cloud and precipitation patterns and processes sandra yuter 1 november 2004

Cloud and Precipitation Cloud and Precipitation Patterns and ProcessesPatterns and Processes

Sandra YuterSandra Yuter

1 November 20041 November 2004

Habitability and sustainability Habitability and sustainability depend on interrelationships among depend on interrelationships among

temperature, clouds, and temperature, clouds, and precipitationprecipitation

AgricultureAgriculture Soil MoistureSoil Moisture Fresh water supplyFresh water supply Flooding and droughtsFlooding and droughts

Key QuestionsKey Questions How will future climate change impact life on How will future climate change impact life on

Earth?Earth?Water Cycle:Water Cycle: Have global patterns of cloud and Have global patterns of cloud and

precipitation changed in the last 30 years? precipitation changed in the last 30 years? Physical interpretation of observations and Physical interpretation of observations and

estimation of their uncertaintiesestimation of their uncertainties How well do global and mesoscale models How well do global and mesoscale models

forecast patterns of cloud and precipitation? forecast patterns of cloud and precipitation? Model parameterizationsModel parameterizations Model evaluationModel evaluation

Research GoalsResearch Goals Improve physical interpretation of Improve physical interpretation of

observations and quantify their uncertaintiesobservations and quantify their uncertainties Impacts:Impacts:

Basic researchBasic research Model physical parameterizations Model physical parameterizations Forecast model evaluationForecast model evaluation Use of model reanalysis as substitute for Use of model reanalysis as substitute for

datadata

Current Areas of ResearchCurrent Areas of Research

Error characterization of satellite Error characterization of satellite precipitation retrievalsprecipitation retrievals

Marine stratocumulus clouds and drizzleMarine stratocumulus clouds and drizzle Orographic precipitationOrographic precipitation

Rain gauge data set used in global precip maps

Global Precipitation Climatology Center (G. Huffman)

NASA’s Tropical Rainfall Measuring NASA’s Tropical Rainfall Measuring Mission (TRMM) SatelliteMission (TRMM) Satellite

35 deg inclination low earth orbit35 deg inclination low earth orbit Precipitation Radar (PR) 2.3 cm Precipitation Radar (PR) 2.3 cm λλ radarradar Infrared (IR) and Visible sensors similar to GOES Infrared (IR) and Visible sensors similar to GOES

satellitessatellites TRMM Microwave Imager (TMI) passive TRMM Microwave Imager (TMI) passive

microwave scattering and emission sensors microwave scattering and emission sensors similar to SSM/I and NPOESS satellitessimilar to SSM/I and NPOESS satellites

Used for research and operations (esp. Used for research and operations (esp. hurricanes)hurricanes)

Storm SchematicStorm Schematic

Cloud boundaryRadar echo boundary

0°C level Surface precipitation

ConvectiveStratiformHouze et al. (1989)

How IR sees stormHow IR sees storm

Area of cloud top above threshold temperature

How PMW emission sees stormHow PMW emission sees storm

How PMW scattering sees stormHow PMW scattering sees storm

IR

PMWScattering and Emission

PMWEmission

2.3 cm Radar

NASA TRMM 32 month average rainfall

mm/day

Global Precipitation UncertaintiesGlobal Precipitation Uncertainties HowHow do radar and passive microwave estimates do radar and passive microwave estimates

differ? differ? Relative error statisticsRelative error statistics Regional and temporal variationsRegional and temporal variations

WhyWhy do radar and passive microwave estimates do radar and passive microwave estimates differ?differ? Diagnostic informationDiagnostic information

Satellite Product User GroupsSatellite Product User Groups Algorithm development - How can retrievals be improved?Algorithm development - How can retrievals be improved? Forecasters-How much confidence?Forecasters-How much confidence? Data Assimilation- How to weight input for each region?Data Assimilation- How to weight input for each region?

