scholarly journals Comparison of Daily Precipitation Statistics for the United States in Observations and in the NCEP Climate Forecast System

2008 ◽  
Vol 21 (22) ◽  
pp. 5993-6014 ◽  
Author(s):  
R. W. Higgins ◽  
V. B. S. Silva ◽  
V. E. Kousky ◽  
W. Shi
Keyword(s):  
United States ◽  
Lead Time ◽  
Daily Precipitation ◽  
The United States ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Station Data ◽  
Forecast Lead Time ◽  
Ncep Climate Forecast System

Abstract An intercomparison of the statistics of daily precipitation within seasonal climate over the conterminous United States is carried out using gridded station data and output from the NCEP Climate Forecast System (CFS). Differences in the occurrence of daily precipitation between the observations and a set of CFS reforecasts are examined as a function of forecast lead time for 1982–2005. Difference patterns show considerable evolution depending on season and lead time, with positive biases in CFS at most locations and leads except along the southern tier of states during the spring and summer months. An examination of differences in daily precipitation statistics by ENSO phase and in the frequencies of wet and dry spells is also conducted using a longer period of gridded daily station data (1948–2006) and a pair of 100-yr CFS coupled simulations. These comparisons expose additional details of the regional and seasonal dependence of the bias in the CFS simulations and reforecasts over the conterminous United States. The analysis motivates additional synoptic studies aimed at improving the linkage between daily precipitation and related circulation features in CFS. Prospects for using this information to develop more reliable ensemble-based probabilistic forecasts in real time at leads of 2–4 weeks (e.g., risks of heavy rain events) are also considered.

scholarly journals Dynamical Prediction of the Early Season Rainfall over Southern China by the NCEP Climate Forecast System

2014 ◽  
Vol 29 (6) ◽  
pp. 1391-1401 ◽  
Author(s):  
Siyu Zhao ◽  
Song Yang
Keyword(s):  
Lead Time ◽  
East Asian Monsoon ◽  
Southern China ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Early Season ◽  
Forecast Lead Time ◽  
Monsoon System ◽  
Ncep Climate Forecast System

Abstract The early season rainfall (ESR) over southern China, usually occurring from April to June, is a prominent meteorological phenomenon of the East Asian monsoon system. In this paper, output from the 45-day hindcast by the NCEP Climate Forecast System, version 2 (CFSv2), and various observational datasets are analyzed to assess the predictability of the ESR and associated atmospheric circulation. Results show that CFSv2 can successfully predict the ESR and associated circulation patterns over southern China. The lower-tropospheric convergence and upper-tropospheric divergence as well as the local upward motion over southern China lead to the formation of ESR. Analysis of bias shows small differences and close relationships between the predicted and observed ESR values when the forecast lead time is less than 2 weeks. The skill in the ESR predictions by CFSv2 decreases significantly when the lead time is longer than 2 weeks. Overall, CFSv2 has a higher level of skill when predicting the southern China ESR compared to the rainfall over other Asian regions during the same period of time.

scholarly journals Temporal and Spatial Variability of Wind Resources in the United States as Derived from the Climate Forecast System Reanalysis

2015 ◽  
Vol 28 (3) ◽  
pp. 1166-1183 ◽  
Author(s):  
Lejiang Yu ◽  
Shiyuan Zhong ◽  
Xindi Bian ◽  
Warren E. Heilman
Keyword(s):  
United States ◽  
Spatial Variability ◽  
El Niño ◽  
El Nino ◽  
The United States ◽  
Total Variance ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis

Abstract This study examines the spatial and temporal variability of wind speed at 80 m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the winter (summer), and higher (lower) speeds over much of the Midwest and U.S. Northeast (U.S. West and Southeast). Trends are also variable spatially, with more upward trends in areas of the Great Plains and Intermountain West of the United States and more downward trends elsewhere. The leading EOF mode, which accounts for 20% (summer) to 33% (winter) of the total variance and represents in-phase variations across the United States, responds mainly to the North Atlantic Oscillation (NAO) in summer and El Niño–Southern Oscillation (ENSO) in the other seasons. The dominant variation pattern can be explained by a southerly/southwesterly (westerly) anomaly over the U.S. East (U.S. West) as a result of the anomalous mean sea level pressure (MSLP) pattern. The second EOF mode, which explains about 15% of the total variance and shows a seesaw pattern, is mainly related to the springtime Arctic Oscillation (AO), the summertime recurrent circumglobal teleconnection (CGT), the autumn Pacific decadal oscillation (PDO), and the winter El Niño Modoki. The anomalous jet stream and MSLP patterns associated with these indices are responsible for the wind variation.

scholarly journals The NCEP Climate Forecast System Version 2

2014 ◽  
Vol 27 (6) ◽  
pp. 2185-2208 ◽  
Author(s):  
Suranjana Saha ◽  
Shrinivas Moorthi ◽  
Xingren Wu ◽  
Jiande Wang ◽  
Sudhir Nadiga ◽  
...  
Keyword(s):  
Energy Use ◽  
Initial Conditions ◽  
Forecast Model ◽  
Seasonal Prediction ◽  
The United States ◽  
Climate Forecast ◽  
Seasonal Forecasts ◽  
Forecast System ◽  
Climate Forecast System ◽  
Ncep Climate Forecast System

Abstract The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979–2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982–2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the United States, and significantly improves global SST forecasts over its predecessor. The CFSv2 not only provides greatly improved guidance at these time scales but also creates many more products for subseasonal and seasonal forecasting with an extensive set of retrospective forecasts for users to calibrate their forecast products. These retrospective and real-time operational forecasts will be used by a wide community of users in their decision making processes in areas such as water management for rivers and agriculture, transportation, energy use by utilities, wind and other sustainable energy, and seasonal prediction of the hurricane season.

