Geoscience Reference
In-Depth Information
information are merged and compared directly in a single system, and their relative con-
tributions to the skill of root zone soil moisture estimates are assessed.
3.4.1 Using Precipitation Observations
The Modern-Era Retrospective Analysis for Research and Applications (MERRA) is a
state-of-the-art atmospheric reanalysis data product based on GEOS-5 that provides, in
addition to atmospheric fields, global estimates of soil moisture, latent heat flux, snow, and
runoff for 1979—present with a latency of about 1 month (Rienecker et al.
2011
). A
supplemental and improved set of land surface hydrological fields (''MERRA-Land'') is
generated routinely using an improved version of the land component of the MERRA
system (Reichle et al.
2011
; Reichle
2012
). Specifically, the MERRA-Land estimates
benefit from corrections to the MERRA precipitation forcing with the global gauge-based
NOAA Climate Prediction Center ''Unified'' (CPCU) precipitation product and from
revised parameter values in the rainfall interception model, changes that effectively correct
for known limitations in the MERRA surface meteorological forcings.
With a few exceptions, the MERRA-Land data appear more accurate than the original
MERRA estimates and are thus recommended for those interested in using MERRA output
for land surface hydrological studies. As an example, Fig.
10
examines the drought con-
ditions experienced across the western United States and along the East Coast. The
MERRA and MERRA-Land drought indicator shown in the figure is derived by ranking,
separately for each grid cell, the normalized, monthly mean root zone soil moisture
anomalies for June, July, and August of 1980 through 2011 and converting the rank into
percentile units. For comparison, the drought severity assessed independently by U.S.
Drought Monitor is also shown. The figure clearly demonstrates that MERRA-Land data
are more consistent with the Drought Monitor than MERRA data.
Reichle et al. (
2011
) and Reichle (
2012
) provide a more comprehensive and quantitative
analysis of the skill (defined as the correlation coefficient of the anomaly time series with
independent observations) in land surface hydrological fields from MERRA, MERRA-
Land, and the latest global atmospheric reanalysis produced by ECWMF (ERA-I; Dee
et al.
2011
). Figure
11
shows that MERRA-Land and ERA-I root zone soil moisture skills
(against in situ observations at 85 US stations) are comparable and significantly greater
than that of MERRA. Furthermore, the runoff skill (against naturalized stream flow
observations from 18 US basins) of MERRA-Land is typically higher than that of MERRA
and ERA-I (not shown). Throughout the northern hemisphere, MERRA and MERRA-Land
agree reasonably well with in situ snow depth measurements (from 583 stations) and with
SWE from an independent analysis (not shown). In summary, through observations-based
corrections of the MERRA precipitation forcing, MERRA-Land provides a supplemental
and significantly improved land surface reanalysis product.
3.4.2 Assimilating Surface Soil Moisture Retrievals
Satellite retrievals of surface soil moisture are not used in MERRA-Land but would almost
certainly have further improved the skill of root zone soil moisture estimates. Draper et al.
(
2012
) illustrate the potential gains from assimilating ASCAT (Bartalis et al.
2007
; Wagner
et al.
1999
) and 10.7 GHz AMSR-E Land Parameter Retrieval Model (LPRM; de Jeu et al.
2008
) surface soil moisture retrievals. The retrievals are assimilated, both separately and
jointly, over 3.5 years into the GEOS-5 LDAS, using MERRA forcing and initial condi-
tions. Soil moisture skill is measured as the anomaly time series correlation coefficient
