Combined assimilation of satellite precipitation and soil moisture: A case study using TRMM and SMOS data

Liao Fan Lin, Ardeshir M. Ebtehaj, Alejandro N. Flores, Satish Bastola, Rafael L. Bras

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).

Original languageEnglish (US)
Pages (from-to)4997-5014
Number of pages18
JournalMonthly Weather Review
Volume145
Issue number12
DOIs
StatePublished - Dec 1 2017

Bibliographical note

Funding Information:
Acknowledgments. This research is part of Liao-Fan Lin’s Ph.D. dissertation (Lin 2016) and is sponsored by the NASA Precipitation Measurement Missions (PMM) science program through Grants NNX13AH35G and NNX16AE36G and by the Science Utilization of the Soil Moisture Active-Passive Mission (SUSMAP) science program through Grant NNX16AM12G. The support by the K. Harrison Brown Family Chair is also gratefully acknowledged. The NCEP FNL data were obtained from the National Weather Service, U.S. Department of Commerce, and NOAA/National Centers for Environmental Prediction (2000) (freely accessible at http://rda.ucar.edu/datasets/ds083.2/). The SMOS data (freely available at http://cp34-bec.cmima.csic.es) were obtained from the SMOS Barcelona Expert Centre, a joint initiative of the Spanish Research Council (CSIC) and Technical University of Catalonia (UPC), mainly funded by the Spanish National Program on Space. The TRMM data were obtained from the NASA PMM webpage (https://pmm.nasa.gov/index. php?q5data-access/downloads/trmm). The NCEP Stage IV data were obtained from the Earth Observing Laboratory at the NCAR (freely available at http://data.eol. ucar.edu/codiac/dss/id521.093). The SCAN data were obtained from the Natural Resources Conservation Service (freely available at http://www.wcc.nrcs.usda.gov/ scan/). The CRN soil moisture data were obtained from the National Centers for Environmental Information, NOAA (freely available at https://www.ncdc.noaa.gov/ crn/). The NLDAS version 2 data were obtained from the NASA Goddard Earth Sciences Data and Information Services Center (freely available at http://disc.sci.gsfc.nasa.gov/ uui/datasets?keywords5NLDAS). The WRF Model was obtained from the NCAR (freely available at http:// www2.mmm.ucar.edu/wrf/users/). We appreciate these agencies for providing the models, data, and technical assistance. The authors would also like to thank three anonymous reviewers and editor Ryan Torn for their helpful comments.

Funding Information:
This research is part of Liao-Fan Lin's Ph. D. dissertation (Lin 2016) and is sponsored by the NASA Precipitation Measurement Missions (PMM) science program through Grants NNX13AH35G and NNX16AE36G and by the Science Utilization of the Soil Moisture Active-Passive Mission (SUSMAP) science program through Grant NNX16AM12G. The support by the K. Harrison Brown Family Chair is also gratefully acknowledged. The NCEP FNL data were obtained from the National Weather Service, U.S. Department of Commerce, and NOAA/National Centers for Environmental Prediction (2000) (freely accessible at http://rda.ucar.edu/datasets/ds083.2/). The SMOS data (freely available at http://cp34-bec.cmima.csic.es) were obtained from the SMOS Barcelona Expert Centre, a joint initiative of the Spanish Research Council (CSIC) and Technical University of Catalonia (UPC), mainly funded by the Spanish National Program on Space. The TRMM data were obtained from the NASA PMM webpage (https://pmm.nasa.gov/index. php?q5data-access/downloads/trmm). The NCEP Stage IV data were obtained from the Earth Observing Laboratory at the NCAR (freely available at http://data.eol. ucar.edu/codiac/dss/id521.093). The SCAN data were obtained from the Natural Resources Conservation Service (freely available at http://www.wcc.nrcs.usda.gov/scan/). The CRN soil moisture data were obtained from the National Centers for Environmental Information, NOAA (freely available at https://www.ncdc.noaa.gov/crn/). The NLDAS version 2 data were obtained from the NASA Goddard Earth Sciences Data and Information Services Center (freely available at http://disc.sci.gsfc.nasa.gov/uui/datasets?keywords5NLDAS). The WRF Model was obtained from the NCAR (freely available at http://www2.mmm.ucar.edu/wrf/users/). We appreciate these agencies for providing the models, data, and technical assistance. The authors would also like to thank three anonymous reviewers and editor Ryan Torn for their helpful comments.

Keywords

  • Atmosphere-land interaction
  • Data assimilation
  • Mesoscale models
  • Numerical weather prediction/forecasting
  • Precipitation
  • Soil moisture

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