SMOS Data Assimilation for Numerical Weather Prediction

This paper presents the Soil Moisture and Ocean Salinity (SMOS) mission data assimilation activities conducted at the European Centre for Medium-Range Weather Forecasts (ECMWF) to analyse soil moisture for Numerical Weather Prediction (NWP) applications. Two different approaches are presented based on SMOS brightness temperature and SMOS neural network soil moisture data assimilation, respectively. For the first approach, SMOS brightness temperature data assimilation relies on forward modelling. Long term results, spanning the SMOS period, of SMOS forward modelling, monitoring and data assimilation are presented. They emphasize the relevance of SMOS data for monitoring and to support NWP model developments. For the second approach, a SMOS soil moisture product has been produced based on a Neural Network (NN) trained on ECMWF soil moisture. So, the SMOS-ECMWF NN soil moisture product captures the SMOS signal variability in time and space, while by design its climatology is consistent with that of the ECMWF soil moisture, which makes it suitable for data assimilation purpose. This approach, initially tested for 2012 in a global scale stand alone approach, shows that SMOS NN data assimilation slightly improves the two-metre air temperature forecast in the short range at regional scale. For NWP applications this approach has been further developed with a near real time production of the SMOS-ECMWF NN soil moisture product, with the implementation of the SMOS NN data assimilation in the ECMWF Integrated Forecasting System (IFS), and with high resolution (9km) global scale testing compatible with the current ECMWF NWP system.

[1]  Qing Liu,et al.  Data Assimilation to extract Soil Moisture Information from SMAP Observations , 2017, Remote. Sens..

[2]  D. Lawrence,et al.  Regions of Strong Coupling Between Soil Moisture and Precipitation , 2004, Science.

[3]  Philippe Richaume,et al.  SMOS near-real-time soil moisture product: processor overview and first validation results , 2017 .

[4]  C. Taylor,et al.  Afternoon rain more likely over drier soils , 2012, Nature.

[5]  Marco L. Carrera,et al.  The Canadian Land Data Assimilation System (CaLDAS): Description and Synthetic Evaluation Study , 2015 .

[6]  Matthias Drusch,et al.  Initializing numerical weather prediction models with satellite‐derived surface soil moisture: Data assimilation experiments with ECMWF's Integrated Forecast System and the TMI soil moisture data set , 2007 .

[7]  Lifeng Luo,et al.  The Second Phase of the Global Land–Atmosphere Coupling Experiment: Soil Moisture Contributions to Subseasonal Forecast Skill , 2011 .

[8]  Jiancheng Shi,et al.  The Soil Moisture Active Passive (SMAP) Mission , 2010, Proceedings of the IEEE.

[9]  Nemesio J. Rodríguez-Fernández,et al.  SMOS Neural Network Soil Moisture Data Assimilation , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[10]  K. Trenberth,et al.  Estimates of the Global Water Budget and Its Annual Cycle Using Observational and Model Data , 2007 .

[11]  Patricia de Rosnay,et al.  Technical Implementation of SMOS Data in the ECMWF Integrated Forecasting System , 2012, IEEE Geoscience and Remote Sensing Letters.

[12]  Y. Kerr,et al.  The SMOS Mission: New Tool for Monitoring Key Elements of the Global Water Cycle This satellite mission will use new algorithms to try to forecast weather and estimate climate change from satellite measurements of the Earth's surface. , 2010 .

[13]  N. Verhoest,et al.  ESA's Soil Moisture and Ocean Salinity mission: From science to operational applications , 2016 .

[14]  L. Isaksen,et al.  A simplified Extended Kalman Filter for the global operational soil moisture analysis at ECMWF , 2013 .

[15]  B. Hurk,et al.  A Revised Hydrology for the ECMWF Model: Verification from Field Site to Terrestrial Water Storage and Impact in the Integrated Forecast System , 2009 .