Precipitation Estimates from MSG SEVIRI Daytime, Nighttime, and Twilight Data with Random Forests

AbstractA new rainfall retrieval technique for determining rainfall rates in a continuous manner (day, twilight, and night) resulting in a 24-h estimation applicable to midlatitudes is presented. The approach is based on satellite-derived information on cloud-top height, cloud-top temperature, cloud phase, and cloud water path retrieved from Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) data and uses the random forests (RF) machine-learning algorithm. The technique is realized in three steps: (i) precipitating cloud areas are identified, (ii) the areas are separated into convective and advective-stratiform precipitating areas, and (iii) rainfall rates are assigned separately to the convective and advective-stratiform precipitating areas. Validation studies were carried out for each individual step as well as for the overall procedure using collocated ground-based radar data. Regarding each individual step, the models for rain area and convective precipitation dete...

[1]  H. Feidas,et al.  Identifying precipitating clouds in Greece using multispectral infrared Meteosat Second Generation satellite data , 2011 .

[2]  P. Bauer,et al.  The International Precipitation Working Group and Its Role in the Improvement of Quantitative Precipitation Measurements. , 2006 .

[3]  Itamar M. Lensky,et al.  Satellite-Based Insights into Precipitation Formation Processes in Continental and Maritime Convective Clouds , 1998 .

[4]  Mahesh Pal,et al.  Random forest classifier for remote sensing classification , 2005 .

[5]  Robert S. Stone,et al.  The Remote Sensing of Thin Cirrus Cloud Using Satellites, Lidar and Radiative Transfer Theory , 1990 .

[6]  V. Levizzani,et al.  Status of satellite precipitation retrievals , 2009 .

[7]  Andreas Stolcke,et al.  A study in machine learning from imbalanced data for sentence boundary detection in speech , 2006, Comput. Speech Lang..

[8]  B. J. Conway,et al.  Delineation of precipitation areas from MODIS visible and infrared imagery with artificial neural networks , 2005 .

[9]  R. A. Roebeling,et al.  SEVIRI rainfall retrieval and validation using weather radar observations , 2009 .

[10]  Adele Cutler,et al.  Random forests for microarrays. , 2006, Methods in enzymology.

[11]  F. Marzano,et al.  Artificial neural-network technique for precipitation nowcasting from satellite imagery , 2006 .

[12]  C. Reudenbach,et al.  Investigation of summertime convective rainfall in Western Europe based on a synergy of remote sensing data and numerical models , 2001 .

[13]  K. Liou,et al.  Remote sensing of cirrus cloud parameters using advanced very-high-resolution radiometer 3.7- and 1 O.9-microm channels. , 1993, Applied optics.

[14]  Vincenzo Levizzani,et al.  Satellite rainfall estimates: new perspectives for meteorology and climate from the EURAINSAT project , 2003 .

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

[16]  J. Peñas,et al.  Phytogeographical relationships among high mountain areas in the Baetic Ranges (South Spain) , 2002 .

[17]  Jörg Bendix,et al.  Rainfall-Rate Assignment Using MSG SEVIRI Data—A Promising Approach to Spaceborne Rainfall-Rate Retrieval for Midlatitudes , 2010 .

[18]  Itamar M. Lensky,et al.  A Night-Rain Delineation Algorithm for Infrared Satellite Data Based on Microphysical Considerations , 2003 .

[19]  G. Visconti,et al.  A Neural Network Approach to Real-Time Rainfall Estimation for Africa Using Satellite Data , 2003 .

[20]  B. N. Meisner,et al.  The Relationship between Large-Scale Convective Rainfall and Cold Cloud over the Western Hemisphere during 1982-84 , 1987 .

[21]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[22]  Barbara Früh,et al.  Verification of precipitation from regional climate simulations and remote-sensing observations with respect to ground-based observations in the upper Danube catchment , 2007 .

[23]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

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

[25]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[26]  Thomas Nauss,et al.  Retrieval of warm cloud optical properties using simple approximations , 2011 .

[27]  Tanvir Islam,et al.  Non-parametric rain/no rain screening method for satellite-borne passive microwave radiometers at 19–85 GHz channels with the Random Forests algorithm , 2014 .

[28]  Garik Gutman,et al.  Retrieving microphysical properties near the tops of potential rain clouds by multispectral analysis of AVHRR data , 1994 .

[29]  Toshiro Inoue,et al.  On the Temperature and Effective Emissivity Determination of Semi-Transparent Cirrus Clouds by Bi-Spectral Measurements in the 10μm Window Region , 1985 .

[30]  Jan Cermak.  SOFOS : a new satellite-based operational fog observation scheme , 2006 .

