SMOS Brightness Temperature Monitoring Quality Control Review and Enhancements

Peter Weston,Patricia De Rosnay

doi:10.3390/rs13204081

Abstract

Brightness temperature (Tb) observations from the European Space Agency (ESA) Soil Moisture Ocean Salinity (SMOS) instrument are passively monitored in the European Centre for Medium-range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). Several quality control procedures are performed to screen out poor quality data and/or data that cannot accurately be simulated from the numerical weather prediction (NWP) model output. In this paper, these quality control procedures are reviewed, and enhancements are proposed, tested, and evaluated. The enhancements presented include improved sea ice screening, coastal and ambiguous land-ocean screening, improved radio frequency interference (RFI) screening, and increased usage of observation at the edge of the satellite swath. Each of the screening changes results in improved agreement between the observations and model equivalent values. This is an important step in advance of future experiments to test the direct assimilation of SMOS Tbs into the ECMWF land data assimilation system.

Highlights

Monitoring Quality Control ReviewThe European Space Agency (ESA) launched the Soil Moisture Ocean Salinity (SMOS)satellite [1] in 2009
5 indicates thatofdata is from the alias-free zoneobservations of the SMOSat different incidence angle bins from snapshot, and only data with this bit set are passed to the monitoring system
The effects are positive by including more observations in the monitoring, reducing mean background departures, and reducing the standard deviation of background departures (e.g., radio frequency interference (RFI) screening)

Summary

Introduction

Monitoring Quality Control ReviewThe European Space Agency (ESA) launched the Soil Moisture Ocean Salinity (SMOS)satellite [1] in 2009. The European Space Agency (ESA) launched the Soil Moisture Ocean Salinity (SMOS). In order to compare the IFS fields of such variables like temperature, humidity, soil moisture etc., with the measured SMOS brightness temperatures (Tbs), the model fields need to be transformed using an observation operator. The chosen operator is the Community Microwave Emission Model (CMEM) [2], which is a radiative transfer model that has been developed at ECMWF. Over land, it uses a combination of vegetation, soil, and snow models and parametrisations to calculate an accurate emissivity and effective temperature from the input model parameters.

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Conclusion