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Objective:

A Gauge-Satellite Merged Analysis of High-Resolution Global Precipitation Pingping Xie NOAA ’ s Climate Prediction Center acknowledgements S.-H. Yoo, R. Joyce, and Y. Yarosh 2011.03.30. Objective:. To develop a high-resolution gauge-satellite merged analysis of global precipitation

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Objective:

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  1. A Gauge-Satellite Merged Analysis of High-Resolution Global PrecipitationPingping XieNOAA’s Climate Prediction CenteracknowledgementsS.-H. Yoo, R. Joyce, and Y. Yarosh2011.03.30.

  2. Objective: To develop a high-resolution gauge-satellite merged analysis of global precipitation Up to 8kmx8km over the globe (60oS-60oN) 30-min from Jan.1998, updated real time Through combining information from gauge-based analysis and CMORPH satellite estimates

  3. Global Daily Gauge Analysis Interpolation of gauge reports from ~30K stations Optimal Interpolation (OI) with orographic correction (Xie et al. 2007) Interpolated on 0.125olat/lon, then averaged on 0.5o/0.25o lat/lon grid over global land / CONUS for release Global fields from1979 to present updated daily on a real-time basis CONUS analysis from 1948 Example for July 1, 2003

  4. CMORPH Satellite Estimates CMORPH : CPC Morphing technique (Joyce et al. 2004) Combined use of satellite PMW and IR data 8kmx8km / 60oS-60oN; 30-min interval / from March1998 / Real-time CMORPH back-extended to 1998 to cover the entire TRMM Era

  5. CMORPH Bias [1]Global Distribution 2000-2009 10-yr annual mean precip CMORPH captures the spatial distribution patterns very well BIAS exists Over-estimates over tropical / sub-tropical areas Under-estimates over mid- and hi-latitudes

  6. CMORPH Bias [2]Time Scales of the Bias Bias over CONUS Bias presents substantial variations of seasonal (top), sub-monthly (middle), and year-to-year (bottom) time scales

  7. CMORPH Bias [3]Range Dependence Bias as a function of CMORPH Rainfall Intensity over CONUS Bias exhibits strong range dependence

  8. Bias Correction [1]General Strategy Seasonal / Year-to-year variations in bias  correction coefficients change with time Sub-monthly variations in bias  against sub-monthly gauge data Range-dependence in bias  PDF matching

  9. PDF matching • Collect co-located daily gauge and satellite data over a time / space domain • Construct cumulated PDF tables for the gauge and satellite data • Match the PDF of satellite data against that of gauge data to remove the bias Bias Correction [2]PDF Bias Correction against daily gauge data

  10. Step 1: Correction using Historical Data • Establish PDF matching tables • using historical data • for each 0.25olat/lon grid • for each calendar date • using data over nearby regions and • over a period of +/- 15 days centering at the target date • At least 500 pairs of non-zero data pairs • to ensure the PDF tables are created using data over a small space domain • Step 2: Correction using Real-Time Data • Perform PDF matching using data over a 30-day period ending at the target date • To account for year-to-year variations in the bias Bias Correction [3]Global Implementation Strategy

  11. Bias Correction [4] Results over Global Land 2000-2009 annual mean Large-scale bias corrected Daily Gauge KF-CMORPH Comparison over Africa Gauge-Adjusted KF-CMORPH

  12. Bias Correction [5]Strategy over Ocean • CMORPH: High-resolution with relatively short record • Pentad GPCP: Low-resolution with relatively homogeneous long record • Adjust the CMORPH against the pentad GPCP • Match the PDF of CMORPH averaged to pentad / 2.5olat/lon to that of pentad GPCP

  13. Bias Correction [6]Applications : Evaluation of CFSR JJA Precip.

  14. Bias Correction [7]Applications : Precip. Diurnal Cycle

  15. Bias Correction [8]Applications : Precip. Diurnal Cycle over Oceans

  16. Bias Correction [9]Applications : Precip. Diurnal Cycle over Land

  17. Bias Correction [10]Applications : Evaluations of MJO Precip in CFSR

  18. Combining Gauge with Satellite [1] Combining bias-corrected satellite estimates with daily gauge over the several regions This is only possible for several regions due to different daily ending time in the gauge reports Africa (06Z) CONUS/MEX (12Z) S. America (12Z) Australia (00Z) China (00Z) Combining the bias-corrected CMORPH with gauge observations through the Optimal Interpolation (OI) over selected regions where gauge observations have the same daily ending time in which the CMORPH and gauge data are used as the first guess and observations, respectively

