scholarly journals Developments in Operational Long-Range Climate Prediction at CPC

2008 ◽  
Vol 23 (3) ◽  
pp. 496-515 ◽  
Author(s):  
Edward A. O’Lenic ◽  
David A. Unger ◽  
Michael S. Halpert ◽  
Kenneth S. Pelman
Keyword(s):  
Long Range ◽  
Southern Oscillation ◽  
Skill Score ◽  
Climate Prediction ◽  
Direct Access ◽  
Production Methods ◽  
Heidke Skill Score ◽  
User Access ◽  
Environmental Prediction ◽  
Climate Prediction Center

Abstract The science, production methods, and format of long-range forecasts (LRFs) at the Climate Prediction Center (CPC), a part of the National Weather Service’s (NWS’s) National Centers for Environmental Prediction (NCEP), have evolved greatly since the inception of 1-month mean forecasts in 1946 and 3-month mean forecasts in 1982. Early forecasts used a subjective blending of persistence and linear regression-based forecast tools, and a categorical map format. The current forecast system uses an increasingly objective technique to combine a variety of statistical and dynamical models, which incorporate the impacts of El Niño–Southern Oscillation (ENSO) and other sources of interannual variability, and trend. CPC’s operational LRFs are produced each midmonth with a “lead” (i.e., amount of time between the release of a forecast and the start of the valid period) of ½ month for the 1-month outlook, and with leads ranging from ½ month through 12½ months for the 3-month outlook. The 1-month outlook is also updated at the end of each month with a lead of zero. Graphical renderings of the forecasts made available to users range from a simple display of the probability of the most likely tercile to a detailed portrayal of the entire probability distribution. Efforts are under way at CPC to objectively weight, bias correct, and combine the information from many different LRF prediction tools into a single tool, called the consolidation (CON). CON ½-month lead 3-month temperature (precipitation) hindcasts over 1995–2005 were 18% (195%) better, as measured by the Heidke skill score for nonequal chances forecasts, than real-time official (OFF) forecasts during that period. CON was implemented into LRF operations in 2006, and promises to transfer these improvements to the official LRF. Improvements in the science and production methods of LRFs are increasingly being driven by users, who are finding an increasing number of applications, and demanding improved access to forecast information. From the forecast-producer side, hope for improvement in this area lies in greater dialogue with users, and development of products emphasizing user access, input, and feedback, including direct access to 5 km × 5 km gridded outlook data through NWS’s new National Digital Forecast Database (NDFD).

scholarly journals Eventos extremos de precipitação no sertão paraibano: Variação espaço-temporal

2017 ◽  
Vol 12 (4) ◽  
pp. 748
Author(s):  
Jaricélia Patrícia De Oliveira Sena ◽  
Daisy Beserra Lucena ◽  
George do Nascimento Ribeiro
Keyword(s):  
Climate Prediction ◽  
Environmental Prediction ◽  
Climate Prediction Center

<p>A variabilidade pluviométrica é característica marcante na região semiárida, não somente nos totais anuais, como também na quantidade e distribuição espacial. Com o objetivo de contribuir para o entendimento dos eventos extremos de precipitação na região semiárida da Paraíba, foi identificado anos de eventos extremos na microrregião do Sertão paraibano, utilizando o Índice de Anomalia de Chuva (IAC). Os dados utilizados foram provenientes do CPC (<em>Climate Prediction Center),</em> centro pertencente ao NCEP (<em>National Centers for Environmental Prediction</em>), compreendendo o período de 1979-2013. Os resultados mostram a distribuição têmporo-espacial bastante homogênea em relação aos eventos extremos, ou seja, os anos chuvosos ou secos, quando ocorrem atingem toda a microrregião. Observou-se que no painel anual um período bem pequeno de precipitação, considera-se o período chuvoso e após este período percebe-se que ocorre uma diminuição drástica na precipitação. Dos 35 anos analisados de precipitação, verificou-se que 19 anos apresentaram precipitações abaixo da média climatológica (54,3%) e 16 anos com precipitações acima da média (45,7%). A contribuição dos meses que não compõe o período chuvoso para a microrregião do Sertão Paraibano (maio a janeiro), apresentou-se de forma significativa nos eventos chuvosos, entretanto, para os eventos secos não teve nenhuma contribuição. A variação espacial da precipitação na região tanto para a climatologia, quanto para as composições dos anos selecionados como secos e chuvosos, apresenta distribuição no sentido Leste-Oeste, com amplitudes altas, comprovando a variação espacial.</p>

