Cloud and Precipitation Patterns and Processes

1. Cloud and Precipitation Patterns and Processes Sandra Yuter 1 November 2004

3. Habitability and sustainability depend on interrelationships among temperature, clouds, and precipitation • Agriculture • Soil Moisture • Fresh water supply • Flooding and droughts

4. Key Questions • How will future climate change impact life on Earth? Water Cycle: • Have global patterns of cloud and precipitation changed in the last 30 years? • Physical interpretation of observations and estimation of their uncertainties • How well do global and mesoscale models forecast patterns of cloud and precipitation? • Model parameterizations • Model evaluation

5. Research Goals • Improve physical interpretation of observations and quantify their uncertainties • Impacts: • Basic research • Model physical parameterizations • Forecast model evaluation • Use of model reanalysis as substitute for data

6. Current Areas of Research • Error characterization of satellite precipitation retrievals • Marine stratocumulus clouds and drizzle • Orographic precipitation

7. Rain gauge data set used in global precip maps Global Precipitation Climatology Center (G. Huffman)

8. NASA’s Tropical Rainfall Measuring Mission (TRMM) Satellite • 35 deg inclination low earth orbit • Precipitation Radar (PR) 2.3 cm λradar • Infrared (IR) and Visible sensors similar to GOES satellites • TRMM Microwave Imager (TMI) passive microwave scattering and emission sensors similar to SSM/I and NPOESS satellites • Used for research and operations (esp. hurricanes)

9. Radar echo boundary Cloud boundary 0°C level Surface precipitation Stratiform Convective Storm Schematic Houze et al. (1989)

10. How IR sees storm Area of cloud top above threshold temperature

11. How PMW emission sees storm

12. How PMW scattering sees storm

13. NASA TRMM 32 month average rainfall IR PMW Scattering and Emission PMW Emission 2.3 cm Radar mm/day

14. Global Precipitation Uncertainties • How do radar and passive microwave estimates differ? • Relative error statistics • Regional and temporal variations • Why do radar and passive microwave estimates differ? • Diagnostic information • Satellite Product User Groups • Algorithm development - How can retrievals be improved? • Forecasters-How much confidence? • Data Assimilation- How to weight input for each region?

15. Prototype for Global Oceanic Error Characterization • Input: TRMM TMI and PR (Vers. 5) • Oceanic pixels only • Grids of ~2000 x 2000 km2 oceanic area • Daily product based on accumulated statistics for previous 47 days (complete diurnal cycle) • Compare • Probability distributions • Statistical variables

16. Prototype Compares Different Data Sets • Spatial scales: • PR and TMI native sensor resolutions • PR ( 5 x 5 km2) rescaled to TMI 10 and 19 GHz scales (30 x 60 km2and 20x30 km2 respectively) • Types: • Surface Rainrate (Rsfc) • Vertically-Integrated Liquid Water Content • Microwave brightness temperatures Tb • % Rain area

17. NASA TRMM 32 month average rainfall IR PMW Scattering and Emission PMW Emission 2.3 cm Radar mm/day

18. PR Rsfc – TMI Overlap Rsfc

20. Similar Spatial Scales: PR Rsfc – TMI Overlap Rsfc

21. 19 GHz Emission Brightness Temperatures

22. % Rain Area of 30 km x 60 km pixel

23. Radiative cooling from large area of persistent low clouds “Problem” area for global models Model representation of marine stratocumulus influences location of the InterTropical Convergence Zone—an energy source driving global atmospheric circulations SE Pacific marine stratocumulus clouds and drizzle

24. E Pacific cross-section along 95° W SEC = South Equatorial Current, NECC = North Equatorial Countercurrent, EUC=Equatorial Undercurrent, x=westward flow, dots=eastward flow Raymond et al. (2004)

25. SE Pacific SST and wind stress Bretherton et al. (2004)

26. 3 am local 6 am local 9 am local SE Pacific Stratocumulus

27. Closed Cells Open Cells Satellite Ship Radar

28. In SE Pacific, Most Drizzle Evaporates Before Reaching the Surface

29. Echo Tracking Comstock et al. (2004)

30. Average cell reflectivity (dBZ) 15 10 5 -1.5 –1 -0.5 0 0.5 1 1.5 Time to reflectivity peak (hours) Structure and evolution of drizzle cells • Drizzle cell lifetime 2+ hours • Time to rain out < ~ 30 minutes • Implies replenishing cloud water Comstock et al. (2004)

31. EPIC Stratocumulus RHIs 18 October 2001 1428 UTC 2 km 18 km 90 dBZ 90 m/s

32. Planned SE Pacific Field Experiment • VAMOS Ocean Cloud Atmosphere and Land Studies (VOCALS) field campaign planned for October 2007 to improve understanding and model simulation of stratocumulus cloud decks in SE Pacific • Interaction with weather systems over South America • Feedback with underlying ocean • Mesoscale variability

33. Orographic Precipitation • New findings from recent field programs • Mesoscale Alpine Programme (MAP) in southern Alps • West coast US coastal mountains and Cascade mountains • How translate field project findings to real-time forecast models?

34. Mt. St. Helens Alps Topography Comparison Portland, OR Eureka, CA Carolinas Central W Coast

35. Different precipitation patterns in stable vs. unstable flow Medina and Houze (2003)

36. During unblocked, unstable case, some precipitation features were locked to terrain while others developed upstream and drifted toward mountains. Smith et al. (2003) Time  Distance topography Gray scale is reflectivity

37. Mean Patterns for 61 Rain Events Eureka, CA WSR-88D radar Oct 1995 – March 1998 Conditional Mean Reflectivity Mean Radial Velocity From James (2004)

38. Mean Reflectivity Cross-Section Enhancement of precipitation over ocean upwind of coastal mountains (James, 2004)

39. Volumetric Statistics--Contoured Frequency by Altitude Diagram (CFAD) Yuter and Houze (1995)

40. Reflectivity Vertical Velocity Time Yuter and Houze (1995)

41. Vertically Pointing Radar from MAP ~2 km Yuter and Houze (2003)

42. Distribution of vertical air velocity with height Yuter and Houze (2003)

43. Input and results of 1D model similar to bulk microphysics parameterization Yuter and Houze (2003)

44. Observed < 2 km scale variability of reflectivity and vertical air motions • Do ensemble characteristics need to be parameterized to obtain correct precipitation patterns and intensities? • How can it best be parameterized? Evaluation of Model Output with Radar Data • Surface fields (2D) • Volumetric fields (3D) • Statistics and spatial patterns Current community emphasis

45. Model to Observation Comparison of Surface Rainfall 1 km cloud resolving model with explicit microphysics (ARPS) of Ft. Worth Texas storm for time=0 (Smedsmo et al, 2004)

46. Volumetric comparison for accumulated storm totals Smedsmo et al. (2004)

47. Different reflectivity patterns for different wind directions Mean Z 2 km altitude James (2004)

48. (a) (b) (c) (d) dBZ Radial velocity (m/s) How well can models reproduce observed orographic precipitation patterns? Plan to collaborate with modeler to prototype comparisons for Portland, OR region.

49. Field project data sets help diagnose observation“wishful thinking”

50. Oregon Cascade mountains particle size data within melting layer at 0°C Rain Subset Wet Snow Subset +13 dB n(D) mm-1 m-3 Diameter (mm) Yuter et al. (2004)