Passive and Active Microwave Remote Sensing of Cold-Cloud Precipitation : Wakasa Bay Field Campaign 2003

Transcription

1 Passive and Active Microwave Remote Sensing of Cold-Cloud Precipitation : Wakasa Bay Field Campaign 3 Benjamin T. Johnson,, Gail Skofronick-Jackson 3, Jim Wang 3, Grant Petty jbenjam@neptune.gsfc.nasa.gov Department of Atmospheric and Oceanic Sciences University of Wisconsin - Madison Joint Center for Earth Systems Technology University of Maryland Baltimore County 3 NASA Goddard Space Flight Center Code 64.6 October 4th, 6: CGMS-IPWG Third Workshop on Precipitation Measurements

2 Introduction Cold-Cloud Precipitation Motivation and Goals Wakasa Bay 3: Observations APR- and MIR Observations 3 Retrieval Method Dual Wavelength Ratio (DWR) Method 4 Snowfall Case Study: January 9th, 3 Example -D Profiles Brightness Temperature Simulations 5 Rainfall Case Study: January 7th, 3 Example -D Profiles Brightness Temperature Simulations 6 Conclusion

3 Introduction Cold-Cloud Precipitation Motivation and Goals Wakasa Bay 3: Observations APR- and MIR Observations 3 Retrieval Method Dual Wavelength Ratio (DWR) Method 4 Snowfall Case Study: January 9th, 3 Example -D Profiles Brightness Temperature Simulations 5 Rainfall Case Study: January 7th, 3 Example -D Profiles Brightness Temperature Simulations 6 Conclusion

4 Introduction Cold-Cloud Precipitation Motivation and Goals Wakasa Bay 3: Observations APR- and MIR Observations 3 Retrieval Method Dual Wavelength Ratio (DWR) Method 4 Snowfall Case Study: January 9th, 3 Example -D Profiles Brightness Temperature Simulations 5 Rainfall Case Study: January 7th, 3 Example -D Profiles Brightness Temperature Simulations 6 Conclusion

5 Introduction Cold-Cloud Precipitation Motivation and Goals Wakasa Bay 3: Observations APR- and MIR Observations 3 Retrieval Method Dual Wavelength Ratio (DWR) Method 4 Snowfall Case Study: January 9th, 3 Example -D Profiles Brightness Temperature Simulations 5 Rainfall Case Study: January 7th, 3 Example -D Profiles Brightness Temperature Simulations 6 Conclusion

6 Introduction Cold-Cloud Precipitation Motivation and Goals Wakasa Bay 3: Observations APR- and MIR Observations 3 Retrieval Method Dual Wavelength Ratio (DWR) Method 4 Snowfall Case Study: January 9th, 3 Example -D Profiles Brightness Temperature Simulations 5 Rainfall Case Study: January 7th, 3 Example -D Profiles Brightness Temperature Simulations 6 Conclusion

7 Introduction Cold-Cloud Precipitation Motivation and Goals Wakasa Bay 3: Observations APR- and MIR Observations 3 Retrieval Method Dual Wavelength Ratio (DWR) Method 4 Snowfall Case Study: January 9th, 3 Example -D Profiles Brightness Temperature Simulations 5 Rainfall Case Study: January 7th, 3 Example -D Profiles Brightness Temperature Simulations 6 Conclusion

8 Zonal Precipitation Occurrence

9 Zonal Precipitation Intensity

10 Uncertainties in Intensity

11 Current Passive Remote Sensing Problems Light snow and rain difficult to detect (signal to noise) In-situ and ground-based instruments limited; Satellites cover mid-to-high latitudes well, issues remain Current passive platforms (TMI, AMSR, AMSU, SSMI, etc.) cannot reliably detect light/shallow snow and rain Retrieval algorithms and models require accurate information about all geophysical quantities within a FOV

12 Current Passive Remote Sensing Problems Light snow and rain difficult to detect (signal to noise) In-situ and ground-based instruments limited; Satellites cover mid-to-high latitudes well, issues remain Current passive platforms (TMI, AMSR, AMSU, SSMI, etc.) cannot reliably detect light/shallow snow and rain Retrieval algorithms and models require accurate information about all geophysical quantities within a FOV

