Xue, Y., B. Huang, Z.-Z. Hu, A. Kumar, C. Wen, D. Behringer, and S. Nadiga, 2011: An assessment of oceanic variability in the NCEP Climate Forecast System Reanalysis. Clim. Dyn., 37 (11-12), 2511-2539, DOI: 10.1007/s00382-010-0954-4.

http://www.springerlink.com/content/x01q261216071647/

At the National Centers for Environmental Prediction (NCEP), a reanalysis of the atmosphere, ocean, sea ice and land over the period 1979-2009, referred to as the Climate Forecast System Reanalysis (CFSR), was recently completed. The oceanic component of CFSR includes many advances: (a) the MOM4 ocean model with an interactive sea-ice, (b) the 6 hour coupled model forecast as the first guess, (c) inclusion of the mean climatological river runoff, and (d) high spatial (0.5^o x 0.5^o) and temporal (hourly) model outputs. Since the CFSR will be used by many in initializing/validating ocean models and climate research, the primary motivation of the paper is to inform the user community about the saline features in the CFSR ocean component, and how the ocean reanalysis compares with in situ observations and previous reanalysis. The net ocean surface heat flux of the CFSR has smaller biases compared to the sum of the latent and sensible heat fluxes from the Objectively Analyzed air-sea Fluxes (OAFlux) and the shortwave and longwave radiation fluxes from the International Satellite Cloud Climatology Project (ISCCP-FD) than the NCEP/NCAR reanalysis (R1) and NCEP/DOE reanalysis (R2) in both the tropics and extratropics. The ocean surface wind stress of the CFSR has smaller biases and higher correlation with the ERA40 produced by the European Centre for Medium-Range Weather Forecasts than the R1 and R2, particularly in the tropical Indian and Pacific Ocean. The CFSR also has smaller errors compared to the QuickSCAT climatology for September 1999 to October 2009 than the R1 and R2. However, the trade winds of the CFSR in the central equatorial Pacific are too strong prior to 1999, and become close to observations once the ATOVS radiance data are assimilated in late 1998. A sudden reduction of easterly wind bias is related to the sudden onset of a warm bias in the eastern equatorial Pacific temperature around 1998/99. The sea surface height and top 300 meter heat content (HC300) of the CFSR compare with observations better than the GODAS in the tropical Indian Ocean and extratropics, but much worse in the tropical Atlantic, probably due to discontinuity in the deep ocean temperature and salinity caused by the six data streams of the CFSR. In terms of climate variability, the CFSR provides a good simulation of Tropical Instability Waves (TIW) and oceanic Kelvin waves in the tropical Pacific, and the dominant modes of HC300 that are associated with El Nino and Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation and Atlantic Meridional Overturning Circulation.

Kumar, A., and Z.-Z. Hu, 2012: Uncertainty in the ocean-atmosphere feedbacks associated with ENSO in the reanalysis products. Clim. Dyn., 39 (3-4), 575-588. DOI: 10.1007/s00382-011-1104-3.



http://www.springerlink.com/content/w66j8vh1n826n711/



 

The evolution of El Niño-Southern Oscillation (ENSO) variability can be characterized by various ocean-atmosphere feedbacks, for example, the influence of ENSO related sea surface temperature (SST) variability on the low-level wind and surface heat fluxes in the equatorial tropical Pacific, which in turn affects the evolution of the SST.  An analysis of these feedbacks requires physically consistent observational data sets.  Availability of various reanalysis data sets produced during the last 15 years provides such an opportunity. A consolidated estimate of ocean surface fluxes based on multiple reanalyses also helps understand biases in ENSO predictions and simulations from climate models. 

