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An Overview of Some Present/Near-Future Satellite Capabilities for Ice Cloud Property Retrievals

An Overview of Some Present/Near-Future Satellite Capabilities for Ice Cloud Property Retrievals. “the competition”. Steve Platnick (GSFC), Dong Wu (JPL), Dave Winker (LaRC), Bryan Baum (LaRC/CIMSS), Jay Mace (U. Utah), et al. Cloud Ice Workshop Madison, WI 10-11 June 2004.

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An Overview of Some Present/Near-Future Satellite Capabilities for Ice Cloud Property Retrievals

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  1. An Overview of Some Present/Near-Future Satellite Capabilities for Ice Cloud Property Retrievals “the competition” Steve Platnick (GSFC), Dong Wu (JPL), Dave Winker (LaRC), Bryan Baum (LaRC/CIMSS), Jay Mace (U. Utah), et al. Cloud Ice Workshop Madison, WI 10-11 June 2004

  2. Dme, re, t, IWP ? Topics Instrument strengths/weaknesses, uncertainties • Microwave Limb Sounder (sub-millimeter) • Shortwavelength instruments/techniques • Passive (MODIS) • Active (CALIPSO) S. Platnick, Cloud Ice Wkshop, 11 June 2004

  3. 103 particle size (µm) ? 102 10 10-1 1 10 102 103 IWP (g/m2) or optical thickness Solution Space S. Platnick, Cloud Ice Wkshop, 11 June 2004

  4. Microwave Limb Sounder (MLS) • Aura MLS Receivers • Frequency v-FOV h-FOV • 118 GHz 6.5 km 13 km • 190 GHz 4.5 km 9 km • 240 GHz 3.5 km 7 km • 640 GHz 1.5 km 3 km • 2.5 THz 2.5 km 2.5 km • UARS MLS Receivers • Frequency v-FOV h-FOV • 183 GHz 3.5 km 15 km • 204 GHz 3 km 15 km Measurements: 1. IWC (averaged over MLS FOV volume) at high altitudes 2. hIWP (path along MLS LOS) at various depths and frequencies A-Train CloudSat Aqua clouds Aura MLS IWC Tangent Pressure (hPa) clouds hIWP 7 min UARS MLS 203-GHz Rad (K) Dong Wu, Cloud Ice Wkshop, 11 June 2004

  5. Aura MLS Cloud Ice Measurements (Estimated uncertainties from pre-launch simulations) IWC (Averaged over MLS FOV volume) Pressure Radiometer Bias Precision Scaling 100 hPa 640 GHz ±1mg/m3 ~1 mg/m3 TBD 100 hPa 240 GHz ±2mg/m3 ~2 mg/m3 TBD 147 hPa 240 GHz ±2mg/m3 ~2 mg/m3 TBD 215 hPa 240 GHz ±20 mg/m3 ~20 mg/m3 TBD hIWP (Path along MLS LOS) Bottom Pressure Radiometer Bias PrecisionScaling ~180, ~100 hPa 118 GHz < 100 g/m2 ~500 g/m2 TBD 500-250 hPa 190 GHz ±300 g/m2 ~300 g/m2 TBD ~500 hPa 240 GHz ±40 g/m2 ~80 g/m2 TBD ~200 hPa 640 GHz ±1 g/m2 ~6 g/m2 TBD ~100 hPa 2.5 THz ±2 g/m2 ~1 g/m2 TBD Note: Scaling error depends largely on assumptions about ice microphysical properties such as size distribution, particle habit, and ice density. Retrievals of hIWP and De together can be done but require careful work to find MLS radiances with matched volumes (i.e., depth, FOV, and location considerations). Dong Wu, Cloud Ice Wkshop, 11 June 2004

  6. CALIPSO Lidar Retrieval - D. Winker • Launch, April 2005, A-train orbit • Instruments • Lidar: 2 l, 3 channel, ~100 m spot size • IIR (IR Imager, 1km, 3 channel) • WFC (VIS CCD camera, 125 m, 1 channel) • Primary cloud data products • Lidar alone: • Profiles of extinction and backscatter coefficient • Cloud base and top height • Layer optical thickness • Ice/water phase • Possibly classification of cirrus particles as either regular or irregular • crystals from depolarization signals • Lidar + IR + VIS: • Effective particle size, emissivity at 8.7, 10.6 and 12 µm S. Platnick, Cloud Ice Wkshop, 11 June 2004

