Overview
This document provides an overview of the snow products from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). MERRA-2 represents a third generation atmospheric global reanalysis produced by the Global Modeling and Assimilation Office (GMAO) at NASA. It begins in 1980 and it replaces the original MERRA reanalysis (Rienecker et al., 2011) using an upgraded version of the Goddard Earth Observing System Model, Version 5 (GEOS-5) data assimilation system. Alongside the meteorological data assimilation using a modern satellite database, MERRA-2 includes an interactive analysis of aerosols that feed back into the circulation, uses NASA's observations of stratospheric ozone and temperature, and takes steps towards representing cryogenic processes by including a representation of ice sheets over Greenland and Antarctica.
Provider's contact information
MERRA-2 is developed by the Global Modeling and Assimilation Office (GMAO) and produced through NASA's Modeling, Analysis and Prediction (MAP) program.
Data Download questions should go to the GES DISC help email: gsfc-help-disc@lists.nasa.gov
Science questions regarding MERRA-2 data be emailed to: merra-questions@lists.nasa.gov
When contacting these emails, provide specific information and links to where you have attempted the data downloads. They also ask you to familiarize yourself with the existing documentation first (MERRA-2: http://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/docs/)
Licensing and citation
MERRA-2 data is freely available from the Goddard Earth Sciences (GES) Data Information Services center (DISC). Note that each MERRA-2 data collection has a citable DOI, that should be used in peer-reviewed publications.
Citing MERRA-2 data has 2 steps for a full citation:
(1) First pick the correct variable here.
(2) When you click on the correct variable, it will take you to a second webpage with tabs that you can click that include: (1) documentation papers you need to cite, and (2) the correct variable citation information. You need both types of citation.
Variable name and units:
Snow depth and Total snow storage are available as time-averaged hourly and monthly products. Each time-averaged collection 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 GMT, 01:30 GMT, 02:30 GMT, etc. Monthly files represent averages for the calendar months, accounting for leap years.
Name | Description | Units |
---|---|---|
SNODP | Snow depth | m |
SNOMAS | Snow mass (or snow water equivalent) | kg m-2 |
FRSNO | fractional area of land snowcover | dimensionless |
Those variables are available in the Land Surface Diagnostics (LSD) collection. In the LND collection of variables, all the data are derived from the land model, and are not weighted according to the land fraction at that grid point. This data is provided to better compute land budgets for soil water and land energy. LND is land only, while all other collections are representative of the whole grid box.
In MERRA-2, Snow depth (SNODP) is recorded as the snow depth within the snow-covered portion only. Snow mass (SNOMAS), on the other hand, is recorded relative to the entire grid cell area, including the snow-covered and snow-free portions. The snow depth averaged across the entire grid cell (including the snow-covered and snow-free portions) can be computed by multiplying SNODP with FRSNO.
More broadly, MERRA-2 File Specification document has a comprehensive list of datasets available, as well as description of the horizontal and vertical grids.
Spatial coverage and resolution:
MERRA-2 data, is a global dataset. All variables are provided on the same horizontal regular latitude-longitude grid that has 576 points in the longitudinal direction and 361 points in the latitudinal direction, corresponding to a resolution of 0.625° × 0.5°.
Temporal coverage and resolution:
MERRA-2 data, is available from 1980 to present.
Snow data is available at hourly and monthly time step.
MERRA-2 data, is updated on a monthly base (each new month is available approximately between the 15th and 20th of the next month.).
Information about observations (number, homogeneity)
Like any other climate variable from a reanalysis product, snow is potentially influenced by all observations assimilated into the product. MERRA-2 assimilates conventional and satellite-based observations.
Conventional observations include surface, upper air, and aircraft measurements. From land based surface meteorology stations, only surface pressure is assimilated in MERRA and MERRA-2. Radiosonde stations may contribute to the lower level analysis (T, Qv, U, V). Likewise, commercial aircraft can provide lower level data on the ascent and descent (T, U, V). There are also wind profilers (U,V). Over ocean, ships and buoys may provide PS, T, Qv, U and V.
Spaceborne observations include satellite radiances and retrieved measurements of the temperature and moisture fields, and satellite observations of wind (derived retrievals of surface and upper-air wind). Spaceborne observations represent the majority of the global observing system, and the percentage of the global observing system that is measured from space increases from 62% in Jan 1980 to 88% in Dec 2014. Modern hyperspectral radiance and microwave observations, along with GPS-Radio Occultation and NASA ozone datasets are now assimilated in MERRA-2.
See the MERRA-2 Observations Tech Memo for more details.
Also, gauge precipitation is not assimilated by the assimilation system, MERRA-2 uses observation-based precipitation data as forcing for the land surface parameterization. However, the forcing precipitation is not purely gauge observations, as it tapers back to MERRA-2 model generated precipitation poleward of 42.5° latitude, and is completely MERRA-2 modelled precipitation poleward of 62.5°, therefore over the northern Canada.
