Active and passive Active and passive microwave remote microwave remote

sensing of sensing of precipitation at high precipitation at high

latitudeslatitudesR. Bennartz - M. Kulie - C. O’Dell R. Bennartz - M. Kulie - C. O’Dell (1)(1)

S. Pinori – A. Mugnai S. Pinori – A. Mugnai (2)(2)

(1) University of Wisconsin – AOS – Madison,WI - USA(2) Institute of Atmospheric Science and Climate, National Research Council, Rome, Italy

Introduction High latitudes and why study light rain snow

Modeling Strategy Light snow/rain validation database Case study

Light snowfall event from radar Satellite-model comparison UW-NMS mesoscale model comparison Sensitivity of the MW frequencies to

perturbation in the IWC Outlook

Towards GPM IPWG

Outline

SNOW AT MID-TO-HIGH SNOW AT MID-TO-HIGH LATITUDES LATITUDES

(Figures from P. Yoe, J. Koistinen)(Figures from P. Yoe, J. Koistinen)

At mid-to-high latitudes, snowfall represents a substantial portion of the precipitation.

Snow to Total Precipitation RatioSnowfall Accumulation

From higher latitudes at least 90% of the precipitation occurs at rates less than 3 mm/hr and 60 % at less than 1 mm/h

What we can observe What we can observe

Radar reflectivity (vertically resolved)Radar reflectivity (vertically resolved)Passive MW brightness temperatures Passive MW brightness temperatures

(vertical integral)(vertical integral)

What we can NOT observe: What we can NOT observe:

Drop size distribution Drop size distribution Ice particle densityIce particle densityIndex of refractionIndex of refraction

..

..

..

What we can NOT observe: What we can NOT observe:

Drop size distribution Drop size distribution Ice particle densityIce particle densityIndex of refractionIndex of refraction

..

..

..We need models to relate the

microphysics tomicrowave optical properties

What we can NOT observe: What we can NOT observe:

Drop size distribution Drop size distribution Ice particle densityIce particle densityIndex of refractionIndex of refraction

..

..

..We need models to relate the

microphysics tomicrowave optical properties

And those models have to agree with all available information

How can we trust our modeling assumptions?

Precip microphysicsmodel

Radar reflectivites

Environmental data

Observed TBs

Radiative transfermodel

Simulated TBs Compare

Change microphysics

How can we trust our modeling assumptions?

X = 1 X = 2

Frozen

Liquid

X = 0.5

Adjustable parameters: Adjustable parameters:

Ice densityIce densitySize of ice relative to liquid particlesSize of ice relative to liquid particles

Consistent description of Radar Refl/ Fall Speed/ Particle Consistent description of Radar Refl/ Fall Speed/ Particle number concentrationnumber concentration

One Microphysics Model (Bennartz & Petty 2001)

High latitude light snow/rain database (2002-ongoing)

Radar dataBALTRAD radar composites BALTRAD gauge adjustmentsGotland radar volume scans

Satellite dataNOAA 15,16,17 AMSU-A/B AQUA AMSR-E

SSMIS (if/when available)

Global/regional model data:global NCEP/GFS dataUW-NMS model (for selected cases)

CASE STUDYCASE STUDYLight snowfall over the Baltic Sea the 12-13 January, 2003.Comparing different ground-based, satellite and modelling data

MODIS 15 March 2003

2003-01-12 0130 UTC

Gotland radar reflectivity (lowest scan)

2003-01-12 0130 UTC

2003-01-12 0130 UTC

2003-01-12 0130 UTC

Radar composite (gauge adjusted surface rain rate)

2003-01-12 0130 UTC

AMSU 89 GHz and 150 GHz NOAA-17 0107 UTC

2003-01-12 0130 UTC

AMSU 89 - 150 GHz NOAA-17 0107 UTC

2003-01-12 0130 UTC

AMSR 89 GHz AQUA 01:31 UTC

RT : Reverse 3D Monte-Carlo with Henyey-Greenstein Phase Function, on a 2 km x 2 km x 1 km grid with 10 vertical levels. FASTEM-2 Ocean emissivity model, everywhere.

89 GHz (a) channel, at 36 GHz resolution

89 GHz (a) channel, at radar resolution

Model vs. Observation Comparison: Little bias, reasonably good correlation. Only areas where there is precip

3 two-way nested grids

18 hr simulation: from 12 UTC 11 January to 06 UTC 12 January 2003

3rd grid: 6 hours from 00UTC 12 Jan

6 category bulk microphysics:Cloud droplets, Rain, Pristine crystals, Snow (rimed crystals/low density graupel), Aggregated crystals, High density graupel

Mixing ratios of total water and 5 hydrometeors categories are predicted: rain, graupel, snow, pristine crystals, and aggregates. Cloud water is diagnosed

UW-NMS MODEL SETUPUW-NMS MODEL SETUP

[Tripoli 1992]

Selected two areas of similar environmental parameters (LWP,WVP).

Take into account the radar beam width at ~100 km from the radar site

RADAR-MODEL COMPARISONRADAR-MODEL COMPARISONdB Z

Relation between scattering index and 89 GHz brightness temperature for model (blue) and AMSR (red) for x=1;Relation between scattering index and 89 GHz brightness temperature for radar (red) and AMSR (black) for x=1.

SCATTERING INDEX FOR PRECIPITATING SCATTERING INDEX FOR PRECIPITATING AREAAREA

Red: radar Black:satellite

Radar and model datasets are in good agreement, with the scattering index ranging from -5 and 20 K.

AMSU–MODEL COMPARISONAMSU–MODEL COMPARISONRelation between TB89-TB150 and the surface precipitation for different size ratio x for observed AMSU-B data (red) and simulated data (blue).

X=1

Where are we?Where are we?

Microphysics model agrees with radar observations

Microphysics model agrees with passive mw observation at various scattering frequencies

Surface rain rates are comparable to gauge-adjusted radar

Channel definition for new sensorsChannel definition for new sensors

The Jacobian is defined as the partial derivative of a function:

The increase the IWC of ε allow us to see the sensitivity of TBs to perturbations in hydrometeor contents.

IWCTB

IWCTBJ

*

150 GHz is more sensitive to the IWC perturbation than the 89GHz especially in the upper levels.

89 GHz

150 GHz

K / (g/m3)

K / (g/m3)

Potential of the OPotential of the O22-sounding channels -sounding channels for frozen precipitation detectionfor frozen precipitation detection118±8.5

GHz

118±4.2 GHz

118±2.3 GHz

K / (g/m3)

Conclusions/Outlook

• Use all observable Tb dBZ to ensure consistency of microphysical assumptions in observation space

• Need for coordination of different groups working towards snowfall/high lat precip. using different microphysics schemes (intercomparison) -> IPWG

Conclusions/Outlook

• Use all observable Tb dBZ to ensure consistency of microphysical assumptions in observation space

• Need for coordination of different groups working towards snowfall/high lat precip. using different microphysics schemes (intercomparison) -> IPWG

• Dedicated experiments necessary to better understand cloud microphysics

Conclusions/Outlook

• Use all observable Tb dBZ to ensure consistency of microphysical assumptions in observation space

• Need for coordination of different groups working towards snowfall/high lat precip. using different microphysics schemes (intercomparison) -> IPWG

• Dedicated experiments necessary to better understand cloud microphysics

• BUT on a global scale we have to go with simple solutions for retrieval algorithms etc…

Two more things for high latitudes

• We need channels that are surface blind

• We need GPM like radars

Thanks