PERSIANN-CDR Global Daily Precipitation Climate Data Record Hamed Ashouri, Scott Sellers, Kuolin Hsu, Soroosh Sorooshian, Dank. Braithwaite Center for Hydrometeorology and Remote Sensing University of California, Irvine, Irvine, California The 7th GEWEX Scientific Conference on Water and Energy Cycle Hague, Netherlands, 14-17 July, 2014 Center for Hydrometeorology and Remote Sensing, University of California, Irvine Extreme Precipitation Events Floods and droughts are the most widespread nature disasters Precipitation measurement at high spatial & temporal over an extended data period are needed for climate variability studies and water resources management. Center for Hydrometeorology and Remote Sensing, University of California, Irvine PERSIANN Precipitation Climate Data Record PERSIANN-CDR Reconstruction of 30-year+ Daily Precipitation Data Center for Hydrometeorology and Remote Sensing, University of California, Irvine LEO Satellites for Precipitation Estimation Limited PMW Samples Before year 2000 Center for Hydrometeorology and Remote Sensing, University of California, Irvine Historical GEO Satellite Data • International Satellite Cloud Climatology Project (ISCCP) 1979 to present 10-km and 3-hour intervals GOES-11 (135°West) GOES-12 (75°West) 1. U.S. Geostationary Operational Environmental Satellite (GOES) MET-9 (0°East) MET-7(57.5°East) 2. European Meteorological satellite (Meteosat) series 3. Japanese Geostationary Meteorological Satellite (GMS) MTSAT-1R(140°East) FY2-C(105°East) 4. The Chinese Fen-yung 2C (FY2) series. Center for Hydrometeorology and Remote Sensing, University of California, Irvine PERSIANN-CDR Algorithm GridSat-B1 IRWIN High Temporal-Spatial Res. Cloud Infrared Images Artificial Neural Network PERSIANN 3-Hourly Rainfall (0.25ox0.25o) PERSIANN Monthly Rainfall (2.5ox2.5o) Adjusted PERSIANN 3-Hourly Rainfall (0.25ox0.25o) GPCP Bias Adjustment GPCP Monthly Precipitation (2.5ox2.5o) Spatiotemporal Accumulation Center for Hydrometeorology and Remote Sensing, University of California, Irvine PERSIANN-CDR daily rainfall During Katrina 2005 Center for Hydrometeorology and Remote Sensing, University of California, Irvine Center for Hydrometeorology and Remote Sensing (CHRS) Center for Hydrometeorology and Remote Sensing, University of California, Irvine GPCP 1-DD and PERSIANN Precipitation (0-30oN) • Comparison of the PERSIANN and Bias-Adjusted PERSIANN with Daily GPCP product (1-DD) • Data evaluation: Data Period: 1997—2009. Data Coverage: 0—30oN • Bias-Adjusted PERSIANN estimates are consistent with the GPCP Daily (1-DD) estimates. Center for Hydrometeorology and Remote Sensing, University of California, Irvine Daily Rainfall --Hurricane Katrina (29 August 2005) Stage IV Estimation. Rain rate (mm/day) Rain rate (mm/day) (mm/day) RainRain raterate (mm/day) PERSIANN-CDR (mm/day) RainRain raterate (mm/day) TMPA V7 Rain rate (mm/day) Rain rate (mm/day) Center for Hydrometeorology and Remote Sensing, University of California, Irvine R10mm: Annual Count of days when rainfall >= 10 mm Center for Hydrometeorology and Remote Sensing, University of California, Irvine Extreme Precipitation Events in China (1983~2006) EA Annual precipitation from wet days SDII Annual count of days when precipitation ≥20mm R20mm Annual count of days when precipitation ≥10mm PERSIANN-CDR Pixel correlation Scatterplot of mean R10mm Annual total precipitation when precipitation ≥ 20mm R20mmTOT Annual total precipitation when precipitation ≥ 10mm R10mmTOT Miao et al., 2014 Center for Hydrometeorology and Remote Sensing, University of California, Irvine Global Drought Monitoring Standard Precipitation Index (SPI) estimated from GPCP 2.5-deg monthly (top) and PERSIANN-CDR 0.25-deg daily (bottom) for the period of 1983-2012. GPCP 2.5-deg monthly PERSIANN-CDR 0.25-deg daily Center for Hydrometeorology and Remote Sensing, University of California, Irvine PERSIANN-CONNECT A Database of Precipitation Systems for Climate Research PERSIANN - CONNected precipitation objECT (CONNECT) Database Sco$ Sellars, Phu Nguyen, Wei Chu, Xiaogang Gao, Kuolin Hsu, Dmitry Kunisky and Soroosh Sorooshian University of California, Irvine Center for Hydrometeorology and Remote