Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China?

Seasonal forecasts from dynamical models are expected to be useful for drought predictions in many regions. This study investigated the usefulness of the Climate Forecast System version 2 (CFSv2) in improving meteorological drought prediction in China based on its 25-year reforecast. The six-month standard precipitation index (SPI6) was used as the drought indicator, and its persistence forecast served as the benchmark against which CFSv2 forecasts were evaluated. The analysis found that the SPI6 persistence forecast shows good skills in all regions at short lead times, and CFSv2 forecast can further improve those skills in most regions. The improvement is particularly pronounced at longer lead times and over the humid regions in the southeast. This study also examined the seasonality and regionality of persistence forecast skills and CFSv2 contributions, and reveals regions where CFSv2 forecast shows no or sometimes even negative contributions.

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Interpretations of systematic errors in the NCEP Climate Forecast System at lead times of 2, 4, 8, ..., 256 days

Journal of Advances in Modeling Earth Systems ◽

10.1029/2011ms000094 ◽

2012 ◽

Vol 4 (3) ◽

pp. n/a-n/a ◽

Cited By ~ 4

Author(s):

Siwon Song ◽

Brian Mapes

Keyword(s):

Systematic Errors ◽

Lead Times ◽

Climate Forecast ◽

Forecast System ◽

Climate Forecast System ◽

Ncep Climate Forecast System

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The German Climate Forecast System 2.1: seasonal forecast performance over Europe

10.5194/egusphere-egu21-9820 ◽

2021 ◽

Author(s):

Kristina Fröhlich ◽

Katharina Isensee ◽

Sascha Brandt ◽

Sebastian Brune ◽

Andreas Paxian ◽

...

Keyword(s):

Large Scale ◽

Atmospheric Model ◽

Prediction Skill ◽

Climate Forecast ◽

Seasonal Forecasts ◽

Max Planck ◽

Forecast System ◽

Climate Forecast System ◽

Forecast Performance ◽

The Impact

In November 2020, the new version of the German Climate Forecast System, GCFS2.1, became operational at Deutscher Wetterdienst (DWD), providing new seasonal forecasts every month. The system is based on the Max Planck Institute for Meteorology Earth-System Model (MPI-ESM-HR) and is developed jointly by DWD, the Max Planck Institute for Meteorology and Universit&#228;t Hamburg.In GCFS2.1, ERA5 and ORAS5 reanalyses are assimilated using atmospheric, oceanic and sea ice nudging, respectively. From the assimilation, 50-member 6-month forecast ensembles are initialized at the start of each month. Prediction skill is assessed with a 30-member 6-month hindcast ensemble covering the time period 1982-2019 for February, May, August and November start months, and 1990-2019 for the remaining start months. Both the forecast and hindcast ensembles are generated by oceanic bred vectors with additional physical perturbations applied to the upper atmospheric model layers.Here, we investigate the performance of GCFS2.1 summer and winter forecasts over Europe. While our main focus is on the prediction of large scale patterns that control the weather regimes during these two seasons, e.g. European blockings, special emphasis is paid on the impact of the January 2021 sudden stratospheric warming (SSW) event on the performance of GCFS2.1. The inclusion of the early phases of the January 2021 SSW event in the forecast initialisation significantly changes the GCFS2.1 forecast for February 2021 European surface climate. Prediction skill of GCFS2.1 for summer European blocking events will be also compared to the previous version GCFS2.0.

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Seasonal predictions of agro-meteorological drought indicators for the Limpopo basin

Hydrology and Earth System Sciences ◽

10.5194/hess-19-2577-2015 ◽

2015 ◽

Vol 19 (6) ◽

pp. 2577-2586 ◽

Cited By ~ 30

Author(s):

F. Wetterhall ◽

H. C. Winsemius ◽

E. Dutra ◽

M. Werner ◽

E. Pappenberger

Keyword(s):

Meteorological Drought ◽

Reanalysis Data ◽

Lead Times ◽

Monthly Precipitation ◽

Seasonal Forecasts ◽

Forecast System ◽

Quantile Mapping ◽

Dry Spells ◽

Precipitation Threshold ◽

Staple Crop

Abstract. The rainfall in southern Africa has a large inter-annual variability, which can cause rain-fed agriculture to fail. The staple crop maize is especially sensitive to dry spells during the early growing season. An early prediction of the probability of dry spells and below normal precipitation can potentially mitigate damages through water management. This paper investigates how well ECMWF's seasonal forecasts predict dry spells over the Limpopo basin during the rainy season December–February (DJF) with lead times from 0 to 4 months. The seasonal forecasts were evaluated against ERA-Interim reanalysis data, which in turn were corrected with GPCP (EGPCP) to match monthly precipitation totals. The seasonal forecasts were also bias-corrected with the EGPCP using quantile mapping as well as post-processed using a precipitation threshold to define a dry day. The results indicate that the forecasts show skill in predicting dry spells in comparison with a climatological ensemble based on previous years. Quantile mapping in combination with a precipitation threshold improved the skill of the forecast. The skill in prediction of dry spells was largest over the most drought-sensitive region. Seasonal forecasts have the potential to be used in a probabilistic forecast system for drought-sensitive crops, though these should be used with caution given the large uncertainties.