Prototype for Global Oceanic Prototype for Global Oceanic Error CharacterizationError Characterization

Input: TRMM TMI and PR (Vers. 5)Input: TRMM TMI and PR (Vers. 5) OceanicOceanic pixels only pixels only Grids of ~2000 x 2000 kmGrids of ~2000 x 2000 km22 oceanic area oceanic area Daily product based on accumulated statistics Daily product based on accumulated statistics

for previous 47 days (complete diurnal cycle)for previous 47 days (complete diurnal cycle) CompareCompare

Probability distributionsProbability distributions Statistical variablesStatistical variables

Prototype Compares Prototype Compares Different Data SetsDifferent Data Sets

Spatial scales:Spatial scales: PR and TMI native sensor resolutionsPR and TMI native sensor resolutions PR ( 5 x 5 kmPR ( 5 x 5 km22) rescaled to TMI 10 and 19 GHz ) rescaled to TMI 10 and 19 GHz

scales scales

((30 x 60 km30 x 60 km22 and 20x30 kmand 20x30 km22 respectively respectively))

Types:Types: Surface Rainrate (Surface Rainrate (RRsfcsfc)) Vertically-Integrated Liquid Water ContentVertically-Integrated Liquid Water Content Microwave brightness temperatures Microwave brightness temperatures TTbb

% Rain area% Rain area

IR

PMWScattering and Emission

PMWEmission

2.3 cm Radar

NASA TRMM 32 month average rainfall

mm/day

PR Rsfc – TMI Overlap RsfcPR Rsfc – TMI Overlap Rsfc

Similar Spatial Scales: PR Rsfc – TMI Overlap RsfcSimilar Spatial Scales: PR Rsfc – TMI Overlap Rsfc

19 GHz Emission Brightness Temperatures19 GHz Emission Brightness Temperatures

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

SE Pacific marine stratocumulus SE Pacific marine stratocumulus clouds and drizzleclouds and drizzle

Radiative cooling from large area of Radiative cooling from large area of persistent low cloudspersistent low clouds

““Problem” area for global modelsProblem” area for global models Model representation of marine Model representation of marine

stratocumulus influences location of the stratocumulus influences location of the InterTropical Convergence Zone—an InterTropical Convergence Zone—an energy source driving global atmospheric energy source driving global atmospheric circulationscirculations

E Pacific cross-section along 95E Pacific cross-section along 95°° W W

Raymond et al. (2004)

SEC = South Equatorial Current, NECC = North Equatorial Countercurrent, EUC=Equatorial Undercurrent, x=westward flow, dots=eastward flow

SE Pacific SST and wind stress

Bretherton et al. (2004)

SE Pacific StratocumulusSE Pacific Stratocumulus 3 am local6 am local9 am local

Open Cells Closed CellsS

atel

lite

Shi

p R

adar

In SE Pacific, Most Drizzle In SE Pacific, Most Drizzle Evaporates Before Reaching Evaporates Before Reaching

the Surfacethe Surface

Echo Echo TrackingTracking

Comstock et al. (2004)

Structure and evolution of Structure and evolution of drizzle cellsdrizzle cells

Drizzle cell Drizzle cell lifetime 2+ lifetime 2+ hourshours

Time to rain out Time to rain out < ~ 30 minutes< ~ 30 minutes

Implies Implies replenishing replenishing cloud watercloud water

Time to reflectivity peak (hours)

Average cell reflectivity (dBZ)1

5

10

5

-1.5 –1 -0.5 0 0.5 1 1.5

Comstock et al. (2004)

EPIC Stratocumulus RHIs 18 October 2001 1428 UTC

90

2 km 18 km

dBZ

90

m/s

VAMOS Ocean Cloud Atmosphere and VAMOS Ocean Cloud Atmosphere and Land Studies (VOCALS) field campaign Land Studies (VOCALS) field campaign planned for October 2007 to improve planned for October 2007 to improve understanding and model simulation of understanding and model simulation of stratocumulus cloud decks in SE Pacificstratocumulus cloud decks in SE Pacific Interaction with weather systems over South Interaction with weather systems over South

AmericaAmerica Feedback with underlying oceanFeedback with underlying ocean Mesoscale variabilityMesoscale variability

Planned SE Pacific Field Experiment

Orographic PrecipitationOrographic Precipitation

New findings from recent field programs New findings from recent field programs Mesoscale Alpine Programme (MAP) in Mesoscale Alpine Programme (MAP) in

southern Alpssouthern Alps West coast US coastal mountains and West coast US coastal mountains and

Cascade mountains Cascade mountains HowHow translate field project findings to real- translate field project findings to real-

time forecast models?time forecast models?

