Atmosphere
Atmospheric Reanalyses Comparison Table
Atmospheric Reanalyses Comparison Table
Name | Source | Time Range | Assimilation Algorithm |
Model Resolution | Model Output Resolution | Model Areal Coverage | Publicly Available Dataset Resolution | Dataset Output Times and Time Averaging |
---|---|---|---|---|---|---|---|---|
Arctic System Reanalysis (ASR) |
Byrd Polar Research Center Polar Meteorology Group |
2000-2010 (30km) 2000-2011 (10km) |
WRFDA-3DVAR |
10 and 30km 71 sigma levels |
10 and 30km | Arctic | 10 and 30km | 3-hourly WRF outputs; selected variables for surface and upper-air fields. Monthly averages of selected fields. |
COSMO Reanalyses (COSMO-REA) | HErZ, DWD |
1995-present (6km)
2007-2013 (2km) |
Continuous nudging Continuous and Latent heat nudging |
6km, 40 eta levels
2km, 50 eta levels |
6km and 2km | 6km and 2km | 15 minute output for 2D variables, hourly output for 3D output, daily and monthly aggregations; selected variables available online, further variables on request | |
ECMWF Interim Reanalysis (ERA Interim) | ECMWF | 1979-present | 4D-VAR | TL255L60 and N128 reduced Gaussian | TL255L60 and N128 reduced Gaussian (~79km globally) | Global | User defined, down to 0.75x0.75 | 3-hourly for most surface fields; 6-hourly for upper-air fields Monthly averages of daily means, and of 6-hourly fields |
ECMWF 40 year Reanalysis (ERA-40) | ECMWF | 1958-2001 | 3D-VAR | TL159L60 and N80 reduced Gaussian | TL159L60 and N80 reduced Gaussian (~125km globally) | Global | 2.5x2.5 / 1.125x1.125 | 3-hourly for most surface fields; 6-hourly for upper-air fields Monthly averages of daily means, and of 6-hourly fields |
ERA5 | ECMWF | 1950-present | 4D-VAR | ? | T639 (HRES) and T319 (EDA) or on a reduced Gaussian grid with a resolution of N320 (HRES) and N160 (EDA) | Global | User defined, down to 30km (0.28x0.28) | hourly estimates of a large number of atmospheric, land and oceanic climate variables. |
ERA-20C | ECMWF | 1900-2010 | 4D-Var | TL159L91 and N80 reduced Gaussian | TL159L91 and N80 reduced Gaussian (~125km globally) | Global | User defined, down to 0.25x0.25 | 3-hourly for most fields, except for some surface fields (6-hourly |
Japanese 25-year Reanalysis (JRA-25)
---------- |
Japan Meteorological Agency (JMA) and Central Research Institute for Electric Power Industry (CRIEPI) ----------
JMA |
1979-2004 2005-Jan.2014 |
3D-VAR | T106L40 | T106L40 Gaussian | Global |
1.25 x 1.25 2.5 x 2.5 |
6-hourly daily monthly |
Japanese 55-year Reanalysis (JRA-55) | Japan Meteolorogical Agency |
1958-2012 (extended to Jan.2024) |
4D-VAR | TL319L60 | TL319L60 reduced Gaussian | Global | 1.25 x 1.25 |
6-hourly, partly 3-hourly daily monthly |
Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) |
Japan Meteolorogical Agency | Sep.1947 - present | 4D-VAR | TL479L100 | TL479L100 reduced Gaussian | Global | 1.25 x 1.25 |
6-hourly, partly 3-hourly or 1-hourly daily monthly |
NASA MERRA | NASA GMAO | 1/1979-2/2016 | 3D-VAR, with incremental update | 2/3 lon x1/2 lat deg; 72 sigma levels | 2/3 lon x1/2 lat deg 3d Analysis and 2d variables; 1.25 deg 3d Diagnostics; 72 model levels and 42 pressure levels | Global | 2/3 lon x1/2 lat deg 3d Analysis and 2d variables; 1.25 deg 3d Diagnostics; 72 model levels and 42 pressure levels | 2d Diagnostics - 1 hourly avg, centered at half hour; 3d Diagnostics - 3 hourly avg, centered at 0130, 0430 ... 2230; 3d Analysis - Instantaneous 6 hourly; 2d Diagnostics, Monthly mean diurnal average; Monthly means for all collections; daily averages processed at servers on-the-fly |
NASA MERRA-2 | NASA GMAO | 1/1980-present | 3D-VAR, with incremental update; Includes aerosol data assimilation, observation corrected precipitation forcing for land surface and aerosol wet deposition | Native cube sphere grid output is interpolated to 5/8 lon x1/2 lat deg; 72 sigma levels | 5/8 lon x1/2 lat deg 3d Analysis and 2d variables; 3d Diagnostics; 72 model levels and 42 pressure levels | Global | 5/8 lon x1/2 lat deg 3d Analysis and 2d variables; 3d Diagnostics; 72 model levels and 42 pressure levels | 2d Diagnostics - 1 hourly avg, centered at half hour; 3d Diagnostics - 3 hourly avg, centered at 0130, 0430 ... 2230; 3d Analysis - Instantaneous 6 hourly; 2d Diagnostics, Monthly mean diurnal average; Monthly means for all collections; Daily min/max T2m; Glacier/Sea Ice; Aerosols; daily averages processed at servers on-the-fly |
NCEP Climate Forecast System Reanalysis (CFSR) | NCEP | 1979-present | 3D-VAR | T382 L64 | .5x.5 and 2.5x2.5 | Global | .5x.5 and 2.5x2.5 | Hourly, 4 times daily |
NCEP/DOE Reanalysis AMIP-II (R2) | NCEP/DOE | 1979-present | 3D-VAR | T62 L28 | 2.5x2.5 | Global | 2.5x2.5 | 4 times daily/daily/monthly, also LTMs |
NCEP/NCAR Reanalysis I (R1) | NCEP/NCAR | 1948-present | 3D-VAR | T62 L28 | 2.5x2.5 and 2x2 gaussian | Global | 2.5x2.5 and 2x2 gaussian | 4 times daily/daily/monthly also LTMs |
NCEP North American Regional Reanalysis (NARR) | NCEP | 1979-present | RDAS | 32km | 32km | N America | 32km | 4/8 times daily/daily/monthly also LTMs. |
NOAA-CIRES 20th Century Reanalysis (20CR) | NOAA PSL | 1871-2012 | Ensemble Kalman Filter | T62 L28 | 2x2 | Global | 2x2 | 4/8 times daily, daily,monthly, also LTMs |
NOAA-CIRES 20th Century Reanalysis (20CRV2c) | NOAA/ESRL PSL | 1851-2014 | Ensemble Kalman Filter | T62 L28 | 2x2 | Global | 2x2 | 4/8 times daily, daily,monthly, also LTMs |
NOAA-CIRES 20th Century Reanalysis (20CRV3) | NOAA/ESRL PSL | 1836-2015 | Ensemble Kalman Filter | T254 L64 | ~0.7x0.7 | Global | 1x1 L28 | 8 times daily, daily,monthly, also LTMs |
OCADA | JMA | 1836-2015 | ? | ? | ? | Global | ? | monthly |
NOAA Last Millennium Reanalysis (LMR) | NOAA | 1-2000 | Ensemble Kalman Filter | T62 | 2x2 | Global | 2x2 | annual |
CORe | NOAA/CPC | 1950-present | ? | ? | ? | Global | ? | 8x daily |
* To insert a new row, select 'edit'. Then select an element in the row you want to add a new row after. Then click your right mouse button. Select 'row' from the list. Then select 'insert row after'.
The satellite errors started…
The satellite errors started around 1979 and hence many of the reanalyses started then (as there many more observations of the atmosphere and particularly the ocean available from the satellites than had been available before. Starting in 1979 results in a more consistent 'climate' in a reanalyses. The downside is there are are observations from before satellites and it is useful to study the atm/ocean before then, of course)
Some Comparison Tables as of 2016
For your information, there are several comparison tables for all available global atmospheric reanalyses as of 2016 in the following paper:
Fujiwara, M., J. S. Wright, G. L. Manney, L. J. Gray, J. Anstey, T. Birner, S. Davis, E. P. Gerber, V. L. Harvey, M. I. Hegglin, C. R. Homeyer, J. A. Knox, K. Krüger, A. Lambert, C. S. Long, P. Martineau, A. Molod, B. M. Monge-Sanz, M. L. Santee, S. Tegtmeier, S. Chabrillat, D. G. H. Tan, D. R. Jackson, S. Polavarapu, G. P. Compo, R. Dragani, W. Ebisuzaki, Y. Harada, C. Kobayashi, W. McCarty, K. Onogi, S. Pawson, A. Simmons, K. Wargan, J. S. Whitaker, and C.-Z. Zou: Introduction to the SPARC Reanalysis Intercomparison Project (S-RIP) and overview of the reanalysis systems, Atmos. Chem. Phys., 17, 1417-1452, doi:10.5194/acp-17-1417-2017, 2017.
http://doi.org/10.5194/acp-17-1417-2017
Please also visit the project page at:
https://s-rip.ees.hokudai.ac.jp/
added to Overview page
Thanks, Masatomo!
