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NASA GEOS-3/TRMM Re-Analysis:

NASA GEOS-3/TRMM Re-Analysis: Capturing Observed Rainfall Variability in Global Analysis Arthur Hou NASA Goddard Space Flight Center. 2 nd IPWG Workshop, Naval Research Laboratory Monterey, CA, 25-28 October 2004. Scope of talk.

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NASA GEOS-3/TRMM Re-Analysis:

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  1. NASA GEOS-3/TRMM Re-Analysis: Capturing Observed Rainfall Variability in Global Analysis Arthur Hou NASA Goddard Space Flight Center 2nd IPWG Workshop, Naval Research Laboratory Monterey, CA, 25-28 October 2004

  2. Scope of talk • Precipitation products from most operational NWP systems are forecasts rather than analyses of precipitation based on rainfall observations and model information. • NASA has been exploring a different approach to precipitation assimilation that uses rainfall observations to directly estimate and correct errors in the model rain within a 6h assimilation cycle. • A brief description of the variational continuous assimilation (VCA) scheme for precipitation assimilation. • Results from the NASA GEOS-3/TRMM reanalysis (Nov. 1997-Dec. 2002): - An atmospheric analysis dynamically consistent with a QPE based on TMI and SSM/I rain rates. GEOS-3 = Goddard Earth Observing System – Version 3

  3. TMI Monthly-Mean SST January 1998 January 1999 GPCP January: 1998 Minus 1999 1 mm/day ~ 30 W/m2 NCEP January: 1998 Minus 1999 ERA40 January 1998 Minus 1999 mm/day Tropical ENSO rainfall variability: Observation vs analyses • Large discrepancies (for the same SST input) • Tropical rainfall analyses are model-dependent and vary with parameterized model physics • Present-generation convective schemes are less than perfect - systematic model errors

  4. Variance of Hadley circulation streamfunction ECMWF Reanalysis (80-93) NCEP/NCAR Reanalysis GEOS-1 Reanalysis Time series of 15-d mean of tropical [v] at 200 hPa Trenberth & Olson, 1988 Sensitivity of tropical analysis to precipitation process • September 1982: Diabatic Nonlinear Normal Initialization (DNNMI) implemented at ECMWF • September 1984: DNNMI introduced at NMC • May 1985: Shallow convection (SC) implemented at ECMWF • May 1986: SC implemented at NMC

  5. Key issues in precipitation assimilation • Conventional data assimilation algorithms are based on the assumption that the underlying observation and model error statistics are random, unbiased, stationary, and normally distributed. • But model clouds and precipitation are derived from parameterized moist physics, which can have large systematic errors. Unless these (largely unknown) systematic model errors are accounted for in the assimilation procedure, one will always make sub-optimal use of these data. • A basic problem is that the observation operator for precipitation is not as accurate as those for conventional data or observables in clear-sky regions.

  6. (u,v,T,q)model grid (u,v,T,q) at observation locations Observations in clear-sky regions Observation operator random error “Perfect model” Systematic error (T,q,u,v)grid Precipitation observation operator with correction, d Cloud, Precipitation Precipitation observation operator What is an observation operator? It relates an observable to model state variables (u,v,T,q, etc.) Developing procedures to make online estimation and correction of biases in the observation operator to make more effective use of precipitation data

  7. Minimizing the cost function: J(x) = (x)TP-1 (x) + ( yo – H(x))TR-1 ( yo – H(x)) • model tendency correction:x • logarithm of observed rain rate:yo • logarithm of model rain estimate:H(x) • error covariance of prior estimate:P • logarithm of relative observation error variance:R A 1+1D observation operator (H) based on a 6h time-integration of a column model of moist physics with large-scale forcing prescribed from “first guess” Variational continuous assimilation (VCA) of surface rain • The strategy is to relax the perfect model assumption - i.e., using the forecast model as a weak constraint. • Assimilation of 6h surface rain accumulation using 6h-mean moisture tendency correction as the control variable,and applying the correction continuously over a 6h analysis window to ensure dynamical consistency. • The scheme estimates and corrects for biases in model’s moisture tendency every 6h to minimize discrepancies in 6h rain between the model and observations.

  8. Replicating observed propagation and intensity of tropical rainfall systems and intraseasonal oscillation GPCP NCEP GDAS ERA-40 GEOS/TRMM mm/d Rain error reduction (30N-30S, ocean) Impact of VCA rainfall assimilation on GEOS-3 analysis Propagation and intensity of tropical rainfall systems are difficult to capture MJO in precipitation over tropical oceans (10N-10S) 2001 GEOS = Goddard Earth Observing System

  9. Avg. Precipitation (120-150E, 4S-4N)(Morlet analysis) Enhanced frequency-time coherence between GPCP and GEOS-3 analysis An atmospheric analysis dynamically consistent with observed rainfall variability Improved temporal and spatial variability

  10. Improved cloud radiative forcing verified against CERES Variational continuous rainfall assimilation improves key climate parameters such as clouds and TOA radiation in the GEOS analysis • 94% reduction in bias • 51% reduction in error standard deviation January 1998

  11. Impact on wind and humidity analyses Improved latent heating patterns and large-scale motion fields leading to improved upper-tropospheric humidity (verified against TOVS brightness temperature) GEOS(TMI+SSM/I PCP+TPW) minus GEOS(CONTROL) Verification: HIRS2 Channel 12 Brightness Temperature Surface rain & Horizontal div. wind at 200 hPa Omega velocity at 500 hPa Specific humidity at 400 hPa GEOS control has a moist/cold bias relative to HIRS2 channel 12 (top) Rainfall assimilation leads to a drier upper-troposphere & reduces the err.std.dev by 11% January 1998

  12. 5-day track forecast from 12UTC 8/20/98 5-day track forecast from 00UTC 9/11/99 Improved initial storm position 5-day rain forecast 5-day rain forecast Blue: No rainfall data in IC Red: With rainfall data in IC Green: NOAA “best track” Hou et al. 2004: MWR, August issue. Impact on hurricane track and precipitation forecasts Bonnie Floyd

  13. Precipitation July 2002 OLR July 2002 OSR July 2002 mm/d W/m2 W/m2 Assimilation of TMI, SSM/I & AMSR-E rain

  14. Summary • Optimal use of precipitation information in global data assimilation poses a special challenge because parameterized physics can have large systematic errors, which must be accounted for in the assimilation procedure. • One effective strategy is to assimilate rainfall data using the forecast model as a weak constraint • Exploring advanced techniques such as ensemble DA, which could provide a unified framework for addressing both initial-condition errors and model errors • The GEOS-3/TRMM reanalysis provides an atmospheric analysis dynamically consistent with the observed tropical rainfall variability: • Improved climate parameters including TOA radiation, upper-tropospheric humidity, and cloud-radiative forcing • Improved short-range forecasts

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