BJohnson_1473_IGARSS_2011_oral_final.pptx

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  • 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. Introduction
    • Midlatitude/Winter precipitation is difficult to measure using radars or radiometers alone.
    • 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 radiation
    B. Johnson IGARSS 2011
    3/22
  • 6. Physically-based microwave precipitation remote sensing methods require (at least):
    • A physical description of the atmosphere and surface properties
    • 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 observations
    B. Johnson IGARSS 2011
    4/22
  • 10. Relevant Key Problems
    • Uncertainties the physical description of the atmosphere: distribution of CLW, WV; particle composition, size distribution, and shape.
    • 11. 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).
    • 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. Part 1 comments:
    • The basic retrieval works surprisingly well using only constant-density spheres
    • 23. approx. 5 K RMS error in precipitating regions, simply by adjusting the CLW and particle density.
    • 24. 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),
    • 25. Include sets of non-spherical “realistically shaped” hydrometeors
    B. 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. Final comments:
    • The present method is designed for testing advances in the physical-radiative properties of a physically based retrieval algorithm
    • 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. Next Steps for this work:
    • (un-break my radiative transfer model… )
    • 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