13 Current Passive Remote Sensing Problems Light snow and rain difficult to detect (signal to noise) In-situ and ground-based instruments limited; Satellites cover mid-to-high latitudes well, issues remain Current passive platforms (TMI, AMSR, AMSU, SSMI, etc.) cannot reliably detect light/shallow snow and rain Retrieval algorithms and models require accurate information about all geophysical quantities within a FOV

14 Current Passive Remote Sensing Problems Light snow and rain difficult to detect (signal to noise) In-situ and ground-based instruments limited; Satellites cover mid-to-high latitudes well, issues remain Current passive platforms (TMI, AMSR, AMSU, SSMI, etc.) cannot reliably detect light/shallow snow and rain Retrieval algorithms and models require accurate information about all geophysical quantities within a FOV

15 Relevant Questions and Issues What are the ranges particle shape, fall speed, composition, and sizes, cold-cloud precipitation? What are the physical relationships between microphysics and microwave radiative transfer, and how can these accurately simulated? How can independent measurements be used to quantify frozen and melting precipitation? How can these quantities be used to improve existing and future retrieval frameworks (e.g., using SSMI/S, GPM, etc.)?

16 Relevant Questions and Issues What are the ranges particle shape, fall speed, composition, and sizes, cold-cloud precipitation? What are the physical relationships between microphysics and microwave radiative transfer, and how can these accurately simulated? How can independent measurements be used to quantify frozen and melting precipitation? How can these quantities be used to improve existing and future retrieval frameworks (e.g., using SSMI/S, GPM, etc.)?

17 Relevant Questions and Issues What are the ranges particle shape, fall speed, composition, and sizes, cold-cloud precipitation? What are the physical relationships between microphysics and microwave radiative transfer, and how can these accurately simulated? How can independent measurements be used to quantify frozen and melting precipitation? How can these quantities be used to improve existing and future retrieval frameworks (e.g., using SSMI/S, GPM, etc.)?

18 Relevant Questions and Issues What are the ranges particle shape, fall speed, composition, and sizes, cold-cloud precipitation? What are the physical relationships between microphysics and microwave radiative transfer, and how can these accurately simulated? How can independent measurements be used to quantify frozen and melting precipitation? How can these quantities be used to improve existing and future retrieval frameworks (e.g., using SSMI/S, GPM, etc.)?

19 Introduction Cold-Cloud Precipitation Motivation and Goals Wakasa Bay 3: Observations APR- and MIR Observations 3 Retrieval Method Dual Wavelength Ratio (DWR) Method 4 Snowfall Case Study: January 9th, 3 Example -D Profiles Brightness Temperature Simulations 5 Rainfall Case Study: January 7th, 3 Example -D Profiles Brightness Temperature Simulations 6 Conclusion

20 January 9th, 3 3: to 3:4 UTC

21 January 7th, 3 5:5 to 5:55 UTC

22 Introduction Cold-Cloud Precipitation Motivation and Goals Wakasa Bay 3: Observations APR- and MIR Observations 3 Retrieval Method Dual Wavelength Ratio (DWR) Method 4 Snowfall Case Study: January 9th, 3 Example -D Profiles Brightness Temperature Simulations 5 Rainfall Case Study: January 7th, 3 Example -D Profiles Brightness Temperature Simulations 6 Conclusion

23 Description of DWR Method DWR goal: estimate two free parameters (N and Λ) of a size distribution (e.g., exponential): N(D) = N exp( Λ D). () DWR independent of N only sensitive to mode diameter (D = 3./Λ) Unknowns: Particle shape/composition, cloud water content, water vapor content, temperature, pressure, and instrument bias Nearby radiosonde obs. constrain WV, T, and P Particle shape/composition ( density in the case of spheres) and CLW free parameters (For further details of the DWR method see, for example, Meneghini et al. (997))

24 Snow Size, Number, and Density relationship to DWR

25

26 Introduction Cold-Cloud Precipitation Motivation and Goals Wakasa Bay 3: Observations APR- and MIR Observations 3 Retrieval Method Dual Wavelength Ratio (DWR) Method 4 Snowfall Case Study: January 9th, 3 Example -D Profiles Brightness Temperature Simulations 5 Rainfall Case Study: January 7th, 3 Example -D Profiles Brightness Temperature Simulations 6 Conclusion