In this paper, the intensity and the spatial structure of ocean-atmosphere feedback terms (precipitation, surface wind stress, and ocean surface heat flux) associated with ENSO are evaluated for six different reanalysis products.  The analysis provides an estimate for the feedback terms that could be used for model validation studies.  The analysis includes the robustness of the estimate across different reanalyses. Results show that one of the “coupled” reanalysis among the six investigated is closer to the ensemble mean of the results, suggesting that the coupled data assimilation may have the potential to better capture the overall atmosphere-ocean feedback processes associated with ENSO than the uncoupled ones.

From: Vose, R. S., S. Applequist, M. J. Menne, C. N. Williams Jr., and P. Thorne. 2012: An intercomparison of temperature trends in the U.S. Historical Climatology Network and recent atmospheric reanalyses. Geophys. Res. Lett., 39, L10703, doi:10.1029/2012GL051387.

Least-squares trends (°C dec^-1) in mean annual temperature over the conterminous United States during the period 1979-2008.

Atmospheric Reanalyses – Recent Progress and Prospects for the Future.

A Report from a Technical Workshop, April 2010

Michele M. Rienecker, Dick Dee, Jack Woollen, Gilbert P. Compo, Kazutoshi Onogi, Ron Gelaro, Michael G. Bosilovich, Arlindo da Silva, Steven Pawson, Siegfried Schubert, Max Suarez, Dale Barker, Hirotaka Kamahori, Robert Kistler, and Suranjana Saha

Abstract

In April 2010, developers representing each of the major reanalysis centers met at Goddard Space Flight Center to discuss technical issues – system advances and lessons learned – associated with recent and ongoing atmospheric reanalyses and plans for the future. The meeting included overviews of each center’s development efforts, a discussion of the issues in observations, models and data assimilation, and, finally, identification of priorities for future directions and potential areas of collaboration. This report summarizes the deliberations and recommendations from the meeting as well as some advances since the workshop.

Summary of Recommendations

From Rienecker et al. (2012)

Target areas for improvements for the next generation of atmospheric reanalyses include:

• The hydrological cycle

• The quality of the reanalyses in the stratosphere

• The quality of the reanalyses over the polar regions

• Representation of surface fluxes

• Observational bias corrections and/or cross-calibration across platforms

• Estimates of uncertainty in the analyses, and

• Reductions of spurious trends and jumps associated with the changing observing system.

Several recommendations were made regarding areas for coordination between reanalysis centers in order to prepare for the next reanalyses:

• Preparation and sharing of lists of anomalous behavior or features to help identify how common anomalies are across the various reanalyses.

• Examination of data utilization, including QC decisions, innovation statistics, bias corrections, outcomes of data selection algorithms, cloud detection outcomes, etc.

• Identification of joint experiments to be conducted to elucidate issues found to be in common in different reanalyses.

• Sharing of results from jointly designed sensitivity experiments.

• Coordination of input observations and ancillary data and centralization of the serving of these observations where possible.

• Expansion of ACRE’s efforts for contributing surface observations to 20CR to contributing to all future reanalysis efforts, possibly acting as a data coordinator and provider of surface data for all future reanalyses in collaboration with working groups of GCOS and WCRP.

• Development of innovative diagnostics and metrics to help quantify observational issues, the quality and also agreement of the reanalyses.

The workshop recommended that a mechanism be established for the timely exchange of information about the quality of the reanalyses, results of experiments, and plans for future developments. This idea was quickly embraced with the establishment of Reanalysis.org. However, further progress is needed in the utilization of such a capability to enhance communications between reanalysis groups.

Finally, consistent with the Arkin et al. (2003) workshop report, the workshop participants recommended extending the reanalysis record for as long as possible, to include the 1970s for reanalyses focused on the satellite era, and to go back at least to 1850 with those reanalyses using sparse observations.

Arkin, P., E. Kalnay, J. Laver, S. Schubert, and K. Trenberth, 2003: Ongoing analysis of the climate system: A workshop report. NASA, NOAA, and NSF, 48 pp.  Link to Full Report.