I have added the link to the paper to the list of Comparison Table links at https://reanalyses.org/atmosphere/overview-current-atmospheric-reanalyses.
If the S-RIP project page has a direct link to the comparison tables, we could add those instead, or in addition. They could also go on the Notes page at http://reanalyses.org/atmosphere/notes-questions-and-discussion-reanalyses-datasets.
best wishes,
gil
added to Notes page
Masatomo,
I also added a link to the paper at http://reanalyses.org/atmosphere/notes-questions-and-discussion-reanalyses-datasets
Please feel free to make changes there, or leave a comment with suggestions.
best wishes,
gil
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There is no easy answer to this question. In part, independent validation is thin (most large sources of water vapor are assimilated already), water vapor is one of the most sensitive prognostic variables to assimilate (see work by Bengtsson), and also low specificity of the question. I would expect each reanalysis to have strengths and weaknesses depending on region, season or time period. There is a fair bit work on these in the literature. So whatever the specific metrics are that answer your question, if you find it through a literature search or evaluating the data itself, there are pages at this www site where you can contribute what you find!
Compliments
Re: Comparison Table
Re: Comparison Table
Thank you for such an helpful comparison table. I would like to know the accuracy of pressure, temperature and rH values given by reanalysis data. As far my knowledge goes, P & T is a 1st order variable and reanalysis values are as good as observations. Is that true? Is the accuracy of P, T & rH is going to affected by soil moisture, radiations, clouds cover, snow melting etc. arround tropics? Thanks
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Both the date updated and the ERA-Interim time extension have been added. Thanks for the suggestions.
Reanalysis comparison
Re: Reanalysis comparison
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Diagnostic Studies: Weather Variability
From the NCAR Research Data Archive blog
- The Atmospheric River event of Dec 2004 - Jan 2005 in the 20CRv2c
- The 1938 Atmospheric River that caused the Los Angeles Flood of 1938
Extreme Winds over the Northern Hemisphere since 1871
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Diagnostic Studies: Climate Variability
This page is intended for recent results in studying the climate variability of reanalyses. (add newer links at the top)
- Evaluating Observation Influence on Regional Water Budgets in Reanalyses - M. Bosilovich (May 2015)
- Differences Between Multidecadal Climate Variability in the GFDL CM3 Model and Reanalyses - S. Kravtsov (October 2014)
- Artificial inhomogeneities identified in the 20CR- C. Ferguson (May 2014)
- Temperature trends for the period 1871-2009 in the midlatitude summer mesosphere - G. Baumgarten (Jan 2014)
- Issues with 20CR near-surface ensemble mean wind speed - N. Swart (Nov 2013)
- Independent Confirmation of global land warming - G. Compo (Sep 2013)
- US Summer Regional Climate Variability - M. Bosilovich (August 2013)
- Strengthening Walker Circulation - M. L'Heureux (April 2013)
- Siberian Snow cover - Y. Peings (Jan 2013)
- The detection of Atmospheric Rivers in Atmospheric Reanalyses - D. Lavers (Dec 2012)
- Weakening Walker Circulation - H. Tokinaga (Nov 2012)
- Interannual Variability in reanalyses - H. Paek (Oct 2012)
- Extratropical cyclone trends in 20CR - X. Wang (July 2012)
- Southern Africa precipitation in reanalyses - Q. Zhong (Aug 2012)
- Decadal-to-Interdecadal Variability and Trend in reanalyses - H. Paek (Aug 2012)
- US Temperature Trends over 1979 to 2008 - R. Vose (June 2012)
- Tropospheric variability in CFSR and other reanalyses - M. Chelliah (May 2012)
- Global precipitation trends from 1979 to 2010 - L. Zhang (April 2012)
- Arctic cloud climatologies from observations and reanalyses - A. Chernokulsky (Mar 2012)
- Studying the observations in reanalyses - M. Bosilovich (Nov 2011)
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Overview of current atmospheric reanalyses
Current / State-of-the-art:
Global: ASR | CERA-20C | ERA5 | ERA-20C | ERA-20CM | JRA-3Q | JRA-55, JRA-55C, JRA-55AMIP | MERRA-2 | NCEP CFSR | NCEP CFSv2 | NOAA-CIRES 20CRv2c | NOAA-CIRES-DOE 20CRv3 | OCADA | CORe
Regional: BARRA (AU) | BARRA2 (AU) | COSMO-REA (Europe) | MERIDA (IT) | NZRA (NZ)
Possible issues: consider other datasets for use in new research projects: NCEP/DOE II | NCEP/NCAR | NCEP NARR
Superseded / Caution use for new research projects: ERA-Interim | ERA-40 | ERA-15 | MERRA | JRA-25
Frequency of updates:
Updated in real-time for public use (1+ days behind): ERA5 | MERRA-2 | NCEP/DOE II | NCEP/NCAR | NCEP NARR | NCEP CFSv2 | JRA-3Q | JRA-55 |
Updated in near real-time for public use (1+ months behind): MERRA-2
Updated irregularly for public use (years behind): CERA-20C |ERA-20C | ERA-20CM | NOAA-CIRES 20CR | NOAA-CIRES 20CRv2c | NOAA-CIRES-DOE 20CRv3 | NASA MERRA
Coming up: ERA6, latest news: 2023-09 (C3S GA) | MERRA-3 | next NOAA reanalysis, latest news: 2024-06
Overview Comparison Table (Reanalyses.org)
Overview Comparison Table (ClimateDataGuide)
Overview Comparison Table (as of 2016, S-RIP)
Notes,questions, and discussion by dataset
Arctic System Reanalysis (ASR): 2000-2012
The Arctic System Reanalysis (ASR), a high-resolution regional assimilation of model output, observations, and satellite data across the mid- and high latitudes of the Northern Hemisphere for the period 2000 – 2012 has been performed at 30 km (ASRv1) and 15 km (ASRv2) horizontal resolution using the polar version of the Weather Research and Forecasting (WRF) model and the WRF Data Assimilation (WRFDA) System.
Source: Polar Meteorology Group, Byrd Polar & Climate Research Center, The Ohio State University
Time Range: 2000-2012
Assimilation: WRFDA-3DVAR
Dataset Output Times and Time Averaging: 3-hourly for surface and upper air fields, Monthly means of selected variables
ASRv1 – 30 km
Model Resolution: 30 km, 71 sigma levels
Dataset location: https://rda.ucar.edu/datasets/ds631.0/
ASRv2 30 km is expected early 2017
ASRv2 – 15 km (Currently updating through 2016 - available end of 2017)
Model Resolution: 15 km, 71 sigma levels
Dataset location: https://rda.ucar.edu/datasets/ds631.1/
Project Website: Arctic System Reanalysis
BARRA
The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) is a high-resolution multi-decadal atmospheric reanalysis. The reanalysis provides information about surface conditions (such as temperature, precipitation, wind speed and direction, humidity, evaporation and soil moisture), information at pressure and model levels, and information on solar radiation and cloud cover. The reanalysis suite is based on the Australian Community Climate Earth-System Simulator (ACCESS) and extends 70 levels (up to 80 km) into the atmosphere. It is nested within the required boundary and/or initial conditions provided by ERA-Interim reanalysis, Operational SST and Sea Ice Analysis, and the Bureau offline soil moisture reanalysis. The region covered by the reanalysis is the Australian continent, and the surrounding region including parts of southeast Asia, New Zealand, and south to the ice edge of the Antarctic continent. About 100 parameters are available at hourly time steps at approximately 12-km resolution, in this dataset. For a small number of subdomains (South-West W.A., S.A., Eastern N.S.W., and Tasmania), ), dynamically downscaled analyses at a 1.5-km resolution are available separately.
Bureau's Atmospheric high-resolution Regional Reanalysis for Australia (BARRA)
BARRA2
BARRA2 is the new version of the regional atmospheric reanalysis over Australia and surrounding regions, spanning 1979-present day time period. When completed, it replaces the first version of BARRA (Su et al., 2019), completed in June 2019, which provided a shorter 1990-2019 February reanalysis.
Bureau's Atmospheric high-resolution Regional Reanalysis for Australia Version 2 (BARRA2)
COSMO Regional Reanalyses: 1995-present
Several reanalysis systems are developed to generate high-resolution regional reanalysis data sets for Europe based on the NWP model COSMO. The first available data set at a horizontal resolution of 6km covers the entire European continent for the years 1995-2015 with output of model variables being available for every hour. A subsequent second data set covers large parts of Central Europe in a convection-permitting setup with 2km horizontal resolution and is available for the period 2007-2013.
Reanalysis consortium: Hans-Ertel-Centre - Climate Monitoring and Diagnostics, German Meteorological Service (DWD) - Climate and Environment, Meteorological Institute of the University of Bonn, Institute for Geophysics and Meteorology of the University of Cologne
Time Range: 1995-2015
Assimilation: Continuous nudging, separate DA modules for soil moisture, snow, SST and sea ice, latent heat nudging scheme
Dataset Output Times: 15 minutes for surface, hourly for upper air fields
COSMO-REA6 – 6.2 km
Model Resolution: 6.2 km, 40 eta levels
Period available: 1995-2018
Domain: Europe (CORDEX-11 domain)
Production of a second (updated) version of COSMO-REA6 is expected to start late 2020
COSMO-REA2 – 2 km
Model Resolution: 2 km, 50 eta levels
Period available: 2007-2013, production for 2014-2015 is currently under way, will be updated regularly
Domain: Central Europe
COSMO-ENS-REA12 – 12 km
Period available: 2006-2010
Data Access: COSMO reanalysis website
ECMWF CERA-20C: 1901-2010
CERA-20C is a global 20th-century reanalysis which aims to reconstruct the past weather and climate of the Earth system including the atmosphere, ocean, land, waves and sea ice. CERA-20C is part of the EU-funded ERA-CLIM2 project and extends the reanalysis capability developed in ERA-20C to the ocean and sea-ice components.
