Title: Quantifying Global Oceanic Precipitation by Combined Use of In Situ and Satellite Observations P' Xi
1Quantifying Global Oceanic Precipitationby
Combined Use of In Situ and Satellite
ObservationsP. Xie, R. Joyce, J.E. Janowiak,
and P.A. Arkin
2Objective
- To review the current status of constructing
observation-based data sets of global oceanic
precipitation - To provide suggestions on what we need to do to
improve the quantitative documentation and
monitoring of oceanic precipitation
3Combination of In Situ Satellite Obs Used in
Defining Precip. Analysis
- Satellite observations provide information of
spatial / temporal variations - In situ instruments (ships, buoys, atolls..) make
direct measurements - Merging improves quality of oceanic precip
analysis - Various merged / combined analyses (e.g. GPCP,
CMAP, TRMM) present similar spatio-temporal
variation patterns
4Problems in Existing Precip Data Sets
- Uncertainty
- in quantitative magnitude
- Inhomogeneity
- in Long-term time series
- Poor Quality
- over high latitudes
- Coarse Resolution
- in long-term data sets
5Quantitative Uncertainty 1Differences among
Data Sets
- Three sets of observation data sets used
- CMAP / GPCP / TRMM
- Annual climatology (mm/day) for 1988 2000
- Largest uncertainties (standard deviation among
observations) over ITCZ and high latitudes - Standard Deviation about 10 of the mean values
6Quantitative Uncertainty 2Sources of the
Differences
- Input Satellite Estimates
- Satellite obs. (IR, PMW)
- Retrieval algorithms
- Calibration methods/Data
- SSM/I-based precip from two different algorithms
- Bias Adjustment Methods
- Against one satellite estimates (e.g. GPCP)
- Against in situ data (e.g. CMAP)
7Long-Term Inhomogeneity 1Differences over the
Data Period
- Inhomogeneity observed in many long-term data
- Rotated EOF of GPCP monthly anomaly for 1979
2005 - Mode 6 associated with inhomogeneity associated
with the use of OLR-based precipitation estimates
before
8Long-Term Inhomogeneity 2Sources of the
Differences
- Input Satellite Data
- MW not available before 1987
- Differences between IR-MW
- ? Histograms of IR- MW-based monthly precip
- Satellite orbit changes
- (observing different phases of a diurnal cycle)
- Instrument calibration
9Poor High-Latitude Estimation Problems and Causes
- PMW unable to detect precip over icy surface
- PMW estimates over open ocean miss light precip
- IR-based estimates relate precip to cloudiness
- ? SSM/I PMW estimates of Wilheit et al.
10Spatial / Temporal Resolution Long-term data
sets vs State-of-the-art estimates for recent
period
- Coarse spatial / temporal resolution for
long-term data sets - 2.5olat/lon
- monthly / pentad
- Fine-res new satellite estimates too short to
define climatology
11Critical Elements Need to be Addressed for
improved observation of oceanic precipitation
- In Situ Measurements
- buoys, ships, special field experiments ..
- Satellite Estimates
- new instruments, new technology,
- new networks
- Combining Information from Various Sources
- different satellites
- in situ satellites
- precip other parameters (e.g. moisture,
temperature ..) -
12In Situ Measurements
- Direct measurements
- Calibration and assessments of satellite
estimates - Correction of local bias in satellite data
- ? Comparison of three SSM/I-based estimates
with buoy - What we need for future improvements
- Quantitative accuracy (wind correction)
- Extended buoy networks over extra-tropical oceans
- (esp. storm tracks)
13Satellite Estimates
- Quasi-complete spatial coverage
- Regionally / temporally varying systematic error
- Poor quality over high latitudes
- Inhomogeneity in long-term time series
- Things Under Going
- Global Precipitation Measurement (GPM)
- Improving estimation of high-latitude precip
using data from AMSU data - ? Preliminary results from an MIT group
Histogram of Precipitation
Courtesy of C. Surussavadee and D. Staelin
14Combining Information from Multiple Satellites
- Defining precipitation estimates with improved
quantitative accuracy at a fine resolution - CMORPH stands out as the best products of hi-res
precipitation for recent years - CPC is working on the further refinement of
CMORPH through using Kalman Filtering technique
and including inputs from more satellites
15Combining Information from Different
Observational Platforms
- Removing of local bias in satellite estimates
requires input of information from in situ
observations - Experiments with gauge / satellite data over
China demonstrate successful correction of biases
and improvements in patterns
16Conclusions / Recommendations
- Substantial progress made over the past decade in
documenting seasonal cycle, interannual
variations intraseasonal variability of oceanic
precipitation - Problems exist in current data sets in
quantitative uncertainty, long-term
inhomogeneity, high-latitude estimation quality,
resolution - Users requirements need to be identified and
weighted to set goals for the next decade - Combining in situ and satellite information an
effective way to construct oceanic precipitation
before data assimilation ultimately outperforms - We need to study and discuss how we, in
collaborations with other communities, can
improve the observations of oceanic precipitation
(in situ, satellites, combining)
17North Pole Precipitation July 20-21, 2006
AMSU-derived precipitation over North Pole sea
ice (pink) evolution over 24 hours High surface
elevation is problematic (dark pink)
Courtesy of C. Surussavadee and D. Staelin