Exploring new pathways in precipitation assimilation Arthur Hou and Sara Zhang

1. Exploring new pathways in precipitation assimilation Arthur Hou and Sara Zhang NASA Goddard Space Flight Center Symposium on the 50th Anniversary of Operational Numerical Weather Prediction 14-17 June 2004, University of Maryland

2. Current usage of precipitation data in NWP and data assimilation • NOAA/NCEP (operational 2001): • 3DVAR rainfall assimilation (reduced over-prediction of tropical convection) • ECMWF (research, operational 2004): • 1D+4DVAR rain assimilation (improved 3-day Zoe forecast) • JMA (operational Oct. 2003): • 3DVAR mesoscale rainfall assimilation • NASA/GMAO (research, reanalysis): • 1D+3DVAR continuous rain assimilation (improved 5-day Bonnie forecast) • FSU (research,semi-operational): • Multi-model super-ensemble forecast (improved hurricane precipitation forecast) Data impact varies with forecast systems and assimilation techniques.

3. Two points of departure for precipitation assimilation • Extending operational use of radiance/retrieval information in clear sky regions to cloudy/rainy areas: • Precipitation assimilated as any other data type using the same technique and assumptions that worked well in clear-sky regions • Data impact determined by the analysis method and accuracy of the observation operator • Exploring how to maximize the usefulness of precipitation data - as a research/application extension of satellite missions: • Begin with considerations of the precipitation observation operator based on parameterized physics, which can have systematic errors • Develop techniques within a given analysis framework to use data effectively in the presence of model deficiencies Two paths should ultimately converge

4. Precipitation observation operator: Key issues • Nonlinear observation operator • Tangent linear and adjoint models may not always be valid for nonlinear/discontinuous physics • Divergent paths to cloud/precipitation parameterization for forecast improvement and analysis algorithm development • Errors in cloud/precipitation parameterization • Errors in model rain diagnosed from changes in specific humidity imply errors in the moisture time-tendency field • Moisture forecast tendency errors arise from uncertainties in IC’s and systematic errors in moist physics schemes, which must be accounted for to make effective use of precipitation data • Need algorithms to address both IC and model errors • Model rain based on parameterized physics with quasi-equilibrium assumptions not designed to match instantaneous rain-rate observations

5. 3 focused research areas at NASA/GSFC • Rainfall assimilation using the forecast model as a weak constraint • Online estimation and correction of biases in precipitation observation operators • Exploring ensemble data assimilation (EnsDA) techniques • A unified framework for handling IC and model errors with direct estimation of error covariance statistics and requiring no tangent linear or adjoint model of moist physics • Using cloud-resolving models to guide the transfer of information from satellite measurements at pixel scales to global models • Identify deficiencies in parameterizations to select control variables for variational assimilation and explore the feasibility of direct assimilation of radiance information using global model physics

6. 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” Rainfall assimilation using forecast model as a weak constraint • Assimilation of 6h surface rain accumulation from TRMM & SSM/I using a 6h-mean moisture tendency correction as the control variable and applying the correction continuously over a 6h analysis window to achieve dynamical consistency • Seeking further improvements through assimilation of convective and stratiform latent heating retrievals from microwave sensors using moist physics parameters as control variables: - convective adjustment time and fraction of cloud-top detrained liquid for Qconvective - critical RHs for condensation and evaporation for Qstratiform

7. 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

8. 5-day Bonnie track and rain forecasts from 12UTC 8/20/98 Improved initial storm position Blue: No rain data in IC Red: With rainfall data Green: Best track January 1998 Hou et al. MWR, 2004 Impact of precipitation assimilation (continued) • Improved cloud/radiation parameters – verified against CERES measurements • Improved upper-tropospheric humidity – verified against HIRS2 ch 12 Tb Improved IR cloud-radiative forcing CERES verification

9. Ensemble techniques for precipitation assimilation • Expected advantages: • no tangent linear or adjoint model • direct estimation and updating of flow-dependent analysis and forecast error covariances • potential capability for addressing initial-condition errors and model biases through augmented control variables • Proof of concept: CSU/GSFC collaboration on EnsDA using GEOS-5 column model physics in idealized model experiments • To test the ability of EnsDA to estimate known model biases and forecast error statistics for IC and model errors • To demonstrate the benefit of direct estimation and updating of flow-dependent forecast error covariances • To determine parametric ranges in ensemble size, width of analysis window, and error amplitudes for obtaining valid results

10. Microwave radiance at 37 GHz A Bayesian rain estimate from MW Tb Modeled Tb Observed Tb Simulated vs. TMI observed Tb Courtesy of W. Olson Use CRM to guide improvements of assimilation procedures Bayesian rain retrieval uses CRM as an interpreter of satellite observations to bridge the gap in physics and spatial resolution between measurements at pixel scales and global models Use radiance-constrained CRM simulations of moist processes that are radiatively consistent with MW measurements in rainy areasto identify deficiencies in precipitation observation operators to guide the selection of control variables for variational assimilation of rainfall data in global systems.

11. Assimilation of CRM-based retrievals + better spatial match between CRM realizations of subgrid-scale processes and satellite measurements Radiance assimilation + better handling of observation errors + realistic atmospheric profiles - difficult to quantify observation errors - no knowledge of actual atmospheric state - scale mismatch between parameterized physics and multi-channel radiance measurements - implementation requires fast simplified RT code Towards direct use of MW radiances in global forecast systems • Examine the feasibility of assimilating MW radiance information in rainy regions in global models by • developing an understanding of the physical relation between CRM processes and satellite measurements • examining the extent to which this relation can be captured by the moist physics parameterized in terms of the grid-mean state

12. Summary • Effective use of precipitation information in data assimilation poses a special challenge because the model rain is diagnosed from parameterized physics, which can have large systematic errors that must be addressed in the assimilation procedure. • Pathways for improvements: • Using the forecast model as a weak constraint • Addressing IC and model errors within the EnsDA framework • Exploring ways to use rain/cloud information to improve global forecast systems through CRM studies within a multi-scale modeling framework • Full benefit of rainfall data can be realized only if analyses of the state variables (u, T, q, ps) are allowed to directly respond to an improved rain field during an analysis cycle.