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Prognóstico das variáveis meteorológicas e da evapotranspiração de referência com o modelo de previsão do tempo GFS/NCEP; Prediction of meteorological variables and reference evapotranspiration with GFS/NCEP weather forecast model

Oliveira Filho, Celso Luís de
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 31/07/2007 PT
Relevância na Pesquisa
106.4%
Avaliou-se o desempenho de um modelo numérico de previsão do tempo (GFS - Global Forecast System – antigo AVN – AViatioN model - do Centro Nacional para Previsão Ambiental – NCEP) no prognóstico de variáveis meteorológicas temperatura, déficit de pressão de vapor do ar, saldo de radiação e velocidade do vento, e da evapotranspiração de referência calculada pelos métodos de Thornthwaite (1948) e de Penman-Monteith (Allen et al., 1998). O desempenho foi avaliado por comparação com dados provenientes de uma estação meteorológica, situada em Piracicaba, São Paulo. A temperatura e o déficit de pressão de vapor do ar foram os elementos melhor prognosticados, com desempenho "muito bom" e "bom", de acordo com o índice de desempenho proposto por Camargo e Sentelhas (1997), para no máximo quatro e três dia de antecedência, respectivamente, durante o período seco. Para o período úmido, somente o prognóstico do déficit de pressão de vapor do ar para o primeiro dia mostrou-se "bom". Os prognósticos de saldo de radiação e velocidade do vento foram ruins para ambos os períodos. Em decorrência do bom desempenho do modelo para prognosticar a temperatura, verificou-se que a estimativa de ETo pelo método de Thornthwaite teve boa concordância com o calculado a partir dos dados da estação meteorológica...

Verificação da previsão do tempo em São Paulo com o modelo operacional WRF; Review of weather in São Paulo with the WRF Operational Model.

Bender, Fabiani Denise
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 01/11/2012 PT
Relevância na Pesquisa
36.19%
Este estudo tem como objetivo a verificação das previsões diárias, das temperaturas máxima e mínima e precipitação acumulada, realizadas pelo modelo operacional de previsão numérica do tempo WRF (Weather Research Forecasting) para o estado de São Paulo. As condições iniciais e de fronteira fornecidas pela análise e previsão das 00UTC do modelo Global Forecast System (GFS), são usados no processamento do WRF, para previsões de 72 horas, em duas grades aninhadas (espaçamentos horizontais de grade de 50 km, D1, e 16,6 km, D2). O período avaliado foi de abril de 2010 a março de 2011. As comparações diárias das temperaturas máxima e mínima foram realizadas entre os valores preditos e observados nas estações de superfície de Registro, São Paulo, Paranapanema, Campinas, Presidente Prudente e Votuporanga (dados da CIIAGRO); através do erro médio (EM) e raiz do erro médio quadrático (REQM), para os prognósticos das 36, 60 e 72 horas. A precipitação acumulada diária é avaliada com relação ao produto MERGE, pela aplicação da ferramenta MODE, na previsão das 36 horas, para um limiar de 0,3 mm, no domínio espacial abrangendo o Estado de São Paulo e vizinhanças. Primeiramente, fez-se uma análise, comparando os pares de grade dos campos previsto e observado...

Subsídios à operação de reservatórios baseada na previsão de variáveis hidrológicas

