Research activities in support of precipitation measurement, analysis and prediction

1. Research activities in support of precipitation measurement, analysis and prediction G. Tripoli1, T. Hashino1, W-Y Leung1, E.A. Smith2 , A. Mugnai3, J. . Hoch1 , M. Kulie1 A.V. Mehta2 1 University of Wisconsin, Madison, Wisconsin 2 Goddard Space Flight Center – Greenbelt, Maryland 3 Institute of Atmospheric Sciences and Climate – Rome, Italy

2. Relevant Activities of MASL (Mesoscale Atmospheric Simulation Laboratory) • CDRD (Cloud Dynamics and Radiation Database) • AMPS(Advanced Microphysics Prediction System) • SHIPS (Spectral Habit Ice Prediction System) • SLIPS (Spectral Liquid Prediction System) • SAPS (Spectral Aerosol Prediction System) • BSSS (Blowing Snow Simulation System) • CRSDAS (Cloud Resolving Satellite Data Assimilation System)

3. CDRD Activity • Creation of CDRD • 4 CRM simulations made daily • 3 at randomly (precipitating) locations around the globe • 1 at a random location within the Mediterranean basin • CRM simulation setup • 2 km grid resolution on an inner grid mesh • Outer grid nested within GFS model output • 24 hour simulation, profiles taken in last 12 hours

4. CDRD Activity • Creation of CDRD • Information saved on CDRD for precipitating grid cells within the inner cloud resolving mesh: • Description of the grid cell: • Simulation number, location and time • Grid dimensions, surface topography, l percent land, albedo, slope • Surface variables, sst, skin T, soil moisture, etc • Vertical Profiles • Atmospheric state • P, T, rv, u,v,w • Microphysics description • Specific humidity , concentration, density, particle skin temperature, fall velocity • Derived Radiatiive transfer • Microwqve Brightness temperatures • Reflectivity factors • IR radiance to space • Grid scale environmental tags • Synoptic scale environmental tags

5. CDRD Access • CDRD made available on the web for users to mine and download profiles from the database. http://mocha.aos.wisc.edu/CDRD/

6. Uses of the CDRD • Precipitation or cloud retrieval Schemes • CDRD contains hundreds of thousands and eventually tens-hundreds of –dertived millions of Global CRM profiles representing- • Light rain • Snow • All types of thunderstorms • Frontal cyclones • Hurricanes and tropical storms • Data mining schemes implemented • FTP files

7. Uses of the CDRD • Basic Research • The CDRD is a unique tool containing CRM-derived information relating microphysics, atmospheric state parameters and precipitation rates to: • geographical, seasonal and synoptic settings • satellite observable radiances • reflectivity for all varieties of precipitating weather everywhere on the globe over all seasons…Wow! • Study regional, seasonal or diurnal differences and relationships • Can look for global relationships among these quantities

8. Potential Problem The formulation of algorithms such as GPROFS6 assume that “the profiles in the model database occur with nearly the same frequency as those found in the region where the inversion method is to be applied.” THUS……… Error in retrieval resulting from the use of database profiles inappropriate for the application can be reduced by only applying locally “relevant” database profiles to the retrieval process. BAYES Theorem – As more constraints are added the more the probability of a “correct” profiles increases

9. CDRD APPROACH Synoptic Setting (short-term model forecast) Satellite Observed Radiance (TBs) Database mining Season CDRD CRD Using CDRD Tags Retrieved Profiles Geographical Location Surface Rain Rate

10. Two Types of Tags saved from CRM simulation and placed in CDRD • 50 km Tags • Describe regional synoptic setting • Can be obtained with good accuracy from most recent global model forecast, eg GFS, ECMWF, etc • 2 km High Resolution Tags • Accurately calculated in real time (topography ) or available from satellite (eg stratiform cloud fraction)

11. 50km Tags

12. High Resolution Tags (2km)

13. Cloud Radiation Tags • Low-Altitude  13.6 GHz Radar Reflectivity Factor (Z) • Mid-Altitude  13.6 GHz Radar Reflectivity Factor (Z) • High-Altitude 13.6 GHz Radar Reflectivity Factor (Z) • Low-Altitude  35.5 GHz Radar Reflectivity Factor (Z) • Mid-Altitude  35.5 GHz Radar Reflectivity Factor (Z) • High-Altitude 35.5 GHz Radar Reflectivity Factor (Z) • 10.65v GHz Brightness Temperature • 10.65h GHz Brightness Temperature • 18.7v GHz Brightness Temperature • 18.7h GHz Brightness Temperature • 23.8v GHz Brightness Temperature • 23.8h GHz Brightness Temperature • 36.5v GHz Brightness Temperature • 36.5h GHz Brightness Temperature • 89.0v GHz Brightness Temperature • 89.0h GHz Brightness Temperature • 150.0v GHz Brightness Temperature • 150.0h GHz Brightness Temperature • 183.3v GHz Brightness Temperature • 183.3h GHz Brightness Temperature

