The NCEP Climate Forecast System Reanalysis

Saha, Sujata; Moorthi, Shrinivas; Pan, Hua-Lu; Wu, Xuan; Wang, Jiande; Nadiga, Sudhir; Tripp, Patrick; Kistler, Robert; Woollen, John S.; Behringer, David; Liu, Haixia; Stokes, Diane; Grumbine, Robert; Gayno, George; Wang, Jun; Hou, Yu-Tai; Chuang, Hui-Ya; Juang, Hann‐Ming Henry; Sela, Joe; Iredell, Mark; Treadon, Russ; Kleist, Daryl; Delst, Paul van; Keyser, Daniel; Derber, John; Ek, Michael; Meng, Jesse; Wei, Helin; Yang, Rongqian; Lord, Stephen J.; Dool, Huug van den; Kumar, Arun; Wang, Wanqiu; Long, Craig S.; Chelliah, Muthuvel; Xue, Yan; Huang, Boyin; Schemm, Jae-Kyung E.; Ebisuzaki, Wesley; Lin, Roger; Xie, Pingping; Chen, Mingyue; Zhou, Shuntai; Higgins, W.; Zou, Cheng‐Zhi; Liu, Quanhua; Chen, Yong; Han, Yong; Cucurull, Lídia; Reynolds, Richard W.; Rutledge, G.; Goldberg, Mitch

doi:10.1175/2010bams3001.1

Cited by 4,573 publications

(3,094 citation statements)

References 107 publications

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“…The Climate Forecast System Reanalysis, operated by NCEP, is a global coupled atmosphere-ocean-land surface-sea ice system designed to provide the best estimate of the state of these domains since 1979 (Saha et al 2010). The CFSR includes coupling of the atmosphere and ocean during the generation of 9-h forecast fields, an interactive sea ice model, and assimilation of satellite radiances over the entire period.…”

Section: Cfsrmentioning

confidence: 99%

“…Each 6-hourly analysis is the most likely state of the global atmosphere and the uncertainty in that analysis is estimated as well. The short-term forecast ensemble is generated in parallel from 56 9-h integrations of the atmospheric component of the Climate Forecast System (CFS) model (Saha et al 2006). The model has a spatial resolution of about 200 km on an irregular Gaussian grid (T62).…”

Section: Crmentioning

confidence: 99%

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Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

et al. 2014

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Atmospheric reanalyses depend on a mix of observations and model forecasts. In data-sparse regions such as the Arctic, the reanalysis solution is more dependent on the model structure, assumptions, and data assimilation methods than in data-rich regions. Applications such as the forcing of ice-ocean models are sensitive to the errors in reanalyses. Seven reanalysis datasets for the Arctic region are compared over the 30-yr period 1981-2010: National Centers for Environmental Prediction (NCEP)-National Center for Atmospheric Research Reanalysis 1 (NCEP-R1) and NCEP-U.S. Department of Energy Reanalysis 2 (NCEP-R2), Climate Forecast System Reanalysis (CFSR), Twentieth-Century Reanalysis (20CR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re-Analysis (ERA-Interim), and Japanese 25-year Reanalysis Project (JRA-25). Emphasis is placed on variables not observed directly including surface fluxes and precipitation and their trends. The monthly averaged surface temperatures, radiative fluxes, precipitation, and wind speed are compared to observed values to assess how well the reanalysis data solutions capture the seasonal cycles. Three models stand out as being more consistent with independent observations: CFSR, MERRA, and ERA-Interim. A coupled ice-ocean model is forced with four of the datasets to determine how estimates of the ice thickness compare to observed values for each forcing and how the total ice volume differs among the simulations. Significant differences in the correlation of the simulated ice thickness with submarine measurements were found, with the MERRA products giving the best correlation (R 5 0.82). The trend in the total ice volume in September is greatest with MERRA (24.1 3 10 3 km 3 decade 21 ) and least with CFSR (22.7 3 10 3 km 3 decade 21 ).