Bravo, Juan Martín
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Tese de Doutorado Formato: application/pdf
POR
Relevância na Pesquisa
36.31%
Diversas atividades humanas são fortemente dependentes do clima e da sua variabilidade, especialmente aquelas relacionadas ao uso da água. A operação integrada de reservatórios com múltiplos usos requer uma série de decisões que definem quanta água deve ser alocada, ao longo do tempo para cada um dos usos, e quais os volumes dos reservatórios a serem mantidos. O conhecimento antecipado das condições climáticas resulta de vital importância para os operadores de reservatórios, pois o insumo dos reservatórios é a vazão dos rios, que por sua vez é dependente de condições atmosféricas e hidrológicas em diferentes escalas de tempo e espaço. A pesquisa trata sobre três importantes elementos de subsídio à tomada de decisão na operação de reservatórios baseada na previsão de variáveis hidrológicas: (a) as previsões de vazão de curto prazo; (b) as previsões de precipitação de longo prazo e (c) as medidas de desempenho das previsões. O reservatório de Furnas, localizado na bacia do Rio Grande, em Minas Gerais, foi selecionado como estudo de caso devido, principalmente, à disponibilidade de previsões quantitativas de chuva e pela importância desse reservatório na região analisada. A previsão de curto prazo de vazão com base na precipitação foi estimada com um modelo empírico (rede neural artificial) e a previsão de precipitação foi obtida pelo modelo regional ETA. Uma metodologia de treinamento e validação da rede neural artificial foi desenvolvida utilizando previsões perfeitas de chuva (considerando a chuva observada como previsão) e utilizando o maior número de dados disponíveis...

Dynamic load-balancing : a new strategy for weather forecast models

Rodrigues, Eduardo Rocha
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Tese de Doutorado Formato: application/pdf
ENG
Relevância na Pesquisa
66.16%
Weather forecasting models are computationally intensive applications and traditionally they are executed in parallel machines. However, some issues prevent these models from fully exploiting the available computing power. One of such issues is load imbalance, i.e., the uneven distribution of load across the processors of the parallel machine. Since weather models are typically synchronous applications, that is, all tasks synchronize at every time-step, the execution time is determined by the slowest task. The causes of such imbalance are either static (e.g. topography) or dynamic (e.g. shortwave radiation, moving thunderstorms). Various techniques, often embedded in the application’s source code, have been used to address both sources. However, these techniques are inflexible and hard to use in legacy codes. In this thesis, we explore the concept of processor virtualization for dynamically balancing the load in weather models. This means that the domain is over-decomposed in more tasks than the available processors. Assuming that many tasks can be safely executed in a single processor, each processor is put in charge of a set of tasks. In addition, the system can migrate some of them from overloaded processors to underloaded ones when it detects load imbalance. This approach has the advantage of decoupling the application from the load balancing strategy. Our objective is to show that processor virtualization can be applied to weather models as long as an appropriate strategy for migrations is used. Our proposal takes into account the communication pattern of the application in addition to the load of each processor. In this text...

Statistical models to forecast summer burned area in Portugal based on meteorological indices of fire danger

Nunes, Sílvia Almeida
Fonte: Universidade de Lisboa Publicador: Universidade de Lisboa
Tipo: Dissertação de Mestrado
Publicado em //2013 ENG
Relevância na Pesquisa
36.06%
Tese de mestrado integrado em Engenharia da Energia e do Ambiente, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2013; Nos últimos anos, os incêndios florestais têm vindo a cativar a atenção da comunidade científica, procurando os investigadores identificar os principais fatores que controlam estes eventos, que têm vindo a tornar-se cada vez mais extremos. No contexto europeu a zona Mediterrânica assume especial relevância na medida em que representa cerca de 85% de toda a área ardida na Europa. Só em Portugal Continental, segundo estatísticas oficiais, arderam cerca de 3 468 986 há no período de1980 a 2011, uma área que é equivalente a 3/5 de toda a floresta portuguesa, sendo que no final deste período foi quando se registou um maior aumento destes valores, que teme-se que continuem a aumentar. Os incêndios em Portugal, tal como em toda a Europa mediterrânica, são um fenómeno natural relacionado com mecanismos meteorológicos, atividade antropogénica e condições da vegetação que se traduzem na quantidade de combustível disponível. Mesmo que as ações do homem, como a migração do interior para o litoral com o consequente abandono das terras, numa escala global, sejam responsáveis por grande parte da ignição dos fogos...

