1. Executive Summary

Reanalyses have become an integral part of Earth system science research across many disciplines. While originating in the atmospheric sciences and Numerical Weather Prediction (NWP), the essential methodology has been adopted in the fields of oceanography and terrestrial ecosystems and hydrology, with emerging research in atmospheric composition, cryosphere and carbon cycle disciplines. Major challenges lie ahead as the disparate nature of each discipline become joined in Earth system analyses. Clearly, substantial progress has been made since the last reanalysis conference (Jan 2008, Tokyo Japan). Newer atmospheric reanalyses (MERRA, CFSR and ERA-Interim) have been evaluated in depth, and many strengths and weaknesses identified. Early results from JRA-55 are becoming available. There is tremendous potential in the NOAA/ESRL 20CR surface data only reanalysis, and in the uncertainty provided from the ensemble. Ensembles of multiple reanalysis systems can provide valuable information. Although there are several reanalyses efforts worldwide at present, the community consensus is that the diversity among them will enable deeper understanding of the reanalyses systems, their strengths and weaknesses and their representation of the underlying earth system processes/phenomena. This is then reflected in the producers’ plans (notably those of JMA and ECMWF) leaning toward “families” of reanalyses (each system producing various configurations of reanalysis). There is much to be learned about the observations, data assimilation, modelling, and coupling the Earth system but new data systems, efficient computing and processing of the multitude of reanalyses products are urgently needed. The integrating nature of reanalyses across components of the Earth system (land, ocean atmosphere) is a key benefit, but to date very few reanalyses systems include all relevant data and assimilate all Earth system components. Observations are the fundamental resource for reanalysis. The need for long-term records of continuous measurements cannot be overstated. Data recovery efforts for in situ and remotely sensed observations are essential to extend the records as far back in time as possible to support long historical reanalyses, while concerted efforts to maintain and develop the observing system forward in time to ensure continuity for the future. Documenting the observations and their uses in past reanalyses can be beneficial both to use in future reanalyses and to understanding of the observations. Expertise for all the observations exists around the world, and so, international coordination of observations for reanalysis should be an imperative for international observations and research coordination programs such as GCOS, GEOSS, CEOS and WCRP. Confronting models with observations continues to be important but also confronting models (e.g. ocean and atmospheric models) and observations (e.g. discrepancies between micro-wave satellite records and radiosonde measurements) among themselves are equally important research tasks for the future. Data assimilation methods continue to improve, but have more challenges ahead, such as the amelioration of shocks associated with changes to the observing system (also better characterizing and reducing model bias) and developing uncertainty estimates for reanalyses. Reanalyses have recently been getting attention from the climate monitoring community, and so, their strengths, weaknesses and uncertainty are increasingly exposed.

Reanalysis users often ask which reanalysis is best for a given topic. As newer reanalyses come along, the answer may not be widely known, if at all. In this regard, the community of users and developers must collaborate, given the diversity of applications. The web site, reanalysis.org, has been promoted as an open media for conveying the latest understanding of reanalyses data. Additionally, NCAR’s Climate Data Guide (https://climatedataguide.ucar.edu/) is a very useful go-to source for scientifically sound information and advice on the strengths, limitations and applications of climate data, including reanalyses. While fundamental information is available primarily on atmosphere and ocean reanalyses, discussions on the latest research and understanding are progressing more slowly. Ultimately, it is incumbent on the researchers to assess the multitude of reanalyses objectively. New data systems are required that allow for more efficient cross comparisons among the various reanalyses (such as those used for AMIP and CMIP studies and the Earth System Grid, ESG). The 4th WCRP International Conference on Reanalyses produced excellent discussions across all the important issues in reanalyses, but continuing the progress and improvements will require sustained efforts over the long term. While progress has been made across the major aspects of reanalyses, significant limitations persist. The conference has identified broad directions to continue the advancement of reanalysis:

1) Quantitative Uncertainty – Reanalyses are based on observations, and can include the errors of observations and the assimilating system. It is recommended to have reanalysis data available in a common framework so as to facilitate the analysis of their strengths and weaknesses. The notion of Families of reanalyses will likewise expose the impact of assimilating observations on the analyses. Ensemble methods can also provide quantitative uncertainty estimates. Lastly, passing observations and innovations into an easily accessible data format can promote deeper investigation of the use of observations in the reanalyses.

2) Qualitative Uncertainty – Often researchers inquire to the applicability of a reanalysis for a given phenomena, or even, which reanalysis is best. Often, this is not satisfactorily known, varies with application and requires significant time and research. Therefore, sharing reanalysis knowledge and research in a timely manner, among researchers and developers is a critical need to allow subsequent exploitation by the climate community. The reanalysis.org effort has provided an initial effort along these lines, but more participation is encouraged. In addition, http://climatedataguide.ucar.edu provides informed commentary on analysis and other datasets. Likely, even more lines of communications are required. 

