Ice-Phase Precipitation Remote Sensing Using Combined Passive and Active Microwave Observations

Benjamin T. JohnsonUMBC/JCET & NASA/GSFC (Code 613.1)

Benjamin.T.Johnson@nasa.gov

Gail Skofronick-JacksonNASA/GSFC (Code 613.1)

IGARSS 2011 – Vancouver, Canada

Figure 1.: whiteout conditions during a snow storm. 2/22

Introduction• Midlatitude/Winter precipitation is difficult to

measure using radars or radiometers alone. • Precipitating clouds consist of a wide range of

particles with variable shape, size, number density, and composition, and microwave radiation is sensitive to these properties

• Furthermore, ice clouds, water clouds, and gases and attenuate/emit microwave radiation

B. Johnson IGARSS 2011 3/22

Physically-based microwave precipitation remote sensing methods require (at least):• A physical description of the atmosphere and

surface properties • Physical descriptions of hydrometeors (PSD,

shape(s), composition)• Appropriate relationships between physical and

scattering/extinction/backscattering properties• An inversion method for retrieving the desired

physical properties given observations

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Relevant Key Problems• Uncertainties the physical description of the atmosphere:

distribution of CLW, WV; particle composition, size distribution, and shape.

• No current method for validating MW scattering properties of ice-phase hydrometeors.

Present Retrieval Approach• Physical method using “consistency matching” -- adjust

simulations until consistent with PMW and radar observations across multiple wavelengths (e.g., Meneghini, 1997).• Pros: Simple to implement, works equally over land and water• Cons: “matches” may not represent reality, geometric issues ignored

(NUBF, beam matching)• Important note: the uncertainty due to unknown particle shape is

orders of magnitude greater than other known sources of uncertainties.

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Observed Reflectivities

(Zku, Zka)

InversionZ-S, DWR, etc.

Large set ofRadar-RetrievedVertical Profiles

of PSD/IWC

Simulated Radiances

(TBsim) PMW RetrievalAlgorithm

Physical ModelPrecip. & Atmos.

HydrometeorModel

Ext., Scat., p( )Q , Z

Physical - Radiative Database

(2) Forward Model

TB Constrained PSD/IWC Profiles

Retrieval Schematic

Attenuation “Correction”

(1) Radar-only Retrieval

Observed Radiances

(TBobs)

Radiative Transfer Model

(3) Radar/Radiometer Retrieval

6/22

Observed Reflectivities and Passive Microwave TBs during the 2003 Wakasa Bay Experiment

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Observables: Zm,14, Zm,35,

DWR

Microphysics: Particle Density,

Shape, PSD Type

Retrieval Inputsat each vertical level

Environment:Pressure, Temperature, Humidity, Cloud Water

Content

Forward Dual Wavelength Ratio Retrieval Method

Ze,35-IWC retrieval, infer D0 / N0

Match DWR with D0 (3.67/L) in

Database; compute N0

Update PIA for air, clouds, and precip.

(A14, A35)

PIA-corrected Reflectivities Ze,14, Ze,35

Starting at storm top (ztop) down to

z=0

Is DWR 1?

no

yes

(Const. Density Spheres)

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WBAY 03: Dual Wavelength Ratio, and retrieved N0, and D0 (assuming a single constant particle density)

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10/22

11/22

12/22

Part 1 comments:• The basic retrieval works surprisingly well using only constant-

density spheres • approx. 5 K RMS error in precipitating regions, simply by adjusting the

CLW and particle density.

• However, constant-density spheres likely are not representative of the true distribution of mass and sizes of particles within the observed volume of the atmosphere…

Improvements:• Inclusion of well-known size-density relationships for spheres

(following Brown and Ruf, 2007), • Include sets of non-spherical “realistically shaped”

hydrometeors

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Magono and Nakamura (1965)Mitchell et al. (1990)Locatelli and Hobbs (1974)Barthazy (1998)UW-NMS (Tripoli, 1992)

Constant Density Spheres

Mass-Density Relationships

(Fixed IWC = 1.0 g m-3)

14/22

Retrieved log10(IWC) [g m-3] using size-density relationships (Brown and Ruf, 2007)

15/22

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Retrieved IWC [g m-3] :: “Realistic” particle shapes, exponential PSD

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18/22

19/22

Final comments:• The present method is designed for testing advances in the

physical-radiative properties of a physically based retrieval algorithm

• The choice of particle shape and size distribution appears to be the largest uncertainty in physically-based precipitation retrieval algorithms (most certainly renders them ill-posed)

• So, prior knowledge of the particle shapes and sizes should significantly constrain physically based retrievals• However, this requires that one has already computed the necessary

physical-radiative properties ahead of time!

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Next Steps for this work:• (un-break my radiative transfer model… )

• Create complete database of IWC as a function of reflectivity, dual-wavelength ratio, and particle shape.

• Add other non-spherical shapes (in progress, e.g., Kuo, G. Liu, others)

• Add melting particles (in progress)• Apply retrieval to GPM satellite simulator data (T.

Matsui, WK Tao, et al.) as a alg. dev. testbed. • Incorporate database(s) into official GPM combined

radar/radiometer algorithm • currently assumes constant-density spheres(?)

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B. Johnson IGARSS 2011 22/22