scholarly journals Assessing the skill of precipitation forecasts on seasonal time scales over East Africa from a Climate Forecast System model

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Emily Bosire ◽  
Franklin Opijah ◽  
Wilson Gitau

It is becoming increasingly important to be able to verify the skill of precipitation forecasts, especially with the advent of high-resolution numerical weather prediction models. This study focused on assessing the skill of climate forecast system (CFS) model in predicting rainfall on seasonal time scales over East Africa region for the period January 1981 to December 2009. The rainfall seasons considered were March to May (MAM) and October to December (OND). The data used in the study included the observed seasonal rainfall totals from January 1981 to December 2009 and CFS model forecast data for the same period. The model had 15 Runs. The measure of skill employed was the categorical skill scores and included Heidke skill scores, bias, probability of detection and false alarm ratio. The results from the categorical skill scores confirmed relatively higher skills during OND season as compared to MAM. When compared with individual Runs, the mean of all the 15 Runs depicted relatively higher accuracy during OND season. Some individual Runs – 1, 7, 9 and 10 – also performed better during OND season. During MAM season, the mean of all the 15 Runs showed relatively lower accuracy in predicting rainfall. Some individual Runs – 5, 10, 12 and 14 – performed better than the mean of all the 15 Runs. The prediction of seasonal rainfall over East Africa region using CFS model depends on the season considered. During MAM, the prediction of seasonal rainfall is better as Runs are fewer, which showed relatively higher averaged skills; on the other hand, during OND the prediction of seasonal rainfall is better when using the mean of all the 15 Runs.

2019 ◽  
Vol 34 (3) ◽  
pp. 751-772 ◽  
Author(s):  
Katherine E. Lukens ◽  
Ernesto Hugo Berbery

Abstract This article examines to what extent the NCEP Climate Forecast System (CFS) weeks 3–4 reforecasts reproduce the CFS Reanalysis (CFSR) storm-track properties, and if so, whether the storm-track behavior can contribute to the prediction of related winter weather in North America. The storm tracks are described by objectively tracking isentropic potential vorticity (PV) anomalies for two periods (base, 1983–2002; validation, 2003–10) to assess their value in a more realistic forecast mode. Statistically significant positive PV biases are found in the storm-track reforecasts. Removal of systematic errors is found to improve general storm-track features. CFSR and Reforecast (CFSRR) reproduces well the observed intensity and spatial distributions of storm-track-related near-surface winds, with small yet significant biases found in the storm-track regions. Removal of the mean wind bias further reduces the error on average by 12%. The spatial distributions of the reforecast precipitation correspond well with the reanalysis, although significant positive biases are found across the contiguous United States. Removal of the precipitation bias reduces the error on average by 25%. The bias-corrected fields better depict the observed variability and exhibit additional improvements in the representation of winter weather associated with strong-storm tracks (the storms with more intense PV). Additionally, the reforecasts reproduce the characteristic intensity and frequency of hazardous strong-storm winds. The findings suggest a potential use of storm-track statistics in the advancement of subseasonal-to-seasonal weather prediction in North America.


2021 ◽  
Vol 893 (1) ◽  
pp. 012037
Author(s):  
F Lubis ◽  
I J A Saragih

Abstract The onset of the rainy season is one of the forecast products that is issued regularly by the Indonesian Agency of Meteorology, Climatology, and Geophysics (BMKG), with deterministic information about the month of which the initial 10-days (dasarian) of the rainy season will occur in each a designated area. On the other hand, state-of-the-art of seasonal forecasting methods suggests that probabilistic forecast products are potentially better for decision making. The probabilistic forecast is also more suitable for Indonesia because of the large rainfall variability that adds up to uncertainty in climate model simulations, besides complex geographical factors. The research aims to determine the onset of rainy season and monsoon over Java Island based on rainfall prediction by Constructed Analogue statistical downscaling of CFSv2 (Climate Forecast System version 2) model output. This research attempted to develop a method to produce a probabilistic forecast of the onset of the rainy season, as well as monsoon onset, by utilizing the freely available seasonal model output of CFSv2 operated by the US National Oceanic and Atmospheric Administration (NOAA). In this case, the output of the global model is dynamically downscaled using the modified Constructed Analogue (CA) method with an observational rainfall database from 26 BMKG stations and TRMM 3B43 gridded dataset. This method was then applied to perform hindcast using CFS-R (re-forecast) for the 2011-2014 period. The results show that downscaled CFS predictions with initial data in September (lead-1) give sufficient accuracy, while that initialized in August (lead-2) have large errors for both onsets of the rainy season and monsoon. Further analysis of forecast skill using the Brier score indicates that the CA scheme used in this study showed good performance in predicting the onset of the rainy season with a skill score in the range of 0.2. The probabilistic skill scores indicate that the prediction for East Java is better than the West- and Central-Java regions. It is also found that the results of CA downscaling can capture year-to-year variations, including delays in the onset of the rainy season.


