Extended-range forecasting of Chinese summer surface air temperature and heat waves

NASA Astrophysics Data System (ADS)

Zhu, Zhiwei; Li, Tim

2018-03-01

Because of growing demand from agricultural planning, power management and activity scheduling, extended-range (5-30-day lead) forecasting of summer surface air temperature (SAT) and heat waves over China is carried out in the present study via spatial-temporal projection models (STPMs). Based on the training data during 1960-1999, the predictability sources are found to propagate from Europe, Northeast Asia, and the tropical Pacific, to influence the intraseasonal 10-80 day SAT over China. STPMs are therefore constructed using the projection domains, which are determined by these previous predictability sources. For the independent forecast period (2000-2013), the STPMs can reproduce EOF-filtered 30-80 day SAT at all lead times of 5-30 days over most part of China, and observed 30-80 and 10-80 day SAT at 25-30 days over eastern China. Significant pattern correlation coefficients account for more than 50% of total forecasts at all 5-30-day lead times against EOF-filtered and observed 30-80 day SAT, and at a 20-day lead time against observed 10-80 day SAT. The STPMs perform poorly in reproducing 10-30 day SAT. Forecasting for the first two modes of 10-30 day SAT only shows useful skill within a 15-day lead time. Forecasting for the third mode of 10-30 day SAT is useless after a 10-day lead time. The forecasted heat waves over China are determined by the reconstructed SAT which is the summation of the forecasted 10-80 day SAT and the lower frequency (longer than 80-day) climatological SAT. Over a large part of China, the STPMs can forecast more than 30% of heat waves within a 15-day lead time. In general, the STPMs demonstrate the promising skill for extended-range forecasting of Chinese summer SAT and heat waves.

Modeling North Atlantic Nor'easters With Modern Wave Forecast Models

NASA Astrophysics Data System (ADS)

Perrie, Will; Toulany, Bechara; Roland, Aron; Dutour-Sikiric, Mathieu; Chen, Changsheng; Beardsley, Robert C.; Qi, Jianhua; Hu, Yongcun; Casey, Michael P.; Shen, Hui

2018-01-01

Three state-of-the-art operational wave forecast model systems are implemented on fine-resolution grids for the Northwest Atlantic. These models are: (1) a composite model system consisting of SWAN implemented within WAVEWATCHIII® (the latter is hereafter, WW3) on a nested system of traditional structured grids, (2) an unstructured grid finite-volume wave model denoted "SWAVE," using SWAN physics, and (3) an unstructured grid finite element wind wave model denoted as "WWM" (for "wind wave model") which uses WW3 physics. Models are implemented on grid systems that include relatively large domains to capture the wave energy generated by the storms, as well as including fine-resolution nearshore regions of the southern Gulf of Maine with resolution on the scale of 25 m to simulate areas where inundation and coastal damage have occurred, due to the storms. Storm cases include three intense midlatitude cases: a spring Nor'easter storm in May 2005, the Patriot's Day storm in 2007, and the Boxing Day storm in 2010. Although these wave model systems have comparable overall properties in terms of their performance and skill, it is found that there are differences. Models that use more advanced physics, as presented in recent versions of WW3, tuned to regional characteristics, as in the Gulf of Maine and the Northwest Atlantic, can give enhanced results.

Lightning-generated whistler waves observed by probes on the Communication/Navigation Outage Forecast System satellite at low latitudes

NASA Astrophysics Data System (ADS)

Holzworth, R. H.; McCarthy, M. P.; Pfaff, R. F.; Jacobson, A. R.; Willcockson, W. L.; Rowland, D. E.

2011-06-01

Direct evidence is presented for a causal relationship between lightning and strong electric field transients inside equatorial ionospheric density depletions. In fact, these whistler mode plasma waves may be the dominant electric field signal within such depletions. Optical lightning data from the Communication/Navigation Outage Forecast System (C/NOFS) satellite and global lightning location information from the World Wide Lightning Location Network are presented as independent verification that these electric field transients are caused by lightning. The electric field instrument on C/NOFS routinely measures lightning-related electric field wave packets or sferics, associated with simultaneous measurements of optical flashes at all altitudes encountered by the satellite (401-867 km). Lightning-generated whistler waves have abundant access to the topside ionosphere, even close to the magnetic equator.

Lightning-Generated Whistler Waves Observed by Probes On The Communication/Navigation Outage Forecast System Satellite at Low Latitudes

NASA Technical Reports Server (NTRS)

Holzworth, R. H.; McCarthy, M. P.; Pfaff, R. F.; Jacobson, A. R.; Willcockson, W. L.; Rowland, D. E.

2011-01-01

Direct evidence is presented for a causal relationship between lightning and strong electric field transients inside equatorial ionospheric density depletions. In fact, these whistler mode plasma waves may be the dominant electric field signal within such depletions. Optical lightning data from the Communication/Navigation Outage Forecast System (C/NOFS) satellite and global lightning location information from the World Wide Lightning Location Network are presented as independent verification that these electric field transients are caused by lightning. The electric field instrument on C/NOFS routinely measures lightning ]related electric field wave packets or sferics, associated with simultaneous measurements of optical flashes at all altitudes encountered by the satellite (401.867 km). Lightning ]generated whistler waves have abundant access to the topside ionosphere, even close to the magnetic equator.

VISIR-I: small vessels, least-time nautical routes using wave forecasts

NASA Astrophysics Data System (ADS)

Mannarini, G.; Pinardi, N.; Coppini, G.; Oddo, P.; Iafrati, A.

2015-09-01

A new numerical model for the on-demand computation of optimal ship routes based on sea-state forecasts has been developed. The model, named VISIR (discoVerIng Safe and effIcient Routes) is designed to support decision-makers when planning a marine voyage. The first version of the system, VISIR-I, considers medium and small motor vessels with lengths of up to a few tens of meters and a displacement hull. The model is made up of three components: the route optimization algorithm, the mechanical model of the ship, and the environmental fields. The optimization algorithm is based on a graph-search method with time-dependent edge weights. The algorithm is also able to compute a voluntary ship speed reduction. The ship model accounts for calm water and added wave resistance by making use of just the principal particulars of the vessel as input parameters. The system also checks the optimal route for parametric roll, pure loss of stability, and surfriding/broaching-to hazard conditions. Significant wave height, wave spectrum peak period, and wave direction forecast fields are employed as an input. Examples of VISIR-I routes in the Mediterranean Sea are provided. The optimal route may be longer in terms of miles sailed and yet it is faster and safer than the geodetic route between the same departure and arrival locations. Route diversions result from the safety constraints and the fact that the algorithm takes into account the full temporal evolution and spatial variability of the environmental fields.

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

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.

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

Heat wave over India during summer 2015: an assessment of real time extended range forecast

NASA Astrophysics Data System (ADS)

Pattanaik, D. R.; Mohapatra, M.; Srivastava, A. K.; Kumar, Arun

2017-08-01

Hot winds are the marked feature of summer season in India during late spring preceding the climatological onset of the monsoon season in June. Some years the conditions becomes very vulnerable with the maximum temperature ( T max) exceeding 45 °C for many days over parts of north-western, eastern coastal states of India and Indo-Gangetic plain. During summer of 2015 (late May to early June) eastern coastal states, central and northwestern parts of India experienced severe heat wave conditions leading to loss of thousands of human life in extreme high temperature conditions. It is not only the loss of human life but also the animals and birds were very vulnerable to this extreme heat wave conditions. In this study, an attempt is made to assess the performance of real time extended range forecast (forecast up to 3 weeks) of this scorching T max based on the NCEP's Climate Forecast System (CFS) latest version coupled model (CFSv2). The heat wave condition was very severe during the week from 22 to 28 May with subsequent week from 29 May to 4 June also witnessed high T max over many parts of central India including eastern coastal states of India. The 8 ensemble members of operational CFSv2 model are used once in a week to prepare the weekly bias corrected deterministic (ensemble mean) T max forecast for 3 weeks valid from Friday to Thursday coinciding with the heat wave periods of 2015. Using the 8 ensemble members separately and the CFSv2 corresponding hindcast climatology the probability of above and below normal T max is also prepared for the same 3 weeks. The real time deterministic and probabilistic forecasts did indicate impending heat wave over many parts of India during late May and early June of 2015 associated with strong northwesterly wind over main land mass of India, delaying the sea breeze, leading to heat waves over eastern coastal regions of India. Thus, the capability of coupled model in providing early warning of such killer heat wave can be very

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 Role of Secondary Frontal Waves in Causing Missed or False Alarm Flood Forecasts During Landfalling Atmospheric Rivers

NASA Astrophysics Data System (ADS)

Martin, A.; Ralph, F. M.; Lavers, D. A.; Kalansky, J.; Kawzenuk, B.

2015-12-01

The previous ten years has seen an explosion in research devoted to the Atmospheric River (AR) phenomena, features of the midlatitude circulation responsible for large horizontal water vapor transport. Upon landfall, ARs can be associated with 30-50% of annual precipitation in some regions, while also causing the largest flooding events in places such as coastal California. Little discussed is the role secondary frontal waves play in modulating precipitation during a landfalling AR. Secondary frontal waves develop along an existing cold front in response to baroclinic frontogenesis, often coinciding with a strong upper-tropospheric jet. If the secondary wave develops along a front associated with a landfalling AR, the resulting precipitation may be much greater or much less than originally forecasted - especially in regions where orographic uplift of horizontally transported water vapor is responsible for a large portion of precipitation. In this study, we present several cases of secondary frontal waves that have occurred in conjunction with a landfalling AR on the US West Coast. We put the impact of these cases in historical perspective using quantitative precipitation forecasts, satellite data, reanalyses, and estimates of damage related to flooding. We also discuss the dynamical mechanisms behind secondary frontal wave development and relate these mechanisms to the high spatiotemporal variability in precipitation observed during ARs with secondary frontal waves. Finally, we demonstrate that even at lead times less than 24 hours, current quantitative precipitation forecasting methods have difficulty accurately predicting the rainfall in the area near the secondary wave landfall, in some cases leading to missed or false alarm flood warnings, and suggest methods which may improve quantitative precipitation forecasts for this type of system in the future.

Preliminary Study on Coupling Wave-Tide-Storm Surges Prediction System

NASA Astrophysics Data System (ADS)

You, S.; Park, S.; Seo, J.; Kim, K.

2008-12-01

The Korean Peninsula is surrounded by the Yellow Sea, East China Sea, and East Sea. This complex oceanographic system includes large tides in the Yellow Sea and seasonally varying monsoon and typhoon events. For Korea's coastal regions, floods caused by wave and storm surges are among the most serious threats. To predict more accurate wave and storm surge, the development of coupling wave-tide-storm surges prediction system is essential. For the time being, wave and storm surges predictions are still made separately in KMA (Korea Meteorological Administration) and most operational institute. However, many researchers have emphasized the effects of tides and storm surges on wind waves and recommended further investigations into the effects of wave-tide-storm surges interactions and coupling module on wave heights. However, tidal height and current give a great effect on the wave prediction in the Yellow sea where is very high tide and related research is not enough. At present, KMA has operated the wave (RWAM : Regional Wave Model) and storm surges/tide prediction system (RTSM : Regional Tide/Storm Surges Model) for ocean forecasting. The RWAM is WAVEWATCH III which is a third generation wave model developed by Tolman (1989). The RTSM is based on POM (Princeton Ocean Model, Blumberg and Mellor, 1987). The RWAM and RTSM cover the northwestern Pacific Ocean from 115°E to 150°E and from 20°N to 52°N. The horizontal grid intervals are 1/12° in both latitudinal and longitudinal directions. The development, testing and application of a coupling module in which wave-tide-storm surges are incorporated within the frame of KMA Ocean prediction system, has been considered as a step forward in respect of ocean forecasting. In addition, advanced wave prediction model will be applicable to the effect of ocean in the weather forecasting system. The main purpose of this study is to show how the coupling module developed and to report on a series of experiments dealing with the

The Spectral Ocean Wave Model (SOWM), a Northern Hemisphere Computer Model for Specifying and Forecasting Ocean Wave Spectra

DTIC Science & Technology

1982-07-01

directions. SIGNIFICANT WAVE HEIGHT A further sua-tion of (3) over the 15 frequency bands yields, within a linear model , the variance of a time history of...SPECTRAL Of.EAN WAVE MODEL (SOWM), A NORTHERN Final Report HEMtISPHEE COMPUTER MODELL Foyt SPECIFYING AND FORECASTING OCEAN WAVE .SftfTRA S EFRIGOG...Ocean Wave Model (SWM() In use at the Fleet Numerical Oceanography Center si.nce 1974 has been used to produce spectra for a 20- year ocean wave

Operational wave now- and forecast in the German Bight as a basis for the assessment of wave-induced hydrodynamic loads on coastal dikes

NASA Astrophysics Data System (ADS)

Dreier, Norman; Fröhle, Peter

2017-12-01

The knowledge of the wave-induced hydrodynamic loads on coastal dikes including their temporal and spatial resolution on the dike in combination with actual water levels is of crucial importance of any risk-based early warning system. As a basis for the assessment of the wave-induced hydrodynamic loads, an operational wave now- and forecast system is set up that consists of i) available field measurements from the federal and local authorities and ii) data from numerical simulation of waves in the German Bight using the SWAN wave model. In this study, results of the hindcast of deep water wave conditions during the winter storm on 5-6 December, 2013 (German name `Xaver') are shown and compared with available measurements. Moreover field measurements of wave run-up from the local authorities at a sea dike on the German North Sea Island of Pellworm are presented and compared against calculated wave run-up using the EurOtop (2016) approach.

MOCASSIM - an operational forecast system for the Portuguese coastal waters.

NASA Astrophysics Data System (ADS)

Vitorino, J.; Soares, C.; Almeida, S.; Rusu, E.; Pinto, J.

2003-04-01

An operational system for the forecast of oceanographic conditions off the Portuguese coast is presently being implemented at Instituto Hidrográfico (IH), in the framework of project MOCASSIM. The system is planned to use a broad range of observations provided both from IH observational networks (wave buoys, tidal gauges) and programs (hydrographic surveys, moorings) as well as from external sources. The MOCASSIM system integrates several numerical models which, combined, are intended to cover the relevant physical processes observed in the geographical areas of interest. At the present stage of development the system integrates a circulation module and a wave module. The circulation module is based on the Harvard Ocean Prediction System (HOPS), a primitive equation model formulated under the rigid lid assumption, which includes a data assimilation module. The wave module is based on the WaveWatch3 (WW3) model, which provides wave conditions in the North Atlantic basin, and on the SWAN model which is used to improve the wave forecasts on coastal or other specific areas of interest. The models use the meteorological forcing fields of a limited area model (ALADIN model) covering the Portuguese area, which are being provided in the framework of a close colaboration with Instituto de Meteorologia. Although still under devellopment, the MOCASSIM system has already been used in several operationnal contexts. These included the operational environmental assessment during both national and NATO navy exercises and, more recently, the monitoring of the oceanographic conditions in the NW Iberian area affected by the oil spill of MV "Prestige". The system is also a key component of ongoing research on the oceanography of the Portuguese continental margin, which is presently being conducted at IH in the framework of national and European funded projects.

An Improved Ocean Observing System for Coastal Louisiana: WAVCIS (WAVE-CURRENT-SURGE Information System )

NASA Astrophysics Data System (ADS)

Zhang, X.; Stone, G. W.; Gibson, W. J.; Braud, D.

2005-05-01

WAVCIS is a regional ocean observing and forecasting system. It was designed to measure, process, forecast, and distribute oceanographic and meteorological information. WAVCIS was developed and is maintained by the Coastal Studies Institute at Louisiana State University. The in-situ observing stations are distributed along the central Louisiana and Mississippi coast. The forecast region covers the entire Gulf of Mexico with emphasis on offshore Louisiana. By using state-of-the-art instrumentation, WAVCIS measures directional waves, currents, temperature, water level, conductivity, turbidity, salinity, dissolved oxygen, chlorophyll, Meteorological parameters include wind speed and direction, air pressure and temperature visibility and humidity. Through satellite communication links, the measured data are transmitted to the WAVCIS laboratory. After processing, they are available to the public via the internet on a near real-time basis. WAVCIS also includes a forecasting capability. Waves, tides, currents, and winds are forecast daily for up to 80 hours in advance. There are a number of numerical wave and surge models that can be used for forecasts. WAM and SWAN are used for operational purposes to forecast sea state. Tides at each station are predicted based on the harmonic constants calculated from past in-situ observations at respective sites. Interpolated winds from the ETA model are used as input forcing for waves. Both in-situ and forecast information are available online to the users through WWW. Interactive GIS web mapping is implemented on the WAVCIS webpage to visualize the model output and in-situ observational data. WAVCIS data can be queried, retrieved, downloaded, and analyzed through the web page. Near real-time numerical model skill assessment can also be performed by using the data from in-situ observing stations.

The diagnosis and forecast system of hydrometeorological characteristics for the White, Barents, Kara and Pechora Seas

NASA Astrophysics Data System (ADS)

Fomin, Vladimir; Diansky, Nikolay; Gusev, Anatoly; Kabatchenko, Ilia; Panasenkova, Irina

2017-04-01

The diagnosis and forecast system for simulating hydrometeorological characteristics of the Russian Western Arctic seas is presented. It performs atmospheric forcing computation with the regional non-hydrostatic atmosphere model Weather Research and Forecasting model (WRF) with spatial resolution 15 km, as well as computation of circulation, sea level, temperature, salinity and sea ice with the marine circulation model INMOM (Institute of Numerical Mathematics Ocean Model) with spatial resolution 2.7 km, and the computation of wind wave parameters using the Russian wind-wave model (RWWM) with spatial resolution 5 km. Verification of the meteorological characteristics is done for air temperature, air pressure, wind velocity, water temperature, currents, sea level anomaly, wave characteristics such as wave height and wave period. The results of the hydrometeorological characteristic verification are presented for both retrospective and forecast computations. The retrospective simulation of the hydrometeorological characteristics for the White, Barents, Kara and Pechora Seas was performed with the diagnosis and forecast system for the period 1986-2015. The important features of the Kara Sea circulation are presented. Water exchange between Pechora and Kara Seas is described. The importance is shown of using non-hydrostatic atmospheric circulation model for the atmospheric forcing computation in coastal areas. According to the computation results, extreme values of hydrometeorological characteristics were obtained for the Russian Western Arctic seas.

a 24/7 High Resolution Storm Surge, Inundation and Circulation Forecasting System for Florida Coast

NASA Astrophysics Data System (ADS)

Paramygin, V.; Davis, J. R.; Sheng, Y.

2012-12-01

A 24/7 forecasting system for Florida is needed because of the high risk of tropical storm surge-induced coastal inundation and damage, and the need to support operational management of water resources, utility infrastructures, and fishery resources. With the anticipated climate change impacts, including sea level rise, coastal areas are facing the challenges of increasing inundation risk and increasing population. Accurate 24/7 forecasting of water level, inundation, and circulation will significantly enhance the sustainability of coastal communities and environments. Supported by the Southeast Coastal Ocean Observing Regional Association (SECOORA) through NOAA IOOS, a 24/7 high-resolution forecasting system for storm surge, coastal inundation, and baroclinic circulation is being developed for Florida using CH3D Storm Surge Modeling System (CH3D-SSMS). CH3D-SSMS is based on the CH3D hydrodynamic model coupled to a coastal wave model SWAN and basin scale surge and wave models. CH3D-SSMS has been verified with surge, wave, and circulation data from several recent hurricanes in the U.S.: Isabel (2003); Charley, Dennis and Ivan (2004); Katrina and Wilma (2005); Ike and Fay (2008); and Irene (2011), as well as typhoons in the Pacific: Fanapi (2010) and Nanmadol (2011). The effects of tropical cyclones on flow and salinity distribution in estuarine and coastal waters has been simulated for Apalachicola Bay as well as Guana-Tolomato-Matanzas Estuary using CH3D-SSMS. The system successfully reproduced different physical phenomena including large waves during Ivan that damaged I-10 Bridges, a large alongshore wave and coastal flooding during Wilma, salinity drop during Fay, and flooding in Taiwan as a result of combined surge and rain effect during Fanapi. The system uses 4 domains that cover entire Florida coastline: West, which covers the Florida panhandle and Tampa Bay; Southwest spans from Florida Keys to Charlotte Harbor; Southeast, covering Biscayne Bay and Miami and

Evaluation of an Early-Warning System for Heat Wave-Related Mortality in Europe: Implications for Sub-seasonal to Seasonal Forecasting and Climate Services

PubMed Central

Lowe, Rachel; García-Díez, Markel; Ballester, Joan; Creswick, James; Robine, Jean-Marie; Herrmann, François R.; Rodó, Xavier

2016-01-01

Heat waves have been responsible for more fatalities in Europe over the past decades than any other extreme weather event. However, temperature-related illnesses and deaths are largely preventable. Reliable sub-seasonal-to-seasonal (S2S) climate forecasts of extreme temperatures could allow for better short-to-medium-term resource management within heat-health action plans, to protect vulnerable populations and ensure access to preventive measures well in advance. The objective of this study is to assess the extent to which S2S climate forecasts could be incorporated into heat-health action plans, to support timely public health decision-making ahead of imminent heat wave events in Europe. Forecasts of apparent temperature at different lead times (e.g., 1 day, 4 days, 8 days, up to 3 months) were used in a mortality model to produce probabilistic mortality forecasts up to several months ahead of the 2003 heat wave event in Europe. Results were compared to mortality predictions, inferred using observed apparent temperature data in the mortality model. In general, we found a decreasing transition in skill between excellent predictions when using observed temperature, to predictions with no skill when using forecast temperature with lead times greater than one week. However, even at lead-times up to three months, there were some regions in Spain and the United Kingdom where excess mortality was detected with some certainty. This suggests that in some areas of Europe, there is potential for S2S climate forecasts to be incorporated in localised heat–health action plans. In general, these results show that the performance of this climate service framework is not limited by the mortality model itself, but rather by the predictability of the climate variables, at S2S time scales, over Europe. PMID:26861369

Evaluation of an Early-Warning System for Heat Wave-Related Mortality in Europe: Implications for Sub-seasonal to Seasonal Forecasting and Climate Services.

PubMed

Lowe, Rachel; García-Díez, Markel; Ballester, Joan; Creswick, James; Robine, Jean-Marie; Herrmann, François R; Rodó, Xavier

2016-02-06

Heat waves have been responsible for more fatalities in Europe over the past decades than any other extreme weather event. However, temperature-related illnesses and deaths are largely preventable. Reliable sub-seasonal-to-seasonal (S2S) climate forecasts of extreme temperatures could allow for better short-to-medium-term resource management within heat-health action plans, to protect vulnerable populations and ensure access to preventive measures well in advance. The objective of this study is to assess the extent to which S2S climate forecasts could be incorporated into heat-health action plans, to support timely public health decision-making ahead of imminent heat wave events in Europe. Forecasts of apparent temperature at different lead times (e.g., 1 day, 4 days, 8 days, up to 3 months) were used in a mortality model to produce probabilistic mortality forecasts up to several months ahead of the 2003 heat wave event in Europe. Results were compared to mortality predictions, inferred using observed apparent temperature data in the mortality model. In general, we found a decreasing transition in skill between excellent predictions when using observed temperature, to predictions with no skill when using forecast temperature with lead times greater than one week. However, even at lead-times up to three months, there were some regions in Spain and the United Kingdom where excess mortality was detected with some certainty. This suggests that in some areas of Europe, there is potential for S2S climate forecasts to be incorporated in localised heat-health action plans. In general, these results show that the performance of this climate service framework is not limited by the mortality model itself, but rather by the predictability of the climate variables, at S2S time scales, over Europe.

