Engineering topics

Verhoest, N. E. C.

Publications and source records attributed to Verhoest, N. E. C..

2 records

NASA NTRS2017

Assimilation of Global Radar Backscatter and Radiometer Brightness Temperature Observations to Improve Soil Moisture and Land Evaporation Estimates

Active radar backscatter (s◦) observations from the Advanced Scatterometer (ASCAT) and passive radiometer brightness temperature (TB) observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated either individually or jointly into the Global Land Evaporation Amsterdam Model (GLEAM) to improve its simulations of soil moisture and land evaporation. To enable s◦ and TB assimilation, GLEAM is coupled to the Water Cloud Model and the L-band Microwave Emission from the Biosphere (L-MEB) model. The innovations, i.e. differences between observations and simulations, are mapped onto the model soil moisture states through an Ensemble Kalman Filter. The validation of surface (0-10 cm) soil moisture simulations over the period 2010-2014 against in situ measurements from the International Soil Moisture Network (ISMN) shows that assimilating s◦ or TB alone improves the average correlation of seasonal anomalies (Ran) from 0.514 to 0.547 and 0.548, respectively. The joint assimilation further improves Ran to 0.559. Associated enhancements in daily evaporative flux simulations by GLEAM are validated based on measurements from 22 FLUXNET stations. Again, the singular assimilation improves Ran from 0.502 to 0.536 and 0.533, respectively for s◦ and TB, whereas the best performance is observed for the joint assimilation (Ran = 0.546). These results demonstrate the complementary value of assimilating radar backscatter observations together with brightness temperatures for improving estimates of hydrological variables, as their joint assimilation outperforms the assimilation of each observation type separately.

GLEAM↗

NASA NTRS2014

Optimization of a Radiative Transfer Forward Operator for Simulating SMOS Brightness Temperatures over the Upper Mississippi Basin, USA

The Soil Moisture and Ocean Salinity (SMOS) satellite mission is routinely providing global multi-angular observations of brightness temperature (TB) at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture (SM). To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multi-angular and multi-polarization top of atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity (VIC) LSM coupled with the Community Microwave Emission Modelling platform (CMEM) for simulating SMOS TB observations over the Upper Mississippi basin, USA. For a period of 2 years (2010-2011), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin averaged bias of 30K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After CDF matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30K to less than 5K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation.

SMOS↗