Many white papers, published papers, and other reports have been issued relating to reanalyses, their current status, and recommendations for improvements.  Please include such reports below in reverse chronological order, with links to a Discussion reanalyses.org page which summarizes the report and a link to the Full Report itself. 

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. Link to Paper. 

Compo, G., J. Carton, X. Dong, A. Kumar, S. Saha, J. S. Woollen, L. Yu, and H. M. Archambault, 2016: Report from the NOAA Climate Reanalysis Task Force Technical Workshop. NOAA Technical Report OAR CPO-4, Silver Spring, MD. doi: 10.7289/V53J39ZZ. Full Report.

Bosilovich, M.G, J.-N.Thépaut, O. Kazutoshi, A. Kumar, D. Dee and Otis Brown, 2015: WCRP Task Team for Intercomparison of ReAnalyses (TIRA). White Paper. Full Report.

CORE CLIMAX, 2014: Procedure for comparing reanalyses, and comparing reanalyses to assimilated observations and CDRs. COordinating Earth observation data validation for RE-analysis for CLIMAte ServiceS, Grant agreement No: 313085, Deliverable D5.53, pp 45. Full report

Bosilovich, M. G., J. Kennedy, D. Dee, R. Allan and A. O’Neill, 2013: On the Reprocessing and Reanalysis of Observations for Climate, Climate Science for Serving Society: Research, Modelling and Prediction Priorities. G. R. Asrar and J. W. Hurrell, Eds. Springer, in press. Full Report. Discussion Page. 

Bosilovich, M.G., M. Rixen, P. van Oevelen, G. Asrar, G. Compo, K. Onogi, A. Simmons, K. Trenberth, D. Behringer, T. H. Bhuiyan, S. Capps, A. Chaudhuri, J. Chen, L. Chen, N. Colasacco-Thumm, M. G. Escobar, C.R. Ferguson, T. Ishibashi, M.L.R. Liberato, J. Meng, A. Molod, P. Poli, J. Roundy, K. Willett, J. Woollen,R. Yang, 2012: Report of the 4th World Climate Research Programme International Conference on Reanalyses, Silver Spring, Maryland, USA, 7-11 May 2012. WCRP Report 12/2012. Full Report. Discussion Page.

Rienecker, M.M., D. Dee, J. Woollen, G.P. Compo, K. Onogi, R. Gelaro, M.G. Bosilovich, A. da Silva, S. Pawson, S. Schubert, M. Suarez, D. Barker, H. Kamahori, R. Kistler, and S. Saha, 2012: Atmospheric Reanalyses—Recent Progress and Prospects for the Future. A Report from a Technical Workshop, April 2010. NASA Technical Report Series on Global Modeling and Data Assimilation, NASA TM–2012-104606, Vol. 29, 56 pp. Full Report. Discussion Page.

Climate Change Science Program, 2008: Weather and Climate Extremes in a Changing Climate. Regions of Focus: North America, Hawaii, Caribbean, and U.S. Pacific Islands. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. [Thomas R. Karl, Gerald A. Meehl, Christopher D. Miller, Susan J. Hassol, Anne M. Waple, and William L. Murray (eds.)]. Department of Commerce, NOAA's National Climatic Data Center, Washington, D.C., USA, 164 pp. Full Report.

Climate Change Science Program, 2008: Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Randall Dole, Martin Hoerling, and Siegfried Schubert (eds.)]. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, 156 pp. Full Report.

Lermusiaux, P.F.J.,  P. Malanotte-Rizzoli, D. Stammer, J. Carton, J. Cummings, and A.M. Moore, 2006: Progress and Prospects in U.S. Data Assimilation in Ocean Research. Oceanography, 19, 172-183. Full Report.

Arkin, P., E. Kalnay, J. Laver, S. Schubert, and K. Trenberth, 2003: Ongoing analysis of the climate system: A workshop report. NASA, NOAA, and NSF, 48 pp.  Full Report.