Application of statistical correction in extended weather forecasting in the southern region of Brazil

Avila,Ana Maria Heuminski de; Cardoso,Andrea de Oliveira
Fonte: Sociedade Brasileira de Meteorologia Publicador: Sociedade Brasileira de Meteorologia
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/12/2012 EN
Relevância na Pesquisa
46.11%
Adverse weather conditions in critical periods of vegetative plant growth affect crop productivity, being a fundamental parameter for yield forecast. An increase in weather forecasting accuracy may be obtained by applying statistical correction to remove model bias. This study used statistical correction of ensemble forecasting with the atmospheric general circulation model (Center for Weather Forecasting and Climate Studies/Center for Ocean - Land - Atmosphere Studies - CPTEC/COLA) by mean error removal for three cities in the South of Brazil. Comparisons were made between corrected and original precipitation forecasts, and between these and data observed at their respective meteorological stations. Results showed that the applied statistical correction method may improve forecasting performance in some situations and that the term of forecast present high accuracy, indicating the importance of ensemble forecasting as an auxiliary tool in agricultural crop monitoring.

Operational maize yield model development and validation based on remote sensing and agro-meteorological data in Kenya

ROJAS MORA OSCAR
Fonte: TAYLOR & FRANCIS LTD Publicador: TAYLOR & FRANCIS LTD
Tipo: Articles in Journals Formato: Printed
ENG
Relevância na Pesquisa
35.92%
Remote-sensing data acquired by satellite have a wide scope for agricultural applications owing to their synoptic and repetitive coverage. On the one hand, spectral indices deduced from visible and near-infrared remote-sensing data have been extensively used for crop characterization, biomass estimation, and crop yield monitoring and forecasting. On the other hand, extensive research has been conducted using agrometerological models to estimate soil moisture to produce indicators of plant-water stress. This paper reports the development of an operational spectro-agrometeorological yield model for maize using a spectral index, the Normalized Difference Vegetation Index (NDVI) derived from SPOTVEGETATION, meteorological data obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) model, and crop-water status indicators estimated by the Crop-Specific Water Balance model (CSWB). Official figures produced by the Government of Kenya (GoK) on crop yield, area planted, and production were used in the model. The statistical multiple regression linear model has been developed for six large maize-growing provinces in Kenya. The spectro-agrometerological yield model was validated by comparing the predicted province-level yields with those estimated by GoK. The performance of the NDVI and land cover weighted NDVI (CNDVI) on the yield model was tested. Using CNDVI instead of NDVI in the model reduces 26% of the unknown variance. Of the output indicators of the CSWB model...

Operational Maize Yield Model Development and Validation Based on Remote Sensing and Agro-Meteorological Data in Kenya

ROJAS MORA OSCAR
Fonte: TAYLOR & FRANCIS LTD Publicador: TAYLOR & FRANCIS LTD
Tipo: Articles in Journals Formato: Printed
ENG
Relevância na Pesquisa
35.92%
Abstract Remote sensing (RS) data acquired by satellite have wide scope for agricultural applications owing to their synoptic and repetitive coverage. On the one hand, spectral indices deduced from visible and near-infrared RS data have been extensively used for crop characterization, biomass estimation and crop yield monitoring and forecasting. On the other hand, extensive research has been conducted using agrometerological models to estimate soil moisture to produce indicators of plant-water stress. This paper reports the development of an operational spectro-agrometeorological yield model for maize using a spectral index, the Normalized Difference Vegetation Index (NDVI) derived from SPOT-VEGETATION, meteorological data obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) model and crop-water status indicators estimated by the Crop Specific Water Balance model (CSWB). Official figures produced by the Government of Kenya (GoK) on crop yield, area planted and production were used in the model. The statistical multiple regression linear model has been developed for six large maize-growing provinces in Kenya. The spectro-agrometerological yield model was validated by comparing the predicted province-level yields with those estimated by GoK. The performance of the NDVI and land cover weighted NDVI (CNDVI) on the yield model was tested. Using CNDVI instead of NDVI in the model reduces 26% of the unknown variance. Of the output indicators of the CSWB model...

