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Ice-Phase Precipitation Remote Sensing Using <br />Combined Passive and Active Microwave Observations<br />Benjamin T. Joh...
Figure 1.: whiteout conditions during a snow storm.<br />2/22<br />
Introduction<br /><ul><li>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 ...
Furthermore,  ice clouds, water clouds, and gases and attenuate/emit microwave radiation</li></ul>B. Johnson   IGARSS 2011...
Physically-based microwave precipitation remote sensing methods require (at least):<br /><ul><li>A physical description of...
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</li></ul>B. Johnson   IGARSS 2011<br...
Relevant Key Problems<br /><ul><li>Uncertainties the physical description of the atmosphere: distribution of CLW, WV; part...
No current method for validating MW scattering properties of ice-phase hydrometeors.  </li></ul>Present Retrieval Approach...
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 u...
Retrieval Schematic<br />(1) Radar-only Retrieval<br />Large set of<br />Radar-Retrieved<br />Vertical Profiles of PSD/IWC...
Observed Reflectivities and Passive Microwave <br />TBs during the 2003 Wakasa Bay Experiment<br />B. Johnson   IGARSS 201...
(Const. Density Spheres)<br />Retrieval Inputs<br />at each vertical level<br />Environment:<br />Pressure, Temperature, H...
WBAY 03:  Dual Wavelength Ratio, and retrieved N0, and D0 <br />(assuming a single constant particle density)<br />B. John...
10/22<br />
11/22<br />
12/22<br />
Part 1 comments:<br /><ul><li>The basic retrieval works surprisingly well using only constant-density spheres
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Transcript of "BJohnson_1473_IGARSS_2011_oral_final.pptx"

  1. 1. Ice-Phase Precipitation Remote Sensing Using <br />Combined Passive and Active Microwave Observations<br />Benjamin T. Johnson<br />UMBC/JCET & NASA/GSFC (Code 613.1)<br />Benjamin.T.Johnson@nasa.gov<br />Gail Skofronick-Jackson<br />NASA/GSFC (Code 613.1)<br />IGARSS 2011 – Vancouver, Canada<br />
  2. 2. Figure 1.: whiteout conditions during a snow storm.<br />2/22<br />
  3. 3. Introduction<br /><ul><li>Midlatitude/Winter precipitation is difficult to measure using radars or radiometers alone.
  4. 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. 5. Furthermore, ice clouds, water clouds, and gases and attenuate/emit microwave radiation</li></ul>B. Johnson IGARSS 2011<br />3/22<br />
  6. 6. Physically-based microwave precipitation remote sensing methods require (at least):<br /><ul><li>A physical description of the atmosphere and surface properties
  7. 7. Physical descriptions of hydrometeors (PSD, shape(s), composition)
  8. 8. Appropriate relationships between physical and scattering/extinction/backscattering properties
  9. 9. An inversion method for retrieving the desired physical properties given observations</li></ul>B. Johnson IGARSS 2011<br />4/22<br />
  10. 10. Relevant Key Problems<br /><ul><li>Uncertainties the physical description of the atmosphere: distribution of CLW, WV; particle composition, size distribution, and shape.
  11. 11. No current method for validating MW scattering properties of ice-phase hydrometeors. </li></ul>Present Retrieval Approach<br /><ul><li>Physical method using “consistency matching” -- adjust simulations until consistent with PMW and radar observations across multiple wavelengths (e.g., Meneghini, 1997).