A new assimilation system (CERA) has been developed to simultaneously ingest atmospheric and ocean observations in the coupled Earth system model used for ECMWF’s ensemble forecasts. It is based on a variational method with a common 24-hour assimilation window. Air-sea interactions are taken into account when observation misfits are computed and when the increments are applied to the initial condition. In this context, ocean observations can have a direct impact on the atmospheric analysis and, conversely, atmospheric observations can have an immediate impact on the analysed state of the ocean.
CERA-20C assimilates only surface pressure and marine wind observations (ISPDv3.2.6 and ICOADSv2.5.1) as well as ocean temperature and salinity profiles (EN4). The air-sea interface is relaxed towards the sea-surface temperature from the HadISST2 monthly product to avoid model drift while enabling the simulation of coupled processes. No data assimilation is performed in the land, wave and sea-ice components, but the use of the coupled model ensures some dynamical consistency.
The evolution of the global weather for the period 1901–2010 is represented by a ten-member ensemble of 3-hourly estimates for ocean, surface and upper-air parameters. The resolution of the atmospheric model is set to TL159L91 (IFS version 41r2), which corresponds to a 1.125° horizontal grid (125 km) with 91 vertical levels going up to 0.1hPa. The ocean model (NEMO version 3.4) uses the ORCA1 grid, which has approximately 1° horizontal resolution with meridional refinement at the equator. There are 42 vertical ocean levels with a first-layer thickness of 10m.
Data Access: ECMWF
References | Data documentation | ClimateDataGuide
ECMWF ERA-20C: 1900 - 2010
ERA-20C is ECMWF's first atmospheric reanalysis of the 20th century, from 1900-2010. It is an outcome of the ERA-CLIM project.
ERA-20C was produced with the same surface and atmospheric forcings as the final version of the atmospheric model integration ERA-20CM. A coupled Atmosphere/Land-surface/Ocean-waves model is used to reanalyse the weather, by assimilating surface observations. The ERA-20C products describe the spatio-temporal evolution of the atmosphere (on 91 vertical levels, between the surface and 0.01 hPa), the land-surface (on 4 soil layers), and the ocean waves (on 25 frequencies and 12 directions). The horizontal resolution is approximately 125 km (spectral truncation T159). Note, atmospheric data are not only available on the native 91 model levels, but also on 37 pressure levels (as in ERA-Interim), 16 potential temperature levels, and the 2 PVU potential vorticity level. Monthly means, daily, and invariant data are available. The temporal resolution of the daily products is usually 3-hourly.
The assimilation methodology is 24-hour 4D-Var analysis, with variational bias correction of surface pressure observations. Analysis increments are at T95 horizontal resolution (aprox. 210 km). The analyses provide the initial conditions for subsequent forecasts that serve as backgrounds to the next analyses. A 10-member ensemble was produced initially, to estimate the spatio-temporal evolution of the background errors.
The observations assimilated in ERA-20C include surface pressures and mean sea level pressures from ISPDv3.2.6 and ICOADSv2.5.1, and surface marine winds from ICOADSv2.5.1. The observation feedback from ERA-20C is available. It includes the observations but also departures before and after assimilation and usage flags.
ECMWF ERA-20CM Model integration (no data assimilation): 1900 - 2010
The ERA-20CM atmospheric model integrations were produced in the framework of the ERA-CLIM project.
There are two versions, ERA-20CM and ERA-20CMv0, each comprising of a 10-member ensemble. The first version is 'final', the second is 'experimental'. The 'experimental' version was an early production and should not be used to initiate new research.
The model integration is forced by radiative forcing from CMIP5 and also by sea-surface temperature (SST) and sea ice cover from HadISST2.
Access: ECMWF
References | ClimateDataGuide
ECMWF ERA5: 1940-present
ERA5 is the latest climate reanalysis produced by ECMWF, providing hourly data on many atmospheric, land-surface and sea-state parameters together with estimates of uncertainty. ERA5 data are available on regular latitude-longitude grids at 0.25o x 0.25o resolution, with atmospheric parameters on 37 pressure levels. Recently, 1940-1978 has been added.
Data Access: Copernicus | NCAR | ECMWF
ECMWF Interim Reanalysis (ERA-Interim): 1979-present
Ends Aug 2019
ERA-Interim was originally planned as an 'interim' reanalysis in preparation for the next-generation extended reanalysis to replace ERA-40. It uses a December 2006 version of the ECMWF Integrated Forecast Model (IFS Cy31r2). It originally covered dates from 1 Jan 1989 but an additional decade, from 1 January 1979, was added later. ERA-Interim is being continued in real time. The spectral resolution is T255 (about 80 km) and there are 60 vertical levels, with the model top at 0.1 hPa (about 64 km). The data assimilation is based on a 12-hourly four-dimensional variational analysis (4D-Var) with adaptive estimation of biases in satellite radiance data (VarBC). With some exceptions, ERA-Interim uses input observations prepared for ERA-40 until 2002, and data from ECMWF's operational archive thereafter. See Dee et al. (2011) in the references below for a full description of the ERA-Interim system.
ECMWF 40 Year Reanalysis (ERA-40): Sep 1957-Aug 2002
Completed in 2003, ERA-40 is a global atmospheric reanalysis of the 45-year period 1 September 1957 - 31 August 2002. It was produced using a June 2001 version of the ECMWF Integrated Forecast Model (IFS Cy28r3). The spectral resolution is T159 (about 125 km) and there are 60 vertical levels, with the model top at 0.1 hPa (about 64 km). Observations were assimilated using a 6-hourly 3D variational analysis (3D-Var). Satellite data used include Vertical Temperature Profile Radiometer radiances starting in 1972, followed by TOVS, SSM/I, ERS and ATOVS data. Cloud Motion Winds are used from 1979 onwards. Various data from past field experiments were used, such as the 1974 Atlantic Tropical Experiment of the Global Atmospheric Research Program, GATE, 1979 FGGE, 1982 Alpine Experiment, ALPEX and 1992-1993 TOGA-COARE.
ECMWF 15 Year Reanalysis (ERA-15): Jan 1979-Dec 1993
Completed in 1996, ERA-15 is a global atmospheric reanalysis of the 15-year period 1 January 1979 - 31 December 1993. It was produced using an April 1995 version of the ECMWF Integrated Forecast Model (IFS Cy13r4). The spectral resolution is T106 (about 190 km) and there are 31 vertical levels, with the model top at 10 hPa (about 31 km altitude). Observations were assimilated using a 6-hourly Optimum Interpolation analysis (OI). Satellite data used were limited to cloud-cleared TOVS radiances and Cloud Motion Winds from GOES, GMS, and METEOSAT. Pseudo-observations of surface pressure (PAOBS) were also used, as well various data from past field experiments: 1979 FGGE, 1982 Alpine Experiment (ALPEX), TOGA, SUBDUCTION, and those found in the COADS dataset.
Japanese 25-year Reanalysis (JRA-25): 1979-2004, JCDAS: 2005-Jan.2014
The Japanese 25-year Reanalysis (JRA-25) represents the first long-term global atmospheric reanalysis undertaken in Asia. Covering the period 1979-2004, it was completed using the Japan Meteorological Agency (JMA) numerical assimilation and forecast system and specially collected and prepared observational and satellite data from many sources including the European Center for Medium-Range Weather Forecasts (ECMWF), the National Climatic Data Center (NCDC), and the Meteorological Research Institute (MRI) of JMA. A primary goal of JRA-25 is to provide a consistent and high-quality reanalysis dataset for climate research, monitoring, and operational forecasts, especially by improving the coverage and quality of analysis in the Asian region. JRA-25 was conducted by JMA and CRIEPI (Central Research Institute of Electric Power Industry). It had been continued as JCDAS (JMA Climate Data Assimilation System) operated by JMA in near real time basis. The data assimilation systems of JRA-25 and JCDAS are the same. Users can use JRA-25 and JCDAS as one continuous reanalysis dataset. JCDAS data provision was terminated in early Feburay 2014 because it was replaced with JRA-55 in operational. The available data period of JRA-25/JCDAS is for 35 years and 1 month (January 1979 to January 2014).
