MERRA-2

Basic steps to find and download data:

1. All users are required to register to the NASA Earthdata system. New users, please read the registration instructions as well as additional required steps when accessing the data via using tools, such as wget for windows or mac, python, Matlab, and IDV.
2. Read the MERRA-2 File Specification carefully and search for any variables of interest to determine which data collection name that contains that variable (e.g., M2TMNXSLV for the air temperature at 2-meters).
3. If you have found the variables of interest are in multiple data collections, please first read the definitions of these collections (e.g., instantaneous or time-averaged), and search possible explanations in the Science FAQ or Data Access FAQ to decide which collection best suits your needs.
4. Search the data collection name using the Data Access and Tools webpage, and click on the data collection name to go to the Dataset Landing Page.
5. Use the data access methods listed on the Dataset Landing Page. Click the blue Data Access box and determine the best data service for your needs. Examples of how to use the data services can be found in Data Access FAQs and Data How-tos. 

Constant 

Sub-Daily and Monthly

(Daily mean can be calculated via the GES DISC Subsetter service.) 

MERRA-2 Derived Products

We strongly recommend users cite the MERRA-2 dataset with the Dataset DOI in their publications to indicate which data collections were used in their research.

The Dataset DOI and notation of “Data Citation” of a MERRA-2 data collection can be found on the dataset landing page (Click M2T1NXSLV_5.12.4 for the example) under the tab "Data Citation"

Template to cite MERRA-2 data with the dataset DOI:

Global Modeling and Assimilation Office (GMAO) (2015), {MERRA-2 data collection title}, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], {Dataset DOI}

For example, M2T1NXSLV_5.12.4:

Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date]. 10.5067/VJAFPLI1CSIV 

To accommodate EOSDIS toolkit requirements, all MERRA files are associated with a nine-character Earth Science Data Types (ESDT). The ESDT is a short (and rather cryptic) handle for users to access sets of files. In MERRA the ESDT will be used to identify the mainstream collections and consists of a compressed version of the collection name of the form:

      M2TFHVGGG (M2 + T + F + HV + GGG)

Where

M2 = MERRA-2

T: Time Description:

   I = Instantaneous: consists of the data field on the hour starting 00:00 UTC. For hourly data, for example, the times are 00:00, 01:00, 02:00, … , 23:00 UTC. The monthly means of instantaneous diagnostics are computed from the corresponding sub-daily data.

   T = Time-averaged: consists of a continuous sequence of data averaged over the indicated interval and time-stamped with the central time of the interval. For hourly data, for example, these times are 00:30, 01:30, 02:30, …, 23:30 (UTC). The monthly means of time-averaged data are computed from the corresponding sub-daily data.

   C = Time-independent: consists of constant data fields.

   S = Statistics: consists of statistics of selected data fields.

   Note: The daily mean (and daily minimum or maximum) can be computed from a sub-daily data using the Subsetting service, click here for an example.

F: Frequency

   1 = Hourly

   3 = 3-Hourly

   6 = 6-Hourly

   M = Monthly mean : consists data averaged over all time steps within a month

   D = Daily statistics

   U = Monthly-Diurnal mean: consists monthly mean of data at each sub-daily time stamp. For example, M2TUNXSLV_5.12.4 consists monthly mean of data at 00:30, 01:30, …23:30 UTC

   C = Climatology monthly mean ( 30 years mean from 1981 to 2010) 

HV: Horizontal and Vertical

   NX = Two-dimensions (single level).

   NP = Three-dimensions at 42 Pressure levels (level=1 is 1000 hPa). [in long-term climatology M2TCNPLTM, NP = 12 Pressure levels]

   NV = Three-dimensions at 72 model layer center (level=1 is at the top of atmosphere).

   NE = Three-dimensional at 73 model layer edge (level=1 is at the top of atmosphere).

GGG: Group

   ANA = direct analysis products.

   ASM = assimilated state variables (please read more in System Document).

   AER = aerosol mixing ratio.

   ADG = aerosol extended diagnostics.

   TDT = tendencies of temperature.

   UDT = tendencies of eastward and northward wind components.

   QDT = tendencies of specific humidity.

   ODT = tendencies of ozone.

   GAS = aerosol optical depth analysis.

   GLC = Land Ice Surface.

   LND = land surface variables.

   LFO = land surface forcing output.

   FLX = surface turbulent fluxes and related quantities.

   MST = moist processes.

   CLD = clouds.

   RAD = radiation.

   CSP = COSP satellite simulator (COSP=CFMIP Observations Simulator Package, CFMIP=Cloud Feedback Model Intercomparison Project).

   TRB = turbulence.

   SLV = single level.

   INT = vertical integrals.

   CHM = chemistry forcing.

   OCN = ocean.

   NAV = vertical coordinates.

