Impact of quality control of satellite soil moisture data on their assimilation into land surface model

Abstract

A global Soil Moisture Operational Product System (SMOPS) has been developed to process satellite soil moisture observational data at the NOAA National Environmental Satellite, Data, and Information Service for improving numerical weather prediction (NWP) models at the NOAA National Weather Service (NWS). A few studies have shown the benefits of assimilating satellite soil moisture data in land surface models (LSMs), which are the components of most NWP models. In this study, synthetic experiments are conducted to determine how soil moisture data quality control may impact the benefit of their assimilation into LSMs. It is found that using green vegetation fraction to quality control the SMOPS soil moisture product may significantly increase the benefit of assimilating it into Noah LSM in terms of increasing the agreement of Noah LSM surface and root zone soil moisture simulations with the corresponding in situ measurements. The quality control procedures and parameters are suggested for the assimilation of SMOPS data into NWS NWP models.

Changes of Soil Moisture from Multiple Sources during 1988–2010 in the Yellow River Basin, China

“…SM ECV appears to perform the best in the Yellow River basin among the analyzed data in terms of the temporal variations from seasonal to interannual time scales. However, we have not made validations of its absolute magnitudes in this study; as Yin et al [64] and Yan et al [65] suggested, further quality control and magnitude adjustment are needed, for example, using assimilations approach with land surface models. Apart from this, further studies can also be conducted with focus on the sources of uncertainties in both reanalysis products over Yellow River basin.…”

Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method

“…Moreover, the performance of DA depends on the quality of the assimilated soil moisture data [48]. This motivates an analysis of the accuracy of AMSR-E soil moisture products for the Little Washita Watershed.…”

An Intercomparison of Noah Model Skills With Benefits of Assimilating SMOPS Blended and Individual Soil Moisture Retrievals

“…Soil moisture (SM) plays a key role in the terrestrial water cycle (Rodriguez‐Iturbe et al, ) and significantly impacts land‐atmosphere water, energy, and carbon exchanges (Yin et al, ). At a continental scale, the positive feedback of SM precipitation is a significant rainfall source (Koster et al, ; van der Schrier & Barkmeijer, ), and thus, precipitation is generally decreased under a lack SM condition.…”

“…Given the availability of the aforementioned satellite microwave SM products, it is desirable to enhance LSM skills and in turn further improvements on weather, hydrological, and climatological predictions, as well as drought and flood monitoring capabilities. Recent research has shown that data assimilation (DA) is a key approach to optimally merge the SM from model simulations and satellite observations in order to obtain the optimal estimates of the state variables and the energy and mass fluxes between land surface and the atmosphere (Crow & Wood, ; Evensen, ; Kumar et al, 2014; Rodell et al, ; Walker & Houser, ; Yin et al, ; Yin, Zhan, Zheng, Liu, et al, ). Ongoing researches show that assimilation of satellite surface SM observations has positive impacts on LSM simulations (Albergel et al, ; Leroux et al, ; Sawada, ).…”