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.

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

NASA Astrophysics Data System (ADS)

Flampouris, Stylianos; Penny, Steve; Alves, Henrique

2017-04-01

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

Wave Extremes in the Northeast Atlantic from Ensemble Forecasts

NASA Astrophysics Data System (ADS)

Breivik, Øyvind; Aarnes, Ole Johan; Bidlot, Jean-Raymond; Carrasco, Ana; Saetra, Øyvind

2013-10-01

A method for estimating return values from ensembles of forecasts at advanced lead times is presented. Return values of significant wave height in the North-East Atlantic, the Norwegian Sea and the North Sea are computed from archived +240-h forecasts of the ECMWF ensemble prediction system (EPS) from 1999 to 2009. We make three assumptions: First, each forecast is representative of a six-hour interval and collectively the data set is then comparable to a time period of 226 years. Second, the model climate matches the observed distribution, which we confirm by comparing with buoy data. Third, the ensemble members are sufficiently uncorrelated to be considered independent realizations of the model climate. We find anomaly correlations of 0.20, but peak events (>P97) are entirely uncorrelated. By comparing return values from individual members with return values of subsamples of the data set we also find that the estimates follow the same distribution and appear unaffected by correlations in the ensemble. The annual mean and variance over the 11-year archived period exhibit no significant departures from stationarity compared with a recent reforecast, i.e., there is no spurious trend due to model upgrades. EPS yields significantly higher return values than ERA-40 and ERA-Interim and is in good agreement with the high-resolution hindcast NORA10, except in the lee of unresolved islands where EPS overestimates and in enclosed seas where it is biased low. Confidence intervals are half the width of those found for ERA-Interim due to the magnitude of the data set.

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.

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.

The Major Stratospheric Sudden Warming of January 2013: Analyses and Forecasts in the GEOS-5 Data Assimilation System

NASA Technical Reports Server (NTRS)

Coy, Lawrence; Pawson, Steven

2014-01-01

We examine the major stratosphere sudden warming (SSW) that occurred on 6 January 2013, using output from the NASA Global Modeling and Assimilation Office (GMAO) GEOS-5 (Goddard Earth Observing System) near-real-time data assimilation system (DAS). Results show that the major SSW of January 2013 falls into the vortex splitting type of SSW, with the initial planetary wave breaking occurring near 10 hPa. The vertical flux of wave activity at the tropopause responsible for the SSW occurred mainly in the Pacific Hemisphere, including the a pulse associated with the preconditioning of the polar vortex by wave 1 identified on 23 December 2012. While most of the vertical wave activity flux was in the Pacific Hemisphere, a rapidly developing tropospheric weather system over the North Atlantic on 28 December is shown to have produced a strong transient upward wave activity flux into the lower stratosphere coinciding with the peak of the SSW event. In addition, the GEOS-5 5-day forecasts accurately predicted the major SSW of January 2013 as well as the upper tropospheric disturbances responsible for the warming. The overall success of the 5-day forecasts provides motivation to produce regular 10-day forecasts with GEOS-5, to better support studies of stratosphere-troposphere interaction.

VISIR-I: small vessels - least-time nautical routes using wave forecasts

NASA Astrophysics Data System (ADS)

Mannarini, Gianandrea; Pinardi, Nadia; Coppini, Giovanni; Oddo, Paolo; Iafrati, Alessandro

2016-05-01

A new numerical model for the on-demand computation of optimal ship routes based on sea-state forecasts has been developed. The model, named VISIR (discoVerIng Safe and effIcient Routes) is designed to support decision-makers when planning a marine voyage. The first version of the system, VISIR-I, considers medium and small motor vessels with lengths of up to a few tens of metres and a displacement hull. The model is comprised of three components: a route optimization algorithm, a mechanical model of the ship, and a processor of the environmental fields. The optimization algorithm is based on a graph-search method with time-dependent edge weights. The algorithm is also able to compute a voluntary ship speed reduction. The ship model accounts for calm water and added wave resistance by making use of just the principal particulars of the vessel as input parameters. It also checks the optimal route for parametric roll, pure loss of stability, and surfriding/broaching-to hazard conditions. The processor of the environmental fields employs significant wave height, wave spectrum peak period, and wave direction forecast fields as input. The topological issues of coastal navigation (islands, peninsulas, narrow passages) are addressed. Examples of VISIR-I routes in the Mediterranean Sea are provided. The optimal route may be longer in terms of miles sailed and yet it is faster and safer than the geodetic route between the same departure and arrival locations. Time savings up to 2.7 % and route lengthening up to 3.2 % are found for the case studies analysed. However, there is no upper bound for the magnitude of the changes of such route metrics, which especially in case of extreme sea states can be much greater. Route diversions result from the safety constraints and the fact that the algorithm takes into account the full temporal evolution and spatial variability of the environmental fields.

Assessment of reservoir system variable forecasts

NASA Astrophysics Data System (ADS)

Kistenmacher, Martin; Georgakakos, Aris P.

2015-05-01

Forecast ensembles are a convenient means to model water resources uncertainties and to inform planning and management processes. For multipurpose reservoir systems, forecast types include (i) forecasts of upcoming inflows and (ii) forecasts of system variables and outputs such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. Forecasts of system variables and outputs are conditional on forecasted inflows as well as on specific management policies and can provide useful information for decision-making processes. Unlike inflow forecasts (in ensemble or other forms), which have been the subject of many previous studies, reservoir system variable and output forecasts are not formally assessed in water resources management theory or practice. This article addresses this gap and develops methods to rectify potential reservoir system forecast inconsistencies and improve the quality of management-relevant information provided to stakeholders and managers. The overarching conclusion is that system variable and output forecast consistency is critical for robust reservoir management and needs to be routinely assessed for any management model used to inform planning and management processes. The above are demonstrated through an application from the Sacramento-American-San Joaquin reservoir system in northern California.

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.

Statistical Downscaling in Multi-dimensional Wave Climate Forecast

NASA Astrophysics Data System (ADS)

Camus, P.; Méndez, F. J.; Medina, R.; Losada, I. J.; Cofiño, A. S.; Gutiérrez, J. M.

2009-04-01

suitability of this methodology to be used for seasonal forecast and for long-term climate change scenario projection of wave climate.

Atmosphere-Wave-Ocean Coupling from Regional to Global Earth System Models for High-Impact Extreme Weather Prediction

NASA Astrophysics Data System (ADS)

Chen, S. S.; Curcic, M.

2017-12-01

The need for acurrate and integrated impact forecasts of extreme wind, rain, waves, and storm surge is growing as coastal population and built environment expand worldwide. A key limiting factor in forecasting impacts of extreme weather events associated with tropical cycle and winter storms is fully coupled atmosphere-wave-ocean model interface with explicit momentum and energy exchange. It is not only critical for accurate prediction of storm intensity, but also provides coherent wind, rian, ocean waves and currents forecasts for forcing for storm surge. The Unified Wave INterface (UWIN) has been developed for coupling of the atmosphere-wave-ocean models. UWIN couples the atmosphere, wave, and ocean models using the Earth System Modeling Framework (ESMF). It is a physically based and computationally efficient coupling sytem that is flexible to use in a multi-model system and portable for transition to the next generation global Earth system prediction mdoels. This standardized coupling framework allows researchers to develop and test air-sea coupling parameterizations and coupled data assimilation, and to better facilitate research-to-operation activities. It has been used and extensively tested and verified in regional coupled model forecasts of tropical cycles and winter storms (Chen and Curcic 2016, Curcic et al. 2016, and Judt et al. 2016). We will present 1) an overview of UWIN and its applications in fully coupled atmosphere-wave-ocean model predictions of hurricanes and coastal winter storms, and 2) implenmentation of UWIN in the NASA GMAO GEOS-5.

Sub-seasonal prediction over East Asia during boreal summer using the ECCC monthly forecasting system

NASA Astrophysics Data System (ADS)

Liang, Ping; Lin, Hai

2018-02-01

A useful sub-seasonal forecast is of great societal and economical value in the highly populated East Asian region, especially during boreal summer when frequent extreme events such as heat waves and persistent heavy rainfalls occur. Despite recent interest and development in sub-seasonal prediction, it is still unclear how skillful dynamical forecasting systems are in East Asia beyond 2 weeks. In this study we evaluate the sub-seasonal prediction over East Asia during boreal summer in the operational monthly forecasting system of Environment and Climate Change Canada (ECCC).Results show that the climatological intra-seasonal oscillation (CISO) of East Asian summer monsoonis reasonably well captured. Statistically significant forecast skill of 2-meter air temperature (T2m) is achieved for all lead times up to week 4 (days 26-32) over East China and Northeast Asia, which is consistent with the skill in 500 hPa geopotential height (Z500). Significant forecast skill of precipitation, however, is limited to the week of days 5-11. Possible sources of predictability on the sub-seasonal time scale are analyzed. The weekly mean T2m anomaly over East China is found to be linked to an eastward propagating extratropical Rossby wave from the North Atlantic across Europe to East Asia. The Madden-Julian Oscillation (MJO) and El Nino-Southern Oscillation (ENSO) are also likely to influence the forecast skill of T2m at the sub-seasonal timescale over East Asia.

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

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

ECMWF (the European Centre for Medium range Weather Forecasts), in collaboration with the EFAS (European Flood Awareness System) and GLOFAS (GLObal Flood Awareness System) teams, has developed a new operational system that post-processes grid box rainfall forecasts from its ensemble forecasting system to provide global probabilistic point-rainfall predictions. The project attains a higher forecasting skill by applying an understanding of how different rainfall generation mechanisms lead to different degrees of sub-grid variability in rainfall totals. In turn this approach facilitates identification of cases in which very localized extreme totals are much more likely. This approach aims also to improve the rainfall input required in different hydro-meteorological applications. Flash flood forecasting, in particular in urban areas, is a good example. In flash flood scenarios precipitation is typically characterised by high spatial variability and response times are short. In this case, to move beyond radar based now casting, the classical approach has been to use very high resolution hydro-meteorological models. Of course these models are valuable but they can represent only very limited areas, may not be spatially accurate and may give reasonable results only for limited lead times. On the other hand, our method aims to use a very cost-effective approach to downscale global rainfall forecasts to a point scale. It needs only rainfall totals from standard global reporting stations and forecasts over a relatively short period to train it, and it can give good results even up to day 5. For these reasons we believe that this approach better satisfies user needs around the world. This presentation aims to describe two phases of the project: The first phase, already completed, is the implementation of this new system to provide 6 and 12 hourly point-rainfall accumulation probabilities. To do this we use a limited number of physically relevant global model parameters (i

Error quantification of abnormal extreme high waves in Operational Oceanographic System in Korea

NASA Astrophysics Data System (ADS)

Jeong, Sang-Hun; Kim, Jinah; Heo, Ki-Young; Park, Kwang-Soon

2017-04-01

In winter season, large-height swell-like waves have occurred on the East coast of Korea, causing property damages and loss of human life. It is known that those waves are generated by a local strong wind made by temperate cyclone moving to eastward in the East Sea of Korean peninsula. Because the waves are often occurred in the clear weather, in particular, the damages are to be maximized. Therefore, it is necessary to predict and forecast large-height swell-like waves to prevent and correspond to the coastal damages. In Korea, an operational oceanographic system (KOOS) has been developed by the Korea institute of ocean science and technology (KIOST) and KOOS provides daily basis 72-hours' ocean forecasts such as wind, water elevation, sea currents, water temperature, salinity, and waves which are computed from not only meteorological and hydrodynamic model (WRF, ROMS, MOM, and MOHID) but also wave models (WW-III and SWAN). In order to evaluate the model performance and guarantee a certain level of accuracy of ocean forecasts, a Skill Assessment (SA) system was established as a one of module in KOOS. It has been performed through comparison of model results with in-situ observation data and model errors have been quantified with skill scores. Statistics which are used in skill assessment are including a measure of both errors and correlations such as root-mean-square-error (RMSE), root-mean-square-error percentage (RMSE%), mean bias (MB), correlation coefficient (R), scatter index (SI), circular correlation (CC) and central frequency (CF) that is a frequency with which errors lie within acceptable error criteria. It should be utilized simultaneously not only to quantify an error but also to improve an accuracy of forecasts by providing a feedback interactively. However, in an abnormal phenomena such as high-height swell-like waves in the East coast of Korea, it requires more advanced and optimized error quantification method that allows to predict the abnormal

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

Usefulness of Wave Data Assimilation to the WAVE WATCH III Modeling System

NASA Astrophysics Data System (ADS)

Choi, J. K.; Dykes, J. D.; Yaremchuk, M.; Wittmann, P.

2017-12-01

In-situ and remote-sensed wave data are more abundant currently than in years past, with excellent accuracy at global scales. Forecast skill of the WAVE WATCH III model is improved by assimilation of these measurements and they are also useful for model validation and calibration. It has been known that the impact of assimilation in wind-sea conditions is not large, but spectra that result in large swell with long term propagation are identified and assimilated, the improved accuracy of the initial conditions improve the long-term forecasts. The Navy's assimilation method started with the simple Optimal Interpolation (OI) method. Operationally, Fleet Numerical Meteorology and Oceanography Center uses the sequential 2DVar scheme, but a new approach has been tested based on an adjoint-free method to variational assimilation in WAVE WATCH III. We will present the status of wave data assimilation into the WAVE WATCH III numerical model and upcoming development of this new adjoint-free variational approach.

Using Seasonal Forecasting Data for Vessel Routing

NASA Astrophysics Data System (ADS)

Bell, Ray; Kirtman, Ben

2017-04-01

We present an assessment of seasonal forecasting of surface wind speed, significant wave height and ocean surface current speed in the North Pacific for potential use of vessel routing from Singapore to San Diego. WaveWatchIII is forced with surface winds and ocean surface currents from the Community Climate System Model 4 (CCSM4) retrospective forecasts for the period of 1982-2015. Several lead time forecasts are used from zero months to six months resulting in 2,720 model years, ensuring the findings from this study are robust. July surface wind speed and significant wave height can be skillfully forecast with a one month lead time, with the western North Pacific being the most predictable region. Beyond May initial conditions (lead time of two months) the El Niño Southern Oscillation (ENSO) Spring predictability barrier limits skill of significant wave height but there is skill for surface wind speed with January initial conditions (lead time of six months). In a separate study of vessel routing between Norfolk, Virginia and Gibraltar we demonstrate the benefit of a multimodel approach using the North American Multimodel Ensemble (NMME). In collaboration with Charles River Analytics an all-encompassing forecast is presented by using machine learning on the various ensembles which can be using used for industry applications.

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

Mediterranean monitoring and forecasting operational system for Copernicus Marine Service

NASA Astrophysics Data System (ADS)

Coppini, Giovanni; Drudi, Massimiliano; Korres, Gerasimos; Fratianni, Claudia; Salon, Stefano; Cossarini, Gianpiero; Clementi, Emanuela; Zacharioudaki, Anna; Grandi, Alessandro; Delrosso, Damiano; Pistoia, Jenny; Solidoro, Cosimo; Pinardi, Nadia; Lecci, Rita; Agostini, Paola; Cretì, Sergio; Turrisi, Giuseppe; Palermo, Francesco; Konstantinidou, Anna; Storto, Andrea; Simoncelli, Simona; Di Pietro, Pier Luigi; Masina, Simona; Ciliberti, Stefania Angela; Ravdas, Michalis; Mancini, Marco; Aloisio, Giovanni; Fiore, Sandro; Buonocore, Mauro

2016-04-01

The MEDiterranean Monitoring and Forecasting Center (Med-MFC) is part of the Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/), provided on an operational mode by Mercator Ocean in agreement with the European Commission. Specifically, Med MFC system provides regular and systematic information about the physical state of the ocean and marine ecosystems for the Mediterranean Sea. The Med-MFC service started in May 2015 from the pre-operational system developed during the MyOcean projects, consolidating the understanding of regional Mediterranean Sea dynamics, from currents to biogeochemistry to waves, interfacing with local data collection networks and guaranteeing an efficient link with other Centers in Copernicus network. The Med-MFC products include analyses, 10 days forecasts and reanalysis, describing currents, temperature, salinity, sea level and pelagic biogeochemistry. Waves products will be available in MED-MFC version in 2017. The consortium, composed of INGV (Italy), HCMR (Greece) and OGS (Italy) and coordinated by the Euro-Mediterranean Centre on Climate Change (CMCC, Italy), performs advanced R&D activities and manages the service delivery. The Med-MFC infrastructure consists of 3 Production Units (PU), for Physics, Biogechemistry and Waves, a unique Dissemination Unit (DU) and Archiving Unit (AU) and Backup Units (BU) for all principal components, guaranteeing a resilient configuration of the service and providing and efficient and robust solution for the maintenance of the service and delivery. The Med-MFC includes also an evolution plan, both in terms of research and operational activities, oriented to increase the spatial resolution of products, to start wave products dissemination, to increase temporal extent of the reanalysis products and improving ocean physical modeling for delivering new products. The scientific activities carried out in 2015 concerned some improvements in the physical, biogeochemical and

Development of Operational Wave-Tide-Storm surges Coupling Prediction System

NASA Astrophysics Data System (ADS)

You, S. H.; Park, S. W.; Kim, J. S.; Kim, K. L.

2009-04-01

The Korean Peninsula is surrounded by the Yellow Sea, East China Sea, and East Sea. This complex oceanographic system includes large tides in the Yellow Sea and seasonally varying monsoon and typhoon events. For Korea's coastal regions, floods caused by wave and storm surges are among the most serious threats. To predict more accurate wave and storm surges, the development of coupling wave-tide-storm surges prediction system is essential. For the time being, wave and storm surges predictions are still made separately in KMA (Korea Meteorological Administration) and most operational institute. However, many researchers have emphasized the effects of tides and storm surges on wind waves and recommended further investigations into the effects of wave-tide-storm surges interactions and coupling module. In Korea, especially, tidal height and current give a great effect on the wave prediction in the Yellow sea where is very high tide and related research is not enough. At present, KMA has operated the wave (RWAM : Regional Wave Model) and storm surges/tide prediction system (STORM : Storm Surges/Tide Operational Model) for ocean forecasting. The RWAM is WAVEWATCH III which is a third generation wave model developed by Tolman (1989). The STORM is based on POM (Princeton Ocean Model, Blumberg and Mellor, 1987). The RWAM and STORM cover the northwestern Pacific Ocean from 115°E to 150°E and from 20°N to 52°N. The horizontal grid intervals are 1/12° in both latitudinal and longitudinal directions. These two operational models are coupled to simulate wave heights for typhoon case. The sea level and current simulated by storm surge model are used for the input of wave model with 3 hour interval. The coupling simulation between wave and storm surge model carried out for Typhoon Nabi (0514), Shanshan(0613) and Nari (0711) which were effected on Korea directly. We simulated significant wave height simulated by wave model and coupling model and compared difference between

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

The international workshop on wave hindcasting and forecasting and the coastal hazards symposium

NASA Astrophysics Data System (ADS)

Breivik, Øyvind; Swail, Val; Babanin, Alexander V.; Horsburgh, Kevin

2015-05-01

Following the 13th International Workshop on Wave Hindcasting and Forecasting and 4th Coastal Hazards Symposium in October 2013 in Banff, Canada, a topical collection has appeared in recent issues of Ocean Dynamics. Here, we give a brief overview of the history of the conference since its inception in 1986 and of the progress made in the fields of wind-generated ocean waves and the modelling of coastal hazards before we summarize the main results of the papers that have appeared in the topical collection.

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.

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.

Coupling of wave and circulation models in coastal-ocean predicting systems: A case study for the German Bight

NASA Astrophysics Data System (ADS)

Staneva, Joanna; Wahle, Kathrin

2015-04-01

This study addresses the coupling between wind wave and circulation models on the example of the German Bight and its coastal area called the Wadden Sea (the area between the barrier islands and the coast). This topic reflects the increased interest in operational oceanography to reduce prediction errors of state estimates at coastal scales. The uncertainties in most of the presently used models result from the nonlinear feedback between strong tidal currents and wind-waves, which can no longer be ignored, in particular in the coastal zone where its role seems to be dominant. A nested modelling system is used in the Helmholtz-Zentrum Geesthacht to producing reliable now- and short-term forecasts of ocean state variables, including wind waves and hydrodynamics. In this study we present analysis of wave and hydrographic observations, as well as the results of numerical simulations. The data base includes ADCP observations and continuous measurements from data stations. The individual and collective role of wind, waves and tidal forcing are quantified. The performance of the forecasting system is illustrated for the cases of several extreme events. Effects of ocean waves on coastal circulation and SST simulations are investigated considering wave-dependent stress and wave breaking parameterization during extreme events, e.g. hurricane Xavier in December, 2013. Also the effect which the circulation exerts on the wind waves is tested for the coastal areas using different parameterizations. The improved skill resulting from the new developments in the forecasting system, in particular during extreme events, justifies further enhancements of the coastal pre-operational system for the North Sea and German Bight.

Forecasting of Radiation Belts: Results From the PROGRESS Project.

NASA Astrophysics Data System (ADS)

Balikhin, M. A.; Arber, T. D.; Ganushkina, N. Y.; Walker, S. N.

2017-12-01

Forecasting of Radiation Belts: Results from the PROGRESS Project. The overall goal of the PROGRESS project, funded in frame of EU Horizon2020 programme, is to combine first principles based models with the systems science methodologies to achieve reliable forecasts of the geo-space particle radiation environment.The PROGRESS incorporates three themes : The propagation of the solar wind to L1, Forecast of geomagnetic indices, and forecast of fluxes of energetic electrons within the magnetosphere. One of the important aspects of the PROGRESS project is the development of statistical wave models for magnetospheric waves that affect the dynamics of energetic electrons such as lower band chorus, hiss and equatorial noise. The error reduction ratio (ERR) concept has been used to optimise the set of solar wind and geomagnetic parameters for organisation of statistical wave models for these emissions. The resulting sets of parameters and statistical wave models will be presented and discussed. However the ERR analysis also indicates that the combination of solar wind and geomagnetic parameters accounts for only part of the variance of the emissions under investigation (lower band chorus, hiss and equatorial noise). In addition, advances in the forecast of fluxes of energetic electrons, exploiting empirical models and the first principles IMPTAM model achieved by the PROGRESS project is presented.

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

Telecommunication service markets through the year 2000 in relation to millimeter wave satellite systems

NASA Technical Reports Server (NTRS)

Stevenson, S. M.

1979-01-01

NASA is currently conducting a series of millimeter wave satellite system market studies to develop 30/20 GHz satellite system concepts that have commercial potential. Four contractual efforts were undertaken: two parallel and independent system studies and two parallel and independent market studies. The marketing efforts are focused on forecasting the total domestic demand for long haul telecommunications services for the 1980-2000 period. Work completed to date and reported in this paper include projections of: geographical distribution of traffic; traffic volume as a function of urban area size; and user identification and forecasted demand.

Coastal and Riverine Flood Forecast Model powered by ADCIRC

NASA Astrophysics Data System (ADS)

Khalid, A.; Ferreira, C.

