C. Draper

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is the latest atmospheric reanalysis of the modern satellite era produced by NASAs Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRAs terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO. This paper provides an overview of the MERRA-2 system and various performance metrics. Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes. Other improvements in the quality of MERRA-2 compared with MERRA include the reduction of some spurious trends and jumps related to changes in the observing system, and reduced biases and imbalances in aspects of the water cycle. Remaining deficiencies are also identified. Production of MERRA-2 began in June 2014 in four processing streams, and converged to a single near-real time stream in mid 2015. MERRA-2 products are accessible online through the NASA Goddard Earth Sciences Data Information Services Center (GES DISC).

Land Surface Precipitation in MERRA-2

AbstractThe Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), features several major advances from the original MERRA reanalysis, including the use, outside of high latitudes, of observations-based precipitation data products to correct the precipitation falling on the land surface in the MERRA-2 system. The method for merging the observed precipitation into MERRA-2 has been refined from that of the (land-only) MERRA-Land reanalysis. This paper describes the method and evaluates the MERRA-2 land surface precipitation. Compared to monthly GPCPv2.2 observations, the corrected MERRA-2 precipitation (M2CORR) is better than the precipitation generated by the atmospheric models within the cycling MERRA-2 and MERRA systems. M2CORR is also better than MERRA-Land precipitation over Africa because in MERRA-2 a merged satellite–gauge precipitation product is used instead of the gauge-only data used for MERRA-Land. Compared to 3-hourly TRMM observations, the M2CORR diurnal cycle ha...

Assessment of MERRA-2 Land Surface Hydrology Estimates

AbstractThe MERRA-2 atmospheric reanalysis product provides global, 1-hourly estimates of land surface conditions for 1980–present at ~50-km resolution. MERRA-2 uses observations-based precipitation to force the land (unlike its predecessor, MERRA). This paper evaluates MERRA-2 and MERRA land hydrology estimates, along with those of the land-only MERRA-Land and ERA-Interim/Land products, which also use observations-based precipitation. Overall, MERRA-2 land hydrology estimates are better than those of MERRA-Land and MERRA. A comparison against GRACE satellite observations of terrestrial water storage demonstrates clear improvements in MERRA-2 over MERRA in South America and Africa but also reflects known errors in the observations used to correct the MERRA-2 precipitation. Validation against in situ measurements from 220–320 stations in North America, Europe, and Australia shows that MERRA-2 and MERRA-Land have the highest surface and root zone soil moisture skill, slightly higher than that of ERA-Interim...

Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS

A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

Clarifications on the “Comparison Between SMOS, VUA, ASCAT, and ECMWF Soil Moisture Products Over Four Watersheds in U.S.”

In a recent paper, Leroux compared three satellite soil moisture data sets (SMOS, AMSR-E, and ASCAT) and ECMWF forecast soil moisture data to in situ measurements over four watersheds located in the United States. Their conclusions stated that SMOS soil moisture retrievals represent “an improvement [in RMSE] by a factor of 2-3 compared with the other products” and that the ASCAT soil moisture data are “very noisy and unstable.” In this clarification, the analysis of Leroux is repeated using a newer version of the ASCAT data and additional metrics are provided. It is shown that the ASCAT retrievals are skillful, although they show some unexpected behavior during summer for two of the watersheds. It is also noted that the improvement of SMOS by a factor of 2-3 mentioned by Leroux is driven by differences in bias and only applies relative to AMSR-E and the ECWMF data in the now obsolete version investigated by Leroux et al.

Comparison of Land Skin Temperature from a Land Model, Remote Sensing, and In-situ Measurement

Land skin temperature (Ts) is an important parameter in the energy exchange between the land surface and atmosphere. Here hourly Ts from the Community Land Model version 4.0, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations, and in situ observations from the Coordinated Energy and Water Cycle Observation Project in 2003 were compared. Both modeled and MODIS Ts were interpolated to the 12 station locations, and comparisons were performed under MODIS clear-sky condition. Over four semiarid stations, both MODIS and modeled Ts show negative biases compared to in situ data, but MODIS shows an overall better performance. Global distribution of differences between MODIS and modeled Ts shows diurnal, seasonal, and spatial variations. Over sparsely vegetated areas, the model Ts is generally lower than the MODIS-observed Ts during the daytime, while the situation is opposite at nighttime. The revision of roughness length for heat and the constraint of minimum friction velocity from Zeng et al. (2012) bring the modeled Ts closer to MODIS during the day and have little effect on Ts at night. Five factors contributing to the Ts differences between the model and MODIS are identified, including the difficulty in properly accounting for cloud cover information at the appropriate temporal and spatial resolutions, and uncertainties in surface energy balance computation, atmospheric forcing data, surface emissivity, and MODIS Ts data. These findings have implications for the cross evaluation of modeled and remotely sensed Ts, as well as the data assimilation of Ts observations into Earth system models.

A Dynamic Approach to Addressing Observation-Minus-Forecast Bias in a Land Surface Skin Temperature Data Assimilation System

AbstractIn land data assimilation, bias in the observation-minus-forecast (O − F) residuals is typically removed from the observations prior to assimilation by rescaling the observations to have the same long-term mean (and higher-order moments) as the corresponding model forecasts. Such observation rescaling approaches require a long record of observed and forecast estimates and an assumption that the O − F residuals are stationary. A two-stage observation bias and state estimation filter is presented here, as an alternative to observation rescaling that does not require a long data record or assume stationary O − F residuals. The two-stage filter removes dynamic (nonstationary) estimates of the seasonal-scale mean O − F difference from the assimilated observations, allowing the assimilation to correct the model for subseasonal-scale errors without adverse effects from observation biases. The two-stage filter is demonstrated by assimilating geostationary skin temperature Tskin observations into the Catch...

Benefits and pitfalls of GRACE data assimilation: A case study of terrestrial water storage depletion in India

This study investigates some of the benefits and drawbacks of assimilating Terrestrial Water Storage (TWS) observations from the Gravity Recovery and Climate Experiment (GRACE) into a land surface model over India. GRACE observes TWS depletion associated with anthropogenic groundwater extraction in northwest India. The model, however, does not represent anthropogenic groundwater withdrawals and is not skillful in reproducing the interannual variability of groundwater. Assimilation of GRACE TWS introduces long-term trends and improves the interannual variability in groundwater. But the assimilation also introduces a negative trend in simulated evapotranspiration whereas in reality evapotranspiration is likely enhanced by irrigation, which is also unmodeled. Moreover, in situ measurements of shallow groundwater show no trend, suggesting that the trends are erroneously introduced by the assimilation into the modeled shallow groundwater, when in reality the groundwater is depleted in deeper aquifers. The results emphasize the importance of representing anthropogenic processes in land surface modeling and data assimilation systems.