2018 ◽  
Vol 33 (3) ◽  
pp. 615-640 ◽  
Author(s):  
Tan Phan-Van ◽  
Thanh Nguyen-Xuan ◽  
Hiep Van Nguyen ◽  
Patrick Laux ◽  
Ha Pham-Thanh ◽  
...  

Abstract This study investigates the ability to apply National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) products and their downscaling by using the Regional Climate Model version 4.2 (RegCM4.2) on seasonal rainfall forecasts over Vietnam. First, the CFS hindcasts (CFS_Rfc) from 1982 to 2009 are used to assess the ability of the CFS to predict the overall circulation and precipitation patterns at forecast lead times of up to 6 months. Second, the operational CFS forecasts (CFS_Ope) and its RegCM4.2 downscaling (RegCM_CFS) for the period 2012–14 are used to derive seasonal rainfall forecasts over Vietnam. The CFS_Rfc and CFS_Ope are validated against the ECMWF interim reanalysis, the Global Precipitation Climatology Centre (GPCC) analyzed rainfall, and observations from 150 meteorological stations across Vietnam. The results show that the CFS_Rfc can capture the seasonal variability of the Asian monsoon circulation and rainfall distribution. The higher-resolution RegCM_CFS product is advantageous over the raw CFS in specific climatic subregions during the transitional, dry, and rainy seasons, particularly in the northern part of Vietnam in January and in the country’s central highlands during July.


2006 ◽  
Vol 19 (15) ◽  
pp. 3483-3517 ◽  
Author(s):  
S. Saha ◽  
S. Nadiga ◽  
C. Thiaw ◽  
J. Wang ◽  
W. Wang ◽  
...  

Abstract The Climate Forecast System (CFS), the fully coupled ocean–land–atmosphere dynamical seasonal prediction system, which became operational at NCEP in August 2004, is described and evaluated in this paper. The CFS provides important advances in operational seasonal prediction on a number of fronts. For the first time in the history of U.S. operational seasonal prediction, a dynamical modeling system has demonstrated a level of skill in forecasting U.S. surface temperature and precipitation that is comparable to the skill of the statistical methods used by the NCEP Climate Prediction Center (CPC). This represents a significant improvement over the previous dynamical modeling system used at NCEP. Furthermore, the skill provided by the CFS spatially and temporally complements the skill provided by the statistical tools. The availability of a dynamical modeling tool with demonstrated skill should result in overall improvement in the operational seasonal forecasts produced by CPC. The atmospheric component of the CFS is a lower-resolution version of the Global Forecast System (GFS) that was the operational global weather prediction model at NCEP during 2003. The ocean component is the GFDL Modular Ocean Model version 3 (MOM3). There are several important improvements inherent in the new CFS relative to the previous dynamical forecast system. These include (i) the atmosphere–ocean coupling spans almost all of the globe (as opposed to the tropical Pacific only); (ii) the CFS is a fully coupled modeling system with no flux correction (as opposed to the previous uncoupled “tier-2” system, which employed multiple bias and flux corrections); and (iii) a set of fully coupled retrospective forecasts covering a 24-yr period (1981–2004), with 15 forecasts per calendar month out to nine months into the future, have been produced with the CFS. These 24 years of fully coupled retrospective forecasts are of paramount importance to the proper calibration (bias correction) of subsequent operational seasonal forecasts. They provide a meaningful a priori estimate of model skill that is critical in determining the utility of the real-time dynamical forecast in the operational framework. The retrospective dataset also provides a wealth of information for researchers to study interactive atmosphere–land–ocean processes.


