Thomas R. Karl Director, National Climatic Data Center, NOAA Editor, Journal of Climate, Climatic Change & IPCC Climate Monitoring Panel Paul D. Try, Moderator.

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Presentation transcript:

Thomas R. Karl Director, National Climatic Data Center, NOAA Editor, Journal of Climate, Climatic Change & IPCC Climate Monitoring Panel Paul D. Try, Moderator Sr. V.P. Science and Technology Corporation Director, International GEWEX Project Office William B. Rossow Goddard Institute for Space Studies, NASA Chairman, GEWEX Radiation Panel, WCRP Director, International Satellite Cloud Climatology Project Stanley Q. Kidder Cooperative Institute for Research in the Atmosphere, CSU Numerous Publications & Books on Meteorological Satellites & Sensors [ “Satellite Meteorology: An Introduction” Kidder and VonderHaar ]

Global Energy and Water Cycle Experiment Observations Models Products International GEWEX Project Office Dr. Paul D. Try, Director GOES Users Conference Climate Monitoring Panel GOES Users Conference Climate Monitoring Panel

Determine the Hydrological Cycle by Global Measurements Model the Hydrological Cycle and its Effects Model the Hydrological Cycle and its Effects Predict Response to Environmental Change Improve Observing Techniques and Data Assimilation Systems OBJECTIVES    

GPCP Global Precipitation

Data Set Progress (10-23yrs 2002) ISCCP (Clouds) GPCP (Precipitation) GACP (Aerosols) GVaP (Water Vapour) Satellite Lifetimes

 PREDICTED OBSERVED Climate Model vs Observed Precipitation Global Intensification of the hydrological cycle ?   Models indicate trend -- observations don’t confirm Errors don’t allow proof

GPCP New 20+ yr Monthly Product GPCP Present shows interannual variability in tropical regions -- El Nino Events El Nino Events

GPCP New 20+ yr Pentad (5dy) Product GPCP Pentad data in tropical regions -- shows Madden-Julian Oscillation (MJO) Events Pentad Monthly [ Ref: P. Xie, NWS/NCEP/CPC ]

Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks PERSIANN System for Hydrological Applications Monthly 1x1 degree from 30 min 0.25 degree GOES-IR data [ Rainfall accumulation for 6 hrly, daily, 5 day and monthly ] [ U of AZ ]

GOES TRMM NEXRAD & Gauges Training Estimation Africa Pan American Pan American SW U.S SW U.S Global-tropical High Temporal, Low Spatial IR: Comparable but low temporal Radar: High Spatial, low temporal, Narrow Swath Radar: High Spatial + Temporal Mountain Blockage Gauges: Spotty Coverage PERSIANN: Medium Spatial and Temporal Global Gridded Coverage PERSIANN System

December, January, and February (DJF) Local time: hr Local time: hr Local time: hr Local time: hr Local time: hr Local time: hr Local time: hr Local time: hr Data Resolution at 1 o x 1 o Lat/Lon PERSIANN: Capturing the diurnal cycle

GOES USERS CONFERENCE: Climate Monitoring

GPCP Results Support IPCC ‘95 IPCC -- “... climate change will lead to an intensification of the global hydrological cycle and can have major impacts on regional water resources”. Global Distribution of Observed Moisture Recycling Using GPCP & TOVS Pathfinder (also GVaP) data sets! Using GPCP & TOVS Pathfinder (also GVaP) data sets! Zonal Average Recycling Rate per Month [Ref: Chahine et al, 1997]