This paper analyzes the role of ocean–atmosphere processes associated with break days and their impact on dry-land biases of Indian summer monsoon in Climate Forecast System version 2 (CFSv2)'s sub daily and monthly hindcasts, which are produced by initializing the forecast system every 5days ...
Diagnostic evaluations of the relative performances of CFSv1 and CFSv2 in prediction of monthly anomalies of the ENSO-related Nino3.4 SST index are conducted using the common hindcast period of 1982–2009 for lead times of up to 9months. CFSv2 outperforms CFSv1 in temporal correlation skill fo...
Spatial Structure, Forecast Errors, and Predictability of the South Asian Monsoon in CFS Monthly Retrospective Forecasts The spatial structure of the boreal summer South Asian monsoon in the ensemble mean of monthly retrospective forecasts by the Climate Forecast System of th... HKL Drbohlav,V Krish...
Monthly Weather ReviewWeber, N.J. and C.F. Mass, 2017: Evaluating CFSv2 Subseasonal Forecast Skill with an 578 Emphasis on Tropical Convection. Mon. Wea. Rev., 145, 3795-3815, 579 https://doi.org/10.1175/MWR-D-17-0109.1Weber NJ, Mass CF (2017) Evaluating CFSv2 subseasonal forecast ...
This study focuses on the Dongjiang Basin and evaluates the prediction accuracy and stability of CFSv2 model products at the monthly scale using the anomaly coefficient of correlation (ACC), normalized root mean square error (NRMSE), mean absolute error (MAE), and the mul...
seasonal predictionWest AfricaWRFdownscalingprecipitationCFSv2Statistical bias correctionSeasonal precipitation forecasts are important sources of information for early drought and famine warnings in West Africa. This study presents an assessment of the monthly precipitation forecast of the Climate Forecast System...
Using the National Center for Environment Prediction-Climate Forecast System version 2 (NCEP-CFSv2), this study comprehensively evaluates the seasonal and monthly prediction of the Siberian high intensity during the winter time (November to February). Results show that the NCEP-CFSv2 model can ...
It has been observed that the daily mean climatology of precipitation over the land points of India is underestimated in the model forecast as compared to observation. The monthly model bias of precipitation shows the dry bias over the land points of India and also over the Bay of Bengal, ...
Submonthly variability accounts for 29% of the total variance in December, 20% in March and June, and 12.5% in September. We assess the ability of CFSv2 to predict the pan-Arctic SIC, as well as regional SIC in nine Arctic regions. Results show that the SIC prediction skill is highly ...
CFSv2 outperforms CFSv1 in temporal correlation skill for predictions at moderate to long lead times that traverse the northern spring ENSO predictability barrier (e.g., a forecast for July made in February). However, for predictions during less challenging times of the year (e.g., a ...