Forecasting Drought in Europe with the Standardized Precipitation Index : An assessment of the performance of the European Centre for Medium Range Weather Forecasts Variable Resolution Ensemble Prediction System

SINGLETON ANDREW
Fonte: Publications Office of the European Union Publicador: Publications Office of the European Union
Tipo: EUR - Scientific and Technical Research Reports Formato: Printed
ENG
Relevância na Pesquisa
36.13%
This report describes an assessment of the performance of the European Centre for Medium Range Weather Forecasts (ECMWF) variable resolution ensemble prediction system (varEPS) as a tool for forecasting drought using the Standardized Precipitation Index (SPI) with one month lead time. The model is verified using standard verification measures of the Brier Score, the Brier Skill Score, reliability and relative operating characteristics. It is found that for the 1-month SPI, the model has little skill in forecasting drought events and the forecast is generally unreliable. For the 3-month SPI the model has more skill, but this skill comes from the use of 2 months of reanalysis precipitation in and 1-month of forecast precipitation in building in the 3-month SPI. Calibration of the forecasts through adjusting the forecast probabilities to observed frequencies improved the verification statistics. Two case studies using the model were analysed and it was found that the model did not give useful guidance, and in fact calibration had the effect of underestimating the probability of extreme events where the model had some skill. It is recommended that ensemble probabilistic forecasts not be used as a tool for decision making with regard to drought without further improvement in the model performance.; JRC.H.7-Climate Risk Management

An analysis of a dust storm impacting Operation Iraqi Freedom, 25-27 March 2003

Anderson, John W.
Fonte: Monterey California. Naval Postgraduate School Publicador: Monterey California. Naval Postgraduate School
Tipo: Tese de Doutorado
Relevância na Pesquisa
35.77%
Approved for public release; distribution in unlimited.; On day five of combat operations during Operation IRAQI FREEDOM, advances by coalition forces were nearly halted by a dust storm, initiated by the passage of a synoptically driven cold front. This storm impacted ground and air operations across the entire Area of Responsibility, and delayed an impending ground attack on the Iraqi capital. Military meteorologists were able to assist military planners in mitigating at least some of the effects of this storm. This thesis examines the synoptic conditions leading to the severe dust storm, evaluates the numerical weather prediction model performance in predicting the event, and reviews metrics pertaining to the overall impacts on the Operation IRAQI FREEDOM combined air campaign. In general, the numerical model guidance correctly predicted the location and onset of the dust storms on 25 March, 2003. As a result of this forecast guidance, mission planners were able to front load Air Tasking Orders with extra sorties prior to the onset of the dust storm, and were able to make changes to planned weapons loads, favoring GPS-guided munitions.

Performance of a high resolution diagnostic model for short range mesoscale wind forecasts in complex terrain

Gallaher, Shawn G.
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Tipo: Tese de Doutorado
Relevância na Pesquisa
35.94%
Approved for public release, distribution is unlimited; This study investigates the feasibility of using a high resolution simple diagnostic model (WOCSS) initialized from a coarser grid full physics prognostic model (COAMPS) to obtain mesoscale winds. This approach using COAMPS 81, 27, and 9 km forecast model soundings to initialize WOCSS at 3 km is compared to COAMPS forecast at 3km horizontal resolution alone. Four case studies were collected during various weather regimes in Central California. Observations were collected from 5 different agencies and were used for verification of the models. The sensitivity of various WOCSS parameters were also explored. The results showed that overall the COAMPS(9km)/WOCSS approach provides winds as good as COAMPS at 3 km at a greatly reduced computation time. The COAMPS/WOCSS methodology performed particularly well during non-frontal situations where low-level inversions were present. Separation of the surface observation data by agency revealed large errors from data networks with low maintenance, monitoring and site specifications standards. The highest flow surface in WOCSS was the only parameter that displayed any significant sensitivity. Further work is needed to test the advantages of this sensitivity. COAMPS/WOCSS mesoscale forecast winds may prove to be very useful as input to emergency response applications such as dispersion and trajectory modeling.

