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Cloud and Precipitation Patterns and Processes Sandra Yuter 1 November 2004.
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Transcript of Cloud and Precipitation Patterns and Processes Sandra Yuter 1 November 2004.
![Page 1: Cloud and Precipitation Patterns and Processes Sandra Yuter 1 November 2004.](https://reader035.fdocuments.net/reader035/viewer/2022070412/56649e615503460f94b5d0fe/html5/thumbnails/1.jpg)
Cloud and Precipitation Cloud and Precipitation Patterns and ProcessesPatterns and Processes
Sandra YuterSandra Yuter
1 November 20041 November 2004
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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
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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
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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
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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
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Rain gauge data set used in global precip maps
Global Precipitation Climatology Center (G. Huffman)
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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)
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Storm SchematicStorm Schematic
Cloud boundaryRadar echo boundary
0°C level Surface precipitation
ConvectiveStratiformHouze et al. (1989)
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How IR sees stormHow IR sees storm
Area of cloud top above threshold temperature
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How PMW emission sees stormHow PMW emission sees storm
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How PMW scattering sees stormHow PMW scattering sees storm
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IR
PMWScattering and Emission
PMWEmission
2.3 cm Radar
NASA TRMM 32 month average rainfall
mm/day
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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?
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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
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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
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IR
PMWScattering and Emission
PMWEmission
2.3 cm Radar
NASA TRMM 32 month average rainfall
mm/day
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PR Rsfc – TMI Overlap RsfcPR Rsfc – TMI Overlap Rsfc
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Similar Spatial Scales: PR Rsfc – TMI Overlap RsfcSimilar Spatial Scales: PR Rsfc – TMI Overlap Rsfc
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19 GHz Emission Brightness Temperatures19 GHz Emission Brightness Temperatures
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% Rain Area of 30 km x 60 km pixel% Rain Area of 30 km x 60 km pixel
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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
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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
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SE Pacific SST and wind stress
Bretherton et al. (2004)
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SE Pacific StratocumulusSE Pacific Stratocumulus 3 am local6 am local9 am local
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Open Cells Closed CellsS
atel
lite
Shi
p R
adar
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In SE Pacific, Most Drizzle In SE Pacific, Most Drizzle Evaporates Before Reaching Evaporates Before Reaching
the Surfacethe Surface
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Echo Echo TrackingTracking
Comstock et al. (2004)
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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)
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EPIC Stratocumulus RHIs 18 October 2001 1428 UTC
90
2 km 18 km
dBZ
90
m/s
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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
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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?
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Topography Topography ComparisonComparison
Alps
Central W Coast
Carolinas
Mt. St. Helens
Portland, OR
Eureka, CA
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Different precipitation patterns in Different precipitation patterns in stable vs. unstable flowstable vs. unstable flow
Medina and Houze (2003)
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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
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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)
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Mean Reflectivity Cross-SectionMean Reflectivity Cross-Section
Enhancement of precipitation over ocean upwind of coastal mountains (James, 2004)
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Volumetric Statistics--Contoured Volumetric Statistics--Contoured Frequency by Altitude Diagram (CFAD)Frequency by Altitude Diagram (CFAD)
Yuter and Houze (1995)
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Reflectivity Vertical Velocity
T
ime
Yuter and Houze (1995)
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Vertically Pointing Radar from MAPVertically Pointing Radar from MAP
Yuter and Houze (2003)
~2 km
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Distribution of vertical air Distribution of vertical air velocity with heightvelocity with height
Yuter and Houze (2003)
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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)
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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
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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)
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Smedsmo et al. (2004)
Volumetric comparison for accumulated storm totals
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Different reflectivity patterns for Different reflectivity patterns for different wind directionsdifferent wind directions
James (2004)
Mean Z2 km altitude
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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.
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Field project data sets Field project data sets help diagnose help diagnose
observationobservation“wishful thinking”“wishful thinking”
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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
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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
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17 Sept 2004 17 Sept 2004 0600 UTC0600 UTC
ReflectivityRadial Velocity
Reflectivity
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Squall lineSquall line18 Sept 2004 0020 18 Sept 2004 0020
UTCUTCKRAX radarKRAX radar
N S
Reflectivity
Reflectivity
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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
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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.
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The End
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Grid used in PrototypeGrid used in Prototype
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Precipitation Area vs. Mean IR TemperaturePrecipitation Area vs. Mean IR Temperature
West Pacific East Pacific
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Storm SchematicStorm Schematic
Cloud boundaryRadar echo boundary
0°C level Surface precipitation
ConvectiveStratiformHouze et al. (1989)
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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
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SE Pacific Stratocumulus RegionSE Pacific Stratocumulus Region
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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
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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
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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
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Accumulated statistics for 47 days
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Conditional Rsfc (PR– TMI)
Tropical West PacificCentral North Atlantic
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Mean % rainy area for rescaled PR pixelsMean % rainy area for rescaled PR pixels
0 5 10 15 20 [%]
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PR Surface rainrate (native resolution)PR Rescaled rainrate (10 GHz TMI res.)
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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
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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
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Comparing rain Comparing rain distributionsdistributionsRescaled PR Rescaled PR against TMIagainst TMI
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Histogram Histogram windowwindow
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Raymond et al. (2004)