scholarly journals Does An ENSO-Conditional Skill Mask Improve Seasonal Predictions?

2013 ◽  
Vol 141 (12) ◽  
pp. 4515-4533 ◽  
Author(s):  
Kathy Pegion ◽  
Arun Kumar
Keyword(s):  
Southern Oscillation ◽  
Seasonal Temperature ◽  
Model Framework ◽  
Temperature And Precipitation ◽  
Skill Scores ◽  
Perfect Model ◽  
Optimal Weighting ◽  
Environmental Prediction ◽  
Model Context ◽  
Climate Prediction Center

Abstract The National Centers for Environmental Prediction Climate Prediction Center uses statistical tools together with the Climate Forecast System (CFS) to produce forecasts for seasonal outlooks of U.S. temperature and precipitation. They are combined using an optimal weighting procedure that depends on a skill mask consisting of the average historical forecast skill of each tool. However, it is likely that skill during El Niño–Southern Oscillation events is higher and the use of this information in developing forecasts could lead to improved seasonal predictions. This study explores the potential to improve the skill of seasonal predictions by developing an ENSO-conditional skill mask. The conditional masks are developed in a perfect-model framework using the CFS version 2 hindcasts and two indices of ENSO. The skill of the indices in forecasting variations in conditional skill is evaluated. The ENSO-conditional skill masks provide improvements in correlation skill over the unconditional mask when averaged over the globe. The masks are applied to tercile forecasts of seasonal temperature and precipitation during the spring and forecasts are verified in a perfect-model context. Application of the conditional masks to tercile forecasts results in modified Heidke skill scores of more than 10% less than using the average mask for temperature and little difference in skill for precipitation. This is attributed to the larger number of equal chances forecasts when using the conditional masks, particularly for temperature. For precipitation, the skill predicted by the average and conditional masks is frequently below 0.3, leading to low skill regardless of which mask is used.

scholarly journals Intercomparison of Daily Precipitation Statistics over the United States in Observations and in NCEP Reanalysis Products

2010 ◽  
Vol 23 (17) ◽  
pp. 4637-4650 ◽  
Author(s):  
R. W. Higgins ◽  
V. E. Kousky ◽  
V. B. S. Silva ◽  
E. Becker ◽  
P. Xie
Keyword(s):  
United States ◽  
Daily Precipitation ◽  
Southern Oscillation ◽  
The United States ◽  
Department Of Energy ◽  
Climate Forecast System ◽  
Daily Data ◽  
Environmental Prediction ◽  
New Generation ◽  
Climate Prediction Center

Abstract A comparison of the statistics of daily precipitation over the conterminous United States is carried out using gridded station data and three generations of reanalysis products in use at the National Centers for Environmental Prediction (NCEP). The reanalysis products are the NCEP–NCAR reanalysis (Kalnay et al.), the NCEP–Department of Energy (DOE) reanalysis (Kanamitsu et al.), and the NCEP Climate Forecast System (CFS) reanalysis (Saha et al.). Several simple measures are used to characterize relationships between the observations and the reanalysis products, including bias, precipitation probability, variance, and correlation. Seasonality is accounted for by examining these measures for four nonoverlapping seasons, using daily data in each case. Relationships between daily precipitation and El Niño–Southern Oscillation (ENSO) phase are also considered. It is shown that the CFS reanalysis represents a clear improvement over the earlier reanalysis products, though significant biases remain. Comparisons of the error patterns in the reanalysis products provide a suitable basis for confident conversion of the Climate Prediction Center (CPC) operational monitoring and prediction products to the new generation of analyses based on CFS.

