Active and passive microwave remote sensing of precipitation at high latitudes
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Active and passive microwave remote sensing of precipitation at high latitudes. R. Bennartz - M. Kulie - C. O’Dell (1) S. Pinori – A. Mugnai (2). (1) University of Wisconsin – AOS – Madison,WI - USA (2) Institute of Atmospheric Science and Climate, National Research Council, Rome, Italy.

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Active and passive microwave remote sensing of precipitation at high latitudes

Active and passive microwave remote sensing of precipitation at high latitudes

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

S. Pinori – A. Mugnai (2)

(1) University of Wisconsin – AOS – Madison,WI - USA

(2)Institute of Atmospheric Science and Climate, National Research Council, Rome, Italy


Outline at high latitudes

  • 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


Snow at mid to high latitudes figures from p yoe j koistinen
SNOW AT MID-TO-HIGH LATITUDES at high latitudes(Figures from P. Yoe, J. Koistinen)

Snowfall Accumulation

Snow to Total Precipitation Ratio

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

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 at high latitudes

Radar reflectivity (vertically resolved)

Passive MW brightness temperatures (vertical integral)


What we can NOT observe: at high latitudes

Drop size distribution

Ice particle density

Index of refraction

.

.

.


What we can NOT observe: at high latitudes

Drop size distribution

Ice particle density

Index of refraction

.

.

.

We need models to relate the microphysics to

microwave optical properties


What we can NOT observe: at high latitudes

Drop size distribution

Ice particle density

Index of refraction

.

.

.

We need models to relate the microphysics to

microwave optical properties

And those models have to agree with all available information



Radar at high latitudes

reflectivites

Environmental

data

How can we trust our modeling assumptions?

Precip microphysics

model

Change

microphysics

Radiative transfer

model

Observed

TBs

Compare

Simulated TBs


One Microphysics Model (Bennartz & Petty 2001) at high latitudes

Adjustable parameters:

Ice densitySize of ice relative to liquid particles

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

X = 0.5

X = 1

X = 2

Frozen

Liquid


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

Radar data

BALTRAD radar composites

BALTRAD gauge adjustments

Gotland radar volume scans

Satellite data

NOAA 15,16,17 AMSU-A/B

AQUA AMSR-E

SSMIS (if/when available)

Global/regional model data:

global NCEP/GFS data

UW-NMS model (for selected cases)


Case study

MODIS 15 March 2003 at high latitudes

CASE STUDY

Light snowfall over the Baltic Sea the 12-13 January, 2003.

Comparing different ground-based, satellite and modelling data


2003-01-12 0130 UTC at high latitudes

Gotland radar reflectivity (lowest scan)


2003-01-12 0130 UTC at high latitudes


2003-01-12 0130 UTC at high latitudes


2003-01-12 0130 UTC at high latitudes

Radar composite (gauge adjusted surface rain rate)


2003-01-12 0130 UTC at high latitudes

AMSU 89 GHz and 150 GHz NOAA-17 0107 UTC


2003-01-12 0130 UTC at high latitudes

AMSU 89 - 150 GHz NOAA-17 0107 UTC


2003-01-12 0130 UTC at high latitudes

AMSR 89 GHz AQUA 01:31 UTC


RT : at high latitudesReverse 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 radar resolution

89 GHz (a) channel, at 36 GHz resolution


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


Uw nms model setup
UW-NMS MODEL SETUP 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

[Tripoli 1992]


Radar model comparison
RADAR-MODEL COMPARISON good correlation. Only areas where there is precip

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

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

dBZ


SCATTERING INDEX FOR PRECIPITATING AREA good correlation. Only areas where there is precip

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.

Red: radar Black:satellite

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


Amsu model comparison
AMSU–MODEL COMPARISON good correlation. Only areas where there is precip

Relation 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? good correlation. Only areas where there is precip

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 sensors
Channel definition for new sensors good correlation. Only areas where there is precip

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.


150 GHz good correlation. Only areas where there is precip

89 GHz

K / (g/m3)

K / (g/m3)

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


118 good correlation. Only areas where there is precip±8.5 GHz

118±4.2 GHz

118±2.3 GHz

K / (g/m3)

Potential of the O2-sounding channels for frozen precipitation detection


Conclusions/Outlook good correlation. Only areas where there is precip

  • 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 good correlation. Only areas where there is precip

  • 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 good correlation. Only areas where there is precip

  • 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 good correlation. Only areas where there is precip

  • We need channels that are surface blind

  • We need GPM like radars


Thanks good correlation. Only areas where there is precip


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