  7. CALIPSO Lidar Retrieval - D. Winker CALIPSO t uncertainty “rough estimate” Lidar-only retrieval, 10 km horiz. averaging cloud attenuation S/N effect retrieval w/ assumed lidar ratio (~30%) retrieval via 2-way extinction S. Platnick, Cloud Ice Wkshop, 11 June 2004

  8. MODIS Solar Reflectance Retrieval MOD06 – Cloud Optical & Microphysical Properties Pixel-level cloud product for daytime observations at 1 km • Cloud optical thickness (t ), effective particle radius (re), water path, thermodynamic phase • liquid water and ice clouds, global retrievals (land, water, snow/ice) • Algorithm overview • Use single water non-absorbing band (0.65, 0.86, 1.2 µm) w/three absorbing bands (1.6, 2.1, 3.7 µm) => 1 t, 3 re(2.1 µm derived re is primary). – Short-wavelength band choice: 0.65 µm (land), 0.86 µm (ocean), 1.2 µm (snow/ice) • Surface spectral albedo from MODIS ecosystem and albedo products • Retrieval gives homogeneous-equivalent cloud properties S. Platnick, Cloud Ice Wkshop, 11 June 2004

  9. 0.8 0.6 Reflectance - 2.1 µm 0.4 0.2 0 0 0.2 0.4 0.6 0.8 1.0 Reflectance - 0.86 µm Solar Reflectance Methodretrieval space example - ice cloud over ocean surface S. Platnick, Cloud Ice Wkshop, 11 June 2004

  10. Cloud optical/microphysical properties from reflectance measurements - Spherical Particles In general: For Mie scattering (spheres, water droplets), 3 optical variables can be reduced to 1 optical & 1 microphysical: S. Platnick, Cloud Ice Wkshop, 11 June 2004

  11. Cloud optical/microphysical properties from reflectance measurements - Spherical Particles, cont. re is a radiative parameter, but with certain assumptions, it can be used with t to estimate column water mass/unit area (water path): Assumption: vertically homogenous cloud layer, i.e., N,re ≠ f(z) S. Platnick, Cloud Ice Wkshop, 11 June 2004

  12. Cloud optical/microphysical properties from reflectance measurements - Crystal/Irregular Particles In general: 3 optical variables can perhaps(?) be reduced to 1 optical & 2 microphysical: S. Platnick, Cloud Ice Wkshop, 11 June 2004

  13. MODIS operational (collection 4)ice crystal library habits/mixtures S. Platnick, Cloud Ice Wkshop, 11 June 2004

  14. Example Pseudo-Empirical re – Dme RelationsB. Baum, A. Heymsfield, P. Yang Note: the tail can wag the Dme S. Platnick, Cloud Ice Wkshop, 11 June 2004

  15. Sensitivity of Scattering Parameters to Habits/MixtureB. Baum S. Platnick, Cloud Ice Wkshop, 11 June 2004

  16. Solar Reflectance Retrieval Pixel-level Uncertainty AnalysisS. Platnick, R. Pincus, B. Wind • Currently incorporating the effect of the following sources on inferred cloud-top reflectance: • Instrument calibration • Atmospheric correction uncertainty • Spectral surface albedo uncertainty • Note: • Uses sensitivity derivatives calculated from reflectance libraries, e.g.: • Represents a likely minimum uncertainty, i.e., other missing components: ( ice cloud models, vertical cloud structure including multi-layer clouds, …) • Random L2 uncertainties may be reduced/eliminated during L3 aggregations S. Platnick, Cloud Ice Wkshop, 11 June 2004

  17. Pixel-level Uncertainty Analysis - Terra MODIS orbit (20 Nov 2002) re: 2-60 µm t: 1-100 log

  18. Pixel-level Uncertainty Analysis - Terra MODIS orbit (20 Nov 2002) Dre/re(%): 1-250 log Dt/t (%): 1-250 log Dt/t (%): 1-250 log

  19. 3 dB 1 dB 0.4 dB Pixel-level Uncertainty AnalysisCyclone granule (20 Nov. 2002)IWP:ice clouds S. Platnick, Cloud Ice Wkshop, 11 June 2004

  20. Retrieval ExampleTerra granule, coastal Chile/Peru, 18 July 2001, 1530 UTC [Platnick et al., IEEE Trans. Geosci. Remote Sens., 41] phase retrieval RGB true-color composite uncertain ice liquid water no retrieval S. Platnick, Cloud Ice Wkshop, 11 June 2004

