Robust estimates of soil moisture and latent heat flux coupling strength obtained from triple collocation

Abstract

Land surface models (LSMs) are often applied to predict the one‐way coupling strength between surface soil moisture (SM) and latent heat (LH) flux. However, the ability of LSMs to accurately represent such coupling has not been adequately established. Likewise, the estimation of SM/LH coupling strength using ground‐based observational data is potentially compromised by the impact of independent SM and LH measurements errors. Here we apply a new statistical technique to acquire estimates of one‐way SM/LH coupling strength which are nonbiased in the presence of random error using a triple collocation approach based on leveraging the simultaneous availability of independent SM and LH estimates acquired from (1) LSMs, (2) satellite remote sensing, and (3) ground‐based observations. Results suggest that LSMs do not generally overestimate the strength of one‐way surface SM/LH coupling.

Use of Satellite Soil Moisture to Diagnose Climate Model Representations of European Soil Moisture‐Air Temperature Coupling Strength

“…Since the variance terms (i.e., β C v v and ) are always positive, equation illustrates that soil moisture and air temperature observation errors will uniformly reduce the magnitude of | R obs | relative to | R true | in . This source of bias in coupling strength estimates has been noted previously by Crow et al () and Findell et al (). As a result, the use of more accurate soil moisture products will produce progressively higher | R obs | values (Crow et al, ).…”

Global Investigation of Soil Moisture and Latent Heat Flux Coupling Strength

“…Instead of estimating the random error variance of an unknown signal observed by three data sets as described above, Crow et al () extended TC to estimate the coupling strength between two different geophysical variables, namely, SM and LH. The basic assumptions are same as in classic TC; that is, all measurements are linearly related to their corresponding true values where α and β represent additive and multiplicative systematic errors of each data set with regard to the (unknown) truth True, ε denotes zero‐mean random error with temporally constant variance, and A and B represent three independent estimates of SM and LH, respectively.…”

“…Therefore, it provides a means for estimating true coupling strength between SM and LH using error‐prone data (e.g., remote sensing retrievals of SM A and LH B ). A more detailed derivation for (8) can be found in Crow et al ().…”

“…Despite the need to accurately represent SM/LH coupling in Earth system models, we currently lack quantitative, observation‐based, and unbiased estimates of its strength. Here we utilize the triple‐collocation (TC) approach introduced in Crow et al () to SM and LH products obtained from multiple satellite remote sensing platforms and land surface models (LSMs) to obtain unbiased global maps of SM/LH coupling strength. Results demonstrate that relative to coupling strength estimates acquired directly from remote sensing‐based data sets, the application of TC generally enhances estimates of warm‐season SM/LH coupling, especially in the western United States, the Sahel, central Asia, and Australia.…”

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

“…These temporal and spatial gaps complicate the sampling of soil moisture‐air temperature coupling strength estimates (Mueller & Seneviratne, ). Furthermore, random errors in soil moisture products induce a low bias into coupling strength estimates (Crow et al, ; Findell et al, ). Due to the inevitable presence of such error, any coupling strength sampled from mutually independent soil moisture and air temperature estimates is most accurately interpreted as a lower bound estimate.…”