scholarly journals Objective and probabilistic long-range forecasts of summertime air temperatures in South Korea based on Gaussian processes

2022 ◽  
Keyword(s):  
Long Range ◽  
Gaussian Processes ◽  
Climate Model ◽  
Model Performance ◽  
Skill Score ◽  
Near Surface ◽  
Mean Temperature ◽  
Air Temperatures ◽  
Improve Model ◽  
Heidke Skill Score

Abstract We propose the objective long-range forecasting model based on Gaussian processes (OLRAF-GP), focusing on summertime near-surface air temperatures in June (1-month lead), July (2-month lead), and August (3-month lead). The predictors were objectively selected based on their relationships with the target variables, either from observations (GP-OBS) or from observations and dynamical climate model results from APEC Climate Center multi-model ensemble (APCC MME) for the period with no observed data (GP-MME). The performances of the OLRAF-GP models were compared with the model with pre-determined predictors from observations (GP-PD). Both GP-MME and GP-OBS outperformed GP-PD in June (Heidke skill score; HSS = 0.46, 0.72, and 0.16 for mean temperature) and July (HSS = 0.53, 0.3, and 0.07 for mean temperature). Furthermore, GP-MME mostly outperformed GP-OBS and GP-PD in August (HSS = 0.52, 0.28, and 0.5, respectively, for mean temperature), implying larger contributions of the additional predictors from MME. OLRAF-GP models, especially GP-MME, are expected to better forecast summertime temperatures in regions where existing models have been struggling. We find that the physical processes associated with the notable predictors are aligned with those in previous studies, such as the attribution of the La Niña conditions in the previous winter, the related Indian Ocean capacitor effect, and the impacts of wintertime Polar/Eurasia pattern. These results imply that the mechanisms of the objectively selected predictors can be physically meaningful, and their inclusion can improve model performance and efficiency.

scholarly journals Análise comparativa de dados de precipitação gerados pelo “Climate Prediction Center – CPC” versus dados observados para o Sul do Brasil (Comparative analysis of precipitation data generated by Climate Prediction Center – CPC versus data observed ...)

2017 ◽  
Vol 10 (4) ◽  
pp. 1180 ◽  
Author(s):  
Camila De Souza Cardoso ◽  
Mário Francisco Leal de Quadro
Keyword(s):  
Statistical Analysis ◽  
Climate Prediction ◽  
Precipitation Data ◽  
Southern Brazil ◽  
Mountainous Regions ◽  
Daily Data ◽  
Observation Network ◽  
Environmental Prediction ◽  
New Generation ◽  
Climate Prediction Center