Chelliah, M., W. Ebisuzaki, S. Weaver, and A. Kumar, 2011: Evaluating the tropospheric variability in National Centers for Environmental Prediction's climate forecast system reanalysis, J. Geophys. Res., 116, D17107, doi:10.1029/2011JD015707.

The National Centers for Environmental Prediction (NCEP) recently completed the latest, and partially coupled, atmosphere/ocean/sea-ice model based climate forecast system reanalysis (CFSR) for the 1979-current satellite era. In the reanalysis, the observed CO2 concentration, and the volcanic aerosols were also prescribed. This paper provides an initial overview of the tropospheric variability in the CFSR by comparing it against available previous reanalyses. CFSR data from 1979-current was generated in six independent and parallel streams covering different periods with a one year overlap between streams. CFSR’s monthly mean zonal and meridional component of wind, U and V, temperature T and geopotential height H at pressure levels up to 100 mb are compared against those of three other readily available reanalyses NCEP/R1, NCEP/R2 and ERA40 for the period from 1979 to 2008 (2002 for ERA40) and also against modern reanalyses such as JRA, MERRA and C20.

Correlation time series of monthly mean CFSR’s H, T, U and V, averaged over the globe at pressure levels up to 100 mb as compared to the R1, R2 and ERA40, show values as high as 0.95 - 0.99 throughout the period from January 1979 till December 2008 and display a gradual increase with time. This demonstrates that the CFSR tropospheric analyses are closer to the other three analyses in the later years than in the previous years. The impact of the assimilation of ATOVS satellite radiances from October 1998 onwards in CFSR is the most likely cause for the agreement with other reanalyses. This is also reflected in the generally higher root mean square (rms) difference values of CFSR with respect to the other reanalyses in the earlier periods which slowly decreases to lower rms values in the later periods at all pressure levels and for all variables considered. Even though the high correlations of CFSR tropospheric variables with R1/R2/ERA40 reanalyses do not show any significant impact of the CFSR data generation in multiple streams, examination of rms maps do show some impact.

While the agreement among all the other reanalyses is relatively better in the later years, during the early period the CFSR 850 mb temperature is colder and hence the 200mb heights are lower than the other reanalyses, particularly before 1998, thus implying a greater trend in CFSR in these and other variables at equatorial latitudes. However, the linear trends of CFSR 850 annual mean T, over the common 1979-2001 period, over the entire globe resemble the ERA40 trends than those of R1 and R2, while the latter two agree better with each other. Over this period, significant differences exist in the tropical east Pacific region, with R1 and R2 showing a cooling trend, whereas CFSR and ERA40 depict a warming trend there and over most regions of the globe as well. At 200 mb, both ERA40, and CFSR show a broad warming trend in the tropics and subtropics over the past 30 years, whereas R1 and R2 show a cooling trend over much of the globe. An examination of time series of some climate indices, such as the various Nino wind indices in the tropical Pacific at 850 and 200 mb suggests that the other reanalysis data sets tend to agree more closely with each other than with CFSR, thus clearly making it an outlier until the late 1990’s. Similar bias in the CFSR temperature field in the near equatorial latitudes is also noted as compared to the other three reanalyses, with CFSR being colder. Consistently, CFSR’s 200 mb heights at the equatorial latitudes are also lower than the other three reanalyses. mb heights at the equatorial latitudes are also lower than the other three reanalyses.

Despite the JRA and MERRA reanalysis monthly mean data sets becoming available toward the completion of this project they were employed to examine the vertical shear (200-850 mb) of the zonal wind from the CFSR over the main hurricane development region (10N-20N, 60W-20W) in the tropical North Atlantic during the peak hurricane season (Aug-Sep-Oct). The CFSR wind shear in the Atlantic MDR is not only lower than R1, R2 and ERA40, but also than that of JRA and MERRA particularly before 1998, thus making CFSR an outlier as compared to all other reanalyses. Interestingly the shear from all five other reanalyses agreed more with each other.

