADS)

Bliefernicht, Jan; Seidel, Jochen; Salack, Seyni; Waongo, Moussa; Laux, Patrick; Kunstmann, Harald

2017-04-01

Seasonal precipitation forecasts are a crucial source of information for an early warning of hydro-meteorological extremes in West Africa. However, the current seasonal forecasting system used by the West African weather services in the framework of the West African Climate Outlook forum (PRESAO) is limited to probabilistic precipitation forecasts of 1-month lead time. To improve this provision, we use an ensemble-based quantile-quantile transformation for bias correction of precipitation forecasts provided by a global seasonal ensemble prediction system, the Climate Forecast System Version 2 (CFS2). The statistical technique eliminates systematic differences between global forecasts and observations with the potential to preserve the signal from the model. The technique has also the advantage that it can be easily implemented at national weather services with low capacities. The statistical technique is used to generate probabilistic forecasts of monthly and seasonal precipitation amount and other precipitation indices useful for an early warning of large-scale drought and floods in West Africa. The evaluation of the statistical technique is done using CFS hindcasts (1982 to 2009) in a cross-validation mode to determine the performance of the precipitation forecasts for several lead times focusing on drought and flood events depicted over the Volta and Niger basins. In addition, operational forecasts provided by PRESAO are analyzed from 1998 to 2015. The precipitation forecasts are compared to low-skill reference forecasts generated from gridded observations (i.e. GPCC, CHIRPS) and a novel in-situ gauge database from national observation networks (see Poster EGU2017-10271). The forecasts are evaluated using state-of-the-art verification techniques to determine specific quality attributes of probabilistic forecasts such as reliability, accuracy and skill. In addition, cost-loss approaches are used to determine the value of probabilistic forecasts for multiple users

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

Real-time drought forecasting system for irrigation managment

NASA Astrophysics Data System (ADS)

Ceppi, Alessandro; Ravazzani, Giovanni; Corbari, Chiara; Masseroni, Daniele; Meucci, Stefania; Pala, Francesca; Salerno, Raffaele; Meazza, Giuseppe; Chiesa, Marco; Mancini, Marco

2013-04-01

In recent years frequent periods of water scarcity have enhanced the need to use water more carefully, even in in European areas traditionally rich of water such as the Po Valley. In dry periods, the problem of water shortage can be enhanced by conflictual use of water such as irrigation, industrial and power production (hydroelectric and thermoelectric). Further, over the last decade the social perspective on this issue is increasing due to climate change and global warming scenarios which come out from the last IPCC Report. The increased frequency of dry periods has stimulated the improvement of irrigation and water management. In this study we show the development and implementation of the real-time drought forecasting system Pre.G.I., an Italian acronym that stands for "Hydro-Meteorological forecast for irrigation management". The system is based on ensemble prediction at long range (30 days) with hydrological simulation of water balance to forecast the soil water content in every parcel over the Consorzio Muzza basin. The studied area covers 74,000 ha in the middle of the Po Valley, near the city of Lodi. The hydrological ensemble forecasts are based on 20 meteorological members of the non-hydrostatic WRF model with 30 days as lead-time, provided by Epson Meteo Centre, while the hydrological model used to generate the soil moisture and water table simulations is the rainfall-runoff distributed FEST-WB model, developed at Politecnico di Milano. The hydrological model was validated against measurements of latent heat flux and soil moisture acquired by an eddy-covariance station. Reliability of the forecasting system and its benefits was assessed on some cases-study occurred in the recent years.

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.

Interval forecasting of cyber-attacks on industrial control systems

NASA Astrophysics Data System (ADS)

Ivanyo, Y. M.; Krakovsky, Y. M.; Luzgin, A. N.

2018-03-01

At present, cyber-security issues of industrial control systems occupy one of the key niches in a state system of planning and management Functional disruption of these systems via cyber-attacks may lead to emergencies related to loss of life, environmental disasters, major financial and economic damage, or disrupted activities of cities and settlements. There is then an urgent need to develop protection methods against cyber-attacks. This paper studied the results of cyber-attack interval forecasting with a pre-set intensity level of cyber-attacks. Interval forecasting is the forecasting of one interval from two predetermined ones in which a future value of the indicator will be obtained. For this, probability estimates of these events were used. For interval forecasting, a probabilistic neural network with a dynamic updating value of the smoothing parameter was used. A dividing bound of these intervals was determined by a calculation method based on statistical characteristics of the indicator. The number of cyber-attacks per hour that were received through a honeypot from March to September 2013 for the group ‘zeppo-norcal’ was selected as the indicator.

Evaluation of Air Force and Navy Demand Forecasting Systems

DTIC Science & Technology

1994-01-01

forecasting approach, the Air Force Material Command is questioning the adoption of the Navy’s Statistical Demand Forecasting System ( Gitman , 1994). The...Recoverable Item Process in the Requirements Data Bank System is to manage reparable spare parts ( Gitman , 1994). Although RDB will have the capability of...D062) ( Gitman , 1994). Since a comparison is made to address Air Force concerns, this research only limits its analysis to the range of Air Force

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/.

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.

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

A simple Lagrangian forecast system with aviation forecast potential

NASA Technical Reports Server (NTRS)

Petersen, R. A.; Homan, J. H.

1983-01-01

A trajectory forecast procedure is developed which uses geopotential tendency fields obtained from a simple, multiple layer, potential vorticity conservative isentropic model. This model can objectively account for short-term advective changes in the mass field when combined with fine-scale initial analyses. This procedure for producing short-term, upper-tropospheric trajectory forecasts employs a combination of a detailed objective analysis technique, an efficient mass advection model, and a diagnostically proven trajectory algorithm, none of which require extensive computer resources. Results of initial tests are presented, which indicate an exceptionally good agreement for trajectory paths entering the jet stream and passing through an intensifying trough. It is concluded that this technique not only has potential for aiding in route determination, fuel use estimation, and clear air turbulence detection, but also provides an example of the types of short range forecasting procedures which can be applied at local forecast centers using simple algorithms and a minimum of computer resources.

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 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 weather forecast systems for storm surge modeling in the Chesapeake Bay

NASA Astrophysics Data System (ADS)

Garzon, Juan L.; Ferreira, Celso M.; Padilla-Hernandez, Roberto

2018-01-01

Accurate forecast of sea-level heights in coastal areas depends, among other factors, upon a reliable coupling of a meteorological forecast system to a hydrodynamic and wave system. This study evaluates the predictive skills of the coupled circulation and wind-wave model system (ADCIRC+SWAN) for simulating storm tides in the Chesapeake Bay, forced by six different products: (1) Global Forecast System (GFS), (2) Climate Forecast System (CFS) version 2, (3) North American Mesoscale Forecast System (NAM), (4) Rapid Refresh (RAP), (5) European Center for Medium-Range Weather Forecasts (ECMWF), and (6) the Atlantic hurricane database (HURDAT2). This evaluation is based on the hindcasting of four events: Irene (2011), Sandy (2012), Joaquin (2015), and Jonas (2016). By comparing the simulated water levels to observations at 13 monitoring stations, we have found that the ADCIR+SWAN System forced by the following: (1) the HURDAT2-based system exhibited the weakest statistical skills owing to a noteworthy overprediction of the simulated wind speed; (2) the ECMWF, RAP, and NAM products captured the moment of the peak and moderately its magnitude during all storms, with a correlation coefficient ranging between 0.98 and 0.77; (3) the CFS system exhibited the worst averaged root-mean-square difference (excepting HURDAT2); (4) the GFS system (the lowest horizontal resolution product tested) resulted in a clear underprediction of the maximum water elevation. Overall, the simulations forced by NAM and ECMWF systems induced the most accurate results best accuracy to support water level forecasting in the Chesapeake Bay during both tropical and extra-tropical storms.

Remote-sensing based approach to forecast habitat quality under climate change scenarios.

PubMed

Requena-Mullor, Juan M; López, Enrique; Castro, Antonio J; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier

2017-01-01

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071-2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological

Remote-sensing based approach to forecast habitat quality under climate change scenarios

PubMed Central

Requena-Mullor, Juan M.; López, Enrique; Castro, Antonio J.; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier

2017-01-01

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071–2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological

NCEP Air Quality Forecast(AQF) Graphics

Science.gov Websites

Forecasts CMAQ PM Bias Corr. Forecasts Change Plot Type: Comparison plots Difference plots Year: 2018 2017 2016 2015 Month: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Day: 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Select Cycle: 06Z 12Z Select Region

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.

Short-Term State Forecasting-Based Optimal Voltage Regulation in Distribution Systems: Preprint

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

Yang, Rui; Jiang, Huaiguang; Zhang, Yingchen

2017-05-17

A novel short-term state forecasting-based optimal power flow (OPF) approach for distribution system voltage regulation is proposed in this paper. An extreme learning machine (ELM) based state forecaster is developed to accurately predict system states (voltage magnitudes and angles) in the near future. Based on the forecast system states, a dynamically weighted three-phase AC OPF problem is formulated to minimize the voltage violations with higher penalization on buses which are forecast to have higher voltage violations in the near future. By solving the proposed OPF problem, the controllable resources in the system are optimally coordinated to alleviate the potential severemore » voltage violations and improve the overall voltage profile. The proposed approach has been tested in a 12-bus distribution system and simulation results are presented to demonstrate the performance of the proposed approach.« less

DMI's Baltic Sea Coastal operational forecasting system

NASA Astrophysics Data System (ADS)

Murawski, Jens; Berg, Per; Weismann Poulsen, Jacob

2017-04-01

Operational forecasting is challenged with bridging the gap between the large scales of the driving weather systems and the local, human scales of the model applications. The limit of what can be represented by local model has been continuously shifted to higher and higher spatial resolution, with the aim to better resolve the local dynamic and to make it possible to describe processes that could only be parameterised in older versions, with the ultimate goal to improve the quality of the forecast. Current hardware trends demand a str onger focus on the development of efficient, highly parallelised software and require a refactoring of the code with a solid focus on portable performance. The gained performance can be used for running high resolution model with a larger coverage. Together with the development of efficient two-way nesting routines, this has made it possible to approach the near-coastal zone with model applications that can run in a time effective way. Denmarks Meteorological Institute uses the HBM(1) ocean circulation model for applications that covers the entire Baltic Sea and North Sea with an integrated model set-up that spans the range of horizontal resolution from 1nm for the entire Baltic Sea to approx. 200m resolution in local fjords (Limfjord). For the next model generation, the high resolution set-ups are going to be extended and new high resolution domains in coastal zones are either implemented or tested for operational use. For the first time it will be possible to cover large stretches of the Baltic coastal zone with sufficiently high resolution to model the local hydrodynamic adequately. (1) HBM stands for HIROMB-BOOS-Model, whereas HIROMB stands for "High Resolution Model for the Baltic Sea" and BOOS stands for "Baltic Operational Oceanography System".

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.

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

Use of Air Quality Observations by the National Air Quality Forecast Capability

NASA Astrophysics Data System (ADS)

Stajner, I.; McQueen, J.; Lee, P.; Stein, A. F.; Kondragunta, S.; Ruminski, M.; Tong, D.; Pan, L.; Huang, J. P.; Shafran, P.; Huang, H. C.; Dickerson, P.; Upadhayay, S.

2015-12-01

The National Air Quality Forecast Capability (NAQFC) operational predictions of ozone and wildfire smoke for the United States (U.S.) and predictions of airborne dust for continental U.S. are available at http://airquality.weather.gov/. NOAA National Centers for Environmental Prediction (NCEP) operational North American Mesoscale (NAM) weather predictions are combined with the Community Multiscale Air Quality (CMAQ) model to produce the ozone predictions and test fine particulate matter (PM2.5) predictions. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model provides smoke and dust predictions. Air quality observations constrain emissions used by NAQFC predictions. NAQFC NOx emissions from mobile sources were updated using National Emissions Inventory (NEI) projections for year 2012. These updates were evaluated over large U.S. cities by comparing observed changes in OMI NO2 observations and NOx measured by surface monitors. The rate of decrease in NOx emission projections from year 2005 to year 2012 is in good agreement with the observed changes over the same period. Smoke emissions rely on the fire locations detected from satellite observations obtained from NESDIS Hazard Mapping System (HMS). Dust emissions rely on a climatology of areas with a potential for dust emissions based on MODIS Deep Blue aerosol retrievals. Verification of NAQFC predictions uses AIRNow compilation of surface measurements for ozone and PM2.5. Retrievals of smoke from GOES satellites are used for verification of smoke predictions. Retrievals of dust from MODIS are used for verification of dust predictions. In summary, observations are the basis for the emissions inputs for NAQFC, they are critical for evaluation of performance of NAQFC predictions, and furthermore they are used in real-time testing of bias correction of PM2.5 predictions, as we continue to work on improving modeling and emissions important for representation of PM2.5.

Design of a Forecasting Service System for Monitoring of Vulnerabilities of Sensor Networks

NASA Astrophysics Data System (ADS)

Song, Jae-Gu; Kim, Jong Hyun; Seo, Dong Il; Kim, Seoksoo

This study aims to reduce security vulnerabilities of sensor networks which transmit data in an open environment by developing a forecasting service system. The system is to remove or monitor causes of breach incidents in advance. To that end, this research first examines general security vulnerabilities of sensor networks and analyzes characteristics of existing forecasting systems. Then, 5 steps of a forecasting service system are proposed in order to improve security responses.

Shared investment projects and forecasting errors: setting framework conditions for coordination and sequencing data quality activities.

PubMed

Leitner, Stephan; Brauneis, Alexander; Rausch, Alexandra

2015-01-01

In this paper, we investigate the impact of inaccurate forecasting on the coordination of distributed investment decisions. In particular, by setting up a computational multi-agent model of a stylized firm, we investigate the case of investment opportunities that are mutually carried out by organizational departments. The forecasts of concern pertain to the initial amount of money necessary to launch and operate an investment opportunity, to the expected intertemporal distribution of cash flows, and the departments' efficiency in operating the investment opportunity at hand. We propose a budget allocation mechanism for coordinating such distributed decisions The paper provides guidance on how to set framework conditions, in terms of the number of investment opportunities considered in one round of funding and the number of departments operating one investment opportunity, so that the coordination mechanism is highly robust to forecasting errors. Furthermore, we show that-in some setups-a certain extent of misforecasting is desirable from the firm's point of view as it supports the achievement of the corporate objective of value maximization. We then address the question of how to improve forecasting quality in the best possible way, and provide policy advice on how to sequence activities for improving forecasting quality so that the robustness of the coordination mechanism to errors increases in the best possible way. At the same time, we show that wrong decisions regarding the sequencing can lead to a decrease in robustness. Finally, we conduct a comprehensive sensitivity analysis and prove that-in particular for relatively good forecasters-most of our results are robust to changes in setting the parameters of our multi-agent simulation model.

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.

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.

Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid (Spanish Version)

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

Tian, Tian; Chernyakhovskiy, Ilya; Brancucci Martinez-Anido, Carlo

This document is the Spanish version of 'Greening the Grid- Forecasting Wind and Solar Generation Improving System Operations'. It discusses improving system operations with forecasting with and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

An experimental system for flood risk forecasting and monitoring at global scale

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Alfieri, Lorenzo; Kalas, Milan; Lorini, Valerio; Salamon, Peter

2017-04-01

Global flood forecasting and monitoring systems are nowadays a reality and are being applied by a wide 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 forecasting, combining streamflow estimations with expected inundated areas and flood impacts. Finally, emerging technologies such as crowdsourcing and social media monitoring can play a crucial role in flood disaster management and preparedness. Here, we present some recent advances of an experimental procedure for near-real time flood mapping and impact assessment. The procedure translates in near real-time the daily streamflow forecasts issued by the Global Flood Awareness System (GloFAS) into event-based flood hazard maps, which are then combined with exposure and vulnerability information at global scale to derive risk forecast. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To increase the reliability of our forecasts we propose the integration of model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification and correction of impact forecasts. Finally, we present the results of preliminary tests which show the potential of the proposed procedure in supporting emergency response and management.

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

Research and Development for Technology Evolution Potential Forecasting System

NASA Astrophysics Data System (ADS)

Gao, Changqing; Cao, Shukun; Wang, Yuzeng; Ai, Changsheng; Ze, Xiangbo

Technology forecasting is a powerful weapon for many enterprises to gain an animate future. Evolutionary potential radar plot is a necessary step of some valuable methods to help the technology managers with right technical strategy. A software system for Technology Evolution Potential Forecasting (TEPF) with automatic radar plot drawing is introduced in this paper. The framework of the system and the date structure describing the concrete evolution pattern are illustrated in details. And the algorithm for radar plot drawing is researched. It is proved that the TEPF system is an effective tool during the technology strategy analyzing process with a referenced case study.

