“The Goddard Multi-Scale Modeling System & Satellite Simulator for NASA PMM”

Wei-Kuo Tao & Toshi Matsui

Representing Goddard Mesoscale Dynamics and Modeling Group: Wei-Kuo Tao, Jiundar Chern, Xiping Zeng, Xiaowen

Li, Jainn Jong Shi , Steve Lang, Bowen Shen, and Toshihisa Matsui

Goddard Multi-Scale Modeling System with Unified Physics

GCEGCE

LocalLocal

WRFWRF

RegionalRegional

MMFMMF

GlobalGlobal

UnifiedUnifiedMicrophysicsMicrophysics

RadiationRadiationLand ModelLand Model

• CRMs can explicitly CRMs can explicitly simulate cloud-simulate cloud-precipitation systems. precipitation systems.

Breaking a deadlock of Breaking a deadlock of cumulus cumulus parameterization. parameterization.

• However, CRMs still suffer However, CRMs still suffer from fundamental from fundamental understanding in microphysics understanding in microphysics processes due to lack of routine processes due to lack of routine observations.observations.

Facing another deadlock of Facing another deadlock of cloud microphysics!cloud microphysics!

Satellite Simulator:Simulate satellite observables (radiance and backscattering) from model-simulated (or

assigned) geophysical parameters.

Scientific Objective:

• Evaluate and improve NASA modeling systems by using direct measurements from space-born, airborne, and ground-based remote sensing.

• Support radiance-based data assimilation for NASA’s modeling systems.

• Support the NASA’s satellite mission (e.g., TRMM, GPM, and A-Train) through providing the virtual satellite measurements as well as simulated geophysical parameters to satellite algorithm developers.

GCE, WRF, MMF output

Lidar SimulatorCALIPSO, ICESAT

Visible-IR simulatorAVHRR,TRMM VIRS, MODIS,

GOES

Radar SimulatorTRMM PR, GPM DPR, CloudSat

CPR

Microwave SimulatorSSM/I, TMI, AMSR-E, AMSU, and

MHS

ISCCP-like SimulatorISCCP DX product

MODIS clouds products

Braodband SimulatorERBE, CERES, TOVS, AIRS

Goddard Satellite Data Simulation Unit

TRMM Triple-senor Three-step Evaluation Framework (T3EF)

T3EF: 1st Step

Precipitating Cloud Classification

• Masunaga Diagrams (Joint TbIR-HET PDF) and Cloud-Precipitation category [Masunaga et al. 2004].

• By using simulators, categorization can be done in identical, simple manner between TRMM and GCE.

• Slight (~10%) overestimation of deep convective systems in GCE simulations (GM03).

T3EF: 2nd Step

Radar Echo CFADs• Contoured frequency with altitude

diagrams (CFADs) of PR reflectivity for shallow, cumulus congestus, deep stratiform, and deep convective precipitation systems.

• Largest simulated CFADs errors appear in deep convective systems. in upper troposphere.

• 15dBZ bias represents that mean particle diameter in the GCE simulations could be nearly twice as large as the TRMM observations in the Rayleigh approximation (Z=D6).

TR

MM

TR

MM

GC

EG

CE

T3EF: 3rd StepCumulative PDF of PCTb85

• Examines microwave brightness temperature depressions caused by scattering from layers of ice particles.

• Simulated PCTb85 in deep convective systems is distributed in very low Tb, indicating too much ice water content in deep convective systems.

(after Liu and Curry 1996).

4 - Deep Convective4 - Deep Convective

3 - Deep Stratiform3 - Deep Stratiform

2 - Congestus2 - Congestus

1 - Shallow1 - Shallow

4 - Deep Convective4 - Deep Convective

Apply A-Train and other satellites for evaluating the WRF simulation in C3VP case

CloudSat observed CPR (94.15GHz) radar reflectivity WRF-SDSD-simulated 94.15GHz radar reflectivity

AMSU-B observed brightness temperature WRF-SDSU simulated brightness temperature

Evaluate vertical Evaluate vertical profile of cloud profile of cloud systems using systems using CPR reflectivityCPR reflectivity

Testing simulated Testing simulated MW Tb against the MW Tb against the AMSU-B Tb for AMSU-B Tb for future GMI sensor. future GMI sensor.

Evaluate spatial Evaluate spatial extent of ISCCP-extent of ISCCP-based cloud types based cloud types using MODIS data.using MODIS data.

MODIS-observed ISCCP cloud type WRF-simulated ISCCP cloud type

How to improve bulk microphysics?

Modify Conversion Rate

• Modified Goddard microphysics (GM07: incorporating Bergeron and ice-nuclei processes, and reducing the collision efficiency in order to reduce the amount of graupel) [Lang et al. 2007] show an improvement in probability distirbution of PCTb than GM03 (default).

GM03GM03

GM07GM07

TRMMTRMM

PCTbPCTb8585

Modify Assumption of Drop-Size Distribution (DSD)

• Constrain DSD assumptions of frozen condensate as a function of temperature (TEDD) based on the GCE spectra-bin microphysics (SBM) [Li et al. 2008].

• Improved droplet effective radius (re) in TEDD against SBM in PRESTROM simulationsSBM: spectra-bin microphysics

N0CTL: control bulk microphysics

N0100: intercept 100 of N0CTL

TEDD: temperature-dependent DSD

NASA SatellitesNASA Satellites

GCE SBMGCE SBM

GCE GCE forced by MERRAforced by MERRA

NASA unifiedNASA unified WRF WRF

MMF MMF (2DGCE+fvGCM)(2DGCE+fvGCM)

SimulatorSimulator Radiance-based Radiance-based evaluationevaluation

Improve SBMImprove SBM

Parameterize DSDParameterize DSDFor bulk microphysicsFor bulk microphysics

SimulatorSimulator

Provide/Improve Provide/Improve a prioria priori database database

of simulated geophysical of simulated geophysical parameters and radiance parameters and radiance

Good enough?Good enough?

Model-Simulator-Satellite ChainModel-Simulator-Satellite Chain

Improve bulk Improve bulk microphysicsmicrophysics

Goddard SDSU future development Plan

Priority Order

1. Code: MPI version (DONE).

2. Surface Properties: Land surface emissivity and BRDF spectrum albedo.

3. Optical properties: Non-spherical optical properties (frozen particles and dust aerosols)

4. Radiative Transfer: 3D radiative transfer with full polarization (numerically worst case)

5. IO process: Options for GEOS5 SCM input (overlapping ensemble statistics)