We also found that based on MSLP differences between the tropical Indian and east Pacific tropical oceans, the strength of the Walker circulation in CFSR did not show any noticeable trend. However, consistent with a few previous modeling and observational studies from other reanalysis data sets of a strengthening Walker circulation in a globally warming climate, R1 and R2 reanalyses data did exhibit a minor strengthening and low level increased easterlies from 1979 to 2008. Finally, our lag-correlation analysis and results indicate that the CFSR model and analyses exhibit a better coupling and internal consistency between the model precipitation and low level wind field in its eastward propagation time scales associated with the MJO, thus making any dynamical prediction of MJO events likely to be improved when compared to the NCEP/R1 based reanalysis and model.

In summary, at any given time (analysis hour, daily or month), for the globe as a whole, CFSR analysis agrees reasonably well with the other reanalyses. The CFSR’s new coupled model and assimilation system makes use of the recent advances in these areas, and hence is possibly an improvement to NCEP’s previous reanalyses R1 and R2 which are fifteen and ten years old respectively. For these long-term climate variability measures the analysis indicates that the CFSR was generally the outlier, with much stronger easterly trades, cooler tropospheric temperatures and lower geopotential heights during much of the earlier part of the analysis period (1979 to ~1998). Consequently, real-time monitoring of many of the ENSO related climate wind indices in the equatorial Pacific or the wind shear index in the tropical North Atlantic from CFSR may be problematic in the context of historical variability.

Some Figures from the paper:Figure 7.  (left) Monthly correlations and (right) root-mean-square differences over 90°N–90°S domain. (a) Correlation of CFSR zonal component of wind U with NCEP/R1 zonal component of wind U at all pressure levels up to 100 mb from January 1979 through December 2008. (b) As in Figure 7a except with NCEP/R2. (c) As in Figure 7a except with ERA40. (d) As in Figure 7a except for root-mean-square difference in m/s between CFSR and NCEP/R1. (e) As in Figure 7d except with NCEP/R2. (f) As in Figure 7d except with ERA40.

Figure 12.  (a) Area-averaged 850 mb zonal wind (M/s) in the tropical west Pacific region (5°N–5°S, E-E) for CFSR, NCEP/R1, NCEP/R2 ERA40, JRA, MERRA, and C20. (b) As in Figure 12a except for central Pacific region (5°N–5°S, E-E). (c) As in Figure 12a except for east Pacific region (5°N–5°S, W-W). (d) Area-averaged 200 mb zonal wind (M/s) in the central Pacific region (5°N–5°S, E-W).

Figure 14. (a) August-September-October (ASO) peak season mean vertical zonal wind (U) shear (U200–U850) averaged over the main hurricane development region (10°N–20°N, 60°W–20°W) in the tropical North Atlantic for CFSR, ERA40, JRA, MERRA, NCEP/R1, and NCEP/R2. (b) As in Figure 14a except for the July-August-September (JAS) peak season in the eastern tropical North Pacific (10°N–20°N, 140°W–105°W).

A recent publication on different Arctic cloud climatologies intercomparison shows a wide spread among observations and reanalyses. Reanalyses generally are not in a close agreement with satellite and surface observations of cloudiness in the Arctic. Several reanalyses show the highest values of total cloud fraction over the central part of the Arctic Ocean but not over the Norwegian Sea and the Barents Sea as observations do. The maximum and minimum of total cloud fraction in the annual cycle are shifted by 1-2 months compared to observations.

December-January-February and June-July-August mean of TCF over the Arctic (north of 60◦N) from different data.

The annual cycle of TCF.

Normalized pattern statistics showing differences among different observational and reanalyses TCF spatial distribution (Taylor diagrams).

Alexander Chernokulsky and Igor I. Mokhov, “Climatology of Total Cloudiness in the Arctic: An Intercomparison of Observations and Reanalyses,” Advances in Meteorology, vol. 2012, Article ID 542093, 15 pages, 2012. doi:10.1155/2012/542093

A set of web-based reanalysis intercomparison tools (WRIT) is available from the NOAA Physical Sciences Laboratory and University of Colorado CIRES.