Solar Resource Mapping in the Maldives; Model Validation Report

World Bank Group
Fonte: World Bank, Washington, DC Publicador: World Bank, Washington, DC
Tipo: Report; Economic & Sector Work :: Energy Study; Economic & Sector Work
ENGLISH; EN_US
Relevância na Pesquisa
35.91%
This model validation report presents results of preliminary validation of solar resource and meteorological modelled data, within phase one of the project renewable energy resource mapping for the Republic of the Maldives. This part of the project focuses on solar resource mapping and measurement services as part of a technical assistance in the renewable energy development implemented by the World Bank in Maldives. It is being undertaken in close coordination with the ministry of environment and energy (MEE) of Maldives, the World Bank’s primary country counterpart for this project. The objective of this report is to document validation of solar resources calculated by satellite-based model SolarGIS and validation of meteorological data derived from the numerical weather model climate forecast system reanalysis (CFSR) and climate forecast system version two (CFSv2). Chapter one gives summary. Chapter two focuses on model quality indicators. Inventory in chapter three identifies the existing data sources in the region: solar...

A Study of Machine Learning Techniques for Daily Solar Energy Forecasting using Numerical Weather Models

Aler, Ricardo; Martín, Ricardo; Valls, José M.; Galván, Inés M.
Fonte: Springer International Publishing Publicador: Springer International Publishing
Tipo: info:eu-repo/semantics/acceptedVersion; info:eu-repo/semantics/conferenceObject; info:eu-repo/semantics/bookPart
Publicado em //2015 ENG
Relevância na Pesquisa
35.88%
Forecasting solar energy is becoming an important issue in the context of renewable energy sources and Machine Learning Algorithms play an important rule in this field. The prediction of solar energy can be addressed as a time series prediction problem using historical data. Also, solar energy forecasting can be derived from numerical weather prediction models (NWP). Our interest is focused on the latter approach.We focus on the problem of predicting solar energy from NWP computed from GEFS, the Global Ensemble Forecast System, which predicts meteorological variables for points in a grid. In this context, it can be useful to know how prediction accuracy improves depending on the number of grid nodes used as input for the machine learning techniques. However, using the variables from a large number of grid nodes can result in many attributes which might degrade the generalization performance of the learning algorithms. In this paper both issues are studied using data supplied by Kaggle for the State of Oklahoma comparing Support Vector Machines and Gradient Boosted Regression. Also, three different feature selection methods have been tested: Linear Correlation, the ReliefF algorithm and, a new method based on local information analysis.; Proceedings of: 8th International Symposium on Intelligent Distributed Computing (IDC'2014). Madrid...

FNMOC model verification system.

Pace, Kyongsuk P.
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Tipo: Tese de Doutorado Formato: xvii, 187 p.;28 cm.
EN_US
Relevância na Pesquisa
45.92%
Approved for public release; distribution is unlimited; Fleet Numerical Meteorology and Oceanography Center (FNMOC) forecasts the atmospheric environment and weather using several meteorological and oceanographic models. These models' forecasting abilities are verified by comparing the model forecast against the observational data and model's analysis. Currently, some models are verified by several inconsistent, maintenance-intense, non-standardized, and hard-to-use model verification systems designed for a particular model. Some models are not verified because there is no model verification system. This thesis demonstrates the concept of a single model verification system for all FNMOC models to eliminate the inconsistencies and redundancies. The single model verification system standardizes the model verifications and provides the ability to verify those models which are currently unverified. The prototype used a GIJI and web browsers to display the model verification statistics. The prototype demonstrates that convenient access to the model verification statistics could aid FNMOC users in evaluating the forecast models' performance. This thesis identifies and documents the user specified verification requirements for several models and implements the most immediate requirements. A complete quantitative model verification system for all FNMOC models will be implemented incrementally...