3) Earth System Coupling – The natural course of reanalysis development is toward, longer data sets with coupled Earth system components that will ultimately contribute to improved coupled predictions. The use of more varied observations (e.g. aerosols) will reinforce the physical representation of the Earth system processes in the reanalysis systems. There is a need to develop independent and innovative modeling, coupling and data assimilation methods to represent the Earth System throughout the time span of the observational record. More interdisciplinary collaborations in the system development and observational research will begin to address this need. 

4) Reanalyses, Observations and Stewardship – While the observational records have been greatly improved since the first reanalyses through research, reprocessing and homogenizations, research and improvements continue their development. Reprocessing and intercalibrations of observed records are critical to improve the quality and consistency of reanalyses. In situ and satellite data need to be found, rescued and archived into suitable formats to extend the reanalysis record back in time. Reanalysis systems for the atmosphere, ocean, cryosphere, land, and coupled earth system are needed that maximize use of the observations as far back as each instrumental record will allow. It is important for the observational data and reanalysis developers to maintain communication, so the latest data are used in reanalyses, and also that output of reanalyses may contribute to the understanding of observations. Such an endeavor should be coordinated at an international level.

 Link to Full Report

Zhang, Q., H. Körnich and K. Holmgren, 2012: How well do reanalyses represent the southern African precipitation? Clim. Dyn., DOI: 10.1007/s00382-012-1423-z.

http://dx.doi.org/10.1007/s00382-012-1423-z

Monthly-mean precipitation observations over southern Africa are used to evaluate the performance of eight global reanalyses: ERA-40, ERA-interim, JRA-25, MERRA, CFSR, NCEP-R1, NCEP-R2 and 20CRv2. All eight reanalyses reproduce the regionally averaged seasonal cycle fairly well; a few spatial mismatches with the observations are found in the climate mean for the rainy season. Principal component analyses show a dipole in the leading modes of all reanalyses, however with crucial differences in its spatial position.

Possible reasons for the differences between the reanalyses are discussed on the basis of the ERA-interim and 20CRv2 results. A comparison between the moisture transports shows that ERA-interim manifests a very strong moisture convergence over the eastern equatorial Atlantic, resulting in the strong precipitation here. This excessive convergence may be due to the water–vapor assimilation and convection parameterization. Over the Indian Ocean, the ITCZ is shifted northward in ERA-interim compared to its position in 20CRv2. This discrepancy is most likely attributable to the meridional SST gradients in the Indian Ocean which are significantly larger in the ERA-interim than those in the 20CRv2, and the resulting atmospheric response prevents a southward shift of the ITCZ.

Overall, the consistent description of the dynamical circulation of the atmosphere and the hydrological cycle appears as a crucial benchmark for reanalysis data. Based on our evaluation, the preferential reanalysis for investigating the climate variability over southern Africa is 20CRv2 that furthermore spans the longest time period, hence permitting the most precise investigations of interannual to decadal variability.

 

An intercomparison of the global relative angular momentum M_R in five reanalysis datasets, including the Twentieth Century Reanalysis (20CR), is performed for the second half of the twentieth century. The intercomparison forms a stringent test for 20CR because the variability of M_R is known to be strongly influenced by the variability of upper-tropospheric zonal wind whereas 20CR assimilated only surface observations. The analysis reveals good agreement for decadal-to-multidecadal variability among all of the datasets, including 20CR, for the second half of the twentieth century. The discrepancies among different datasets are mainly in the slowest component, the long-term trend, of M_R. Once the data are detrended, the resulting decadal-to-multidecadal variability shows even better agreement among all of the datasets. This result indicates that 20CR can be reliably used for the analysis of decadal-to-interdecadal variability in the pre-1950 era, provided that the data are properly detrended. As a quick application, it is found that the increase in M_Rduring the 1976/77 climate-shift event remains the sharpest over the entire period from 1871 to 2008 covered by 20CR. The nontrivial difference in the long-term trend between 20CR and the other reanalysis datasets found in this study provides a caution against using 20CR to determine the trend on the centennial time scale that is relevant to climate change. These conclusions are restricted to the quantities that depend strongly on the upper-tropospheric zonal wind, but the approach adopted in this work will be useful for future intercomparisons of the low-frequency behavior of other climate indices in the reanalysis datasets. Paek and Huang 2012.

   (a) The 5-year running averaged monthly anomalies of global relative angular momentum, ΔM_R, for the 5 reanalysis datasets. (b) Same as (a) but for ΔM₂₀₀, the angular momentum (per unit pressure thickness) calculated at only 200 hPa level.

