Thomas R. KarlDirector, National Climatic Data Center, NOAAEditor, Journal of Climate, Climatic Change & IPCC

Climate Monitoring Panel

Paul D. Try, ModeratorSr. V.P. Science and Technology CorporationDirector, International GEWEX Project Office

William B. RossowGoddard Institute for Space Studies, NASAChairman, GEWEX Radiation Panel, WCRPDirector, International Satellite Cloud Climatology Project

Stanley Q. KidderCooperative 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 OfficeDr. Paul D. Try, Director

GOES Users ConferenceClimate Monitoring Panel GOES Users Conference

Climate Monitoring Panel

Determine the Hydrological Cycle by Determine the Hydrological Cycle by Global MeasurementsGlobal Measurements

ModelModel the Hydrological the Hydrological Cycle and its EffectsCycle and its Effects

Predict Predict Response to Response to Environmental ChangeEnvironmental Change

Improve Observing Improve Observing Techniques and Data Techniques and Data Assimilation SystemsAssimilation Systems

OBJECTIVES

Precipitation Evaporation

RunoffRunoff

StorageStorage

WV Flux

Energy and Water CycleEnergy and Water Cycle

GPCP Global PrecipitationGPCP Global Precipitation

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

(mm

)

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

oC

Water Vapor Anomalies, Ocean Only - NVAP

Sea Surface Temperature Anomalies - Reynolds

Data Set Progress (10-23yrs 2002)

ISCCP (Clouds)

GPCP (Precipitation)

GACP (Aerosols)

GVaP (Water Vapour)

Satellite Lifetimes

PREDICTED

OBSERVED

Climate Model vs Observed PrecipitationClimate Model vs Observed Precipitation

Global Intensification of Global Intensification of the hydrological cyclethe hydrological cycle ??

Models indicate trend --Models indicate trend --observations don’t confirmobservations don’t confirm

Errors don’t allow proofErrors don’t allow proof

GPCP New 20+ yr Monthly ProductGPCP New 20+ yr Monthly Product

GPCP 1979 - PresentGPCP 1979 - Presentshows interannualshows interannualvariability in tropicalvariability in tropicalregions --regions --

El Nino EventsEl Nino Events

GPCP New 20+ yr Pentad (5dy) ProductGPCP New 20+ yr Pentad (5dy) Product

GPCP Pentad data in GPCP Pentad data in tropical regions -- tropical regions -- shows Madden-Julian shows Madden-Julian Oscillation (MJO) Oscillation (MJO) EventsEvents

PentadPentad MonthlyMonthly

[ Ref: P. Xie, NWS/NCEP/CPC ][ Ref: P. Xie, NWS/NCEP/CPC ]

Precipitation Estimation from RemotelyPrecipitation Estimation from RemotelySensed Information using Artificial Neural NetworksSensed Information using Artificial Neural Networks

Precipitation Estimation from RemotelyPrecipitation Estimation from RemotelySensed Information using Artificial Neural NetworksSensed Information using Artificial Neural Networks

PERSIANNPERSIANN System for Hydrological Applications System for Hydrological ApplicationsPERSIANNPERSIANN System for Hydrological Applications System for Hydrological Applications

Monthly 1x1 degree from 30 min 0.25 degree GOES-IR dataMonthly 1x1 degree from 30 min 0.25 degree GOES-IR data Monthly 1x1 degree from 30 min 0.25 degree GOES-IR dataMonthly 1x1 degree from 30 min 0.25 degree GOES-IR data

[ Rainfall accumulation for 6 hrly, daily, 5 day and monthly ][ Rainfall accumulation for 6 hrly, daily, 5 day and monthly ][ Rainfall accumulation for 6 hrly, daily, 5 day and monthly ][ Rainfall accumulation for 6 hrly, daily, 5 day and monthly ]

[ U of AZ ][ U of AZ ]

GO

ES

TR

MM

NE

XR

AD

& G

aug

es

Training

Estimation

AfricaAfrica

PanAmericanPanAmerican

SWU.SSWU.S

Global-tropicalGlobal-tropical

High Temporal, Low Spatial

IR: Comparable but low temporalRadar: High Spatial, low temporal, Narrow Swath

Radar: High Spatial + TemporalMountain BlockageGauges: Spotty Coverage

PERSIANN: Medium Spatial and Temporal Global Gridded Coverage

PERSIANN SystemPERSIANN System

December, January, and February (DJF)Local time: 00-02 hr

Local time: 03-05 hr

Local time: 06-08 hr

Local time: 09-11 hr

Local time: 18-20 hr

Local time: 21-23 hr

Local time: 15-17 hr

Local time: 12-14 hr

Data Resolution at 1o x 1o 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 MonthZonal Average Recycling Rate per Month

[Ref: Chahine et al , 1997][Ref: Chahine et al , 1997]