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Assimilation of TRMM Precipitation into Reanalysis and its Impact on Tropical Divergence Fields

Explore assimilation of TRMM data into reanalysis and its effects on tropical divergence fields, addressing inconsistencies between precipitation and divergence in existing reanalyses.

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Assimilation of TRMM Precipitation into Reanalysis and its Impact on Tropical Divergence Fields

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  1. Baek-Min Kim(金伯珉 ) J.-H. Oh, and G.-H. Lim Seoul National University Steven Cocke and D.-W. Shin FSU/COAPS Assimilation of TRMM Precipitation into Reanalysis and its Impact on Tropical Divergence Fields

  2. ERA40-GPCP PRCP. From ECMWF Web site(http:://www.ecmwf.int)

  3. Mean divergence in winter Contour interval= Mean precipitation in winter Contour interval=1.5mm/day No Direct PRCP. ASSIM. Despite the importance of the diabatic heating related to precipitations to climate studies, the data assimilation system of long-term reanalyses do not exploit the benefit of precipitation assimilation. Inconsistency between the precipitations and divergences in existing reanalyses over tropic and subtropic has been demonstrated. (Newman et al, BAMS 2000)

  4. Motivation • Different models produce wide range of mean seasonal precipitation, yet tropical SST/diabatic heating forcing is a main ingredient for understanding climate variability and predictability. • Reanalyses (NCEP, ERA40, NASA etc) have widely varying representation of intraseasonal oscillations such as MJO (Newman et al, 2000). • Tropical divergence for tropical cyclones significantly under-represented. • Models have difficulty simulating ISOs.

  5. Data Preparation (TRMM,RII,ERA40) • Assimilate the precipitation data to reanalysis using physical initialization. Preprocessing For Assimilation Examination on the convectively-coupled variability over tropics(i.e.: MJO) with high quality PAReanalysis Test Run And Tuning of Nudging Coeff. Production of PAReanalysis (Current) Diagnostic Studies Procedure & Object

  6. Model

  7. Assimilation Method • Based on physical initialization procedure using FSUGSM • Rain rate determined by Kuo scheme • Prognostic variables nudged using Newtonian relaxation toward NCEP II reanalysis • Continuous assimilation for many months

  8. 12Z 00Z 06Z 18Z Schematics of Analysis Cycle Observation Background Simplified Physical Initialization TRMM precipitarion rate AVHRR OLR RII Rain Matching Dynamic Nudging improved moisture vertical profile Reanalized atmospheric variables strong relaxation Relaxation weak nudging Model Run Analysis Cycle

  9. Rainfall Matching Technique • The rainfall nudging basically modifies the vertical profile of humidity as a function of the observed and predicted model rain rates. • Through the continuous application, the model rain is brought closer to the observed rain rate. • Humidity profile is modified using simple structure function to match model rain rate against TRMM:

  10. Pros/Cons • PA is crude, but effective. Simple, easy to implement. • Can be used to diagnose model errors (Jeuken et al, 1996) • No cost function to distinguish quality of data • Big assumptions are made (2D field determines 3D field) – results could be largely model/cumulus parameterization dependent • Choice of nudging coefficients rely on painful procedure-”Try and See”

  11. Preliminary Focus Areas • Impact in the Western Pacific Warm Pool Region • Tropical cyclone circulation(Preliminary stage) • Intraseasonal oscillation(Preliminary stage) • Eastward propagating mode (winter)

  12. Feasibility Experiment • Continuous assimilation for JJA 2000, NOV2004 • Sensitivity to nudging coefficients • 6 hourly analyses and 3 hourly rain rates interpolated to model time step • FSUGSM at T63L27 resolution

  13. Data Sets • NCEP-2 Reanalysis (Kanamitsu et al 2002), 6 hourly interval • Tropical Rainfall Measuring Mission(TRMM) Multi-satellite Precipitation Analysis(MPA;3B42) is used. • 3B42 is a merged product of passive microwave-only product(3B40) and microwave-calibrated IR(3B41). • Temporal resolution is 3 hour and spatial resolution is 0.25 deg.

  14. PA-Reanalysis TRMM Total Precipitation for November 2004

  15. TRMM PAR November 15 0Z NCEP R2

  16. Correlation with TRMM

  17. Area-average rain in Eastern Pacific ITCZ Black – TRMM Green – PA Rean Red – NCEP R2

  18. Impact in Western Pacific Warm Pool (B.-M. Kim et al, 2007) • Assimilation of TRMM 3B42 into NCEP R2 • Seasonal assimilation from 1 May to 31 Aug 2000 • Sensitivity of nudging coefficients • Compare with ERA40, GPCP • Statistics(Mean, Var., Corr., RMSE) are compared. • This region is interesting because of large differences in the reanalyses

  19. Dynamic relaxation(nudging) • Dynamic nudging provides background(reanalysis) of precipitation assimilation. • A is model variable (vor., Psfc, div., T, q ). • A* (model prediction prior to relaxation) gets closer to A0 (reanalysis) • Bigger N forces more toward reanalysis.

  20. Sensitivity of nudging coeff. (N) Unit=day^-1

  21. Statistics over the Western Warm Pool Region (15S-25N, 60-180E)

  22. Mean PRCP(TRMM vs RII)

  23. Mean PRCP(TRMM vs NUDG3)

  24. Mean Div.(NUDG3 vs RII) Contour:1e-6 s^-1

  25. Tropical Cyclones(Steven Cocke) • Examine impact on circulation/intensity of tropical cyclones • Current experiments used T63 resolution, but future experiments need to be done a higher resolution: • Hurricane-like vortices are exaggerated in size at low resolutions • TRMM is available at 0.25 deg. -don’t waste it.

  26. TS Bonnie & Charlie (2004/08/11) GOES-12 RGB=CH1,CH1,CH4 08/11/2004 14:45 UTC

  27. Intraseasonal Oscillation(J.-H. Oh) • Assimilation period: 1 Jan 1998 to 31 Dec 2005 • NUDG3 setting (vor:8,Psfc:8,div:2,T:4,q:4) • Composite based on Wheeler and Hendon(2004) • Eastward propagating mode (winter MJO)

  28. New reanalysis GPCP NCEP R2 MJJA rainfall variability (98~05) Upper: MJJA ISO rainfall standard deviation, lower: MJJA rainfall standard deviation

  29. Western Pacfic West.Hem. And Africa Naritime continent Indian Ocean NCEP R2 New reanalysis NOAA OLR New reanalysis rainfall NCEP rainfall OLR -0.821 -0.501 Eastward propagating ISO Composite strategy: based on Wheeler and Hendon(2004) -MJO index Pattern correlation

  30. New reanalysis-wind & divergence (200hPa) NCEP R2-wind & divergence (200hPa) New reanalysis-wind(850hPa) & vertical velocity(500hPa) New reanalysis-wind(850hPa) & vertical velocity(500hPa)

  31. Summary • The TRMM data is very well assimilated, and the associated model variables are consistent with the assimilated precipitation. • Tropical Pacific rainfall and variability and its associated divergent circulation appear to be improved. • Tropical Cyclone circulation appears improved and more consistent with independent observations. • ISO signature is much more pronounced in the PA-reanalysis when compared to NCEP R2.

  32. Summary • The meteorological variables such as divergence and vertical velocity of the reanalyses are consistent with their respective reanalysis precipitation – so, if the reanalysis rain is not correct, then.....

  33. One Possiblity…. PAReanalysis Reanalysis Rossby wave Train Rossby Wave Train Div. Div. Observed precipitation Model precipitation Vorticity budget equation: Vorticity budget equation: More balanced Imbalanced

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