WRIT Maps WRIT Time-series WRIT Correlations WRIT Trajectories WRIT Distributions

The "WRIT" Maps tool allows users to examine 20CR, ERA-Interim, ERA-20C, JRA-55, MERRA, MERRA-2, NCEP R1, NCEP R2, and NCEP CFSR reanalyses datasets. Pressure level data are available for most reanalyses, as well as 5 single level variables including sea level pressure, 2 m air temperature, 10 m winds, precipitation. Maps and pressure-level by longitude and pressure-level by latitude can be generated for monthly means, anomalies, and climatologies. Observational datasets have been made available that can be compared to 2m air temperature and precipitation. These quantities can be differenced between datasets.

Future enhancements include different time scales.

is also available from WRIT. It allows users to examine 20CR, ERA-Interim, ERA-20C, JRA-55, MERRA, MERRA-2, NCEP R1,  NCEP R2, and NCEP-CFSR as well as some observational dataset. Users can compare timeseries from different datasets, regions, variables and levels. Distributions, scatter plots, and auto-correlation are also available. Statistics for each timeseries including means, standaed deviations, slope and correlations are provided.

The "WRIT" Trajectory Tool is a new tool available from WRIT. It allows users to plot forward and backward trajectories from different reanalyses (currently NCEP R1, NCEP R2, and 20CR with more planned). Users can plot  the trajectories of one or more levels on a single plot. The output is plotted and is available as netCDF and as KMZ files suitable for Google Earth.

Other analysis products are planned.

Feedback and suggestions are welcome. Please give comments/issues/suggestions in the Post a comment or question below.

Acknowledgments

The Twentieth Century Reanalysis Project (20CR) used resources of the National Energy Research Scientific Computing Center managed by Lawrence Berkeley National Laboratory and of the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory, which are supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 and Contract No. DE-AC05-00OR22725, respectively. Support is provided by the U.S. Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) program, and Office of Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office. Data are freely available from NOAA, NCAR, the IRI, KNMI, and DOE NERSC.

NASA's Modern Era Retrospective analysis for Research and Applications (MERRA) was developed by the Global Modeling and Assimilation Office (GMAO) and produced through NASA's Modeling, Analysis and Prediction (MAP) program, and is freely available from the Goddard Earth Sciences (GES) Data Information Services center (DISC).

NCEP's Climate Forecast System Reanalysis (CFSR) was developed by the Environmental Modeling Center (EMC) and was partially funded through the NOAA Climate Program Office. It is available free to the public from both NCDC and NCAR.

The ERA-Interim reanalysis is being produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) in Reading, UK. ERA-Interim data with near-real time updates are freely available from the ECMWF data server and from the CISL Data Archive at NCAR. 

WRIT contributes to the Atmospheric Circulation Reconstructions over the Earth (ACRE) Initiative.

WRIT is supported in part by NOAA and by the US Department of Energy Office of Science (BER).

Saha, Suranjana, and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015–1057.  doi: 10.1175/2010BAMS3001.1 .

