1. “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

2. Goddard Multi-Scale Modeling System with Unified Physics GCELocal WRFRegional MMFGlobalUnifiedMicrophysicsRadiation Land Model CRMs can explicitly simulate cloud- precipitation systems. CRMs can explicitly simulate cloud- precipitation systems. Breaking a deadlock of cumulus parameterization. However, CRMs still suffer from fundamental understanding in microphysics processes due to lack of routine observations. However, CRMs still suffer from fundamental understanding in microphysics processes due to lack of routine observations. Facing another deadlock of cloud microphysics!

3. 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 Simulator CALIPSO, ICESAT Visible-IR simulator AVHRR,TRMM VIRS, MODIS, GOES Radar Simulator TRMM PR, GPM DPR, CloudSat CPR Microwave Simulator SSM/I, TMI, AMSR-E, AMSU, and MHS ISCCP-like Simulator ISCCP DX product MODIS clouds products Braodband Simulator ERBE, CERES, TOVS, AIRS Goddard Satellite Data Simulation Unit

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

5. T3EF: 1st Step Precipitating Cloud Classification Masunaga Diagrams (Joint Tb IR -H ET 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).

6. 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=D 6 ). TRMM GCE

7. T3EF: 3rd Step Cumulative PDF of PCTb 85 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 Convective 3 - Deep Stratiform 2 - Congestus 1 - Shallow 4 - Deep Convective

8. Apply A-Train and other satellites for evaluating the WRF simulation in C3VP case Evaluate vertical profile of cloud systems using CPR reflectivity Testing simulated MW Tb against the AMSU-B Tb for future GMI sensor. Evaluate spatial extent of ISCCP- based cloud types using MODIS data.

9. 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). GM03 GM07 TRMM PCTb 85 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 (r e ) in TEDD against SBM in PRESTROM simulations SBM: spectra-bin microphysics N 0 CTL: control bulk microphysics N 0 100: intercept  100 of N 0 CTL TEDD: temperature-dependent DSD

10. NASA Satellites GCE SBM GCE forced by MERRA NASA unified WRF MMF (2DGCE+fvGCM) Simulator Radiance-basedevaluation Improve SBM Parameterize DSD For bulk microphysics Simulator Provide/Improve a priori database of simulated geophysical parameters and radiance Good enough? Model-Simulator-Satellite Chain Improve bulk microphysics

11. 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)