14. Global Simulation Domains Middle Grid – 10km Outer Grid – 50km Inner Grid Locations – 2km resolution

15. Selected CDRD Tag Correlations Correlation coefficients computed between every CDRD variable

16. Correlation Coefficients for TBs

17. We are Investigating: • How well do the simulated brightness channels alone sort the simulated precipitation rates when we look at a global cross section of storms? • Does the addition of dynamics tags help sort simulated precipitation rates over and above what the brightness channels are capable of implying?

18. Use CDRD to depict how particular precipitation types of precipitation are captured globally Surface snowfall rate (ordinate) Brightness Temperature (abcissa) 183 GHz 89 GHz 23.8 GHz 6.6 GHz

19. Correlation between brightness channels colored by potential vorticity advection tag

20. Correlation between brightness temperatures colored by freezing level tag

21. Rain rate vs 89 Ghz brightness colored by 200 mb divergence High rain rates 571,967 profiles plotted 200 mb divergence

22. Colorado Snowstorm October 10th, 2005 CDRD Tags can INCREASE the probability of detecting snowfall Selected Range: 215 – 230K

23. Two CDRD Dynamical Tags Selected Range: 1 - 8 Selected Range: 273 – 279K

24. NO TAGS Add Surface Temperature Tag Add Lifted Index Tag A – Snowfall Rate > 1mm/hr B – 150 GHz Brightness Temperatures from 215 – 230 K C – Surface Temperature from 273 – 279 K D – Lifted Index from 1 - 8

25. Summary of CDRD Research • Global CDRD is operational • 3D cloud resolving model simulations of precipitation in randomly chosen (precipitating) locations around the globe • 4 daily runs • Dynamics and thermodynamics tags placed in database along with profiles of state parameters, microphysics and radiative transfer • Available online http://cup.aos.wisc.edu/CDRD • CDRD promises to be useful with a Bayesian approach for precipitation assessment combining traditional retrieval and tags • We are investigating the additional information content present in the tags and are working to optimize the choices of tags • CDRD may be useful to predict overall success of microwave radiometer channels over a wide cross section of storm types

26. Advanced Microphysics Prediction System (AMPS) • The overarching goal is to take advantage of modern computing power to bring 3D models of microphysics processes to the next level in order to: • Realistically model the evolution and associated evolved structures of liquid and ice hydrometeors in complex cloud forms • Facilitate the realistic modeling of radiative transfer in cloudy air to better understand the relationship between satelliute observed brightness temperature and precipitation

27. Advanced Microphysics Prediction System (AMPS) • Explicitly evolve characteristics ofshape, density, phase and size distributionof aerosol, liquid and ice particles in a 3D Eulerian framework. • Computationally efficient enough to run for operational use in cloud resolving models. • Goal is to reduce the computation, while keeping the degree of freedom in resulting physical properties.

28. Spectral Aerosol Prediction System Partially soluble AP Lognormal distribution Pure insoluble AP Uniform distribution • Currently the accumulation mode is modeled with the size distribution. • This can be expanded to bin approach, or more size distributions can be added to describe nucleation mode and coarse mode as done by Wilson (2001).

29. Aerosol Microphysics • Nucleation scavenging and evaporation of hydrometeors are considered. • Neither interaction among aerosols nor between aerosols and hydrometeors are considered.

30. Spectral LIquid Prediction System (SLIPS) • About 20 bins seems to be optimal • The vapor deposition is assumed to transfer mass of activated droplets into multiple bins to compensate the time step.

31. Liquid microphysics • CCN activation process • Kohler equation • Vapor deposition process • Capacitance approach • Collision-coalescence process • Quasi-stochastic model • Collision-breakup process • Low and List (1982) formulation • Aerosol mass prediction in the liquid hydrometeors • Auto-Conversion….future work

32. PPVs • Integrated based on local conditions and history of particles • Each bin has different properties of ice particles. • The properties change in time and space. Bin model Bulk micro. par. Spectral Habit Ice Prediction System (SHIPS) No use of categorization!

