Latest Version

Version 2015E26. Updated May.26.2015.


Installation and compatibility of GOAT was verified for the MATLAB versions and Operating systems listed below. If you encountered problems or successfully installed GOAT on one of the MATLAB versions with unknown (??) status shown below, please report here.

MATLAB version   Mac   Windows  Linux
2014b ok ?? ??
2014a ok ?? ??
2013b  ok ?? ??
2013a ok ?? ??
2012b  ok** ?? ??
2012a ??  ?? ??
2011b  ok ?? ??
2011a ok ?? ??
2010b* ok ?? ??
2010a* ok ?? ??

* LINK archives cannot be used for MATLAB versions earlier than 2011 since the ncread and ncinfo functions are only available for MATLAB versions 2011a and onwards.

** Failure to use polar stereographic projections has been reported for this version.

Installation instructions

Download latest version. Unzip and place the GOAT folder where you would like GOAT to be installed. Set directory to GOAT/Install. Type GOAT_Install at the command line. The relevant paths and some shortcuts will be added automatically. To launch GOAT, press the GOAT shortcut or set directory to GOAT/Code and type GOAT at the command-line. To download data, press the DataDownload shortcut or set directory to GOAT/Code and type GOAT_AutoDownload at the command-line.

Note: on MATLAB versions earlier than 2013a, the shortcuts need to be manually moved to the shortcut toolbar (right click the ‘shortcuts’ toolbar, ‘Organise Shortcuts’, ‘Move to Category’).

Update instructions

Option 1: Press the ‘UpdateVersion’ shortcut. The latest version will be automatically downloaded and installed without affecting personal settings, the MyScripts, WORK and Data folders.

Option2: set directory to …GOAT/Code and type GOAT_VersionUpdateCode at the command-window. The latest version will be automatically downloaded and installed without affecting personal settings, the MyScripts, WORK and Data folders.

Option3: Reinstall. Replace the GOAT/Code, GOAT/Scripts, GOAT/Documentation, GOAT/MyScripts/ExampleCode and GOAT/Installation folders. Set directory to GOAT/Installation and type GOAT_Install at the command-window.


The latest GOAT version contains some initial data. Additional LINK archive data can be downloaded from the OPeNDAP and CMIP5 page or by using GOAT’s auto-download utility. GOAT’s auto-download utility (GOAT_AutoDownload.m, also called by the DataDownload shortcut) automatically installs the datasets listed bellow, by downloading directly from free data-servers, and converting the downloaded data to GOAT MAT standard (see GOAT_GUIDE for a description of GOAT archiving standards and the GOAT Auto Download Protocol). Changes related to data-servers may result in failure to download, misplaced or erroneous data. Please verify the downloaded data and report in case errors are encountered.

Additional dataset details, errata and referencing information can be found at NCAR/UCAR’s Climate Data Guide.