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.

Fuzzy neural network technique for system state forecasting.

PubMed

Li, Dezhi; Wang, Wilson; Ismail, Fathy

2013-10-01

In many system state forecasting applications, the prediction is performed based on multiple datasets, each corresponding to a distinct system condition. The traditional methods dealing with multiple datasets (e.g., vector autoregressive moving average models and neural networks) have some shortcomings, such as limited modeling capability and opaque reasoning operations. To tackle these problems, a novel fuzzy neural network (FNN) is proposed in this paper to effectively extract information from multiple datasets, so as to improve forecasting accuracy. The proposed predictor consists of both autoregressive (AR) nodes modeling and nonlinear nodes modeling; AR models/nodes are used to capture the linear correlation of the datasets, and the nonlinear correlation of the datasets are modeled with nonlinear neuron nodes. A novel particle swarm technique [i.e., Laplace particle swarm (LPS) method] is proposed to facilitate parameters estimation of the predictor and improve modeling accuracy. The effectiveness of the developed FNN predictor and the associated LPS method is verified by a series of tests related to Mackey-Glass data forecast, exchange rate data prediction, and gear system prognosis. Test results show that the developed FNN predictor and the LPS method can capture the dynamics of multiple datasets effectively and track system characteristics accurately.

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.

The Discriminant Analysis Flare Forecasting System (DAFFS)

NASA Astrophysics Data System (ADS)

Leka, K. D.; Barnes, Graham; Wagner, Eric; Hill, Frank; Marble, Andrew R.

2016-05-01

The Discriminant Analysis Flare Forecasting System (DAFFS) has been developed under NOAA/Small Business Innovative Research funds to quantitatively improve upon the NOAA/SWPC flare prediction. In the Phase-I of this project, it was demonstrated that DAFFS could indeed improve by the requested 25% most of the standard flare prediction data products from NOAA/SWPC. In the Phase-II of this project, a prototype has been developed and is presently running autonomously at NWRA.DAFFS uses near-real-time data from NOAA/GOES, SDO/HMI, and the NSO/GONG network to issue both region- and full-disk forecasts of solar flares, based on multi-variable non-parametric Discriminant Analysis. Presently, DAFFS provides forecasts which match those provided by NOAA/SWPC in terms of thresholds and validity periods (including 1-, 2-, and 3- day forecasts), although issued twice daily. Of particular note regarding DAFFS capabilities are the redundant system design, automatically-generated validation statistics and the large range of customizable options available. As part of this poster, a description of the data used, algorithm, performance and customizable options will be presented, as well as a demonstration of the DAFFS prototype.DAFFS development at NWRA is supported by NOAA/SBIR contracts WC-133R-13-CN-0079 and WC-133R-14-CN-0103, with additional support from NASA contract NNH12CG10C, plus acknowledgment to the SDO/HMI and NSO/GONG facilities and NOAA/SWPC personnel for data products, support, and feedback. DAFFS is presently ready for Phase-III development.

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.

A pan-African medium-range ensemble flood forecast system

NASA Astrophysics Data System (ADS)

Thiemig, Vera; Bisselink, Bernard; Pappenberger, Florian; Thielen, Jutta

2015-04-01

The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions of the ECMWF and critical hydrological thresholds. In this study the predictive capability is investigated, to estimate AFFS' potential as an operational flood forecasting system for the whole of Africa. This is done in a hindcast mode, by reproducing pan-African hydrological predictions for the whole year of 2003 where important flood events were observed. Results were analysed in two ways, each with its individual objective. The first part of the analysis is of paramount importance for the assessment of AFFS as a flood forecasting system, as it focuses on the detection and prediction of flood events. Here, results were verified with reports of various flood archives such as Dartmouth Flood Observatory, the Emergency Event Database, the NASA Earth Observatory and Reliefweb. The number of hits, false alerts and missed alerts as well as the Probability of Detection, False Alarm Rate and Critical Success Index were determined for various conditions (different regions, flood durations, average amount of annual precipitations, size of affected areas and mean annual discharge). The second part of the analysis complements the first by giving a basic insight into the prediction skill of the general streamflow. For this, hydrological predictions were compared against observations at 36 key locations across Africa and the Continuous Rank Probability Skill Score (CRPSS), the limit of predictability and reliability were calculated. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas

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.

3D cloud detection and tracking system for solar forecast using multiple sky imagers

DOE PAGES

Peng, Zhenzhou; Yu, Dantong; Huang, Dong; ...

2015-06-23

We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together intomore » larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.« less

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.

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

Verification of Ensemble Forecasts for the New York City Operations Support Tool

NASA Astrophysics Data System (ADS)

Day, G.; Schaake, J. C.; Thiemann, M.; Draijer, S.; Wang, L.

2012-12-01

The New York City water supply system operated by the Department of Environmental Protection (DEP) serves nine million people. It covers 2,000 square miles of portions of the Catskill, Delaware, and Croton watersheds, and it includes nineteen reservoirs and three controlled lakes. DEP is developing an Operations Support Tool (OST) to support its water supply operations and planning activities. OST includes historical and real-time data, a model of the water supply system complete with operating rules, and lake water quality models developed to evaluate alternatives for managing turbidity in the New York City Catskill reservoirs. OST will enable DEP to manage turbidity in its unfiltered system while satisfying its primary objective of meeting the City's water supply needs, in addition to considering secondary objectives of maintaining ecological flows, supporting fishery and recreation releases, and mitigating downstream flood peaks. The current version of OST relies on statistical forecasts of flows in the system based on recent observed flows. To improve short-term decision making, plans are being made to transition to National Weather Service (NWS) ensemble forecasts based on hydrologic models that account for short-term weather forecast skill, longer-term climate information, as well as the hydrologic state of the watersheds and recent observed flows. To ensure that the ensemble forecasts are unbiased and that the ensemble spread reflects the actual uncertainty of the forecasts, a statistical model has been developed to post-process the NWS ensemble forecasts to account for hydrologic model error as well as any inherent bias and uncertainty in initial model states, meteorological data and forecasts. The post-processor is designed to produce adjusted ensemble forecasts that are consistent with the DEP historical flow sequences that were used to develop the system operating rules. A set of historical hindcasts that is representative of the real-time ensemble

Validation of the CME Geomagnetic forecast alerts under COMESEP alert system

NASA Astrophysics Data System (ADS)

Dumbovic, Mateja; Srivastava, Nandita; Khodia, Yamini; Vršnak, Bojan; Devos, Andy; Rodriguez, Luciano

2017-04-01

An automated space weather alert system has been developed under the EU FP7 project COMESEP (COronal Mass Ejections and Solar Energetic Particles: http://comesep.aeronomy.be) to forecast solar energetic particles (SEP) and coronal mass ejection (CME) risk levels at Earth. COMESEP alert system uses automated detection tool CACTus to detect potentially threatening CMEs, drag-based model (DBM) to predict their arrival and CME geo-effectiveness tool (CGFT) to predict their geomagnetic impact. Whenever CACTus detects a halo or partial halo CME and issues an alert, DBM calculates its arrival time at Earth and CGFT calculates its geomagnetic risk level. Geomagnetic risk level is calculated based on an estimation of the CME arrival probability and its likely geo-effectiveness, as well as an estimate of the geomagnetic-storm duration. We present the evaluation of the CME risk level forecast with COMESEP alert system based on a study of geo-effective CMEs observed during 2014. The validation of the forecast tool is done 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 DBM and CGFT (self standing tools available at Hvar Observatory website: http://oh.geof.unizg.hr). The results implicate that the success rate of the forecast is higher with human intervention and using more advanced tools. This work has received funding from the European Commission FP7 Project COMESEP (263252). We acknowledge the support of Croatian Science Foundation under the project 6212 „Solar and Stellar Variability".

Convective Weather Forecast Quality Metrics for Air Traffic Management Decision-Making

NASA Technical Reports Server (NTRS)

Chatterji, Gano B.; Gyarfas, Brett; Chan, William N.; Meyn, Larry A.

2006-01-01

the process described in Refs. 5 through 7, in terms of percentage coverage or confidence level is notionally sound compared to characterizing in terms of probabilities because the probability of the forecast being correct can only be determined using actual observations. References 5 through 7 only use the forecast data and not the observations. The method for computing the probability of detection, false alarm ratio and several forecast quality metrics (Skill Scores) using both the forecast and observation data are given in Ref. 2. This paper extends the statistical verification method in Ref. 2 to determine co-occurrence probabilities. The method consists of computing the probability that a severe weather cell (grid location) is detected in the observation data in the neighborhood of the severe weather cell in the forecast data. Probabilities of occurrence at the grid location and in its neighborhood with higher severity, and with lower severity in the observation data compared to that in the forecast data are examined. The method proposed in Refs. 5 through 7 is used for computing the probability that a certain number of cells in the neighborhood of severe weather cells in the forecast data are seen as severe weather cells in the observation data. Finally, the probability of existence of gaps in the observation data in the neighborhood of severe weather cells in forecast data is computed. Gaps are defined as openings between severe weather cells through which an aircraft can safely fly to its intended destination. The rest of the paper is organized as follows. Section II summarizes the statistical verification method described in Ref. 2. The extension of this method for computing the co-occurrence probabilities in discussed in Section HI. Numerical examples using NCWF forecast data and NCWD observation data are presented in Section III to elucidate the characteristics of the co-occurrence probabilities. This section also discusses the procedure for computing

An Evaluation of Real-time Air Quality Forecasts and their Urban Emissions over Eastern Texas During the Summer of 2006 Second Texas Air Quality Study Field Study

EPA Science Inventory

Forecasts of ozone (O₃) and particulate matter (diameter less than 2.5 µm, PM_2.5) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during August and September of 2006 (49 days) through the AIRNow netwo...

A national-scale seasonal hydrological forecast system: development and evaluation over Britain

NASA Astrophysics Data System (ADS)

Bell, Victoria A.; Davies, Helen N.; Kay, Alison L.; Brookshaw, Anca; Scaife, Adam A.

2017-09-01

Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for ˜ 70 % of the

A computerized system to measure and predict air quality for emission control

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

Crooks, G.; Ciccone, A.; Frattolillo, P.

1997-12-31

A Supplementary Emission Control (SEC) system has been developed on behalf of the Association Industrielle de l`Est de Montreal (AIEM). The objective of the SEC is to avoid exceedences of the Montreal Urban Community (MUC) 24 hour ambient Air Quality Standard (AQS) for sulphur dioxide in the industrial East Montreal area. The SEC system is comprised of: 3 continuous SO{sub 2} monitoring stations with data loggers and remote communications; a meteorological tower with data logger and modem for acquiring local meteorology; communications with Environment Canada to download meteorological forecast data; a polling PC for data retrieval; and Windows NT basedmore » software running on the AIEM computer server. The SEC software utilizes relational databases to store and maintain measured SO{sub 2} concentration data, emission data, as well as observed and forecast meteorological data. The SEC system automatically executes a numerical dispersion model to forecast SO{sub 2} concentrations up to six hours in the future. Based on measured SO{sub 2} concentrations at the monitoring stations and the six hour forecast concentrations, the system determines if local sources should reduce their emission levels to avoid potential exceedences of the AQS. The SEC system also includes a Graphical User Interface (GUI) for user access to the system. The SEC system and software are described, and the accuracy of the system at forecasting SO{sub 2} concentrations is examined.« less

Performance of an Advanced MOS System in the 1996-97 National Collegiate Weather Forecasting Contest.

NASA Astrophysics Data System (ADS)

Vislocky, Robert L.; Fritsch, J. Michael

1997-12-01

A prototype advanced model output statistics (MOS) forecast system that was entered in the 1996-97 National Collegiate Weather Forecast Contest is described and its performance compared to that of widely available objective guidance and to contest participants. The prototype system uses an optimal blend of aviation (AVN) and nested grid model (NGM) MOS forecasts, explicit output from the NGM and Eta guidance, and the latest surface weather observations from the forecast site. The forecasts are totally objective and can be generated quickly on a personal computer. Other "objective" forms of guidance tracked in the contest are 1) the consensus forecast (i.e., the average of the forecasts from all of the human participants), 2) the combination of NGM raw output (for precipitation forecasts) and NGM MOS guidance (for temperature forecasts), and 3) the combination of Eta Model raw output (for precipitation forecasts) and AVN MOS guidance (for temperature forecasts).Results show that the advanced MOS system finished in 20th place out of 737 original entrants, or better than approximately 97% of the human forecasters who entered the contest. Moreover, the advanced MOS system was slightly better than consensus (23d place). The fact that an objective forecast system finished ahead of consensus is a significant accomplishment since consensus is traditionally a very formidable "opponent" in forecast competitions. Equally significant is that the advanced MOS system was superior to the traditional guidance products available from the National Centers for Environmental Prediction (NCEP). Specifically, the combination of NGM raw output and NGM MOS guidance finished in 175th place, and the combination of Eta Model raw output and AVN MOS guidance finished in 266th place. The latter result is most intriguing since the proposed elimination of all NGM products would likely result in a serious degradation of objective products disseminated by NCEP, unless they are replaced with equal

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.

The skill of ECMWF long range Forecasting System to drive impact models for health and hydrology in Africa

NASA Astrophysics Data System (ADS)

Di Giuseppe, F.; Tompkins, A. M.; Lowe, R.; Dutra, E.; Wetterhall, F.

2012-04-01

As the quality of numerical weather prediction over the monthly to seasonal leadtimes steadily improves there is an increasing motivation to apply these fruitfully to the impacts sectors of health, water, energy and agriculture. Despite these improvements, the accuracy of fields such as temperature and precipitation that are required to drive sectoral models can still be poor. This is true globally, but particularly so in Africa, the region of focus in the present study. In the last year ECMWF has been particularly active through EU research founded projects in demonstrating the capability of its longer range forecasting system to drive impact modeling systems in this region. A first assessment on the consequences of the documented errors in ECMWF forecasting system is therefore presented here looking at two different application fields which we found particularly critical for Africa - vector-born diseases prevention and hydrological monitoring. A new malaria community model (VECTRI) has been developed at ICTP and tested for the 3 target regions participating in the QWECI project. The impacts on the mean malaria climate is assessed using the newly realized seasonal forecasting system (Sys4) with the dismissed system 3 (Sys3) which had the same model cycle of the up-to-date ECMWF re-analysis product (ERA-Interim). The predictive skill of Sys4 to be employed for malaria monitoring and forecast are also evaluated by aggregating the fields to country level. As a part of the DEWFORA projects, ECMWF is also developing a system for drought monitoring and forecasting over Africa whose main meteorological input is precipitation. Similarly to what is done for the VECTRI model, the skill of seasonal forecasts of precipitation is, in this application, translated into the capability of predicting drought while ERA-Interim is used in monitoring. On a monitoring level, the near real-time update of ERA-Interim could compensate the lack of observations in the regions. However, ERA

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...

Seasonal Climate Forecasts and Adoption by Agriculture

NASA Astrophysics Data System (ADS)

Garbrecht, Jurgen; Meinke, Holger; Sivakumar, Mannava V. K.; Motha, Raymond P.; Salinger, Michael J.

2005-06-01

Recent advances in atmospheric and ocean sciences and a better understanding of the global climate have led to skillful climate forecasts at seasonal to interannual timescales, even in midlatitudes. These scientific advances and forecasting capabilities have opened the door to practical applications that benefit society. The benefits include the reduction of weather/climate related risks and vulnerability, increased economic opportunities, enhanced food security, mitigation of adverse climate impacts, protection of environmental quality, and so forth. Agriculture in particular can benefit substantially from accurate long-lead seasonal climate forecasts. Indeed, agricultural production very much depends on weather, climate, and water availability, and unexpected departures from anticipated climate conditions can thwart the best laid management plans. Timely climate forecasts offer means to reduce losses in drought years, increase profitability in good years, deal more effectively with climate variability, and choose from targeted risk-management strategies. In addition to benefiting farmers, forecasts can also help marketing systems and downstream users prepare for anticipated production outcomes and associated consequences.

A Real-time Irrigation Forecasting System in Jiefangzha Irrigation District, China

NASA Astrophysics Data System (ADS)

Cong, Z.