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Section: Cfsrmentioning

confidence: 99%

Section: Crmentioning

confidence: 99%

Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

et al. 2014

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“…The (re)analysis products used in this study are 1) the National Centers for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) 40-Year Reanalysis (NCEP-NCAR hereafter; Kalnay et al 1996;Kistler et al 2001), 2) the NCEP-Department of Energy (DOE) Reanalysis 2 (NCEP-DOE hereafter; Kanamitsu et al 2002), 3) the NCEP Climate Forecast System Reanalysis (CFSR; Saha et al 2010), 4) the National Aeronautics and Space Administration's (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA; Rienecker et al 2008, Rienecker et al 2011, 5) the ECMWF ERA-Interim (ERA-Int hereafter; Dee et al 2011), 6) the ECMWF operational analysis (ECMWF-op hereafter), and 7) the NCEP Final (FNL) operational Global Forecast System (GFS) analysis. Details of all these products can be found in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Disagreements in Low-Level Moisture between (Re)Analyses over Summertime West Africa

Roberts

Marsham

Knippertz

2015

Monthly Weather Review

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Reanalysis and operational analysis products are routinely used as the best estimates of the atmospheric state for operational and research purposes. However, different models, assimilation techniques, and assimilated datasets lead to differences between products. Here, such differences in the distribution of low-level water vapor over summertime West Africa are analyzed, as reflected in the zonal mean position of the leading edge of the West African monsoon [the intertropical discontinuity (ITD)] using five reanalyses [NCEP-NCAR, NCEP-Department of Energy (DOE), the Modern-Era Retrospective Analysis for Research and Applications (MERRA), the Climate Forecast System Reanalysis (CFSR), and the Interim ECMWF ReAnalysis (ERA-Interim)] and two operational analyses [Global Forecast System (GFS) and ECMWF] during the 11 monsoon seasons (April-September) from 2000 to 2010. Specific humidity differences regularly reach 50% of the mean value over areas spanning hundreds of kilometers and often coincide with northward excursions of the ITD that last several days and bring unusual rainfall to the Sahel and Sahara. The largest disagreements occur during the southward retreat of the ITD and are connected with anomalously high values of aerosol optical depth, consistent with the production of haboob dust storms. The results suggest that known errors in the representation of moist convection and cold pools may contribute to the identified disagreements. A large reduction in disagreement occurs in 2006, when upper-air observations were enhanced during the African Monsoon Multidisciplinary Analysis (AMMA) campaign, pointing to an insufficient observational constraint of the (re)analyses in other years. It is hoped that this work will raise awareness of the limited reliability of (re)analysis products over West Africa during the summer, particularly during northward surges of the ITD, and will instigate further work to improve their quality.

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“…Fifty vertical levels are defined throughout the domain, which are distributed every ∼ 10 m in the ABL and stretched in the free atmosphere. Initial and boundary conditions for all domains are taken from the Climate Forecast System Reanalysis (Saha et al, 2010) at a spatial resolution of ∼ 38 km. Topography for the highest resolution domain is taken from the Shuttle Radar Topography Mission (Jarvis et al, 2008) at a 90 m spatial resolution, and land use from GlobCover (Bontemps et al, 2011) at a ∼300 m spatial resolution.…”

Section: Simulationsmentioning

confidence: 99%

Analysis of Different Gray Zone Treatments in WRF-LES Real Case Simulations

Doubrawa

Montornès²,

Barthelmie

et al. 2018

Preprint

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Abstract. When conducting meso-micro scale coupled atmospheric simulations, it is crucial to ensure an adequate treatment of gray zone or terra incognita resolutions in which a large portion of the kinetic energy is naturally produced by the momentum balance equations in the model, while the remaining part still needs to be parameterized. In this work, we conduct three multiday, real case, full-physics atmospheric simulations that are fully coupled from the meso to the micro scale and in which the only difference is the treatment of boundary layer physics at the gray zone domain. One simulation uses a well-established 5 parameterization, another uses its scale-aware version previously modified to accommodate gray zone resolutions, and a final one uses no parameterization at all and assumes that the gray zone domain can be run in large-eddy simulation (LES) mode.The simulated fields are cross-compared, and further compared to measurements collected during the Prince Edward Island Wind Energy Experiment. Use of LES in the gray zone domain influences the flow fields in a manner that is robust to temporal averaging. The best predictions of vertical wind shear were found for the simulations in which the gray zone is parameterized, 10 and the inclusion of a micro scale nest run in LES mode within the gray zone domains increased the model errors by producing overly homogeneous flow fields. The parameterized simulations also produced better agreement in terms of kinetic energy spectra at the two innermost simulation domains. In the gray zone domain, the energy decays as f −3 throughout most of the spectral range considered. In the micro scale domain, the same is only seen in the low-frequency end of the gray zone spectral range. In the high-frequency end, the energy decay follows a f −1 slope. Outside the gray zone spectral range, the micro scale 15 simulated spectra follow the expected f −5/3 slope and produce good agreement with measurements.

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The NCEP Climate Forecast System Reanalysis

Cited by 4,573 publications

References 107 publications

Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

Disagreements in Low-Level Moisture between (Re)Analyses over Summertime West Africa

Analysis of Different Gray Zone Treatments in WRF-LES Real Case Simulations

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