Cloud Radiation Simulation for GPM and TRMM

1. Cloud Radiation Simulationfor GPM and TRMM Gregory J. Tripoli Tempei Hashino Giulia Panegrossi University of Wisconsin – Madison

2. Purpose ofCloud Radiation Simulation • Provide GPM with a comprehensive 5D (multivariate in time and space) data base of : • simulated cloud structure • attendant physical and dynamical processes • simulated brightness temperatures that would be observed by a remote sensing platform. • Provide data base for a variety of different weather systems

3. CRS – Cloud Radiation Simulation CRM – Cloud Resolving Model PRM – Passive Radiation Model CRVS – Cloud Radiation Verification Studies CRDB – Cloud Radiation Data Base

4. Key CRS Technologies • Cloud Resolving Models • Evolution of weather system • Quantitative Precipitation Forecast (QPF) • Simulation of detailed of cloud microphysics • Simulation of details of cloud impact on larger scales • Passive Radiation Models • Upwelling radiation from surface • 3D scattering and absorption of microwave radiation • Ice and liquid hydrometeors • Vapor • Dry air

5. Key CRM Issues • Integrity of simulated microphysical structure • Liquid vs. ice • Number concentration • Size distribution • Ice habit • Integrity of dynamical simulation • Cloud initiated from hot bubble in a representantative sounding • Clouds in context of parent weather system • Representativeness of data sets • Location on Earth • Parent weather system, i.e. tropical cyclone, cumulus, PBL MCC, etc • Where in storm, i.e., anvil, new convective tower, old convective tower, over cold, occluded or stationary front? • Depth • Stratiform vs. convective • Column representativeness, i.e. relationship of view at top to processes below (in radiation stream) Idealized supercell Idealized Lake effectt Genoa flash flood Chicago Frontal snow Piemonte orographic flooding raqin Hurricane Bonnie

6. Cloud Radiation Verification Studies • Evaluate Simulated Brightness Temperature of CRM/PRM against TRMM (or other observations) • Evaluate predicted microphysical structure against in situ observations • Adjust “parameters” in microphysical parameterization to “improve” scheme?

7. UW-NMS Simulation 3: Aug 26, 1130UTC UW-NMS Aug 26, 1200UTC TMI Bonnie: Aug 26, 1130UTC TMI overpass: Aug 26, 1130 UTC

8. Old microphysics

9. New microphysics

10. What this is telling us • The errors in simulated brightness temperature are sensitive to simulation of microphysics process • Our simulation of process, especially for ice is not robust and dependent on • an arbitrary conversion decision tree • An assumed size distribution • The current microphysics parameterization paradigm critically flawed

12. New Approach • Use some of the new computing resources to drive a more “physical” microphysics simulation • Abandon categorization approach • Take optimal advantage of solid empirical understanding and physical relationships

13. Example: We have documented that there are well defined crystal growth regimes

14. Ice Crystal Model 2 Geometrical relationship Reformulate with physical relationship (minimize surface free energy?) • May be used for calculation of • terminal velocity • radiative transfer

15. Test of Vapor deposition • Looking at one falling ice crystal • Fixed temperature and water saturation Chen and Lamb (1994) Tends to be larger than Chen and Lamb

16. Proposed New Microphysics Simulation Paradigm • Replace categorization of ice with a continuum of growth characteristics • Replace assignment of arbitrary distributions with flexibility to evolve distributions • Base predictions on basic physical or well-defined empirical principles when possible • Use the computer power that we expect to have • Flexibility to run at various complexities

17. Spectral Habit Ice Prediction System 6 particle masses Piecewise continuous logarithmic distribution ln 6 Mass bins

18. Flow Diagram

19. Some Characteristics • No arbitrary Categories • List of extensive parameters define particle characteristics • Mixed as mass moves between bins • Eulerien dynamics • Empirical growth rates as a function of temperature, fall velocity and humidity • Remember history of growth • Specific habits can be diagnosed from parameters • Particle characteristics can be diagnosed • Drag • Collision efficiency • Coalescence efficiency • Capacitance • Radiative characteristics

20. Summary • CRS will provide a physically competent 5D database for developing techniques to relate BT to cloud structure and ultimately surface precipitation. This will benefit retrieval formulation and the development of direct 4D data assimilation techniques. • GPM, together with supporting super sites and IOPs, will provide a new microphysics process verification database and new insights into why models succeed or fail enabling major strides in the science of cloud process modeling. This will benefit not only the effective use of GPM to diagnose precipitation and initialize NWP models, but also the skillful quantitative precipitation forecast of the future.