Quantifying Global Oceanic PrecipitationQuantifying Global Oceanic Precipitationby Combined Use by Combined Use

of In Situ and Satellite Observationsof In Situ and Satellite Observations

P. Xie, P. Xie, R. Joyce, J.E. Janowiak, and P.A. ArkinR. Joyce, J.E. Janowiak, and P.A. Arkin

Objective:Objective:

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

Combination of In Situ & Satellite Obs Combination of In Situ & Satellite Obs Used in Defining Precip. AnalysisUsed 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

Problems in Existing Precip Data SetsProblems in Existing Precip Data Sets

Uncertainty in quantitative magnitude

Inhomogeneity in Long-term time series

Poor Qualityover high latitudes

Coarse Resolution in long-term data sets

Quantitative Uncertainty [1]Quantitative Uncertainty [1]Differences among Data SetsDifferences 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

Quantitative Uncertainty [2]Quantitative Uncertainty [2]Sources of the DifferencesSources 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)

Long-Term Inhomogeneity [1]Long-Term Inhomogeneity [1]Differences over the Data PeriodDifferences 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

Long-Term Inhomogeneity [2]Long-Term Inhomogeneity [2]Sources of the DifferencesSources 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

Poor High-Latitude Estimation Poor High-Latitude Estimation Problems and CausesProblems 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.

Spatial / Temporal Resolution Spatial / Temporal Resolution Long-term data sets vs Long-term data sets vs State-of-the-art estimates for recent periodState-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

Critical Elements Need to be Addressed Critical Elements Need to be Addressed for improved observation of oceanic precipitationfor improved observation of oceanic precipitation

In Situ Measurementsbuoys, ships, special field experiments ..

Satellite Estimatesnew instruments, new technology, new networks

Combining Information from Various Sources different satellitesin situ & satellitesprecip & other parameters (e.g. moisture, temperature ..)

In Situ MeasurementsIn 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)

Satellite EstimatesSatellite 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

Ocean, Summer

AMSU/NOAA biased low

103

102

10

1

AMSR-E biased low

0.01 0.1 1 10 mm/h

Histogram of Precipitation

Courtesy of C. Surussavadee and D. Staelin

Combining Information Combining Information from Multiple Satellitesfrom 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

Combining Information Combining Information from Different Observational Platformsfrom 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

Conclusions / RecommendationsConclusions / 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)

North Pole Precipitation July 20-21, 2006

70N

80N

July 20, 2006

70N

80N

July 21, 2006

AMSU-derived precipitation over North Pole sea ice (pink) – evolution over 24 hoursHigh surface elevation is problematic (dark pink)

mm/h0.2 0.5 1 2 4 8 16 25

Courtesy of C. Surussavadee and D. Staelin