Evaluation of variations in the late-80s observing system on theGEOS-5 data assimilation system
Michael Bosilovich1, Junye Chen1,2, Siegfried Schubert1, Philip Pegion1, Emily Liu1 andFranklin R. Robertson3
1

GMAO, NASA GSFC, USA
Division, ESSIC, U. Maryland, USA
3
NASA MSFC, USA
Correspondence: Michael.Bosilovich@nasa.gov
2

INTRODUCTION
The impact of observing system changes on retrospective-analyses (or reanalyses) has been known for some time (e.g.
Chen et al. 2008), and continues to be an issue in recent reanalyses. For example, Onogi et al (2007) show the spatial
correlation of Japanese 25 Year Reanalysis (JRA-25) precipitation to the Global Precipitation Climatology Project(GPCP) merged satellite/gauge observations. The result shows that the JRA-25 produces very good spatial patterns ofprecipitation. However, there is a distinct change (improvement) when SSMI total column water retrievals become
available (their Figure 5). Variations in the total column water, evaporation and precipitation time series’ are also
apparent (figures not shown).
The Global Modeling and Assimilation Office (GMAO) has developed the GEOS5 data assimilation system tosupport NASA instrument teams, field experiments and scientific priorities. In preparing to begin a retrospectiveanalysis of the satellite era (called the Modern Era Retrospective-analysis for Research and Applications, MERRA),
the GMAO has performed several validation experiments to evaluate the GEOS5 system.
Briefly, the GEOS5 data assimilation system includes NASA modeling components, coupled with the NCEP
Gridpoint Statitical Interpolation (GSI) for data assimilation. The GSI allows for radiance assimilation of theoperational NOAA instruments, SSMI and AIRS. While analysis and forecast cycles are run every 6 hours, anadditional segment uses the analysis tendencies to update the model state equations at every time step (called
Incremental Analyiss Update, IAU, Bloom et al. 1996). The IAU segment produces the research diagnostics evaluated
here. Rienecker et al. (2008) discusses the development of the GEOS5 data assimilation system.
To evaluate the impact of SSMI on the GEOS5 data assimilation system, as it pertains to reanalysis, we haveconducted a short sensitivity experiment at the native MERRA spatial resolution (1/2°×2/3°), and also a long coarse
spatial resolution experiment (2°×2.5°, Jan1979-Dec1990, call the Scout experiment). In the short sensitivity
experiment, a control experiment was run from July through August of 1987, with the full observing system available
at the time, except that SSMI is not included. In the SSMI experiment, SSMI (Version 6 from Wentz, RSS) retrievalsof rain rate and surface wind are assimilated starting on July 11. The radiances are analyzed in passive mode for twoweeks, building the adaptive bias corrections, and begin to be assimilated on July 25. In the Scout experiment, allobservations that would be used for MERRA are used. The Scout system is a fundamentally identical configuration of
the short sensitivity test and the data assimilation system that will be used for MERRA, with the primary difference
being the spatial resolution.

SENSITIVITY EXPERIMENT
After SSMI radiances are assimilated, the analysis increments generally add more water (add more in the lower
troposphere, take away less in the mid-troposphere) to the system (figure in the presentation available from the WCRP
Reanalysis Conference WWW site). Figure 1 shows the difference of the sensitivity experiment and the control for themonthly mean of August 1987 (the first full month when SSMI is available).
a) Precipitation (mm day-1)b) Total Column Water (mm)c) 10m Wind Speed (m s-1)d) Evaporation (mm day-1)e) Outgoing Longwave Radiation (W m-2)f) Cloud Fraction

Figure 1 Monthly mean differences (August 1987) of several quantities for the SSMI experiment minus
the control (not assimilating SSMI retrievals or radiances).

Large scale increases in total column water occur (Figure 1 b), predominantly focused on the tropics. Precipitation
generally increases, where precipitation occurs, with the largest increases are in the ITCZ. The strongest tropical windspeed increases contribute to increases in surface evaporation. There are significant changes to the wind speed outsideof the tropics (primarily decreases of the wind speed in the 40-60S latitude band), likely from the assimilation of SSMI

retrieved winds. Additionally, the cloud fraction increases, while OLR decreases. While there are some intermittent
regions that do not follow the generality, it is reasonably consistent across the tropics. Away from the tropics, thedifferences between the experiments tend to be small.
Table 1 GEOS5 experiment Tropics area averages.

Table 1 summarizes the differences across the tropics.
Precipitation increases in the SSMI experiment by ~10%of the Control experiment precipitation. There is also arobust response to the increasing water vapor, where cloud
fraction increases, and the outgoing longwave radiation
decreases. While the changes are apparently robust, they
are only a few percent of the control experiment. In thefollowing experiment, we evaluate the long time series compared to existing reanalyses.

Tropics Average (15°S-15°N)
August 1987
No SSMI
GEOS5
4.98
5.17
|V| 10m (m s-1)
3.70
4.04
P (mm day-1)
E (mm day-1)
3.99
4.06
TPW (mm)
41.3
42.2
CLDFRC
0.64
0.66
256254
OLR (W m-2)

Figure 2 Annual mean precipitation (mm day-1) for GPCP (Adler et al 2003), CMAP (Xie and Arkin,1996) and reanalyses data.