2017-12-01

Coastal flooding is becoming a major threat to increased population in the coastal areas. To protect coastal communities from tropical storms & hurricane damages, early warning systems are being developed. These systems have the capability of real time flood forecasting to identify hazardous coastal areas and aid coastal communities in rescue operations. State of the art hydrodynamic models forced by atmospheric forcing have given modelers the ability to forecast storm surge, water levels and currents. This helps to identify the areas threatened by intense storms. Study on Chesapeake Bay area has gained national importance because of its combined riverine and coastal phenomenon, which leads to greater uncertainty in flood predictions. This study presents an automated flood forecast system developed by following Advanced Circulation (ADCIRC) Surge Guidance System (ASGS) guidelines and tailored to take in riverine and coastal boundary forcing, thus includes all the hydrodynamic processes to forecast total water in the Potomac River. As studies on tidal and riverine flow interaction are very scarce in number, our forecast system would be a scientific tool to examine such area and fill the gaps with precise prediction for Potomac River. Real-time observations from National Oceanic and Atmospheric Administration (NOAA) and field measurements have been used as model boundary feeding. The model performance has been validated by using major historical riverine and coastal flooding events. Hydrodynamic model ADCIRC produced promising predictions for flood inundation areas. As better forecasts can be achieved by using coupled models, this system is developed to take boundary conditions from Global WaveWatchIII for the research purposes. Wave and swell propagation will be fed through Global WavewatchIII model to take into account the effects of swells and currents. This automated forecast system is currently undergoing rigorous testing to include any missing parameters which

Flood Forecasting in Wales: Challenges and Solutions

NASA Astrophysics Data System (ADS)

How, Andrew; Williams, Christopher

2015-04-01

With steep, fast-responding river catchments, exposed coastal reaches with large tidal ranges and large population densities in some of the most at-risk areas; flood forecasting in Wales presents many varied challenges. Utilising advances in computing power and learning from best practice within the United Kingdom and abroad have seen significant improvements in recent years - however, many challenges still remain. Developments in computing and increased processing power comes with a significant price tag; greater numbers of data sources and ensemble feeds brings a better understanding of uncertainty but the wealth of data needs careful management to ensure a clear message of risk is disseminated; new modelling techniques utilise better and faster computation, but lack the history of record and experience gained from the continued use of more established forecasting models. As a flood forecasting team we work to develop coastal and fluvial forecasting models, set them up for operational use and manage the duty role that runs the models in real time. An overview of our current operational flood forecasting system will be presented, along with a discussion on some of the solutions we have in place to address the challenges we face. These include: • real-time updating of fluvial models • rainfall forecasting verification • ensemble forecast data • longer range forecast data • contingency models • offshore to nearshore wave transformation • calculation of wave overtopping

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.

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.

Verification of Meteorological and Oceanographic Ensemble Forecasts in the U.S. Navy

NASA Astrophysics Data System (ADS)

Klotz, S.; Hansen, J.; Pauley, P.; Sestak, M.; Wittmann, P.; Skupniewicz, C.; Nelson, G.

2013-12-01

The Navy Ensemble Forecast Verification System (NEFVS) has been promoted recently to operational status at the U.S. Navy's Fleet Numerical Meteorology and Oceanography Center (FNMOC). NEFVS processes FNMOC and National Centers for Environmental Prediction (NCEP) meteorological and ocean wave ensemble forecasts, gridded forecast analyses, and innovation (observational) data output by FNMOC's data assimilation system. The NEFVS framework consists of statistical analysis routines, a variety of pre- and post-processing scripts to manage data and plot verification metrics, and a master script to control application workflow. NEFVS computes metrics that include forecast bias, mean-squared error, conditional error, conditional rank probability score, and Brier score. The system also generates reliability and Receiver Operating Characteristic diagrams. In this presentation we describe the operational framework of NEFVS and show examples of verification products computed from ensemble forecasts, meteorological observations, and forecast analyses. The construction and deployment of NEFVS addresses important operational and scientific requirements within Navy Meteorology and Oceanography. These include computational capabilities for assessing the reliability and accuracy of meteorological and ocean wave forecasts in an operational environment, for quantifying effects of changes and potential improvements to the Navy's forecast models, and for comparing the skill of forecasts from different forecast systems. NEFVS also supports the Navy's collaboration with the U.S. Air Force, NCEP, and Environment Canada in the North American Ensemble Forecast System (NAEFS) project and with the Air Force and the National Oceanic and Atmospheric Administration (NOAA) in the National Unified Operational Prediction Capability (NUOPC) program. This program is tasked with eliminating unnecessary duplication within the three agencies, accelerating the transition of new technology, such as multi

Verification of Meteorological and Oceanographic Ensemble Forecasts in the U.S. Navy

NASA Astrophysics Data System (ADS)

Klotz, S. P.; Hansen, J.; Pauley, P.; Sestak, M.; Wittmann, P.; Skupniewicz, C.; Nelson, G.

2012-12-01

The Navy Ensemble Forecast Verification System (NEFVS) has been promoted recently to operational status at the U.S. Navy's Fleet Numerical Meteorology and Oceanography Center (FNMOC). NEFVS processes FNMOC and National Centers for Environmental Prediction (NCEP) meteorological and ocean wave ensemble forecasts, gridded forecast analyses, and innovation (observational) data output by FNMOC's data assimilation system. The NEFVS framework consists of statistical analysis routines, a variety of pre- and post-processing scripts to manage data and plot verification metrics, and a master script to control application workflow. NEFVS computes metrics that include forecast bias, mean-squared error, conditional error, conditional rank probability score, and Brier score. The system also generates reliability and Receiver Operating Characteristic diagrams. In this presentation we describe the operational framework of NEFVS and show examples of verification products computed from ensemble forecasts, meteorological observations, and forecast analyses. The construction and deployment of NEFVS addresses important operational and scientific requirements within Navy Meteorology and Oceanography (METOC). These include computational capabilities for assessing the reliability and accuracy of meteorological and ocean wave forecasts in an operational environment, for quantifying effects of changes and potential improvements to the Navy's forecast models, and for comparing the skill of forecasts from different forecast systems. NEFVS also supports the Navy's collaboration with the U.S. Air Force, NCEP, and Environment Canada in the North American Ensemble Forecast System (NAEFS) project and with the Air Force and the National Oceanic and Atmospheric Administration (NOAA) in the National Unified Operational Prediction Capability (NUOPC) program. This program is tasked with eliminating unnecessary duplication within the three agencies, accelerating the transition of new technology, such as

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).

Predictability of the European heat and cold waves

NASA Astrophysics Data System (ADS)

Lavaysse, Christophe; Naumann, Gustavo; Alfieri, Lorenzo; Salamon, Peter; Vogt, Jürgen

2018-06-01

Heat and cold waves may have considerable human and economic impacts in Europe. Recent events, like the heat waves observed in France in 2003 and Russia in 2010, illustrated the major consequences to be expected. Reliable Early Warning Systems for extreme temperatures would, therefore, be of high value for decision makers. However, they require a clear definition and robust forecasts of these events. This study analyzes the predictability of heat and cold waves over Europe, defined as at least three consecutive days of {T}_{min} and {T}_{max} above the quantile Q90 (under Q10), using the extended ensemble system of ECMWF. The results show significant predictability for events within a 2-week lead time, but with a strong decrease of the predictability during the first week of forecasts (from 80 to 40% of observed events correctly forecasted). The scores show a higher predictive skill for the cold waves (in winter) than for the heat waves (in summer). The uncertainties and the sensitivities of the predictability are discussed on the basis of tests conducted with different spatial and temporal resolutions. Results demonstrate the negligible effect of the temporal resolution (very few errors due to bad timing of the forecasts), and a better predictability of large-scale events. The onset and the end of the waves are slightly less predictable with an average of about 35% (30%) of observed heat (cold) waves onsets or ends correctly forecasted with a 5-day lead time. Finally, the forecasted intensities show a correlation of about 0.65 with those observed, revealing the challenge to predict this important characteristic.

Extreme precipitation event over North China in August 2010: observations, monthly forecasting, and link to intra-seasonal variability of the Silk-Road wave-train across Eurasia

NASA Astrophysics Data System (ADS)

Orsolini, Yvan; Zhang, Ling; Peters, Dieter; Fraedrich, Klaus

2014-05-01

Forecast of regional precipitation events at the sub-seasonal timescale remains a big challenge for operational global prediction systems. Over the Far East in summer, climate and precipitation are strongly influenced by the fluctuating western Pacific subtropical high (WPSH) and strong precipitation is often associated with southeasterly low-level wind that brings moist-laden air from the southern China seas. The WPSH variability is partly influenced by quasi-stationary wave-trains propagating eastwards from Europe across Asia along the two westerly jets: the Silk-Road wave-train along the Asian jet at mid-latitudes and, on a more northern route, the polar wave-train along the sub-polar jet. While the Silk-Road wave-train appears as a robust, internal mode of variability in seasonal predictions models, its predictability is very low on the sub-seasonal to seasonal time scale. A case in point is the unusual summer of 2010, when China experienced its worst seasonal flooding for a decade, triggered by unusually prolonged and severe monsoonal rains. In addition that summer was also characterized by record-breaking heat wave over Eastern Europe and Russia as well as catastrophic monsoonal floods in Pakistan 2010. The impact of the latter circulation anomalies on the precipitation further east over China, has been little explored. Here, we examine the role and the actual predictability of the Silk-Road wave-train, and its impact on precipitation over Northeastern China throughout August 2010, using the high-resolution IFS forecast model of ECMWF, realistic initialized and run in an ensemble mode. We demonstrate that the forecast failure with regard to flooding and extreme precipitation over Northeastern China in August 2010 is linked to the failure to represent intra-seasonal variations of the Silk-Road wave-train and the associated intensification of the WPSH.

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

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.

Coupling of WRF meteorological model to WAM spectral wave model through sea surface roughness at the Balearic Sea: impact on wind and wave forecasts

NASA Astrophysics Data System (ADS)

Tolosana-Delgado, R.; Soret, A.; Jorba, O.; Baldasano, J. M.; Sánchez-Arcilla, A.

2012-04-01

Meteorological models, like WRF, usually describe the earth surface characteristics by tables that are function of land-use. The roughness length (z0) is an example of such approach. However, over sea z0 is modeled by the Charnock (1955) relation, linking the surface friction velocity u*2 with the roughness length z0 of turbulent air flow, z0 = α-u2* g The Charnock coefficient α may be considered a measure of roughness. For the sea surface, WRF considers a constant roughness α = 0.0185. However, there is evidence that sea surface roughness should depend on wave energy (Donelan, 1982). Spectral wave models like WAM, model the evolution and propagation of wave energy as a function of wind, and include a richer sea surface roughness description. Coupling WRF and WAM is thus a common way to improve the sea surface roughness description of WRF. WAM is a third generation wave model, solving the equation of advection of wave energy subject to input/output terms of: wind growth, energy dissipation and resonant non-linear wave-wave interactions. Third generation models work on the spectral domain. WAM considers the Charnock coefficient α a complex yet known function of the total wind input term, which depends on the wind velocity and on the Charnock coefficient again. This is solved iteratively (Janssen et al., 1990). Coupling of meteorological and wave models through a common Charnock coefficient is operationally done in medium-range met forecasting systems (e.g., at ECMWF) though the impact of coupling for smaller domains is not yet clearly assessed (Warner et al, 2010). It is unclear to which extent the additional effort of coupling improves the local wind and wave fields, in comparison to the effects of other factors, like e.g. a better bathymetry and relief resolution, or a better circulation information which might have its influence on local-scale meteorological processes (local wind jets, local convection, daily marine wind regimes, etc.). This work, within the

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

Did we see the 2011 summer heat wave coming?

NASA Astrophysics Data System (ADS)

Luo, Lifeng; Zhang, Yan

2012-05-01

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

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

Sea-Salt Aerosol Forecasts Compared with Wave and Sea-Salt Measurements in the Open Mediterranean Sea

NASA Astrophysics Data System (ADS)

Kishcha, P.; Starobinets, B.; Bozzano, R.; Pensieri, S.; Canepa, E.; Nickovie, S.; di Sarra, A.; Udisti, R.; Becagli, S.; Alpert, P.

2012-03-01

Sea-salt aerosol (SSA) could influence the Earth's climate acting as cloud condensation nuclei. However, there were no regular measurements of SSA in the open sea. At Tel-Aviv University, the DREAM-Salt prediction system has been producing daily forecasts of 3-D distribution of sea-salt aerosol concentrations over the Mediterranean Sea (http://wind.tau.ac.il/saltina/ salt.html). In order to evaluate the model performance in the open sea, daily modeled concentrations were compared directly with SSA measurements taken at the tiny island of Lampedusa, in the Central Mediterranean. In order to further test the robustness of the model, the model performance over the open sea was indirectly verified by comparing modeled SSA concentrations with wave height measurements collected by the ODAS Italia 1 buoy and the Llobregat buoy. Model-vs.-measurement comparisons show that the model is capable of producing realistic SSA concentrations and their day-today variations over the open sea, in accordance with observed wave height and wind speed.

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

Demystifying the Complexities of Gravity Wave Dynamics in the Middle Atmosphere: a Roadmap to Improved Weather Forecasts through High-Fidelity Modeling

NASA Astrophysics Data System (ADS)

Mixa, T.; Fritts, D. C.; Bossert, K.; Laughman, B.; Wang, L.; Lund, T.; Kantha, L. H.

2017-12-01

Gravity waves play a profound role in the mixing of the atmosphere, transporting vast amounts of momentum and energy among different altitudes as they propagate vertically. Above 60km in the middle atmosphere, high wave amplitudes enable a series of complex, nonlinear interactions with the background environment that produce highly-localized wind and temperature variations which alter the layering structure of the atmosphere. These small-scale interactions account for a significant portion of energy transport in the middle atmosphere, but they are difficult to characterize, occurring at spatial scales that are both challenging to observe with ground instruments and prohibitively small to include in weather forecasting models. Using high fidelity numerical simulations, these nuanced wave interactions are analyzed to better our understanding of these dynamics and improve the accuracy of long-term weather forecasting.

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

Improving the health forecasting alert system for cold weather and heat-waves in England: a case-study approach using temperature-mortality relationships

NASA Astrophysics Data System (ADS)

Masato, Giacomo; Cavany, Sean; Charlton-Perez, Andrew; Dacre, Helen; Bone, Angie; Carmicheal, Katie; Murray, Virginia; Danker, Rutger; Neal, Rob; Sarran, Christophe

2015-04-01

The health forecasting alert system for cold weather and heatwaves currently in use in the Cold Weather and Heatwave plans for England is based on 5 alert levels, with levels 2 and 3 dependent on a forecast or actual single temperature action trigger. Epidemiological evidence indicates that for both heat and cold, the impact on human health is gradual, with worsening impact for more extreme temperatures. The 60% risk of heat and cold forecasts used by the alerts is a rather crude probabilistic measure, which could be substantially improved thanks to the state-of-the-art forecast techniques. In this study a prototype of a new health forecasting alert system is developed, which is aligned to the approach used in the Met Office's (MO) National Severe Weather Warning Service (NSWWS). This is in order to improve information available to responders in the health and social care system by linking temperatures more directly to risks of mortality, and developing a system more coherent with other weather alerts. The prototype is compared to the current system in the Cold Weather and Heatwave plans via a case-study approach to verify its potential advantages and shortcomings. The prototype health forecasting alert system introduces an "impact vs likelihood matrix" for the health impacts of hot and cold temperatures which is similar to those used operationally for other weather hazards as part of the NSWWS. The impact axis of this matrix is based on existing epidemiological evidence, which shows an increasing relative risk of death at extremes of outdoor temperature beyond a threshold which can be identified epidemiologically. The likelihood axis is based on a probability measure associated with the temperature forecast. The new method is tested for two case studies (one during summer 2013, one during winter 2013), and compared to the performance of the current alert system. The prototype shows some clear improvements over the current alert system. It allows for a much greater

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...

Human-model hybrid Korean air quality forecasting system.

PubMed

Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun

2016-09-01

The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the

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

Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 3. Evaluation by means of case studies

NASA Astrophysics Data System (ADS)

Mangold, A.; de Backer, H.; de Paepe, B.; Dewitte, S.; Chiapello, I.; Derimian, Y.; Kacenelenbogen, M.; LéOn, J.-F.; Huneeus, N.; Schulz, M.; Ceburnis, D.; O'Dowd, C.; Flentje, H.; Kinne, S.; Benedetti, A.; Morcrette, J.-J.; Boucher, O.

2011-02-01

A near real-time system for assimilation and forecasts of aerosols, greenhouse and trace gases, extending the ECMWF Integrated Forecasting System (IFS), has been developed in the framework of the Global and regional Earth-system Monitoring using Satellite and in-situ data (GEMS) project. The GEMS aerosol modeling system is novel as it is the first aerosol model fully coupled to a numerical weather prediction model with data assimilation. A reanalysis of the period 2003-2009 has been carried out with the same system. During its development phase, the aerosol system was first run for the time period January 2003 to December 2004 and included sea salt, desert dust, organic matter, black carbon, and sulfate aerosols. In the analysis, Moderate Resolution Imaging Spectroradiometer (MODIS) total aerosol optical depth (AOD) at 550 nm over ocean and land (except over bright surfaces) was assimilated. This work evaluates the performance of the aerosol system by means of case studies. The case studies include (1) the summer heat wave in Europe in August 2003, characterized by forest fire aerosol and conditions of high temperatures and stagnation, favoring photochemistry and secondary aerosol formation, (2) a large Saharan dust event in March 2004, and (3) periods of high and low sea salt aerosol production. During the heat wave period in 2003, the linear correlation coefficients between modeled and observed AOD (550 nm) and between modeled and observed PM2.5 mass concentrations are 0.82 and 0.71, respectively, for all investigated sites together. The AOD is slightly and the PM2.5 mass concentration is clearly overestimated by the aerosol model during this period. The simulated sulfate mass concentration is significantly correlated with observations but is distinctly overestimated. The horizontal and vertical locations of the main features of the aerosol distribution during the Saharan dust outbreak are generally well captured, as well as the timing of the AOD peaks. The

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.

Mechanistic Drifting Forecast Model for A Small Semi-Submersible Drifter Under Tide-Wind-Wave Conditions

NASA Astrophysics Data System (ADS)

Zhang, Wei-Na; Huang, Hui-ming; Wang, Yi-gang; Chen, Da-ke; Zhang, lin

2018-03-01

Understanding the drifting motion of a small semi-submersible drifter is of vital importance regarding monitoring surface currents and the floating pollutants in coastal regions. This work addresses this issue by establishing a mechanistic drifting forecast model based on kinetic analysis. Taking tide-wind-wave into consideration, the forecast model is validated against in situ drifting experiment in the Radial Sand Ridges. Model results show good performance with respect to the measured drifting features, characterized by migrating back and forth twice a day with daily downwind displacements. Trajectory models are used to evaluate the influence of the individual hydrodynamic forcing. The tidal current is the fundamental dynamic condition in the Radial Sand Ridges and has the greatest impact on the drifting distance. However, it loses its leading position in the field of the daily displacement of the used drifter. The simulations reveal that different hydrodynamic forces dominate the daily displacement of the used drifter at different wind scales. The wave-induced mass transport has the greatest influence on the daily displacement at Beaufort wind scale 5-6; while wind drag contributes mostly at wind scale 2-4.

The 14th international workshop on wave hindcasting and forecasting and the 5th coastal hazards symposium

NASA Astrophysics Data System (ADS)

Breivik, Øyvind; Alves, Jose Henrique; Greenslade, Diana; Horsburgh, Kevin; Swail, Val

2017-04-01

Following the 14th International Workshop on Wave Hindcasting and Forecasting and 5th Coastal Hazards Symposium in November 2014 in Key West, Florida, a topical collection has appeared in recent issues of Ocean Dynamics. Here, we give a brief overview of the 16 papers published in this topical collection as well as an overview of the widening scope of the conference in recent years. A general trend in the field has been towards closer integration between the wave and ocean modelling communities. This is also seen in this topical collection, with several papers exploring the interaction between surface waves and mixed layer dynamics and sea ice.

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

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

Using HPC within an operational forecasting configuration

NASA Astrophysics Data System (ADS)

Jagers, H. R. A.; Genseberger, M.; van den Broek, M. A. F. H.

2012-04-01

Various natural disasters are caused by high-intensity events, for example: extreme rainfall can in a short time cause major damage in river catchments, storms can cause havoc in coastal areas. To assist emergency response teams in operational decisions, it's important to have reliable information and predictions as soon as possible. This starts before the event by providing early warnings about imminent risks and estimated probabilities of possible scenarios. In the context of various applications worldwide, Deltares has developed an open and highly configurable forecasting and early warning system: Delft-FEWS. Finding the right balance between simulation time (and hence prediction lead time) and simulation accuracy and detail is challenging. Model resolution may be crucial to capture certain critical physical processes. Uncertainty in forcing conditions may require running large ensembles of models; data assimilation techniques may require additional ensembles and repeated simulations. The computational demand is steadily increasing and data streams become bigger. Using HPC resources is a logical step; in different settings Delft-FEWS has been configured to take advantage of distributed computational resources available to improve and accelerate the forecasting process (e.g. Montanari et al, 2006). We will illustrate the system by means of a couple of practical applications including the real-time dynamic forecasting of wind driven waves, flow of water, and wave overtopping at dikes of Lake IJssel and neighboring lakes in the center of The Netherlands. Montanari et al., 2006. Development of an ensemble flood forecasting system for the Po river basin, First MAP D-PHASE Scientific Meeting, 6-8 November 2006, Vienna, Austria.

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.

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

Framework of distributed coupled atmosphere-ocean-wave modeling system

NASA Astrophysics Data System (ADS)

Wen, Yuanqiao; Huang, Liwen; Deng, Jian; Zhang, Jinfeng; Wang, Sisi; Wang, Lijun

2006-05-01

In order to research the interactions between the atmosphere and ocean as well as their important role in the intensive weather systems of coastal areas, and to improve the forecasting ability of the hazardous weather processes of coastal areas, a coupled atmosphere-ocean-wave modeling system has been developed. The agent-based environment framework for linking models allows flexible and dynamic information exchange between models. For the purpose of flexibility, portability and scalability, the framework of the whole system takes a multi-layer architecture that includes a user interface layer, computational layer and service-enabling layer. The numerical experiment presented in this paper demonstrates the performance of the distributed coupled modeling system.

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...

Optimal Control of a Surge-Mode WEC in Random Waves

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

Chertok, Allan; Ceberio, Olivier; Staby, Bill

2016-08-30

The objective of this project was to develop one or more real-time feedback and feed-forward (MPC) control algorithms for an Oscillating Surge Wave Converter (OSWC) developed by RME called SurgeWEC™ that leverages recent innovations in wave energy converter (WEC) control theory to maximize power production in random wave environments. The control algorithms synthesized innovations in dynamic programming and nonlinear wave dynamics using anticipatory wave sensors and localized sensor measurements; e.g. position and velocity of the WEC Power Take Off (PTO), with predictive wave forecasting data. The result was an advanced control system that uses feedback or feed-forward data from anmore » array of sensor channels comprised of both localized and deployed sensors fused into a single decision process that optimally compensates for uncertainties in the system dynamics, wave forecasts, and sensor measurement errors.« less

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.

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

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

Space Weather Forecasting at IZMIRAN

NASA Astrophysics Data System (ADS)

Gaidash, S. P.; Belov, A. V.; Abunina, M. A.; Abunin, A. A.