2015-12-01

In order to improve the irrigation efficiency, we need to know when and how much to irrigate in real time. If we know the soil moisture content at this time, we can forecast the soil moisture content in the next days based on the rainfall forecasting and the crop evapotranspiration forecasting. Then the irrigation should be considered when the forecasting soil moisture content reaches to a threshold. Jiefangzha Irrigation District, a part of Hetao Irrigation District, is located in Inner Mongolia, China. The irrigated area of this irrigation district is about 140,000 ha mainly planting wheat, maize and sunflower. The annual precipitation is below 200mm, so the irrigation is necessary and the irrigation water comes from the Yellow river. We set up 10 sites with 4 TDR sensors at each site (20cm, 40cm, 60cm and 80cm depth) to monitor the soil moisture content. The weather forecasting data are downloaded from the website of European Centre for Medium-Range Weather Forecasts (ECMWF). The reference evapotranspiration is estimated based on FAO-Blaney-Criddle equation with only the air temperature from ECMWF. Then the crop water requirement is forecasted by the crop coefficient multiplying the reference evapotranspiration. Finally, the soil moisture content is forecasted based on soil water balance with the initial condition is set as the monitoring soil moisture content. When the soil moisture content reaches to a threshold, the irrigation warning will be announced. The irrigation mount can be estimated through three ways: (1) making the soil moisture content be equal to the field capacity; (2) making the soil moisture saturated; or (3) according to the irrigation quota. The forecasting period is 10 days. The system is developed according to B2C model with Java language. All the databases and the data analysis are carried out in the server. The customers can log in the website with their own username and password then get the information about the irrigation forecasting

A quantitative comparison of precipitation forecasts between the storm-scale numerical weather prediction model and auto-nowcast system in Jiangsu, China

NASA Astrophysics Data System (ADS)

Wang, Gaili; Yang, Ji; Wang, Dan; Liu, Liping

2016-11-01

Extrapolation techniques and storm-scale Numerical Weather Prediction (NWP) models are two primary approaches for short-term precipitation forecasts. The primary objective of this study is to verify precipitation forecasts and compare the performances of two nowcasting schemes: a Beijing Auto-Nowcast system (BJ-ANC) based on extrapolation techniques and a storm-scale NWP model called the Advanced Regional Prediction System (ARPS). The verification and comparison takes into account six heavy precipitation events that occurred in the summer of 2014 and 2015 in Jiangsu, China. The forecast performances of the two schemes were evaluated for the next 6 h at 1-h intervals using gridpoint-based measures of critical success index, bias, index of agreement, root mean square error, and using an object-based verification method called Structure-Amplitude-Location (SAL) score. Regarding gridpoint-based measures, BJ-ANC outperforms ARPS at first, but then the forecast accuracy decreases rapidly with lead time and performs worse than ARPS after 4-5 h of the initial forecast. Regarding the object-based verification method, most forecasts produced by BJ-ANC focus on the center of the diagram at the 1-h lead time and indicate high-quality forecasts. As the lead time increases, BJ-ANC overestimates precipitation amount and produces widespread precipitation, especially at a 6-h lead time. The ARPS model overestimates precipitation at all lead times, particularly at first.

Model-Aided Altimeter-Based Water Level Forecasting System in Mekong River

NASA Astrophysics Data System (ADS)

Chang, C. H.; Lee, H.; Hossain, F.; Okeowo, M. A.; Basnayake, S. B.; Jayasinghe, S.; Saah, D. S.; Anderson, E.; Hwang, E.

2017-12-01

Mekong River, one of the massive river systems in the world, has drainage area of about 795,000 km2 covering six countries. People living in its drainage area highly rely on resources given by the river in terms of agriculture, fishery, and hydropower. Monitoring and forecasting the water level in a timely manner, is urgently needed over the Mekong River. Recently, using TOPEX/Poseidon (T/P) altimetry water level measurements in India, Biancamaria et al. [2011] has demonstrated the capability of an altimeter-based flood forecasting system in Bangladesh, with RMSE from 0.6 - 0.8 m for lead times up to 5 days on 10-day basis due to T/P's repeat period. Hossain et al. [2013] further established a daily water level forecasting system in Bangladesh using observations from Jason-2 in India and HEC-RAS hydraulic model, with RMSE from 0.5 - 1.5 m and an underestimating mean bias of 0.25 - 1.25 m. However, such daily forecasting system relies on a collection of Jason-2 virtual stations (VSs) to ensure frequent sampling and data availability. Since the Mekong River is a meridional river with few number of VSs, the direct application of this system to the Mekong River becomes challenging. To address this problem, we propose a model-aided altimeter-based forecasting system. The discharge output by Variable Infiltration Capacity hydrologic model is used to reconstruct a daily water level product at upstream Jason-2 VSs based on the discharge-to-level rating curve. The reconstructed daily water level is then used to perform regression analysis with downstream in-situ water level to build regression models, which are used to forecast a daily water level. In the middle reach of the Mekong River from Nakhon Phanom to Kratie, a 3-day lead time forecasting can reach RMSE about 0.7 - 1.3 m with correlation coefficient around 0.95. For the lower reach of the Mekong River, the water flow becomes more complicated due to the reversal flow between the Tonle Sap Lake and the Mekong River

Sensitivity of monthly streamflow forecasts to the quality of rainfall forcing: When do dynamical climate forecasts outperform the Ensemble Streamflow Prediction (ESP) method?

NASA Astrophysics Data System (ADS)

Tanguy, M.; Prudhomme, C.; Harrigan, S.; Smith, K. A.; Parry, S.

2017-12-01

representative of the range of UK's hydro-climatic conditions. These forecasts were then benchmarked against the traditional ESP method. It is hoped that the results of this work will help the meteorological community to identify where to focus their efforts in order to increase the usefulness of their forecasts within hydrological forecasting systems.

A Performance Evaluation of the National Air Quality Forecast Capability for the Summer of 2007

EPA Science Inventory

This paper provides a performance evaluation of the real-time, CONUS-scale National Air Quality Forecast Capability (NAQFC), developed collaboratively by the National Oceanic and Atmospheric Administration (NOAA) and Environmental Protection Agency (EPA), that supported, in part,...

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

The Value of Humans in the Operational River Forecasting Enterprise

NASA Astrophysics Data System (ADS)

Pagano, T. C.

2012-04-01

The extent of human control over operational river forecasts, such as by adjusting model inputs and outputs, varies from nearly completely automated systems to those where forecasts are generated after discussion among a group of experts. Historical and realtime data availability, the complexity of hydrologic processes, forecast user needs, and forecasting institution support/resource availability (e.g. computing power, money for model maintenance) influence the character and effectiveness of operational forecasting systems. Automated data quality algorithms, if used at all, are typically very basic (e.g. checks for impossible values); substantial human effort is devoted to cleaning up forcing data using subjective methods. Similarly, although it is an active research topic, nearly all operational forecasting systems struggle to make quantitative use of Numerical Weather Prediction model-based precipitation forecasts, instead relying on the assessment of meteorologists. Conversely, while there is a strong tradition in meteorology of making raw model outputs available to forecast users via the Internet, this is rarely done in hydrology; Operational river forecasters express concerns about exposing users to raw guidance, due to the potential for misinterpretation and misuse. However, this limits the ability of users to build their confidence in operational products through their own value-added analyses. Forecasting agencies also struggle with provenance (i.e. documenting the production process and archiving the pieces that went into creating a forecast) although this is necessary for quantifying the benefits of human involvement in forecasting and diagnosing weak links in the forecasting chain. In hydrology, the space between model outputs and final operational products is nearly unstudied by the academic community, although some studies exist in other fields such as meteorology.

Emissions Inventory of Anthropogenic PM2.5 and PM10 in Mega city, Delhi, India for Air Quality Forecasting during CWG- 2010

NASA Astrophysics Data System (ADS)

Sahu, S.; Beig, G.; Schultz, M.; Parkhi, N.; Stein, O.

2012-04-01

The mega city of Delhi is the second largest urban agglomeration in India with 16.7 mio. inhabitants. Delhi has the highest per capita power consumption of electricity in India and the demand has risen by more than 50% during the last decade. Emissions from commercial, power, domestic and industrial sectors have strongly increased causing more and more environmental problems due to air pollution and its adverse impacts on human health. Particulate matter (PM) of size less than 2.5-micron (PM2.5) and 10 micron (PM10) have emerged as primary pollutants of concern due to their adverse impact on human health. As part of the System of Air quality Forecasting and Research (SAFAR) project developed for air quality forecasting during the Commonwealth Games (CWG) - 2010, a high resolution Emission Inventory (EI) of PM10 and PM2.5 has been developed for the metropolitan city Delhi for the year 2010. The comprehensive inventory involves detailed activity data and has been developed for a domain of 70km×65km with a 1.67km×1.67km resolution covering Delhi and its surrounding region (i.e. National Capital Region (NCR)). In creating this inventory, Geographical Information System (GIS) based techniques were used for the first time in India. The major sectors considered are, transport, thermal power plants, industries, residential and commercial cooking along with windblown road dust which is found to play a major role for the megacity environment. Extensive surveys were conducted among the Delhi slum dwellers (Jhuggi) in order to obtain more robust estimates for the activity data related to domestic cooking and heating. Total emissions of PM10 and PM2.5 including wind blown dust over the study area are found to be 236 Gg/yr and 94 Gg/yr respectively. About half of the PM10 emissions stem from windblown road dust. The new emission inventory has been used for regional air quality forecasts in the Delhi region during the Commonwealth games (SAFAR project), and they will soon be

METEOR - an artificial intelligence system for convective storm forecasting

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

Elio, R.; De haan, J.; Strong, G.S.

1987-03-01

An AI system called METEOR, which uses the meteorologist's heuristics, strategies, and statistical tools to forecast severe hailstorms in Alberta, is described, emphasizing the information and knowledge that METEOR uses to mimic the forecasting procedure of an expert meteorologist. METEOR is then discussed as an AI system, emphasizing the ways in which it is qualitatively different from algorithmic or statistical approaches to prediction. Some features of METEOR's design and the AI techniques for representing meteorological knowledge and for reasoning and inference are presented. Finally, some observations on designing and implementing intelligent consultants for meteorological applications are made. 7 references.

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

Spectral Analysis of Forecast Error Investigated with an Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, N. C.; Errico, Ronald M.

2015-01-01

The spectra of analysis and forecast error are examined using the observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASAGMAO). A global numerical weather prediction model, the Global Earth Observing System version 5 (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation, is cycled for two months with once-daily forecasts to 336 hours to generate a control case. Verification of forecast errors using the Nature Run as truth is compared with verification of forecast errors using self-analysis; significant underestimation of forecast errors is seen using self-analysis verification for up to 48 hours. Likewise, self analysis verification significantly overestimates the error growth rates of the early forecast, as well as mischaracterizing the spatial scales at which the strongest growth occurs. The Nature Run-verified error variances exhibit a complicated progression of growth, particularly for low wave number errors. In a second experiment, cycling of the model and data assimilation over the same period is repeated, but using synthetic observations with different explicitly added observation errors having the same error variances as the control experiment, thus creating a different realization of the control. The forecast errors of the two experiments become more correlated during the early forecast period, with correlations increasing for up to 72 hours before beginning to decrease.

Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 1. Experimental design and forcing verification

NASA Astrophysics Data System (ADS)

Brown, James D.; Wu, Limin; He, Minxue; Regonda, Satish; Lee, Haksu; Seo, Dong-Jun

2014-11-01

Retrospective forecasts of precipitation, temperature, and streamflow were generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) for a 20-year period between 1979 and 1999. The hindcasts were produced for two basins in each of four River Forecast Centers (RFCs), namely the Arkansas-Red Basin RFC, the Colorado Basin RFC, the California-Nevada RFC, and the Middle Atlantic RFC. Precipitation and temperature forecasts were produced with the HEFS Meteorological Ensemble Forecast Processor (MEFP). Inputs to the MEFP comprised ;raw; precipitation and temperature forecasts from the frozen (circa 1997) version of the NWS Global Forecast System (GFS) and a climatological ensemble, which involved resampling historical observations in a moving window around the forecast valid date (;resampled climatology;). In both cases, the forecast horizon was 1-14 days. This paper outlines the hindcasting and verification strategy, and then focuses on the quality of the temperature and precipitation forecasts from the MEFP. A companion paper focuses on the quality of the streamflow forecasts from the HEFS. In general, the precipitation forecasts are more skillful than resampled climatology during the first week, but comprise little or no skill during the second week. In contrast, the temperature forecasts improve upon resampled climatology at all forecast lead times. However, there are notable differences among RFCs and for different seasons, aggregation periods and magnitudes of the observed and forecast variables, both for precipitation and temperature. For example, the MEFP-GFS precipitation forecasts show the highest correlations and greatest skill in the California Nevada RFC, particularly during the wet season (November-April). While generally reliable, the MEFP forecasts typically underestimate the largest observed precipitation amounts (a Type-II conditional bias). As a statistical technique, the MEFP cannot detect, and thus

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.

Impact of chemical lateral boundary conditions in a regional air quality forecast model on surface ozone predictions during stratospheric intrusions

NASA Astrophysics Data System (ADS)

Pendlebury, Diane; Gravel, Sylvie; Moran, Michael D.; Lupu, Alexandru

2018-02-01

A regional air quality forecast model, GEM-MACH, is used to examine the conditions under which a limited-area air quality model can accurately forecast near-surface ozone concentrations during stratospheric intrusions. Periods in 2010 and 2014 with known stratospheric intrusions over North America were modelled using four different ozone lateral boundary conditions obtained from a seasonal climatology, a dynamically-interpolated monthly climatology, global air quality forecasts, and global air quality reanalyses. It is shown that the mean bias and correlation in surface ozone over the course of a season can be improved by using time-varying ozone lateral boundary conditions, particularly through the correct assignment of stratospheric vs. tropospheric ozone along the western lateral boundary (for North America). Part of the improvement in surface ozone forecasts results from improvements in the characterization of near-surface ozone along the lateral boundaries that then directly impact surface locations near the boundaries. However, there is an additional benefit from the correct characterization of the location of the tropopause along the western lateral boundary such that the model can correctly simulate stratospheric intrusions and their associated exchange of ozone from stratosphere to troposphere. Over a three-month period in spring 2010, the mean bias was seen to improve by as much as 5 ppbv and the correlation by 0.1 depending on location, and on the form of the chemical lateral boundary condition.

Superensemble forecasts of dengue outbreaks

PubMed Central

Kandula, Sasikiran; Shaman, Jeffrey

2016-01-01

In recent years, a number of systems capable of predicting future infectious disease incidence have been developed. As more of these systems are operationalized, it is important that the forecasts generated by these different approaches be formally reconciled so that individual forecast error and bias are reduced. Here we present a first example of such multi-system, or superensemble, forecast. We develop three distinct systems for predicting dengue, which are applied retrospectively to forecast outbreak characteristics in San Juan, Puerto Rico. We then use Bayesian averaging methods to combine the predictions from these systems and create superensemble forecasts. We demonstrate that on average, the superensemble approach produces more accurate forecasts than those made from any of the individual forecasting systems. PMID:27733698

Shared Investment Projects and Forecasting Errors: Setting Framework Conditions for Coordination and Sequencing Data Quality Activities

PubMed Central

Leitner, Stephan; Brauneis, Alexander; Rausch, Alexandra

2015-01-01

In this paper, we investigate the impact of inaccurate forecasting on the coordination of distributed investment decisions. In particular, by setting up a computational multi-agent model of a stylized firm, we investigate the case of investment opportunities that are mutually carried out by organizational departments. The forecasts of concern pertain to the initial amount of money necessary to launch and operate an investment opportunity, to the expected intertemporal distribution of cash flows, and the departments’ efficiency in operating the investment opportunity at hand. We propose a budget allocation mechanism for coordinating such distributed decisions The paper provides guidance on how to set framework conditions, in terms of the number of investment opportunities considered in one round of funding and the number of departments operating one investment opportunity, so that the coordination mechanism is highly robust to forecasting errors. Furthermore, we show that—in some setups—a certain extent of misforecasting is desirable from the firm’s point of view as it supports the achievement of the corporate objective of value maximization. We then address the question of how to improve forecasting quality in the best possible way, and provide policy advice on how to sequence activities for improving forecasting quality so that the robustness of the coordination mechanism to errors increases in the best possible way. At the same time, we show that wrong decisions regarding the sequencing can lead to a decrease in robustness. Finally, we conduct a comprehensive sensitivity analysis and prove that—in particular for relatively good forecasters—most of our results are robust to changes in setting the parameters of our multi-agent simulation model. PMID:25803736

4 km Forecasting System to Support DISCOVER-AQ Campaigns: Model Configuration, Testing and Evaluation

NASA Astrophysics Data System (ADS)

Lee, P.; Pan, L.; Kim, H. C.; Chai, T.; Hu, Y.; Tong, D.; Ngan, F.; Wong, D.; Dornblaser, B.; Tanrikulu, S.; Pickering, K. E.