Verification of the AFWA 3-Element Severe Weather Forecast Algorithm

Pagliaro, Daniel E.
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Tipo: Tese de Doutorado Formato: xvi, 87 p. : maps ;
Relevância na Pesquisa
56.2%
Accurate severe thunderstorm forecasts are critical to providing sufficient leadtime to protect lives and property. The Air Force Weather Agency has developed a 3-Element Severe Weather Forecast Algorithm that when applied to model forecasts gives and outlook region for severe thunderstorms. Improvements were made in this study to enhance the algorithm's forecast skill, reduce its "false alarm" rate, and thereby increase the amount of lead-time for installation commanders to take decisive action to protect personnel and resources. This paper discusses the performance of the 3-Element Algorithm in its original form, and the adjustments made to overcome some of its limitations. The 3-Element Algorithm techniques and results of a performance evaluation are presented. Based on the amount of forecast improvement, eight configurations were retained for analysis across the entire dataset containing six severe weather cases. A new stability proxy, the Elevated Total-Totals Index, was developed and integrated into the algorithm to improve severe weather forecasts over high-elevation regions where some traditional severe weather indices cannot be accurately computed. Additionally, the horizontal gradient of convective available potential energy was studied as a new indicator to the presence of dynamic forcing. It is hoped that improvements discussed in this paper will make the 3-Element Algorithm an effective tool in the early forecasting of severe weather...

High-Performance Computing in Geoscience - Data Preprocessing by Domain Decomposition and Load Balancing; High-Performance Computing in den Geowissenschaften - Datenvorverarbeitung mittels Gebietszerlegung und Lastbalancierung

Kemmler, Dany
Fonte: Universität Tübingen Publicador: Universität Tübingen
Tipo: Dissertation; info:eu-repo/semantics/doctoralThesis
EN
Relevância na Pesquisa
45.86%
The popularity and availability of computers is a simple fact of life in most of today's world. Computer simulation is a quick and relatively inexpensive alternative to physical experimentation in the scientific realm. Computer operations are meanwhile performed increasingly rapidly; one trillion operations per second are not anything extraordinary these days and are necessary in the field of automotive engineering, weather forecast or applied geology, for example. An interdependency of speed, power and storage space means that if one of these attributes is insufficient or impaired, it will limit the efficacy of the others. Data preprocessing and problem subdivision play a vital role in making real-world problems "computable." High-performance computing is not only a cornerstone of modern life, but also a compelling topic in itself. The task of descretizing real-world problems and processing models so that these problems can be solved by computers involves obtaining finite amounts of data from real-world problem domains and replacing them by grids consisting of inter-connected nodes and elements which will serve to model the problem in question on a computer. Altering the size of the grid, its number of nodes, etc. to determine the optimal structure to simulate a certain problem on a computer is the ultimate goal here. The more carefully discretization is accomplished...

Weather forecasting for Eastern Amazon with OLAM model

Silva,Renato Ramos da; Gandú,Adilson Wagner; Cohen,Julia Clarinda; Kuhn,Paulo; Mota,Maria Aurora
Fonte: Sociedade Brasileira de Meteorologia Publicador: Sociedade Brasileira de Meteorologia
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/12/2014 EN
Relevância na Pesquisa
35.94%
The OLAM model has as its characteristics the advantage to represent simultaneously the global and regional meteorological phenomena using the application of a grid refinement scheme. During the REMAM project the model was applied for a few case studies to evaluate its performance on numerical weather prediction for the eastern Amazon region. Case studies were performed for the twelve months of the year of 2009. The model results for those numerical experiments were compared with the observed data for the region of study. Precipitation data analysis showed that OLAM is able to represent the average mean accumulated precipitation and the seasonal features of the events occurrence, but can't predict the local total amount of precipitation. However, individual evaluation for a few cases had shown that OLAM was able to represent the dynamics and forecast a few days in advance the development of coastal meteorological systems such as the squall lines that are one of the most important precipitating systems of the Amazon.