2017-12-01

Since 1998, the Institute of Terrestrial Magnetism, Ionosphere, and Radio Wave Propagation (IZMIRAN) has had an operating heliogeophysical service—the Center for Space Weather Forecasts. This center transfers the results of basic research in solar-terrestrial physics into daily forecasting of various space weather parameters for various lead times. The forecasts are promptly available to interested consumers. This article describes the center and the main types of forecasts it provides: solar and geomagnetic activity, magnetospheric electron fluxes, and probabilities of proton increases. The challenges associated with the forecasting of effects of coronal mass ejections and coronal holes are discussed. Verification data are provided for the center's forecasts.

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

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.

A multidisciplinary system for monitoring and forecasting Etna volcanic plumes

NASA Astrophysics Data System (ADS)

Coltelli, Mauro; Prestifilippo, Michele; Spata, Gaetano; Scollo, Simona; Andronico, Daniele

2010-05-01

One of the most active volcanoes in the world is Mt. Etna, in Italy, characterized by frequent explosive activity from the central craters and from fractures opened along the volcano flanks which, during the last years, caused several damages to aviation and forced the closure of the Catania International Airport. To give precise warning to the aviation authorities and air traffic controller and to assist the work of VAACs, a novel system for monitoring and forecasting Etna volcanic plumes, was developed at the Istituto Nazionale di Geofisica e Vulcanologia, sezione di Catania, the managing institution for the surveillance of Etna volcano. Monitoring is carried out using multispectral infrared measurements from the Spin Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation geosynchronous satellite able to track the volcanic plume with a high time resolution, visual and thermal cameras used to monitor the explosive activity, three continuous wave X-band disdrometers which detect ash dispersal and fallout, sounding balloons used to evaluate the atmospheric fields, and finally field data collected after the end of the eruptive event needed to extrapolate important features of explosive activity. Forecasting is carried out daily using automatic procedures which download weather forecast data obtained by meteorological mesoscale models from the Italian Air Force national Meteorological Office and from the hydrometeorological service of ARPA-SIM; run four different tephra dispersal models using input parameters obtained by the analysis of the deposits collected after few hours since the eruptive event similar to 22 July 1998, 21-24 July 2001 and 2002-03 Etna eruptions; plot hazard maps on ground and in air and finally publish them on a web-site dedicated to the Italian Civil Protection. The system has been already tested successfully during several explosive events occurring at Etna in 2006, 2007 and 2008. These events produced eruption

Impact of soil moisture initialization on boreal summer subseasonal forecasts: mid-latitude surface air temperature and heat wave events

NASA Astrophysics Data System (ADS)

Seo, Eunkyo; Lee, Myong-In; Jeong, Jee-Hoon; Koster, Randal D.; Schubert, Siegfried D.; Kim, Hye-Mi; Kim, Daehyun; Kang, Hyun-Suk; Kim, Hyun-Kyung; MacLachlan, Craig; Scaife, Adam A.

2018-05-01

This study uses a global land-atmosphere coupled model, the land-atmosphere component of the Global Seasonal Forecast System version 5, to quantify the degree to which soil moisture initialization could potentially enhance boreal summer surface air temperature forecast skill. Two sets of hindcast experiments are performed by prescribing the observed sea surface temperature as the boundary condition for a 15-year period (1996-2010). In one set of the hindcast experiments (noINIT), the initial soil moisture conditions are randomly taken from a long-term simulation. In the other set (INIT), the initial soil moisture conditions are taken from an observation-driven offline Land Surface Model (LSM) simulation. The soil moisture conditions from the offline LSM simulation are calibrated using the forecast model statistics to minimize the inconsistency between the LSM and the land-atmosphere coupled model in their mean and variability. Results show a higher boreal summer surface air temperature prediction skill in INIT than in noINIT, demonstrating the potential benefit from an accurate soil moisture initialization. The forecast skill enhancement appears especially in the areas in which the evaporative fraction—the ratio of surface latent heat flux to net surface incoming radiation—is sensitive to soil moisture amount. These areas lie in the transitional regime between humid and arid climates. Examination of the extreme 2003 European and 2010 Russian heat wave events reveal that the regionally anomalous soil moisture conditions during the events played an important role in maintaining the stationary circulation anomalies, especially those near the surface.

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

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.

The relevance of the whitecapping term in wave forecasting. An analysis for the wave period of the Catalan coast.

NASA Astrophysics Data System (ADS)

Pallares, Elena; Espino, Manuel; Sánchez-Arcilla, Agustín

2013-04-01

The Catalan Coast is located in the North Western Mediterranean Sea. It is a region with highly heterogeneous wind and wave conditions, characterized by a microtidal environment, and economically very dependent from the sea and the coastal zone activities. Because some of the main coastal conflicts and management problems occur within a few kilometers of the land-ocean boundary, the level of resolution and accuracy from meteo-oceanographic predictions required is not currently available. The current work is focused on improving high resolution wave forecasting very near the coast. The SWAN wave model is used to simulate the waves in the area, and various buoy data and field campaigns are used to validate the results. The simulations are structured in four different domains covering all the North Western Mediterranean Sea, with a grid resolution from 9 km to 250 meters in coastal areas. Previous results show that the significant wave height is almost always underpredicted in this area, and the underprediction is higher during storm events. However, the error in the peak period and the mean period is almost always constantly under predicted with a bias between one and two seconds, plus some residual error. This systematic error represents 40% of the total error. To improve the initial results, the whiteccaping dissipation term is studied and modified. In the SWAN model, the whitecapping is mainly controlled by the steepness of the waves. Although the by default parameter is not depending on the wave number, there is a new formulation in the last SWAN version (40.81) to include it in the calculations. Previous investigations show that adjusting the dependence for the wave number improved the predictions for the wave energy at lower frequencies, solving the underprediction of the period mentioned before. In the present work different simulations are developed to calibrate the new formulation, obtaining important improvements in the results. For the significant wave

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.

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

Integrating Unified Gravity Wave Physics into the NOAA Next Generation Global Prediction System

NASA Astrophysics Data System (ADS)

Alpert, J. C.; Yudin, V.; Fuller-Rowell, T. J.; Akmaev, R. A.

2017-12-01

The Unified Gravity Wave Physics (UGWP) project for the Next Generation Global Prediction System (NGGPS) is a NOAA collaborative effort between the National Centers for Environmental Prediction (NCEP), Environemntal Modeling Center (EMC) and the University of Colorado, Cooperative Institute for Research in Environmental Sciences (CU-CIRES) to support upgrades and improvements of GW dynamics (resolved scales) and physics (sub-grid scales) in the NOAA Environmental Modeling System (NEMS)†. As envisioned the global climate, weather and space weather models of NEMS will substantially improve their predictions and forecasts with the resolution-sensitive (scale-aware) formulations planned under the UGWP framework for both orographic and non-stationary waves. In particular, the planned improvements for the Global Forecast System (GFS) model of NEMS are: calibration of model physics for higher vertical and horizontal resolution and an extended vertical range of simulations, upgrades to GW schemes, including the turbulent heating and eddy mixing due to wave dissipation and breaking, and representation of the internally-generated QBO. The main priority of the UGWP project is unified parameterization of orographic and non-orographic GW effects including momentum deposition in the middle atmosphere and turbulent heating and eddies due to wave dissipation and breaking. The latter effects are not currently represented in NOAA atmosphere models. The team has tested and evaluated four candidate GW solvers integrating the selected GW schemes into the NGGPS model. Our current work and planned activity is to implement the UGWP schemes in the first available GFS/FV3 (open FV3) configuration including adapted GFDL modification for sub-grid orography in GFS. Initial global model results will be shown for the operational and research GFS configuration for spectral and FV3 dynamical cores. †http://www.emc.ncep.noaa.gov/index.php?branch=NEMS

Deterministic Wave Predictions from the WaMoS II

DTIC Science & Technology

2014-10-23

Monitoring System WaMoS II as input to a wave pre- diction system . The utility of wave prediction is primarily ves- sel motion prediction. Specific...successful prediction. The envisioned prediction system may provide graphical output in the form of a decision support system (Fig. 1). Predictions are...quality and accuracy of WaMoS as input to a deterministic wave prediction system . In the context of this paper, the Time Now Forecast H e a v e Hindcast

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

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

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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.

Development, testing, and applications of site-specific tsunami inundation models for real-time forecasting

NASA Astrophysics Data System (ADS)

Tang, L.; Titov, V. V.; Chamberlin, C. D.

2009-12-01

The study describes the development, testing and applications of site-specific tsunami inundation models (forecast models) for use in NOAA's tsunami forecast and warning system. The model development process includes sensitivity studies of tsunami wave characteristics in the nearshore and inundation, for a range of model grid setups, resolutions and parameters. To demonstrate the process, four forecast models in Hawaii, at Hilo, Kahului, Honolulu, and Nawiliwili are described. The models were validated with fourteen historical tsunamis and compared with numerical results from reference inundation models of higher resolution. The accuracy of the modeled maximum wave height is greater than 80% when the observation is greater than 0.5 m; when the observation is below 0.5 m the error is less than 0.3 m. The error of the modeled arrival time of the first peak is within 3% of the travel time. The developed forecast models were further applied to hazard assessment from simulated magnitude 7.5, 8.2, 8.7 and 9.3 tsunamis based on subduction zone earthquakes in the Pacific. The tsunami hazard assessment study indicates that use of a seismic magnitude alone for a tsunami source assessment is inadequate to achieve such accuracy for tsunami amplitude forecasts. The forecast models apply local bathymetric and topographic information, and utilize dynamic boundary conditions from the tsunami source function database, to provide site- and event-specific coastal predictions. Only by combining a Deep-ocean Assessment and Reporting of Tsunami-constrained tsunami magnitude with site-specific high-resolution models can the forecasts completely cover the evolution of earthquake-generated tsunami waves: generation, deep ocean propagation, and coastal inundation. Wavelet analysis of the tsunami waves suggests the coastal tsunami frequency responses at different sites are dominated by the local bathymetry, yet they can be partially related to the locations of the tsunami sources. The study

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.

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.

Probability of US Heat Waves Affected by a Subseasonal Planetary Wave Pattern

NASA Technical Reports Server (NTRS)

Teng, Haiyan; Branstator, Grant; Wang, Hailan; Meehl, Gerald A.; Washington, Warren M.

2013-01-01

Heat waves are thought to result from subseasonal atmospheric variability. Atmospheric phenomena driven by tropical convection, such as the Asian monsoon, have been considered potential sources of predictability on subseasonal timescales. Mid-latitude atmospheric dynamics have been considered too chaotic to allow significant prediction skill of lead times beyond the typical 10-day range of weather forecasts. Here we use a 12,000-year integration of an atmospheric general circulation model to identify a pattern of subseasonal atmospheric variability that can help improve forecast skill for heat waves in the United States. We find that heat waves tend to be preceded by 15-20 days by a pattern of anomalous atmospheric planetary waves with a wavenumber of 5. This circulation pattern can arise as a result of internal atmospheric dynamics and is not necessarily linked to tropical heating.We conclude that some mid-latitude circulation anomalies that increase the probability of heat waves are predictable beyond the typical weather forecast range.

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.

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

Heterogeneity: The key to failure forecasting

PubMed Central

Vasseur, Jérémie; Wadsworth, Fabian B.; Lavallée, Yan; Bell, Andrew F.; Main, Ian G.; Dingwell, Donald B.

2015-01-01

Elastic waves are generated when brittle materials are subjected to increasing strain. Their number and energy increase non-linearly, ending in a system-sized catastrophic failure event. Accelerating rates of geophysical signals (e.g., seismicity and deformation) preceding large-scale dynamic failure can serve as proxies for damage accumulation in the Failure Forecast Method (FFM). Here we test the hypothesis that the style and mechanisms of deformation, and the accuracy of the FFM, are both tightly controlled by the degree of microstructural heterogeneity of the material under stress. We generate a suite of synthetic samples with variable heterogeneity, controlled by the gas volume fraction. We experimentally demonstrate that the accuracy of failure prediction increases drastically with the degree of material heterogeneity. These results have significant implications in a broad range of material-based disciplines for which failure forecasting is of central importance. In particular, the FFM has been used with only variable success to forecast failure scenarios both in the field (volcanic eruptions and landslides) and in the laboratory (rock and magma failure). Our results show that this variability may be explained, and the reliability and accuracy of forecast quantified significantly improved, by accounting for material heterogeneity as a first-order control on forecasting power. PMID:26307196

Heterogeneity: The key to failure forecasting.

PubMed

Vasseur, Jérémie; Wadsworth, Fabian B; Lavallée, Yan; Bell, Andrew F; Main, Ian G; Dingwell, Donald B

2015-08-26

Elastic waves are generated when brittle materials are subjected to increasing strain. Their number and energy increase non-linearly, ending in a system-sized catastrophic failure event. Accelerating rates of geophysical signals (e.g., seismicity and deformation) preceding large-scale dynamic failure can serve as proxies for damage accumulation in the Failure Forecast Method (FFM). Here we test the hypothesis that the style and mechanisms of deformation, and the accuracy of the FFM, are both tightly controlled by the degree of microstructural heterogeneity of the material under stress. We generate a suite of synthetic samples with variable heterogeneity, controlled by the gas volume fraction. We experimentally demonstrate that the accuracy of failure prediction increases drastically with the degree of material heterogeneity. These results have significant implications in a broad range of material-based disciplines for which failure forecasting is of central importance. In particular, the FFM has been used with only variable success to forecast failure scenarios both in the field (volcanic eruptions and landslides) and in the laboratory (rock and magma failure). Our results show that this variability may be explained, and the reliability and accuracy of forecast quantified significantly improved, by accounting for material heterogeneity as a first-order control on forecasting power.

Heterogeneity: The key to failure forecasting

NASA Astrophysics Data System (ADS)

Vasseur, Jérémie; Wadsworth, Fabian B.; Lavallée, Yan; Bell, Andrew F.; Main, Ian G.; Dingwell, Donald B.

2015-08-01

Elastic waves are generated when brittle materials are subjected to increasing strain. Their number and energy increase non-linearly, ending in a system-sized catastrophic failure event. Accelerating rates of geophysical signals (e.g., seismicity and deformation) preceding large-scale dynamic failure can serve as proxies for damage accumulation in the Failure Forecast Method (FFM). Here we test the hypothesis that the style and mechanisms of deformation, and the accuracy of the FFM, are both tightly controlled by the degree of microstructural heterogeneity of the material under stress. We generate a suite of synthetic samples with variable heterogeneity, controlled by the gas volume fraction. We experimentally demonstrate that the accuracy of failure prediction increases drastically with the degree of material heterogeneity. These results have significant implications in a broad range of material-based disciplines for which failure forecasting is of central importance. In particular, the FFM has been used with only variable success to forecast failure scenarios both in the field (volcanic eruptions and landslides) and in the laboratory (rock and magma failure). Our results show that this variability may be explained, and the reliability and accuracy of forecast quantified significantly improved, by accounting for material heterogeneity as a first-order control on forecasting power.

Set-up and validation of a Delft-FEWS based coastal hazard forecasting system

NASA Astrophysics Data System (ADS)

Valchev, Nikolay; Eftimova, Petya; Andreeva, Nataliya

2017-04-01

European coasts are increasingly threatened by hazards related to low-probability and high-impact hydro-meteorological events. Uncertainties in hazard prediction and capabilities to cope with their impact lie in both future storm pattern and increasing coastal development. Therefore, adaptation to future conditions requires a re-evaluation of coastal disaster risk reduction (DRR) strategies and introduction of a more efficient mix of prevention, mitigation and preparedness measures. The latter presumes that development of tools, which can manage the complex process of merging data and models and generate products on the current and expected hydro-and morpho-dynamic states of the coasts, such as forecasting system of flooding and erosion hazards at vulnerable coastal locations (hotspots), is of vital importance. Output of such system can be of an utmost value for coastal stakeholders and the entire coastal community. In response to these challenges, Delft-FEWS provides a state-of-the-art framework for implementation of such system with vast capabilities to trigger the early warning process. In addition, this framework is highly customizable to the specific requirements of any individual coastal hotspot. Since its release many Delft-FEWS based forecasting system related to inland flooding have been developed. However, limited number of coastal applications was implemented. In this paper, a set-up of Delft-FEWS based forecasting system for Varna Bay (Bulgaria) and a coastal hotspot, which includes a sandy beach and port infrastructure, is presented. It is implemented in the frame of RISC-KIT project (Resilience-Increasing Strategies for Coasts - toolKIT). The system output generated in hindcast mode is validated with available observations of surge levels, wave and morphodynamic parameters for a sequence of three short-duration and relatively weak storm events occurred during February 4-12, 2015. Generally, the models' performance is considered as very good and

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.

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

Automated turbulence forecasts for aviation hazards

NASA Astrophysics Data System (ADS)

Sharman, R.; Frehlich, R.; Vandenberghe, F.

2010-09-01

An operational turbulence forecast system for commercial and aviation use is described that is based on an ensemble of turbulence diagnostics derived from standard NWP model outputs. In the U. S. this forecast product is named GTG (Graphical Turbulence Guidance) and has been described in detail in Sharman et al., WAF 2006. Since turbulence has many sources in the atmosphere, the ensemble approach of combining diagnostics has been shown to provide greater statistical accuracy than the use of a single diagnostic, or of a subgrid tke parameterization. GTG is sponsored by the FAA, and has undergone rigorous accuracy, safety, and usability evaluations. The GTG product is now hosted on NOAA's Aviation Data Service (ADDS), web site (http://aviationweather.gov/), for access by pilots, air traffic controllers, and dispatchers. During this talk the various turbulence diagnostics, their statistical properties, and their relative performance (based on comparisons to observations) will be presented. Importantly, the model output is ɛ1/3 (where ɛ is the eddy dissipation rate), so is aircraft independent. The diagnostics are individually and collectively calibrated so that their PDFs satisfy the expected log normal distribution of ɛ^1/3. Some of the diagnostics try to take into account the role of gravity waves and inertia-gravity waves in the turbulence generation process. Although the current GTG product is based on the RUC forecast model running over the CONUS, it is transitioning to a WRF based product, and in fact WRF-based versions are currently running operationally over Taiwan and has also been implemented for use by the French Navy in climatological studies. Yet another version has been developed which uses GFS model output to provide global turbulence forecasts. Thus the forecast product is available as a postprocessing program for WRF or other model output and provides 3D maps of turbulence likelihood of any region where NWP model data is available. Although the

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 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.

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

NASA Astrophysics Data System (ADS)

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

2003-04-01

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

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.

The impact of sea surface currents in wave power potential modeling

NASA Astrophysics Data System (ADS)

Zodiatis, George; Galanis, George; Kallos, George; Nikolaidis, Andreas; Kalogeri, Christina; Liakatas, Aristotelis; Stylianou, Stavros

2015-11-01

The impact of sea surface currents to the estimation and modeling of wave energy potential over an area of increased economic interest, the Eastern Mediterranean Sea, is investigated in this work. High-resolution atmospheric, wave, and circulation models, the latter downscaled from the regional Mediterranean Forecasting System (MFS) of the Copernicus marine service (former MyOcean regional MFS system), are utilized towards this goal. The modeled data are analyzed by means of a variety of statistical tools measuring the potential changes not only in the main wave characteristics, but also in the general distribution of the wave energy and the wave parameters that mainly affect it, when using sea surface currents as a forcing to the wave models. The obtained results prove that the impact of the sea surface currents is quite significant in wave energy-related modeling, as well as temporally and spatially dependent. These facts are revealing the necessity of the utilization of the sea surface currents characteristics in renewable energy studies in conjunction with their meteo-ocean forecasting counterparts.

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

Skill assessment of a real-time forecast system utilizing a coupled hydrologic and coastal hydrodynamic model during Hurricane Irene (2011)

NASA Astrophysics Data System (ADS)

Dresback, Kendra M.; Fleming, Jason G.; Blanton, Brian O.; Kaiser, Carola; Gourley, Jonathan J.; Tromble, Evan M.; Luettich, Richard A.; Kolar, Randall L.; Hong, Yang; Van Cooten, Suzanne; Vergara, Humberto J.; Flamig, Zac L.; Lander, Howard M.; Kelleher, Kevin E.; Nemunaitis-Monroe, Kodi L.

2013-12-01

Due to the devastating effects of recent hurricanes in the Gulf of Mexico (e.g., Katrina, Rita, Ike and Gustav), the development of a high-resolution, real-time, total water level prototype system has been accelerated. The fully coupled model system that includes hydrology is an extension of the ADCIRC Surge Guidance System (ASGS), and will henceforth be referred to as ASGS-STORM (Scalable, Terrestrial, Ocean, River, Meteorological) to emphasize the major processes that are represented by the system.The ASGS-STORM system incorporates tides, waves, winds, rivers and surge to produce a total water level, which provides a holistic representation of coastal flooding. ASGS-STORM was rigorously tested during Hurricane Irene, which made landfall in late August 2011 in North Carolina. All results from ASGS-STORM for the advisories were produced in real-time, forced by forecast wind and pressure fields computed using a parametric tropical cyclone model, and made available via the web. Herein, a skill assessment, analyzing wind speed and direction, significant wave heights, and total water levels, is used to evaluate ASGS-STORM's performance during Irene for three advisories and the best track from the National Hurricane Center (NHC). ASGS-STORM showed slight over-prediction for two advisories (Advisory 23 and 25) due to the over-estimation of the storm intensity. However, ASGS-STORM shows notable skill in capturing total water levels, wind speed and direction, and significant wave heights in North Carolina when utilizing Advisory 28, which had a slight shift in the track but provided a more accurate estimation of the storm intensity, along with the best track from the NHC. Results from ASGS-STORM have shown that as the forecast of the advisories improves, so does the accuracy of the models used in the study; therefore, accurate input from the weather forecast is a necessary, but not sufficient, condition to ensure the accuracy of the guidance provided by the system. While

A Pro-active Real-time Forecasting and Decision Support System for Daily Management of Marine Works

NASA Astrophysics Data System (ADS)

Bollen, Mark; Leyssen, Gert; Smets, Steven; De Wachter, Tom

2016-04-01

Marine Works involving turbidity generating activities (eg. dredging, dredge spoil placement) can generate environmental stress in and around a project area in the form of sediment plumes causing light reduction and sedimentation. If these works are situated near sensitive habitats like sea-grass beds, coral reefs or sensitive human activities eg. aquaculture farms or water intakes, or if contaminants are present in the water soil environmental scrutiny is advised. Environmental Regulations can impose limitations to these activities in the form of turbidity thresholds, spill budgets, contaminant levels. Breaching environmental regulations can result in increased monitoring, adaptation of the works planning and production rates and ultimately in a (temporary) stop of activities all of which entail time and cost impacts for a contractor and/or client. Sediment plume behaviour is governed by the dredging process, soil properties and ambient conditions (currents, water depth) and can be modelled. Usually this is done during the preparatory EIA phase of a project, for estimation of environmental impact based on climatic scenarios. An operational forecasting tool is developed to adapt marine work schedules to the real-time circumstances and thus evade exceedance of critical threshold levels at sensitive areas. The forecasting system is based on a Python-based workflow manager with a MySQL database and a Django frontend web tool for user interaction and visualisation of the model results. The core consists of a numerical hydrodynamic model with sediment transport module (Mike21 from DHI). This model is driven by space and time varying wind fields and wave boundary conditions, and turbidity inputs (suspended sediment source terms) based on marine works production rates and soil properties. The resulting threshold analysis allows the operator to indicate potential impact at the sensitive areas and instigate an adaption of the marine work schedule if needed. In order to use

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.