  21. 3 dB 1 dB 0.4 dB Pixel-level Uncertainty AnalysisPeru granule (18 July 2001)IWP:ice clouds over ocean S. Platnick, Cloud Ice Wkshop, 11 June 2004

  22. 3 dB 1 dB 0.4 dB Pixel-level Uncertainty AnalysisPeru granule (18 July 2001)IWP:ice clouds over land S. Platnick, Cloud Ice Wkshop, 11 June 2004

  23. Pixel-level Uncertainty AnalysisPeru granule (18 July 2001)t :ice clouds over ocean S. Platnick, Cloud Ice Wkshop, 11 June 2004

  24. Example Validation Efforts S. Platnick, Cloud Ice Wkshop, 11 June 2004

  25. MODIS Terra, 6 March 2001, 1735 UTC MMCR Cirrus Validation - SGP ARM siteMace, Zhang, Platnick, King, Minnis, Yang (J. Appl. Meteor., accepted) S. Platnick, Cloud Ice Wkshop, 11 June 2004

  26. Cirrus Validation - SGP ARM site, cont. 6 March 2001 (MOD06 vs. Z-Velocity algorithm case study) S. Platnick, Cloud Ice Wkshop, 11 June 2004

  27. Cirrus Validation - SGP ARM site, cont. Case study 6 March 2001 6 March 2001 (MOD06 vs. Z-Velocity algorithm case study) S. Platnick, Cloud Ice Wkshop, 11 June 2004

  28. ~ 3dB relative error Cirrus Validation - SGP ARM site, cont. 15 overpasses, single layer cirrus (MOD06 vs. Z-Radiance algorithm case study) S. Platnick, Cloud Ice Wkshop, 11 June 2004

  29. 103 particle size (µm) 102 10 10-1 1 10 102 103 IWP (g/m2) or optical thickness Approx. Solution Space~ 3dB (100% rel error) Disadvantages Daytime only Multilevel clouds Sfc. albedo (t, re for small t) Vertical inhomog. (IWP) Habit mixture, size dist. Advantages High spatial resolution Future capability (VIIRS - NPP, NPOESS) solar (MODIS, land sfc) re t S. Platnick, Cloud Ice Wkshop, 11 June 2004

  30. solar (MODIS, land) ? (vert. inhomog.) re IWP Approx. Solution Space~ 3dB (100% rel error) 103 particle size (µm) 102 10 10-1 1 10 102 103 IWP (g/m2) or optical thickness S. Platnick, Cloud Ice Wkshop, 11 June 2004

  31. 103 particle size (µm) 102 CALIPSO lidar (t) 10 10-1 1 10 102 103 IWP (g/m2) or optical thickness Approx. Solution Space~ 3dB (100% rel error) Advantages Sensitivity for thin clouds Separate individual layers Disadvantages Limited cloud thickness Use of lidar ratio for small t Nadir only S. Platnick, Cloud Ice Wkshop, 11 June 2004

  32. 103 particle size (µm) 102 10 10-1 1 10 102 103 IWP (g/m2) or optical thickness Approx. Solution Space~ 3dB (100% rel error) Advantages Novel technique Vertical info Good sensitivity UARS, Aura Disadvantages hIWP vs vertical IWP Horiz. spatial resolution Habit mixture, size dist. MLS (hIWP) S. Platnick, Cloud Ice Wkshop, 11 June 2004

  33. 103 particle size (µm) 102 IR re 10 t 10-1 1 10 102 103 IWP (g/m2) or optical thickness Approx. Solution Space~ 3dB (100% rel error) Disadvantages Upwelling radiance B.C. Limited cloud thickness Habit mixture, size dist. Advantages High spatial resolution Future capability (VIIRS, CrIS - NPP, NPOESS) S. Platnick, Cloud Ice Wkshop, 11 June 2004

  34. 103 particle size (µm) 102 10 10-1 1 10 102 103 IWP (g/m2) or optical thickness Approx. Solution Space~ 3dB (100% rel error) Disadvantages Upwelling radiance B.C. Limited cloud thickness Habit mixture, size dist. Advantages High spatial resolution Future capability (VIIRS, CrIS - NPP, NPOESS) IR re IWP S. Platnick, Cloud Ice Wkshop, 11 June 2004

  35. 103 particle size (µm) 102 10 CALIPSO lidar (t) 10-1 1 10 102 103 IWP (g/m2) or optical thickness Approx. Solution Space~ 3dB (100% rel error) solar (MODIS, land sfc) IR re re re IWP t t IWP MLS (hIWP) S. Platnick, Cloud Ice Wkshop, 11 June 2004

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