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The Climate Hazards group InfraRed Precipitation (CHIRP) with Stations (CHIRPS): Development and Validation Overview Estimating precipitation variations in space and time is an important aspect of drought early warning and environmental monitoring. An evolving drier- than-normal season must be placed in historical context so that the severity of rainfall deficits may quickly be evaluated. To this end, scientists at the U.S. Geological Survey Earth Resources Observation and Science Center, working closely with collaborators at the University of California, Santa Barbara Climate Hazard Group (CHG), have developed a quasi-global (50 o S-50 o N, 180 o E-180 o W), 0.05 o resolution, 1981 to near-present gridded precipitation time series: the Climate Hazard group InfraRed Precipitation with Station (CHIRPS) data archive. The primary time step is the pentad which is used to create 6-hourly, daily, dekad, monthly and 3-monthly precipitation products. Quick preliminary pentad and daily CHIRPS are available every pentad on the 2 nd day of the following pentad. Final products for all time steps are produced after the 15 th of the following month to allow for the acquisition of all available station data. Citations Funk, C., J. Michaelsen, and M. Marshall, 2012: Mapping recent decadal climate variations in precipitation and temperature across Eastern Africa and the Sahel. Remote Sensing of Drought: Innovative Monitoring Approaches, M. A. a. J. V. B. Wardlow, Ed., Taylor and Francis, 25 pages. Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales. Journal of Hydrometeorology, 8, Janowiak, J. E., R. J. Joyce, and Y. Yarosh, 2001: A real-time global half-hourly pixel-resolution infrared dataset and its applications. Bull. Amer. Meteor. Soc., 82, Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydromet., 5. Knapp, K. R., and Coauthors, 2011: Globally gridded satellite (GriSat) observations for climate studies. Bulletin of the American Meteorological Society 92, Peterson, T. C., and R. S. Vose, 1997: An overview of the Global Historical Climatology Network temperature database. Bulletin of the American Meteorological Society, 78, CHIRP Validation - How good is CHIRPS? Trends chg.geog.ucsb.edu Pete Peterson 1, Chris C Funk 2,1, Gregory J Husak 1, Diego H Pedreros 2, Martin Landsfeld 1, James P Verdin 2 and Shraddhanand Shukla 1 1 Geography, University of California, Santa Barbara, CA, United States. 2 US Geological Survey B1 IR coverage CHIRPS = CHIRP + Blended Stations Three worst seasonal (MAM) rainfall deficits for Horn of Africa Regress Cold Cloud Duration (CCD) to TRMM-V7 pentad precipitation at each pixel, for each month ( ) and use to calculate near real time precipitation (IRP) from CPC-IR (½ hourly). Apply to B1 IR data (3-hourly) from to extend IRP time series. CHG Station Climatology Database (CSCD) 22 sources (global – regional - national) 198,000 stations (some duplicates) 500 million daily rainfall data since 1981 Another 500 million back to million daily temperature data since 1833 American Geophysical Union Fall Meeting, , San Francisco, CA, USA GPCC 2.5 o, CHIRPS 0.05 o GPCC ends in 2012, CHIRPS topped off in near-real time Independent stations Scott Adams © CHPclim is the CHG Precipitation gridded 0.05 o monthly climatology derived from station data.
Oct 2012 The FEWS NET’s Rainfall Enhancement Process Chris Funk 1, Pete Peterson 2, Marty Landsfeld 2, Andrew Verdin, Diego Pedreros 1, Joel Michaelsen.
Comparison of Precipitation Products for 2014 Ethiopian Growing Season chg.geog.ucsb.edu Pete Peterson 1, Chris C Funk 2,1, Gregory.
The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) Dataset: Quasi-Global Precipitation Estimates for Drought Monitoring and Trend.
CHG rainfall products Make best possible rainfall products for monitoring crop stress in areas of rain fed agriculture. Objectives:
We are developing a seasonal forecast system for agricultural drought early warning in sub- Saharan Africa and other food insecure locations around the.
U.S. Department of the Interior U.S. Geological Survey Evaluating the drought monitoring capabilities of rainfall estimates for Africa Chris Funk Pete.
The Evaluation of a Passive Microwave-Based Satellite Rainfall Estimation Algorithm with an IR-Based Algorithm at Short time Scales Robert Joyce RS Information.
1 Inter-comparing high resolution satellite precipitation estimates at different scales Phil Arkin and Matt Sapiano Cooperative Institute for Climate Studies.
Monitoring Global Droughts from Space Zhong Liu 1,4, W.L. Teng 2,4, S. Kempler 4, H. Rui 3,4, G. Leptoukh 4, and E. Ocampo 3,4 1 George Mason University,
FTIPP FCLIM / TRMM / IRP Precipitation Pentads aboaf (A bit of Acronym Forensics)
All about DATASETS Description and Algorithms Description and Algorithms Source Source Spatial and temporal Resolutions Spatial and temporal Resolutions.
U.S. Department of the Interior U.S. Geological Survey February 29, 2012 – Washington, D.C. Detection and Monitoring of Agricultural Drought for Famine.
Diurnal Cycle of Cloud and Precipitation Associated with the North American Monsoon System Pingping Xie, Yelena Yarosh, Mingyue Chen, Robert Joyce, John.