33. Outputs of SHIPS • Concentration, mass content, and Particle Property Variables (PPVs) for a bin. • Habit of ice crystals and type of solid hydrometeors in the bin can be diagnosed with PPVs. • Predicted maximum dimension, circumscribing volume, aspect ratio, bulk density of solid hydrometeors. • Aerosol distribution outside and inside hydrometeors, and solubility of the aerosols.

34. Ice microphysics • Ice nucleation process • deposition-condensation nucleation, contact freezing, immersion freezing, secondary nucleation. • Vapor deposition process • Capacitance analogy, empirical mass growth rate, probabilistic growth • Collision-coalescence process • Quasi-stochastic approach for aggregation process and riming process • Hydrodynamic breakup process • Melting-shedding process • Aerosol mass prediction in the solid hydrometeors

35. Snow crystals obs sounding Woods et al (2005) 2D orographic snow storm simulation – IMPROVE-2 (13-14 Dec 2001) • Key microphysical processes for precipitation on the ground • Aggregation • Riming From WMO Cloud Modeling Workshop (http://www.rap.ucar.edu/~gthompsn/workshop2004/) IMPROVE 2 website (http://improve.atmos.washington.edu/) Habit dependent!

36. Observed ice particles Woods et al. (2005)

37. Simulation with all processes Ice crystal habit of pristine and rimed crystals 12 hours of only liquid mic + 30 minutes vd and agg of ice mic + 1 hour of all the processes EXP1 • Plates and columns are forming in high level due to immersion freezing. • Bullet rosettes are forming in upper level and grow large due to less concentration. • Columnar crystals dominates in lower levels. • Dendrites were consumed by aggregation and riming before.

38. Simulation with all processes Type of solid hydrometers 12 hours of only liquid mic + 30 minutes vd and agg of ice mic + 1 hour of all the processes EXP1 • Aggregates forming in higher level from bullet rosettes. • Immersion process supplies crystals to the aggregation. • Rimed crystals exist in high level due to small consumption by vapor deposition.

39. Simulation with all processes 12 hours of only liquid mic + 30 minutes vd and agg of ice mic + 1 hour of all the processes Ice crystal habit of pristine and rimed crystals UNI230 • Plates dominate in upper level due to vapor competition among high concentration of ice crystals. • Dendrites form on plates falling from the above. • Columnar crystals dominate in lower levels. • Irregular crystals were only seen at very beginning of simulation.

40. Simulation with all processes 12 hours of only liquid mic + 30 minutes vd and agg of ice mic + 1 hour of all the processes Type of solid hydrometeors UNI230 • More aggregates at 3-5 km due to active formation by dendrites. • Ice crystals are available for aggregation once the process starts. • More active riming process in lower level • The altitude of active riming corresponds to observation.

41. Low level riming Middle level aggregation Secondary nucleation Precipitation rate and accumulation Time (hour) EXP1 UNI230 UNIMAX Accumulation (mm) Time (hour) EXP1-NIM UNI230-NIM EXP1-DST230 Accumulation (mm) Woods et al (2005) Elevation (km)

42. Case Study : Genoa, 1992 Floods From Tripoli et al (200?) 500 kPa height contour, surface wind vectors equivalent potential temperature (shaded over low elevations) and topography (shaded over higher elevations) for Genoa simulation valid at 1500 UTC, 27 October 1992.

43. Experiment Design Weather Prediction Model • UW-NMS (University of Wisconsin-Non-hydrostatic Modeling System) Tripoli (1992) Two categories of aerosols predicted: CCN and IN • nucleation scavenge and evaporation of hydrometeors considered. Four vertical profiles for IN CCN vertical initial profile IN vertical initial profile

44. Habit of predicted solid hydrometeors 4 hours simulation CL

45. Habit of predicted solid hydrometeors 4 hours simulation DL Stronger Updraft • More supply of moisture. • More active depositional nucleation mode. • More cloud droplets available in convective core for riming for the aggregates

46. Type of predicted solid hydrometeors 2 hours 30 min CL

47. Type of predicted solid hydrometeors 2 hours 30 min DL

48. Mean concentration of ice particles (150<y<300km) CL DL DA

49. Surface precipitation CL DL 6 hours

50. Maximum vertical motion (150<y<300km) CL DL