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Ice-Phase Precipitation Remote Sensing Using Combined Passive and Active Microwave Observations
Benjamin T. JohnsonUMBC/JCET & NASA/GSFC (Code 613.1)
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
B. Johnson IGARSS 2011 4/22
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.
B. Johnson IGARSS 2011 5/22
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( , 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
B. Johnson IGARSS 2011 7/22
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/ ) 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)
B. Johnson IGARSS 2011 8/22
WBAY 03: Dual Wavelength Ratio, and retrieved N0, and D0 (assuming a single constant particle density)
B. Johnson IGARSS 2011 9/22
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
B. Johnson IGARSS 2011 13/22
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
B. Johnson IGARSS 2011 16/22
Retrieved IWC [g m-3] :: “Realistic” particle shapes, exponential PSD
B. Johnson IGARSS 2011 17/22
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!
B. Johnson IGARSS 2011 20/22
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(?)
B. Johnson IGARSS 2011 21/22
B. Johnson IGARSS 2011 22/22