  19. Combining Gauge with Satellite [2]Quantifying error in the inputs Key to the construction of an OI-analysis is the definition of input errors Quantified error in the gauge and bias-corrected CMORPH through comparison against real data Error variance in gauge analysis Proportional to precip intensity Inversely proportional to local gauge density Error variance in bias-crtd CMORPH Proportional to precip intensity Correlation between CMORPH error at two different grid boxes Decreases exponentially with separation distance

  20. Combining Gauge with Satellite [3]Example for Pakistan Flooding Gauge analysis depict heavy rain but tend to extend the raining area Satellite data tend to under-estimate Merged analysis present improved depiction of the heavy rain

  21. Combining Gauge with Satellite [4]Independent tests using data over Korea • Reports from up to 28 stns available in a 0.25olat/lon grid box over Seoul • Arithmetic mean of 28-stn reports taken as the ‘truth’ • Gauge analyses using 1000 combinations of sub-set stations are combined with bias-corrected CMORPH and compared to the ‘truth’ at the grid box over Seoul • Correlation is calculated for the combined analyses and the input gauge / CMORPH. Results are plotted in different colors for different gauge network densities

  22. Gauge Analysis (0.25olat/lon) CMORPH (0.25olat/lon) Operation System PDF Matching Bias Correction Bias-Corrected CMORPH OI Combining Merged Analysis

  23. We are in final stage of constructing gauge-satellite merged analyses of global precipitation; • Two sets of gauge-satellite precipitation analyses • Bias-corrected Satellite Estimates • Global • 8kmx8km; 30-min • 1998 to the present • Gauge-satellite combined analyses • Regional • 0.25olat/lon; daily • 1998 to the present Summary

  24. Developing 2nd generation Kalman filter based CMORPH satellite estimates (Part of PMM project) • Capable of including information from additional sources (e.g. IR, model) • Integrating information based on more accurate statistical framework Related R&D Activities [1] Simulation tests of the original and KF CMORPH with inputs from 1,2,4,7, and 9 PMW satellites Comparisons against radar observations over CONUS for different local times

  25. Related R&D Activities [2] Gauge-radar-satellite merged analysis of hourly precipitation over CONUS Final goal: a portable module to combine global high-resolution satellite estimates (e.g. CMORPH) with regionally available additional information (e.g. gauge, radar, model) to create precip analysis of higher quality and resolution

  26. Extending the CMORPH to cover the entire globe (pole-to-pole) • Cloud advection vectors • Cold season precipitation rates • Taking advantage of model simulations Related R&D Activities [3]

  27. Thank You !!

  28. Backup Slides

  29. Gauge Error [1] • Gauge error is defined by comparing the gauge analysis derived from reports of different gauge network configurations against the ’truth’ • Daily data from an extremely dense station network over Korea is used. Over a 0.25olat/lon grid box over Seoul, Korea, reports from 28 stations are available and their arithmetic mean is used as the ‘truth’ for that grid box • Gauge analyses are constructed using reports from 1,000 combinations of stations to mimic the configuration of those over China; and they are compared to the ‘truth’ for 2005-2007

  30. Gauge Error [2] • Results are analyzed according to the precipitation intensity and gauge network density • Gauge network density is defined using a parameter called Number of Equivalent Gauges (Neg) • Neg = Ng0 + Ng1 + Ng2 • Ng0 : # of gauges inside the target grid box • Ng1 : # of gauges inside the grid boxes neighboring to the target grid box • Ng2: # of gauges inside the grid boxes one f urther layer away.

  31. Gauge Error [3] • Error variance of the gauge analysis linearly proportional to the precipitation intensity and inversely proportional to the local gauge network density measured by the Number of Equivalent Gauges (Neg) • An empirical relation established between the error variance and the precipitation and the Neg: • E2 = a + b ∙ R / ( Neg + 1 ) • a = 0.15 (mm/day)2 • b = 4.09 (mm/day)2

  32. CMORPH Error [1] • Assuming CMORPH error variance is proportional to the estimated precipitation amount • Proportional constant is determined through comparison with gauge analysis over grid boxes with at least one gauge using data over China for summer 2007 • CMORPH error is defined for each grid box of 0.25olat/lon using the curve in the bottom panel of the figure

  33. CMORPH Error Correlation • Correlation between the bias- corrected CMOROH error at one location and that at a different location • Assuming the error correlation is only a function of separation distance • Correlation calculated for all combinations of grid boxes using data over China for summer 2007 • Results fitted to a Gaussian function

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