27 Snow: Retrieved Quantities

28 Snow: Derived Quantities (IWC and precip. rate)

29 (Unconstrained ρ snow ): Retrieved N and Λ: Snow Observed DWR 35, Alt. (km) Alt. (km).5 observed Z 4 observed Z Reflectivity Factor (dbz) Dual Wavelength Ratio: 35/ Alt [km] Alt [km].5.5 Retrieved Λ [m ] Retrieved N [m 4 ] Λ [m ] log (N ) [m 4 ])

30 Snow: Retrieved N and Λ observed Z 4 Observed DWR 35,4 3 observed Z Alt. (km) Alt. (km) Reflectivity Factor (dbz) Dual Wavelength Ratio: 35/4 4.5 Retrieved N [m 4 ] 4 3 Retrieved Λ [m ] 3.5 Alt [km].5 Alt [km] Λ [m ] log (N ) [m 4 ])

31 (Unconstrained ρ snow ): Retrieved R and IWC: Snow observed Z 4 DWR 35,4 3 observed Z Alt. (km) Alt. (km) Reflectivity Factor (dbz) Dual Wavelength Ratio: 35/4 3 Retrieved LWC (kg/m 3 ) Z 35 LWC for Snow Z 35 LWC for Rain 3 Retrieved R (mm/hr) Z 35 R for Snow Z 35 R for Rain.5.5 Alt. (km) Alt. (km) (melted) Liquid Water Content (kg/m 3 ) Liquid Equivalent Rainfall Rate (mm/hr)

32 Snow: Retrieved R and IWC vs Z 35 -IWC and Z 35 -R observed Z 4 DWR 35,4 3 observed Z Alt. (km) Alt. (km) Reflectivity Factor (dbz) Dual Wavelength Ratio: 35/4 3 Retrieved LWC (kg/m 3 ) Z LWC for Snow 35 Z 35 LWC for Rain 3 Retrieved R (mm/hr) Z R for Snow 35 Z 35 R for Rain.5.5 Alt. (km) Alt. (km) (melted) Liquid Water Content (kg/m 3 ) Liquid Equivalent Precipitation Rate (mm/hr)

33 Snow: Simulated and Observed profile TBs Constrained TB Observed TB No CLW TB Obs. Clear TB Snow Case 4 TB [K] ± 83.3±3 83.3±7 34 Frequency (GHz)

34 Snow: Observed & Simulated Optimal-Fit TBs Segment TB [K] 4 Simulated & Matched 89 GHz Observed 89 GHz TB [K] Simulated & Matched 5 GHz Observed 5 GHz TB [K] 6 4 Simulated & Matched 83+/ 3 GHz Observed 83+/ 3 GHz TB [K] Simulated & Matched 83+/ 7 GHz Observed 83+/ 7 GHz TB [K] 6 4 Simulated & Matched GHz 8 Observed GHz Scan #

35 Introduction Cold-Cloud Precipitation Motivation and Goals Wakasa Bay 3: Observations APR- and MIR Observations 3 Retrieval Method Dual Wavelength Ratio (DWR) Method 4 Snowfall Case Study: January 9th, 3 Example -D Profiles Brightness Temperature Simulations 5 Rainfall Case Study: January 7th, 3 Example -D Profiles Brightness Temperature Simulations 6 Conclusion

36 Rain Case: Retrieved Quantities

37 Rain: Derived Quantities (IWC and precip. rate)

38 Rain: Retrieved N and Λ 5 5 observed Z 4 Observed DWR 35,4 4.5 observed Z Alt. (km) Reflectivity Factor (dbz) Alt. (km) Dual Wavelength Ratio: 35/4 Alt [km] Retrieved Λ [m ] Retrieved N [m 4 ] Alt [km] Λ [m ] log (N ) [m 4 ])

39 Rain: Retrieved R and IWC vs Z 35 -IWC and Z 35 R 5 5 observed Z 4 DWR 35,4 4.5 observed Z Alt. (km) Reflectivity Factor (dbz) Alt. (km) Dual Wavelength Ratio: 35/ Retrieved LWC (kg/m 3 ) Z LWC for Snow 35 Z 35 LWC for Rain Retrieved R (mm/hr) Z 35 R for Snow Z 35 R for Rain 3 3 Alt. (km).5 Alt. (km) (melted) Liquid Water Content (kg/m 3 ) Liquid Equivalent Precipitation Rate (mm/hr)