Data Access: NCAR
Homepage | Atlas | References | ClimateDataGuide
Japanese 55-year Reanalysis (JRA-55): 1958-2012, and extended to present
[JRA-55C(1972-2012) and JRA-55AMIP(1958-2012)] <-- not extended to present
JMA has carried out the second reanalysis project named the Japanese 55-year Reanalysis (JRA-55) (nicknamed JRA Go! Go!) using a more sophisticated NWP system, which is based on the operational system as of December 2009, and newly prepared past observations. The analysis period is extended to 55 years starting from 1958, when the regular radiosonde observations became operational on the global basis. Many of deficiencies in JRA-25 have been diminished or reduced in JRA-55 because many improvements achieved after JRA-25 have been introduced. JRA-55 provides a consistent climate dataset over the last half century. JRA-55 has been continued in near real time basis after 2013. If you need real time basis (2 days behind) data, please access to JMA. The real time basis data are also provided to other cooperating organizations (Note: half year behind). Note that the products for extended period after 2013 are also called JRA-55. As "JRA-55 family", there are two subproducts JRA-55C and JRA-55AMIP produced by MRI/JMA. JRA-55C assimilated conventional observations only. JRA-55C covers from 1972 to 2012. Before 1971, use JRA-55 instead because no satellite data was assimilated in JRA-55 before 1971. JRA-55AMIP (AMIP type run of JRA-55, with no observations) covers 1958 to 2012. JRA-55C and JRA-55AMIP data are available from DIAS and NCAR. Note that JRA-55C and JRA-55AMIP are not extended to present. JRA-55 Atlas (climate charts) is now available.
Data Access: JMA | DIAS (JRA-55) (JRA-55C) (JRA-55AMIP) | NCAR (JRA-55: Daily 3-Hourly and 6-Hourly Data, Monthly Means and Variances) (JRA-55C: Daily(3-hourly,6-hourly), Monthly) (JRA-55AMIP: Daily(3-hourly,6-hourly), Monthly) | ESGF/NASA/WCRP | ECMWF |
Homepage | References | JRA-55 Atlas |
Japanese Reanalysis for Three Quarters of a Century (JRA-3Q): Sep.1947 to present
JMA is currently conducting the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q), which covers the period from September 1947 onward to extend the current period of data coverage and improve the quality of long-term reanalysis. The project involves a sophisticated data assimilation system (based on the operational set-up as of December 2018) incorporating development results from the operational NWP system and sea surface temperature analysis achieved since JRA-55 (based on the operational set-up as of :2009). New datasets of past observations are also assimilated, including rescued historical observations and reprocessed satellite data supplied by meteorological and satellite centers worldwide. Many of the deficiencies of JRA-55 are alleviated in JRA-3Q, providing a high-quality homogeneous reanalysis dataset that covers the previous 75 years.
Homepage | References |
Sub-product JRA-3Q-COBE
In JRA-3Q, the sea surface temperature (SST) specified as the lower boundary condition is the Centennial In Situ Observation-based Estimates of the Variability of SSTs and Marine Meteorological Variables Version 2 (COBE-SST2) with a resolution of 1° based on in situ observations until May 1985 and the Merged Satellite and In-Situ Data Global Daily Sea Surface Temperature (MGDSST) with a resolution of 0.25° based on satellite observations since June 1985. To enable evaluation of changes in product characteristics following the switch from COBE-SST2 to MGDSST, a sub-product using COBE-SST2 (JRA-3Q-COBE) is also provided for the period from June 1985 to December 1990.
Data Access: DIAS |
NASA Modern Era Reanalysis for Research and Applications (MERRA): 1979-2016(Feb)
MERRA is a NASA reanalysis for the satellite era using a major new version (circa 2008) of the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5) produced by the NASA GSFC Global Modeling and Assimilation Office (GMAO). The Project focused on historical analyses of the hydrological cycle on a broad range of weather and climate time scales and placed the NASA EOS suite of observations in a climate context.
Home Page | References | FAQ | Atlas | ClimateDataGuide | AMS Special Collection
NASA Modern Era Reanalysis for Research and Applications Version-2 (MERRA-2): 1980-present
MERRA-2 is a NASA reanalysis for the satellite era using a major new version of the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5) produced by the NASA GSFC Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observations not available to MERRA during the 2010s, and therefore, will continue processing in real time longer than MERRA. There are numerous improvements and updates to the data assimilaiton, model and observing system. One notable change is the assimilation of aerosol observations, including black and organic carbon, sulfate and dust. Production began in the spring of 2014 and is presently available for access.
Data Access: GES MDISC | FTP Subsetter
Home Page | File Specification | Documentation | AMS Special Collection
NCEP Climate Forecast System Reanalysis (CFSR): 1979-present
The National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) spans 1979 to present. The CFSR was designed and executed as a global, high resolution, coupled atmosphere-ocean-land surface-sea ice system to provide the best estimate of the state of these coupled domains over this period. The T382 resolution atmospheric data spans 1979 to 2010. The current T574 analysis is an extension of the CFSR as an operational, real time CFSv2 product from 2011 into the future.
Data Access: NCEP | NCDC NOMADS | NCAR (includes real time CFSv2) | ESGF
NCEP Climate Forecast System Reanalysis version 2 (CFSv2): 2011-present
The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the interaction between the Earth's oceans, land and atmosphere. The four-times-daily, 9-month control runs, consist of all 6-hourly forecasts, and the monthly means and variable time-series (all variables). The CFSv2 outputs include: 2-D Energetics (EGY); 2-D Surface and Radiative Fluxes (FLX); 3-D Pressure Level Data (PGB); 3-D Isentropic Level Data (IPV); 3-D Ocean Data (OCN); Low-resolution output (GRBLOW); Dumps (DMP); and High- and Low-resolution Initial Conditions (HIC and LIC). The monthly CDAS variable timeseries includes all variables. The CFSv2 period of record begins on April 1, 2011 and continues onward. CFS output is in GRIB-2 file format.
Data Access: https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00877
NCEP/DOE Reanalysis II: 1979-near present
NCEP produced a second version of their first reanalysis starting from the beginning of the major satellite era. More observations were added, assimilation errors were corrected and a better version of the model was used.
Data Access: NCEP NOMADS | NCAR | ESRL | KNMI | IRI
NCEP/NCAR Reanalysis I: 1948-present
This reanalysis was the first of its kind for NOAA. NCEP used the same climate model that were initialized with a wide variety of weather observations: ships, planes, RAOBS, station data, satellite observations and many more. By using the same model, scientists can examine climate/weather statistics and dynamic processes without the complication that model changes can cause. The dataset is kept current using near real-time observations.
Data access: NCEP NOMADS| NCAR | NOAA PSL | IRI | KNMI
References | FAQ | FGDC | ClimateDataGuide
NCEP North American Regional Reanalysis (NARR): 1979-near present
The NARR reanalysis was done to produce very high resolution output over the North American region. Observational inputs were similar to NCEP I with the addition of assimilated precipitation. The NARR model region was nested in a global, lower resolution model. Outputs are similar to the NCEP I and II models but with more snow, ice and precipitation related variables.
Data Access: NCDC and NCEP NOMADS | NCAR | NOAA PSL
References | FAQ | ClimateDataGuide
NOAA-CIRES 20th Century Reanalysis version 2 (20CRv2): 1871-2012
The 20th Century Reanalysis version 2 (20CRv2) dataset contains global weather conditions and their uncertainty in six hour intervals from the year 1871 to 2012. Surface and sea level pressure observations are combined with a short-term forecast from an ensemble of 56 integrations of an NCEP numerical weather prediction model using the Ensemble Kalman Filter technique to produce an estimate of the complete state of the atmosphere, and the uncertainty in that estimate. The uncertainty is approximately inversely proportional to the density of observations. Additional observations and a newer version of the NCEP model that includes time-varying CO2 concentrations, solar variability, and volcanic aerosols are used in version 2. The long time range of this dataset allows scientists to examine better long time scale climate processes such as the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation as well as looking at the dynamics of historical climate and weather events. Verification tests have shown that using only pressure creates reasonable atmospheric fields up to the tropopause. Additional tests suggest some correspondence with observed variations in the lower stratosphere.
Data Access: NOAA PSL | KNMI | IRI | NCAR | NERSC | BADC | ESGF
Homepage | References | Related Publications | ClimateDataGuide
NOAA-CIRES 20th Century Reanalysis version 2c (20CRv2c): 1851-2012 [2013-2014]
The 20th Century Reanalysis version 2 (20CRv2c) dataset contains global weather conditions and their uncertainty in six hour intervals from the year 1851 to 2012. Surface and sea level pressure observations are combined with a short-term forecast from an ensemble of 56 integrations of an NCEP numerical weather prediction model using the Ensemble Kalman Filter technique to produce an estimate of the complete state of the atmosphere, and the uncertainty in that estimate. The uncertainty is approximately inversely proportional to the density of observations. Additional observations from ISPDv3.2.9 and new boundary conditions from the Simple Ocean Data Assimilation with sparse observational input (SODAsi.2) pentad sea surface temperature and COBE-SST2 monthly sea ice concentration are used. For the 2013 and 2014 extension, SSTs are from the NOAA daily 1/4 degree Optimal Interpolation version 2. 2014 does not include tropical cyclone pressure observations after April.
Data Access: NOAA PSL | NCAR | ESGF | NERSC (every member)
Homepage | References | Related Publications | ClimateDataGuide
NOAA-CIRES-DOE 20th Century Reanalysis version 3 (20CRv3): 1806-2015
The 20th Century Reanalysis version 3 (20CRv3) is now available. It provides 8-times daily estimates of global tropospheric variability across ~75 km grids, spanning 1836 to 2015 and experimentally back to 1806 at NERSC.