Dataset Name Description
NCEP-I The original reanalysis by the National Oceanic and Atmospheric Administration, Earth Science Research Laboratory (NOAA-ESRL).
• Kalnay et al.,The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77, 437-470, 1996
• NCEP-DEO AMIP-II Reanalysis (R-2): M. Kanamitsu, W. Ebisuzaki, J. Woollen, S-K Yang, J.J. Hnilo, M. Fiorino, and G. L. Potter. 1631-1643, Nov 2002, Bulletin of the American Meteorological Society
20th Cent. Reanalysis  by NOAA-ESRL.
• Compo, G.P., J.S. Whitaker, P.D. Sardeshmukh, N. Matsui, R.J. Allan, X. Yin, B.E. Gleason, R.S. Vose, G. Rutledge, P. Bessemoulin, S. Brönnimann, M. Brunet, R.I. Crouthamel, A.N. Grant, P.Y. Groisman, P.D. Jones, M. Kruk, A.C. Kruger, G.J. Marshall, M. Maugeri, H.Y. Mok, Ø. Nordli, T.F. Ross, R.M. Trigo, X.L. Wang, S.D. Woodruff, and S.J. Worley, 2011: The Twentieth Century Reanalysis Project. Quarterly J. Roy. Meteorol. Soc., 137, 1-28. DOI: 10.1002/qj.776 Free and Open Access.
• Compo,G.P., J.S. Whitaker, and P.D. Sardeshmukh, 2006: Feasibility of a 100 year reanalysis using only surface pressure data. Bull. Amer. Met. Soc., 87, 175-190.
• Whitaker, J.S., G.P.Compo, X. Wei, and T.M. Hamill 2004: Reanalysis without radiosondes using ensemble data assimilation. Mon. Wea. Rev., 132, 1190-1200.
GODAS  Global Ocean Data Assimilation System by NOAA-ESRL.
• Behringer, D.W., M. Ji, and A. Leetmaa, 1998: An improved coupled model for ENSO prediction and implications for ocean initialization. Part I: The ocean data assimilation system. Mon. Wea. Rev., 126, 1013-1021.
• Behringer, D.W., and Y. Xue, 2004: Evaluation of the global ocean data assimilation system at NCEP: The Pacific Ocean. Eighth Symposium on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, AMS 84th Annual Meeting, Washington State Convention and Trade Center, Seattle, Washington, 11-15. Derber, J.C., and A. Rosati, 1989: A global oceanic data assimilation system. J. Phys. Oceanogr., 19, 1333-1347.
• Ji, M., A. Leetmaa, and J. Derber, 1995: An ocean analysis system for seasonal to interannual climate studies. Mon. Wea. Rev., 123, 460-481. S. Saha, S. Nadiga, C. Thiaw, J. Wang, W. Wang, Q. Zhang, H. M. van den Dool, H.-L. Pan, S. Moorthi, D. Behringer, D. Stokes, M. Pe–a, S. Lord, G. White, W. Ebisuzaki, P. Peng, P. Xie , 2006 : The NCEP Climate Forecast System. Accepted J. Climate.
ERSST Extended Reconstructed Sea Surface Temperature NOAA Extended Reconstructed Sea Surface Temperatutures v3b.
• ERSST.v3 Smith, T.M., R.W. Reynolds, Thomas C. Peterson, and Jay Lawrimore 2007: Improvements to NOAA's Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006). In press. Journal of Climate. Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871-2000. J. Climate, 16, 1601-1612.
TRMM Tropical Rainfall Measurement Mission (V7). by NASA.
Huffman et al, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales. iyear, combined- sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 38–55.
Ebert, E. E., J. E. Janowiak, and C. Kidd, 2007: Comparison of near-real-time precipitation estimates from satellite observa- tions and numerical models. Bull. Amer. Meteor. Soc., 88, 47–64.
Basic information on 3B43, TRMM and Other Data Precipitation Products
Liu, Z. et al (2012): Tropical Rainfall Measuring Mission (TRMM): Precipitation Data and Services for Research and Applications. BAMS: 1317
• Huffman et al, 2010: The TRMM Multi-satellite Precipitation Analysis (TMPA). Chapter 1 in Satellite Rainfall Applications for Surface Hydrology, F. Hossain and M. Gebremichael, Eds. Springer Verlag, ISBN: 978-90-481-2914-0, 3-22
Prakash et al (2013): Comparison of TRMM Multisatellite Precipitation Analysis (TMPA)-3B43 version 6 and 7 products with rain gauge data from ocean buoys, Remote Sensing Letters, 4, 677-685
Rasmussen, K. L., S. L. Choi, M. D. Zuluaga, and R. A. Houze Jr. (2013), TRMM precipitation bias in extreme storms in South America, Geophys. Res. Lett., 40, 3457–3461, doi:10.1002/grl.50651.
MERRA Modern Era Retrospective Analysis for Research and Applications. by NASA.
• Rienecker, M.M., M.J. Suarez, R. Gelaro, R. Todling, J. Bacmeister, E. Liu, M.G. Bosilovich, S.D. Schubert, L. Takacs, G.-K. Kim, S. Bloom, J. Chen, D. Collins, A. Conaty, A. da Silva, et al. (2011), MERRA: NASA's Modern-Era Retrospective Analysis for Research and ApplicationsJ. Climate24, 3624-3648, doi:10.1175/JCLI-D-11-00015.1. Link.
ORAS4 Ocean Reanalysis System, by ECMWF.
Balmaseda, M. A., Mogensen, K. and Weaver, A. T. (2013), Evaluation of the ECMWF ocean reanalysis system ORAS4. Q.J.R. Meteorol. Soc., 139: 1132–1161. doi: 10.1002/qj.2063
Balmaseda, M. A., K. E. Trenberth, and E. Källén (2013), Distinctive climate signals in reanalysis of global ocean heat content, Geophys. Res. Lett., 40, 1754–1759, doi:10.1002/grl.50382.
Balmaseda, M.A. et al (2008): The ECMWF Ocean Analysis System: ORA-S3
WOA 2013 World Ocean Atlas (2013). By NOAA-National Oceanographic Data Center (NODC). Objectively analysed climatological oceanic fields.
GISTEMP by NASA-Goddard Institute for Space Studies.
• Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change. Rev. Geophys., 48, RG4004, doi:10.1029/2010RG000345
CLOUDSAT and CLIPSO Combined CLOUDSAT spaceborne radar and CLIPSO spaceborne lidar cloud fraction dataset. More information the the Climate-Data-Guide and here.
Kay and Gettelman 2009: Cloud influence on and response to seasonal Arctic sea ice loss