Thomas R. Karl Director, National Climatic Data Center, NOAA Editor, Journal of Climate, Climatic Change & IPCC Climate Monitoring Panel Paul D. Try, Moderator.
VERIFICATION OF RAINFALL ESTIMATES OVER AFRICA USING RFE, NASA MPA-RT, AND CMORPH VERIFICATION OF RAINFALL ESTIMATES OVER AFRICA USING RFE, NASA MPA-RT,
T ropical A pplications of M eteorology using SAT ellite data Ross Maidment TAMSAT Research Group, University of Reading.
Global Flood and Drought Prediction GEWEX 2005 Meeting, June Role of Modeling in Predictability and Prediction Studies Nathalie Voisin, Dennis P.
The Climate Prediction Center Rainfall Estimation Algorithm Version 2 Tim Love -- RSIS/CPC.
1. Session Goals 2 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Become familiar with the available data sources for.
Precipitation in IGWCO The objectives of IGWCO require time series of accurate gridded precipitation fields with fine spatial and temporal resolution for.
1. Session Goals 2 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Understand use of the terms climatology and variability.
Weather Station Data Quality and Interpolation Issues in Modeling Joe Russo International Workshop on Plant Epidemiology Surveillance for the Pest Forecasting.
U.S. Department of the Interior U.S. Geological Survey Exploring Western and Eastern Pacific contributions to the 21st century Walker circulation intensification.
U.S. Department of the Interior U.S. Geological Survey NASA/USDA Workshop on Evapotranspiration April 6, 2011 – Silver Spring, Maryland ET for famine early.
High Resolution Gauge – Satellite Merged Analyses of Precipitation: A 15-Year Record Pingping Xie, Soo-Hyun Yoo, Robert Joyce, Yelena Yarosh, Shaorong.
Phil Arkin, ESSIC University of Maryland With thanks to: Pingping Xie, John Janowiak, and Bob Joyce Climate Prediction Center/NOAA Describing the Diurnal.
THE USE OF REMOTE SENSING DATA/INFORMATION AS PROXY OF WEATHER AND CLIMATE IN THE GREATER HORN OF AFRICA Gilbert O Ouma IGAD Climate Applications and Prediction.
1/13/2003IRI Presentation E D C F E W S An empirical study of the links between NDVI and atmospheric variables in Africa with applications to forecasting.
The Global Precipitation Climatology Project – Accomplishments and future outlook Arnold Gruber Director of the GPCP NOAA NESDIS IPWG September 2002,
Vulnerability to near-term warming in the Sahel Laura Harrison UCSB Geography Climate Hazards Group Famine Early Warning System Network.
Rainfall estimation for food security in Africa, using the Meteosat Second Generation (MSG) satellite. Robin Chadwick.
A new high resolution satellite derived precipitation data set for climate studies Renu Joseph, T. Smith, M. R. P. Sapiano, and R. R. Ferraro Cooperative.
Arctic Discharge Observations The Arctic RIMS and the R-Arctic Net datasets Åsa Rennermalm Princeton University.
Model representation of the diurnal cycle and moist surges along the Gulf of California during NAME Emily J. Becker and Ernesto Hugo Berbery Department.
An Overview of Satellite Rainfall Estimation for Flash Flood Monitoring Timothy Love NOAA Climate Prediction Center with USAID- FEWS-NET, MFEWS, AFN Presented.
Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:
Data Issues for Latin America Dr. Hugo G. Hidalgo School of Physics University of Costa Rica GEOSS Support for the IPCC Assessments February 2011
Flood Risk Modeling in Thailand … and more … Alexander Lotsch Commodity Risk Management Group Agriculture and Rural Development The World Bank Group.
Bob Joyce : RSIS, Inc. John Janowiak : Climate Prediction Center/NWS Phil Arkin : ESSIC/Univ. Maryland Pingping Xie: Climate Prediction Center/NWS 0000Z,
Development of Bias-Corrected Precipitation Database and Climatology for the Arctic Regions Daqing Yang, Principal Investigator Douglas L. Kane, Co-Investigator.
Arctic Land Surface Hydrology: Moving Towards a Synthesis Global Datasets.
John Janowiak Climate Prediction Center/NCEP/NWS Jianyin Liang China Meteorological Agency Pingping Xie Climate Prediction Center/NCEP/NWS Robert Joyce.
Electrical & Computer Engineering High Resolution Satellite Precipitation Estimate Using Cluster Ensemble Cloud Classification Majid Mahrooghy, Nicolas.
Spatial and Temporal Variability of GPCP Precipitation Estimates By C. F. Ropelewski Summarized from the generous input Provided by G. Huffman, R. Adler,
Intercomparing and evaluating high- resolution precipitation products M. R. P. Sapiano*, P. A. Arkin*, S. Sorooshian +, K. Hsu + * ESSIC, University of.
“CMORPH” is a method that creates spatially & temporally complete information using existing precipitation products that are derived from passive microwave.
Integrating Global Species Distributions, Remote Sensing and Climate Station Data to Assess Biodiversity Response to Climate Change Adam Wilson & Walter.
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