Methodology
Like any other reanalysis, MERRA-2 data is strongly influenced by the data assimilation methodology. MERRA-2 is currently being produced with the GMAO/GEOS-5 Data Assimilation System Version 5.12.4, which incorporates the Global Statistical Interpolation (GSI) analysis scheme of Wu et al. (2002). The system utilizes a revised version of the GEOS global atmospheric model (Molod et al., 2014). MERRA-2 is intended to replace the MERRA reanalysis product (which was created with GEOS-5.2.0). Details of the MERRA-2 system, including the major changes from the MERRA system, are summarized in the companion GMAO Office Note No. 10. The major motivation for replacing MERRA with MERRA-2 is the fact that the MERRA data assimilation system was frozen in 2008 and is not capable of ingesting several important new data types as the newer microwave sounders and hyperspectral infrared radiance instruments. The GEOS-5 system actively assimilates roughly 2 × 106 observations for each analysis, including about 7.5 × 105 AIRS radiance data. The input stream is roughly twice this volume, but because of the large volume, the data are thinned commensurate with the analysis grid to reduce the computational burden. Data are also rejected from the analysis through quality control procedures.
There is a fundamental change between MERRA and MERRA-2 over land surfaces. Soil moisture in MERRA-2 is initialized using a separate observation-based precipitation product (variable PRECTOTCORR in “flx” collections). This approach improves the representation of land surface properties and runoff, and is similar to the soil moisture initialization scheme developed for MERRA-Land (Reichle et al., 2011; Reichle, 2012; Reichle and Liu, 2014). The forcing precipitation is primarily based on gauge observations at low and midlatitudes, and gradually tapers to the MERRA-2 modelled precipitation over a zonal range from 42.5° to 62.5° latitude. The forcing precipitation is entirely composed of the MERRA-2 modelled precipitation poleward of 62.5°.
MERRA-2 is produced as four production Streams, each of the first three covering approximately a third of the MERRA-2 period, with the fourth stream starting within a couple years of real time. Initial conditions for the four MERRA-2 streams were derived from MERRA with a subsequent single year spin-up period, which has not been released in MERRA-2.
Information about the technical and scientific quality
MERRA-2 replaces the original NASA MERRA reanalysis (Rienecker et al., 2011) using an upgraded version of the data assimilation system, and of the forecast model. It is accompanied by extensive technical documentation (see section below on reference to s describing the methodology or/and the dataset). It incorporates observations from the more recent satellite instruments, uses observation-corrected precipitation forcing for the land surface, includes stratospheric ozone products and assimilates interactive aerosols and observed time varying emissions.
A webpage is provided with FAQ answers here: https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/FAQ/#
Limitations and strengths for application in North Canada
Key general limitations:
- Discontinuities occur in the sea ice and SST boundary condition fields that affect certain time series analysis
- Discontinuities associated with major observing system variations do occur
- The forcing precipitation is entirely composed of the MERRA-2 modelled precipitation poleward of 62.5°. Care must be taken in mass balance studies as the difference between the observation-based and model-generated precipitation will affect the water budget when land and atmosphere budgets are combined. More generally, precipitation is thought to be too large over polar oceans, and is excessive over high topography in tropical latitudes
References to documents describing the methodology or/and the dataset
MERRA-2 Overview: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Ronald Gelaro, et al., 2017, J. Clim., doi: 10.1175/JCLI-D-16-0758.1
The American Meteorological Society has a special collection of articles relevant to MERRA-2. This collection, coordinated by Mike Bosilovich, is available at http://journals.ametsoc.org/collection/MERRA2.
There are several MAO Technical Memoranda that document and evaluate different aspects of the MERRA-2 system aspects of the MERRA-2 system:
#43, Bosilovich et al. – MERRA-2: Initial Evaluation of the Climate
#45, Randles et al. – The MERRA-2 Aerosol Assimilation
#46, McCarty et al. – MERRA-2 Input Observations: Summary and Assessment
Description of the observation corrected precipitation process used in MERRA-2:
Reichle, R., Q. Liu, R. Koster, C. Draper, S. Mahanama, and G. Partyka, 2017: Land Surface Precipitation in MERRA-2. J. Clim. doi:10.1175/JCLI-D-16-0570.1 Link.
Description of the GEOS-5 model changes between the MERRA and MERRA-2 systems:
Molod, A., L.T akacs, M. Suarez, and J. Bacmeister, 2015: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA-2, Geosci. Model Dev., 8, 1339-1356, doi:10.5194/gmd-8-1339-2015. Link.
Description of the mass constraint used in MERRA-2:
Takacs, L. L., M. Suarez, and R. Todling, 2015: Maintaining Atmospheric Mass and Water Balance Within Reanalysis. NASA/TM–2014-104606, Vol. 37 Document.
Link to download the data and format of data:
The MERRA-2 data are available online through the Goddard Earth Sciences (GES) Data and Information Services Center (DISC) (http://disc.sci.gsfc.nasa.gov/mdisc/). All MERRA-2 data are organized into file collections that contain fields with common characteristics. Snow related data are grouped in the following collections:
MERRA-2 tavg1_2d_lnd_Nx: 1-Hourly time averaged data containing Land Surface Diagnostics
MERRA-2 tavgM_2d_lnd_Nx: Monthly time average data containing Land Surface Diagnostics.
MERRA-2 data files are provided in netCDF-4 format. Due to the size of the MERRA-2 archive, most product collections are compressed with a GRIB like method that is invisible to the user. This method does degrade the precision of the data, but every effort has been made to ensure that differences between the product and the original, non-degraded data are not scientifically meaningful.