Com o aumento significativo da rede de observação pluviométrica no Brasil, a partir da instalação de estações meteorológicas automáticas, cada vez mais se tem a necessidade de uniformizar, tanto no espaço como no tempo, as séries diárias de precipitação. Em função disso, este estudo tem por objetivo analisar o desempenho da nova geração de dados de precipitação do Climate Prediction Center (CPC) para região Sul do Brasil, comparando com dados observados em estações meteorológicas. Neste trabalho, são utilizados dados acumulados diários de precipitação fornecidos pelo CPC/NCEP/NOAA (Climate Prediction Center/National Centers for Environmental Prediction/national Oceanic and Atmospheric Administration), que possui resolução espacial de 0.5°x0.5°, no período de 01 de janeiro de 1979 a 31 de dezembro de 2015. As análises foram realizadas através de técnicas estatísticas comparando com dados de precipitação observados em 81 estações localizadas nos três estados da região Sul do Brasil, disponibilizados pela Agência Nacional de Águas (ANA) e Instituto Nacional de Meteorologia (INMET). A etapa de consistência dos dados observados mostrou que as séries observadas possuem falhas à nível diário, mensal e anual, que podem ter alterado o padrão climatológico da precipitação no Sul do Brasil. A análise estatística dos dados mostrou que o CPC possui bom desempenho em representar a precipitação no Sul do Brasil, com tendência a subestimar a precipitação em regiões montanhosas e os maiores erros ocorreram nas regiões oeste e litorâneas do Sul do Brasil.  A B S T R A C TWith the significant increase of the rainfall observation network in Brazil, with the installation of automatic meteorological stations, there is an increasing need to standardize, both in space and in time, the daily series of precipitation. Therefore, this study aims to analyze the performance of the new generation of precipitation data from the Climate Prediction Center (CPC) for the southern region of Brazil, comparing with data observed in meteorological stations. This work uses daily cumulative precipitation data provided by the CPC/NCEP/NOAA (Climate Prediction Center/National Centers for Environmental Prediction/National Oceanic and Atmospheric Administration), which has spatial resolution of 0.5°x0.5°, considering the period from January 1st, 1979 to December 31st, 2015. The analyzes were performed by using statistical techniques to make a comparison with precipitation data observed in 81 stations located in the three southern states of Brazil, made available by the National Water Agency (ANA) and the National Institute of Meteorology (INMET). The stage of consistency of the observed data showed that the evaluated series have daily, monthly and annual faults that may have altered the precipitation climatological pattern in southern Brazil. The statistical analysis of the data showed that CPC has a good performance in representing precipitation in southern Brazil, with a trend to underestimate precipitation in mountainous regions, and the major errors occurred in the western and coastal regions of southern Brazil.Keywords: Precipitation daily data, statistical analysis, Southern Brazil. 

scholarly journals Skilful predictions of the Asian summer monsoon one year ahead

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yuhei Takaya ◽  
Yu Kosaka ◽  
Masahiro Watanabe ◽  
Shuhei Maeda
Keyword(s):  
Long Range ◽  
Summer Monsoon ◽  
Southern Oscillation ◽  
Asian Summer Monsoon ◽  
Boreal Summer ◽  
Climate Modelling ◽  
Large Ensemble ◽  
Asian Society ◽  
Asian Summer ◽  
One Year

AbstractThe interannual variability of the Asian summer monsoon has significant impacts on Asian society. Advances in climate modelling have enabled us to make useful predictions of the seasonal Asian summer monsoon up to approximately half a year ahead, but long-range predictions remain challenging. Here, using a 52-member large ensemble hindcast experiment spanning 1980–2016, we show that a state-of-the-art climate model can predict the Asian summer monsoon and associated summer tropical cyclone activity more than one year ahead. The key to this long-range prediction is successfully simulating El Niño-Southern Oscillation evolution and realistically representing the subsequent atmosphere–ocean response in the Indian Ocean–western North Pacific in the second boreal summer of the prediction. A large ensemble size is also important for achieving a useful prediction skill, with a margin for further improvement by an even larger ensemble.

scholarly journals CARACTERIZAÇÃO ATMOSFÉRICA QUANDO DA OCORRÊNCIA DE EVENTOS EXTREMOS DE CHUVA NA REGIÃO SUL DE MINAS GERAIS

2017 ◽  
Vol 21 ◽  
Author(s):  
Michelle Simões Reboita ◽  
Diogo Malagutti Gonçalves Marietto ◽  
Amanda Souza ◽  
Marina Barbosa
Keyword(s):  
Climate Prediction ◽  
Minas Gerais ◽  
Weather Forecasts ◽  
European Centre ◽  
Medium Range ◽  
Climate Prediction Center

O objetivo deste estudo é apresentar uma descrição das características da atmosfera que contribuíram para elevados totais de precipitação no sul de Minas Gerais e que foram precursores de dois episódios de inundação e alagamento na cidade de Itajubá: um em 16 de janeiro de 1991 e outro em 02 de janeiro de 2000. Para tanto, foram utilizados dados do Climate Prediction Center e da reanálise ERA-Interim do European Centre for Medium-Range Weather Forecasts (ECMWF). Entre os resultados, têm-se que os episódios de inundação e alagamento ocorridos na cidade de Itajubá, em ambos os anos, estiveram associados à atuação da Zona de Convergência do Atlântico Sul, que se estendia da Amazônia, passando pelo sudeste do Brasil, e chegava ao Atlântico Sul.