Data Access: NOAA PSL | NCAR | NERSC (every member back to 1806)
Homepage | References | Related Publications
NOAA Last Millennium Reanalysis version 2 (LMRv2): 1-2000CE
The Last Millennium Reanalysis (LMR) uses an ensemble methodology to assimilate paleoclimate data for the production of annually resolved, globally gridded, climate field reconstructions of the Common Era. File and variable naming conventions follow as closely as possible those for the NOAA 20th Century Reanalysis. Available fields include 2m air temperature, sea surface temperature, mean-sea-level pressure, 500 hPa geopotential height, precipitation, precipitable water, Palmer Drought Severity Index (PDSI), and a range of climate indices.
Data Access: NCEI | University of Washington
NCEI Home Page | References |
NZRA (NZ)
https://www.jstor.org/stable/27226715
MEteorological Reanalysis Italian DAtaset (MERIDA)
The new MEteorological Reanalysis Italian DAtaset (MERIDA) has been developed to deal with the increasingly frequent extreme weather conditions of the last 20 years, which have caused several disruptions to the Italian electricity system. This work has been developed following the indications emerged from the 'Working table for resilience' established by the Regulatory Authority for Energy Networks and the Environment (ARERA). MERIDA is able to respond to energy stakeholders, who need reliable meteorological data to implement effective adaptation strategies for safely operating the electricity system.
OCADA from the JMA 1836-2015
A historical atmospheric reanalysis from 1836 to 2015, assimilating only surface pressure observations with an atmospheric general circulation model. The reanalysis is called OCADA (Over-Centennial Atmospheric Data Assimilation), and it provides the evolution of the three-dimensional atmosphere and the quantitative information of the uncertainties.
Data Access: https://climate.mri-jma.go.jp/pub/archives/Ishii-et-al_OCADA/
No Homepage | References
CORe from the NOAA/CPC 1950-near present
CORe was designed for climate monitoring. It assimilates most observations but does not include satellite observations with the exception of those used in the SST dataset used as a boundary forcing and for atmospheric motion vectors. This avoids spurious jumps when the satellite period began around 1979.
Data Access: NOAA/CPC has an avaluation dataset available.
Have a question or comment on a specific reanalysis dataset or in general?
Use this link to Post Notes, Questions, and Comments on specific reanalyses
Assimilated surface data from several reanalyses
Hi, I need to know what variables (T, UV winds, surface pressure, etc.) ERA-Interim, NCEP-CFSR, MERRA2 and JRA55 assimilate from land met stations and moored buoy arrays such as TAO, TRITON and RAMA.
Also, do ERA-Interim, NCEP-CFSR, MERRA2 and JRA55 assimilate any variables measured by moored buoys offshore the Spanish Atlantic coast, operated by the Spanish Agency Puertos del Estado?
Any help would be much appreciated, thanks!
respond under the specific reanalyses
Dear David,
Nice job also putting this wide-ranging question under each reanalysis question page. I would expect responses to go there.
I would have expected the answer to your question to already be in the provided references. Did you not find this information in the references that are linked for each dataset? I believe that each reference paper describes the assimilated platforms and variables in some detail. I suggest that you update your specific comments with the page of each paper where you would have expected the information to be but are not finding it. Also, you may not be aware that the International Comprehensive Ocean Atmosphere Data Set (ICOADS, www.icoads.gov) ingests all of the marine observations and is used in all reanalyses. But, your specific question about which observed variables are assimilated is still a good one if you aren't finding this information in the references.
best wishes,
gil
Hi Gil, thanks for your…
Hi Gil, thanks for your quick reply.
Actually I went through all the mentioned reanalyses reference papers, and many more including the tech docs, and I cannot really find a conclusive answer to my (apparently not so) simple question. I've been digging through the literature for some months now...
From what I have found, I believe that ERA-Interim and JRA55 only assimilate surface pressure from land stations and moored buoys. MERRA2 doesnt assimilate UV from land stations (only sfc press), but seems to assimilate UV from some TAO buoys (not PIRATA though), but I'm not 100% sure if MERRA2 assimilates winds from moored buoys as a rule. Have no idea what CFSR does, although I'm inclined to think MERRA2 follows similar rules for conventional obs as CFSR.
Will hope for some insight in this forum!
Hi Gil, haven't seen any…
Hi Gil, haven't seen any more answers to my question, am I missing them or simply no one replied?
Thanks
No other replies yet
David,
Sorry, it looks like no other replies have been posted, yet. In general, if the data are transmitted to the global telecommunications system (GTS), they are assimilated. Hopefully, you will receive some other replies soon.
best wishes,
gil
Dear Yiyi,…
Dear Yiyi,
In ERA-20C data the monthly means of daily means are averages over the available hours in a day, then over all days in the month, to represent the whole or complete monthly mean. Synoptic monthly means are averages over days in the month for a particular synoptic time eg 12 UTC, or in the case of accumulations, for only a particular accumulation period within the day eg 06 to 12 UTC.
For most applications you would use the monthly means of daily means. You would only use the synoptic monthly means if you wanted to restrict your attention to a particular point in, or portion of, the diurnal cycle.
Regards,
Paul Berrisford
ERA20C vs 20CRv2c
Question on forecast period of cloud and radiation fluxes
Hello everyone,
Currently I am using the cloud fraction and radiation flux monthly data from MERRA-2, 20CRv2c, ERA-Interim, JRA-55 and CFSR. As far as I know, these variables are calculated in the forecast portion of forecast-analysis update cycles. So I was wondering over which period the radiation fluxes and cloud variables are calculated (e.g., the first 12 hours of each forecast) in each reanalysis. I would be really appreciated if you can provide information for any one of them.
Best,
Yiyi
20CRv2c cloud fraction and radiation flux
Dear Yiyi,
For 20CRv2c, these data are calculated from the first 0-3 hours of the forecast and from the 3-6 hours of the forecast. The 3 hourly data are averaged for the monthly mean.
best wishes,
gil compo
Alberto,…
Alberto,
For your storm surge model you probably want surface winds (primary), surface pressure (secondary) and geopotential and temperature (tertiary).
The GFS analyses use satellite scatterometers to estimate the surface wind speed over water. Both NCEP/NCAR and NCEP/DOE do not use these instruments. However, CFSR, ERA-Interim, MERRA and JRA-55 do use scatterometers.
The first generation reanalyses had the resolution to resolve synoptic-scale+ features. For many phenomenon, such as ENSO, this is adequate. For storm surges, you want something that that can resolve much finer scale features. The modern reanalyses (CFSR, ERA-Interim, MERRA, JRA-55) are run at a much higher resolution and are more suited. However, they are run at a much lower resolution than the current GFS (GFS:T1534 vs CFSR:T384/T574)
The near-surface winds are strongly influenced by the physics in the model. The CFSR will more similar to the GFS, so the CFSR is looking more favorable. The CFSR products have not restriction for commercial use which is another advantage. One disadvantage of the CFSR is difficulty in obtaining it from the official NOAA archive (NCEI) especially data from 2010 and onwards. (I don't follow the situation at NCEI and the data may be available.)
My suggestion is to use the CFSR if you can get the data. If you are affected by the commercial restriction, then the MERRA-2 reanalysis is available without a restriction for commercial use. If the commercial restriction doesn't apply, I don't know which of the modern reanalyses gives the best surface winds.
Info about historical GFS analysis and reanalysis
Dear all, we are group working on storm surge forecasting. We are interested in using GFM as input for our statistical and deterministic models for storm surges. We have a couple of questions on this regard:
1) Our statistical models need a large historical dataset for calibration (mainly surface wind speed, surface pressure, geopotential and temperature), therefore we were planning to use reanalysis for calibration. In the website (http://www.esrl.noaa.gov/psd/data/gridded/reanalysis/ ) it looks like the best reanalysis are NCEP/DOE Reanalysis II (2.5 deg resolution) and 20th Century Reanalysis (V2 and V2c) (2 deg resolution). Which one do you think is the one closer to the GFS operational model, which has a 0.25 degrees resolution, and which we will use operationally?
2) Our Deterministic (shallow water) models need a smaller dataset for calibration (only surface pressure and surface wind ). 5 or 10 years would be enough. Are the past GFS analysis at 0.25 degrees of resolution available for the past 5-10 years? Again we will use GSF operationally, therefore we need to calibrate using the same model.
Thanks a lot. Regards
Alberto
Is the same sigma…
Is the same sigma coordinate used in the reanalysis data from UCAR FNL ? 0.25 x 0.25 degree resolution ? Are the 28 levels the same ?
Need more information
Dear Ashwin,
I will need more information to be able to provide any assistance on this. What is the UCAR FNL? Do you have a web address?
What is the reference reanalysis you are asking about? Is it JRA55, ERA-Interim, CFSR? You mention 28 levels. That could be 20CR or NCEP-NCAR reanalysis, but 0.25 resolution is more like a modern full-input reanalysis.
I will look forward to hearing from you.
Best wishes,
gil
ERA-interim data
I am doing solar modelling and have downloaded the data but it is at 0.00 which is UTC and our region is UTC +10. so it means that the data is for 10AM here which is not useful and next hour is 6 which is 16hrs so it means 4 PM over here. So with the 10AM data and 4 PM data how can i do the modelling?
How can i download the daily data on hourly basis? Any idea highly appreciated.
