The Global Precipitation Climatology Project Accomplishments and future outlook

1
The Global Precipitation Climatology Project
Accomplishments and future outlook
Arnold Gruber Director of the GPCP NOAA
NESDIS IPWG 23-27 September 2002, Madrid, Spain
2
(No Transcript)
3
(No Transcript)
4
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

5
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- UOK- M.Morrissey)
(ocean)
(landocean)
Algorithm Intercompararison Program

NASA-GSFC
NOAA-NESDIS
Validation
A. Chang
R.Ferraro
A. Chang
R. Ferraro
GPC Merge Development Centre
Merged Global Analysis
NASA - GSFC R.Adler
Station Observations
(CLIMAT, SYNOP National Collections)
Gauge - Only Analysis
Global Precipitation Climatology Centre
DWD - GERMANY, B. Rudolf
6
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 and GPI
  techniques is poor
• OPI
• OLR precipitation Index

• 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

7
Monthly Mean Analysis Procedures Monthly means
stepwise bias corrections i.e., IR, adjusted to
MW, satellite, adjusted to gauges, final blending
uses inverse error weighting ( Huffman, et al
1995 and Huffman et al, 1997) Pentad combines
satellite estimates by maximum likelihood
estimates, then bias removal by solving a Poisson
equation with gauges as boundary conditions. (
Xie and Arkin, 1996,1997) All products sum to
monthly means
8
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)

9
Global Precipitation Climatology Project

• Current Products
• Monthly mean 2.5 x 2.5 latitude/longitude
  (Adler et al., 2002, submitted J Hydromet )
• Merged satellite and gauge, error estimates
• Satellite components microwave and infrared
  estimates, error estimates
• Gauge analysis, error estimates (Rudolf, DWD
  Germany)
• Intermediate analysis products, e.g., merged
  satellite estimates
• Daily 1 x 1 degree, ( Huffman et al, 2001, J.
  Hydromet)
• Pentad ( Xie, et al, 2002, In press, J Climate)

http//lwf.ncdc.noaa.gov/oa/wmo/wdcamet-ncdc.html
1985
2000
1995
1990

• Continuing- Version 2 , Pentad
  1997 Daily 1x 1, deg

10
Global Precipitation Climatology Project Annual
Mean Precipitation
mm/day
11
(No Transcript)
12
1 x 1 degree, daily precipitation January 1,
1998
mm/day
13
(No Transcript)
14
(No Transcript)
15
Validation
16
Validation Surface Reference Data Center EVAC
Univ Oklahoma Director Mark Morrissey Monthly,
Daily various locations around
world http//srdc.evac.ou.edu
17
(No Transcript)
18
(No Transcript)
19
Applications
20
(No Transcript)
21
Courtesy R. Adler
22
(No Transcript)
23
(No Transcript)
24
(No Transcript)
25
(No Transcript)
26
(No Transcript)
27
Future Outlook/Issues

• New Instruments/Improved Algorithms
• TRMM a calibration source
• AMSR improved MW algorithm
• Use of Multiple Satellites
• Operational and research satellites e.g. multiple
  microwave observations from AMSU, AMSR, SSM/I,
  TRMM
• Solid precipitation
• Snow rate
• Precipitation in complex terrain
• A challenge - microphysical cloud properties to
  detect warm top rain

28
(No Transcript)
29
Observation Times by end of 2002
0
21
3
6
18
DMSP NOAA Aqua ADEOS
9
15
12
30
(No Transcript)
31

• 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

32
(No Transcript)
33
Use of Multiple Satellites Currently GPCP uses IR
data from Geostationary and Polar orbiting data
and MW data from one SSM/I. We now have MW data
available from multiple SSM/I orbits, AMSU data,
TRMM data and soon will have AMSR data. The
challenge is to utilize these data effectively.
We are proposing to utilize these data to develop
a three hourly 1 x 1 degree product. This would
be Version 3. Solid Precipitation Solid
precipitation not measured explicitly but is
included over land through use of gauges. Liu
and Curry have done some early work on solid
precipitation over the oceans and recently
Ferraro has been studying the use of AMSU 150
and 176 GHz data to help identify solid
precipitation over land.