scholarly journals The skill assessment of ENSO prediction issued by JMA ensemble prediction system and CFSv2

2021 ◽  
Vol 893 (1) ◽  
pp. 012047
Author(s):  
R Rahmat ◽  
A M Setiawan ◽  
Supari
Keyword(s):  
Southern Oscillation ◽  
Skill Score ◽  
Skill Assessment ◽  
Japan Meteorological Agency ◽  
Ensemble Prediction ◽  
In Situ Observation ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Enso Prediction ◽  
The Government

Abstract Indonesian climate is strongly affected by El Niño-Southern Oscillation (ENSO) as one of climate-driven factor. ENSO prediction during the upcoming months or year is crucial for the government in order to design the further strategic policy. Besides producing its own ENSO prediction, BMKG also regularly releases the status and ENSO prediction collected from other climate centers, such as Japan Meteorological Agency (JMA) and National Oceanic and Atmospheric Administration (NOAA). However, the skill of these products is not well known yet. The aim of this study is to conduct a simple assessment on the skill of JMA Ensemble Prediction System (EPS) and NOAA Climate Forecast System version 2 (CFSv2) ENSO prediction using World Meteorological Organization (WMO) Standard Verification System for Long Range Forecast (SVS-LRF) method. Both ENSO prediction results also compared each other using Student's t-test. The ENSO predictions data were obtained from the ENSO JMA and ENSO NCEP forecast archive files, while observed Nino 3.4 were calculated from Centennial in situ Observation-Based Estimates (COBE) Sea Surface Temperature Anomaly (SSTA). Both ENSO prediction issued by JMA and NCEP has a good skill on 1 to 3 months lead time, indicated by high correlation coefficient and positive value of Mean Square Skill Score (MSSS). However, the skill of both skills significantly reduced for May-August target month. Further careful interpretation is needed for ENSO prediction issued on this mentioned period.

Large-Sample Application of Radar Reflectivity Object-Based Verification to Evaluate HRRR Warm-Season Forecasts

2021 ◽  
Author(s):  
Jeffrey D. Duda ◽  
David D. Turner
Keyword(s):  
Warm Season ◽  
Skill Score ◽  
Central Plains ◽  
Attribute Weights ◽  
Object Based ◽  
Convective Scale ◽  
Heidke Skill Score ◽  
Sample Application ◽  
Storm Cells ◽  
The Impact

AbstractThe Method of Object-based Diagnostic Evaluation (MODE) is used to perform an object-based verification of approximately 1400 forecasts of composite reflectivity from the operational HRRR from April – September 2019. In this study, MODE is configured to prioritize deep, moist convective storm cells typical of those that produce severe weather across the central and eastern US during the warm season. In particular, attributes related to distance and size are given the greatest attribute weights for computing interest in MODE.HRRR tends to over-forecast all objects, but substantially over-forecasts both small objects at low reflectivity thresholds and large objects at high reflectivity thresholds. HRRR tends to either under-forecast objects in the southern and central Plains or has a correct frequency bias there, whereas it over-forecasts objects across the southern and eastern US. Attribute comparisons reveal the inability of the HRRR to fully resolve convective scale features and the impact of data assimilation and loss of skill during the initial hours of the forecasts.Scalar metrics are defined and computed based on MODE output, chiefly relying on the interest value. The object-based threat score (OTS), in particular, reveals similar performance of HRRR forecasts as does the Heidke Skill Score, but with differing magnitudes, suggesting value in adopting an object-based approach to forecast verification. The typical distance between centroids of objects is also analyzed and shows gradual degradation with increasing forecast length.

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