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Research activities in support of precipitation measurement, analysis and prediction. G. Tripoli 1 , T. Hashino 1 , W-Y Leung 1 , E.A. Smith 2 , A. Mugnai 3 , J. . Hoch 1 , M. Kulie 1 A.V. Mehta 2 1 University of Wisconsin, Madison, Wisconsin

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Research activities in support of precipitation measurement analysis and prediction

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


Relevant activities of masl mesoscale atmospheric simulation laboratory
Relevant Activities of MASL analysis and prediction (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)


Cdrd activity
CDRD Activity analysis and prediction

  • 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


Cdrd activity1
CDRD Activity analysis and prediction

  • 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


Cdrd access
CDRD Access analysis and prediction

  • CDRD made available on the web for users to mine and download profiles from the database.

http://mocha.aos.wisc.edu/CDRD/


Uses of the cdrd
Uses of the CDRD analysis and prediction

  • 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


Uses of the cdrd1
Uses of the CDRD analysis and prediction

  • 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


Potential problem
Potential Problem analysis and prediction

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


CDRD APPROACH analysis and prediction

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


Two types of tags saved from crm simulation and placed in cdrd
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)


50km Tags CDRD



Cloud radiation tags
Cloud Radiation Tags CDRD

  • 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


Global simulation domains
Global Simulation Domains CDRD

Middle Grid – 10km

Outer Grid – 50km

Inner Grid Locations – 2km resolution


Selected CDRD Tag Correlations CDRD

Correlation coefficients computed between every CDRD variable



We are investigating
We are Investigating: CDRD

  • 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?


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




Rain rate vs 89 ghz brightness colored by 200 mb divergence
Rain rate vs 89 Ghz brightness colored by 200 mb divergence freezing level tag

High rain rates

571,967 profiles plotted

200 mb divergence


Colorado snowstorm october 10 th 2005
Colorado Snowstorm freezing level tagOctober 10th, 2005

CDRD Tags can INCREASE the probability of detecting snowfall

Selected Range: 215 – 230K


Two cdrd dynamical tags
Two CDRD Dynamical Tags freezing level tag

Selected Range: 1 - 8

Selected Range: 273 – 279K


NO TAGS freezing level tag

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


Summary of cdrd research
Summary of CDRD Research freezing level tag

  • 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


Advanced microphysics prediction system amps
Advanced Microphysics Prediction System (AMPS) freezing level tag

  • 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


Advanced microphysics prediction system amps1
Advanced Microphysics Prediction System (AMPS) freezing level tag

  • 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.


Spectral aerosol prediction system
Spectral Aerosol Prediction System freezing level tag

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).


Aerosol microphysics
Aerosol Microphysics freezing level tag

  • Nucleation scavenging and evaporation of hydrometeors are considered.

  • Neither interaction among aerosols nor between aerosols and hydrometeors are considered.


Spectral liquid prediction system slips
Spectral LIquid Prediction System (SLIPS) freezing level tag

  • 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.


Liquid microphysics
Liquid microphysics freezing level tag

  • 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


Spectral habit ice prediction system ships

PPVs freezing level tag

  • 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!


Outputs of ships
Outputs of SHIPS freezing level tag

  • 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.


Ice microphysics
Ice microphysics freezing level tag

  • 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


2d orographic snow storm simulation improve 2 13 14 dec 2001

Snow crystals obs freezing level tag

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!


Observed ice particles
Observed ice particles freezing level tag

Woods et al. (2005)


Simulation with all processes
Simulation with all processes freezing level tag

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.


Simulation with all processes1
Simulation with all processes freezing level tag

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.


Simulation with all processes2
Simulation with all processes freezing level tag

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.


Simulation with all processes freezing level tag

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.


Precipitation rate and accumulation

Low level riming freezing level tag

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)


Case study genoa 1992 floods
Case Study : Genoa, 1992 Floods freezing level tag

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.


Experiment design
Experiment Design freezing level tag

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


Habit of predicted solid hydrometeors
Habit of predicted solid hydrometeors freezing level tag

4 hours simulation

CL


Habit of predicted solid hydrometeors1
Habit of predicted solid hydrometeors freezing level tag

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


Type of predicted solid hydrometeors
Type of predicted solid hydrometeors freezing level tag

2 hours 30 min

CL


Type of predicted solid hydrometeors1
Type of predicted solid hydrometeors freezing level tag

2 hours 30 min

DL



Surface precipitation
Surface precipitation freezing level tag

CL

DL

6 hours


Maximum vertical motion 150 y 300km
Maximum vertical motion (150<y<300km) freezing level tag

CL

DL




Summary
Summary freezing level tag

  • Convective cloud system shows sensitivity to different vertical profiles of Sahara dust layer.

    • The case with Sahara dust layer indicate stronger updraft in earlier time than clean case.

    • More active aggregation process and subsequent riming process led to more precipitation in the dust case.

    • The dust case produced surface precipitation than clean case in the early stage of cloud development, but 10 hour accumulated surface precipitation is similar.

  • The simulation supports the results of sensitivity test for Florida storms (CRYSTAL-FACE) by Van Den Heever et al. (2006) qualitatively.


Future work with mugnai
Future Work with Mugnai freezing level tag

  • Calculate radiative transfer from explicit microiphysics

    • Explicit size bins

    • Structure characteristics of each bin defined

      • Progress from simple treatment to more complex treatment

        • Spheres

        • Equivalent spheres

        • Complex shapes

    • Multiple phase of each bin


Crsdas will lewis
CRSDAS freezing level tag(Will Lewis)

  • Early work with Nexrad In Space (Eric Smith, Eastwoord Im)

  • Ensemble Kahlman Filter

  • Assumed Geostationary reflectivity and Doppler velocity data

  • Applied to simulation of

    • Supercell

    • Hurricane Genesis


Conclusions
Conclusions freezing level tag

  • CDRD, AMPS and CRMSDAS all show great promise for methodologies to measure

    • CDRD promises major returns in short term

    • AMPS is providing new insight into how microphysics works and promises to catapult the science of microwave radiatiative transfer to a new level

    • CRMSDAS is how it should be done in the long run, but it is expensive and we have a lot to learn about error characyteristics and the correct representation of microphysics and radiative transfer before we can expect forward models to be competent.


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