Hou/JTST2000 - 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

Arthur Hou, IPWG Workshop, 25-28 October 2004 - 2

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

Scope of talkScope of talk

Arthur Hou, IPWG Workshop, 25-28 October 2004 - 3

Tropical ENSO rainfall variability: Observation vs analysesTropical ENSO rainfall variability: Observation vs analyses

TMI Monthly-Mean SST January 1998

January 1999

GPCP January: 1998 Minus 1999

NCEP January: 1998 Minus 1999

ERA40 January 1998 Minus 1999

mm/day

1 mm/day 30 W/m2

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

Arthur Hou, IPWG Workshop, 25-28 October 2004 - 4

Sensitivity of tropical analysis to precipitation processSensitivity of tropical analysis to precipitation process

Time series of 15-d mean of tropical [v] at 200 hPa

Trenberth & Olson, 1988Trenberth & Olson, 1988

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

ECMWF Reanalysis (80-93) NCEP/NCAR Reanalysis GEOS-1 Reanalysis

Variance of Hadley circulation streamfunction

Arthur Hou, IPWG Workshop, 25-28 October 2004 - 5

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.

Key issues in precipitation assimilationKey issues in precipitation assimilation

Arthur Hou, IPWG Workshop, 25-28 October 2004 - 6

What is an observation operator?What is an observation operator?

It relates an observable to model state variables (u,v,T,q, etc.)

(u,v,T,q)model grid (u,v,T,q) at observation locations

Observation operator

(T,q,u,v)grid

Precipitation observation operator

Cloud, Precipitation

random error

“Perfect model”

Observations in clear-sky regions

Systematic error

Developing procedures to make online estimation and correction of biases in the observation operator to make more effective use of precipitation data

Precipitation observation operator with correction,

Arthur Hou, IPWG Workshop, 25-28 October 2004 - 7

Variational continuous assimilation (VCA) of surface rainVariational continuous assimilation (VCA) of surface rain

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”

Minimizing the cost function:

J(x) = (x)T P-1 (x) + ( yo – H(x))T R -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

• 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.

• The strategy is to relax the perfect model assumption - i.e., using the forecast model as a weak constraint.

Arthur Hou, IPWG Workshop, 25-28 October 2004 - 8

Impact of VCA rainfall assimilation on GEOS-3 analysisImpact of VCA rainfall assimilation on GEOS-3 analysis

MJO in precipitation over tropical oceans (10N-10S) 2001

mm/d

GPCP NCEP GDAS ERA-40

Propagation and intensity of tropical rainfall systems are difficult to capturePropagation and intensity of tropical

rainfall systems are difficult to capture GEOS/TRMM

Replicating observed propagation and intensity of tropical rainfall systems and intraseasonal oscillationReplicating observed propagation and intensity of

tropical rainfall systems and intraseasonal oscillation

Rain error reduction (30N-30S, ocean)

GEOS = Goddard Earth Observing System

Arthur Hou, IPWG Workshop, 25-28 October 2004 - 9

Improved temporal and spatial variabilityImproved temporal and spatial variability

Enhanced frequency-time coherence between GPCP and GEOS-3 analysis

Avg. Precipitation (120-150E, 4S-4N)(Morlet analysis)

An atmospheric analysis dynamically consistent with observed rainfall variability

Arthur Hou, IPWG Workshop, 25-28 October 2004 - 10

Improved cloud radiative forcing verified against CERESImproved cloud radiative forcing verified against CERES

94% reduction in bias 51% reduction in error

standard deviation

January 1998

Variational continuous rainfall assimilation improves key climate parameters such as clouds and TOA radiation in the GEOS analysis

Arthur Hou, IPWG Workshop, 25-28 October 2004 - 11

Impact on wind and humidity analysesImpact on wind and humidity analyses

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

Improved latent heating patterns and large-scale motion fields leading to improved upper-tropospheric humidity (verified against TOVS brightness temperature)

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

Arthur Hou, IPWG Workshop, 25-28 October 2004 - 12

Impact on hurricane track and precipitation forecastsImpact on hurricane track and precipitation forecasts

Improved initial storm position

5-day track forecast from 12UTC 8/20/98

Blue: No rainfall data in ICRed: With rainfall data in ICGreen: NOAA “best track”

5-day rain forecast

Hou et al. 2004: MWR, August issue.

5-day track forecast from 00UTC 9/11/99

5-day rain forecast

Bonnie Floyd

Arthur Hou, IPWG Workshop, 25-28 October 2004 - 13

Assimilation of TMI, SSM/I & AMSR-E rainAssimilation of TMI, SSM/I & AMSR-E rain

OLR July 2002W/m2

OSR July 2002W/m2

Precipitation July 2002mm/d

Arthur Hou, IPWG Workshop, 25-28 October 2004 - 14

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

SummarySummary