Hello,…
Hello,
There is no hourly data in ERA-Interim. The analyses are 6 hourly and the twice daily forecasts (from 00 and 12 UTC) provide 3 hourly output to T+24 hours on pressure levels and at the surface, with output becoming less frequent to T+240, and to T+12 hours on model levels. Only a sub-set of this data is available on the point and click web interface (http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/), but all can be obtained by using script access to the data, see https://software.ecmwf.int/wiki/display/WEBAPI/Access+ECMWF+Public+Datasets.
Regards, Paul Berrisford
What is the software I need to see the data?
Re: What is the software I need to see the data?
CFSR precipitation data
Re: CFSR precipitation data
Re: CFSR precipitation data
Re: CFSR precipitation data
Wich atmospheric reanalyses is better
Re: Wich atmospheric reanalyses is better
Re: Wich atmospheric reanalyses is better
ERA-interim's Precipiation and evaporation
Re: ERA-interim's Precipiation and evaporation
Re: ERA-interim's Precipiation and evaporation
Re: ERA-interim's Precipiation and evaporation
Time coordinate in MERRA2 accessed via OpenDAP
Hi all,
I've run across what is either a 2-day discrepancy or a misunderstanding on my part of the time coordinate in MERRA2 data accessed through OpenDAP. The description of the 'time' variable is 'days since 1-1-1 00:00:00'. However, the first element of the MERRA2 time vector, corresponding to 1980-1-1 00:00:00, is 722816 (days), whereas when I compute the difference between 1980-1-1 and 1-1-1 using Python's 'datetime', I get 722814 days. I've checked 'datetime' against test data in Appendix C of Dershowitz and Reinhold's book 'Calendrical Calculations', and it appears to be correct. Any ideas? I've appended a short iPython session demonstrating the issue below.
Thanks,
Scott Paine
Smithsonian Astrophysical Observatory
=================================
In [1]: import pydap.client
In [2]: import datetime
In [3]: dataset = pydap.client.open_url('http://goldsmr4.sci.gsfc.nasa.gov:80/dods/M2I1NXASM')
In [4]: var = dataset['time']
In [5]: var.attributes
Out[5]:
{'grads_dim': 't',
'grads_mapping': 'linear',
'grads_min': '00z01jan1980',
'grads_size': '324362',
'grads_step': '60mn',
'long_name': 'time',
'maximum': '01z01jan2017',
'minimum': '00z01jan1980',
'resolution': 0.0416666679084301,
'units': 'days since 1-1-1 00:00:0.0'}
In [6]: var[0]
Out[6]: array([ 722816.])
In [7]: d1 = datetime.date(1980, 1, 1)
In [8]: d0 = datetime.date(1, 1, 1)
In [9]: delta = d1 - d0
In [10]: delta.days
Out[10]: 722814
Re: Time coordinate in MERRA2 accessed via OpenDAP
Re: Time coordinate in MERRA2 accessed via OpenDAP
Many thanks for this explanation. Sounds like the robust starting point for computing time offsets is to read T[0] from the server, rather than compute it for 1980 Jan 01 on the client side using potentially incorrect calendar functions.
gird area for MERRA-2 and JRA-55
Re: gird area for MERRA-2 and JRA-55
Please look at JRA-55 data manual.
http://jra.kishou.go.jp/JRA-55/document/JRA-55_handbook_LL125_en.pdf
The data has surface data such as anl_surf125, anl_land125, fcst_surf125, fcst_land 125.
Re: gird area for MERRA-2 and JRA-55
Surface area of a MERRA-2 grid box can be computed using the geometry of a sphere, with radius of 6371.0E3 m. It is worthwhile to mention that the model does not work on an oblate spheroid.
Downloading members of 20CR
Re: Downloading members of 20CR
elevation data in ERA Interim
Re: elevation data in ERA Interim
era-i precipitation
Cloud water content in 20CRv2c
Re: Cloud water content in 20CRv2c
Re: Cloud water content in 20CRv2c
Re: Cloud water content in 20CRv2c
Re: Overview of current atmospheric reanalyses
Re: Overview of current atmospheric reanalyses
Re: Overview of current atmospheric reanalyses
For land surface modeling, it is useful to know temporal frequency of reanalysis output (hourly, 3-hourly, etc...). Some have this mentioned, others make it hard to figure out. Is there a simple list somewhere? In particular, for some of my applications, temporal frequency of surface meteorology trumps spatial resolution. Not sure if any hourly output exists. Thanks. -ankur, UW-Madison, desai@aos.wisc.edu
Re: Overview of current atmospheric reanalyses
Re: Overview of current atmospheric reanalyses
Ankur, For offline land modeling, the MERRA-2 data includes our lowest model level data. This is on the terrain following coordinate, so the height AGL is variable, roughly 60m (but also provided). The advantages of lowest model level to land modeling are 1) this level includes a portion of the analysis increment, so the observations that help constrain the prognostic state fields and 2) it does not include the surface layer similarity parameterization interpolation that we use to determine fluxes for our land model. This should allow your land model and surface layer to develop independently of what the reanalyses systems do. In this way you can also compare back to the 2m meteorology and even diffusion coefficients that MERRA-2 provides.
MERRA-2 and CFSR provide 1 hourly frequency data, and CFSR does have higher spatial resolutions.
Re: Overview of current atmospheric reanalyses
Re: Information about SWGDN & SWGNT
Re: Information about SWGDN & SWGNT
In MERRA, there are two fundamental components of the radiation in shortwave. Downward, the solar radiation that reaches the surface through clouds and other atmospheric attenuation. Upward, the reflected radiation which depends on the downward radiation, and the albedo. The net shortwave is the downward minus the upward. It is described as net downward to provide the direction (or sign) of the data. For more information on MERRA data, you can refer to the File Specification document, and the budget appendix.
http://gmao.gsfc.nasa.gov/research/merra/file_specifications.php
Shapefiles for NARR data?
NCEP/NCAR Reanalysis- potential evaporation rate (W/m2)
Hi all,
I am currently looking for gridded datasets of evapotranspiration (daily, ideally, at national/global scale). I have derived daily potential evapotranspiration estimates for the U.S. using the Hargreaves-Samani equation and the NCEP/NCAR Reanalysis dataset. However, I would like to compare these estimates to other datasets/derivations if possible. I know that Penman-Monteith is another standard method for calculating, but I have not attempted to calculate using P-M yet because of the greater number of inputs needed.
I am interested in learning more about the potential evaporation rate (W/m2) in the NCEP/NCAR Reanalysis dataset (Kalnay et al., 1996) - Does anyone have familiarity with this particular variable and/or know how it was derived? I know that actual evapotranspiration, potential evaporation, and potential evapotranspiration are often distinguised in the literature, so I also wanted to confirm what the potential evaporation rate in this dataset represents.
I also recently asked ESRL PSD about the potential evaporation and transpiration variables in the NOAA-CIRES 20CR dataset, and Gil Compo was very helpful in providing those references (Mahrt and Ek, 1984 - eq 9; Chen et al., 1996 - eqn 11).
If there are other national (or global) gridded datasets of daily evapotranspiration that you know of (eventually I will need to have PET in mm/day), please let me know.
Thank you in advance!
Best regards,
Meridith Fry (EPA/OPP)
Re: How CAPE was computed in ERA Interim please
Re: How CAPE was computed in ERA Interim please
tropical africa
Reanalysis precipitation data
Re: Reanalysis precipitation data
analysis and forecast datasets in reanalyses products
Reanalysis Surface Pressure Data - Is this spatially averaged?
Re: Reanalysis Surface Pressure Data - Is this spatially ...
Re: HELP PLEASE!!!! Overview of current atmospheric reanalyses
Re: HELP PLEASE!!!! Overview of current atmospheric reanalyses
Sea Ice Cover and Sea Ice extent
SLP drops in 20CR not present in ERA-interim reanalysis
Re: SLP drops in 20CR not present in ERA-interim reanalysis
Re: SLP drops in 20CR not present in ERA-interim reanalysis
Question on precipitation data in ERA-20C
Re: Question on precipitation data in ERA-20C
Re: Question on precipitation data in ERA-20C
Re: Question on precipitation data in ERA-20C
Re: Question on precipitation data in ERA-20C
ERA-Interim surface pressures
Re: ERA-Interim surface pressures
ERA-Interim surface moisture variables
Re: ERA-Interim surface moisture variables
Re: ERA-Interim surface moisture variables
Re: ERA-Interim surface moisture variables
Re: ERA-Interim surface moisture variables
Re: ERA-Interim surface moisture variables
Re: Overview of current atmospheric reanalyses
Re: Overview of current atmospheric reanalyses
The modeling and assimilation in the GEOS5 system that performs the MERRA reanalysis runs at 72 terrain following (eta) coordinates. These are not typically what most meteorological studies would favor, so the models eta coordinate is interpolated to 42 pressure levels. For certain variables it is beneficial to provide data in both coordinates. Please see the MERRA File Specification Document for details: http://gmao.gsfc.nasa.gov/research/merra/file_specifications.php
NCEP FNL (Final) Operational Global Analysis data
Re: NCEP FNL (Final) Operational Global Analysis data
Re: Overview of current atmospheric reanalyses
Re: Overview of current atmospheric reanalyses
whither ERA-15 in this section?
