Daytime Rainy Cloud Detection and Convective Precipitation Delineation Based on a Deep Neural Network Method Using GOES-16 ABI Images

Precipitation, especially convective precipitation, is highly associated with hydrological disasters (e.g., floods and drought) that have negative impacts on agricultural productivity, society, and the environment. To mitigate these negative impacts, it is crucial to monitor the precipitation status in real time. The new Advanced Baseline Imager (ABI) onboard the GOES-16 satellite provides such a precipitation product in higher spatiotemporal and spectral resolutions, especially during the daytime. This research proposes a deep neural network (DNN) method to classify rainy and non-rainy clouds based on the brightness temperature differences (BTDs) and reflectances (Ref) derived from ABI. Convective and stratiform rain clouds are also separated using similar spectral parameters expressing the characteristics of cloud properties. The precipitation events used for training and validation are obtained from the IMERG V05B data, covering the southeastern coast of the U.S. during the 2018 rainy season. The performance of the proposed method is compared with traditional machine learning methods, including support vector machines (SVMs) and random forest (RF). For rainy area detection, the DNN method outperformed the other methods, with a critical success index (CSI) of 0.71 and a probability of detection (POD) of 0.86. For convective precipitation delineation, the DNN models also show a better performance, with a CSI of 0.58 and POD of 0.72. This automatic cloud classification system could be deployed for extreme rainfall event detection, real-time forecasting, and decision-making support in rainfall-related disasters.

[1]  Qiang Huang,et al.  Hierarchical Learning for DNN-Based Acoustic Scene Classification , 2016, DCASE.

[2]  Timothy J. Schmit,et al.  A Closer Look at the ABI on the GOES-R Series , 2017 .

[3]  Rich Caruana,et al.  An empirical comparison of supervised learning algorithms , 2006, ICML.

[4]  Manzhu Yu,et al.  Big Earth data analytics: a survey , 2019, Big Earth Data.

[5]  Kohei Arai,et al.  Thresholding Based Method for Rainy Cloud Detection with NOAA/AVHRR Data by Means of Jacobi Itteration Method , 2016 .

[6]  S. Liang,et al.  GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Document For , 2010 .

[7]  M. Lazri,et al.  Combination of Spectral and Textural Features in the MSG Satellite Remote Sensing Images for Classifying Rainy Area into Different Classes , 2017, Journal of the Indian Society of Remote Sensing.

[8]  Alfred T. C. Chang,et al.  A statistical technique for determining rainfall over land employing Nimbus-6 ESMR measurements , 1979 .

[9]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[10]  Wade T. Crow,et al.  Comprehensive Evaluation of GPM-IMERG, CMORPH, and TMPA Precipitation Products with Gauged Rainfall over Mainland China , 2018, Advances in Meteorology.

[11]  D. Wilson-Barker CLOUD CLASSIFICATION. , 1893, Science.

[12]  Soltane Ameur,et al.  Convective rainfall estimation from MSG/SEVIRI data based on different development phase duration of convective systems (growth phase and decay phase) , 2014 .

[13]  M. Valipour Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms , 2016 .

[14]  Y. Fu,et al.  Precipitation Clouds Delineation Scheme in Tropical Cyclones and Its Validation Using Precipitation and Cloud Parameter Datasets from TRMM , 2018 .

[15]  Jörg Bendix,et al.  Discriminating raining from non-raining clouds at mid-latitudes using meteosat second generation daytime data , 2007 .

[16]  WSR-88 D Radar Rainfall Estimation : Capabilities , Limitations and Potential Improvements , 2009 .

[17]  R. Scofield The NESDIS Operational Convective Precipitation- Estimation Technique , 1987 .

[18]  T. Nakajima,et al.  Wide-Area Determination of Cloud Microphysical Properties from NOAA AVHRR Measurements for FIRE and ASTEX Regions , 1995 .

[19]  B. Baum,et al.  Introduction to MODIS Cloud Products , 2006 .