Sensing, University of California, Irvine PERSIANN-CONNECT • PERSIANN - CONNected precipitation objECT (CONNECT) Database • • Near Global Precipitation Data (10+ Years) Hourly Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) precipitation data (mm/hour) Parameters: • – – – – • 0.25 degree (~25kmx25km) 480 rows x 1440 columns 600 North - 600 South 01 March 2000 – 01 January 2011 Store in PostgreSQL database *Sellars, S., P. Nguyen, W. Chu, X. Gao, K. Hsu, and S. Sorooshian (2013), ComputaDonal Earth Science: Big Data Transformed Into Insight, EOS Trans. AGU, 94(32),277 Center for Hydrometeorology and Remote Sensing, University of California, Irvine PERSIANN-CONNECT: Object Approach a) Traditional Approach b) 4-Dimensional Approach (voxel) 5mm/hr Rainfall Time Latitude 5mm/hr Rainfall Latitude Longitude Longitude Center for Hydrometeorology and Remote Sensing, University of California, Irvine Precipitation Object CONNECT algorithm criteria: 1) Must have 1mm/hr 2) Must exist for 24 hours 3) 6 voxel connections Time 5mm/hr Rainfall Longitu de Center for Hydrometeorology and Remote Sensing, University of California, Irvine Precipitation Object Time Calculate Object Characteristics: 1) Max Intensity 2) Duration 3) Track 4) Speed B A Longitu de Center for Hydrometeorology and Remote Sensing, University of California, Irvine Precipitation Object Characteristics Get characteristics from each object: 1) 2) 3) 4) 5) 6) 7) 8) Volume Max Intensity Average Intensity Duration Average Speed Centroid Lat Centroid Lon … … 69) NINO3+4, 70) NINO4 71) Start Centroid Latitude Nxd Matrix ! 1 X # 1 # X12 X =# # ! # XN " 1 1 2 ! 2 2 ! X X X 1 d X 2 d X 2N ! X dN N = # of events d = # of dimensions (71 in this case) Center for Hydrometeorology and Remote Sensing, University of California, Irvine $ & & & & & % Large Scale Precipitation Object Example *Sellars, S., P. Nguyen, W. Chu, X. Gao, K. Hsu, and S. Sorooshian (2013), ComputaDonal Earth Science: Big Data Transformed Into Insight, EOS Trans. AGU, 94(32),277 -115 28 Center for Hydrometeorology and Remote Sensing, University of California, Irvine Object Segmentation and Storage CONNECT: Object Segmentation Data Cube: 10+ Years of Data Object Storage (PostgreSQL) 5mm/hr Rainfall Longitude Time t=5 t=4 t=3 t=2 t=1 Longitu de Data Set Object Criteria: Database Indexes: 1. 60N-60S, 0-360 lat and long 2. Hourly time step 3. March 1st, 2000 to January 1st, 2011 1. Each voxel must have 1mm/hr 2. Each object must exist for 24 hours 3. 6 voxel connections (i.e. voxel face connections) 1. Object ID Number 2. Latitude (of each voxel in objects) 3. Longitude (of each voxel in objects) 4. Time (hour) 5. Precipitation Intensity (mm/hour) Calculate Characteristics ~1.2 billion rows, 55,000+ Objects Center for Hydrometeorology and Remote Sensing, University of California, Irvine PERSIANN-CONNECT database Search… a) Search For Storms http://chrs.web.uci.edu/research/voxel/index.html b) Select Single Storms or All Storms Download data and statistics for further analysis Center for Hydrometeorology and Remote Sensing, University of California, Irvine All Storms (2000-2010) *Sellars, S., P. Nguyen, W. Chu, X. Gao, K. Hsu, and S. Sorooshian (2013), ComputaDonal Earth Science: Big Data Transformed Into Insight, EOS Trans. AGU, 94(32),277 Center for Hydrometeorology and Remote Sensing, University of California, Irvine Case Study: Western U.S. Database Search *Returns ~ 626 large scale precipitation events Center for Hydrometeorology and Remote Sensing, University of California, Irvine Conclusions PERSIANN-CDR: • A more than 30 year daily precipitation data is introduced. • With extended data coverage, PERSIANN-CDR can be a very useful source for climate study of extreme events, such as floods and droughts. • The dataset is available for download through NCDC. PERSIANN-CONNECT: • Data driven, database technologies and 4D methods provide unique perspective for precipitation analysis • Data cover 10 years from March 2000 to Jan. 2011 • The data base will be extended to the 30+ years using PERSIANN-CDR data Center for Hydrometeorology and Remote Sensing, University of California, Irvine
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