The Added Value of Assimilating Remotely Sensed Soil Moisture for Estimating Summertime Soil Moisture‐Air Temperature Coupling Strength

Abstract

To date, the direct use of remote‐sensing soil moisture data sets for examining surface/atmosphere coupling strengths has been hampered by the presence of significant random errors and data gaps in these products. This study investigates the potential for obtaining an improved observation‐based lower bound of summertime soil moisture‐air temperature coupling strength via the assimilation of long‐term, remote‐sensing soil moisture data sets into a simple, prognostic model driven by observed rainfall. In particular, we utilize simultaneous scatterometer‐ and radiometer‐based soil moisture products obtained from the European Space Agency Climate Change Initiative soil moisture product and a triple collocation analysis approach to estimate the variance of modeling and observation errors (required as input by a data assimilation system). Results show that assimilating remotely sensed soil moisture leads to larger coupling strength estimates as measured by the absolute anomaly correlation with independent temperature observation data than those obtained from modeled or remotely sensed soil moisture alone. According to an analyses of soil moisture error impacts on coupling strength estimates, this increase in coupling strength is likely attributable to an improvement in the precision of the soil moisture product used to estimate it. Based on this, we conclude that data assimilation provides an improved lower bound on the magnitude of true coupling strength between soil moisture and screen‐level air temperature.

A Global Assessment of Added Value in the SMAP Level 4 Soil Moisture Product Relative to Its Baseline Land Surface Model

“…Figure 2a further illustrates that the L4 product demonstrates much higher levels of skill relative to the OL simulation in areas of low rain gauge density (i.e., areas with large distances to the nearest rain gauge in Figure 2d), such as central Australia, and in regions where CPCU data are not used to derive the L4 and OL products, including Africa and the high latitudes (Reichle et al, 2017a). Hence, assimilating satellite-based Tb is associated with a relatively larger increase in soil moisture skill (Dong & Crow, 2018b;Liu et al, 2018;Reichle et al, 2008a). Due to the sparsity of rain gauge observations in these regions, the OL simulation contains elevated levels of random error.…”

“…Following previous soil moisture error analyses (e.g., Dong & Crow, 2018b;Dorigo et al, 2010), we assume that SMAP L4 ( L4 ) anomalies are linearly related to the unknown true soil moisture anomaly ( ):…”

“…In such densely gauged areas, the skill of the OL simulation is expected to be high, and the additional information provided by assimilating Tb (or soil moisture) into a land model is expected to be low (Dong & Crow, 2018b;Liu et al, 2018;Reichle et al, 2008a). Under such conditions, even small uncertainties in the model and observation error characterization can degrade data assimilation estimates relative to OL estimates (Crow & Van Loon, 2006;Dong & Crow, 2018b;Reichle et al, 2008b).…”

“…Due to the sparsity of rain gauge observations in these regions, the OL simulation contains elevated levels of random error. Hence, assimilating satellite-based Tb is associated with a relatively larger increase in soil moisture skill (Dong & Crow, 2018b;Liu et al, 2018;Reichle et al, 2008a).…”

Coupling of Soil Moisture and Air Temperature from Multiyear Data During 1980–2013 over China

“…The changes of large-scale atmospheric circulations may be the main cause of temperature anomalies, and small-scale physical processes of local energy balance such as soil moisture-atmosphere coupling could also make a contribution to them [5]. Many studies have shown that soil moisture anomalies play an important role in soil moisture-temperature coupling [6,7], as it could control the energy budget by the partitioning of latent heat flux and sensible heat flux, further impacting the air temperature [8,9]. When soil moisture decreases, less water can be used for evapotranspiration, resulting in a decrease of latent heat flux [8,10].…”