  12. 12. Pros: Simple to implement, works equally over land and water
  13. 13. Cons: “matches” may not represent reality, geometric issues ignored (NUBF, beam matching)
  14. 14. Important note: the uncertainty due to unknown particle shape is orders of magnitude greater than other known sources of uncertainties.</li></ul>B. Johnson IGARSS 2011<br />5/22<br />
  15. 15. Retrieval Schematic<br />(1) Radar-only Retrieval<br />Large set of<br />Radar-Retrieved<br />Vertical Profiles of PSD/IWC<br />Observed Reflectivities <br />(Zku, Zka)<br />Inversion<br />Z-S, DWR, etc.<br />Attenuation “Correction”<br />(2) Forward Model<br />Physical - Radiative <br />Database<br />Physical Model<br />Precip. & Atmos. <br />Hydrometeor<br />Model<br />Ext., Scat., p(Q), Z<br />Radiative Transfer <br />Model <br />(3) Radar/Radiometer Retrieval<br />Simulated Radiances<br />(TBsim)<br />TB Constrained PSD/IWC Profiles <br />PMW Retrieval<br />Algorithm<br />Observed Radiances<br />(TBobs)<br />6/22<br />
  16. 16. Observed Reflectivities and Passive Microwave <br />TBs during the 2003 Wakasa Bay Experiment<br />B. Johnson IGARSS 2011<br />7/22<br />
  17. 17. (Const. Density Spheres)<br />Retrieval Inputs<br />at each vertical level<br />Environment:<br />Pressure, Temperature, Humidity, Cloud Water Content<br />Microphysics:<br />Particle Density, Shape, PSD Type<br />Observables:<br />Zm,14, Zm,35, DWR<br />Forward Dual Wavelength Ratio Retrieval Method<br />Update PIA for air, clouds, and precip. <br />(A14, A35)<br />Starting at storm top (ztop) down to z=0<br />PIA-corrected Reflectivities <br />Ze,14, Ze,35<br />B. Johnson IGARSS 2011<br />8/22<br />Match DWR with D0 (3.67/L) in Database; compute N0<br />Is <br />DWR  1?<br />no<br />yes<br />Ze,35-IWC retrieval, infer D0 / N0<br />
  18. 18. WBAY 03: Dual Wavelength Ratio, and retrieved N0, and D0 <br />(assuming a single constant particle density)<br />B. Johnson IGARSS 2011<br />9/22<br />
  19. 19. 10/22<br />
  20. 20. 11/22<br />
  21. 21. 12/22<br />
  22. 22. Part 1 comments:<br /><ul><li>The basic retrieval works surprisingly well using only constant-density spheres
  23. 23. approx. 5 K RMS error in precipitating regions, simply by adjusting the CLW and particle density.
  24. 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… </li></ul>Improvements:<br /><ul><li>Inclusion of well-known size-density relationships for spheres (following Brown and Ruf, 2007),
  25. 25. Include sets of non-spherical “realistically shaped” hydrometeors</li></ul>B. Johnson IGARSS 2011<br />13/22<br />
  26. 26. (Fixed IWC = 1.0 g m-3)<br />Constant Density Spheres<br />Mass-Density Relationships<br />Magono and Nakamura (1965)<br />Mitchell et al. (1990)<br />Locatelli and Hobbs (1974)<br />Barthazy (1998)<br />UW-NMS (Tripoli, 1992)<br />14/22<br />
  27. 27. Retrieved log10(IWC) [g m-3] using size-density relationships (Brown and Ruf, 2007)<br />15/22<br />
  28. 28. B. Johnson IGARSS 2011<br />16/22<br />
  29. 29. Retrieved IWC [g m-3] :: “Realistic” particle shapes, exponential PSD<br />B. Johnson IGARSS 2011<br />17/22<br />
  30. 30. 18/22<br />
  31. 31. 19/22<br />
  32. 32. Final comments:<br /><ul><li>The present method is designed for testing advances in the physical-radiative properties of a physically based retrieval algorithm
  33. 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. 34. So, prior knowledge of the particle shapes and sizes should significantly constrain physically based retrievals
  35. 35. However, this requires that one has already computed the necessary physical-radiative properties ahead of time!</li></ul>B. Johnson IGARSS 2011<br />20/22<br />
  36. 36. Next Steps for this work:<br /><ul><li>(un-break my radiative transfer model… )
  37. 37. Create complete database of IWC as a function of reflectivity, dual-wavelength ratio, and particle shape.
  38. 38. Add other non-spherical shapes (in progress, e.g., Kuo, G. Liu, others)
  39. 39. Add melting particles (in progress)
  40. 40. Apply retrieval to GPM satellite simulator data (T. Matsui, WK Tao, et al.) as a alg. dev. testbed.
  41. 41. Incorporate database(s) into official GPM combined radar/radiometer algorithm
  42. 42. currently assumes constant-density spheres(?)</li></ul>B. Johnson IGARSS 2011<br />21/22<br />
  43. 43. B. Johnson IGARSS 2011<br />22/22<br />
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