[31]  Emmanouil N. Anagnostou,et al.  Stratiform and Convective Classification of Rainfall Using SSM/I 85-GHz Brightness Temperature Observations , 1997 .

[32]  W. Menzel,et al.  Discriminating clear sky from clouds with MODIS , 1998 .

[33]  Jennifer A. Miller,et al.  Contextual land-cover classification: incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic , 2010 .

[34]  Jörg Bendix,et al.  A novel approach to fog/low stratus detection using Meteosat 8 data , 2008 .

[35]  D. Aminou.  MSG's SEVIRI instrument , 2002 .

[36]  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.

[37]  Thomas Nauss,et al.  Satellite‐based retrieval of ice cloud properties using a semianalytical algorithm , 2005 .

[38]  Johannes Schmetz,et al.  Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation , 2001 .

[39]  Dawei Han,et al.  An exploratory investigation of an adaptive neuro fuzzy inference system (ANFIS) for estimating hydrometeors from TRMM/TMI in synergy with TRMM/PR , 2014 .

[40]  J. Janowiak,et al.  COMPARISON OF NEAR-REAL-TIME PRECIPITATION ESTIMATES FROM SATELLITE OBSERVATIONS AND NUMERICAL MODELS , 2007 .

[41]  Samia Boukir,et al.  Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests , 2011 .

[42]  Tanvir Islam,et al.  Tree-based genetic programming approach to infer microphysical parameters of the DSDs from the polarization diversity measurements , 2012, Comput. Geosci..

[43]  Robert F. Adler,et al.  Thunderstorm cloud height-rainfall rate relations for use with satellite rainfall estimation techniques , 1984 .

[44]  W. Paul Menzel,et al.  Cloud Properties inferred from 812-µm Data , 1994 .

[45]  Georges Dupret,et al.  Bootstrap re-sampling for unbalanced data in supervised learning , 2001, Eur. J. Oper. Res..

[46]  Thomas Nauss,et al.  Assignment of rainfall confidence values using multispectral satellite data at mid-latitudes: first results , 2007 .

[47]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[48]  Robert F. Adler,et al.  A Satellite Infrared Technique to Estimate Tropical Convective and Stratiform Rainfall , 1988 .

[49]  James D. Malley,et al.  Statistical Learning for Biomedical Data , 2011 .

[50]  M. Cheng,et al.  Delineation of Precipitation Areas by Correlation of Meteosat Visible and Infrared Data with Radar Data , 1995 .

[51]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[52]  Anne Ruiz,et al.  Storms prediction : Logistic regression vs random forest for unbalanced data , 2007, 0804.0650.

[53]  Thomas Nauss,et al.  Discriminating raining from non-raining clouds at mid-latitudes using multispectral satellite data , 2006 .

[54]  Mohan K. Ramamurthy,et al.  Preface Earth System Science Data access, distribution and use for education and research , 2006 .

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

[56]  Nikunj C. Oza,et al.  Online Ensemble Learning , 2000, AAAI/IAAI.

[57]  G. Tutz,et al.  An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. , 2009, Psychological methods.

[58]  K. Liou,et al.  Removal of the Solar Component in AVHRR 3.7-µm Radiances for the Retrieval of Cirrus Cloud Parameters , 1995 .

[59]  S. Sorooshian,et al.  Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks , 1997 .

[60]  Johannes Schmetz,et al.  Warm water vapour pixels over high clouds as observed by METEOSAT , 1997 .

[61]  Tim Appelhans,et al.  An evaluation of a semi-analytical cloud property retrieval using MSG SEVIRI, MODIS and CloudSat , 2013 .

[62]  Kuolin Hsu,et al.  Intercomparison of High-Resolution Precipitation Products over Northwest Europe , 2012 .

[63]  Anne-Laure Boulesteix,et al.  Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics , 2012, WIREs Data Mining Knowl. Discov..

[64]  Tim Appelhans,et al.  Improving the accuracy of rainfall rates from optical satellite sensors with machine learning — A random forests-based approach applied to MSG SEVIRI , 2014 .

[65]  Johannes Schmetz,et al.  Monitoring deep convection and convective overshooting with METEOSAT , 1997 .

[66]  Jörg Bendix,et al.  Discriminating raining from non‐raining cloud areas at mid‐latitudes using meteosat second generation SEVIRI night‐time data , 2008 .

[67]  A. Gruber,et al.  GOES Multispectral Rainfall Algorithm (GMSRA) , 2001 .

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

[69]  Sunny Sun-Mack,et al.  CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part I: Algorithms , 2011, IEEE Transactions on Geoscience and Remote Sensing.