2019 Spring Cyberseminar Series: Exploring deep neural networks to retrieve rain and snow in high latitudes using multi-sensor and reanalysis data

Talk Description

Satellite remote sensing is able to provide information on global rain and snow, but challenges remain in accurate estimation of precipitation rates, particularly in snow retrieval. In this work, the deep neural network (DNN) is applied to estimate rain and snow rates in high latitudes. The reference data for DNN training are provided by two spaceborne radars onboard the GPM Core Observatory and CloudSat. Passive microwave data from the GPM Microwave Imager (GMI), infrared (IR) data from MODIS and environmental data from ECMWF are trained to the reference precipitation. The DNN estimates are compared to data from the Goddard Profiling Algorithm (GPROF) which is used to retrieve passive microwave precipitation for the Global Precipitation Measurement (GPM) mission. First, the DNN-based retrieval method performs well in both training and testing periods. Second, the DNN can reveal the advantages and disadvantages of different channels of GMI and MODIS. Additionally, IR and environmental data can improve precipitation estimation of the DNN, particularly for snowfall. Finally, based on the optimized DNN, precipitation is estimated in 2017 from orbital GMI brightness temperatures and compared to ERA-Interim and MERRA2 reanalysis data. Evaluation results show that: (1) the DNN can largely mitigate the underestimation of precipitation rates in high latitudes by GPROF; (2) the DNN-based snowfall estimates largely outperform those of GPROF; and (3) the spatial distributions of DNN-based precipitation are closer to reanalysis data. The method and assessment presented in this study could potentially contribute to the substantial improvement of satellite precipitation products in high latitudes.

CUAHSI's 2019 Spring Cyberseminar Series: Recent advances in big data machine learning in Hydrology

Hosted by Chaopeng Shen, Pennsylvania State University

Recently big data machine learning has led to substantial changes across many areas of study. In Hydrology, the introduction of big data and machine learning methods have substantially improved our ability to address existing challenges and encouraged novel perspectives and new applications. These advances present new opportunities methods that aid scientific discovery, data discovery, and predictive modeling. This series cover new techniques and findings that have emerged in Hydrology during the previous year, with a focus on catchment and land surface hydrology.

All talks take place on Fridays at 1:00 p.m. ET.

Dates, Speakers, and Topics:

  • March 29, 2019: Machine Learning & Information Theory for Land Model Benchmarking & Process Diagnostics | Grey Nearing, University of Alabama
  • April 5, 2019: Long Short-Term Memory (LSTM) networks for rainfall-runoff modeling | Frederik Kratzert, Johannes Kepler University
  • April 12, 2019: Use deep convolutional neural nets to learn patterns of mismatch between a land surface model and GRACE satellite | Alex Sun, University of Texas at Austin
  • April 19, 2019: Long-term projections of soil moisture using deep learning and SMAP data with aleatoric and epistemic uncertainty estimates | Chaopeng Shen, Pennsylvania State University
  • April 26, 2019: Exploring deep neural networks to retrieve rain and snow in high latitudes using multi-sensor and reanalysis data | Guoqiang Tang, Tsinghua University
  • May 3, 2019: Multioutput neural networks for estimating flow-duration curves in ungaged catchments | Scott Worland, Cornell University and USGS
  • May 10, 2019: Remote sensing precipitation using artificial neural networks and machine learning methods | Kuolin Hsu, University of California, Irvine

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