Improving Soil Moisture and Surface Turbulent Heat Flux Estimates by Assimilation of SMAP Brightness Temperatures or Soil Moisture Retrievals and GOES Land Surface Temperature Retrievals

Abstract

Surface heat fluxes are vital to hydrological and environmental studies, but mapping them accurately over a large area remains a problem. In this study, brightness temperature (TB) observations or soil moisture retrievals from the NASA Soil Moisture Active Passive (SMAP) mission and land surface temperature (LST) product from the Geostationary Operational Environmental Satellite (GOES) are assimilated together into a coupled water and heat transfer model to improve surface heat flux estimates. A particle filter is used to assimilate SMAP data, while a particle smoothing method is adopted to assimilate GOES LST time series, correcting for both systematic biases via parameter updating and for short-term error via state updating. One experiment assimilates SMAP TB at horizontal polarization and GOES LST, a second experiment assimilates SMAP TB at vertical polarization and GOES LST, and a third experiment assimilates SMAP soil moisture retrievals along with GOES LST. The aim is to examine if the assimilation of physically consistent TB and LST observations could yield improved surface heat flux estimates. It is demonstrated that all three assimilation experiments improved flux estimates compared to a no-assimilation case. Assimilating TB data tends to produce smaller bias in soil moisture estimates compared to assimilating soil moisture retrievals, but the estimates are influenced by the respective bias correction approaches. Despite the differences in soil moisture estimates, the flux estimates from different assimilation experiments are in general very similar.

Impacts of Non-Local versus Local Moisture Sources on a Heavy (and Deadly) Rain Event in Israel

“…The Quantitative Precipitation Estimation (QPE) analysis is based on a constant altitude plan precipitation indicator (CAPPI at height of 1 km) from a C-Band Doppler radar located at Israel Meteorological Service (IMS) headquarters in Bet Dagan (32.01 N, 34.85 E). The Marshall-Palmer Z-R relationship, Z = 200R 1.6 [13], is used to convert the reflectivity into rainfall intensity.…”

“…The quantitively derived precipitation values were used to initialize the volumetric soil moisture in the model. The hypothesis was that a more realistic depiction of soil moisture would lead to a more accurate simulation of surface humidity fluxes [34]. Very few in-situ observations of soil moisture (both surface and by soil layers) and/or soil heat flux exists, so instead, it was supposed that assimilated rainfall, derived from radar observations, could be used to test the potential impact of soil moisture on the simulation of the convective events of 25 and 26 April 2018.…”

Improve the Performance of the Noah‐MP‐Crop Model by Jointly Assimilating Soil Moisture and Vegetation Phenology Data

“…Data assimilation (DA) methods originated from estimation theory and cybernetics to merge observational information into process‐based models to mitigate uncertainties in model variables and optimize model parameters (X. Li et al., 2020; Liang & Qin, 2008; Xia et al., 2019). This is done within variational‐based or ensemble‐based DA schemes to improve the model performances (He, Xu, et al., 2019; He, Xu, Bateni, et al., 2020; He et al., 2018; Lu et al., 2016, 2017, 2020; Margulis et al., 2002; Xu, Bateni, et al., 2018; Xu, Chen, et al., 2019; Xu, He, et al., 2019; T. Xu et al., 2011, 2015). Studies have assimilated various observational variables such as land surface temperature (LST), leaf area index (LAI), soil moisture (SM), and solar‐induced chlorophyll fluorescence (SIF) into crop models and/or LSMs, which has improved the estimated crop yields (Ines et al., 2013; X. Li et al., 2018; Wang et al., 2014; Xie et al., 2017), vegetation biomass, evapotranspiration (ET), and gross primary production (GPP) within LSMs (Huang et al., 2008; Kumar et al., 2019; Xu, He, et al., 2019; T. Xu et al., 2015).…”

“…Studies have assimilated various observational variables such as land surface temperature (LST), leaf area index (LAI), soil moisture (SM), and solar‐induced chlorophyll fluorescence (SIF) into crop models and/or LSMs, which has improved the estimated crop yields (Ines et al., 2013; X. Li et al., 2018; Wang et al., 2014; Xie et al., 2017), vegetation biomass, evapotranspiration (ET), and gross primary production (GPP) within LSMs (Huang et al., 2008; Kumar et al., 2019; Xu, He, et al., 2019; T. Xu et al., 2015). The assimilation of SM observations improved the estimation of SM, runoff, ET (Han et al., 2012; D. Liu & Mishra, 2017; Lu et al., 2016, 2020; Tian et al., 2017), and drought monitoring and forecasting (Kumar et al., 2014; Yan et al., 2017). Remotely sensed SIF, as an indicator of photochemical processes, provides additional constraints for photosynthesis simulation (Camino et al., 2019; Y. Zhang et al., 2014; Zhang, Xiao, et al., 2016).…”

“…Li et al, 2020;Liang & Qin, 2008;Xia et al, 2019). This is done within variational-based or ensemble-based DA schemes to improve the model performances (He, Xu, et al, 2019;He et al, 2018;Lu et al, 2016Lu et al, , 2017Lu et al, , 2020Margulis et al, 2002;Xu, Bateni, et al, 2018;Xu, Chen, et al, 2019;Xu, He, et al, 2019;T. Xu et al, 2011T.…”