CLIMATE EXPERIMENT
A coarse resolution (Scout) experiment has been run for the period Jan 1979 – Dec 1990 to test the variations ofsatellite data in the 1980s for this system. The settings for the data assimilation are identical to what would be used forMERRA, with the exception that the model and data assimilation are run at 2° latitude × 2.5° longitude spatialresolution. Schubert et al (2008) presented some results from the GEOS5 validation at the WCRP Reanalysis
Conference. Bosilovich et al (2008) evaluate precipitation among the existing long reanalyses and also the GEOS5validation experiments. GEOS5 has a smaller tropical precipitation bias compared to reanalyses, regardless of theresolution (Figure 2). However at 2 degrees resolution, low oceanic precipitation occurs off the east coast of theNorthern hemisphere continents. At ½ degree resolution, dynamically driven precipitation in these regions increases
substantially.

In spatial correlation (as in Onogi et al 2007, their Figure 5), the coarse version of the GEOS5 reproduces the spatial
distribution of the precipitation very well, compared to previous reanalyses for the early satellite period discussed here
(Figure 3). There is a slight decline in the GEOS5 correlations after 1987, but without data beyond 1990, it is not clearif that continues or ameliorates. Even with this, the temporal variability of the GOES5 spatial correlations are
generally smaller than that of the previous reanalyses. In Figure 3, the increasing correlations of JRA-25 are apparent,
but across the GOES5 Scout experiment, the change is smaller.

Figure 3 Annual average of the area weighted spatial correlation of monthly GPCP precipitation toCMAP and the reanalyses.
August 1987 Precipitation Spatial Correlation
1.00
0.95
CMAP0.90

JRA25ERA400.85

NCEPR1NCEPR20.80

Scout0.75

W/ SSMINo/ SSMI0.70GlobalLand

Ocean

15S- 15N

Figure 4 Spatial correltion for August 1987
monthly mean fields of GPCP to CMAP,
existing satellite era reanalyses, and the GEOS5
Scout and SSMI experiments.

In the validation of the GEOS5 system for MERRA, there is
only the ½ degree analysis for August 1987 to compare to the2 degree system. Figure 4 shows spatial correlations for thelong reanalyses and the GEOS5 experiments. While the 2degree system (including SSMI) is good, oceanic precipitation
correlations are improved both by including SSMI and byincreasing spatial resolution. While this is a limited
comparison for the purposes here, Bosilovich et al (2008)
discuss further statistics of the GOES5 precipitation forexperiments when AIRS is also assimilated, where the spatial
correlations are also high.

SUMMARY
Sensitivity tests of the initial weeks of SSMI availability show that there is a systematic response of the analysis
diagnostics and state variables. Tropical precipitation shows some of the largest response, at ~10% increase of thecontrol precipitation without SSMI assimilation. Other variables have a smaller percent change. In the coarseresolution Scout experiment, the change of correlation in time, is less dramatic than the JRA-25, and the interannual
variability of spatial correlation is smaller. These statistics will be monitored during the production of MERRA data.
MERRA data production began in February 2008, and should be completed by Mid-2009. Status and information on
GEOS5 MERRA data production can be found at http://gmao.gsfc.nasa.gov/merra/.

REFERENCES
Chen J., A.D. Del Genio, B.E. Carlson, M.G. Bosilovich, 2008: The spatiotemporal structure of 20th century climate
variations in observations and reanalyses. Part I: long-term trend. J. Clim., acceptedAdler, R. F., G. J. Huffman, GJ; A. Chang, and co-authors., 2003: The version-2 global precipitation climatology project(GPCP) monthly precipitation analysis (1979-present). J. Hydromet., 4, 1147-1167.
Bloom, S. C., L. L. Takacs, A. M. da Silva, and D. Ledvina, 1996: Data assimilation using incremental analysis updates.
Mon. Wea. Rev., 124, 1256–1271.
Bosilovich, M. G., J. Chen, F. R. Robertson and R. F. Adler, 2008: Evaluation of Precipitation in Reanalyses. Submitted
to J. Appl. Meteor. Climatol.
Onogi, K., J. Tsutsui, H. Koide, M. Sakamoto, S. Kobayashi, H. Hatsushika, T. Matsumoto, N. Yamazaki, H. Kamahori,K. Takahashi, S. Kadokura, K. Wada, K. Kato, R. Oyama, T. Ose, N. Mannoji and R. Taira 2007: The JRA-25
Reanalysis. J. Meteor. Soc. Japan, 85, 369-432.
Rienecker, M.M.. M.J. Suarez, R. Todling, J. Bacmeister, L. Takacs, H.-C. Liu, W. Gu, M. Sienkiewicz, R.D. Koster, R.
Gelaro, and I. Stajner, 2007: The GEOS-5 Data Assimilation System – Documentation of Versions 5.0.1 and 5.1.0.
NASA GSFC Technical Report Series on Global Modeling and Data Assimilation, NASA/TM-2007-104606, Vol. 27.,
pp. 92.
Schubert, S. D. and coauthors, 2008: Assimilating Earth System Observations at NASA: MERRA and Beyond. 3rd
WCRP International Conference on Reanalysis, Jan 26 – Feb 1, 2008, Tokyo Japan.
Xie P., and P. A. Arkin, 1996: Global precipitation: a 17-year monthly analysis based on gauge observations, satellite
estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 2539-2558.