Use of ground-based wind profiles in mesoscale forecasting

NASA Technical Reports Server (NTRS)

Schlatter, Thomas W.

1985-01-01

A brief review is presented of recent uses of ground-based wind profile data in mesoscale forecasting. Some of the applications are in real time, and some are after the fact. Not all of the work mentioned here has been published yet, but references are given wherever possible. As Gage and Balsley (1978) point out, sensitive Doppler radars have been used to examine tropospheric wind profiles since the 1970's. It was not until the early 1980's, however, that the potential contribution of these instruments to operational forecasting and numerical weather prediction became apparent. Profiler winds and radiosonde winds compare favorably, usually within a few m/s in speed and 10 degrees in direction (see Hogg et al., 1983), but the obvious advantage of the profiler is its frequent (hourly or more often) sampling of the same volume. The rawinsonde balloon is launched only twice a day and drifts with the wind. In this paper, I will: (1) mention two operational uses of data from a wind profiling system developed jointly by the Wave Propagation and Aeronomy Laboratories of NOAA; (2) describe a number of displays of these same data on a workstation for mesoscale forecasting developed by the Program for Regional Observing and Forecasting Services (PROFS); and (3) explain some interesting diagnostic calculations performed by meteorologists of the Wave Propagation Laboratory.

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.

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

Improving the Health Forecasting Alert System for Cold Weather and Heat-Waves In England: A Proof-of-Concept Using Temperature-Mortality Relationships.

PubMed

Masato, Giacomo; Bone, Angie; Charlton-Perez, Andrew; Cavany, Sean; Neal, Robert; Dankers, Rutger; Dacre, Helen; Carmichael, Katie; Murray, Virginia

2015-01-01

In this study a prototype of a new health forecasting alert system is developed, which is aligned to the approach used in the Met Office's (MO) National Severe Weather Warning Service (NSWWS). This is in order to improve information available to responders in the health and social care system by linking temperatures more directly to risks of mortality, and developing a system more coherent with other weather alerts. The prototype is compared to the current system in the Cold Weather and Heatwave plans via a case-study approach to verify its potential advantages and shortcomings. The prototype health forecasting alert system introduces an "impact vs likelihood matrix" for the health impacts of hot and cold temperatures which is similar to those used operationally for other weather hazards as part of the NSWWS. The impact axis of this matrix is based on existing epidemiological evidence, which shows an increasing relative risk of death at extremes of outdoor temperature beyond a threshold which can be identified epidemiologically. The likelihood axis is based on a probability measure associated with the temperature forecast. The new method is tested for two case studies (one during summer 2013, one during winter 2013), and compared to the performance of the current alert system. The prototype shows some clear improvements over the current alert system. It allows for a much greater degree of flexibility, provides more detailed regional information about the health risks associated with periods of extreme temperatures, and is more coherent with other weather alerts which may make it easier for front line responders to use. It will require validation and engagement with stakeholders before it can be considered for use.

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.

Rogue waves in terms of multi-point statistics and nonequilibrium thermodynamics

NASA Astrophysics Data System (ADS)

Hadjihosseini, Ali; Lind, Pedro; Mori, Nobuhito; Hoffmann, Norbert P.; Peinke, Joachim

2017-04-01

Ocean waves, which lead to rogue waves, are investigated on the background of complex systems. In contrast to deterministic approaches based on the nonlinear Schroedinger equation or focusing effects, we analyze this system in terms of a noisy stochastic system. In particular we present a statistical method that maps the complexity of multi-point data into the statistics of hierarchically ordered height increments for different time scales. We show that the stochastic cascade process with Markov properties is governed by a Fokker-Planck equation. Conditional probabilities as well as the Fokker-Planck equation itself can be estimated directly from the available observational data. This stochastic description enables us to show several new aspects of wave states. Surrogate data sets can in turn be generated allowing to work out different statistical features of the complex sea state in general and extreme rogue wave events in particular. The results also open up new perspectives for forecasting the occurrence probability of extreme rogue wave events, and even for forecasting the occurrence of individual rogue waves based on precursory dynamics. As a new outlook the ocean wave states will be considered in terms of nonequilibrium thermodynamics, for which the entropy production of different wave heights will be considered. We show evidence that rogue waves are characterized by negative entropy production. The statistics of the entropy production can be used to distinguish different wave states.

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.

Detecting primordial gravitational waves with circular polarization of the redshifted 21 cm line. II. Forecasts

NASA Astrophysics Data System (ADS)

Mishra, Abhilash; Hirata, Christopher M.

2018-05-01

In the first paper of this series, we showed that the CMB quadrupole at high redshifts results in a small circular polarization of the emitted 21 cm radiation. In this paper we forecast the sensitivity of future radio experiments to measure the CMB quadrupole during the era of first cosmic light (z ˜20 ). The tomographic measurement of 21 cm circular polarization allows us to construct a 3D remote quadrupole field. Measuring the B -mode component of this remote quadrupole field can be used to put bounds on the tensor-to-scalar ratio r . We make Fisher forecasts for a future Fast Fourier Transform Telescope (FFTT), consisting of an array of dipole antennas in a compact grid configuration, as a function of array size and observation time. We find that a FFTT with a side length of 100 km can achieve σ (r )˜4 ×10-3 after ten years of observation and with a sky coverage fsky˜0.7 . The forecasts are dependent on the evolution of the Lyman-α flux in the pre-reionization era, that remains observationally unconstrained. Finally, we calculate the typical order of magnitudes for circular polarization foregrounds and comment on their mitigation strategies. We conclude that detection of primordial gravitational waves with 21 cm observations is in principle possible, so long as the primordial magnetic field amplitude is small, but would require a very futuristic experiment with corresponding advances in calibration and foreground suppression techniques.

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.

Design and Prototype Implementation of non-Triggered Database-driven Real-time Tsunami Forecast System using Multi-index Method

NASA Astrophysics Data System (ADS)

Yamamoto, N.; Aoi, S.; Suzuki, W.; Hirata, K.; Takahashi, N.; Kunugi, T.; Nakamura, H.

2016-12-01

We have launched a new project to develop real-time tsunami inundation forecast system for the Pacific coast of Chiba prefecture (Kujukuri-Sotobo region), Japan (Aoi et al., 2015, AGU). In this study, we design a database-driven real-time tsunami forecast system using the multi-index method (Yamamoto et al., 2016, EPS) and implement a prototype system. In the previous study (Yamamoto et al., 2015, AGU), we assumed that the origin-time of tsunami was known before a forecast based on comparing observed and calculated ocean-bottom pressure waveforms stored in the Tsunami Scenario Bank (TSB). As shown in the figure, we assume the scenario origin-times by defining the scenario elapsed timeτp to compare observed and calculated waveforms. In this design, when several appropriate tsunami scenarios were selected by multiple indices (two variance reductions and correlation coefficient), the system could make tsunami forecast using the selected tsunami scenarios for the target coastal region without any triggered information derived from observed seismic and/or tsunami data. In addition, we define the time range Tq shown in the figure for masking perturbations contaminated by ocean-acoustic and seismic waves on the observed pressure records (Saito, 2015, JpGU). Following the proposed design, we implement a prototype system of real-time tsunami inundation forecast system for the exclusive use of the target coastal region using ocean-bottom pressure data from the Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench (S-net) (Kanazawa et al., 2012, JpGU; Uehira et al., 2015, IUGG), which is constructed by National Research institute for Earth Science and Disaster Resilience (NIED). For the prototype system, we construct a prototype TSB using interplate earthquake fault models located along the Japan Trench (Mw 7.6-9.8), the Sagami Trough (Mw 7.6-8.6), and the Nankai Trough (Mw 7.6-8.6) as well as intraplate earthquake fault models (Mw 7.6-8.6) within

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.

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.

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

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

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

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

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

Coastal Wave Studies

DTIC Science & Technology

2011-09-30

Directional wave spectra analysis from a cross-shore array of acoustic Doppler profilers, accepted paper, 12th International Workshop on Wave Hindcasting and Forecasting, 30 October – 4 November 2011, Hilo , Hawaii .

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).

Operational forecasting of human-biometeorological conditions

NASA Astrophysics Data System (ADS)

Giannaros, T. M.; Lagouvardos, K.; Kotroni, V.; Matzarakis, A.

2018-03-01

This paper presents the development of an operational forecasting service focusing on human-biometeorological conditions. The service is based on the coupling of numerical weather prediction models with an advanced human-biometeorological model. Human thermal perception and stress forecasts are issued on a daily basis for Greece, in both point and gridded format. A user-friendly presentation approach is adopted for communicating the forecasts to the public via the worldwide web. The development of the presented service highlights the feasibility of replacing standard meteorological parameters and/or indices used in operational weather forecasting activities for assessing the thermal environment. This is of particular significance for providing effective, human-biometeorology-oriented, warnings for both heat waves and cold outbreaks.

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".

Capturing rogue waves by multi-point statistics

NASA Astrophysics Data System (ADS)

Hadjihosseini, A.; Wächter, Matthias; Hoffmann, N. P.; Peinke, J.

2016-01-01

As an example of a complex system with extreme events, we investigate ocean wave states exhibiting rogue waves. We present a statistical method of data analysis based on multi-point statistics which for the first time allows the grasping of extreme rogue wave events in a highly satisfactory statistical manner. The key to the success of the approach is mapping the complexity of multi-point data onto the statistics of hierarchically ordered height increments for different time scales, for which we can show that a stochastic cascade process with Markov properties is governed by a Fokker-Planck equation. Conditional probabilities as well as the Fokker-Planck equation itself can be estimated directly from the available observational data. With this stochastic description surrogate data sets can in turn be generated, which makes it possible to work out arbitrary statistical features of the complex sea state in general, and extreme rogue wave events in particular. The results also open up new perspectives for forecasting the occurrence probability of extreme rogue wave events, and even for forecasting the occurrence of individual rogue waves based on precursory dynamics.

Improving the Health Forecasting Alert System for Cold Weather and Heat-Waves In England: A Proof-of-Concept Using Temperature-Mortality Relationships

PubMed Central

Masato, Giacomo; Bone, Angie; Charlton-Perez, Andrew; Cavany, Sean; Neal, Robert; Dankers, Rutger; Dacre, Helen; Carmichael, Katie; Murray, Virginia

2015-01-01

Objectives In this study a prototype of a new health forecasting alert system is developed, which is aligned to the approach used in the Met Office’s (MO) National Severe Weather Warning Service (NSWWS). This is in order to improve information available to responders in the health and social care system by linking temperatures more directly to risks of mortality, and developing a system more coherent with other weather alerts. The prototype is compared to the current system in the Cold Weather and Heatwave plans via a case-study approach to verify its potential advantages and shortcomings. Method The prototype health forecasting alert system introduces an “impact vs likelihood matrix” for the health impacts of hot and cold temperatures which is similar to those used operationally for other weather hazards as part of the NSWWS. The impact axis of this matrix is based on existing epidemiological evidence, which shows an increasing relative risk of death at extremes of outdoor temperature beyond a threshold which can be identified epidemiologically. The likelihood axis is based on a probability measure associated with the temperature forecast. The new method is tested for two case studies (one during summer 2013, one during winter 2013), and compared to the performance of the current alert system. Conclusions The prototype shows some clear improvements over the current alert system. It allows for a much greater degree of flexibility, provides more detailed regional information about the health risks associated with periods of extreme temperatures, and is more coherent with other weather alerts which may make it easier for front line responders to use. It will require validation and engagement with stakeholders before it can be considered for use. PMID:26431427

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

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

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

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 CMEMS IBI-MFC Forecasting Service in 2017: Evolution and Novelties associated to the CMEMS service release

NASA Astrophysics Data System (ADS)

Lorente, Pablo; Sotillo, Marcos G.; Gutknecht, Elodie; Dabrowski, Tomasz; Aouf, Lotfi; Toledano, Cristina; Amo-Baladron, Arancha; Aznar, Roland; De Pascual, Alvaro; Levier, Bruno; Bowyer, Peter; Rainaud, Romain; Alvarez-Fanjul, Enrique

2017-04-01

The IBI-MFC (Iberia-Biscay-Ireland Monitoring & Forecasting Centre) has been providing daily ocean model estimates and forecasts of diverse physical parameters for the IBI regional seas since 2011, first in the frame of MyOcean projects and later as part of the Copernicus Marine Environment Monitoring Service (CMEMS). By April 2017, coincident with the V3 CMEMS Service Release, the IBI-MFC will extend their near real time (NRT) forecast capabilities. Two new operational IBI forecast systems will be operationally run to generate high resolution biochemical (BIO) and wave (WAV) products on the IBI area. The IBI-NRT-BIO forecast system, based on a 1/36° NEMO-PISCES model application, is run once a week coupled with the IBI physical forecast solution and nested to the CMEMS GLOBAL-BIO solution. On the other hand, the IBI-NRT-WAV system, based on a MeteoFrance-WAM 10km resolution model application, runs twice a day using ECMWF wind forcing. Among other novelties related to the evolution of the IBI physical (PHY) solution, it is worthwhile mentioning the provision, as part of the IBI-NRT-PHY product daily updated, of three-dimensional hourly data on specific areas within the IBI domain. The delivery of these new hourly data along the whole water column has been achieved after the request from IBI users, in order to foster downscaling approaches by providing coherent open boundary conditions to any potential high-resolution coastal model nested to IBI regional solution. An extensive skill assessment of IBI-NRT forecast products has been conducted through the NARVAL (North Atlantic Regional VALidation) web tool, by means of the automatic computation of statistical metrics and quality indicators. By now, this tool has been focused on the validation of the IBI-NRT-PHY system. Nowadays, NARVAL is facing a significant upgrade to validate the aforementioned new biogeochemical and wave IBI products. To this aim, satellite derived observations of chlorophyll and significant wave

Parcel-scale urban coastal flood mapping: Leveraging the multi-scale CoSMoS model for coastal flood forecasting

NASA Astrophysics Data System (ADS)

Gallien, T.; Barnard, P. L.; Sanders, B. F.

2011-12-01

California coastal sea levels are projected to rise 1-1.4 meters in the next century and evidence suggests mean tidal range, and consequently, mean high water (MHW) is increasing along portions of Southern California Bight. Furthermore, emerging research indicates wind stress patterns associated with the Pacific Decadal Oscillation (PDO) have suppressed sea level rise rates along the West Coast since 1980, and a reversal in this pattern would result in the resumption of regional sea level rise rates equivalent to or exceeding global mean sea level rise rates, thereby enhancing coastal flooding. Newport Beach is a highly developed, densely populated lowland along the Southern California coast currently subject to episodic flooding from coincident high tides and waves, and the frequency and intensity of flooding is expected to increase with projected future sea levels. Adaptation to elevated sea levels will require flood mapping and forecasting tools that are sensitive to the dominant factors affecting flooding including extreme high tides, waves and flood control infrastructure. Considerable effort has been focused on the development of nowcast and forecast systems including Scripps Institute of Oceanography's Coastal Data Information Program (CDIP) and the USGS Multi-hazard model, the Southern California Coastal Storm Modeling System (CoSMoS). However, fine scale local embayment dynamics and overtopping flows are needed to map unsteady flooding effects in coastal lowlands protected by dunes, levees and seawalls. Here, a recently developed two dimensional Godunov non-linear shallow water solver is coupled to water level and wave forecasts from the CoSMoS model to investigate the roles of tides, waves, sea level changes and flood control infrastructure in accurate flood mapping and forecasting. The results of this study highlight the important roles of topographic data, embayment hydrodynamics, water level uncertainties and critical flood processes required for

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

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

Tsunami Forecasting in the Atlantic Basin

NASA Astrophysics Data System (ADS)

Knight, W. R.; Whitmore, P.; Sterling, K.; Hale, D. A.; Bahng, B.

2012-12-01

The mission of the West Coast and Alaska Tsunami Warning Center (WCATWC) is to provide advance tsunami warning and guidance to coastal communities within its Area-of-Responsibility (AOR). Predictive tsunami models, based on the shallow water wave equations, are an important part of the Center's guidance support. An Atlantic-based counterpart to the long-standing forecasting ability in the Pacific known as the Alaska Tsunami Forecast Model (ATFM) is now developed. The Atlantic forecasting method is based on ATFM version 2 which contains advanced capabilities over the original model; including better handling of the dynamic interactions between grids, inundation over dry land, new forecast model products, an optional non-hydrostatic approach, and the ability to pre-compute larger and more finely gridded regions using parallel computational techniques. The wide and nearly continuous Atlantic shelf region presents a challenge for forecast models. Our solution to this problem has been to develop a single unbroken high resolution sub-mesh (currently 30 arc-seconds), trimmed to the shelf break. This allows for edge wave propagation and for kilometer scale bathymetric feature resolution. Terminating the fine mesh at the 2000m isobath keeps the number of grid points manageable while allowing for a coarse (4 minute) mesh to adequately resolve deep water tsunami dynamics. Higher resolution sub-meshes are then included around coastal forecast points of interest. The WCATWC Atlantic AOR includes eastern U.S. and Canada, the U.S. Gulf of Mexico, Puerto Rico, and the Virgin Islands. Puerto Rico and the Virgin Islands are in very close proximity to well-known tsunami sources. Because travel times are under an hour and response must be immediate, our focus is on pre-computing many tsunami source "scenarios" and compiling those results into a database accessible and calibrated with observations during an event. Seismic source evaluation determines the order of model pre

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

Sensitivity of mesoscale-model forecast skill to some initial-data characteristics, data density, data position, analysis procedure and measurement error

NASA Technical Reports Server (NTRS)

Warner, Thomas T.; Key, Lawrence E.; Lario, Annette M.

1989-01-01

The effects of horizontal and vertical data resolution, data density, data location, different objective analysis algorithms, and measurement error on mesoscale-forecast accuracy are studied with observing-system simulation experiments. Domain-averaged errors are shown to generally decrease with time. It is found that the vertical distribution of error growth depends on the initial vertical distribution of the error itself. Larger gravity-inertia wave noise is produced in forecasts with coarser vertical data resolution. The use of a low vertical resolution observing system with three data levels leads to more forecast errors than moderate and high vertical resolution observing systems with 8 and 14 data levels. Also, with poor vertical resolution in soundings, the initial and forecast errors are not affected by the horizontal data resolution.

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

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

Effects of Parameterized Orographic Drag on Weather Forecasting and Simulated Climatology Over East Asia During Boreal Summer

NASA Astrophysics Data System (ADS)

Choi, Hyun-Joo; Choi, Suk-Jin; Koo, Myung-Seo; Kim, Jung-Eun; Kwon, Young Cheol; Hong, Song-You

2017-10-01

The impact of subgrid orographic drag on weather forecasting and simulated climatology over East Asia in boreal summer is examined using two parameterization schemes in a global forecast model. The schemes consider gravity wave drag (GWD) with and without lower-level wave breaking drag (LLWD) and flow-blocking drag (FBD). Simulation results from sensitivity experiments verify that the scheme with LLWD and FBD improves the intensity of a summertime continental high over the northern part of the Korean Peninsula, which is exaggerated with GWD only. This is because the enhanced lower tropospheric drag due to the effects of lower-level wave breaking and flow blocking slows down the wind flowing out of the high-pressure system in the lower troposphere. It is found that the decreased lower-level divergence induces a compensating weakening of middle- to upper-level convergence aloft. Extended experiments for medium-range forecasts for July 2013 and seasonal simulations for June to August of 2013-2015 are also conducted. Statistical skill scores for medium-range forecasting are improved not only in low-level winds but also in surface pressure when both LLWD and FBD are considered. A simulated climatology of summertime monsoon circulation in East Asia is also realistically reproduced.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

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 �

Forecasting extreme temperature health hazards in Europe

NASA Astrophysics Data System (ADS)

Di Napoli, Claudia; Pappenberger, Florian; Cloke, Hannah L.

2017-04-01

Extreme hot temperatures, such as those experienced during a heat wave, represent a dangerous meteorological hazard to human health. Heat disorders such as sunstroke are harmful to people of all ages and responsible for excess mortality in the affected areas. In 2003 more than 50,000 people died in western and southern Europe because of a severe and sustained episode of summer heat [1]. Furthermore, according to the Intergovernmental Panel on Climate Change heat waves are expected to get more frequent in the future thus posing an increasing threat to human lives. Developing appropriate tools for extreme hot temperatures prediction is therefore mandatory to increase public preparedness and mitigate heat-induced impacts. A recent study has shown that forecasts of the Universal Thermal Climate Index (UTCI) provide a valid overview of extreme temperature health hazards on a global scale [2]. UTCI is a parameter related to the temperature of the human body and its regulatory responses to the surrounding atmospheric environment. UTCI is calculated using an advanced thermo-physiological model that includes the human heat budget, physiology and clothing. To forecast UTCI the model uses meteorological inputs, such as 2m air temperature, 2m water vapour pressure and wind velocity at body height derived from 10m wind speed, from NWP models. Here we examine the potential of UTCI as an extreme hot temperature prediction tool for the European area. UTCI forecasts calculated using above-mentioned parameters from ECMWF models are presented. The skill in predicting UTCI for medium lead times is also analysed and discussed for implementation to international health-hazard warning systems. This research is supported by the ANYWHERE project (EnhANcing emergencY management and response to extreme WeatHER and climate Events) which is funded by the European Commission's HORIZON2020 programme. [1] Koppe C. et al., Heat waves: risks and responses. World Health Organization. Health and

Atlantic Tropical Cyclogenetic Processes During SOP-3 NAMMA in the GEOS-5 Global Data Assimilation and Forecast System

NASA Technical Reports Server (NTRS)

Reale, Oreste; Lau, William K.; Kim, Kyu-Myong; Brin, Eugenia

2009-01-01

This article investigates the role of the Saharan air layer (SAL) in tropical cyclogenetic processes associated with a nondeveloping and a developing African easterly wave observed during the Special Observation Period (SOP-3) phase of the 2006 NASA African. Monsoon Multidisciplinary Analyses (NAMMA). The two waves are chosen because they both interact heavily with Saharan air. A glottal data assimilation and forecast system, the NASA Goddard Earth Observing System. version 5 (GEOS-5), is being run to produce a set of high-9 uality global analyses, inclusive of all observations used operationally but with additional satellite information. In particular, following previous works by the same authors, the duality-controlled data from the Atmospheric Infrared Sounder (AIRS) used to produce these analyses have a better coverage than the one adopted by operational centers. From these improved analyses, two sets of 31 five-day high-resolution forecasts, at horizontal resolutions of both half and quarter degrees, are produced. Results indicate that very steep moisture gradients are associated with the SAL in forecasts and analyses, even at great distances from their source over the Sahara. In addition, a thermal dipole in the vertiieat (warm above, cool below) is present in the nondeveloping case. The Moderate Resolution Imaging Spoctroradiometer (MODIS) aboard NASA's Terra and Aqua satellites shows that aerosol optical thickness, indicative of more dust as opposed to other factors, is higher in the nondeveloping case. Altogether, results suggest that the radiative effect of dust may play some role in producing a thermal structure less favorable to cyclogenesis. Results also indicate that only global horizontal resolutions on the order of 20-30 km can capture the large-scale transport and the tine thermal structure of the SAL, inclusive of the sharp moisture gradients, reproducing the effect of tropical cyclone suppression that has been hypothesized by previous authors

A proposal of monitoring and forecasting system for crustal activity in and around Japan using a large-scale high-fidelity finite element simulation codes

NASA Astrophysics Data System (ADS)

Hori, T.; Ichimura, T.