40 Rain: Simulated and Observed profile TBs 8 Rain Case 7 6 TB [K] Constrained TB Observed TB No CLW TB Obs. Clear TB ± 83.3±3 83.3±7 34 Frequency (GHz)

41 TB Constrained Profile and Insensitivity to CLW 5 5 Alt [km] (No Cloud) Λ [m ] TB Constr. Case Λ [m ] Alt [km] (No Cloud) N [m 4 ] TB Constr. Case N [m 4 ] Λ [m ] log (N ) [m 4 ]) Alt. (km) Alt. (km) (No Cloud) R (mm/hr) TB Constr. R (mm/hr).5 (No Cloud) LWC (kg/m 3 ) TB Constr. Case LWC (kg/m 3 ) (melted) Liquid Water Content (kg/m 3 ) Liquid Equivalent Rainfall Rate (mm/hr)

42 Introduction Cold-Cloud Precipitation Motivation and Goals Wakasa Bay 3: Observations APR- and MIR Observations 3 Retrieval Method Dual Wavelength Ratio (DWR) Method 4 Snowfall Case Study: January 9th, 3 Example -D Profiles Brightness Temperature Simulations 5 Rainfall Case Study: January 7th, 3 Example -D Profiles Brightness Temperature Simulations 6 Conclusion

43 Future Work Short-Term: Database of TB-constrained retrieved profiles consistent with observations Short-Term: Validate wind-emissivity algorithm: wind-sensitivity analysis Short-Term: Parameterize retrievals for use in PMW retrievals Medium-Term: Replace Mie-sphere with more realistic shape model (DDA?) compare Medium-Term: Address conically scanning TBs geometry issues vs. nadir radar (i.e., GMI & PR- on GPM) Long-term: Over-land retrievals: Land surface emissivity / radar cross-sections Long-term: GPM: Combined GMI/PR radar retrieval algorithm(s) for light rain and snow over both land and ocean

44 Conclusions Model database is robust enough to capture small variations in DWR, only when DWR <.; Retrievals of N and Λ appear to be consistent with published Z-R and Z-IWC relationships for rain and snow, given some guidance about particle density; Retrievals in rain cases require prior knowledge of melting layer altitude and extent, difficult when no melting layer is obvious (LDR is helpful for this); Multifrequency TB constraints look promising for rain and snow; simulations sensitive to free parameters

45 Selected References I Johnson, B. T. and G. W. Petty, 6: Methods and uncertainties in modeling of microwave scattering and extinction properties of frozen and mixed phase precipitation:. theory. In preparation for submission. Meneghini, R., H. Kumagai, J. Wang, T. Iguchi, and T. Kozu, 997: Microphysical Retrievals over Stratiform Rain Using Measurements from an Airborne Dual-Wavelength Radar-Radiometer. IEEE Trans. Geosci. Rem. Sens.., 35(3), Petty, G. W., : Physical and microwave radiative properties of precipitating clouds. Part I: Principal component analysis of observed multichannel microwave radiances in tropical stratiform rainfall. J. Appl. Meteorol., 4(), 5 4.

46 Growing Snowflakes

47 Theoretical Basis (Forward DWR Method) [ ] j A i (r j ) = A ext,i (r j )exp. ln()h N (r k )I e,i (Λ(r k )) k= f (r j ) = Z obs, (r j )A (r j ) Z obs, (r j )A (r j ) g (Λ (r j )) = Z sim, (r j, Λ, D, m) Z sim, (r j, Λ, D, m) solving for the slope parameter of the size distribution: () (3) (4) Λ (r j ) = g [f (r j )] D,m (5) and the coefficient N : N (r j ) Z obs,i (r j ) {Z sim,i (Λ (r j ))A (r j )} (6)

48 Constraining simulated TBs using observed TBs 5 5 T B 89 GHz T B 89 GHz T 5 GHz B T GHz B 5 T B 5 GHz T GHz B T B GHz T B 89 GHz T B 5 GHz

49 Rain Case: /7/3

50 Snow Case: /9/3

51 Middle to High Latitude Cold Cloud Precipitation Source