2012-12-01

This work presents the development and evaluation of a high-resolution air quality forecasting system to support two NASA Earth Venture campaigns (DISCOVER-AQ) in 2013. These campaigns aim to further understanding of column-integrated and vertically resolved observations in determining air pollution conditions near the surface (http://science.nasa.gov/missions/discover-aq/). The first one will be carried out in San Joaquin Valley (SJV) in winter and the second one in Houston (HOU) area in late summer. Accurate forecast of pollution plumes is critical for on-site deployment and co-ordination of the various observation platforms. We develop of a fine resolution forecasting system to provide dynamic prediction of the chemical fields over these regions. This system utilizes meteorology fields from the US National Centers for Environmental Prediction North American Model (NAM) that is equipped with an elaborative NAM Data Assimilation System (NDAS) for its Land Surface Model (LSM) and initialization processes. NAM output is used to drive the US EPA Community Multi-scale Air Quality Model (CMAQ) with identical horizontal resolution. The SJV campaign is believed to be subjected to rather high particulate matter loading and possible frequent occurrence of multiple-day fog. NDAS provides advanced methodology to constrain atmospheric stability and soil moisture characteristics. These meteorological parameters are critical for the winter campaign. Special attention is paid to emission modeling for agricultural dust aerosols, which were found important for the SJV area. In contrast to the winter campaign where strong atmospheric stability will likely be a challenge, the HOU campaign in September of 2013 will be challenged with strong atmospheric convection and rather rapid growth of and a sustained deep Planetary Boundary Layer (PBL) during mid-morning and afternoon, respectively. Convection often results in lightning. Wild-fires can contribute significantly to pollution

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

The impact of implementing a demand forecasting system into a low-income country's supply chain.

PubMed

Mueller, Leslie E; Haidari, Leila A; Wateska, Angela R; Phillips, Roslyn J; Schmitz, Michelle M; Connor, Diana L; Norman, Bryan A; Brown, Shawn T; Welling, Joel S; Lee, Bruce Y

2016-07-12

To evaluate the potential impact and value of applications (e.g. adjusting ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country's vaccine supply chain with different levels of population change to urban areas. Using our software, HERMES, we generated a detailed discrete event simulation model of Niger's entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. Demand forecasting systems have the potential to greatly improve vaccine demand fulfilment, and decrease logistics cost/dose when implemented with storage and transportation increases. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements. Copyright �

The Impact of Atmospheric InfraRed Sounder (AIRS) Profiles on Short-term Weather Forecasts

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Brad; Jedlovec, Gary J.; Lapenta, William

2007-01-01

The Atmospheric Infrared Sounder (AIRS), together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced spacebased atmospheric sounding systems. The combined AlRS/AMSU system provides radiance measurements used to retrieve temperature profiles with an accuracy of 1 K over 1 km layers under both clear and partly cloudy conditions, while the accuracy of the derived humidity profiles is 15% in 2 km layers. Critical to the successful use of AIRS profiles for weather and climate studies is the use of profile quality indicators and error estimates provided with each profile Aside form monitoring changes in Earth's climate, one of the objectives of AIRS is to provide sounding information of sufficient accuracy such that the assimilation of the new observations, especially in data sparse region, will lead to an improvement in weather forecasts. The purpose of this paper is to describe a procedure to optimally assimilate highresolution AIRS profile data in a regional analysis/forecast model. The paper will focus on the impact of AIRS profiles on a rapidly developing east coast storm and will also discuss preliminary results for a 30-day forecast period, simulating a quasi-operation environment. Temperature and moisture profiles were obtained from the prototype version 5.0 EOS science team retrieval algorithm which includes explicit error information for each profile. The error profile information was used to select the highest quality temperature and moisture data for every profile location and pressure level for assimilation into the ARPS Data Analysis System (ADAS). The AIRS-enhanced analyses were used as initial fields for the Weather Research and Forecast (WRF) system used by the SPORT project for regional weather forecast studies. The ADASWRF system will be run on CONUS domain with an emphasis on the east coast. The preliminary assessment of the impact of the AIRS profiles will focus on quality control issues associated with AIRS

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.

Operational monitoring and forecasting of bathing water quality through exploiting satellite Earth observation and models: The AlgaRisk demonstration service

NASA Astrophysics Data System (ADS)

Shutler, J. D.; Warren, M. A.; Miller, P. I.; Barciela, R.; Mahdon, R.; Land, P. E.; Edwards, K.; Wither, A.; Jonas, P.; Murdoch, N.; Roast, S. D.; Clements, O.; Kurekin, A.

2015-04-01

Coastal zones and shelf-seas are important for tourism, commercial fishing and aquaculture. As a result the importance of good water quality within these regions to support life is recognised worldwide and a number of international directives for monitoring them now exist. This paper describes the AlgaRisk water quality monitoring demonstration service that was developed and operated for the UK Environment Agency in response to the microbiological monitoring needs within the revised European Union Bathing Waters Directive. The AlgaRisk approach used satellite Earth observation to provide a near-real time monitoring of microbiological water quality and a series of nested operational models (atmospheric and hydrodynamic-ecosystem) provided a forecast capability. For the period of the demonstration service (2008-2013) all monitoring and forecast datasets were processed in near-real time on a daily basis and disseminated through a dedicated web portal, with extracted data automatically emailed to agency staff. Near-real time data processing was achieved using a series of supercomputers and an Open Grid approach. The novel web portal and java-based viewer enabled users to visualise and interrogate current and historical data. The system description, the algorithms employed and example results focussing on a case study of an incidence of the harmful algal bloom Karenia mikimotoi are presented. Recommendations and the potential exploitation of web services for future water quality monitoring services are discussed.

Validation of Seasonal Forecast of Indian Summer Monsoon Rainfall

NASA Astrophysics Data System (ADS)

Das, Sukanta Kumar; Deb, Sanjib Kumar; Kishtawal, C. M.; Pal, Pradip Kumar

2015-06-01

The experimental seasonal forecast of Indian summer monsoon (ISM) rainfall during June through September using Community Atmosphere Model (CAM) version 3 has been carried out at the Space Applications Centre Ahmedabad since 2009. The forecasts, based on a number of ensemble members (ten minimum) of CAM, are generated in several phases and updated on regular basis. On completion of 5 years of experimental seasonal forecasts in operational mode, it is required that the overall validation or correctness of the forecast system is quantified and that the scope is assessed for further improvements of the forecast over time, if any. The ensemble model climatology generated by a set of 20 identical CAM simulations is considered as the model control simulation. The performance of the forecast has been evaluated by assuming the control simulation as the model reference. The forecast improvement factor shows positive improvements, with higher values for the recent forecasted years as compared to the control experiment over the Indian landmass. The Taylor diagram representation of the Pearson correlation coefficient (PCC), standard deviation and centered root mean square difference has been used to demonstrate the best PCC, in the order of 0.74-0.79, recorded for the seasonal forecast made during 2013. Further, the bias score of different phases of experiment revealed the fact that the ISM rainfall forecast is affected by overestimation in predicting the low rain-rate (less than 7 mm/day), but by underestimation in the medium and high rain-rate (higher than 11 mm/day). Overall, the analysis shows significant improvement of the ISM forecast over the last 5 years, viz. 2009-2013, due to several important modifications that have been implemented in the forecast system. The validation exercise has also pointed out a number of shortcomings in the forecast system; these will be addressed in the upcoming years of experiments to improve the quality of the ISM prediction.

Forecasting the quality of water-suppressed 1 H MR spectra based on a single-shot water scan.

PubMed

Kyathanahally, Sreenath P; Kreis, Roland

2017-08-01

To investigate whether an initial non-water-suppressed acquisition that provides information about the signal-to-noise ratio (SNR) and linewidth is enough to forecast the maximally achievable final spectral quality and thus inform the operator whether the foreseen number of averages and achieved field homogeneity is adequate. A large range of spectra with varying SNR and linewidth was simulated and fitted with popular fitting programs to determine the dependence of fitting errors on linewidth and SNR. A tool to forecast variance based on a single acquisition was developed and its performance evaluated on simulated and in vivo data obtained at 3 Tesla from various brain regions and acquisition settings. A strong correlation to real uncertainties in estimated metabolite contents was found for the forecast values and the Cramer-Rao lower bounds obtained from the water-suppressed spectra. It appears to be possible to forecast the best-case errors associated with specific metabolites to be found in model fits of water-suppressed spectra based on a single water scan. Thus, nonspecialist operators will be able to judge ahead of time whether the planned acquisition can possibly be of sufficient quality to answer the targeted clinical question or whether it needs more averages or improved shimming. Magn Reson Med 78:441-451, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Evolving forecasting classifications and applications in health forecasting

PubMed Central

Soyiri, Ireneous N; Reidpath, Daniel D

2012-01-01

Health forecasting forewarns the health community about future health situations and disease episodes so that health systems can better allocate resources and manage demand. The tools used for developing and measuring the accuracy and validity of health forecasts commonly are not defined although they are usually adapted forms of statistical procedures. This review identifies previous typologies used in classifying the forecasting methods commonly used in forecasting health conditions or situations. It then discusses the strengths and weaknesses of these methods and presents the choices available for measuring the accuracy of health-forecasting models, including a note on the discrepancies in the modes of validation. PMID:22615533

Analyses and forecasts of a tornadic supercell outbreak using a 3DVAR system ensemble

NASA Astrophysics Data System (ADS)

Zhuang, Zhaorong; Yussouf, Nusrat; Gao, Jidong

2016-05-01

As part of NOAA's "Warn-On-Forecast" initiative, a convective-scale data assimilation and prediction system was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The system was then evaluated using retrospective short-range ensemble analyses and probabilistic forecasts of the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA. A 36-member multi-physics ensemble system provided the initial and boundary conditions for a 3-km convective-scale ensemble system. Radial velocity and reflectivity observations from four WSR-88Ds were assimilated into the ensemble using the ARPS 3DVAR technique. Five data assimilation and forecast experiments were conducted to evaluate the sensitivity of the system to data assimilation frequencies, in-cloud temperature adjustment schemes, and fixed- and mixed-microphysics ensembles. The results indicated that the experiment with 5-min assimilation frequency quickly built up the storm and produced a more accurate analysis compared with the 10-min assimilation frequency experiment. The predicted vertical vorticity from the moist-adiabatic in-cloud temperature adjustment scheme was larger in magnitude than that from the latent heat scheme. Cycled data assimilation yielded good forecasts, where the ensemble probability of high vertical vorticity matched reasonably well with the observed tornado damage path. Overall, the results of the study suggest that the 3DVAR analysis and forecast system can provide reasonable forecasts of tornadic supercell storms.

Forecasting approaches to the Mekong River

NASA Astrophysics Data System (ADS)

Plate, E. J.

2009-04-01

other hand, have as objective to obtain an accurate hydrograph of the near future. The method by means of which this is done is not as important as the accuracy of the forecast. A mathematical rainfall-runoff model is not necessarily a good forecast model. It has to be very carefully designed, and in many cases statistical models are found to give better results than mathematical models. Forecasters have the advantage of knowing the course of the hydrographs up to the point in time where forecasts have to be made. Therefore, models can be calibrated on line against the hydrograph of the immediate past. To assess the quality of a forecast, the quality criterion should not be based on the mean value, as does the Nash-Sutcliffe criterion, but should be based on the best forecast given the information up to the forecast time. Without any additional information, the best forecast when only the present day value is known is to assume a no-change scenario, i.e. to assume that the present value does not change in the immediate future. For the Mekong there exists a forecasting system which is based on a rainfall-runoff model operated by the Mekong River Commission. This model is found not to be adequate for forecasting for periods longer than one or two days ahead. Improvements are sought through two approaches: a strictly deterministic rainfall-runoff model, and a strictly statistical model based on regression with upstream stations. The two approaches are com-pared, and suggestions are made how to best combine the advantages of both approaches. This requires that due consideration is given to critical hydraulic conditions of the river at and in between the gauging stations. Critical situations occur in two ways: when the river overtops, in which case the rainfall-runoff model is incomplete unless overflow losses are considered, and at the confluence with tributaries. Of particular importance is the role of the large Tonle Sap Lake, which dampens the hydrograph downstream of

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

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.

Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang

PubMed Central

Liu, Bing-Chun; Binaykia, Arihant; Chang, Pei-Chann; Tiwari, Manoj Kumar; Tsao, Cheng-Chin

2017-01-01

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction. PMID:28708836

Toward a Marine Ecological Forecasting System

DTIC Science & Technology

2010-06-01

advance. Two beaches in Lake Michigan have been selected for initial implementation. Forecasting Coral Bleaching in relation to Ocean Temperatures...The coral reef is a unique and very rich ecosystem which supports a vast array of animal and plant species. Corals form the structural and...ecological foundation of the reef system, and consist of a symbiotic relationship between the coral animal (polyp) and associated algae (zooxanthellae

Improved Weather Forecasting for the Dynamic Scheduling System of the Green Bank Telescope

NASA Astrophysics Data System (ADS)

Henry, Kari; Maddalena, Ronald

2018-01-01

The Robert C Byrd Green Bank Telescope (GBT) uses a software system that dynamically schedules observations based on models of vertical weather forecasts produced by the National Weather Service (NWS). The NWS provides hourly forecasted values for ~60 layers that extend into the stratosphere over the observatory. We use models, recommended by the Radiocommunication Sector of the International Telecommunications Union, to derive the absorption coefficient in each layer for each hour in the NWS forecasts and for all frequencies over which the GBT has receivers, 0.1 to 115 GHz. We apply radiative transfer models to derive the opacity and the atmospheric contributions to the system temperature, thereby deriving forecasts applicable to scheduling radio observations for up to 10 days into the future. Additionally, the algorithms embedded in the data processing pipeline use historical values of the forecasted opacity to calibrate observations. Until recently, we have concentrated on predictions for high frequency (> 15 GHz) observing, as these need to be scheduled carefully around bad weather. We have been using simple models for the contribution of rain and clouds since we only schedule low-frequency observations under these conditions. In this project, we wanted to improve the scheduling of the GBT and data calibration at low frequencies by deriving better algorithms for clouds and rain. To address the limitation at low frequency, the observatory acquired a Radiometrics Corporation MP-1500A radiometer, which operates in 27 channels between 22 and 30 GHz. By comparing 16 months of measurements from the radiometer against forecasted system temperatures, we have confirmed that forecasted system temperatures are indistinguishable from those measured under good weather conditions. Small miss-calibrations of the radiometer data dominate the comparison. By using recalibrated radiometer measurements, we looked at bad weather days to derive better models for forecasting the

Development, Implementation, and Skill Assessment of the NOAA/NOS Great Lakes Operational Forecast System

DTIC Science & Technology

2011-01-01

USA) 2011 Abstract The NOAA Great Lakes Operational Forecast System ( GLOFS ) uses near-real-time atmospheric observa- tions and numerical weather...Operational Oceanographic Products and Services (CO-OPS) in Silver Spring, MD. GLOFS has been making operational nowcasts and forecasts at CO-OPS... GLOFS ) uses near-real-time atmospheric observations and numerical weather prediction forecast guidance to produce three-dimensional forecasts of water

Wave ensemble forecast in the Western Mediterranean Sea, application to an early warning system.