2015-12-01

Here we propose a system for monitoring and forecasting of crustal activity, especially great interplate earthquake generation and its preparation processes in subduction zone. Basically, we model great earthquake generation as frictional instability on the subjecting plate boundary. So, spatio-temporal variation in slip velocity on the plate interface should be monitored and forecasted. Although, we can obtain continuous dense surface deformation data on land and partly at the sea bottom, the data obtained are not fully utilized for monitoring and forecasting. It is necessary to develop a physics-based data analysis system including (1) a structural model with the 3D geometry of the plate interface and the material property such as elasticity and viscosity, (2) calculation code for crustal deformation and seismic wave propagation using (1), (3) inverse analysis or data assimilation code both for structure and fault slip using (1)&(2). To accomplish this, it is at least necessary to develop highly reliable large-scale simulation code to calculate crustal deformation and seismic wave propagation for 3D heterogeneous structure. Actually, Ichimura et al. (2014, SC14) has developed unstructured FE non-linear seismic wave simulation code, which achieved physics-based urban earthquake simulation enhanced by 10.7 BlnDOF x 30 K time-step. Ichimura et al. (2013, GJI) has developed high fidelity FEM simulation code with mesh generator to calculate crustal deformation in and around Japan with complicated surface topography and subducting plate geometry for 1km mesh. Further, for inverse analyses, Errol et al. (2012, BSSA) has developed waveform inversion code for modeling 3D crustal structure, and Agata et al. (2015, this meeting) has improved the high fidelity FEM code to apply an adjoint method for estimating fault slip and asthenosphere viscosity. Hence, we have large-scale simulation and analysis tools for monitoring. Furthermore, we are developing the methods for

Remote wave measurements using autonomous mobile robotic systems

NASA Astrophysics Data System (ADS)

Kurkin, Andrey; Zeziulin, Denis; Makarov, Vladimir; Belyakov, Vladimir; Tyugin, Dmitry; Pelinovsky, Efim

2016-04-01

The project covers the development of a technology for monitoring and forecasting the state of the coastal zone environment using radar equipment transported by autonomous mobile robotic systems (AMRS). Sought-after areas of application are the eastern and northern coasts of Russia, where continuous collection of information on topographic changes of the coastal zone and carrying out hydrodynamic measurements in inaccessible to human environment are needed. The intensity of the reflection of waves, received by radar surveillance, is directly related to the height of the waves. Mathematical models and algorithms for processing experimental data (signal selection, spectral analysis, wavelet analysis), recalculation of landwash from data on heights of waves far from the shore, determination of the threshold values of heights of waves far from the shore have been developed. There has been developed the program complex for functioning of the experimental prototype of AMRS, comprising the following modules: data loading module, reporting module, module of georeferencing, data analysis module, monitoring module, hardware control module, graphical user interface. Further work will be connected with carrying out tests of manufactured experimental prototype in conditions of selected routes coastline of Sakhalin Island. Conducting field tests will allow to reveal the shortcomings of development and to identify ways of optimization of the structure and functioning algorithms of AMRS, as well as functioning the measuring equipment. The presented results have been obtained in Nizhny Novgorod State Technical University n.a. R. Alekseev in the framework of the Federal Target Program «Research and development on priority directions of scientific-technological complex of Russia for 2014 - 2020 years» (agreement № 14.574.21.0089 (unique identifier of agreement - RFMEFI57414X0089)).

Correcting wave predictions with artificial neural networks

NASA Astrophysics Data System (ADS)

Makarynskyy, O.; Makarynska, D.

2003-04-01

The predictions of wind waves with different lead times are necessary in a large scope of coastal and open ocean activities. Numerical wave models, which usually provide this information, are based on deterministic equations that do not entirely account for the complexity and uncertainty of the wave generation and dissipation processes. An attempt to improve wave parameters short-term forecasts based on artificial neural networks is reported. In recent years, artificial neural networks have been used in a number of coastal engineering applications due to their ability to approximate the nonlinear mathematical behavior without a priori knowledge of interrelations among the elements within a system. The common multilayer feed-forward networks, with a nonlinear transfer functions in the hidden layers, were developed and employed to forecast the wave characteristics over one hour intervals starting from one up to 24 hours, and to correct these predictions. Three non-overlapping data sets of wave characteristics, both from a buoy, moored roughly 60 miles west of the Aran Islands, west coast of Ireland, were used to train and validate the neural nets involved. The networks were trained with error back propagation algorithm. Time series plots and scatterplots of the wave characteristics as well as tables with statistics show an improvement of the results achieved due to the correction procedure employed.

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.

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

The Global Signature of Ocean Wave Spectra

NASA Astrophysics Data System (ADS)

Portilla-Yandún, Jesús

2018-01-01

A global atlas of ocean wave spectra is developed and presented. The development is based on a new technique for deriving wave spectral statistics, which is applied to the extensive ERA-Interim database from European Centre of Medium-Range Weather Forecasts. Spectral statistics is based on the idea of long-term wave systems, which are unique and distinct at every geographical point. The identification of those wave systems allows their separation from the overall spectrum using the partition technique. Their further characterization is made using standard integrated parameters, which turn out much more meaningful when applied to the individual components than to the total spectrum. The parameters developed include the density distribution of spectral partitions, which is the main descriptor; the identified wave systems; the individual distribution of the characteristic frequencies, directions, wave height, wave age, seasonal variability of wind and waves; return periods derived from extreme value analysis; and crossing-sea probabilities. This information is made available in web format for public use at http://www.modemat.epn.edu.ec/#/nereo. It is found that wave spectral statistics offers the possibility to synthesize data while providing a direct and comprehensive view of the local and regional wave conditions.

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.

Tracking, sensing and predicting flood wave propagation using nomadic satellite communication systems and hydrodynamic models

NASA Astrophysics Data System (ADS)

Hostache, R.; Matgen, P.; Giustarini, L.; Tailliez, C.; Iffly, J.-F.

2011-11-01

The main objective of this study is to contribute to the development and the improvement of flood forecasting systems. Since hydrometric stations are often poorly distributed for monitoring the propagation of extreme flood waves, the study aims at evaluating the hydrometric value of the Global Navigation Satellite System (GNSS). Integrated with satellite telecommunication systems, drifting or anchored floaters equipped with navigation systems such as GPS and Galileo, enable the quasi-continuous measurement and near real-time transmission of water level and flow velocity data, from virtually any point in the world. The presented study investigates the effect of assimilating GNSS-derived water level and flow velocity measurements into hydraulic models in order to reduce the associated predictive uncertainty.

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

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.

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

EPA Science Inventory

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

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

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

Sea level forecasts for Pacific Islands based on Satellite Altimetry

NASA Astrophysics Data System (ADS)

Yoon, H.; Merrifield, M. A.; Thompson, P. R.; Widlansky, M. J.; Marra, J. J.

2017-12-01

Coastal flooding at tropical Pacific Islands often occurs when positive sea level anomalies coincide with high tides. To help mitigate this risk, a forecast tool for daily-averaged sea level anomalies is developed that can be added to predicted tides at tropical Pacific Island sites. The forecast takes advantage of the observed westward propagation that sea level anomalies exhibit over a range of time scales. The daily near-real time altimetry gridded data from Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) is used to specify upstream sea level at each site, with lead times computed based on mode-one baroclinic Rossby wave speeds. To validate the forecast, hindcasts are compared to tide gauge and nearby AVISO gridded time series. The forecast skills exceed persistence at most stations out to a month or more lead time. The skill is highest at stations where eddy variability is relatively weak. The impacts on the forecasts due to varying propagation speed, decay time, and smoothing of the AVISO data are examined. In addition, the inclusion of forecast winds in a forced wave equation is compared to the freely propagating results. Case studies are presented for seasonally high tide events throughout the Pacific Island region.

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.

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.

Simulation studies of the application of SEASAT data in weather and state of sea forecasting models

NASA Technical Reports Server (NTRS)

Cardone, V. J.; Greenwood, J. A.

1979-01-01

The design and analysis of SEASAT simulation studies in which the error structure of conventional analyses and forecasts is modeled realistically are presented. The development and computer implementation of a global spectral ocean wave model is described. The design of algorithms for the assimilation of theoretical wind data into computers and for the utilization of real wind data and wave height data in a coupled computer system are presented.

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

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.

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.

Gravity Wave Predictability and Dynamics in Deepwave

NASA Astrophysics Data System (ADS)

Doyle, J. D.; Fritts, D. C.; Smith, R. B.; Eckermann, S. D.; Taylor, M. J.; Dörnbrack, A.; Uddstrom, M.; Reynolds, C. A.; Reinecke, A.; Jiang, Q.

2015-12-01

The DEEP propagating gravity WAVE program (DEEPWAVE) is a comprehensive, airborne and ground-based measurement and modeling program centered on New Zealand and focused on providing a new understanding of gravity wave dynamics and impacts from the troposphere through the mesosphere and lower thermosphere (MLT). This program employed the NSF/NCAR GV (NGV) research aircraft from a base in New Zealand in a 6-week field measurement campaign in June-July 2014. During the field phase, the NGV was equipped with new lidar and airglow instruments, as well as dropwindsondes and a full suite of flight level instruments including the microwave temperature profiler (MTP), providing temperatures and vertical winds spanning altitudes from immediately above the NGV flight altitude (~13 km) to ~100 km. The region near New Zealand was chosen since all the relevant GW sources (e.g., mountains, cyclones, jet streams) occur strongly here, and upper-level winds in austral winter permit gravity waves to propagate to very high altitudes. The COAMPS adjoint modeling system provided forecast sensitivity in real time during the six-week DEEPWAVE field phase. Five missions were conducted using the NGV to observe regions of high forecast sensitivity, as diagnosed using the COAMPS adjoint model. In this presentation, we provide a summary of the sensitivity characteristics and explore the implications for predictability of low-level winds crucial for gravity wave launching, as well as predictability of gravity wave characteristics in the stratosphere. In general, the sensitive regions were characterized by localized strong dynamics, often involving intense baroclinic systems with deep convection. The results of the adjoint modeling system suggest that gravity wave launching and the characteristics of the gravity waves can be linked to these sensitive regions near frontal zones within baroclinic systems. The predictability links between the tropospheric fronts, cyclones, jet regions, and gravity

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...

The predictability of Iowa's hydroclimate through analog forecasts

NASA Astrophysics Data System (ADS)

Rowe, Scott Thomas

Iowa has long been affected by periods characterized by extreme drought and flood. In 2008, Cedar Rapids, Iowa was devastated by a record flood with damages around 3 billion. Several years later, Iowa was affected by severe drought in 2012, causing upwards of 30 billion in damages and losses across the United States. These climatic regimes can quickly transition from one regime to another, as was observed in the June 2013 major floods to the late summer 2013 severe drought across eastern Iowa. Though it is not possible to prevent a natural disaster from occurring, we explore how predictable these events are by using forecast models and analogs. Iowa's climate records are analyzed from 1950 to 2012 to determine if there are specific surface and upper-air pressure patterns linked to climate regimes (i.e., cold/hot and dry/wet conditions for a given month). We found that opposing climate regimes in Iowa have reversed anomalies in certain geographical regions of the northern hemisphere. These defined patterns and waves suggested to us that it could be possible to forecast extreme temperature and precipitation periods over Iowa if given a skillful forecast system. We examined the CMC, COLA, and GFDL models within the National Multi-Model Ensemble suite to create analog forecasts based on either surface or upper-air pressure forecasts. The verification results show that some analogs have predictability skill at the 0.5-month lead time exceeding random chance, but our overall confidence in the analog forecasts is not high enough to allow us to issue statewide categorical temperature and precipitation climate forecasts.

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

Forecasting European cold waves based on subsampling strategies of CMIP5 and Euro-CORDEX ensembles

NASA Astrophysics Data System (ADS)

Cordero-Llana, Laura; Braconnot, Pascale; Vautard, Robert; Vrac, Mathieu; Jezequel, Aglae

2016-04-01

Forecasting future extreme events under the present changing climate represents a difficult task. Currently there are a large number of ensembles of simulations for climate projections that take in account different models and scenarios. However, there is a need for reducing the size of the ensemble to make the interpretation of these simulations more manageable for impact studies or climate risk assessment. This can be achieved by developing subsampling strategies to identify a limited number of simulations that best represent the ensemble. In this study, cold waves are chosen to test different approaches for subsampling available simulations. The definition of cold waves depends on the criteria used, but they are generally defined using a minimum temperature threshold, the duration of the cold spell as well as their geographical extend. These climate indicators are not universal, highlighting the difficulty of directly comparing different studies. As part of the of the CLIPC European project, we use daily surface temperature data obtained from CMIP5 outputs as well as Euro-CORDEX simulations to predict future cold waves events in Europe. From these simulations a clustering method is applied to minimise the number of ensembles required. Furthermore, we analyse the different uncertainties that arise from the different model characteristics and definitions of climate indicators. Finally, we will test if the same subsampling strategy can be used for different climate indicators. This will facilitate the use of the subsampling results for a wide number of impact assessment studies.

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.

NOAA Propagation Database Value in Tsunami Forecast Guidance

NASA Astrophysics Data System (ADS)

Eble, M. C.; Wright, L. M.

2016-02-01

The National Oceanic and Atmospheric Administration (NOAA) Center for Tsunami Research (NCTR) has developed a tsunami forecasting capability that combines a graphical user interface with data ingestion and numerical models to produce estimates of tsunami wave arrival times, amplitudes, current or water flow rates, and flooding at specific coastal communities. The capability integrates several key components: deep-ocean observations of tsunamis in real-time, a basin-wide pre-computed propagation database of water level and flow velocities based on potential pre-defined seismic unit sources, an inversion or fitting algorithm to refine the tsunami source based on the observations during an event, and tsunami forecast models. As tsunami waves propagate across the ocean, observations from the deep ocean are automatically ingested into the application in real-time to better define the source of the tsunami itself. Since passage of tsunami waves over a deep ocean reporting site is not immediate, we explore the value of the NOAA propagation database in providing placeholder forecasts in advance of deep ocean observations. The propagation database consists of water elevations and flow velocities pre-computed for 50 x 100 [km] unit sources in a continuous series along all known ocean subduction zones. The 2011 Japan Tohoku tsunami is presented as the case study

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.

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

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

Application Of Multi-grid Method On China Seas' Temperature Forecast

NASA Astrophysics Data System (ADS)

Li, W.; Xie, Y.; He, Z.; Liu, K.; Han, G.; Ma, J.; Li, D.

2006-12-01

Correlation scales have been used in traditional scheme of 3-dimensional variational (3D-Var) data assimilation to estimate the background error covariance for the numerical forecast and reanalysis of atmosphere and ocean for decades. However there are still some drawbacks of this scheme. First, the correlation scales are difficult to be determined accurately. Second, the positive definition of the first-guess error covariance matrix cannot be guaranteed unless the correlation scales are sufficiently small. Xie et al. (2005) indicated that a traditional 3D-Var only corrects some certain wavelength errors and its accuracy depends on the accuracy of the first-guess covariance. And in general, short wavelength error can not be well corrected until long one is corrected and then inaccurate first-guess covariance may mistakenly take long wave error as short wave ones and result in erroneous analysis. For the purpose of quickly minimizing the errors of long and short waves successively, a new 3D-Var data assimilation scheme, called multi-grid data assimilation scheme, is proposed in this paper. By assimilating the shipboard SST and temperature profiles data into a numerical model of China Seas, we applied this scheme in two-month data assimilation and forecast experiment which ended in a favorable result. Comparing with the traditional scheme of 3D-Var, the new scheme has higher forecast accuracy and a lower forecast Root-Mean-Square (RMS) error. Furthermore, this scheme was applied to assimilate the SST of shipboard, AVHRR Pathfinder Version 5.0 SST and temperature profiles at the same time, and a ten-month forecast experiment on sea temperature of China Seas was carried out, in which a successful forecast result was obtained. Particularly, the new scheme is demonstrated a great numerical efficiency in these analyses.

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.

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

Space-Wave Routing via Surface Waves Using a Metasurface System.

PubMed

Achouri, Karim; Caloz, Christophe

2018-05-15

We introduce the concept of a metasurface system able to route space waves via surface waves. This concept may be used to laterally shift or modulate the beam width of scattered waves. The system is synthesized based on a momentum transfer approach using phase-gradient metasurfaces. The concept is experimentally verified in an "electromagnetic periscope". Additionally, we propose two other potential applications namely a beam expander and a multi-wave refractor.

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.

An Investigation of Marine Fog Forecast Concepts.

DTIC Science & Technology

1981-01-01

8217ANTA ANA C FORECASTING WEST COAST MARINE FOG or which the forecast is to be made .) SENT (TYPICALLY IN LATE I S M~IDDLE OR HIGH CLOUD PRESENT’ THERE...following discussions. Much mention will be made in the ensuing discussion of downslope motion and its role in lowering the inversion. Along a large portion...layer below 850 mb. In those cases, reference will be made to the time sequence of vertical profiles of wind at radiosonde stations. 25 Long Wave

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

Guided acoustic wave inspection system

DOEpatents

Chinn, Diane J.

2004-10-05

A system for inspecting a conduit for undesirable characteristics. A transducer system induces guided acoustic waves onto said conduit. The transducer system detects the undesirable characteristics of the conduit by receiving guided acoustic waves that contain information about the undesirable characteristics. The conduit has at least two sides and the transducer system utilizes flexural modes of propagation to provide inspection using access from only the one side of the conduit. Cracking is detected with pulse-echo testing using one transducer to both send and receive the guided acoustic waves. Thinning is detected in through-transmission testing where one transducer sends and another transducer receives the guided acoustic waves.

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.

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.

Impact of shortwave radiation biases on ocean conditions in the Pacific Northwest waters: Results from the seasonal forecast system J-SCOPE.

NASA Astrophysics Data System (ADS)

Siedlecki, S. A.; Nguyen, T. T.; Hermann, A. J.; Bond, N. A.; Ackerman, T. P.; Hinkelman, L. M.

2016-02-01

JISAO Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE) is an experimental seasonal forecast system of ocean conditions that is designed to support ecosystem-based management of fisheries in the Northwest Pacific ocean. The forecast system consists of a high resolution ROMS model with biogeochemistry forced by atmospheric and oceanic fields from the Climate Forecast System (CFS). Recent research has focused on the systematic errors in this forcing. In 2013, the predicted CFS shortwave radiation fluxes for summer were higher than the observation fluxes by nearly 100 W/m2. This forecast bias varies interannually and regionally. Hindcast experiments were set up for 2013 to estimate the impact of the shortwave radiation bias on ocean conditions in the Pacific Northwest waters. Results demonstrate that a 20% increase (decrease) in radiation fluxes can cause a warm (cold) bias in sea surface temperature (SST) of up to 1 - 1.5°C on average, and an even higher bias (± 2°C) during the June - August upwelling season. In the response to an increased radiation flux, the increased stratification from the warmer SSTs can reduce mixing and deepen the maximum phytoplankton growth zone, which consequently modifies the oxygen concentration of the water column. The effect of the change in short wave radiation fluxes on the oxygen concentrations of shelf waters is more complicated than the effect on SST. A change of up to 1 to 1.5 ml/l in bottom oxygen concentration occurs in some areas in the region. Two potential mechanisms that govern the response of the shelf water oxygen concentration are explored in this study: reduced mixing and altered chlorophyll distributions. Through the use of an oxygen budget, we can examine the relative importance of each of these mechanisms to the change in radiation.

Understanding Variability in Beach Slope to Improve Forecasts of Storm-induced Water Levels

NASA Astrophysics Data System (ADS)

Doran, K. S.; Stockdon, H. F.; Long, J.

2014-12-01

The National Assessment of Hurricane-Induced Coastal Erosion Hazards combines measurements of beach morphology with storm hydrodynamics to produce forecasts of coastal change during storms for the Gulf of Mexico and Atlantic coastlines of the United States. Wave-induced water levels are estimated using modeled offshore wave height and period and measured beach slope (from dune toe to shoreline) through the empirical parameterization of Stockdon et al. (2006). Spatial and temporal variability in beach slope leads to corresponding variability in predicted wave setup and swash. Seasonal and storm-induced changes in beach slope can lead to differences on the order of a meter in wave runup elevation, making accurate specification of this parameter essential to skillful forecasts of coastal change. Spatial variation in beach slope is accounted for through alongshore averaging, but temporal variability in beach slope is not included in the final computation of the likelihood of coastal change. Additionally, input morphology may be years old and potentially very different than the conditions present during forecast storm. In order to improve our forecasts of hurricane-induced coastal erosion hazards, the temporal variability of beach slope must be included in the final uncertainty of modeled wave-induced water levels. Frequently collected field measurements of lidar-based beach morphology are examined for study sites in Duck, North Carolina, Treasure Island, Florida, Assateague Island, Virginia, and Dauphin Island, Alabama, with some records extending over a period of 15 years. Understanding the variability of slopes at these sites will help provide estimates of associated water level uncertainty which can then be applied to other areas where lidar observations are infrequent, and improve the overall skill of future forecasts of storm-induced coastal change. Stockdon, H. F., Holman, R. A., Howd, P. A., and Sallenger Jr, A. H. (2006). Empirical parameterization of setup

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

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.

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

Smooth Sailing for Weather Forecasting

NASA Technical Reports Server (NTRS)

2002-01-01

Through a cooperative venture with NASA's Stennis Space Center, WorldWinds, Inc., developed a unique weather and wave vector map using space-based radar satellite information and traditional weather observations. Called WorldWinds, the product provides accurate, near real-time, high-resolution weather forecasts. It was developed for commercial and scientific users. In addition to weather forecasting, the product's applications include maritime and terrestrial transportation, aviation operations, precision farming, offshore oil and gas operations, and coastal hazard response support. Target commercial markets include the operational maritime and aviation communities, oil and gas providers, and recreational yachting interests. Science applications include global long-term prediction and climate change, land-cover and land-use change, and natural hazard issues. Commercial airlines have expressed interest in the product, as it can provide forecasts over remote areas. WorldWinds, Inc., is currently providing its product to commercial weather outlets.

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

Communicating Climate Change - Weather Forecast Need Assessment and Information Dissemination Mechanism to Farmers in Nepal

NASA Astrophysics Data System (ADS)

Panthi, J., Sr.

2014-12-01

Climate Change is becoming one of the major threats to the fragile Himalayan ecosystem. It is affecting all sectors mainly fresh water, agriculture, forest, biodiversity and species. The subsistence agriculture system of Nepal is mainly rain-fed; therefore, climate change and climate extremes do have direct impacts on it. Weather extremes like droughts, floods and landslides long-lasting fog, hot and cold waves are affecting the agriculture sectors of Nepal. As human-induced climate change has already showing its impacts and it is going to be there for a long time to come, it is paramount importance to move towards the adaptation. Early warning system is an effective way for reducing the impacts of disasters. Forecasting of weather parameters (temperature, precipitation, and wind) helps farmers for their preparedness activities. With consultation with farmers and other relevant institutions, a research project was carried out, for the first time in Nepal, to identify the forecast information need to farmers and their dissemination mechanism. Community consultation workshops, key informant survey, and field observations were the techniques used for this research. Two ecological locations: Bageshwori VDC in Banke (plain) and Dhaibung VDC in Rasuwa (mountain) were taken as the pilot sites for this assessment. People in both the districts are dependent highly on agriculture and the weather extremes like hailstone, untimely rainfall; droughts are affecting their agriculture practices. They do not have confidence in the weather forecast information disseminated by the government of Nepal currently being done because it is a general forecast not done for a smaller domain and the forecast is valid only for 24 hours. The weather forecast need to the farmers in both the sites are: rainfall (intensity, duration and time), drought, and hailstone but in Banke, people wished to have the information of heat and cold waves too as they are affecting their wheat and tomato crops

A Cause and A Solution for the Underprediction of Extreme Wave Events in the Northeast Pacific

NASA Astrophysics Data System (ADS)

Ellenson, A. N.; Ozkan-Haller, H. T.; Thomson, J.; Brown, A. C.; Haller, M. C.

