The NCEP Climate Forecast System

2006 ◽  
Vol 19 (15) ◽  
pp. 3483-3517 ◽  
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.

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.

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

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2010
Author(s):  
Yang Lang ◽  
Lifeng Luo ◽  
Aizhong Ye ◽  
Qingyun Duan
Keyword(s):  
Mainland China ◽  
Meteorological Drought ◽  
Lead Times ◽  
Climate Forecast ◽  
Seasonal Forecasts ◽  
Dynamical Models ◽  
Standard Precipitation Index ◽  
Forecast System ◽  
Climate Forecast System ◽  
Drought Prediction

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.

scholarly journals Winter Storm Tracks and Related Weather in the NCEP Climate Forecast System Weeks 3–4 Reforecasts for North America

2019 ◽  
Vol 34 (3) ◽  
pp. 751-772 ◽  
Author(s):  
Katherine E. Lukens ◽  
Ernesto Hugo Berbery
Keyword(s):  
North America ◽  
Weather Prediction ◽  
Storm Track ◽  
Storm Tracks ◽  
Spatial Distributions ◽  
Climate Forecast ◽  
Winter Weather ◽  
Forecast System ◽  
Climate Forecast System ◽  
Ncep Climate Forecast System

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.

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

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

<p>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 <strong>is based</strong><strong> </strong>on the Max Planck Institute for Meteorology Earth-System Model <strong>(MPI-ESM-HR)</strong> and is developed jointly by DWD, the Max Planck Institute for Meteorology and Universität Hamburg.</p><p>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.</p><p>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.</p>

Impact of improved Ocean initial condition in Climate Forecast System (CFSv2) Hindcast run

2020 ◽  
Author(s):  
Samir Pokhrel ◽  
Hasibur Rahaman ◽  
Hemantkumar Chaudhari ◽  
Subodh Kumar Saha ◽  
Anupam Hazra
Keyword(s):  
Monsoon Rainfall ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Climate Forecast ◽  
Surface Currents ◽  
Forecast System ◽  
Surface Salinity ◽  
Climate Forecast System ◽  
The Indian Ocean

<p>IITM provides seasonal monsoon rainfall forecast using modified CGCM CFSv2. The present operational CFSv2 initilized with the INCOIS-GODAS ocean analysis based on MOM4p0d and 3DVar assimilation schemes. Recently new Ocean analysis GODAS-Mom4p1 using Moduler Ocean Model (MOM) upgraded physical model MOM4p1 is generated. This analysis has shown improvement in terms of subsurface temperature, salinity , current as well as sea surface temperature (SST), sea surface salinity (SSS) and surface currents over the Indian Ocean domain with respect to present operational INCOIS-GODAS analysis (Rahaman et al. 2017;Rahman et al. 2019). This newly generated ocean analysis is used to initialize NCEP Climate Forecast System (CFSv2) for the retrospective run from 2011 to 2018. The simulated coupled run has shown improvement in both oceanic as well atmospheric parameters. The more realistic nature of coupled simulations across the atmosphere and ocean may be promising to get better forecast skill.</p>

scholarly journals A Comparison of Skill between Two Versions of the NCEP Climate Forecast System (CFS) and CPC’s Operational Short-Lead Seasonal Outlooks

2013 ◽  
Vol 28 (2) ◽  
pp. 445-462 ◽  
Author(s):  
Peitao Peng ◽  
Anthony G. Barnston ◽  
Arun Kumar
Keyword(s):  
Surface Temperature ◽  
Co2 Concentration ◽  
Seasonal Prediction ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Use Of Time ◽  
Predictive Skill ◽  
Two Generations ◽  
Decision Making Processes

Abstract Analyses of the relative prediction skills of NOAA’s Climate Forecast System versions 1 and 2 (CFSv1 and CFSv2, respectively), and the NOAA/Climate Prediction Center’s (CPC) operational seasonal outlook, are conducted over the 15-yr common period of 1995–2009. The analyses are applied to predictions of seasonal mean surface temperature and total precipitation over the conterminous United States for the shortest and most commonly used lead time of 0.5 months. The assessments include both categorical and probabilistic verification diagnostics—their seasonalities, spatial distributions, and probabilistic reliability. Attribution of skill to specific physical sources is attempted when possible. Motivations for the analyses are to document improvements in skill between two generations of NOAA’s dynamical seasonal prediction system and to inform the forecast producers, but more importantly the user community, of the skill of the CFS model now in use (CFSv2) to help guide the users’ decision-making processes. The CFSv2 model is found to deliver generally higher mean predictive skill than CFSv1. This result is strongest for surface temperature predictions, and may be related to the use of time-evolving CO2 concentration in CFSv2, in contrast to a fixed (and now outdated) concentration used in CFSv1. CFSv2, and especially CFSv1, exhibit more forecast “overconfidence” than the official seasonal outlooks, despite that the CFSv2 hindcasts have outperformed the outlooks more than half of the time. Results justify the greater weight given to CFSv2 in developing the final outlooks than given to previous dynamical input tools (e.g., CFSv1) and indicate that CFSv2 should be of greater interest to users.

A first look at Climate Forecast System version 2 (CFSv2) for hydrological seasonal prediction

2011 ◽  
Vol 38 (13) ◽  
pp. n/a-n/a ◽  
Author(s):  
Xing Yuan ◽  
Eric F. Wood ◽  
Lifeng Luo ◽  
Ming Pan
Keyword(s):  
Seasonal Prediction ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System

scholarly journals Seasonal Prediction Skills in the CAMS-CSM Climate Forecast System

2021 ◽  
Author(s):  
Bo Liu ◽  
Jingzhi SU ◽  
Libin MA ◽  
Yanli TANG ◽  
Xinyao RONG ◽  
...  
Keyword(s):  
East Asia ◽  
Air Temperature ◽  
Lead Time ◽  
Southern Oscillation ◽  
Seasonal Prediction ◽  
Good Prediction ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Enso Events

Abstract The seasonal predictability in the CAMS-CSM climate forecast system is evaluated with a set of retrospective forecast experiments during the period of 1981-2019. The CAMS-CSM, which has been registered for the sixth phase of the coupled model intercomparison project (CMIP6), is an atmosphere-ocean-land-sea ice fully coupled general model. The assimilation scheme used in the forecast system is the 3-dimentional nudging, including both the atmospheric and oceanic components. The analyses mainly focus on the seasonal predictable skill of sea surface temperature, 2-m air temperature, and precipitation anomalies. The analyses revealed that the model shows a good prediction skill for the SST anomalies, especially in the tropical Pacific, such as El Niño-Southern Oscillation (ENSO) events. The anomaly correlation coefficient (ACC) score for ENSO can reach 0.75 at 6-month lead time. Furthermore, the extreme warm/cold Indian Ocean dipole (IOD) events are successfully predicted at 3- and even 6-month lead times. The whole ACC of IOD events between the observation and the prediction can reach 0.51 at 2-month lead time. There are reliable seasonal prediction skills for 2-m air temperature anomalies over most of the Northern Hemisphere, where the correlation is mainly above 0.4 at 2-month lead time, especially over the East Asia, North America and South America. However, the seasonal prediction for precipitation still faces a big challenge. The source of precipitation predictability over the East Asia can be partly related to strong ENSO events. Additionally, the anomalous anticyclone over the western North Pacific (WPAC) which connects the ENSO events and the East Asian summer monsoon (EASM) can be well predicted at 6-month lead time.

scholarly journals Did the Climate Forecast System Anticipate the 2015 Caribbean Drought?

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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