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Technical Note

Development of a Machine Learning-Based Radiometric Bias Correction for NOAA’s Microwave Integrated Retrieval System (MiRS)

Cooperative Institute for Satellite and Earth System Studies, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742, USA
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Remote Sens. 2020, 12(19), 3160; https://doi.org/10.3390/rs12193160
Received: 28 August 2020 / Revised: 22 September 2020 / Accepted: 24 September 2020 / Published: 26 September 2020
We present the development of a dynamic over-ocean radiometric bias correction for the Microwave Integrated Retrieval System (MiRS) which accounts for spatial, temporal, spectral, and angular dependence of the systematic differences between observed and forward model-simulated radiances. The dynamic bias correction, which utilizes a deep neural network approach, is designed to incorporate dependence on the atmospheric and surface conditions that impact forward model biases. The approach utilizes collocations of observed Suomi National Polar-orbiting Partnership/Advanced Technology Microwave Sounder (SNPP/ATMS) radiances and European Centre for Medium-Range Weather Forecasts (ECMWF) model analyses which are used as input to the Community Radiative Transfer Model (CRTM) forward model to develop training data of radiometric biases. Analysis of the neural network performance indicates that in many channels, the dynamic bias is able to reproduce realistically both the spatial patterns of the original bias and its probability distribution function. Furthermore, retrieval impact experiments on independent data show that, compared with the baseline static bias correction, using the dynamic bias correction can improve temperature and water vapor profile retrievals, particularly in regions with higher Cloud Liquid Water (CLW) amounts. Ocean surface emissivity retrievals are also improved, for example at 23.8 GHz, showing an increase in correlation from 0.59 to 0.67 and a reduction of standard deviation from 0.035 to 0.026. View Full-Text
Keywords: machine learning; neural network; bias correction; MiRS machine learning; neural network; bias correction; MiRS
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MDPI and ACS Style

Zhou, Y.; Grassotti, C. Development of a Machine Learning-Based Radiometric Bias Correction for NOAA’s Microwave Integrated Retrieval System (MiRS). Remote Sens. 2020, 12, 3160. https://doi.org/10.3390/rs12193160

AMA Style

Zhou Y, Grassotti C. Development of a Machine Learning-Based Radiometric Bias Correction for NOAA’s Microwave Integrated Retrieval System (MiRS). Remote Sensing. 2020; 12(19):3160. https://doi.org/10.3390/rs12193160

Chicago/Turabian Style

Zhou, Yan, and Christopher Grassotti. 2020. "Development of a Machine Learning-Based Radiometric Bias Correction for NOAA’s Microwave Integrated Retrieval System (MiRS)" Remote Sensing 12, no. 19: 3160. https://doi.org/10.3390/rs12193160

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