2016-12-01

Along the coastlines of Washington and Oregon, at least one 10 m wave height event occurs every year, and the strongest storms produce wave heights of 14-15 m. Extremely high wave heights can cause severe damage to coastal infrastructure and pose hazards to stakeholders along the coast. A system which can accurately predict such sea states is important for quantifying risk and aiding in preparation for extreme wave events. This study explores how to optimize forecast model performance for extreme wave events by utilizing different physics packages or wind input in four model configurations. The different wind input products consist of a reanalyzed Global Forecasting System (GFS) wind input and a Climate Forecast System Reanalysis (CFSR) from the National Center of Environmental Prediction (NCEP). The physics packages are the Tolman-Chalikov (1996) ST2 physics package and the Ardhuin et al (2009) ST4 physics package associated with version 4.18 of WaveWatch III. A hindcast was previously performed to assess the wave character along the Pacific Northwest Coastline for wave energy applications. Inspection of hindcast model results showed that the operational model, which consisted of ST2 physics and GFS wind, underpredicted events where wave height exceeded six meters.The under-prediction is most severe for cases with the combined conditions of a distant cyclone and a strong coastal jet. Three such cases were re-analyzed with the four model configurations. Model output is compared with observations at NDBC buoy 46050, offshore of Newport, OR. The model configuration consisting of ST4 physics package and CFSR wind input performs best as compared with the original model, reducing significant wave height underprediction from 1.25 m to approximately 0.67 m and mean wave direction error from 30 degrees to 17 degrees for wave heights greater than 6 m. Spectral analysis shows that the ST4-CFSR model configuration best resolves southerly wave energy, and all model

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.

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.

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...

The propagation of wind errors through ocean wave hindcasts

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

Holthuijsen, L.H.; Booij, N.; Bertotti, L.

1996-08-01

To estimate uncertainties in wave forecast and hindcasts, computations have been carried out for a location in the Mediterranean Sea using three different analyses of one historic wind field. These computations involve a systematic sensitivity analysis and estimated wind field errors. This technique enables a wave modeler to estimate such uncertainties in other forecasts and hindcasts if only one wind analysis is available.

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

EMISSIONS PROCESSING FOR THE ETA/ CMAQ AIR QUALITY FORECAST SYSTEM

EPA Science Inventory

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

Improving the wave forecast in the Catalan Coast

NASA Astrophysics Data System (ADS)

Pallares, Elena; Sanchez-Arcilla, Agustin; Espino, Manuel

2014-05-01

This study has been motivated by the limited accuracy of wave models under short-duration, fetch-limited conditions. This applies particularly to the wave period, and can be illustrated by the case of semi-enclosed domains with highly variable wind patterns such as the Catalan coast in the Spanish Mediterranean. The wave model SWAN version 40.91A is used here in three nested grids covering all the North-western Mediterranean Sea with resolution from 9 to 1 km, forced with high resolution wind patterns from BSC (Barcelona Supercomputing Center) for two study periods, the winter 2010 and the spring 2011. The results are validated in eight locations with different types of instrumentation. In order to improve the results, a modification of the whitecapping well-known formulation of Hasselmann (1974) has been considered. The delta coefficient is increased to adapt the dissipation to the growth rates actually observed in the region. This correction introduces a dependence on the squared wave number, improving the prediction of the energy spectra at lower frequencies. However, one may note that an over-prediction will occur for waves with longer fetch and/or duration. The results obtained show a clear improvement of the mean and peak wave periods for the study area, decreasing considerably the negative bias observed previously, while almost no change is observed in wave height due to the proposed modifications. These results can be generalized to the Spanish Mediterranean coast and could be exported to similar environments, characterized by young/moderate sea wave conditions due to limited fetch and transient wind driving. References: - Hasselmann, K., 1974. On the spectral dissipation of ocean waves due to whitecapping. Boundary-layer Meteorology,6,107-127.

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

Atlantic Tropical Cyclogenetic Processes during SOP-3 NAMMA in the GEOS-5 Global Data Assimilation and Forecast System

NASA Technical Reports Server (NTRS)

Reale, Oreste; Lau, William K.; Kim, Kyu-Myong; Brin, Eugenia

2009-01-01

This article investigates the role of the Saharan Air Layer (SAL) in tropical cyclogenetic processes associated with a non-developing and a developing African easterly wave observed during the Special Observation Period (SOP-3) phase of the 2006 NASA African Monsoon Multidisciplinary Analyses (NAMMA). The two waves are chosen because both interact heavily with Saharan air. A global data assimilation and forecast system, the NASA GEOS-5, is being run to produce a set of high-quality global analyses, inclusive of all observations used operationally but with denser satellite information. In particular, following previous works by the same Authors, the quality-controlled data from the Atmospheric Infrared Sounder (AIRS) used to produce these analyses have a better coverage than the one adopted by operational centers. From these improved analyses, two sets of 31 5-day high resolution forecasts, at horizontal resolutions of both half and quarter degrees, are produced. Results show that very steep moisture gradients are associated with the SAL in forecasts and analyses even at great distance from the Sahara. In addition, a thermal dipole (warm above, cool below) is present in the non-developing case. Moderate Resolution Imaging Spectroradiometer (MODIS) show that aerosol optical thickness is higher in the non-developing case. Altogether, results suggest that radiative effect of dust may play some role in producing a thermal structure less favorable to cyclogenesis. Results also indicate that only global horizontal resolutions on the order of 20-30 kilometers can capture the large-scale transport and the fine thermal structure of the SAL, inclusive of the sharp moisture gradients, reproducing the effect of tropical cyclone suppression which has been hypothesized by previous authors from observational and regional modeling perspectives. These effects cannot be fully represented at lower resolutions. Global resolution of a quarter of a degree is a minimum critical threshold

Implementation of remote sensing data for flood forecasting

NASA Astrophysics Data System (ADS)

Grimaldi, S.; Li, Y.; Pauwels, V. R. N.; Walker, J. P.; Wright, A. J.

2016-12-01

Flooding is one of the most frequent and destructive natural disasters. A timely, accurate and reliable flood forecast can provide vital information for flood preparedness, warning delivery, and emergency response. An operational flood forecasting system typically consists of a hydrologic model, which simulates runoff generation and concentration, and a hydraulic model, which models riverine flood wave routing and floodplain inundation. However, these two types of models suffer from various sources of uncertainties, e.g., forcing data initial conditions, model structure and parameters. To reduce those uncertainties, current forecasting systems are typically calibrated and/or updated using streamflow measurements, and such applications are limited in well-gauged areas. The recent increasing availability of spatially distributed Remote Sensing (RS) data offers new opportunities for flood events investigation and forecast. Based on an Australian case study, this presentation will discuss the use 1) of RS soil moisture data to constrain a hydrologic model, and 2) of RS-derived flood extent and level to constrain a hydraulic model. The hydrological model is based on a semi-distributed system coupled with a two-soil-layer rainfall-runoff model GRKAL and a linear Muskingum routing model. Model calibration was performed using either 1) streamflow data only or 2) both streamflow and RS soil moisture data. The model was then further constrained through the integration of real-time soil moisture data. The hydraulic model is based on LISFLOOD-FP which solves the 2D inertial approximation of the Shallow Water Equations. Streamflow data and RS-derived flood extent and levels were used to apply a multi-objective calibration protocol. The effectiveness with which each data source or combination of data sources constrained the parameter space was quantified and discussed.

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

A proposal of monitoring and forecasting system for crustal activity in and around Japan using a large-scale high-fidelity finite element simulation codes

NASA Astrophysics Data System (ADS)

Hori, Takane; Ichimura, Tsuyoshi; Takahashi, Narumi

2017-04-01

Here we propose a system for monitoring and forecasting of crustal activity, such as spatio-temporal variation in slip velocity on the plate interface including earthquakes, seismic wave propagation, and crustal deformation. Although, we can obtain continuous dense surface deformation data on land and partly on the sea floor, the obtained data are not fully utilized for monitoring and forecasting. It is necessary to develop a physics-based data analysis system including (1) a structural model with the 3D geometry of the plate interface and the material property such as elasticity and viscosity, (2) calculation code for crustal deformation and seismic wave propagation using (1), (3) inverse analysis or data assimilation code both for structure and fault slip using (1) & (2). To accomplish this, it is at least necessary to develop highly reliable large-scale simulation code to calculate crustal deformation and seismic wave propagation for 3D heterogeneous structure. Actually, Ichimura et al. (2015, SC15) has developed unstructured FE non-linear seismic wave simulation code, which achieved physics-based urban earthquake simulation enhanced by 1.08 T DOF x 6.6 K time-step. Ichimura et al. (2013, GJI) has developed high fidelity FEM simulation code with mesh generator to calculate crustal deformation in and around Japan with complicated surface topography and subducting plate geometry for 1km mesh. Fujita et al. (2016, SC16) has improved the code for crustal deformation and achieved 2.05 T-DOF with 45m resolution on the plate interface. This high-resolution analysis enables computation of change of stress acting on the plate interface. Further, for inverse analyses, Errol et al. (2012, BSSA) has developed waveform inversion code for modeling 3D crustal structure, and Agata et al. (2015, AGU Fall Meeting) has improved the high-fidelity FEM code to apply an adjoint method for estimating fault slip and asthenosphere viscosity. Hence, we have large-scale simulation and

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

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.

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

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

[Combined forecasting system of peritonitis outcome].

PubMed

Lebedev, N V; Klimov, A E; Agrba, S B; Gaidukevich, E K

To create a reliable system for assessing of severity and prediction of the outcome of peritonitis. Critical analysis of the systems for peritonitis severity assessment is presented. The study included outcomes of 347 patients who admitted at the Department of Faculty Surgery of Peoples' Friendship University of Russia in 2015-2016. The cause of peritonitis were destructive forms of acute appendicitis, cholecystitis, perforated gastroduodenal ulcer, various perforation of small and large intestines (including tumor). Combined forecasting system for peritonitis severity assessment is created. The system includes clinical, laboratory data, assessment of systemic inflammatory response (SIRS) and severity of organ failure (qSOFA). The authors focused on easily identifiable parameters which are available in virtually any surgical hospital. Threshold value (lethal outcome probability over 50%) is 8 scores in this system. Sensitivity, specificity and accuracy were 93.3, 99.7 and 98.9%, respectively according to ROC-curve that exceeds those parameters of MPI and APACHE II.

Better Forecasting for Better Planning: A Systems Approach.

ERIC Educational Resources Information Center

Austin, W. Burnet

Predictions and forecasts are the most critical features of rational planning as well as the most vulnerable to inaccuracy. Because plans are only as good as their forecasts, current planning procedures could be improved by greater forecasting accuracy. Economic factors explain and predict more than any other set of factors, making economic…

Demonstrating the Alaska Ocean Observing System in Prince William Sound

NASA Astrophysics Data System (ADS)

Schoch, G. Carl; McCammon, Molly

2013-07-01

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

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.

Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

NASA Astrophysics Data System (ADS)

Radziukynas, V.; Klementavičius, A.

2016-04-01

The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011) and planned wind power capacities (the year 2023).

Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil.

PubMed

Lowe, Rachel; Coelho, Caio As; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier

2016-02-24

Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics.

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

NASA Astrophysics Data System (ADS)

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

2013-04-01

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

JPSS application in a near real time regional numerical forecast system at CIMSS

NASA Astrophysics Data System (ADS)

Li, J.; Wang, P.; Han, H.; Zhu, F.; Schmit, T. J.; Goldberg, M.

2015-12-01

Observations from next generation of environmental sensors onboard the Suomi National Polar-Orbiting Parnership (S-NPP) and its successor, the Joint Polar Satellite System (JPSS), provide us the critical information for numerical weather forecast (NWP). How to better represent these satellite observations and how to get value added information into NWP system still need more studies. Recently scientists from Cooperative Institute of Meteorological Satellite Studies (CIMSS) at University of Wisconsin-Madison have developed a near realtime regional Satellite Data Assimilation system for Tropical storm forecasts (SDAT) (http://cimss.ssec.wisc.edu/sdat). The system is built with the community Gridpoint Statistical Interpolation (GSI) assimilation and advanced Weather Research Forecast (WRF) model. With GSI, SDAT can assimilate all operational available satellite data including GOES, AMSUA/AMSUB, HIRS, MHS, ATMS, AIRS and IASI radiances and some satellite derived products. In addition, some research products, such as hyperspectral IR retrieved temperature and moisture profiles, GOES imager atmospheric motion vector (AMV) and GOES sounder layer precipitable water (LPW), are also added into the system. Using SDAT as a research testbed, studies have been conducted to show how to improve high impact weather forecast by better handling cloud information in satellite data. Previously by collocating high spatial resolution MODIS data with hyperspectral resolution AIRS data, precise clear pixels of AIRS can be identified and some partially or thin cloud contamination from pixels can be removed by taking advantage of high spatial resolution and high accurate MODIS cloud information. The results have demonstrated that both of these strategies have greatly improved the hurricane track and intensity forecast. We recently have extended these methodologies into processing CrIS/VIIRS data. We also tested similar ideas in microwave sounders by the collocation of AMSU/MODIS and ATMS

An Intelligent Decision Support System for Workforce Forecast

DTIC Science & Technology

2011-01-01

ARIMA ) model to forecast the demand for construction skills in Hong Kong. This model was based...Decision Trees ARIMA Rule Based Forecasting Segmentation Forecasting Regression Analysis Simulation Modeling Input-Output Models LP and NLP Markovian...data • When results are needed as a set of easily interpretable rules 4.1.4 ARIMA Auto-regressive, integrated, moving-average ( ARIMA ) models

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

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

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

2012-08-01

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

Reply [to: Atlantic Tropical Cyclogenetic Processes during SOP-3 NAMMA in the GEOS-5 Global Data Assimilation and Forecast System

NASA Technical Reports Server (NTRS)

Reale, Oreste; Lau, William K.

2010-01-01

This article is a Reply to a Comment by Scott Braun on a previously published article by O. Reale, K.-M. Lau, and E. Brin: "Atlantic tropical cyclogenetic processes during SOP-3 NAMMA in the GEOS-5 global data assimilation and forecast system", by Reale, Lau and Brin, hereafter referred to as RA09. RA09 investigated the role of the Saharan Air Layer (SAL) in tropical cyclogenetic processes associated with a non-developing easterly wave observed during the Special Observation Period (SOP-3) phase of the 2006 NASA African Monsoon Multidisciplinary Analyses (MAMMA). The wave was chosen because both interact heavily with Saharan air. Results showed: a) very steep moisture gradients are associated with the SAL in forecasts and analyses even at great distance from the Sahara; b) a thermal dipole (warm above, cool below) in the non-developing case. RA09A suggested that radiative effect of dust may play some role in producing a thermal structure less favorable to cyclogenesis, and also indicated that only global horizontal resolutions on the order of 20-30 kilometers can capture the large-scale transport and the fine thermal structure of the SAL Braun (2010) questions those results attributing the wave dissipation to midlatitude air. The core discussion is on a dry filament preceding the wave, on the presence of dust, and on the origin of the air contained in this dry filament. In the 'Reply', higher resolution analyses than the ones used by Braun, taken at almost coincident times with Aqua and Terra passes, are shown, to emphasize how the channel of dry air associated with W1 is indeed rich in dust. Backtrajectories on a higher resolution grid are also performed, leading to results drastically different from Braun (2010), and in particularly showing that there is a clear contribution of Saharan air. Finally, the 'Reply' presents evidence on that analyses at a horizontal resolution of one degree are inadequate to investigate such feature.

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

NASA Astrophysics Data System (ADS)

Danhelka, Jan; Vlasak, Tomas

2010-05-01

Czech Hydrometeorological Institute (CHMI) is responsible for flood forecasting and warning in the Czech Republic. To meet that issue CHMI operates hydrological forecasting systems and publish flow forecast in selected profiles. Flood forecast and warning is an output of system that links observation (flow and atmosphere), data processing, weather forecast (especially NWP's QPF), hydrological modeling and modeled outputs evaluation and interpretation by forecaster. Forecast users are interested in final output without separating uncertainties of separate steps of described process. Therefore an evaluation of final operational forecasts was done for profiles within Elbe river basin produced by AquaLog forecasting system during period 2002 to 2008. Effects of uncertainties of observation, data processing and especially meteorological forecasts were not accounted separately. Forecast of flood levels exceedance (peak over the threshold) during forecasting period was the main criterion as flow increase forecast is of the highest importance. Other evaluation criteria included peak flow and volume difference. In addition Nash-Sutcliffe was computed separately for each time step (1 to 48 h) of forecasting period to identify its change with the lead time. Textual flood warnings are issued for administrative regions to initiate flood protection actions in danger of flood. Flood warning hit rate was evaluated at regions level and national level. Evaluation found significant differences of model forecast skill between forecasting profiles, particularly less skill was evaluated at small headwater basins due to domination of QPF uncertainty in these basins. The average hit rate was 0.34 (miss rate = 0.33, false alarm rate = 0.32). However its explored spatial difference is likely to be influenced also by different fit of parameters sets (due to different basin characteristics) and importantly by different impact of human factor. Results suggest that the practice of interactive

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

PubMed

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

2016-01-01

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

Operational air quality forecasting system for Spain: CALIOPE

NASA Astrophysics Data System (ADS)

Baldasano, J. M.; Piot, M.; Jorba, O.; Goncalves, M.; Pay, M.; Pirez, C.; Lopez, E.; Gasso, S.; Martin, F.; García-Vivanco, M.; Palomino, I.; Querol, X.; Pandolfi, M.; Dieguez, J. J.; Padilla, L.

2009-12-01

The European Commission (EC) and the United States Environmental Protection Agency (US-EPA) have shown great concerns to understand the transport and dynamics of pollutants in the atmosphere. According to the European directives (1996/62/EC, 2002/3/EC, 2008/50/EC), air quality modeling, if accurately applied, is a useful tool to understand the dynamics of air pollutants, to analyze and forecast the air quality, and to develop programs reducing emissions and alert the population when health-related issues occur. The CALIOPE project, funded by the Spanish Ministry of the Environment, has the main objective to establish an air quality forecasting system for Spain. A partnership of four research institutions composes the CALIOPE project: the Barcelona Supercomputing Center (BSC), the center of investigation CIEMAT, the Earth Sciences Institute ‘Jaume Almera’ (IJA-CSIC) and the CEAM Foundation. CALIOPE will become the official Spanish air quality operational system. This contribution focuses on the recent developments and implementation of the integrated modelling system for the Iberian Peninsula (IP) and Canary Islands (CI) with a high spatial and temporal resolution (4x4 sq. km for IP and 2x2 sq. km for CI, 1 hour), namely WRF-ARW/HERMES04/CMAQ/BSC-DREAM. The HERMES04 emission model has been specifically developed as a high-resolution (1x1 sq. km, 1 hour) emission model for Spain. It includes biogenic and anthropogenic emissions such as on-road and paved-road resuspension production, power plant generation, ship and plane traffic, airports and ports activities, industrial and agricultural sectors as well as domestic and commercial emissions. The qualitative and quantitative evaluation of the model was performed for a reference year (2004) using data from ground-based measurement networks. The products of the CALIOPE system will provide 24h and 48h forecasts for O3, NO2, SO2, CO, PM10 and PM2.5 at surface level. An operational evaluation system has been developed

Optimal Power Flow for Distribution Systems under Uncertain Forecasts: Preprint

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

Dall'Anese, Emiliano; Baker, Kyri; Summers, Tyler

2016-12-01

The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative boundsmore » that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.« less

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.

Wave Climate and Wave Mixing in the Marginal Ice Zones of Arctic Seas, Observations and Modelling

DTIC Science & Technology

2014-09-30

At the same time, the PIs participate in Australian efforts of developing wave-ocean- ice coupled models for Antarctica . Specific new physics modules...Wave Mixing in the Marginal Ice Zones of Arctic Seas, Observations and Modelling Alexander V. Babanin Swinburne University of Technology, PO Box...operational forecast. Altimeter climatology and the wave models will be used to study the current and future wind/wave and ice trends. APPROACH

GIM-TEC adaptive ionospheric weather assessment and forecast system

NASA Astrophysics Data System (ADS)

Gulyaeva, T. L.; Arikan, F.; Hernandez-Pajares, M.; Stanislawska, I.

2013-09-01

The Ionospheric Weather Assessment and Forecast (IWAF) system is a computer software package designed to assess and predict the world-wide representation of 3-D electron density profiles from the Global Ionospheric Maps of Total Electron Content (GIM-TEC). The unique system products include daily-hourly numerical global maps of the F2 layer critical frequency (foF2) and the peak height (hmF2) generated with the International Reference Ionosphere extended to the plasmasphere, IRI-Plas, upgraded by importing the daily-hourly GIM-TEC as a new model driving parameter. Since GIM-TEC maps are provided with 1- or 2-days latency, the global maps forecast for 1 day and 2 days ahead are derived using an harmonic analysis applied to the temporal changes of TEC, foF2 and hmF2 at 5112 grid points of a map encapsulated in IONEX format (-87.5°:2.5°:87.5°N in latitude, -180°:5°:180°E in longitude). The system provides online the ionospheric disturbance warnings in the global W-index map establishing categories of the ionospheric weather from the quiet state (W=±1) to intense storm (W=±4) according to the thresholds set for instant TEC perturbations regarding quiet reference median for the preceding 7 days. The accuracy of IWAF system predictions of TEC, foF2 and hmF2 maps is superior to the standard persistence model with prediction equal to the most recent ‘true’ map. The paper presents outcomes of the new service expressed by the global ionospheric foF2, hmF2 and W-index maps demonstrating the process of origin and propagation of positive and negative ionosphere disturbances in space and time and their forecast under different scenarios.

Refrigeration system having standing wave compressor

DOEpatents

Lucas, Timothy S.

1992-01-01

A compression-evaporation refrigeration system, wherein gaseous compression of the refrigerant is provided by a standing wave compressor. The standing wave compressor is modified so as to provide a separate subcooling system for the refrigerant, so that efficiency losses due to flashing are reduced. Subcooling occurs when heat exchange is provided between the refrigerant and a heat pumping surface, which is exposed to the standing acoustic wave within the standing wave compressor. A variable capacity and variable discharge pressure for the standing wave compressor is provided. A control circuit simultaneously varies the capacity and discharge pressure in response to changing operating conditions, thereby maintaining the minimum discharge pressure needed for condensation to occur at any time. Thus, the power consumption of the standing wave compressor is reduced and system efficiency is improved.

