1. Test of Microphysical Schemes (WRF) with C3VP Snow Events J.-J. Shi, T. Matsui, S. Lang A. Hou, G. S. Jackson, C. Peters-Lidard W. Petersen, R. Cifelli, S. Rutledge Wei-Kuo Tao Objectives Cases: a lake event followed by a synoptic event NASA Unified WRF - Satellite Simulator Comparison: Observation (Snow Fall, CloudSat, AMSU) vs simulated Summary and future work Simulated DBz

2. Objectives To improve our understanding of precipitation processes at high latitudes To utilize the Earth satellite simulator to identify the strengths and weaknesses of model-simulated microphysical processes To provide consistent 4-D thermodynamic and dynamic cloud data sets for GPM snow retrieval To examine the sensitivity of microphysical schemes on precipitation processes associated with snowstorms

3. NASA Unified WRF Christa Peters-Lidard, Wei-Kuo Tao, Mian Chin Scott Braun, Jonathan Case, Arthur Hou, Sujay Kumar, William Lau, Toshi Matsui, Roger Shi, Qian Tan, Sara Zhang Objectives is to Integrated Modeling of Aerosol, Cloud, Precipitation and Land Processes at Satellite-Resolved Scales Blue Boxes: Goddard Physical Packages

4. Goddard Bulk Microphysical Scheme Warm Rain (Soong and Ogura 1973) Ice-Water Saturation Adjustment (Tao et al. 1989) Option for 3ICE-Graupel (Rutledge and Hobb 1984) or 3ICE-Hail (Lin et al. 1983) scheme (Tao and Simpson 1989, 1993; McCumber et al. 1990) The sum of all the sink processes associated with one species will not exceed its mass - (water budget balance) All transfer processes from one type of hydrometeor to another are calculated based on one thermodynamic state (ensure all processes are equal) 3ICE Modification (Tao et al. 2003a) Saturation adjustment Conversion from Ice to Snow 2ICE scheme (Tao et al. 2003b) Ice and Snow 3ICE-Graupel Modification (Lang et al. 2007) Conversion from cloud to snow Dry growth of graupel Observation Improved CFAD - Radar Reflectivity dBZ Z(km) MM5 WRF <-- GCE qg qs CFAD: Contoured Frequency Altitude Diagram (i.e., stacked PDFs) Lang, S., W.-K. Tao, R. Cifelli, W. Olson, J. Halverson, S. Rutledge, and J. Simpson, 2007: Improving simulations of convective system from TRMM LBA: Easterly and Westerly regimes. J. Atmos. Sci., 64, 1141-1164. Tao, W.-K., J. Simpson, D. Baker, S. Braun, M.-D. Chou, B. Ferrier, D. Johnson, A. Khain, S. Lang, B. Lynn, C.-L. Shie, D. Starr, C.-H. Sui, Y. Wang and P. Wetzel, 2003a: Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model, A Special Issue on Non-hydrostatic Mesoscale Modeling, Meteorology and Atmospheric Physics, 82, 97-137.

5. Resolutions: 9, 3, and 1 km Grid sizes: 301X241, 430X412, and 457X457 31 vertical layers  t = 30 seconds Starting time: 00Z 1/20/2007 Initial and Boundary Conditions: NCEP/GFS Physics: Cu parameterization: Grell-Devenyi scheme (for the outer grid only) *Cloud microphysics (6) + sensitivity tests (2) Radiation: Goddard shortwave and RRTM longwave PBL parameterization: Mellor-Yamada-Janjic TKE scheme Surface Layer: Monin-Obukhov (Janjic) scheme Land Surface Model: Noah WRF C3VP Simulation 20 - 22 January 2007 (a lake event followed by a synoptic event)

6. January 20 (Lake Effect)January 22 (Synoptic Event) Simulated Snow Fall January 20January 22

7. January 20 (Lake Snow) --> <-- January 22 ( Synoptic Event) Radar Reflectivity Observation Simulation

8. 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 current and future 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

9. CloudSat Simulated WRF captures the multi-layered cloud system as seen by CloudSat. However, Ze in the mid-layer clouds appears to be stronger than the CloudSat measurements. CFADs CloudSat Simulated

10. AMSU-B (Tb)Simulated Tb CloudSat (CPR)Simulated (CPR)

11. 3ICE2ICE Vertical profiles of domain- and 1st 24-hour time-average cloud species for the 3ICE (cloud ice, snow and graupel) and 2ICE (cloud ice and snow) schemes O O Large precipitating particles (rain and graupel) did not form in either experiment ==> weak vertical velocities (~50 cm/s). O O Similar profiles for cloud water, cloud ice and snow for both experiments. O O Goddard 3ICE microphysical scheme responded well to the cloud dynamics and did not produce large precipitating ice (graupel). O O Presence of cloud water during snow event has been observed and simulated (also found in many other snow events)

12. Lin WSM6 Thompson Goddard Sensitivity of microphysical schemes on the vertical profiles of domain and time- average cloud species (1st 24h of integration for lake effect event) No cloud ice, large cloud water Snow and graupel at ground Cloud ice is dominant species, little cloud water No cloud ice, little cloud water Snow and graupel at ground

13. Test 1: Pimlt = 0 Test 2: reduced condensation Synoptic Effect Storm A. Heymsfield: No liquid water observed What is the microphysical process to produce liquid water? Control Run No MeltingNo Condensation

14. Summary and Future Work Preliminary WRF simulation captures the basic cloud properties as seen by ground-based radar and satellite (CloudSat, AMSU-B) observations. However, the model under predicts low cloud for the lake effect snow case. WRF simulation with two different microphysical schemes (3ICE and 2ICE scheme) shows almost identical results (due to weak vertical velocities and therefore no large precipitating liquid or ice presence). WRF simulation with other WRF microphysical schemes (Thompson, Purue Lin and WSM6) shows a great sensitivity in vertical cloud profiles (important for both radiation Budget and hydrologic/energy cycles). WRF-simulated cloud data set is available to GPM science team through the Goddard Cloud library web-site (http://portal.nccs.nasa.gov/cloudlibrary/index2.html). WRF simulation with higher-resolution initial conditions (NCEP Eta 32 km), more and higher vertical resolution (low and upper troposphere), microphysics (liquid phase) PBL will be conducted. WRF-Earth satellite simulator with realistic ground emissivity is required.

15. Goddard SDSU development Plan Priority Order 1. Code: MPI version. 2. Surface Properties: Land surface emissivity and bidirectional reflectance distribution function ( BRDF) spectrum albedo. 3. Optical properties: Non-spherical optical properties (frozen particles and dust aerosols - MODIS, GoCART) 4. Radiative Transfer: 3D radiative transfer with full polarization 5. IO process: Options for GEOS5 Single Column Model (SCM) input (overlapping ensemble statistics)

16. Goddard WRF Microphysics New 3Ice-Graupel (see CFAD) 2-Moment (cloud-aerosol interactions) Multi-moment (mass, concentration, shape) Hybrid (Spectral bin and bulk microphysics) Observed WRFNew 3Ice-Graupel --> Reduce 40dBZ at high altitude Satellite (Earth) simulators (microwave, dual frequency precipitation radar, lidar, cloud radar, IR…) - identify the strengths/weakness of microphysics in a global context

18. Comparing CloudSat-observed and WRF-simulated Ze Low cloud Middle-high cloud Multi-layer cloud