I know ERA-15 is kind of 'OBE' (overtaken by events) relative to ERA-40 (I worked on both reanalyses)...However...
ERA-15 would be useful in assessing observing system variability and the benefits of observation data set 'cleansing'/improvement that were brought about from the initial comparisons of older obs to modern models in the pioneering reanalyses (NCEP/NCAR I NCEP/DOE II and ERA-15).
from an old reanalysis guy...
best /R Mike Fiorino
Re: Overview of current atmospheric reanalyses
And the DAO reanalysis too! (Sorry, I think that was lost some time ago) ERA-15 is still available at the ECMWF data server (http://apps.ecmwf.int/datasets/data/era15/) and is also described at the Climate Data Guide (https://climatedataguide.ucar.edu/climate-data/era-15). As you say, it is probably best for marking the progression of reanalyses, but there are known issues that require new investigations to update to more recent data.
Re: Overview of current atmospheric reanalyses
Tropical Moored Buoy System data assimilated in Reanalyses?
reanalysis in the Tropics
Re: reanalysis in the Tropics
Re: reanalysis in the Tropics
Field campaign incorporation
Turbulent fluxes averaged over the Arctic region
Re: Turbulent fluxes averaged over the Arctic region
Reanalysis data vertical heights?
Re: Reanalysis data vertical heights?
Net Heat Flux at air-sea interface?
Re: Net Heat Flux at air-sea interface?
Rainfall analysis
Re: Rainfall analysis
Re: Rainfall analysis
I can't say anything specific, since your comment has very little detail about where and when you are working. One thing that will be universal when considering reanalysis precipitation is that the quantity is derived from the background model forecast of the assimilation process. So, while the environment that produces precipitation is affected by the observational analysis, precipitation relies on the model physics, and has significant uncertainty. If observations of precipitation are available, you'll want to use those, at the least, in a comparison with the reanalysis data. Some reanalyses produce 1 hourly data, and some assimilate rainfall (see the North American Regional Reanalysis). Biases and uncertainty in reanalysis precipitation depends greatly on where and when you are looking. The papers listed in the pages Gil provided are a start, but be sure to follow those that they cite as well.
Vertical velocities in the ERA Interim model levels data
Re: Vertical velocities in the ERA Interim model levels data
Land/Sea Mask
Re: Land/Sea Mask
Re: Overview of current atmospheric reanalyses
Elevation of 20CR-grid points
Re: Elevation of 20CR-grid points
Re: Elevation of 20CR-grid points
Accuracy of NCEP reanalysis in summer and winter
Re: Accuracy of NCEP reanalysis in summer and winter
Re: Overview of current atmospheric reanalyses
Re: Overview of current atmospheric reanalyses
Re: Overview of current atmospheric reanalyses
Re: Overview of current atmospheric reanalyses
Re: Overview of current atmospheric reanalyses
Re: Overview of current atmospheric reanalyses
Re: Overview of current atmospheric reanalyses
MERRA and CFSR have 1 hourly surface temperature and pressure. Both, I believe assimilate surface pressure, but not surface temperature. In MERRA sea surface temperature is prescribed, and in CFSR sst is analyzed. Both have horizontal resolution better than 100km.
Re: Overview of current atmospheric reanalyses
NARR "transports" vs. "fluxes"
Re: NARR "transports" vs. "fluxes"
Re: Overview of current atmospheric reanalyses
Re: Overview of current atmospheric reanalyses
Land veg mask
Re: Land veg mask
Cathy
Re: Land veg mask
Re: Overview of current atmospheric reanalyses
Re: Overview of current atmospheric reanalyses
Re: High resolution reanalyses data for watershed modeling
Re: High resolution reanalyses data for watershed modeling
Re: Overview of current atmospheric reanalyses
Determining which observations are assimilated into reanalyes
Re: Determining which observations are assimilated into ...
I just took a quick look at the MERRA assimilated observations in Jan2000. I can see data near Summit in surface pressure, but not near Humbolt, GITS, Tunu-N or Petermann. I'm not so familiar with the stations data availability. If they were in the GTS, then they would be input. Note, that MERRA assimilates surface pressure from surface meteorology stations, not temperature, moisture or wind.
To look at this, I used MERRA's Gridded Innovations and Observations data, on openDAP at: http://opendap.nccs.nasa.gov/dods/MerraObs
It can also be downloaded from GES DISC: http://disc.sci.gsfc.nasa.gov/mdisc/data-holdings/merra-innov
These are not yet well documented.
Re: Determining which observations are assimilated into ...
Re: Determining which observations are assimilated into ...
Re: Determining which observations are assimilated into ...
For the 20th Century Reanalysis (20CR, http://reanalyses.org/atmosphere/overview-current-reanalyses#TWENT ), you can obtain all of the observations used from the International Surface Pressure Databank version 2 ( http://reanalyses.org/observations/international-surface-pressure-datab… ) for the entire global domain and time period (1871-2010) or for a subset period or region using the tools
courtesy of the Data Support Section of the Computational and Information Systems Laboratory at the National Center for Atmospheric Research from http://rda.ucar.edu/datasets/ds132.0/.
Maps of the stations available to the 20CR can be viewed at http://www.esrl.noaa.gov/psd/data/ISPD/v2.0/.
A text file with the stations available is at http://reanalyses.org/sites/default/files/groups/users/gilbert.p.compo/… .
See the ISPD home page http://reanalyses.org/observations/international-surface-pressure-datab… for more information.
Please let me know if I can of more help.
best wishes,
gil compo
Re: Determining which observations are assimilated into ...
Questions regarding ERA-Interim
Re: Questions regarding ERA-Interim
Re: Questions regarding ERA-Interim
Re: Questions regarding ERA-Interim
Re: Overview of current atmospheric reanalyses
Re: Overview of current atmospheric reanalyses
I would say the Arctic System Reanalysis is precisely what you are looking for.
http://polarmet.osu.edu/ASR/asr_domains.pdf
Check the status of it at the links at the top of this page. -Mike
Which reanalysis most suitable for sat. product comparisons?
Hello,
I would like to compare globally gridded monthly means of cloud products derived from satellite observations (AVHRR,MODIS) with reanalysis products. I am tending towards using ERA-Interim but would be interested in the general opinion on this question. I would also be very interested in any recommendations which products to look at. I am aiming for cloud fractional coverage, cloud liquid and ice water path and possibly cloud top height. Any comments on this also wrt common traps and problems, parameters to investigate etc. are highly appreciated.
Thanks,
Matthias
Re: Which reanalysis most suitable for sat. product comparisons?
I can't say which you should compare, as it ultimately depends on your metrics and purpose. However, direct cloud data comparisons have been tricky, in my experience, since there are inherent differences in what is observed, and how the background models compute cloud quantities. Part of this is also the motivation for data simulators (e.g. ISSCP Simulator). I would encourage you to include radiation observations into the comparison, as those should be another variable where the feedback from clouds to the atmosphere manifests.
Re: Which reanalysis most suitable for sat. product comparisons?
Thanks for your answer. You are certainly raising an important point. We might include direct radiation comparisons but the current scope of the project will not allow us to do much. Therefore, we may have to stick with direct cloud data comparisons for the time being.
Thanks,
Matthias
ERA-Inerim
Re: ERA-Inerim
Re: Overview of current reanalyses
Re: Overview of current reanalyses
Re: Overview of current reanalyses
Hello all,
I am studying applicability of met parameter (P, T & Rh) derived from reanalysis products to GPS PWV estimation. I selected NCEP R1, NCEP FNL and ERA-Interim for a decade and use the values (P, T & Rh) derived from them for inter-comparison. R1 and ERA-Interim are reanalysis products whereas FNL is operational GDAS analysis. Is it feasible to use them for inter-comparison? Any help will be highly appreciated.
Prakash
CFSV2
Hello all,
I have a question regarding the CFSV2. UCAR website states that it is a continuation of CFSR. But is it a reanalysis like CFSR or is it an analysis? Can we consider that the quality of CFSV2 data are the same than CFSR?
Thanks,
G.L
Monthly uncertainy estimates of 20th CR
Re: Monthly uncertainy estimates of 20th CR
related to the use of two different data set
Re: related to the use of two different data set
Re: related to the use of two different data set
Re: related to the use of two different data set
Re: Overview of current reanalyses
Solar irradiances in 20th Century reanalysis
Re: Solar irradiances in 20th Century reanalysis
Re: Solar irradiances in 20th Century reanalysis
Thank you very much for the quick reply.
The NCEP model is forced by spectral irradiances but, if i understood correctly, the solar cycle variation refers to total solar irradiance only. This means that every spectral band of the radiation code increases equally (~0.1%) from the minimum to the maximum phase of the 11-yr solar cycle.
The last question was referring to stratospheric ozone. There is a prognostic equation for ozone, right? So, i guess there is a weak ozone variation in the course of the 11-yr solar cycle.
Stergios Misios
Which is best for wind related issues
Re: Which is best for wind related issues
It depends on your needs, and which winds you are referring to. Ocean/SHem will have diffs in N/N when satellite data becomes available. 20CR is an ensemble of surface pressure only assimilation, no wind assimilation. the pressure gradients where obs are available should help the low level winds, but there is no upper level wind assimilation.
bottom line is that the 20CR is so new, you will likely have to determine this for your self, and we hope that you can share that information back here, as others may have similar questions. Be sure to search for new papers coming out and conference papers, 20CR is getting a lot of attention!
