Meteorological Data Overview¶
The POWER meteorological parameters provided by the project and listed within this validation section are based on NASA Goddard’s Global Modeling and Assimilation Office (GMAO) assimilation model, the Modern Era Retrospective-Analysis for Research and Applications (MERRA-2) (See Rienecker et al. (2008, 2011), Bosilovich, et al. 2016, and Molod et al. (2011, 2015) for more details on the Reanalysis.)
Each of the parameters is either obtained directly from or calculated using meteorological parameters taken from NASA's MERRA-2 assimilation model. The meteorological parameters emerging from the assimilation model are estimated via:
An atmospheric analysis performed within a data assimilation context [that] seeks to combine in some 'optimal' fashion the information from irregularly distributed atmospheric observations with a model state obtained from a forecast initialized from a previous analysis (Bloom, et al., 2005; Bosilovich, et al. 2016).
The model seeks to assimilate and optimize observational data and model estimates of atmospheric variables. Types of observations used in the analysis include (1) land surface observations of surface pressure; (2) ocean surface observations of sea level pressure and winds; (3) sea level winds inferred from backscatter returns from space-borne radars; (4) conventional upper-air data from rawinsondes (e.g., height, temperature, wind and moisture); (5) additional sources of upper-air data include drop sondes, pilot balloons, and aircraft winds; and (6) remotely sensed information from satellites (e.g., height and moisture profiles, total perceptible water, and single level cloud motion vector winds obtained from geostationary satellite images). Emerging from MERRA-2 are hourly global estimates of the vertical distribution of a range of atmospheric parameters.
Values from MERRA-2 are on a ½° x ⅝° global grid. The POWER archived data parameters are transformed from Coordinated Universal Time (UTC) to solar local time (i.e. noon is defined to be solar noon without local time zone definitions taken into account). The MERRA-2 meteorological data available through POWER encompasses the time period from January 1, 1981 through a few months within near-real time. Each of the POWER MERRA-2 parameters is provided in a time series of hourly (or larger time scale) values. All of the MERRA-2 parameters represent the average value over the spatial grid. The wind speed is at 2m, 10m, and 50m above the average elevation of the grid and precipitation surface value averaged over the grid. The remaining parameters are taken from the model at 2m above the average elevation of the grid box. As noted in the preceding paragraph the MERRA-2 parameters are calculated on hourly increments and converted by the POWER project to local time. The daily maximum and minimum temperatures are obtained from the 24 hourly temperature values, not an average of those values. All other parameters are based upon averages or sums (i.e. HDD and CDD) of the hourly values.
The validation of the MERRA-2 meteorological parameters is based upon comparisons of the primary parameter to surface observations of the corresponding parameters. Statistics associated with the MERRA-2 vs. surface based values are reported to provide users with information necessary to assess the applicability of the MERRA-2 data to their particular project. Scatter plots of the MERRA-2 parameters vs. surface based values along with the correlation and accuracy parameters for each scatter plots are typically provided. The statistical parameters associated with a linear least squares fit to the respective scatter plots that are reported include: Pearson's correlation coefficient; the bias between the mean of the respective MERRA-2 parameter and the surface observations; the Root Mean Square Error (RMSE) calculated as the root mean square difference between the respective MERRA-2 and observational values. Additional statistics typically provided are the variance in the MERRA-2 and observational data and the number of MERRA-2 / observational data pairs.