NASA Astrophysics Data System (ADS)

Pallares, Elena; Hernandez, Hector; Moré, Jordi; Espino, Manuel; Sairouni, Abdel

2015-04-01

The Western Mediterranean Sea is a highly heterogeneous and variable area, as is reflected on the wind field, the current field, and the waves, mainly in the first kilometers offshore. As a result of this variability, the wave forecast in these regions is quite complicated to perform, usually with some accuracy problems during energetic storm events. Moreover, is in these areas where most of the economic activities take part, including fisheries, sailing, tourism, coastal management and offshore renewal energy platforms. In order to introduce an indicator of the probability of occurrence of the different sea states and give more detailed information of the forecast to the end users, an ensemble wave forecast system is considered. The ensemble prediction systems have already been used in the last decades for the meteorological forecast; to deal with the uncertainties of the initial conditions and the different parametrizations used in the models, which may introduce some errors in the forecast, a bunch of different perturbed meteorological simulations are considered as possible future scenarios and compared with the deterministic forecast. In the present work, the SWAN wave model (v41.01) has been implemented for the Western Mediterranean sea, forced with wind fields produced by the deterministic Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS). The wind fields includes a deterministic forecast (also named control), between 11 and 21 ensemble members, and some intelligent member obtained from the ensemble, as the mean of all the members. Four buoys located in the study area, moored in coastal waters, have been used to validate the results. The outputs include all the time series, with a forecast horizon of 8 days and represented in spaghetti diagrams, the spread of the system and the probability at different thresholds. The main goal of this exercise is to be able to determine the degree of the uncertainty of the wave forecast, meaningful

A Real-time 1/16° Global Ocean Nowcast/Forecast System

NASA Astrophysics Data System (ADS)

Shriver, J. F.; Rhodes, R. C.; Hurlburt, H. E.; Wallcraft, A. J.; Metzger, E. J.; Smedstad, O. M.; Kara, A. B.

2001-05-01

A 1/16° eddy-resolving global ocean prediction system that uses the NRL Layered Ocean Model (NLOM) has been transitioned to the Naval Oceanographic Office (NAVO), Stennis Space Center, MS. The system gives a real time view of the ocean down to the 50-100 mile scale of ocean eddies and the meandering of ocean currents and fronts, a view with unprecedented resolution and clarity, and demonstrated forecast skill for a month or more for many ocean features. It has been running in real time at NAVO since 19 Oct 2000 with assimilation of real-time altimeter sea surface height (SSH) data (currently ERS-2, GFO and TOPEX/POSEIDON) and sea surface temperature (SST). The model is updated daily and 4-day forecasts are made daily. 30-day forecasts are made once a week. Nowcasts and forecasts using this model are viewable on the web, including SSH, SST and 30-day forecast verification statistics for many zoom regions. The NRL web address is http://www7320.nrlssc.navy.mil/global_nlom/index.html. The NAVO web address is: http://www.navo.navy.mil. Click on "Operational Products", then "Product Search Form", then "Product Type View", then select "Model Navy Layered Ocean Model" and a region and click on "Submit Query". This system is used at NAVO for ocean front and eddy analyses and predictions and to provide accurate sea surface height for use in computing synthetic temperature and salinity profiles, among other applications.

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.

Rough Precipitation Forecasts based on Analogue Method: an Operational System

NASA Astrophysics Data System (ADS)

Raffa, Mario; Mercogliano, Paola; Lacressonnière, Gwendoline; Guillaume, Bruno; Deandreis, Céline; Castanier, Pierre

2017-04-01

In the framework of the Climate KIC partnership, has been funded the project Wat-Ener-Cast (WEC), coordinated by ARIA Technologies, having the goal to adapt, through tailored weather-related forecast, the water and energy operations to the increased weather fluctuation and to climate change. The WEC products allow providing high quality forecast suited in risk and opportunities assessment dashboard for water and energy operational decisions and addressing the needs of sewage/water distribution operators, energy transport & distribution system operators, energy manager and wind energy producers. A common "energy water" web platform, able to interface with newest smart water-energy IT network have been developed. The main benefit by sharing resources through the "WEC platform" is the possibility to optimize the cost and the procedures of safety and maintenance team, in case of alerts and, finally to reduce overflows. Among the different services implemented on the WEC platform, ARIA have developed a product having the goal to support sewage/water distribution operators, based on a gradual forecast information system ( at 48hrs/24hrs/12hrs horizons) of heavy precipitation. For each fixed deadline different type of operation are implemented: 1) 48hour horizon, organisation of "on call team", 2) 24 hour horizon, update and confirm the "on call team", 3) 12 hour horizon, secure human resources and equipment (emptying storage basins, pipes manipulations …). More specifically CMCC have provided a statistical downscaling method in order to provide a "rough" daily local precipitation at 24 hours, especially when high precipitation values are expected. This statistical technique consists of an adaptation of analogue method based on ECMWF data (analysis and forecast at 24 hours). One of the most advantages of this technique concerns a lower computational burden and budget compared to running a Numerical Weather Prediction (NWP) model, also if, of course it provides only this

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.

Demand forecasting for automotive sector in Malaysia by system dynamics approach

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

Zulkepli, Jafri, E-mail: zhjafri@uum.edu.my; Abidin, Norhaslinda Zainal, E-mail: nhaslinda@uum.edu.my; Fong, Chan Hwa, E-mail: hfchan7623@yahoo.com

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 frommore » 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.« less

Evaluation of Wind Power Forecasts from the Vermont Weather Analytics Center and Identification of Improvements

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

Optis, Michael; Scott, George N.; Draxl, Caroline

The goal of this analysis was to assess the wind power forecast accuracy of the Vermont Weather Analytics Center (VTWAC) forecast system and to identify potential improvements to the forecasts. Based on the analysis at Georgia Mountain, the following recommendations for improving forecast performance were made: 1. Resolve the significant negative forecast bias in February-March 2017 (50% underprediction on average) 2. Improve the ability of the forecast model to capture the strong diurnal cycle of wind power 3. Add ability for forecast model to assess internal wake loss, particularly at sites where strong diurnal shifts in wind direction are present.more » Data availability and quality limited the robustness of this forecast assessment. A more thorough analysis would be possible given a longer period of record for the data (at least one full year), detailed supervisory control and data acquisition data for each wind plant, and more detailed information on the forecast system input data and methodologies.« less

Season-ahead water quality forecasts for the Schuylkill River, Pennsylvania

NASA Astrophysics Data System (ADS)

Block, P. J.; Leung, K.

2013-12-01

Anticipating and preparing for elevated water quality parameter levels in critical water sources, using weather forecasts, is not uncommon. In this study, we explore the feasibility of extending this prediction scale to a season-ahead for the Schuylkill River in Philadelphia, utilizing both statistical and dynamical prediction models, to characterize the season. This advance information has relevance for recreational activities, ecosystem health, and water treatment, as the Schuylkill provides 40% of Philadelphia's water supply. The statistical model associates large-scale climate drivers with streamflow and water quality parameter levels; numerous variables from NOAA's CFSv2 model are evaluated for the dynamical approach. A multi-model combination is also assessed. Results indicate moderately skillful prediction of average summertime total coliform and wintertime turbidity, using season-ahead oceanic and atmospheric variables, predominantly from the North Atlantic Ocean. Models predicting the number of elevated turbidity events across the wintertime season are also explored.

Comparative assessment of several post-processing methods for correcting evapotranspiration forecasts derived from TIGGE datasets.

NASA Astrophysics Data System (ADS)

Tian, D.; Medina, H.

2017-12-01

Post-processing of medium range reference evapotranspiration (ETo) forecasts based on numerical weather prediction (NWP) models has the potential of improving the quality and utility of these forecasts. This work compares the performance of several post-processing methods for correcting ETo forecasts over the continental U.S. generated from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database using data from Europe (EC), the United Kingdom (MO), and the United States (NCEP). The pondered post-processing techniques are: simple bias correction, the use of multimodels, the Ensemble Model Output Statistics (EMOS, Gneitting et al., 2005) and the Bayesian Model Averaging (BMA, Raftery et al., 2005). ETo estimates based on quality-controlled U.S. Regional Climate Reference Network measurements, and computed with the FAO 56 Penman Monteith equation, are adopted as baseline. EMOS and BMA are generally the most efficient post-processing techniques of the ETo forecasts. Nevertheless, the simple bias correction of the best model is commonly much more rewarding than using multimodel raw forecasts. Our results demonstrate the potential of different forecasting and post-processing frameworks in operational evapotranspiration and irrigation advisory systems at national scale.

SCADA-based Operator Support System for Power Plant Equipment Fault Forecasting

NASA Astrophysics Data System (ADS)

Mayadevi, N.; Ushakumari, S. S.; Vinodchandra, S. S.

2014-12-01

Power plant equipment must be monitored closely to prevent failures from disrupting plant availability. Online monitoring technology integrated with hybrid forecasting techniques can be used to prevent plant equipment faults. A self learning rule-based expert system is proposed in this paper for fault forecasting in power plants controlled by supervisory control and data acquisition (SCADA) system. Self-learning utilizes associative data mining algorithms on the SCADA history database to form new rules that can dynamically update the knowledge base of the rule-based expert system. In this study, a number of popular associative learning algorithms are considered for rule formation. Data mining results show that the Tertius algorithm is best suited for developing a learning engine for power plants. For real-time monitoring of the plant condition, graphical models are constructed by K-means clustering. To build a time-series forecasting model, a multi layer preceptron (MLP) is used. Once created, the models are updated in the model library to provide an adaptive environment for the proposed system. Graphical user interface (GUI) illustrates the variation of all sensor values affecting a particular alarm/fault, as well as the step-by-step procedure for avoiding critical situations and consequent plant shutdown. The forecasting performance is evaluated by computing the mean absolute error and root mean square error of the predictions.

Consequences Identification in Forecasting and Ethical Decision-making

PubMed Central

Stenmark, Cheryl K.; Antes, Alison L.; Thiel, Chase E.; Caughron, Jared J.; Wang, Xiaoqian; Mumford, Michael D.

2015-01-01

Forecasting involves predicting outcomes based on observations of the situation at hand. We examined the impact of the number and types of consequences considered on the quality of ethical decision-making. Undergraduates role-played several ethical problems in which they forecast potential outcomes and made decisions. Performance pressure (difficult demands placed on the situation) and interpersonal conflict (clashes among people in the problem situation) were manipulated within each problem scenario. The results indicated that the identification of potential consequences was positively associated with both higher quality forecasts and more ethical decisions. Neither performance pressure nor interpersonal conflict affected the quality of forecasts or decisions. Theoretical and practical implications of these findings and the use of this research approach are discussed. PMID:21460584

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.

LINKING ETA MODEL WITH THE COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODELING SYSTEM: OZONE BOUNDARY CONDITIONS

EPA Science Inventory

A prototype surface ozone concentration forecasting model system for the Eastern U.S. has been developed. The model system is consisting of a regional meteorological and a regional air quality model. It demonstrated a strong prediction dependence on its ozone boundary conditions....

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

Hydro-economic assessment of hydrological forecasting systems

NASA Astrophysics Data System (ADS)

Boucher, M.-A.; Tremblay, D.; Delorme, L.; Perreault, L.; Anctil, F.

2012-01-01

SummaryAn increasing number of publications show that ensemble hydrological forecasts exhibit good performance when compared to observed streamflow. Many studies also conclude that ensemble forecasts lead to a better performance than deterministic ones. This investigation takes one step further by not only comparing ensemble and deterministic forecasts to observed values, but by employing the forecasts in a stochastic decision-making assistance tool for hydroelectricity production, during a flood event on the Gatineau River in Canada. This allows the comparison between different types of forecasts according to their value in terms of energy, spillage and storage in a reservoir. The motivation for this is to adopt the point of view of an end-user, here a hydroelectricity production society. We show that ensemble forecasts exhibit excellent performances when compared to observations and are also satisfying when involved in operation management for electricity production. Further improvement in terms of productivity can be reached through the use of a simple post-processing method.

A system for forecasting and monitoring cash flow : phase I : forecasting payments on construction contracts.

DOT National Transportation Integrated Search

1983-01-01

The research on which this paper is based was performed as part of a study to develop a system for generating a one-to-two year forecast of monthly cash flows for the Virginia Department of Highways and Transportation. It revealed that presently used...

Development of the ClearSky smoke dispersion forecast system for agricultural field burning in the Pacific Northwest

NASA Astrophysics Data System (ADS)

Jain, Rahul; Vaughan, Joseph; Heitkamp, Kyle; Ramos, Charleston; Claiborn, Candis; Schreuder, Maarten; Schaaf, Mark; Lamb, Brian

The post-harvest burning of agricultural fields is commonly used to dispose of crop residue and provide other desired services such as pest control. Despite careful regulation of burning, smoke plumes from field burning in the Pacific Northwest commonly degrade air quality, particularly for rural populations. In this paper, ClearSky, a numerical smoke dispersion forecast system for agricultural field burning that was developed to support smoke management in the Inland Pacific Northwest, is described. ClearSky began operation during the summer through fall burn season of 2002 and continues to the present. ClearSky utilizes Mesoscale Meteorological Model version 5 (MM5v3) forecasts from the University of Washington, data on agricultural fields, a web-based user interface for defining burn scenarios, the Lagrangian CALPUFF dispersion model and web-served animations of plume forecasts. The ClearSky system employs a unique hybrid source configuration, which treats the flaming portion of a field as a buoyant line source and the smoldering portion of the field as a buoyant area source. Limited field observations show that this hybrid approach yields reasonable plume rise estimates using source parameters derived from recent field burning emission field studies. The performance of this modeling system was evaluated for 2003 by comparing forecast meteorology against meteorological observations, and comparing model-predicted hourly averaged PM 2.5 concentrations against observations. Examples from this evaluation illustrate that while the ClearSky system can accurately predict PM 2.5 surface concentrations due to field burning, the overall model performance depends strongly on meteorological forecast error. Statistical evaluation of the meteorological forecast at seven surface stations indicates a strong relationship between topographical complexity near the station and absolute wind direction error with wind direction errors increasing from approximately 20° for sites in

Short-term Forecasting Tools for Agricultural Nutrient Management.

PubMed

Easton, Zachary M; Kleinman, Peter J A; Buda, Anthony R; Goering, Dustin; Emberston, Nichole; Reed, Seann; Drohan, Patrick J; Walter, M Todd; Guinan, Pat; Lory, John A; Sommerlot, Andrew R; Sharpley, Andrew

2017-11-01

The advent of real-time, short-term farm management tools is motivated by the need to protect water quality above and beyond the general guidance offered by existing nutrient management plans. Advances in high-performance computing and hydrologic or climate modeling have enabled rapid dissemination of real-time information that can assist landowners and conservation personnel with short-term management planning. This paper reviews short-term decision support tools for agriculture that are under various stages of development and implementation in the United States: (i) Wisconsin's Runoff Risk Advisory Forecast (RRAF) System, (ii) New York's Hydrologically Sensitive Area Prediction Tool, (iii) Virginia's Saturated Area Forecast Model, (iv) Pennsylvania's Fertilizer Forecaster, (v) Washington's Application Risk Management (ARM) System, and (vi) Missouri's Design Storm Notification System. Although these decision support tools differ in their underlying model structure, the resolution at which they are applied, and the hydroclimates to which they are relevant, all provide forecasts (range 24-120 h) of runoff risk or soil moisture saturation derived from National Weather Service Forecast models. Although this review highlights the need for further development of robust and well-supported short-term nutrient management tools, their potential for adoption and ultimate utility requires an understanding of the appropriate context of application, the strategic and operational needs of managers, access to weather forecasts, scales of application (e.g., regional vs. field level), data requirements, and outreach communication structure. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

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

Jiang, Huaiguang; Zhang, Yingchen

This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vectormore » regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.« less

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.

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.

An Experimental Real-Time Ocean Nowcast/Forecast System for Intra America Seas

NASA Astrophysics Data System (ADS)

Ko, D. S.; Preller, R. H.; Martin, P. J.

2003-04-01

An experimental real-time Ocean Nowcast/Forecast System has been developed for the Intra America Seas (IASNFS). The area of coverage includes the Caribbean Sea, the Gulf of Mexico and the Straits of Florida. The system produces nowcast and up to 72 hours forecast the sea level variation, 3D ocean current, temperature and salinity fields. IASNFS consists an 1/24 degree (~5 km), 41-level sigma-z data-assimilating ocean model based on NCOM. For daily nowcast/forecast the model is restarted from previous nowcast. Once model is restarted it continuously assimilates the synthetic temperature/salinity profiles generated by a data analysis model called MODAS to produce nowcast. Real-time data come from satellite altimeter (GFO, TOPEX/Poseidon, ERS-2) sea surface height anomaly and AVHRR sea surface temperature. Three hourly surface heat fluxes, including solar radiation, wind stresses and sea level air pressure from NOGAPS/FNMOC are applied for surface forcing. Forecasts are produced with available NOGAPS forecasts. Once the nowcast/forecast are produced they are distributed through the Internet via the updated web pages. The open boundary conditions including sea surface elevation, transport, temperature, salinity and currents are provided by the NRL 1/8 degree Global NCOM which is operated daily. An one way coupling scheme is used to ingest those boundary conditions into the IAS model. There are 41 rivers with monthly discharges included in the IASNFS.