MB
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Gil's Example
Here's some text.
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asr-references
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Kohnemann, S. H. E., G. Heinemann, D. H. Bromwich, and O. Gutjahr, 2017: Extreme warming in the Kara Sea and Barents Sea during the winter period 2000-2016. J. Clim., 30, 8913-8927, doi: 10.1175/JCLI-D-16-0693.
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Kolstad, E. W., 2017: Higher ocean wind speeds during marine cold air outbreaks. Q. J. R. Meteorol. Soc., 143, 2084-2092, doi: 10.1002/qj.23068.
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Smirnova, J., and P. Golubkin, 2017: Comparing polar lows in atmospheric reanalyses: Arctic System Reanalysis versus ERA-Interim. Mon. Wea. Rev., 145, 2375-2383, doi: 10.1175/MWR-D-16-0333.1.
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Bromwich, D. H., A. B. Wilson, L. Bai, G. W. K. Moore, and P. Bauer, 2016: A comparison of the regional Arctic System Reanalysis and the global ERA-Interim Reanalysis for the Arctic. Q. J. R. Meteorol. Soc., 142, 644-658, doi: 10.1002/qj.2527.
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Moore, G. W. K., D. H. Bromwich, A. B. Wilson, I. Renfrew, and L. Bai, 2016: Arctic System Reanalysis improvements in topographically-forced winds near Greenland. Q. J. R. Meteorol. Soc., 142, 2033-2045, doi: 10.1002/qj.2798.
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Moore, G. W. K., I. A. Renfrew, B. E. Harden, and S. H. Nernild, 2015: The impact of resolution on the representation of southeast Greenland barrier winds and katabatic flows. Geophys. Res. Letts., 42, doi: 10.1002/2015GL063550.
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Rampal, P., S. Bouillon, E. Ólason, and M. Morlighem, 2015: neXtSIM: a new Lagrangian sea ice model, The Cryosphere Discuss., 9, 5885-5941, doi:10.5194/tcd-9-5885-2015.
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Tuononen, M., V. A. Sinclair, and T. Vihma, 2015: A climatology of low-level jets in the mid-latituesand polar regions of the Northern Hemisphere. Atmos. Sci. Let., 16, 492-499, doi: 10.1002/asl.587.
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Moore, G. W. K., 2014: A new look at Southeast Greenland barrier winds and katabatic flow. US CLIVAR Variations Newsletter, 12, No. 2, 13-19.
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Tilinina, N., S. K. Gulev, and D. H. Bromwich, 2014: New view of Arctic cyclone activity from the Arctic System Reanalysis. Geophys. Res. Letts., 41, doi: 10.1002/2013gl058924.
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Moore, G. W. K., 2013: The Novaya Zemlya Bora and its impact on Barents Sea air-sea interaction. Geophys. Res. Letts., 40, 3462-3467, doi: 10.1002/grl.50641.
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Wilson, A. B., D. H. Bromwich, K. M. Hines, 2012: Evaluation of Polar WRF forecasts on the Arctic System Reanalysis domain. 2. Atmospheric hydrologic cycle. J. Geophys. Res., 117, D04107, doi: 10.1029/2011JD016765.
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Wilson, A. B., D. H. Bromwich, K. M. Hines, 2011: Evaluation of Polar WRF forecasts on the Arctic System Reanalysis domain: Surface and upper air analysis. J. Geophys. Res., 116, D11112, doi: 10.1029/2010JD015013.
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Hines, K. M., D. H. Bromwich, L.-S. Bai, M. Barlage, and A. G. Slater, 2011: Development and testing of Polar WRF. Part III. Arctic land. J. Climate, 24, 26-48, doi: 10.1175/2010JCLI3460.1.
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Bromwich, D., Y.-H. Kuo, M. Serreze, J. Walsh, L.S. Bai, M. Barlage, K. Hines, and A, Slater, 2010: Arctic System Reanalysis: Call for community involvement. EOS Trans. AGU, 91, 13-14.
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Bromwich, D. H., K. M. Hines, and L.-S. Bai, 2009: Development and testing of Polar WRF: 2. Arctic Ocean. J. Geophys. Res., 114, D08122, doi:10.1029/2008JD010300.
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Hines, K. M., and D. H. Bromwich, 2008: Development and Testing of Polar WRF. Part I. Greenland Ice Sheet Meteorology. Mon. Wea. Rev., 136, 1971-1989, doi: 10.1175/2007MWR2112.1.
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Pregenerated Images and Reanalysis Comparisons
MERRA: comparisons with observations: NVAP, GPCP
JRA-25 Atlas: climatologies (annual, seasonal) of most variables. Long timeseries of some
IRI Maproom: recent climate on weekly, monthly and seasonal timescale including animations (uses NCEP-NCAR Reanalysis).
ESRL/PSD maproom: recent climate on weekly, monthly, and seasonal timescale including animations (uses NCEP-NCAR Reanalysis).
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analysis software
List of Plotting/Analysis Software useful for Reanalysis Datasets
- CDAT: Climate Data Analysis Tools (CDAT) is a software system designed to provide access to and management of gridded climate data. It uses the Python scripting language which provides a general purpose and full-featured scripting language with a variety of user interfaces including command-line interaction, stand-alone scripts (applications) and graphical user interfaces (GUI). The modular CDAT subsystems provide access to the data, to large-array numerical operations (via Numerical Python), and visualization.
- Ferret*: Ferret is an interactive computer visualization and analysis environment designed to meet the needs of oceanographers and meteorologists analyzing large and complex gridded data sets. It runs on most Unix systems, and on Windows XP/NT/9x using X windows for display.
- GrADS*: The Grid Analysis and Display System (GrADS) is an interactive desktop tool that is used for easy access, manipulation, and visualization of earth science data. GrADS has two data models for handling gridded and station data. GrADS supports many data file formats, including binary (stream or sequential), GRIB (version 1 and 2), NetCDF, HDF (version 4 and 5), and BUFR (for station data). GrADS has been implemented worldwide on a variety of commonly used operating systems and is freely distributed over the Internet.
- IDL*: IDL is a commercial cross platform data analysis/mapping/plotting code that is extremely customizable.
- IDV*: is a Java(TM)-based software framework for analyzing and visualizing geoscience data. The IDV brings together the ability to display and work with satellite imagery, gridded data, surface observations, balloon soundings, NWS WSR-88D Level II and Level III radar data, and NOAA National Profiler Network data, all within a unified interface.
- Matlab: MATLAB is a high-level language and interactive environment w/extensive plotting and numerical processing available.
- ncBrowse: ncBrowse is a Java application that provides flexible, interactive graphical displays of data and attributes from a wide range of netCDF data file conventions.
- NCL*: Is an interpreted language designed specifically for data analysis and visualization.
- Panoply: Panoply is a cross-platform application which plots geo-gridded arrays from netCDF, HDF and GRIB datasets. You can plot data as well as do some simple data analysis. Outputs are images, postscript and kml
*'d are OPeNDAP enabled
Dataset Analysis Tools
- Excel: A desktop analysis tool. It can read netCDF files.
- CDO: CDO is a collection of command line Operators to manipulate and analyse Climate and forecast model Data.
Supported data formats are GRIB, netCDF, SERVICE, EXTRA and IEG. There are more than 400 operators available. - NCO: A (free) suite of programs that operate on netCDF files. Each operator is a standalone, command line program which is executed at the UNIX (or NT) shell-level like, e.g., ls or mkdir. The operators take netCDF or HDF4 files as input, then perform a set of operations (e.g., deriving new data, averaging, hyperslabbing, or metadata manipulation) and produce a netCDF file as output. The operators are primarily designed to aid manipulation and analysis of gridded scientific data. These tools are a powerful and easy way to perform simple manipulations on netCDF files without a major programming effort.
Read Files Remotely
- GDS: The GrADS Data Server (GDS, formerly known as GrADS-DODS Server) is a stable, secure data server that provides subsetting and analysis services across the internet.
- TDS: The THREDDS Data Server (TDS) is a web server that provides metadata and data access for scientific datasets, using OPeNDAP, OGC WMS and WCS, HTTP, and other remote data access protocols.
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In the News
Articles in the popular press about the use of any reanalysis data:
- Aug 24, 2010 US Dept of Energy: War, Pandemics, and Climate: the 1918 El Niño.
- May 14, 201 ABC News: Weather records probed in climate study.
- Feb 10, 2010 Climate Watch Magazine: Reconstructing Weather to Predict Climate
- February 4, 2010 Discovery News: The big El Niño that nobody saw.
- September 29, 2009 ASCR Newsletter: Reanalysis Project targets once-and-future weather.
- Spring 2009 ESRL Newsletter: Reconstructing History: Understanding past weather events to improve climate forecasts.
- April 1, 2009 AUSSMC: Weather Secrets of the past help predict the future.
- March 25, 2009 NCCS: Modeling the Weather's Extreme Mood Swings.
- September 2008 SciDAC Review: Bridging the Gap between Science and Weather.
time period query
why most of the reanalysis dataset starts from 1980/or1979?