[20]  Sandra Cruz-Pol,et al.  Rain-rate estimate algorithm evaluation and rainfall characterization in tropical environments using 2DVD, rain gauges and TRMM data , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[21]  H. Feidas,et al.  Classification of convective and stratiform rain based on the spectral and textural features of Meteosat Second Generation infrared data , 2013, Theoretical and Applied Climatology.

[22]  S. E. Haupt,et al.  Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather , 2017 .

[23]  Riko Oki,et al.  THE GLOBAL PRECIPITATION MEASUREMENT (GPM) MISSION FOR SCIENCE AND SOCIETY. , 2017, Bulletin of the American Meteorological Society.

[24]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition , 2012 .

[25]  Paul D. Bates,et al.  Flood Detection in Urban Areas Using TerraSAR-X , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[26]  G. Huffman,et al.  Integrated Multi-satellitE Retrievals for GPM (IMERG) Technical Documentation , 2015 .

[27]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[28]  W. Paul Menzel,et al.  Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS: 2. Cloud thermodynamic phase , 2000 .

[29]  Frank K. Soong,et al.  On the training aspects of Deep Neural Network (DNN) for parametric TTS synthesis , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[30]  Kuolin Hsu,et al.  Bias adjustment of infrared‐based rainfall estimation using Passive Microwave satellite rainfall data , 2017 .

[31]  P. O'Gorman,et al.  Precipitation Extremes Under Climate Change , 2015, Current Climate Change Reports.

[32]  D. Hartmann Global Physical Climatology , 1994 .

[33]  W. M. Brown,et al.  Real-Time Landslide Warning During Heavy Rainfall , 1987, Science.

[34]  Christian D. Kummerow,et al.  Differences between east and west Pacific rainfall systems , 2002 .

[35]  Jutta Thielen,et al.  A European precipitation index for extreme rain‐storm and flash flood early warning , 2015 .

[36]  Boualem Haddad,et al.  Artificial intelligence systems for rainy areas detection and convective cells' delineation for the south shore of Mediterranean Sea during day and nighttime using MSG satellite images , 2016 .

[37]  J. Janowiak,et al.  CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution , 2004 .

[38]  P. Vijaykumar,et al.  Distribution of cloudiness and categorization of rainfall types based on INSAT IR brightness temperatures over Indian subcontinent and adjoining oceanic region during south west monsoon season , 2017 .

[39]  Y. Hong,et al.  Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System , 2004 .

[40]  Haralambos Feidas,et al.  Classifying convective and stratiform rain using multispectral infrared Meteosat Second Generation satellite data , 2011, Theoretical and Applied Climatology.

[41]  Tara N. Sainath,et al.  Improving deep neural networks for LVCSR using rectified linear units and dropout , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[42]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[43]  A. Lacis,et al.  Near-Global Survey of Effective Droplet Radii in Liquid Water Clouds Using ISCCP Data. , 1994 .

[44]  Jörg Bendix,et al.  Precipitation process and rainfall intensity differentiation using Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager data , 2008 .

[45]  Tim Appelhans,et al.  Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals , 2015 .

[46]  Hidde Leijnse,et al.  Evaluation of Rainfall Products Derived From Satellites and Microwave Links for The Netherlands , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[47]  Dawei Han,et al.  Artificial intelligence techniques for clutter identification with polarimetric radar signatures , 2012 .

[48]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[49]  The nonlinear relationship between albedo and cloud fraction on near‐global, monthly mean scale in observations and in the CMIP5 model ensemble , 2015 .

[50]  V. Chandrasekar,et al.  DSD characterization and computations of expected reflectivity using data from a two-dimensional video disdrometer deployed in a tropical environment , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[51]  J. Reid,et al.  Relationships between cloud droplet effective radius, liquid water content, and droplet concentration for warm clouds in Brazil embedded in biomass smoke , 1999 .