Forecasting Cloud Cover and Atmospheric Seeing for Astronomical Observing: Application and Evaluation of the Global Forecast System

Ye, Q. -z
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.07%
To explore the issue of performing a non-interactive numerical weather forecast with an operational global model in assist of astronomical observing, we use the Xu-Randall cloud scheme and the Trinquet-Vernin AXP seeing model with the global numerical output from the Global Forecast System to generate 3-72h forecasts for cloud coverage and atmospheric seeing, and compare them with sequence observations from 9 sites from different regions of the world with different climatic background in the period of January 2008 to December 2009. The evaluation shows that the proportion of prefect forecast of cloud cover forecast varies from ~50% to ~85%. The probability of cloud detection is estimated to be around ~30% to ~90%, while the false alarm rate is generally moderate and is much lower than the probability of detection in most cases. The seeing forecast has a moderate mean difference (absolute mean difference <0.3" in most cases) and root-mean-square-error or RMSE (0.2"-0.4" in most cases) comparing with the observation. The probability of forecast with <30% error varies between 40% to 60% for entire atmosphere forecast and 40% to 50% for free atmosphere forecast for almost all sites, which being placed in the better cluster among major seeing models. However...

Joint probabilistic forecasting of wind speed and temperature using Bayesian model averaging

Baran, Sándor; Möller, Annette
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 14/04/2014
Relevância na Pesquisa
26.21%
Ensembles of forecasts are typically employed to account for the forecast uncertainties inherent in predictions of future weather states. However, biases and dispersion errors often present in forecast ensembles require statistical post-processing. Univariate post-processing models such as Bayesian model averaging (BMA) have been successfully applied for various weather quantities. Nonetheless, BMA and many other standard post-processing procedures are designed for a single weather variable, thus ignoring possible dependencies among weather quantities. In line with recently upcoming research to develop multivariate post-processing procedures, e.g., BMA for bivariate wind vectors, or flexible procedures applicable for multiple weather quantities of different types, a bivariate BMA model for joint calibration of wind speed and temperature forecasts is proposed based on the bivariate truncated normal distribution. It extends the univariate truncated normal BMA model designed for post-processing ensemble forecast of wind speed by adding a normally distributed temperature component with a covariance structure representing the dependency among the two weather quantities. The method is applied to wind speed and temperature forecasts of the eight-member University of Washington mesoscale ensemble and of the eleven-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service and its predictive performance is compared to that of the general Gaussian copula method. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison to the raw ensemble and the overall performance of this model is able to keep up with that of the Gaussian copula method.; Comment: 22 pages...

Surface temperature forecast skill comparison for the west coast of Saudi Arabia

ATHAR,HUSSAIN; SARA,ATHAR
Fonte: Centro de Ciencias de la Atmósfera, UNAM Publicador: Centro de Ciencias de la Atmósfera, UNAM
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2014 EN
Relevância na Pesquisa
46.13%
Given the growing interest of the general public in accessing commercial weather forecasts through various media outlets and the available impetuses for promoting tourism in Saudi Arabia (SA), a first attempt is made to present a forecast skill comparison for surface temperature in four cities (Wejh, Yenbo, Jeddah, and Gizan) along the west coast of SA, for the 61-day transitional period (from January 16 to March 16) between the December-January-February (DJF) and the March-April-May (MAM) seasons. A simple skill score comparison method is used to assess the next-day city forecasts for surface temperature from six commercial weather forecast providers based on the operational numerical weather prediction (NWP) model outputs. All the NWP model forecast providers performed better than the respective daily climatology (Clm) for each station. Depending upon the station and the provider, the absolute average maximum daily surface temperature difference between the forecasts and the observations was less than 2 °C. Daily surface temperature forecasts from two versions of an atmospheric-ocean general circulation model are also compared to assess their performance for these coastal locations.