2013 ◽  
Vol 118 (3) ◽  
pp. 1312-1328 ◽  
Author(s):  
Xingwen Jiang ◽  
Song Yang ◽  
Yueqing Li ◽  
Arun Kumar ◽  
Wanqiu Wang ◽  
...  

2020 ◽  
Author(s):  
Kristina Fröhlich ◽  
Mikhail Dobrynin ◽  
Katharina Isensee ◽  
Claudia Gessner ◽  
Andreas Paxian ◽  
...  

2018 ◽  
Vol 18 (18) ◽  
pp. 13547-13579 ◽  
Author(s):  
Zachary D. Lawrence ◽  
Gloria L. Manney ◽  
Krzysztof Wargan

Abstract. We compare herein polar processing diagnostics derived from the four most recent “full-input” reanalysis datasets: the National Centers for Environmental Prediction Climate Forecast System Reanalysis/Climate Forecast System, version 2 (CFSR/CFSv2), the European Centre for Medium-Range Weather Forecasts Interim (ERA-Interim) reanalysis, the Japanese Meteorological Agency's 55-year (JRA-55) reanalysis, and the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). We focus on diagnostics based on temperatures and potential vorticity (PV) in the lower-to-middle stratosphere that are related to formation of polar stratospheric clouds (PSCs), chlorine activation, and the strength, size, and longevity of the stratospheric polar vortex. Polar minimum temperatures (Tmin) and the area of regions having temperatures below PSC formation thresholds (APSC) show large persistent differences between the reanalyses, especially in the Southern Hemisphere (SH), for years prior to 1999. Average absolute differences of the reanalyses from the reanalysis ensemble mean (REM) in Tmin are as large as 3 K at some levels in the SH (1.5 K in the Northern Hemisphere – NH), and absolute differences of reanalysis APSC from the REM up to 1.5 % of a hemisphere (0.75 % of a hemisphere in the NH). After 1999, the reanalyses converge toward better agreement in both hemispheres, dramatically so in the SH: average Tmin differences from the REM are generally less than 1 K in both hemispheres, and average APSC differences less than 0.3 % of a hemisphere. The comparisons of diagnostics based on isentropic PV for assessing polar vortex characteristics, including maximum PV gradients (MPVGs) and the area of the vortex in sunlight (or sunlit vortex area, SVA), show more complex behavior: SH MPVGs showed convergence toward better agreement with the REM after 1999, while NH MPVGs differences remained largely constant over time; differences in SVA remained relatively constant in both hemispheres. While the average differences from the REM are generally small for these vortex diagnostics, understanding such differences among the reanalyses is complicated by the need to use different methods to obtain vertically resolved PV for the different reanalyses. We also evaluated other winter season summary diagnostics, including the winter mean volume of air below PSC thresholds, and vortex decay dates. For the volume of air below PSC thresholds, the reanalyses generally agree best in the SH, where relatively small interannual variability has led to many winter seasons with similar polar processing potential and duration, and thus low sensitivity to differences in meteorological conditions among the reanalyses. In contrast, the large interannual variability of NH winters has given rise to many seasons with marginal conditions that are more sensitive to reanalysis differences. For vortex decay dates, larger differences are seen in the SH than in the NH; in general, the differences in decay dates among the reanalyses follow from persistent differences in their vortex areas. Our results indicate that the transition from the reanalyses assimilating Tiros Operational Vertical Sounder (TOVS) data to advanced TOVS and other data around 1998–2000 resulted in a profound improvement in the agreement of the temperature diagnostics presented (especially in the SH) and to a lesser extent the agreement of the vortex diagnostics. We present several recommendations for using reanalyses in polar processing studies, particularly related to the sensitivity to changes in data inputs and assimilation. Because of these sensitivities, we urge great caution for studies aiming to assess trends derived from reanalysis temperatures. We also argue that one of the best ways to assess the sensitivity of scientific results on polar processing is to use multiple reanalysis datasets.


2013 ◽  
Vol 42 (7-8) ◽  
pp. 1925-1947 ◽  
Author(s):  
J. S. Chowdary ◽  
H. S. Chaudhari ◽  
C. Gnanaseelan ◽  
Anant Parekh ◽  
A. Suryachandra Rao ◽  
...  

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