A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine.

PubMed

Wang, Deyun; Wei, Shuai; Luo, Hongyuan; Yue, Chenqiang; Grunder, Olivier

2017-02-15

The randomness, non-stationarity and irregularity of air quality index (AQI) series bring the difficulty of AQI forecasting. To enhance forecast accuracy, a novel hybrid forecasting model combining two-phase decomposition technique and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm is developed for AQI forecasting in this paper. In phase I, the complementary ensemble empirical mode decomposition (CEEMD) is utilized to decompose the AQI series into a set of intrinsic mode functions (IMFs) with different frequencies; in phase II, in order to further handle the high frequency IMFs which will increase the forecast difficulty, variational mode decomposition (VMD) is employed to decompose the high frequency IMFs into a number of variational modes (VMs). Then, the ELM model optimized by DE algorithm is applied to forecast all the IMFs and VMs. Finally, the forecast value of each high frequency IMF is obtained through adding up the forecast results of all corresponding VMs, and the forecast series of AQI is obtained by aggregating the forecast results of all IMFs. To verify and validate the proposed model, two daily AQI series from July 1, 2014 to June 30, 2016 collected from Beijing and Shanghai located in China are taken as the test cases to conduct the empirical study. The experimental results show that the proposed hybrid model based on two-phase decomposition technique is remarkably superior to all other considered models for its higher forecast accuracy. Copyright © 2016 Elsevier B.V. All rights reserved.

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

Subseasonal forecast skills and biases of global summer monsoons in the NCEP Climate Forecast System version 2

NASA Astrophysics Data System (ADS)

Liu, Xiangwen; Yang, Song; Li, Qiaoping; Kumar, Arun; Weaver, Scott; Liu, Shi

2014-03-01

Subseasonal forecast skills and biases of global summer monsoons are diagnosed using daily data from the hindcasts of 45-day integrations by the NCEP Climate Forecast System version 2. Predictions for subseasonal variability of zonal wind and precipitation are generally more skillful over the Asian and Australian monsoon regions than other monsoon regions. Climatologically, forecasts for the variations of dynamical monsoon indices have high skills at leads of about 2 weeks. However, apparent interannual differences exist, with high skills up to 5 weeks in exceptional cases. Comparisons for the relationships of monsoon indices with atmospheric circulation and precipitation patterns between skillful and unskillful forecasts indicate that skills for subseasonal variability of a monsoon index depend partially on the degree to which the observed variability of the index attributes to the variation of large-scale circulation. Thus, predictions are often more skillful when the index is closely linked to atmospheric circulation over a broad region than over a regional and narrow range. It is also revealed that, the subseasonal variations of biases of winds, precipitation, and surface temperature over various monsoon regions are captured by a first mode with seasonally independent biases and a second mode with apparent phase transition of biases during summer. The first mode indicates the dominance of overall weaker-than-observed summer monsoons over major monsoon regions. However, at certain stages of monsoon evolution, these underestimations are regionally offset or intensified by the time evolving biases portrayed by the second mode. This feature may be partially related to factors such as the shifts of subtropical highs and intertropical convergence zones, the reversal of biases of surface temperature over some monsoon regions, and the transition of regional circulation system. The significant geographical differences in bias growth with increasing lead time reflect the

Post LANDSAT D Advanced Concept Evaluation (PLACE). [with emphasis on mission planning, technological forecasting, and user requirements

NASA Technical Reports Server (NTRS)

1977-01-01

An outline is given of the mission objectives and requirements, system elements, system concepts, technology requirements and forecasting, and priority analysis for LANDSAT D. User requirements and mission analysis and technological forecasting are emphasized. Mission areas considered include agriculture, range management, forestry, geology, land use, water resources, environmental quality, and disaster assessment.

Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model.

PubMed

Wang, Jianzhou; Niu, Tong; Wang, Rui

2017-03-02

The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the area. In this paper, firstly a comprehensive warning system is proposed, which consists of two vital indispensable modules, namely effective forecasting and scientific evaluation, respectively. For the forecasting module, a novel hybrid model combining the theory of data preprocessing and numerical optimization is first developed to implement effective forecasting for air pollutant concentration. Especially, in order to further enhance the accuracy and robustness of the warning system, interval forecasting is implemented to quantify the uncertainties generated by forecasts, which can provide significant risk signals by using point forecasting for decision-makers. For the evaluation module, a cloud model, based on probability and fuzzy set theory, is developed to perform comprehensive evaluations of air quality, which can realize the transformation between qualitative concept and quantitative data. To verify the effectiveness and efficiency of the warning system, extensive simulations based on air pollutants data from Dalian in China were effectively implemented, which illustrate that the warning system is not only remarkably high-performance, but also widely applicable.

Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model

PubMed Central

Wang, Jianzhou; Niu, Tong; Wang, Rui

2017-01-01

The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the area. In this paper, firstly a comprehensive warning system is proposed, which consists of two vital indispensable modules, namely effective forecasting and scientific evaluation, respectively. For the forecasting module, a novel hybrid model combining the theory of data preprocessing and numerical optimization is first developed to implement effective forecasting for air pollutant concentration. Especially, in order to further enhance the accuracy and robustness of the warning system, interval forecasting is implemented to quantify the uncertainties generated by forecasts, which can provide significant risk signals by using point forecasting for decision-makers. For the evaluation module, a cloud model, based on probability and fuzzy set theory, is developed to perform comprehensive evaluations of air quality, which can realize the transformation between qualitative concept and quantitative data. To verify the effectiveness and efficiency of the warning system, extensive simulations based on air pollutants data from Dalian in China were effectively implemented, which illustrate that the warning system is not only remarkably high-performance, but also widely applicable. PMID:28257122

The Impact of Implementing a Demand Forecasting System into a Low-Income Country’s Supply Chain

PubMed Central

Mueller, Leslie E.; Haidari, Leila A.; Wateska, Angela R.; Phillips, Roslyn J.; Schmitz, Michelle M.; Connor, Diana L.; Norman, Bryan A.; Brown, Shawn T.; Welling, Joel S.; Lee, Bruce Y.

2016-01-01

OBJECTIVE To evaluate the potential impact and value of applications (e.g., ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country’s vaccine supply chain with different levels of population change to urban areas. MATERIALS AND METHODS Using our software, HERMES, we generated a detailed discrete event simulation model of Niger’s entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. RESULTS Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. DISCUSSION The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. CONCLUSION Demand forecasting systems have the potential to greatly improve vaccine demand fulfillment, and decrease logistics cost/dose when implemented with storage and transportation increases direct vaccines. Simulation modeling can demonstrate the potential

A Relocatable Environmental Prediction System for Volcanic Ash Forecasts

NASA Astrophysics Data System (ADS)

Cook, J.; Geiszler, D.

2009-12-01

Timeliness is an essential component for any system generating volcanic ash forecasts for aviation. Timeliness implies that the steps required for estimating the concentration of volcanic ash in the atmosphere are streamlined into a process that can accurately identify the volcano’s source function, utilize atmospheric conditions to predict the movement of the volcanic ash plume, and ultimately produce a volcanic ash forecast product in a useable format for aviation interests. During the past decade, the Naval Research Laboratory (NRL) has developed a suite of software integrated with the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) that is designed with a similar automated purpose in support of the Navy’s operational (24/7) schedule and diverse mission requirements worldwide. The COAMPS-OS® (On-demand System) provides web-based interfaces to COAMPS that allows Navy users to rapidly (in a few minutes) set up and start a new forecast in response to short-fused requests. A unique capability in COAMPS unlike many regional numerical weather prediction models is the option to initialize a volcanic ash plume and use the model’s full three-dimensional atmospheric grid (e.g. winds and precipitation) to predict the movement and concentration of the plume. This paper will describe the efforts to automate volcanic ash forecasts using COAMPS-OS including the specification of the source function, initialization and configuration of COAMPS, and generation of output products for aviation. This research is in response to requirements and funding by the Federal Aviation Administration (FAA). The views expressed are those of the authors and do not necessarily represent the official policy or position of the FAA. COAMPS® and COAMPS-OS® are registered trademarks of the Naval Research Laboratory.

Global Ocean Forecast System (GOFS) Version 2.6. User’s Manual

DTIC Science & Technology

2010-03-31

odimens.D, which takes the rivers.dat flow levels, inputs an SST and sea surface salinity (SSS) climatology from GDEM , and outputs the orivs_1.D...Center for Medium-range Weather Forecast GB GigaByte GDEM Global Digital Elevation Map GOFS Global Ocean Forecast System HPCMP High Performance

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

Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.

PubMed

Vlachas, Pantelis R; Byeon, Wonmin; Wan, Zhong Y; Sapsis, Themistoklis P; Koumoutsakos, Petros

2018-05-01

We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.

Chemical weather forecasting for the Yangtze River Delta

NASA Astrophysics Data System (ADS)

Xie, Y.; Xu, J.; Zhou, G.; Chang, L.; Chen, B.

2016-12-01

Shanghai is one of the largest megacities in the world. With rapid economic growth of the city and its surrounding areas in recent years, air pollution has posed adverse effects on public health and ecosystem. In winter heavy pollution episodes are often associated with PM exceedances under stagnant conditions or transport events, whereas in summer the region frequently experiences elevated O3 levels. Chemical weather prediction systems with the WRF-Chem and CMAQ models are being developed to support air quality and haze forecasting for Shanghai and the Yangtze River Delta region. We will present main components of the modeling system, forecasting products, as well as evaluation results. Evaluation of the WRF-Chem forecasts show the model has generally good ability to capture the temporal variations of O3 and PM2.5. Substantial regional differences exist, with the best performance in Shanghai. Meanwhile, the forecasts tend to degrade during highly polluted episodes and transitional time periods, which highlights the need to improve model representation of key process (e.g. meteorological fields and formation of secondary pollutants). Recent work includes using the ECMWF global model forecasts as chemical boundary conditions for our regional model. We investigate the impact of chemical downscaling, and also compare the results from different models participated in the PANDA (PArtnership with chiNa on space Data) project. Results from ongoing efforts (e.g. chemical weather forecasting driven by SMS regional high resolution NWP) will also be presented.

Waste information management system: a web-based system for DOE waste forecasting

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

Geisler, T.J.; Shoffner, P.A.; Upadhyay, U.

2007-07-01

The implementation of the Department of Energy (DOE) mandated accelerated cleanup program has created significant potential technical impediments that must be overcome. The schedule compression will require close coordination and a comprehensive review and prioritization of the barriers that may impede treatment and disposition of the waste streams at each site. Many issues related to site waste treatment and disposal have now become potential critical path issues under the accelerated schedules. In order to facilitate accelerated cleanup initiatives, waste managers at DOE field sites and at DOE headquarters in Washington, D.C., need timely waste forecast information regarding the volumes andmore » types of waste that will be generated by DOE sites over the next 25 years. Each local DOE site has historically collected, organized, and displayed site waste forecast information in separate and unique systems. However, waste information from all sites needs a common application to allow interested parties to understand and view the complete complex-wide picture. A common application would allow identification of total waste volumes, material classes, disposition sites, choke points, and technological or regulatory barriers to treatment and disposal. The Applied Research Center (ARC) at Florida International University (FIU) in Miami, Florida, has completed the development of this web-based forecast system. (authors)« less

High-resolution visibility and air quality forecasting using multi-layer urban canopy model for highly urbanized Hong Kong and the Pearl River Delta

NASA Astrophysics Data System (ADS)

Piu NG, Chak; HAO, Song; Fat LAM, Yun

2015-04-01

Visibility is a universally critical element which affects the public in many aspects, including economic activities, health of local citizens and safety of marine transportation and aviation. The Interagency Monitoring of Protected Visual Environments (IMPROVE) visibility equation, an empirical equation developed by USEPA, has been modified by various studies to fit into the application upon the Asian continent including Hong Kong and China. Often these studies focused on the improvement of the existing IMPROVE equation by modifying its particulate speciation using local observation data. In this study, we developed an Integrated Forecast System (IFS) to predict the next-day air quality and visibility using Weather Research and Forecasting model with Building Energy Parameterization and Building Energy Model (WRF-BEP+BEM) and Community Multi-scale Air Quality Model (CMAQ). Unlike the other studies, the core of this study is to include detailed urbanization impacts with calibrated "IMPROVE equation for PRD" into the modeling system for Hong Kong's environs. The ultra-high resolution land cover information (~1km x 1km) from Google images, was digitized into the Geographic Information System (GIS) for preparing the model-ready input for IFS. The NCEP FNL (Final) Operation Global Analysis (FNL) and the Global Forecasting System (GFS) datasets were tested for both hind-cast and forecast cases, in order to calibrate the input of urban parameters in the WRF-BEP+BEM model. The evaluation of model performance with sensitivity cases was performed on sea surface temperature (SST), surface temperature (T), wind speed/direction with the major pollutants (i.e., PM10, PM2.5, NOx, SO2 and O3) using local observation and will be presented/discussed in this paper. References: 1. Y. L. Lee, R. Sequeira, Visibility degradation across Hong Kong its components and their relative contribution. Atmospheric Environment 2001, 35, 5861-5872. doi:10.1016/S1352-2310(01)00395-8 2. R. Zhang, Q

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

Forecasting Electric Power Generation of Photovoltaic Power System for Energy Network

NASA Astrophysics Data System (ADS)

Kudo, Mitsuru; Takeuchi, Akira; Nozaki, Yousuke; Endo, Hisahito; Sumita, Jiro

Recently, there has been an increase in concern about the global environment. Interest is growing in developing an energy network by which new energy systems such as photovoltaic and fuel cells generate power locally and electric power and heat are controlled with a communications network. We developed the power generation forecast method for photovoltaic power systems in an energy network. The method makes use of weather information and regression analysis. We carried out forecasting power output of the photovoltaic power system installed in Expo 2005, Aichi Japan. As a result of comparing measurements with a prediction values, the average prediction error per day was about 26% of the measured power.

Enhancing Famine Early Warning Systems with Improved Forecasts, Satellite Observations and Hydrologic Simulations

NASA Astrophysics Data System (ADS)

Funk, C. C.; Verdin, J.; Thiaw, W. M.; Hoell, A.; Korecha, D.; McNally, A.; Shukla, S.; Arsenault, K. R.; Magadzire, T.; Novella, N.; Peters-Lidard, C. D.; Robjohn, M.; Pomposi, C.; Galu, G.; Rowland, J.; Budde, M. E.; Landsfeld, M. F.; Harrison, L.; Davenport, F.; Husak, G. J.; Endalkachew, E.

2017-12-01

Drought early warning science, in support of famine prevention, is a rapidly advancing field that is helping to save lives and livelihoods. In 2015-2017, a series of extreme droughts afflicted Ethiopia, Southern Africa, Eastern Africa in OND and Eastern Africa in MAM, pushing more than 50 million people into severe food insecurity. Improved drought forecasts and monitoring tools, however, helped motivate and target large and effective humanitarian responses. Here we describe new science being developed by a long-established early warning system - the USAID Famine Early Warning Systems Network (FEWS NET). FEWS NET is a leading provider of early warning and analysis on food insecurity. FEWS NET research is advancing rapidly on several fronts, providing better climate forecasts and more effective drought monitoring tools that are being used to support enhanced famine early warning. We explore the philosophy and science underlying these successes, suggesting that a modal view of climate change can support enhanced seasonal prediction. Under this modal perspective, warming of the tropical oceans may interact with natural modes of variability, like the El Niño-Southern Oscillation, to enhance Indo-Pacific sea surface temperature gradients during both El Niño and La Niña-like climate states. Using empirical data and climate change simulations, we suggest that a sequence of droughts may commence in northern Ethiopia and Southern Africa with the advent of a moderate-to-strong El Niño, and then continue with La Niña/West Pacific related droughts in equatorial eastern East Africa. Scientifically, we show that a new hybrid statistical-dynamic precipitation forecast system, the FEWS NET Integrated Forecast System (FIFS), based on reformulations of the Global Ensemble Forecast System weather forecasts and National Multi-Model Ensemble (NMME) seasonal climate predictions, can effectively anticipate recent East and Southern African drought events. Using cross-validation, we

Change in Weather Research and Forecasting (WRF) Model Accuracy with Age of Input Data from the Global Forecast System (GFS)

DTIC Science & Technology

2016-09-01

Laboratory Change in Weather Research and Forecasting (WRF) Model Accuracy with Age of Input Data from the Global Forecast System (GFS) by JL Cogan...analysis. As expected, accuracy generally tended to decline as the large-scale data aged , but appeared to improve slightly as the age of the large...19 Table 7 Minimum and maximum mean RMDs for each WRF time (or GFS data age ) category. Minimum and

Consensus Seasonal Flood Forecasts and Warning Response System (FFWRS): an alternate for nonstructural flood management in Bangladesh.