Topography Topography ComparisonComparison

Alps

Central W Coast

Carolinas

Mt. St. Helens

Portland, OR

Eureka, CA

Different precipitation patterns in Different precipitation patterns in stable vs. unstable flowstable vs. unstable flow

Medina and Houze (2003)

During unblocked, During unblocked, unstable case, unstable case, some precipitation some precipitation features were features were locked to terrain locked to terrain while others while others developed developed upstream and upstream and drifted toward drifted toward mountains. mountains. Smith et al. (2003)Smith et al. (2003)

Tim

e

Distance topography

Gray scale is reflectivity

Mean Patterns for 61 Rain EventsMean Patterns for 61 Rain Events

Mean Radial Velocity Conditional Mean Reflectivity

Eureka, CA WSR-88D radar Oct 1995 – March 1998

From James (2004)

Mean Reflectivity Cross-SectionMean Reflectivity Cross-Section

Enhancement of precipitation over ocean upwind of coastal mountains (James, 2004)

Volumetric Statistics--Contoured Volumetric Statistics--Contoured Frequency by Altitude Diagram (CFAD)Frequency by Altitude Diagram (CFAD)

Yuter and Houze (1995)

Reflectivity Vertical Velocity

T

ime

Yuter and Houze (1995)

Vertically Pointing Radar from MAPVertically Pointing Radar from MAP

Yuter and Houze (2003)

~2 km

Distribution of vertical air Distribution of vertical air velocity with heightvelocity with height

Yuter and Houze (2003)

Input and results of 1D model similar to Input and results of 1D model similar to bulk microphysics parameterizationbulk microphysics parameterization

Yuter and Houze (2003)

Observed < 2 km scale variability of Observed < 2 km scale variability of reflectivity and vertical air motionsreflectivity and vertical air motions

Do ensemble characteristics need to be Do ensemble characteristics need to be parameterized to obtain correct parameterized to obtain correct precipitation patterns and intensities?precipitation patterns and intensities?

How can it best be parameterized?How can it best be parameterized?Evaluation of Model Output with Radar Evaluation of Model Output with Radar

DataData Surface fields (2D) Surface fields (2D) Volumetric fields (3D) Volumetric fields (3D) Statistics and spatial patternsStatistics and spatial patterns

Current community emphasis

Model to Model to Observation Observation Comparison of Comparison of Surface RainfallSurface Rainfall

1 km cloud resolving model with explicit microphysics (ARPS) of Ft. Worth Texas storm for time=0 (Smedsmo et al, 2004)

Smedsmo et al. (2004)

Volumetric comparison for accumulated storm totals

Different reflectivity patterns for Different reflectivity patterns for different wind directionsdifferent wind directions

James (2004)

Mean Z2 km altitude

dBZ Radial velocity (m/s)

(a) (b)

(c) (d)

How well can models reproduce observed orographic precipitation patterns?

Plan to collaborate with modeler to prototype comparisons for Portland, OR region.

Field project data sets Field project data sets help diagnose help diagnose

observationobservation“wishful thinking”“wishful thinking”

Oregon Cascade mountains Oregon Cascade mountains particle size data within particle size data within

melting layer at 0melting layer at 0°°CC

Diameter (mm) Yuter et al. (2004)

n(D

) m

m-1 m

-3

Rain Subset Wet Snow Subset

+13 dB

Severe Weather ExampleSevere Weather Example

Hurricane Ivan remnants September Hurricane Ivan remnants September 17 and 18, 2004 as observed by 17 and 18, 2004 as observed by regional radar networkregional radar network

Possible long term objective- Possible long term objective- evaluation of model ensemble evaluation of model ensemble members in near-real time to aid members in near-real time to aid nowcasting and forecasting nowcasting and forecasting

