Objective:

1. Quantifying Global Oceanic Precipitationby Combined Use of In Situ and Satellite ObservationsP. Xie, R. Joyce, J.E. Janowiak, and P.A. Arkin

2. 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

3. Combination 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

4. Problems 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

5. Quantitative Uncertainty [1]Differences 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

6. Quantitative Uncertainty [2]Sources 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)

7. Long-Term Inhomogeneity [1]Differences 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

8. Long-Term Inhomogeneity [2]Sources 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

9. Poor 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.

10. Spatial / 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

11. Critical 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 ..)

12. In 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)

13. Satellite Estimates Histogram of Precipitation Ocean, Summer 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 103 102 10 1 AMSR-E biased low AMSU/NOAA biased low Courtesy of C. Surussavadee and D. Staelin 0.01 0.1 1 10 mm/h

14. Combining 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

15. Combining 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

16. Conclusions / 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)

17. North Pole Precipitation July 20-21, 2006 70N 70N 80N 80N July 21, 2006 July 20, 2006 mm/h 0.2 0.5 1 2 4 8 16 25 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