PubMed

Chowdhury, Rashed

2005-06-01

Despite advances in short-range flood forecasting and information dissemination systems in Bangladesh, the present system is less than satisfactory. This is because of short lead-time products, outdated dissemination networks, and lack of direct feedback from the end-user. One viable solution is to produce long-lead seasonal forecasts--the demand for which is significantly increasing in Bangladesh--and disseminate these products through the appropriate channels. As observed in other regions, the success of seasonal forecasts, in contrast to short-term forecast, depends on consensus among the participating institutions. The Flood Forecasting and Warning Response System (henceforth, FFWRS) has been found to be an important component in a comprehensive and participatory approach to seasonal flood management. A general consensus in producing seasonal forecasts can thus be achieved by enhancing the existing FFWRS. Therefore, the primary objective of this paper is to revisit and modify the framework of an ideal warning response system for issuance of consensus seasonal flood forecasts in Bangladesh. The five-stage FFWRS-i) Flood forecasting, ii) Forecast interpretation and message formulation, iii) Warning preparation and dissemination, iv) Responses, and v) Review and analysis-has been modified. To apply the concept of consensus forecast, a framework similar to that of the Southern African Regional Climate Outlook Forum (SARCOF) has been discussed. Finally, the need for a climate Outlook Fora has been emphasized for a comprehensive and participatory approach to seasonal flood hazard management in Bangladesh.

Sea Ice in the NCEP Seasonal Forecast System

NASA Astrophysics Data System (ADS)

Wu, X.; Saha, S.; Grumbine, R. W.; Bailey, D. A.; Carton, J.; Penny, S. G.

2017-12-01

Sea ice is known to play a significant role in the global climate system. For a weather or climate forecast system (CFS), it is important that the realistic distribution of sea ice is represented. Sea ice prediction is challenging; sea ice can form or melt, it can move with wind and/or ocean current; sea ice interacts with both the air above and ocean underneath, it influences by, and has impact on the air and ocean conditions. NCEP has developed coupled CFS (version 2, CFSv2) and also carried out CFS reanalysis (CFSR), which includes a coupled model with the NCEP global forecast system, a land model, an ocean model (GFDL MOM4), and a sea ice model. In this work, we present the NCEP coupled model, the CFSv2 sea ice component that includes a dynamic thermodynamic sea ice model and a simple "assimilation" scheme, how sea ice has been assimilated in CFSR, the characteristics of the sea ice from CFSR and CFSv2, and the improvements of sea ice needed for future seasonal prediction system, part of the Unified Global Coupled System (UGCS), which is being developed and under testing, including sea ice data assimilation with the Local Ensemble Transform Kalman Filter (LETKF). Preliminary results from the UGCS testing will also be presented.

System load forecasts for an electric utility. [Hourly loads using Box-Jenkins method

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

Uri, N.D.

This paper discusses forecasting hourly system load for an electric utility using Box-Jenkins time-series analysis. The results indicate that a model based on the method of Box and Jenkins, given its simplicity, gives excellent results over the forecast horizon.

Financial forecasts accuracy in Brazil's social security system.

PubMed

Silva, Carlos Patrick Alves da; Puty, Claudio Alberto Castelo Branco; Silva, Marcelino Silva da; Carvalho, Solon Venâncio de; Francês, Carlos Renato Lisboa

2017-01-01

Long-term social security statistical forecasts produced and disseminated by the Brazilian government aim to provide accurate results that would serve as background information for optimal policy decisions. These forecasts are being used as support for the government's proposed pension reform that plans to radically change the Brazilian Constitution insofar as Social Security is concerned. However, the reliability of official results is uncertain since no systematic evaluation of these forecasts has ever been published by the Brazilian government or anyone else. This paper aims to present a study of the accuracy and methodology of the instruments used by the Brazilian government to carry out long-term actuarial forecasts. We base our research on an empirical and probabilistic analysis of the official models. Our empirical analysis shows that the long-term Social Security forecasts are systematically biased in the short term and have significant errors that render them meaningless in the long run. Moreover, the low level of transparency in the methods impaired the replication of results published by the Brazilian Government and the use of outdated data compromises forecast results. In the theoretical analysis, based on a mathematical modeling approach, we discuss the complexity and limitations of the macroeconomic forecast through the computation of confidence intervals. We demonstrate the problems related to error measurement inherent to any forecasting process. We then extend this exercise to the computation of confidence intervals for Social Security forecasts. This mathematical exercise raises questions about the degree of reliability of the Social Security forecasts.

Solar Storm GIC Forecasting: Solar Shield Extension Development of the End-User Forecasting System Requirements

NASA Technical Reports Server (NTRS)

Pulkkinen, A.; Mahmood, S.; Ngwira, C.; Balch, C.; Lordan, R.; Fugate, D.; Jacobs, W.; Honkonen, I.

2015-01-01

A NASA Goddard Space Flight Center Heliophysics Science Division-led team that includes NOAA Space Weather Prediction Center, the Catholic University of America, Electric Power Research Institute (EPRI), and Electric Research and Management, Inc., recently partnered with the Department of Homeland Security (DHS) Science and Technology Directorate (S&T) to better understand the impact of Geomagnetically Induced Currents (GIC) on the electric power industry. This effort builds on a previous NASA-sponsored Applied Sciences Program for predicting GIC, known as Solar Shield. The focus of the new DHS S&T funded effort is to revise and extend the existing Solar Shield system to enhance its forecasting capability and provide tailored, timely, actionable information for electric utility decision makers. To enhance the forecasting capabilities of the new Solar Shield, a key undertaking is to extend the prediction system coverage across Contiguous United States (CONUS), as the previous version was only applicable to high latitudes. The team also leverages the latest enhancements in space weather modeling capacity residing at Community Coordinated Modeling Center to increase the Technological Readiness Level, or Applications Readiness Level of the system http://www.nasa.gov/sites/default/files/files/ExpandedARLDefinitions4813.pdf.

Air Quality Forecasting through Different Statistical and Artificial Intelligence Techniques

NASA Astrophysics Data System (ADS)

Mishra, D.; Goyal, P.

2014-12-01

Urban air pollution forecasting has emerged as an acute problem in recent years because there are sever environmental degradation due to increase in harmful air pollutants in the ambient atmosphere. In this study, there are different types of statistical as well as artificial intelligence techniques are used for forecasting and analysis of air pollution over Delhi urban area. These techniques are principle component analysis (PCA), multiple linear regression (MLR) and artificial neural network (ANN) and the forecasting are observed in good agreement with the observed concentrations through Central Pollution Control Board (CPCB) at different locations in Delhi. But such methods suffers from disadvantages like they provide limited accuracy as they are unable to predict the extreme points i.e. the pollution maximum and minimum cut-offs cannot be determined using such approach. Also, such methods are inefficient approach for better output forecasting. But with the advancement in technology and research, an alternative to the above traditional methods has been proposed i.e. the coupling of statistical techniques with artificial Intelligence (AI) can be used for forecasting purposes. The coupling of PCA, ANN and fuzzy logic is used for forecasting of air pollutant over Delhi urban area. The statistical measures e.g., correlation coefficient (R), normalized mean square error (NMSE), fractional bias (FB) and index of agreement (IOA) of the proposed model are observed in better agreement with the all other models. Hence, the coupling of statistical and artificial intelligence can be use for the forecasting of air pollutant over urban area.

A novel hybrid forecasting model for PM₁₀ and SO₂ daily concentrations.

PubMed

Wang, Ping; Liu, Yong; Qin, Zuodong; Zhang, Guisheng

2015-02-01

Air-quality forecasting in urban areas is difficult because of the uncertainties in describing both the emission and meteorological fields. The use of incomplete information in the training phase restricts practical air-quality forecasting. In this paper, we propose a hybrid artificial neural network and a hybrid support vector machine, which effectively enhance the forecasting accuracy of an artificial neural network (ANN) and support vector machine (SVM) by revising the error term of the traditional methods. The hybrid methodology can be described in two stages. First, we applied the ANN or SVM forecasting system with historical data and exogenous parameters, such as meteorological variables. Then, the forecasting target was revised by the Taylor expansion forecasting model using the residual information of the error term in the previous stage. The innovation involved in this approach is that it sufficiently and validly utilizes the useful residual information on an incomplete input variable condition. The proposed method was evaluated by experiments using a 2-year dataset of daily PM₁₀ (particles with a diameter of 10 μm or less) concentrations and SO₂ (sulfur dioxide) concentrations from four air pollution monitoring stations located in Taiyuan, China. The theoretical analysis and experimental results demonstrated that the forecasting accuracy of the proposed model is very promising. Copyright © 2014 Elsevier B.V. All rights reserved.

SASS wind forecast impact studies using the GLAS and NEPRF systems: Preliminary conclusions

NASA Technical Reports Server (NTRS)

Kalnay, E.; Atlas, R.; Baker, W. E.; Duffy, D.; Halem, M.; Helfand, M.

1984-01-01

For this project, a version of the GLAS Analysis/Forecast System was developed that includes an objective dealiasing scheme as an integral part of the analysis cycle. With this system the (100 sq km) binned SASS wind data generated by S. Peteherych of AER, Canada corresponding of the period 0000 GMT 7 September 1978 to 1200 GMT 13 September 1978 was objectively dealiased. The dealiased wind fields have been requested and received by JPL, NMC and the British Meteorological Office. The first 3.5 days of objectively dealiased fields were subjectively enhanced on the McIDAS system. Approximately 20% of the wind directions were modified, and of these, about 70% were changed by less than 90 deg. Two SASS forecast impact studies, were performed using the dealiased fields, with the GLAS and the NEPRF (Navy Environmental Prediction Research Facility) analysis/forecast systems.

Toward a Global Water Quality Observing and Forecasting System

EPA Science Inventory

The Group on Earth Observations (GEO) Coastal and Inland Water Quality Working Group held a Water Quality Summit at the World Meteorological Organization (WMO) in Geneva, Switzerland April 20 to 22, 2015. The goal was to define specific water quality component requirements and de...

Using a Software Tool in Forecasting: a Case Study of Sales Forecasting Taking into Account Data Uncertainty

NASA Astrophysics Data System (ADS)

Fabianová, Jana; Kačmáry, Peter; Molnár, Vieroslav; Michalik, Peter

2016-10-01

Forecasting is one of the logistics activities and a sales forecast is the starting point for the elaboration of business plans. Forecast accuracy affects the business outcomes and ultimately may significantly affect the economic stability of the company. The accuracy of the prediction depends on the suitability of the use of forecasting methods, experience, quality of input data, time period and other factors. The input data are usually not deterministic but they are often of random nature. They are affected by uncertainties of the market environment, and many other factors. Taking into account the input data uncertainty, the forecast error can by reduced. This article deals with the use of the software tool for incorporating data uncertainty into forecasting. Proposals are presented of a forecasting approach and simulation of the impact of uncertain input parameters to the target forecasted value by this case study model. The statistical analysis and risk analysis of the forecast results is carried out including sensitivity analysis and variables impact analysis.

East Asian winter monsoon forecasting schemes based on the NCEP's climate forecast system

NASA Astrophysics Data System (ADS)

Tian, Baoqiang; Fan, Ke; Yang, Hongqing

2017-12-01

The East Asian winter monsoon (EAWM) is the major climate system in the Northern Hemisphere during boreal winter. In this study, we developed two schemes to improve the forecasting skill of the interannual variability of the EAWM index (EAWMI) using the interannual increment prediction method, also known as the DY method. First, we found that version 2 of the NCEP's Climate Forecast System (CFSv2) showed higher skill in predicting the EAWMI in DY form than not. So, based on the advantage of the DY method, Scheme-I was obtained by adding the EAWMI DY predicted by CFSv2 to the observed EAWMI in the previous year. This scheme showed higher forecasting skill than CFSv2. Specifically, during 1983-2016, the temporal correlation coefficient between the Scheme-I-predicted and observed EAWMI was 0.47, exceeding the 99% significance level, with the root-mean-square error (RMSE) decreased by 12%. The autumn Arctic sea ice and North Pacific sea surface temperature (SST) are two important external forcing factors for the interannual variability of the EAWM. Therefore, a second (hybrid) prediction scheme, Scheme-II, was also developed. This scheme not only involved the EAWMI DY of CFSv2, but also the sea-ice concentration (SIC) observed the previous autumn in the Laptev and East Siberian seas and the temporal coefficients of the third mode of the North Pacific SST in DY form. We found that a negative SIC anomaly in the preceding autumn over the Laptev and the East Siberian seas could lead to a significant enhancement of the Aleutian low and East Asian westerly jet in the following winter. However, the intensity of the winter Siberian high was mainly affected by the third mode of the North Pacific autumn SST. Scheme-I and Scheme-II also showed higher predictive ability for the EAWMI in negative anomaly years compared to CFSv2. More importantly, the improvement in the prediction skill of the EAWMI by the new schemes, especially for Scheme-II, could enhance the forecasting skill of

Testing for ontological errors in probabilistic forecasting models of natural systems

PubMed Central

Marzocchi, Warner; Jordan, Thomas H.

2014-01-01

Probabilistic forecasting models describe the aleatory variability of natural systems as well as our epistemic uncertainty about how the systems work. Testing a model against observations exposes ontological errors in the representation of a system and its uncertainties. We clarify several conceptual issues regarding the testing of probabilistic forecasting models for ontological errors: the ambiguity of the aleatory/epistemic dichotomy, the quantification of uncertainties as degrees of belief, the interplay between Bayesian and frequentist methods, and the scientific pathway for capturing predictability. We show that testability of the ontological null hypothesis derives from an experimental concept, external to the model, that identifies collections of data, observed and not yet observed, that are judged to be exchangeable when conditioned on a set of explanatory variables. These conditional exchangeability judgments specify observations with well-defined frequencies. Any model predicting these behaviors can thus be tested for ontological error by frequentist methods; e.g., using P values. In the forecasting problem, prior predictive model checking, rather than posterior predictive checking, is desirable because it provides more severe tests. We illustrate experimental concepts using examples from probabilistic seismic hazard analysis. Severe testing of a model under an appropriate set of experimental concepts is the key to model validation, in which we seek to know whether a model replicates the data-generating process well enough to be sufficiently reliable for some useful purpose, such as long-term seismic forecasting. Pessimistic views of system predictability fail to recognize the power of this methodology in separating predictable behaviors from those that are not. PMID:25097265

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.

An Operational System for Surveillance and Ecological Forecasting of West Nile Virus Outbreaks

NASA Astrophysics Data System (ADS)

Wimberly, M. C.; Davis, J. K.; Vincent, G.; Hess, A.; Hildreth, M. B.

2017-12-01

Mosquito-borne disease surveillance has traditionally focused on tracking human cases along with the abundance and infection status of mosquito vectors. For many of these diseases, vector and host population dynamics are also sensitive to climatic factors, including temperature fluctuations and the availability of surface water for mosquito breeding. Thus, there is a potential to strengthen surveillance and predict future outbreaks by monitoring environmental risk factors using broad-scale sensor networks that include earth-observing satellites. The South Dakota Mosquito Information System (SDMIS) project combines entomological surveillance with gridded meteorological data from NASA's North American Land Data Assimilation System (NLDAS) to generate weekly risk maps for West Nile virus (WNV) in the north-central United States. Critical components include a mosquito infection model that smooths the noisy infection rate and compensates for unbalanced sampling, and a human infection model that combines the entomological risk estimates with lagged effects of meteorological variables from the North American Land Data Assimilation System (NLDAS). Two types of forecasts are generated: long-term forecasts of statewide risk extending through the entire WNV season, and short-term forecasts of the geographic pattern of WNV risk in the upcoming week. Model forecasts are connected to public health actions through decision support matrices that link predicted risk levels to a set of phased responses. In 2016, the SDMIS successfully forecast an early start to the WNV season and a large outbreak of WNV cases following several years of low transmission. An evaluation of the 2017 forecasts will also be presented. Our experiences with the SDMIS highlight several important lessons that can inform future efforts at disease early warning. These include the value of integrating climatic models with recent observations of infection, the critical role of automated workflows to facilitate

Comparison of the economic impact of different wind power forecast systems for producers

NASA Astrophysics Data System (ADS)

Alessandrini, S.; Davò, F.; Sperati, S.; Benini, M.; Delle Monache, L.