Groups for the derived products: 

   EDI = extremes detection indices 

   PCT = percentiles

   LTM = long-term mean (30 years mean)

The full name for the MERRA-2 standrad products consists of three dot-delimited nodes:

      runid.collection.timestamp

where, 

      runid = MERRA2_SVv 

S and Vv denote the production Stream and the Version numbers. MERRA-2 was run in four production Streams. The Version number is usually zero, denoting the original processing. If the version number is none zero, it is a reprocessed data. The runid values for the four streams are listed below:

An example of a runid for reprocessed data is the month of September 2020, where runid = MERRA2_401 (rather than MERRA2_400). 

Read more information on "collection" and "timestamp" in Section 5.1 of the MERRA-2 File Specification.

Find MERRA-2 data which have been reprocessed.

Pressure-level data will be output on the following 42 pressure levels:

Products on the native vertical grid will be output on the following model layers. Pressures are nominal for a 1000 hPa surface pressure and refer to the top edge of the layer. Note that the bottom layer has a nominal thickness of 15 hPa.

MERRA-2 long-term mean data (M2TCNPLTM) will be output on the following 12 pressure levels:

Migration Status

• All MERRA-2 collections have been migrated to the cloud archive in the AWS US-West-2 region.  Direct S3 access and HTTPS access from the cloud archive is now available for all MERRA-2 data.
• The migration of the cloud data services is in progress:
  • The status of enabling the Cloud-based OPeNDAP is available on the page:  
    Enabling Cloud-based OPeNDAP Services for MERRA-2 ;  Read more information about OPeNDAP in the Cloud. 
  • The enhanced time series service is available for the selected MERRA-2 variables, read the page: Giovanni in the Cloud: Time Series Service
• The current on-premise data and services remain the same until further notice. 

Access data in the Cloud 

• How to Directly Access MERRA-2 Data from an S3 Bucket with Python from a Cloud Environment.
• Find and access cloud-enabled collections from GES DISC with Python
• The GES DISC Cloud Migration page provides more information on data How-tos, FAQ, and the cloud computing. 

Gelaro, R., W. McCarty, M. J. Suárez, R. Todling, A. Molod, L. Takacs, C. A. Randles, A. Darmenov, M. G. Bosilovich, R. Reichle, et al. (2017), The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)., J. Climate, 30(14), doi:10.1175/JCLI-D-16-0758.1

Molod, A., L. Takacs, M. Suarez, and J. Bacmeister (2015), Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2., Geosci. Model Dev., 8, doi:10.5194/gmd-8-1339-2015

Randles, C. A., A. M. da Silva, V. Buchard, P. R. Colarco, A. Darmenov, R. Govindaraju, A. Smirnov, B. Holben, R. Ferrare, J. Hair, Y.Shinozuka, and C.J. Flynn (2017), The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation., J. Climate, 30(17), doi:10.1175/JCLI-D-16-0609.1

Buchard V., C. A. Randles, A. M. da Silva, A. Darmenov, P. R. Colarco, R. Govindaraju, R. Ferrare, J. Hair, A. J. Beyersdorf, L. D. Ziemba, H. Yu (2017), The MERRA-2 aerosol reanalysis, 1980 onward. Part II: Evaluation and case studies., J. Climate, 30(17), doi:10.1175/JCLI-D-16-0613.1

Reichle, R.H., Q. Liu, R.D. Koster, C.S. Draper, S.P.P. Mahanama, and G.S. Partyka (2017), Land Surface Precipitation in MERRA-2., J. Climate, 30(5), doi:10.1175/JCLI-D-16-0570.1

Reichle, R. H., C. S. Draper, Q. Liu, M. Girotto, S. P. P. Mahanama, R. D. Koster, and G. J. M. De Lannoy (2017), Assessment of MERRA-2 Land Surface Hydrology Estimates., J. Climate, 30(8), doi:10.1175/JCLI-D-16-0720.1

Rienecker, M. M., and Coauthors, 2008: The GEOS-5 Data Assimilation System—Documentation of versions 5.0.1 and 5.1.0, and 5.2.0. NASA Tech. Rep. Series on Global Modeling and Data Assimilation, NASA/TM-2008-104606, Vol. 27, 92 pp.

Bosilovich, M. G., S. Akella, L. Coy, R. Cullather, C. Draper, R. Gelaro, R. Kovach, Q.Liu, A. Molod, P. Norris, K. Wargan, W. Chao, R. Reichle, L. Takacs, Y. Vikhliaev, S. Bloom, A. Collow, S. Firth, G. Labow, G. Partyka, S. Pawson, O. Reale, S. D. Schubert, and M. Suarez (2015), 2015b: MERRA-2: Initial Evaluation of the Climate, Technical Report Series on Global Modeling and Data Assimilation, 43, NASA/TM–2015-104606/Vol. 43

Collins, N., G. Theurich, C. DeLuca, M. Suarez, A. Trayanov, V. Balaji, P. Li, W. Yang, C. Hill, and A. da Silva (2005), Design and implementation of components in the Earth System Modeling Framework, International Journal of High Performance Computing Applications, doi:10.1177/1094342005056120

Collow, A., V. Buchard, M. Chin, P. Colarco, A. Darmenov, and A. da Silva, 2023: Supplemental Documentation for GEOS Aerosol Products. GMAO Office Note No. 22 (Version 1.0), 8 pp.