Performance analysis of coupled and uncoupled hydrodynamic and wave models in the northern Adriatic Sea

NASA Astrophysics Data System (ADS)

Busca, Claudia; Coluccelli, Alessandro; Valentini, Andrea; Benetazzo, Alvise; Bonaldo, Davide; Bortoluzzi, Giovanni; Carniel, Sandro; Falcieri, Francesco; Paccagnella, Tiziana; Ravaioli, Mariangela; Riminucci, Francesco; Sclavo, Mauro; Russo, Aniello

2014-05-01

The complex dynamics of the Adriatic Sea are the result of geographical position, orography and bathymetry, as well as rivers discharge and meteorological conditions that influence, more strongly, the shallow northern part. Such complexity requires a constant monitoring of marine conditions in order to support several activities (marine resources management, naval operations, emergency management, shipping, tourism, as well as scientific ones). Platforms, buoys and mooring located in Adriatic Sea supply almost continuously real time punctual information, which can be spatially extended, with some limitations, by drifters and remote sensing. Operational forecasting systems represent valid tools to provide a complete tridimensional coverage of the area, with a high spatial and temporal resolution. The Hydro-Meteo-Clima Service of the Emilia-Romagna Environmental Agency (ARPA-SIMC, Bologna, Italy) and the Dept. of Life and Environmental Sciences of Università Politecnica delle Marche (DISVA-UNIVPM, Ancona, Italy), in collaboration with the Institute of Marine Science of the National Research Council (ISMAR-CNR, Italy) operationally run several wave and hydrodynamic models on the Adriatic Sea. The main implementations are based on the Regional Ocean Modeling System (ROMS), the wave model Simulating WAves Nearshore (SWAN), and the coupling of the former two models in the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) system. Horizontal resolutions of the different systems range from the 2 km of AdriaROMS to the 0.5 km of the recently implemented northern Adriatic COAWST. Forecasts are produced every day for the subsequent 72 hour with hourly resolution. All the systems compute the fluxes exchanged through the interface with the atmosphere from the numerical weather prediction system named COSMO-I7, an implementation for Italy of the Consortium for Small-scale Modeling (COSMO) model, at 7 km horizontal resolution. Considering the several operational

iFLOOD: A Real Time Flood Forecast System for Total Water Modeling in the National Capital Region

NASA Astrophysics Data System (ADS)

Sumi, S. J.; Ferreira, C.

2017-12-01

Extreme flood events are the costliest natural hazards impacting the US and frequently cause extensive damages to infrastructure, disruption to economy and loss of lives. In 2016, Hurricane Matthew brought severe damage to South Carolina and demonstrated the importance of accurate flood hazard predictions that requires the integration of riverine and coastal model forecasts for total water prediction in coastal and tidal areas. The National Weather Service (NWS) and the National Ocean Service (NOS) provide flood forecasts for almost the entire US, still there are service-gap areas in tidal regions where no official flood forecast is available. The National capital region is vulnerable to multi-flood hazards including high flows from annual inland precipitation events and surge driven coastal inundation along the tidal Potomac River. Predicting flood levels on such tidal areas in river-estuarine zone is extremely challenging. The main objective of this study is to develop the next generation of flood forecast systems capable of providing accurate and timely information to support emergency management and response in areas impacted by multi-flood hazards. This forecast system is capable of simulating flood levels in the Potomac and Anacostia River incorporating the effects of riverine flooding from the upstream basins, urban storm water and tidal oscillations from the Chesapeake Bay. Flood forecast models developed so far have been using riverine data to simulate water levels for Potomac River. Therefore, the idea is to use forecasted storm surge data from a coastal model as boundary condition of this system. Final output of this validated model will capture the water behavior in river-estuary transition zone far better than the one with riverine data only. The challenge for this iFLOOD forecast system is to understand the complex dynamics of multi-flood hazards caused by storm surges, riverine flow, tidal oscillation and urban storm water. Automated system

Modeling influence of tide stages on forecasts of the 2010 Chilean tsunami

NASA Astrophysics Data System (ADS)

Uslu, B. U.; Chamberlin, C.; Walsh, D.; Eble, M. C.

2010-12-01

The impact of the 2010 Chilean tsunami is studied using the NOAA high-resolution tsunami forecast model augmented to include modeled tide heights in addition to deep-water tsunami propagation as boundary-condition input. The Chilean tsunami was observed at the Los Angeles tide station at mean low water, Hilo at low, Pago Pago at mid tide and Wake Island near high tide. Because the tsunami arrived at coastal communities at a representative variety of tide stages, 2010 Chile tsunami provides opportunity to study the tsunami impacts at different tide levels to different communities. The current forecast models are computed with a constant tidal stage, and this study evaluates techniques for adding an additional varying predicted tidal component in a forecasting context. Computed wave amplitudes, wave currents and flooding are compared at locations around the Pacific, and the difference in tsunami impact due to tidal stage is studied. This study focuses on how tsunami impacts vary with different tide levels, and helps us understand how the inclusion of tidal components can improve real-time forecast accuracy.

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

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

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

2015-10-05

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

Financial forecasts accuracy in Brazil’s social security system

PubMed Central

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. PMID:28859172

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

NASA Astrophysics Data System (ADS)

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

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

Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil

PubMed Central

Lowe, Rachel; Coelho, Caio AS; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier

2016-01-01

Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics. DOI: http://dx.doi.org/10.7554/eLife.11285.001 PMID:26910315

An integrated sea monitoring system based on a X-band wave radar to support the removal activities of the Costa Concordia wreck.

NASA Astrophysics Data System (ADS)

Gozzini, Bernardo; Serafino, Francesco; Lugni, Claudio; Antonini, Andrea; Costanza, Letizia; Orlandi, Andrea; Arturi, Daniele; Ludeno, Giovanni; Natale, Antonio; Soldovieri, Francesco; Ortolani, Alberto; Brandini, Carlo

2013-04-01

The planning and management of different types of operations at sea requires a number of sea state data as much in real-time as possible, for rapid and effective response to different situations. This need is particularly strong in emergency management practices, in accidents due to man-made or natural causes, that require the planning of civil protection activities (such as search-and-rescue, cleaning of pollution, ship recovery), transport planning etc. The use of X-band radar technology nowadays provides great advantages over traditional in-situ and satellite-based techniques for sea state measuring, to update information on waves and currents over a sea area with high spatial and temporal resolution. Other advantages include a good spatial coverage around the area of interest, the flexibility of use, the capacity to provide, on-demand and when necessary, complementary information (possible oil spills detection, integration with VTS, etc.). X-band coastal radars (so-called "wave-radars") are widely used in the monitoring of large marine areas, in integration with in-situ measurements, satellites and other radar types (HF), as a key element of the observational component of present operational oceanography systems. Outside of these systems, the use of this technology to support emergency management practices is very promising for both the quality and quantity of available parameters, and for an easy integration with all other available monitoring and forecasting tools. A case study particularly relevant is offered by the presence of the Costa Concordia ship near the Giglio Island. The management of this disaster has requested at an early stage a large number of data to support the monitoring of marine environment around the ship, e.g. to optimally plan water samples. In the next and present phase, to support the highly risky and costly activities linked to the wreck removal, which are extremely sea-state dependent, the installation of a wave-radar allows to

Bayesian Hierarchical Models to Augment the Mediterranean Forecast System

DTIC Science & Technology

2010-09-30

In part 2 (Bonazzi et al., 2010), the impact of the ensemble forecast methodology based on MFS-Wind-BHM perturbations is documented. Forecast...absence of dt data stage inputs, the forecast impact of MFS-Error-BHM is neutral. Experiments are underway now to introduce dt back into the MFS-Error...BHM and quantify forecast impacts at MFS. MFS-SuperEnsemble-BHM We have assembled all needed datasets and completed algorithmic development

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

Anvil Forecast Tool in the Advanced Weather Interactive Processing System (AWIPS)

NASA Technical Reports Server (NTRS)

Barrett, Joe H., III; Hood, Doris

2009-01-01

Launch Weather Officers (LWOs) from the 45th Weather Squadron (45 WS) and forecasters from the National Weather Service (NWS) Spaceflight Meteorology Group (SMG) have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violating the Lightning Launch Commit Criteria (LLCC) (Krider et al. 2006; Space Shuttle Flight Rules (FR), NASA/JSC 2004)). As a result, the Applied Meteorology Unit (AMU) developed a tool that creates an anvil threat corridor graphic that can be overlaid on satellite imagery using the Meteorological Interactive Data Display System (MIDDS, Short and Wheeler, 2002). The tool helps forecasters estimate the locations of thunderstorm anvils at one, two, and three hours into the future. It has been used extensively in launch and landing operations by both the 45 WS and SMG. The Advanced Weather Interactive Processing System (AWIPS) is now used along with MIDDS for weather analysis and display at SMG. In Phase I of this task, SMG tasked the AMU to transition the tool from MIDDS to AWIPS (Barrett et aI., 2007). For Phase II, SMG requested the AMU make the Anvil Forecast Tool in AWIPS more configurable by creating the capability to read model gridded data from user-defined model files instead of hard-coded files. An NWS local AWIPS application called AGRID was used to accomplish this. In addition, SMG needed to be able to define the pressure levels for the model data, instead of hard-coding the bottom level as 300 mb and the top level as 150 mb. This paper describes the initial development of the Anvil Forecast Tool for MIDDS, followed by the migration of the tool to AWIPS in Phase I. It then gives a detailed presentation of the Phase II improvements to the AWIPS tool.

Valuing year-to-go hydrologic forecast improvements for a peaking hydropower system in the Sierra Nevada

NASA Astrophysics Data System (ADS)

Rheinheimer, David E.; Bales, Roger C.; Oroza, Carlos A.; Lund, Jay R.; Viers, Joshua H.

2016-05-01

We assessed the potential value of hydrologic forecasting improvements for a snow-dominated high-elevation hydropower system in the Sierra Nevada of California, using a hydropower optimization model. To mimic different forecasting skill levels for inflow time series, rest-of-year inflows from regression-based forecasts were blended in different proportions with representative inflows from a spatially distributed hydrologic model. The statistical approach mimics the simpler, historical forecasting approach that is still widely used. Revenue was calculated using historical electricity prices, with perfect price foresight assumed. With current infrastructure and operations, perfect hydrologic forecasts increased annual hydropower revenue by 0.14 to 1.6 million, with lower values in dry years and higher values in wet years, or about $0.8 million (1.2%) on average, representing overall willingness-to-pay for perfect information. A second sensitivity analysis found a wider range of annual revenue gain or loss using different skill levels in snow measurement in the regression-based forecast, mimicking expected declines in skill as the climate warms and historical snow measurements no longer represent current conditions. The value of perfect forecasts was insensitive to storage capacity for small and large reservoirs, relative to average inflow, and modestly sensitive to storage capacity with medium (current) reservoir storage. The value of forecasts was highly sensitive to powerhouse capacity, particularly for the range of capacities in the northern Sierra Nevada. The approach can be extended to multireservoir, multipurpose systems to help guide investments in forecasting.

Post-processing of a low-flow forecasting system in the Thur basin (Switzerland)

NASA Astrophysics Data System (ADS)

Bogner, Konrad; Joerg-Hess, Stefanie; Bernhard, Luzi; Zappa, Massimiliano

2015-04-01

Low-flows and droughts are natural hazards with potentially severe impacts and economic loss or damage in a number of environmental and socio-economic sectors. As droughts develop slowly there is time to prepare and pre-empt some of these impacts. Real-time information and forecasting of a drought situation can therefore be an effective component of drought management. Although Switzerland has traditionally been more concerned with problems related to floods, in recent years some unprecedented low-flow situations have been experienced. Driven by the climate change debate a drought information platform has been developed to guide water resources management during situations where water resources drop below critical low-flow levels characterised by the indices duration (time between onset and offset), severity (cumulative water deficit) and magnitude (severity/duration). However to gain maximum benefit from such an information system it is essential to remove the bias from the meteorological forecast, to derive optimal estimates of the initial conditions, and to post-process the stream-flow forecasts. Quantile mapping methods for pre-processing the meteorological forecasts and improved data assimilation methods of snow measurements, which accounts for much of the seasonal stream-flow predictability for the majority of the basins in Switzerland, have been tested previously. The objective of this study is the testing of post-processing methods in order to remove bias and dispersion errors and to derive the predictive uncertainty of a calibrated low-flow forecast system. Therefore various stream-flow error correction methods with different degrees of complexity have been applied and combined with the Hydrological Uncertainty Processor (HUP) in order to minimise the differences between the observations and model predictions and to derive posterior probabilities. The complexity of the analysed error correction methods ranges from simple AR(1) models to methods including

Operational flood forecasting system of Umbria Region "Functional Centre

NASA Astrophysics Data System (ADS)

Berni, N.; Pandolfo, C.; Stelluti, M.; Ponziani, F.; Viterbo, A.

2009-04-01

The hydrometeorological alert office (called "Decentrate Functional Centre" - CFD) of Umbria Region, in central Italy, is the office that provides technical tools able to support decisions when significant flood/landslide events occur, furnishing 24h support for the whole duration of the emergency period, according to the national directive DPCM 27 February 2004 concerning the "Operating concepts for functional management of national and regional alert system during flooding and landslide events for civil protection activities purposes" that designs, within the Italian Civil Defence Emergency Management System, a network of 21 regional Functional Centres coordinated by a central office at the National Civil Protection Department in Rome. Due to its "linking" role between Civil Protection "real time" activities and environmental/planning "deferred time" ones, the Centre is in charge to acquire and collect both real time and quasi-static data: quantitative data from monitoring networks (hydrometeorological stations, meteo radar, ...), meteorological forecasting models output, Earth Observation data, hydraulic and hydrological simulation models, cartographic and thematic GIS data (vectorial and raster type), planning studies related to flooding areas mapping, dam managing plans during flood events, non instrumental information from direct control of "territorial presidium". A detailed procedure for the management of critical events was planned, also in order to define the different role of various authorities and institutions involved. Tiber River catchment, of which Umbria region represents the main upper-medium portion, includes also regional trans-boundary issues very important to cope with, especially for what concerns large dam behavior and management during heavy rainfall. The alert system is referred to 6 different warning areas in which the territory has been divided into and based on a threshold system of three different increasing critical levels according

Oceanic sources of predictability for MJO propagation across the Maritime Continent in a subset of S2S forecast models

NASA Astrophysics Data System (ADS)

DeMott, C. A.; Klingaman, N. P.

2017-12-01

Skillful prediction of the Madden-Julian oscillation (MJO) passage across the Maritime Continent (MC) has important implications for global forecasts of high-impact weather events, such as atmospheric rivers and heat waves. The North American teleconnection response to the MJO is strongest when MJO convection is located in the western Pacific Ocean, but many climate and forecast models are deficient in their simulation of MC-crossing MJO events. Compared to atmosphere-only general circulation models (AGCMs), MJO simulation skill generally improves with the addition of ocean feedbacks in coupled GCMs (CGCMs). Using observations, previous studies have noted that the degree of ocean coupling may vary considerably from one MJO event to the next. The coupling mechanisms may be linked to the presence of ocean Equatorial Rossby waves, the sign and amplitude of Equatorial surface currents, and the upper ocean temperature and salinity profiles. In this study, we assess the role of ocean feedbacks to MJO prediction skill using a subset of CGCMs participating in the Subseasonal-to-Seasonal (S2S) Project database. Oceanic observational and reanalysis datasets are used to characterize the upper ocean background state for observed MJO events that do and do not propagate beyond the MC. The ability of forecast models to capture the oceanic influence on the MJO is first assessed by quantifying SST forecast skill. Next, a set of previously developed air-sea interaction diagnostics is applied to model output to measure the role of SST perturbations on the forecast MJO. The "SST effect" in forecast MJO events is compared to that obtained from reanalysis data. Leveraging all ensemble members of a given forecast helps disentangle oceanic model biases from atmospheric model biases, both of which can influence the expression of ocean feedbacks in coupled forecast systems. Results of this study will help identify areas of needed model improvement for improved MJO forecasts.

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.

Evaluation of precipitation forecasts from 3D-Var and hybrid GSI-based system during Indian summer monsoon 2015

NASA Astrophysics Data System (ADS)

Singh, Sanjeev Kumar; Prasad, V. S.

2018-02-01

This paper presents a systematic investigation of medium-range rainfall forecasts from two versions of the National Centre for Medium Range Weather Forecasting (NCMRWF)-Global Forecast System based on three-dimensional variational (3D-Var) and hybrid analysis system namely, NGFS and HNGFS, respectively, during Indian summer monsoon (June-September) 2015. The NGFS uses gridpoint statistical interpolation (GSI) 3D-Var data assimilation system, whereas HNGFS uses hybrid 3D ensemble-variational scheme. The analysis includes the evaluation of rainfall fields and comparisons of rainfall using statistical score such as mean precipitation, bias, correlation coefficient, root mean square error and forecast improvement factor. In addition to these, categorical scores like Peirce skill score and bias score are also computed to describe particular aspects of forecasts performance. The comparison results of mean precipitation reveal that both the versions of model produced similar large-scale feature of Indian summer monsoon rainfall for day-1 through day-5 forecasts. The inclusion of fully flow-dependent background error covariance significantly improved the wet biases in HNGFS over the Indian Ocean. The forecast improvement factor and Peirce skill score in the HNGFS have also found better than NGFS for day-1 through day-5 forecasts.

Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

NASA Astrophysics Data System (ADS)

Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.

2016-11-01

All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.

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

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

Forecasting Influenza Epidemics in Hong Kong.

PubMed

Yang, Wan; Cowling, Benjamin J; Lau, Eric H Y; Shaman, Jeffrey

2015-07-01

Recent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A(H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions.

Forecasting Influenza Epidemics in Hong Kong

PubMed Central

Yang, Wan; Cowling, Benjamin J.; Lau, Eric H. Y.; Shaman, Jeffrey

2015-01-01

Recent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A(H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions. PMID:26226185

VERIFICATION OF SURFACE LAYER OZONE FORECASTS IN THE NOAA/EPA AIR QUALITY FORECAST SYSTEM IN DIFFERENT REGIONS UNDER DIFFERENT SYNOPTIC SCENARIOS

EPA Science Inventory

An air quality forecast (AQF) system has been established at NOAA/NCEP since 2003 as a collaborative effort of NOAA and EPA. The system is based on NCEP's Eta mesoscale meteorological model and EPA's CMAQ air quality model (Davidson et al, 2004). The vision behind this system is ...

An Experimental High-Resolution Forecast System During the Vancouver 2010 Winter Olympic and Paralympic Games

NASA Astrophysics Data System (ADS)

Mailhot, J.; Milbrandt, J. A.; Giguère, A.; McTaggart-Cowan, R.; Erfani, A.; Denis, B.; Glazer, A.; Vallée, M.

2014-01-01

Environment Canada ran an experimental numerical weather prediction (NWP) system during the Vancouver 2010 Winter Olympic and Paralympic Games, consisting of nested high-resolution (down to 1-km horizontal grid-spacing) configurations of the GEM-LAM model, with improved geophysical fields, cloud microphysics and radiative transfer schemes, and several new diagnostic products such as density of falling snow, visibility, and peak wind gust strength. The performance of this experimental NWP system has been evaluated in these winter conditions over complex terrain using the enhanced mesoscale observing network in place during the Olympics. As compared to the forecasts from the operational regional 15-km GEM model, objective verification generally indicated significant added value of the higher-resolution models for near-surface meteorological variables (wind speed, air temperature, and dewpoint temperature) with the 1-km model providing the best forecast accuracy. Appreciable errors were noted in all models for the forecasts of wind direction and humidity near the surface. Subjective assessment of several cases also indicated that the experimental Olympic system was skillful at forecasting meteorological phenomena at high-resolution, both spatially and temporally, and provided enhanced guidance to the Olympic forecasters in terms of better timing of precipitation phase change, squall line passage, wind flow channeling, and visibility reduction due to fog and snow.

Forecast and virtual weather driven plant disease risk modeling system

USDA-ARS?s Scientific Manuscript database

We describe a system in use and development that leverages public weather station data, several spatialized weather forecast types, leaf wetness estimation, generic plant disease models, and online statistical evaluation. Convergent technological developments in all these areas allow, with funding f...

Trapped mountain wave excitations over the Kathmandu valley, Nepal

NASA Astrophysics Data System (ADS)

Regmi, Ram P.; Maharjan, Sangeeta

2015-11-01

Mid-wintertime spatial and temporal distributions of mountain wave excitation over the Kathmandu valley has been numerically simulated using Weather Research and Forecasting (WRF) modeling system. The study shows that low-level trapped mountain waves may remain very active during the night and early morning in the sky over the southern rim of the surrounding mountains, particularly, over the lee of Mt. Fulchoki. Calculations suggest that mountain wave activities are at minimum level during afternoon. The low-level trapped mountain waves in the sky over southern gateway of Tribhuvan International Airport (TIA) may pose risk for landings and takeoffs of light aircrafts. Detailed numerical and observational studies would be very important to reduce risk of air accidents and discomfort in and around the Kathmandu valley.

An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models

DOE PAGES

Yuan, Xing

2016-06-22

This is the second paper of a two-part series on introducing an experimental seasonal hydrological forecasting system over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, the added value from eight North American Multimodel Ensemble (NMME) climate forecast models with a grand ensemble of 99 members is assessed in this paper, with an implicit consideration of human-induced uncertainty in the hydrological models through a post-processing procedure. The forecast skill in terms of anomaly correlation (AC) for 2 m air temperature and precipitation does not necessarily decrease overmore » leads but is dependent on the target month due to a strong seasonality for the climate over the Yellow River basin. As there is more diversity in the model performance for the temperature forecasts than the precipitation forecasts, the grand NMME ensemble mean forecast has consistently higher skill than the best single model up to 6 months for the temperature but up to 2 months for the precipitation. The NMME climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model regionalized over the Yellow River basin to produce forecasts of soil moisture, runoff and streamflow. And the NMME/VIC forecasts are compared with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month hindcast experiments for each calendar month during 1982–2010. As verified by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC for the soil moisture forecasts, and the former has higher skill than the latter only for the forecasts at long leads and for those initialized in the rainy season. The forecast skill for runoff is lower for both forecast approaches, but the added value from NMME/VIC is more obvious, with an increase of the average AC by 0.08–0.2. To compare with

An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models

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

Yuan, Xing

This is the second paper of a two-part series on introducing an experimental seasonal hydrological forecasting system over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, the added value from eight North American Multimodel Ensemble (NMME) climate forecast models with a grand ensemble of 99 members is assessed in this paper, with an implicit consideration of human-induced uncertainty in the hydrological models through a post-processing procedure. The forecast skill in terms of anomaly correlation (AC) for 2 m air temperature and precipitation does not necessarily decrease overmore » leads but is dependent on the target month due to a strong seasonality for the climate over the Yellow River basin. As there is more diversity in the model performance for the temperature forecasts than the precipitation forecasts, the grand NMME ensemble mean forecast has consistently higher skill than the best single model up to 6 months for the temperature but up to 2 months for the precipitation. The NMME climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model regionalized over the Yellow River basin to produce forecasts of soil moisture, runoff and streamflow. And the NMME/VIC forecasts are compared with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month hindcast experiments for each calendar month during 1982–2010. As verified by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC for the soil moisture forecasts, and the former has higher skill than the latter only for the forecasts at long leads and for those initialized in the rainy season. The forecast skill for runoff is lower for both forecast approaches, but the added value from NMME/VIC is more obvious, with an increase of the average AC by 0.08–0.2. To compare with

Development and application of an atmospheric-hydrologic-hydraulic flood forecasting model driven by TIGGE ensemble forecasts

NASA Astrophysics Data System (ADS)

Bao, Hongjun; Zhao, Linna

2012-02-01

A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction (NWP) information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centers for Environmental Prediction (NCEP). The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a

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.