2014-05-01

Deterministic forecasts of wind production for the next 72 h at a single wind farm or at the regional level are among the main end-users requirement. However, for an optimal management of wind power production and distribution it is important to provide, together with a deterministic prediction, a probabilistic one. A deterministic forecast consists of a single value for each time in the future for the variable to be predicted, while probabilistic forecasting informs on probabilities for potential future events. This means providing information about uncertainty (i.e. a forecast of the PDF of power) in addition to the commonly provided single-valued power prediction. A significant probabilistic application is related to the trading of energy in day-ahead electricity markets. It has been shown that, when trading future wind energy production, using probabilistic wind power predictions can lead to higher benefits than those obtained by using deterministic forecasts alone. In fact, by using probabilistic forecasting it is possible to solve economic model equations trying to optimize the revenue for the producer depending, for example, on the specific penalties for forecast errors valid in that market. In this work we have applied a probabilistic wind power forecast systems based on the "analog ensemble" method for bidding wind energy during the day-ahead market in the case of a wind farm located in Italy. The actual hourly income for the plant is computed considering the actual selling energy prices and penalties proportional to the unbalancing, defined as the difference between the day-ahead offered energy and the actual production. The economic benefit of using a probabilistic approach for the day-ahead energy bidding are evaluated, resulting in an increase of 23% of the annual income for a wind farm owner in the case of knowing "a priori" the future energy prices. The uncertainty on price forecasting partly reduces the economic benefit gained by using a

Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Weather Forecasting

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Bradley T.; Jedlovee, Gary J.

2010-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimension variational (3DVAR) analysis component (WRF-Var). Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in both clear and partly cloudy regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts due to instability added in the forecast soundings by the AIRS profiles. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

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

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.

Evaluation of the Plant-Craig stochastic convection scheme in an ensemble forecasting system

NASA Astrophysics Data System (ADS)

Keane, R. J.; Plant, R. S.; Tennant, W. J.

2015-12-01

The Plant-Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic element only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant-Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant-Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.

Challenges for operational forecasting and early warning of rainfall induced landslides

NASA Astrophysics Data System (ADS)

Guzzetti, Fausto

2017-04-01

models. Building on the experience gained in designing, implementing, and operating national and regional landslide forecasting systems in Italy, and on a preliminary review of the existing literature on regional landslide early warning systems, the talk discusses concepts, limitations and challenges inherent to the design of reliable forecasting and early warning systems for rainfall-triggered landslides, the evaluation of the performances of the systems, and on problems related to the use of the forecasts and the issuing of landslide warnings. Several of the typical elements of an operational landslide forecasting system are considered, including: (i) the rainfall and landslide information used to establish the threshold models, (ii) the methods and tools used to define the empirical rainfall thresholds, and their associated uncertainty, (iii) the quality (e.g., the temporal and spatial resolution) of the rainfall information used for operational forecasting, including rain gauge and radar measurements, satellite estimates, and quantitative weather forecasts, (iv) the ancillary information used to prepare the forecasts, including e.g., the terrain subdivisions and the landslide susceptibility zonations, (v) the criteria used to transform the forecasts into landslide warnings and the methods used to communicate the warnings, and (vi) the criteria and strategies adopted to evaluate the performances of the systems, and to define minimum or optimal performance levels.

Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay.

PubMed

Jacobs, J M; Rhodes, M; Brown, C W; Hood, R R; Leight, A; Long, W; Wood, R

2014-11-01

To construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters of Chesapeake Bay for implementation in ecological forecasting systems. We evaluated and applied previously published qPCR assays to water samples (n = 1636) collected from Chesapeake Bay from 2007-2010 in conjunction with State water quality monitoring programmes. A variety of statistical techniques were used in concert to identify water quality parameters associated with V. vulnificus presence, abundance and virulence markers in the interest of developing strong predictive models for use in regional oceanographic modeling systems. A suite of models are provided to represent the best model fit and alternatives using environmental variables that allow them to be put to immediate use in current ecological forecasting efforts. Environmental parameters such as temperature, salinity and turbidity are capable of accurately predicting abundance and distribution of V. vulnificus in Chesapeake Bay. Forcing these empirical models with output from ocean modeling systems allows for spatially explicit forecasts for up to 48 h in the future. This study uses one of the largest data sets compiled to model Vibrio in an estuary, enhances our understanding of environmental correlates with abundance, distribution and presence of potentially virulent strains and offers a method to forecast these pathogens that may be replicated in other regions. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

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.

Study on grain quality forecasting method and indicators by using hyperspectral data in wheat

NASA Astrophysics Data System (ADS)

Huang, Wenjiang; Wang, Jihua; Liu, Liangyun; Wang, Zhijie; Tan, Changwei; Song, Xiaoyu; Wang, Jingdi

2005-01-01

Field experiments were conducted to examine the influence factors of cultivar, nitrogen application and irrigation on grain protein content, gluten content and grain hardness in three winter wheat cultivars under four levels of nitrogen and irrigation treatments. Firstly, the influence of cultivars and environment factors on grain quality were studied, the effective factors were cultivars, irrigation, fertilization, et al. Secondly, total nitrogen content around winter wheat anthesis stage was proved to be significant correlative with grain protein content, and spectral vegetation index significantly correlated to total nitrogen content around anthesis stage were the potential indicators for grain protein content. Accumulation of total nitrogen content and its transfer to grain is the physical link to produce the final grain protein, and total nitrogen content at anthesis stage was proved to be an indicator of final grain protein content. The selected normalized photochemical reflectance index (NPRI) was proved to be able to predict of grain protein content on the close correlation between the ratio of total carotenoid to chlorophyll a and total nitrogen content. The method contributes towards developing optimal procedures for predicting wheat grain quality through analysis of their canopy reflected spectrum at anthesis stage. Regression equations were established for forecasting grain protein and dry gluten content by total nitrogen content at anthesis stage, so it is feasible for forecasting grain quality by establishing correlation equations between biochemical constitutes and canopy reflected spectrum.

Short-term integrated forecasting system : 1993 model documentation report

DOT National Transportation Integrated Search

1993-12-01

The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the U.S. Energy Department (DOE) developed the STIFS model to generate shor...

ASSESSMENT OF ETA-CMAQ FORECASTS OF PARTICULATE MATTER DISTRIBUTIONS THROUGH COMPARISONS WITH SURFACE NETWORK AND SPECIALIZED MEASUREMENTS

EPA Science Inventory

An air-quality forecasting (AQF) system based on the National Weather Service (NWS) National Centers for Environmental Prediction's (NCEP's) Eta model and the U.S. EPA's Community Multiscale Air Quality (CMAQ) Modeling System is used to simulate the distributions of tropospheric ...

Seasonal scale water deficit forecasting in Africa and the Middle East using NASA's Land Information System (LIS)

NASA Astrophysics Data System (ADS)

Peters-Lidard, C. D.; Arsenault, K. R.; Shukla, S.; Getirana, A.; McNally, A.; Koster, R. D.; Zaitchik, B. F.; Badr, H. S.; Roningen, J. M.; Kumar, S.; Funk, C. C.

2017-12-01

A seamless and effective water deficit monitoring and early warning system is critical for assessing food security in Africa and the Middle East. In this presentation, we report on the ongoing development and validation of a seasonal scale water deficit forecasting system based on NASA's Land Information System (LIS) and seasonal climate forecasts. First, our presentation will focus on the implementation and validation of drought and water availability monitoring products in the region. Next, it will focus on evaluating drought and water availability forecasts. Finally, details will be provided of our ongoing collaboration with end-user partners in the region (e.g., USAID's Famine Early Warning Systems Network, FEWS NET), on formulating meaningful early warning indicators, effective communication and seamless dissemination of the products through NASA's web-services. The water deficit forecasting system thus far incorporates NASA GMAO's Catchment and the Noah Multi-Physics (MP) LSMs. In addition, the LSMs' surface and subsurface runoff are routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics. To establish a climatology from 1981-2015, the two LSMs are driven by NASA/GMAO's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS and UCSB Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily rainfall dataset. Comparison of the models' energy and hydrological budgets with independent observations suggests that major droughts are well-reflected in the climatology. The system uses seasonal climate forecasts from NASA's GEOS-5 (the Goddard Earth Observing System Model-5) and NCEP's Climate Forecast System-2, and it produces forecasts of soil moisture, ET and streamflow out to 6 months in the future. Forecasts of those variables are formulated in terms of indicators to provide forecasts of drought and water availability in the region. Current work suggests

Improving Forecast Skill by Assimilation of AIRS Temperature Soundings

NASA Technical Reports Server (NTRS)

Susskind, Joel; Reale, Oreste

2010-01-01

AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The AIRS Version 5 retrieval algorithm, is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates delta T(p) for retrieved quantities and the use of these error estimates for Quality Control. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 deg. latitude X 0.67 deg longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates delta (p) were used as the uncertainty for each measurement in the data assimilation process. We compared forecasts analyses generated from the analyses done by assimilation of AIRS temperature profiles with three different sets of thresholds; Standard, Medium, and Tight. Assimilation of Quality Controlled AIRS temperature profiles significantly improve 5-7 day forecast skill compared to that obtained without the benefit of AIRS data in all of the cases studied. In addition, assimilation of Quality Controlled AIRS temperature soundings performs better than assimilation of AIRS observed radiances. Based on the experiments shown, Tight Quality Control of AIRS temperature profile performs best

Climate forecasting services: coming down from the ivory tower

NASA Astrophysics Data System (ADS)

Doblas-Reyes, F. J.; Caron, L. P.; Cortesi, N.; Soret, A.; Torralba, V.; Turco, M.; González Reviriego, N.; Jiménez, I.; Terrado, M.

2016-12-01

Subseasonal-to-seasonal (S2S) climate forecasts are increasingly used across a range of application areas (energy, water management, agriculture, health, insurance) through tailored services using the climate services paradigm. In this contribution we show the value of climate forecasting services through several examples of their application in the energy, reinsurance and agriculture sectors. Climate services aim at making climate information action oriented. In a climate forecasting context the task starts with the identification of climate variables, thresholds and events relevant to the users. These elements are then analysed to determine whether they can be both reliably and skilfully predicted at appropriate time scales. In this contribution we assess climate predictions of precipitation, temperature and wind indices from state-of-the-art operational multi-model forecast systems and if they respond to the expectations and requests from a range of users. This requires going beyond the more traditional assessment of monthly mean values to include assessments of global forecast quality of the frequency of warm, cold, windy and wet extremes (e.g. [1], [2]), as well as of using tools like the Euro-Atlantic weather regimes [3]. The forecast quality of extremes is generally similar to or slightly lower than that of monthly or seasonal averages, but offers a kind of information closer to what some users require. In addition to considering local climate variables, we also explore the use of large-scale climate indices, such as ENSO and NAO, that are associated with large regional synchronous variations of wind or tropical storm frequency. These indices help illustrating the relative merits of climate forecast information to users and are the cornerstone of climate stories that engage them in the co-production of climate information. [1] Doblas-Reyes et al, WIREs, 2013 [2] Pepler et al, Weather and Climate Extremes, 2015 [3] Pavan and Doblas-Reyes, Clim Dyn, 2013

A Study on the Potential Applications of Satellite Data in Air Quality Monitoring and Forecasting

NASA Technical Reports Server (NTRS)

Li, Can; Hsu, N. Christina; Tsay, Si-Chee

2011-01-01

In this study we explore the potential applications of MODIS (Moderate Resolution Imaging Spectroradiometer) -like satellite sensors in air quality research for some Asian regions. The MODIS aerosol optical thickness (AOT), NCEP global reanalysis meteorological data, and daily surface PM(sub 10) concentrations over China and Thailand from 2001 to 2009 were analyzed using simple and multiple regression models. The AOT-PM(sub 10) correlation demonstrates substantial seasonal and regional difference, likely reflecting variations in aerosol composition and atmospheric conditions, Meteorological factors, particularly relative humidity, were found to influence the AOT-PM(sub 10) relationship. Their inclusion in regression models leads to more accurate assessment of PM(sub 10) from space borne observations. We further introduced a simple method for employing the satellite data to empirically forecast surface particulate pollution, In general, AOT from the previous day (day 0) is used as a predicator variable, along with the forecasted meteorology for the following day (day 1), to predict the PM(sub 10) level for day 1. The contribution of regional transport is represented by backward trajectories combined with AOT. This method was evaluated through PM(sub 10) hindcasts for 2008-2009, using ohservations from 2005 to 2007 as a training data set to obtain model coefficients. For five big Chinese cities, over 50% of the hindcasts have percentage error less than or equal to 30%. Similar performance was achieved for cities in northern Thailand. The MODIS AOT data are responsible for at least part of the demonstrated forecasting skill. This method can be easily adapted for other regions, but is probably most useful for those having sparse ground monitoring networks or no access to sophisticated deterministic models. We also highlight several existing issues, including some inherent to a regression-based approach as exemplified by a case study for Beijing, Further studies will be

THE EMERGENCE OF NUMERICAL AIR QUALITY FORCASTING MODELS AND THEIR APPLICATIONS

EPA Science Inventory

In recent years the U.S. and other nations have begun programs for short-term local through regional air quality forecasting based upon numerical three-dimensional air quality grid models. These numerical air quality forecast (NAQF) models and systems have been developed and test...

An assessment of a North American Multi-Model Ensemble (NMME) based global drought early warning forecast system

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Sheffield, J.; Pan, M.; Roundy, J.

2013-12-01

One of the key recommendations of the WCRP Global Drought Information System (GDIS) workshop is to develop an experimental real-time global monitoring and prediction system. While great advances has been made in global drought monitoring based on satellite observations and model reanalysis data, global drought forecasting has been stranded in part due to the limited skill both in climate forecast models and global hydrologic predictions. Having been working on drought monitoring and forecasting over USA for more than a decade, the Princeton land surface hydrology group is now developing an experimental global drought early warning system that is based on multiple climate forecast models and a calibrated global hydrologic model. In this presentation, we will test its capability in seasonal forecasting of meteorological, agricultural and hydrologic droughts over global major river basins, using precipitation, soil moisture and streamflow forecasts respectively. Based on the joint probability distribution between observations using Princeton's global drought monitoring system and model hindcasts and real-time forecasts from North American Multi-Model Ensemble (NMME) project, we (i) bias correct the monthly precipitation and temperature forecasts from multiple climate forecast models, (ii) downscale them to a daily time scale, and (iii) use them to drive the calibrated VIC model to produce global drought forecasts at a 1-degree resolution. A parallel run using the ESP forecast method, which is based on resampling historical forcings, is also carried out for comparison. Analysis is being conducted over global major river basins, with multiple drought indices that have different time scales and characteristics. The meteorological drought forecast does not have uncertainty from hydrologic models and can be validated directly against observations - making the validation an 'apples-to-apples' comparison. Preliminary results for the evaluation of meteorological drought onset

System for NIS Forecasting Based on Ensembles Analysis

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

2014-01-02

BMA-NIS is a package/library designed to be called by a script (e.g. Perl or Python). The software itself is written in the language of R. The software assists electric power delivery systems in planning resource availability and demand, based on historical data and current data variables. Net Interchange Schedule (NIS) is the algebraic sum of all energy scheduled to flow into or out of a balancing area during any interval. Accurate forecasts for NIS are important so that the Area Control Error (ACE) stays within an acceptable limit. To date, there are many approaches for forecasting NIS but all nonemore » of these are based on single models that can be sensitive to time of day and day of week effects.« less

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

Selection and Classification Using a Forecast Applicant Pool.

ERIC Educational Resources Information Center

Hendrix, William H.

The document presents a forecast model of the future Air Force applicant pool. By forecasting applicants' quality (means and standard deviations of aptitude scores) and quantity (total number of applicants), a potential enlistee could be compared to the forecasted pool. The data used to develop the model consisted of means, standard deviation, and…

EMC: Air Quality Forecast Home page

Science.gov Websites

archive NAM Verification Meteorology Error Time Series EMC NAM Spatial Maps Real Time Mesoscale Analysis Precipitation verification NAQFC VERIFICATION CMAQ Ozone & PM Error Time Series AOD Error Time Series HYSPLIT Smoke forecasts vs GASP satellite Dust and Smoke Error Time Series HYSPLIT WCOSS Upgrade (July