Data Extraction

1. NCEI NOMADS and NCMP:  reanalysis (CFSR,NARR,R1,R2); NWP (NAM, GFS, RUC); GENS ensembles, SST
2. NOAA PSL Search and Plot. (R1,R2,20CR, NARR)
3. NOAA IRI (CFSR,20CR,R1,R2)
4. ECMWF: 
   1. ERA5: access via the Climate Data Store https://cds.climate.copernicus.eu/#!/home and https://cds-beta.climate.copernicus.eu/ 
   2. Alternative: https://www.ecmwf.int/en/forecasts/datasets/search 
5. NASA 
   1. MERRA: GES DISC Data Collections: MERRA
   2. MERRA-2: GES DISC Data Collections: MERRA-2
   3. MERRA and MERRA2 Data Subsetter  (variable list for MERRA-2)
6. NCAR, Highest-resolution files for all reanalyses, except MERRA. GRIB parameter field extraction using cURL, and some conversion to netCDF as noted. 
   1. JRA-25: Data Access > Web File Listing > Create Your Own File List (e.g. anl_p), use cURL or wget (for files).
   2. JRA-55
   3. ERA5, ERA-Interim
   4. CFSR - (also subset with net CDF format conversion)
   5. 20CR
   6. 20CRv2c
   7. 20CRv3
7. OpenDAP servers: 
   1. NOAA PSL (20CR,R1,R2,NARR), NCEP, MERRA2D, MERRA3D
   2. MERRA Gridded Innovations and Observations (GIO)
8. OpenGrADS.org: GrADS software with additional user functionality, including GUI for reanalyses including NCEP and MERRA
9. Earth System Grid Federation (CFSR, MERRA, 20CR, JRA-25, ERA-Interim) Obs4MIPS: Easy access to NetCDF reanalyses data of selected variables corresponding to the CMIP5 climate model output
10. GOAT: Geophysical Observations Analysis Tool for MATLAB

Step-by-Step Guide to obtaining data files

• Step by step descriptions of how to get the data at the reanalyses centers are available.

Post Processing Routines and Algorithms

1. Extrapolate MERRA pressure-level data below the surface

Webtools to plot/analyze data by Function

Basic Maps

1. NOAA IRI (CFSR,20CR,R1,R2)
2. NOAA PSL Search and Plot (20CR,R1,R2,NARR)
3. NASA MERRA: Uses Giovanni to produce maps or animations of some monthly fields. Can average over successive times.
4. NOAA NOMADS (CFSR,NARR,R1,R2)
5. ECMWF ERA-40, ERA-Interim (Plot maps)
6. NASA MERRA Atlas
7. NOAA/PSL Web-based Reanalysis Intercomparison Tool for maps makes user-selected reanalysis fields for monthly data. It can also difference two reanalyses or selected observational datasets with user-selected climatologies.
8. The Climate Reanalyzer makes user-selected reanalysis fields and differences for monthly data.
9. MeteoCentre provides pre-generated synoptic maps of SLP, 1000-500 thickness, and 500 hPa height (20CR and R1).
10. GOAT: Geophysical Observations Analysis Tool for MATLAB

Other Basic Geographic Plots

1. NOAA PSL:Crossections
2. NOAA PSL: Search and Plot
3. MERRA: Uses Giovanni to produce hovmollers of some monthly fields.
4. The Royal Netherlands Meteorological Institute (KNMI) Climate Explorer
5. GOAT: Management and Analysis of Geophysical-Data Made Simple for Matlab
6. Met Data Explorer

Hovmollers

1. NOAA/PSL: Hovmollers (R1)
2. IRI
3. GOAT: Geophysical Observations Analysis Tool:Management and Analysis of Geophysical-Data Made Simple for Matlab

Advanced Plots

1. NOAA PSL: Can plot monthly, daily and sub-daily of crossections from composite plotting pages (R1). Anomalies are available.
2. NOAA/PSL: Hovmollers of means, anomalies of daily data. Anomalies are available (R1).
3. GOAT: Temporal and spatial subsettting is supported via a GUI or built in function. Built-in calculation of anomalies and climatology. Superimpose or show the difference between two fields. Display land-cover or topography.

Composite Maps (Average different, possibly non-contiguous dates together)

1. NOAA PSL: Can plot composite maps and vertical crossectons from composite plotting pages on monthly, daily and sub-daily time scales for R1. Anomalies are available.
2. NOAA PSL: Can plot composite maps from plotting pages on monthly, daily and sub-daily timescales for 20CR. Anomalies are available. For monthly, plot composite maps of the 20CR ensemble spread (uncertainty).
3. GCOS/WGSP: Can plot composite maps of sea level pressure from plotting pages on monthly timescales. Anomalies are available.
4. WRIT maps: Can plot composite maps of a reanalysis or the difference of composites from two reanalyses.
5. GOAT: Can plot composites of non-contiguous dates.