Dynamical prediction of the East Asian winter monsoon by the NCEP Climate Forecast System

2013 ◽  
Vol 118 (3) ◽  
pp. 1312-1328 ◽  
Author(s):  
Xingwen Jiang ◽  
Song Yang ◽  
Yueqing Li ◽  
Arun Kumar ◽  
Wanqiu Wang ◽  
...  
Keyword(s):  
East Asian Winter Monsoon ◽  
East Asian ◽  
Winter Monsoon ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Asian Winter Monsoon ◽  
Ncep Climate Forecast System

Summer monsoon circulation and precipitation over the tropical Indian Ocean during ENSO in the NCEP climate forecast system

2013 ◽  
Vol 42 (7-8) ◽  
pp. 1925-1947 ◽  
Author(s):  
J. S. Chowdary ◽  
H. S. Chaudhari ◽  
C. Gnanaseelan ◽  
Anant Parekh ◽  
A. Suryachandra Rao ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Summer Monsoon ◽  
Tropical Indian Ocean ◽  
Monsoon Circulation ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Ncep Climate Forecast System

scholarly journals A Hybrid Global Ocean Data Assimilation System at NCEP

2015 ◽  
Vol 143 (11) ◽  
pp. 4660-4677 ◽  
Author(s):  
Stephen G. Penny ◽  
David W. Behringer ◽  
James A. Carton ◽  
Eugenia Kalnay
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Global Ocean ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Ocean Data Assimilation ◽  
Data Assimilation System ◽  
Ncep Climate Forecast System ◽  
Assimilation System

Abstract Seasonal forecasting with a coupled model requires accurate initial conditions for the ocean. A hybrid data assimilation has been implemented within the National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS) as a future replacement of the operational three-dimensional variational data assimilation (3DVar) method. This Hybrid-GODAS provides improved representation of model uncertainties by using a combination of dynamic and static background error covariances, and by using an ensemble forced by different realizations of atmospheric surface conditions. An observing system simulation experiment (OSSE) is presented spanning January 1991 to January 1999, with a bias imposed on the surface forcing conditions to emulate an imperfect model. The OSSE compares the 3DVar used by the NCEP Climate Forecast System (CFSv2) with the new hybrid, using simulated in situ ocean observations corresponding to those used for the NCEP Climate Forecast System Reanalysis (CFSR). The Hybrid-GODAS reduces errors for all prognostic model variables over the majority of the experiment duration, both globally and regionally. Compared to an ensemble Kalman filter (EnKF) used alone, the hybrid further reduces errors in the tropical Pacific. The hybrid eliminates growth in biases of temperature and salinity present in the EnKF and 3DVar, respectively. A preliminary reanalysis using real data shows that reductions in errors and biases are qualitatively similar to the results from the OSSE. The Hybrid-GODAS is currently being implemented as the ocean component in a prototype next-generation CFSv3, and will be used in studies by the Climate Prediction Center to evaluate impacts on ENSO prediction.

scholarly journals Confidence Interval Estimation for Quantitative Precipitation Forecasts (QPF) Using Short-Range Ensemble Forecasts (SREF)

2006 ◽  
Vol 21 (1) ◽  
pp. 24-41 ◽  
Author(s):  
Jung-Sun Im ◽  
Keith Brill ◽  
Edwin Danaher
Keyword(s):  
United States ◽  
Confidence Interval ◽  
Lead Time ◽  
Short Range ◽  
Grid Point ◽  
The United States ◽  
Regression Equations ◽  
Ensemble Forecasts ◽  
Forecast Lead Time ◽  
Quantitative Precipitation Forecasts

Abstract The Hydrometeorological Prediction Center (HPC) at the NCEP has produced a suite of deterministic quantitative precipitation forecasts (QPFs) for over 40 yr. While the operational forecasts have proven to be useful in their present form, they offer no information concerning the uncertainties of individual forecasts. The purpose of this study is to develop a methodology to quantify the uncertainty in manually produced 6-h HPC QPFs (HQPFs) using NCEP short-range ensemble forecasts (SREFs). Results presented herein show the SREFs can predict the uncertainty of HQPFs. The correlation between HQPF absolute error (AE) and ensemble QPF spread (SP) is greater than 0.5 at 90.5% of grid points in the continental United States, exceeding 0.8 at 10% of these, for the 6-h forecast in winter. On the basis of the high correlation, the linear regression equations of AE on SP are derived at each point on a grid covering the United States. In addition, the regression equations for data categorized according to the observed and forecasted precipitation amounts are obtained and evaluated. Using the regression model equation parameters for 15 categorized ranges of HQPF at each horizontal grid point for each season and individual forecast lead time, an AE associated with an individual SP is predicted, as is the 95% confidence interval (CI) of the AE. Based on the AE CI forecast and the HQPF itself, the 95% CI of the HQPF is predicted as well. This study introduces an efficient and advanced method, providing an estimate of the uncertainty in the deterministic HQPF. Verification demonstrates the usefulness of the CI forecasts for a variety of classifications, such as season, CI range, HQPF, and forecast lead time.

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