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Seasonal predictions of agro-meteorological drought indicators for the Limpopo basin

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-861-2014 ◽

2014 ◽

Vol 11 (1) ◽

pp. 861-888 ◽

Cited By ~ 5

Author(s):

F. Wetterhall ◽

H. C. Winsemius ◽

E. Dutra ◽

M. Werner ◽

F. Pappenberger

Keyword(s):

Meteorological Drought ◽

Spatial Filter ◽

Reanalysis Data ◽

Lead Times ◽

Monthly Precipitation ◽

Seasonal Forecasts ◽

Forecast System ◽

Dry Spells ◽

Precipitation Threshold ◽

Staple Crop

Abstract. The rainfall in Southern Africa has a large interannual variability, which can cause rain-fed agriculture to fail. The staple crop maize is especially sensitive to dry spells during the early growing season. An early prediction of the probability of dry spells and below normal precipitation can potentially mitigate damages through water management. This paper investigates how well ECMWF's seasonal forecasts predict dry spells over the Limpopo basin during the rainy season December–February (DJF) with lead times from 1 to 5 months. The seasonal forecasts were evaluated against ERA-Interim reanalysis data which in turn was corrected with GPCP (EGPCP) to match monthly precipitation totals. The seasonal forecasts were also bias-corrected with the EGPCP using quantile matching as well as post-processed using a precipitation threshold to define a dry day as well as spatial filtering. The results indicate that the forecasts show skill in predicting dry spells in comparison with a "climatological ensemble" based on previous years. Quantile matching in combination with a precipitation threshold improved the skill of the forecast, whereas a spatial filter had no effect. The skill in prediction of dry spells was largest over the most drought-sensitive region. Seasonal forecasts have potential to be used in a probabilistic forecast system for drought-sensitive crops, though these should be used with caution given the large uncertainties.

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The NCEP Climate Forecast System Version 2

Journal of Climate ◽

10.1175/jcli-d-12-00823.1 ◽

2014 ◽

Vol 27 (6) ◽

pp. 2185-2208 ◽

Cited By ~ 1314

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.

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Did the Climate Forecast System Anticipate the 2015 Caribbean Drought?

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0284.1 ◽

2020 ◽

Vol 21 (6) ◽

pp. 1245-1258

Author(s):

Paul W. Miller ◽

Craig A. Ramseyer

Keyword(s):

Lead Time ◽

Lead Times ◽

Climate Forecast ◽

Forecast System ◽

Climate Forecast System ◽

Caribbean Islands ◽

Potential Index ◽

Drought Early Warning ◽

Hydroclimatic Variability ◽

Fully Coupled

AbstractIn groundwater-limited settings, such as Puerto Rico and other Caribbean islands, societal, ecological, and agricultural water needs depend on regular rainfall. Though long-range numerical weather predication models explicitly predict precipitation, such quantitative precipitation forecasts (QPF) critically failed to detect the historic 2015 Caribbean drought. Consequently, this work examines the feasibility of developing a drought early warning tool using the Gálvez–Davison index (GDI), a tropical convective potential index, derived from the Climate Forecast System, version 2 (CFSv2). Drought forecasts are focused on Puerto Rico’s early rainfall season (ERS; April–July), which is susceptible to intrusions of strongly stable Saharan air and represents the largest source of hydroclimatic variability for the island. A fully coupled atmosphere–ocean–land model, the CFSv2 can plausibly detect the transatlantic advection of low-GDI Saharan air with multimonth lead times. The mean ERS GDI is calculated from semidaily CFSv2 forecasts beginning 1 January of each year between 2012 and 2018 and monitored as the initialization approaches 1 April. The CFSv2 demonstrates a broad region of statistically significant correlations with observed GDI across the eastern Caribbean up to 30 days prior to the ERS. During 2015, the CFSv2 forecast a low-GDI tongue extending across the Atlantic toward the Caribbean with 60–90 days lead time and placed Puerto Rico’s 2015 ERS beneath the 15th percentile of all 1982–2018 ERS forecasts with up to 30 days lead time. A preliminary GDI-based QPF tool tested herein is a statistically significant improvement over climatology for the driest years.

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The NCEP Climate Forecast System

Journal of Climate ◽

10.1175/jcli3812.1 ◽

2006 ◽

Vol 19 (15) ◽

pp. 3483-3517 ◽

Cited By ~ 778

Author(s):

S. Saha ◽

S. Nadiga ◽

C. Thiaw ◽

J. Wang ◽

W. Wang ◽

...

Keyword(s):

Weather Prediction ◽

Seasonal Prediction ◽

Ocean Model ◽

Climate Forecast ◽

Seasonal Forecasts ◽

Forecast System ◽

Coupled Modeling ◽

Dynamical Modeling ◽

Climate Forecast System ◽

Fully Coupled

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.

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