Correlation Maps

1. NOAA PSL WRIT Correlations
2. NOAA PSL: From monthly NCEP/NCAR R1
3. KNMI
4. The Climate Reanalyzer (ERA-Interim, NCEP/NCAR R1, NCEP/DOE R2, 20CR, observational datasets: PRISM precipitation, ERSSTv3b)

Timeseries Plots

1. NOAA PSL Plot simple timeseries of NCEP/NCAR R1 and 20thC Reanalysis monthly variables
2. IRI Data Library
3. Met Data Explorer: Unidata
4. Web-based Reanalysis Intercomparison Tool for timeseries (WRIT) makes user-selected reanalysis timeseries, scatter plots, cross-correlation functions, and probability density functions for monthly data. It can also difference two reanalyses or selected observational datasets.
5. The Climate Reanalyzer makes user-selected reanalysis time series with land/ocean masking.

Timeseries Analysis

1. KNMI: Plot, compute annual cycle, filter and other tools available for time series analysis. Provides many climate/ocean time-series.
2. NOAA/PSL: Correlate and do some other simple analysis on pregenerated or user supplied monthly time-series
3. NOAA/PSL: Extract daily timeseries from datasets. Can supply user criteria (e.g. top 10 temperatures in January for a point). R1,20CR
4. NOAA/PSL: Extract monthly timeseries from datasets.  R1,20CR
5. IRI Data Library
6. NOAA PSL: Web-based Reanalysis Intercomparison Tool for timeseries (WRIT) makes user-selected reanalysis timeseries, scatter plots, cross-correlation functions, wavelets, and probability density functions for monthly data. It can also difference two reanalyses or selected observational datasets.

Google Earth

1. NOAA PSL: Create in KML plots (20CR, R1)

Miscellaneous

1. NOAA/PSL Lead/Lag for Composites
2. NOAA\/PSL Lead/Lag for Correlations (maps)
3. KNMI Smoothed fields, EOF, SVD and other analysis
4. NOAA/PSL WRIT Trajectory calculator (20CR, CFSR)

Applications that read/plot/analyze netCDF and/or grib data (non-web)

A complete list is at Unidata

1. NCL: NCAR Command Language (no longer updated but has full functionality)
2. GrADS: The Grid Analysis and Display System (GrADS)
3. IDV:  Integrated Data Viewer
4. Ferret: Data Visualization and Analysis: From NOAA/PMEL
5. NCO: NetCDF Operators. The NCO toolkit manipulates and analyzes data stored in netCDF-accessible formats, including DAP, HDF4, andHDF5. It exploits the geophysical expressivity of many CF (Climate & Forecast) metadata conventions, the flexible description of physical dimensions translated by UDUnits, the network transparency of OPeNDAP, the storage features (e.g., compression, chunking, groups) of HDF (the Hierarchical Data Format), and many powerful mathematical and statistical algorithms of GSL (the GNU Scientific Library). NCO is fast, powerful, and free.
6. CDO: Climate Data operators.  A collection of command-line operators to manipulate and analyze climate and numerical weather prediction data; includes support for netCDF-3, netCDF-4 and GRIB1, GRIB2, and other formats.
7. MATLAB
8. IDL: Interactive Data Language
9. CDAT Community Data Analysis Tools: CDAT: Note this will be replaced soon by xCDAT
10. xCDAT: xCDAT is an extension of xarray for climate data analysis on structured grids. It serves as a spiritual successor to the Community Data Analysis Tools (CDAT) library.
11. GOAT: Geophysical Observations Analysis Tool:  A MATLAB based tool that integrates with NetCDF files and OPeNDAP sources.