   (a) The least-square quadratic fit (dashed curve) to ΔM_R for NCEP Reanalysis I, ERA-40, and 20CR for 1949-1978. (b) the “detrended” time series for the same period, with the quadratic curve removed from the time series. (c) Same as (a) but for 1979-2008 and with the addition of NCEP Reanalysis II and ERA-Interim.  (d) Same as (b) but for 1979-2008 and with the addition of NCEP Reanalysis II and ERA-Interim.

  (a) The global relative angular momentum, M_R, with the long-term mean retained, for the five reanalysis datasets. (b) Same as (a) but for M₂₀₀.  (c) Same as (a) but with the integration carried out only from 150-10hPa and 10°S-10°N to show the contribution from tropical upper atmosphere.

  (a) The summer (JJA)  climatology of zonal mean zonal wind from 1979-2008 for 20CR. (b) Same as (a) but for ERA-Interim. (c) The difference between 20CR and ERA-Interim, i.e., (b) minus (a). (d) is similar to (c) but for the difference between NCEP Reanalysis II and ERA-Interim.  (e)-(h) are similar to (a)-(d) but for winter (DJF). (i)-(l) are similar to (a)-(d) but for the annual mean.  (m) and (n) are the 1979-2008 linear trends for 20CR and ERA-Interim.  Contour intervals are 4 m s^-1 for (a), (b), (e), (f), (i), and (j);  1 m s^-1 for (c), (d), (g), (h), (k), (l), (m) and (n).

The extended intercomparison to CFSR and MERRA can be found at http://icr4.org/posters/Paek_AT-43.pdf.

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.

RAOBCORE and RICH

online Viewers now available!

RAOBCORE => RAdiosone OBservation COrrection using REanalyses

RICH              => Radiosonde Innovation Composite Homogenization

New visualization utility for RAOBCORE and RICH adjusted global radiosonde dataset now available:

RAOBCORE/RICH

Version 1.5.1

http://srvx7.img.univie.ac.at/~leo/richvis/newindex.html 

New visualization utility for RAOBCORE 2.0 global radiosonde dataset now available.

Adjustments are not yet provided.

http://srvx7.img.univie.ac.at/~lorenzo/DEVL_rrvis_2.0/html/

Notes: Viewer RAOBCORE 2.0

• Station : WMO radiosonde station number (not all the stations ara available for all the archives)
• Varibale;
• Pressure level [hPa];
• Type: observation, biascorrection (if available), FirstGuess departures (if available) and Analysis departures ( if available); 
• Smooth: running mean over the time serie;
• Experiment: only Control is available;
• Data from: in this menu are shown all the possible soure of data we are using:
  • Stations_ERA_INTERIM: data from ERA_INTERIM (observations and departures);
  • Stations_ERA_40: data from ERA_40 (obseravtiona and departures);
  • Stations_CHUAN_montly: monthly data from the CHUAN archive (observations from CHUAN);
  • Stations_CHUAN_daily: daily data from the CHUAN archive (observation from CHUAN, Analysis departures from 20CR);
  • 20CR: recoverd (only in presence of observation) time series from the 20CR interpolated at the station latitude and longitude;
  • ERA_40: recoverd (only in presence of observation) time series from ERA_40 interpolated at the station latitude and longitude;
  • long_20CR: recoverd time series (covers all the 20CR length) from the 20CR interpolated at the station latitude and longitude;
  • MERGED_archive: it contains merged obseravtions data from ERA_INTERIM, ERA_40 and CHUAN; the analysis departures are from 20CR.
• Adjustment_Method: only Unadjusted is now available;
• Version: only v2.0 is available;
• Long_Time: 
  • 00h: data at Midnight;
  • 12h: data at Noon;
  • 00h - 12h: difference between Midnight  and Noon;
• PlotSetting:
  • Jump_Missing: if there is a missind value, nothing is plotted;
  • Linear_Connection: if there is a missing value, a linear connection between the last available and the new available will be plotted;
• Metadata: shows (if available) the metadata for the selected station; 
• Overplot: allows plotting many time series on the same frame;
• Tooltip: gives hints about the different menus;
• Autorefresh: at each selection it tries to plot the current setting;
• Range: scaled for the time axis;
• Delete Last: deletes the last plot form the frame;
• Delete All : deletes all the plots from the frame;

In order to zoom in: select with a mouse and drag the area that you would like to inspect. It will be magnified in the main frame. 

Suggested stations for a easy start :

• Temperature:

Station: 010393, Merged archive, pressure 1000 or 850 hPa -> long time series back to 1905

Station: 004018, Merged archive, pressure 850 hPa -> continue time serie back to 1946;

• U and V Wind component:

Station: 016716, Merged archive, pressure 200 hPa, Type: Observations and Analysis depertures -> big shift (better visible with smooth bigger than 100);

•  Wind Speed:

Station: 016716, Merged archive, pressure 200 hPa, Type: Observations and Analysis depertures -> big shift (better visible with smooth bigger than 100)  

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).

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.