Ice-Phase Precipitation Remote Sensing Using Combined Passive and Active Microwave Observations Benjamin T. Johnson UMBC/JCET & NASA/GSFC (Code 613.1) Benjamin.T.Johnson@nasa.gov Gail Skofronick-Jackson NASA/GSFC (Code 613.1) IGARSS 2011 – Vancouver, Canada
Figure 1.: whiteout conditions during a snow storm. 2/22
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
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
Retrieval Schematic (1) Radar-only Retrieval Large set of Radar-Retrieved Vertical Profiles of PSD/IWC Observed Reflectivities (Zku, Zka) Inversion Z-S, DWR, etc. Attenuation “Correction” (2) Forward Model Physical - Radiative Database Physical Model Precip. & Atmos. Hydrometeor Model Ext., Scat., p(Q), Z Radiative Transfer Model (3) Radar/Radiometer Retrieval Simulated Radiances (TBsim) TB Constrained PSD/IWC Profiles PMW Retrieval Algorithm Observed Radiances (TBobs) 6/22
Observed Reflectivities and Passive Microwave TBs during the 2003 Wakasa Bay Experiment B. Johnson IGARSS 2011 7/22
(Const. Density Spheres) Retrieval Inputs at each vertical level Environment: Pressure, Temperature, Humidity, Cloud Water Content Microphysics: Particle Density, Shape, PSD Type Observables: Zm,14, Zm,35, DWR Forward Dual Wavelength Ratio Retrieval Method Update PIA for air, clouds, and precip. (A14, A35) Starting at storm top (ztop) down to z=0 PIA-corrected Reflectivities Ze,14, Ze,35 B. Johnson IGARSS 2011 8/22 Match DWR with D0 (3.67/L) in Database; compute N0 Is DWR 1? no yes Ze,35-IWC retrieval, infer D0 / N0
WBAY 03: Dual Wavelength Ratio, and retrieved N0, and D0 (assuming a single constant particle density) B. Johnson IGARSS 2011 9/22
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…
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
(Fixed IWC = 1.0 g m-3) Constant Density Spheres Mass-Density Relationships Magono and Nakamura (1965) Mitchell et al. (1990) Locatelli and Hobbs (1974) Barthazy (1998) UW-NMS (Tripoli, 1992) 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
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