17 Sept 2004 17 Sept 2004 0600 UTC0600 UTC

ReflectivityRadial Velocity

Reflectivity

Squall lineSquall line18 Sept 2004 0020 18 Sept 2004 0020

UTCUTCKRAX radarKRAX radar

N S

Reflectivity

Reflectivity

Incomplete Incomplete and/or and/or ErroneousErroneous

Sound and Sound and completecomplete

Wishful Wishful ThinkingThinking

FictionFiction

Mix of Fiction, Mix of Fiction, Plausible Plausible

Fiction, and Fiction, and RealityReality

Sound with Sound with errors errors characterizedcharacterized

Mix of Fiction, Mix of Fiction, Plausible Plausible

Fiction, and Fiction, and RealityReality

RealityReality

Model PhysicsO

bse

rvat

ion

P

hys

ical

Inte

rpre

tati

on

Models and Observations Need to Improve

Cloud and Precipitation Cloud and Precipitation Challenges for the First Quarter Challenges for the First Quarter

of the 21of the 21stst Century Century

Utilize operational observations and Utilize operational observations and mesoscale models to improve regional mesoscale models to improve regional forecasting and basic science.forecasting and basic science.

Prioritize and improve surface-based and Prioritize and improve surface-based and satellite observations.satellite observations.

Retrospectively evaluate global changes Retrospectively evaluate global changes and improve climate forecasts.and improve climate forecasts.

The End

Grid used in PrototypeGrid used in Prototype

Precipitation Area vs. Mean IR TemperaturePrecipitation Area vs. Mean IR Temperature

West Pacific East Pacific

Storm SchematicStorm Schematic

Cloud boundaryRadar echo boundary

0°C level Surface precipitation

ConvectiveStratiformHouze et al. (1989)

Radar-derived precipitation Radar-derived precipitation productsproducts

Existence, Precip.Area--Min. detectable Existence, Precip.Area--Min. detectable surface precip ratesurface precip rate

Classification of precip structure in vertical Classification of precip structure in vertical and horizontal into rain, snow, mixed, and horizontal into rain, snow, mixed, graupel/hailgraupel/hail

Spatial pattern of precip. intensitySpatial pattern of precip. intensity Quantitative estimate of precip. intensityQuantitative estimate of precip. intensity

Uncertainty

SE Pacific Stratocumulus RegionSE Pacific Stratocumulus Region

Tropical West Pacific (x)Tropical West Pacific (x)Jul-Aug 02Jul-Aug 02

-1.0 -0.6 -0.2 0.2 0.6 1.0 1.4 1.8

log10(Rsfc [mm hr-1])

PRPRTMITMI

Central North Atlantic Central North Atlantic Jul-Aug 02Jul-Aug 02

PRPRTMITMI

log10(Rsfc [mm hr-1])

-1.0 -0.6 -0.2 0.2 0.6 1.0 1.4 1.8

RescalingRescaling

• PR data aggregated PR data aggregated to TMI pixel scale to TMI pixel scale (10 and 19 GHz)(10 and 19 GHz)

• Ensures comparison Ensures comparison of statistical properties of statistical properties at a common scaleat a common scale

• Useful to investigate Useful to investigate TMI subpixel variabilityTMI subpixel variability

Accumulated statistics for 47 days

Conditional Rsfc (PR– TMI)

Tropical West PacificCentral North Atlantic

Mean % rainy area for rescaled PR pixelsMean % rainy area for rescaled PR pixels

0 5 10 15 20 [%]

PR Surface rainrate (native resolution)PR Rescaled rainrate (10 GHz TMI res.)

TMI Swath (872 km)

PR Swath (245 km)

EFOVs

Alo

ng-t

rack

axi

sEFOV

IFOV end

IFOV start

Satellite subpt 15 km between swaths

Meth BlueMeth Blue• 33 Samples Obtained• Counted by Hand• Rain rates from 0.0001 to 4.1 mm/hr• Does not resolve drops < 0.2 mm at all, and drops

< 0.4 mm well.• ASSUME size range of drops resolved by method

are sufficient to determine Z-R relation

Num

ber/

seco

nd -2 dBZ0.01

mm/hr

22 dBZ1.6

mm/hr

15 dBZ0.7

mm/hr

Comparing rain Comparing rain distributionsdistributionsRescaled PR Rescaled PR against TMIagainst TMI

Histogram Histogram windowwindow

Raymond et al. (2004)