Global Precipitation Climatology Project GPCP

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Global Precipitation Climatology Project(GPCP)
Arnold Gruber Director of the GPCP WCRP Workshop
on Detrermination of Solid Precipitation in Cold
Climate Regions 9-14 June 2002, Fairbanks, Alaska
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Global Precipitation Climatology Project

• Organized in 1986
• Component of the Global Energy and Water
  Cycle Experiment (GEWEX) of WCRP
• Objectives
• Improve understanding of seasonal to inter-annual
  and longer term variability of the global
  hydrological cycle
• Determine the atmospheric heating needed for
  climate prediction models
• Provide an observational data set for model
  validation and initialization and other
  hydrological applications

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Global Precipitation Climatology Project
IR Component
Data Processing Centres
GMS
Meteosat
GOES
NOAA
JAPAN
EUROPE
UNITED STATES
MW Component
CAL/VAL Component
Polar Satellite Precipitation Data Center
Geostationary Satellite Precipitation Data Centre
Surface Reference Data Centre
scattering
emission
(EVAC - U.OK)
(ocean)
(landocean)
Algorithm Intercompararison Program

NASA-GSFC
NOAA-NESDIS
New Observations
GPC Merge Development Centre
Merged Global Analysis
NASA - GSFC
Station Observations
(CLIMAT, SYNOP National Collections)
Gauge - Only Analysis
Global Precipitation Climatology Centre
DWD - GERMANY
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Remote Sensing Estimates used in GPCP

• Infra red
• GOES
• RR linearly related to fractional pixels
  Tcldlt235K
• Most effective for deep convective clouds, used
  only in 40N,S zone
• High spatial and temporal resolution
• false signatures, insensitive to warm top rain
• TOVS
• Regression between cloud parameters and rain
  gauges
• Used in high latitudes where MW techniques are
  poor

• Microwave (SSM/I)
• Closely related to hydrometeors
• Emission from cloud drops ( 29 GHz). Most
  effective over water surfaces ( Tsfc ltltTcld)
• Scattering by ice particles over land over land (
  89, Tcldlt Ta)
• only ice clouds over land, low resolution, no
  estimate over snow and ice

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IMPORTANT POINT Algorithms are designed for
liquid precipitation Gauges Used to produce a
gridded analysis, incorporates water equivalent
of solid precipitation Final GPCP Precipitation
Field satellite estimates adjusted to large
scale gauge analysis ( water equivalent of
solid precipitation incorporated in this stage)
Satellite data merged with gauge analysis using
inverse error variance weighting
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Global Precipitation Climatology Project

• Current Products
• Monthly mean 2.5x2.5 latitude/longitude
• Merged satellite and gauge, error estimates
• Satellite components microwave and infrared
  estimates, error estimates
• Gauge analysis, error estimates
• Intermediate analysis products, e.g., merged
  satellite estimates
• Daily 1 x 1 degree, Pentad

July 1987 and continuing -Version 1
1985
2000
1995
1990
1979 Continuing- Version 2 , Pentad
1997 Daily 1x 1, deg
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Global Precipitation Climatology Project Annual
Mean Precipitation
mm/day
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1 x 1 degree, daily precipitation January 1,
1998
mm/day
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• Summary
• Needs for solid precipitation
• Atmosphere Latent heat of fusion is important
  diabatic heat source
• Surface albedo affects land atmosphere energy
  exchange important for surface hydrologic
  applications. e.g., floods, water resources, etc.
• GPCP
• No direct measure of solid precipitation rate
  only water equivalent when adjusted to gauges
• Can lead to bias where there are no gauges
• Need validation and feedback on GPCP
  precipitation estimates over land areas
• Techniques being developed to identify solid
  precipitation need observed rates for
  calibration/validation

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• Global Precipitation Climatology Data
• Monthly Mean 2.5 x 2.5 degree 1979 and
  continuing
• Pentad ( 5 day) 2.5 x 2.5 degree 1979 and
  continuing
• Daily, 1 x 1 degree - 1997 and continuing
• Available On Line from World Data Center A at
  The National Climatic Data Center
• http//lwf.ncdc.noaa.gov/oa/wmo/wdcamet-ncdc.html

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Potential of detecting falling snow over land
using AMSU-B

• Preliminary study (ongoing) using AMSU-B (89,
  150, 1831, 3, 7 GHz)
• Great Plains U.S (flat, homogeneous)
• Cases where no snow existed, active snowfall and
  remaining snowcover after precipitation event
• Ancillary data
• NEXRAD composites
• Synoptic weather reports/first order stations
• Hourly precipitation amounts
• QC of AMSU surface reports to insure that
  proper surface and weather types have been
  classified

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Preliminary Findings

• Use of AMSU-B 150 and 176 GHz appears to be best
  set of channels
• Single channel inadequate
• More channels may not add much more information
• Application to case studies seems promising, but
• Need to consider false alarm rate
• Need to determine global applicability
• Need to determine sensitivity to snowrate, cloud
  physics, etc.

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Algorithm
Enter Rain Rate Algorithm
Snow on Ground?
NO
YES
Snow Index 6.4 0.213TB150-0.043TB176
No Falling Snow
Snow Index gt 0.60?
NO
YES
TB176 gt TB180?
NO
YES
Snow is Falling
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