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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
Introduction ,[object Object]
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 radiationB. Johnson   IGARSS 2011 3/22
Physically-based microwave precipitation remote sensing methods require (at least): ,[object Object]
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 observationsB. Johnson   IGARSS 2011 4/22
Relevant Key Problems ,[object Object]
No current method for validating MW scattering properties of ice-phase hydrometeors.  Present Retrieval Approach ,[object Object]
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
10/22
11/22
12/22
Part 1 comments: ,[object Object]

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BJohnson_1473_IGARSS_2011_oral_final.pptx

  • 1. 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
  • 2. Figure 1.: whiteout conditions during a snow storm. 2/22
  • 3.
  • 4. 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
  • 5. Furthermore, ice clouds, water clouds, and gases and attenuate/emit microwave radiationB. Johnson IGARSS 2011 3/22
  • 6.
  • 7. Physical descriptions of hydrometeors (PSD, shape(s), composition)
  • 8. Appropriate relationships between physical and scattering/extinction/backscattering properties
  • 9. An inversion method for retrieving the desired physical properties given observationsB. Johnson IGARSS 2011 4/22
  • 10.
  • 11.
  • 12. Pros: Simple to implement, works equally over land and water
  • 13. Cons: “matches” may not represent reality, geometric issues ignored (NUBF, beam matching)
  • 14. 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
  • 15. 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
  • 16. Observed Reflectivities and Passive Microwave TBs during the 2003 Wakasa Bay Experiment B. Johnson IGARSS 2011 7/22
  • 17. (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
  • 18. WBAY 03: Dual Wavelength Ratio, and retrieved N0, and D0 (assuming a single constant particle density) B. Johnson IGARSS 2011 9/22
  • 19. 10/22
  • 20. 11/22
  • 21. 12/22
  • 22.
  • 23. approx. 5 K RMS error in precipitating regions, simply by adjusting the CLW and particle density.
  • 24.
  • 25. Include sets of non-spherical “realistically shaped” hydrometeorsB. Johnson IGARSS 2011 13/22
  • 26. (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
  • 27. Retrieved log10(IWC) [g m-3] using size-density relationships (Brown and Ruf, 2007) 15/22
  • 28. B. Johnson IGARSS 2011 16/22
  • 29. Retrieved IWC [g m-3] :: “Realistic” particle shapes, exponential PSD B. Johnson IGARSS 2011 17/22
  • 30. 18/22
  • 31. 19/22
  • 32.
  • 33. 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)
  • 34. So, prior knowledge of the particle shapes and sizes should significantly constrain physically based retrievals
  • 35. However, this requires that one has already computed the necessary physical-radiative properties ahead of time!B. Johnson IGARSS 2011 20/22
  • 36.
  • 37. Create complete database of IWC as a function of reflectivity, dual-wavelength ratio, and particle shape.
  • 38. Add other non-spherical shapes (in progress, e.g., Kuo, G. Liu, others)
  • 39. Add melting particles (in progress)
  • 40. Apply retrieval to GPM satellite simulator data (T. Matsui, WK Tao, et al.) as a alg. dev. testbed.
  • 41. Incorporate database(s) into official GPM combined radar/radiometer algorithm
  • 42. currently assumes constant-density spheres(?)B. Johnson IGARSS 2011 21/22
  • 43. B. Johnson IGARSS 2011 22/22