Evaluation of MERRA Land Surface Estimates in Preparation for the Soil Moisture Active Passive Mission

Abstract

The authors evaluated several land surface variables from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) product that are important for global ecological and hydrological studies, including daily maximum (T max ) and minimum (T min ) surface air temperatures, atmosphere vapor pressure deficit (VPD), incident solar radiation (SW rad ), and surface soil moisture. The MERRA results were evaluated against in situ measurements, similar global products derived from satellite microwave [the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E)] remote sensing and earlier generation atmospheric analysis [Goddard Earth Observing System version 4 (GEOS-4)] products. Relative to GEOS-4, MERRA is generally warmer (;0.58C for T min and T max ) and drier (;50 Pa for VPD) for low-and middle-latitude regions (,508N) associated with reduced cloudiness and increased SW rad . MERRA and AMSR-E temperatures show relatively large differences (.38C) in mountainous areas, tropical forest, and desert regions. Surface soil moisture estimates from MERRA (0-2-cm depth) and two AMSR-E products (;0-1-cm depth) are moderately correlated (R ; 0.4) for middle-latitude regions with low to moderate vegetation biomass. The MERRA derived surface soil moisture also corresponds favorably with in situ observations (R 5 0.53 6 0.01, p , 0.001) in the midlatitudes, where its accuracy is directly proportional to the quality of MERRA precipitation. In the high latitudes, MERRA shows inconsistent soil moisture seasonal dynamics relative to in situ observations. The study's results suggest that satellite microwave remote sensing may contribute to improved reanalysis accuracy where surface meteorological observations are sparse and in cold land regions subject to seasonal freeze-thaw transitions. The upcoming NASA Soil Moisture Active Passive (SMAP) mission is expected to improve MERRA-type reanalysis accuracy by providing accurate global mapping of freeze-thaw state and surface soil moisture with 2-3-day temporal fidelity and enhanced (#9 km) spatial resolution.

Estimating downwelling solar irradiance at the surface of the tropical Atlantic Ocean: A comparison of PIRATA measurements against several re-analyses and satellite-derived data sets

“…with increasing occurrence of cloud-free conditions, it may be speculated that MERRA-2 is as a whole more accurate in cloud-free conditions that in other conditions. This is in agreement with the findings of Kennedy et al (2011), Yi et al (2011), Zib et al (2012, Zhao et al (2013) or Boilley and Wald (2015) for MERRA. The 2D histograms show that the dots for E are fairly well aligned along the 1:1 line with a very large scattering (see e.g.…”

Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska

“…Our study may underestimate the modeled ALT uncertainties associated with SMAP SM data. Large uncertainty is associated with global reanalysis or satellite SM data (Yi et al, 2011). This uncertainty is due to many factors, including insufficient understanding of the effect of permafrost-thaw-induced transitions on active layer hydrology (Rawlins et al, 2013) and the predominance of wet soil conditions and standing water in permafrost landscapes, which constrains satellite microwave penetration and sensitivity to active layer properties (Du et al, 2015).…”

“…For the model sensitivity analysis, a soil wetness uncertainty (±10 %) was assigned to the SMAP Nature Run and L4SM soil moisture records based on prior global soil moisture assessments using MERRA reanalysis data (Yi et al, 2011). The ±10 % wetness uncertainty translates into an uncertainty of ∼ 0.04 m 3 m −3 for mineral soils and ∼ 0.08 m 3 m −3 for organic soils, which typically have a higher porosity.…”

A baseline probabilistic drought forecasting framework using standardized soil moisture index: application to the 2012 United States drought

“…Uncertainties in MERRA data sets have been evaluated against different observations (e.g., Yi et al, 2011;Kennedy et al, 2011). The results show that MERRA provides valuable information consistent with observations especially in the midlatitudes, while uncertainties in high latitudes are often large (Yi et al, 2011;.…”