1. Use of Profilers in TRMM Ground Validation Kenneth Gage 1, and Christopher Williams 1,2 1 NOAA Aeronomy Laboratory, Tropical Dynamics and Climate Group 2 University of Colorado, Cooperative Institute for Research in Environmental Sciences (CIRES)

2. Outline   Introduction   Profiler as a Reflectivity Standard   Profiler determination of vertical reflectivity gradient structure in stratiform and convective precipitation   Profiler retrieval of DSD and precipitation parameters as an alternative to Z-R.

3. NASA-TRMM Ground Validation Program The Aeronomy Lab was involved with these field campaigns: Texas (April 1998) TEFLUN-A Florida (July-Sept 1998)TEFLUN-B Brazil (Jan-Feb 1999)TRMM-LBA Kwajalein (July 1999-Present)KWAJEX Darwin (1997-Present)

4. TEFLUN-B, Triple-N-Ranch, Florida July – September 1998 Joss Waldvogel Disdrometer 2-Dimensional Video Disdrometer 2835 MHz Profiler 915 MHz Profiler

5. Reflectivity(amplitude) Doppler Velocity (air motion + particle motion) Spectral Width (turbulence and particle size variation) == 2*sqrt(var)

6. How Can Profilers Help with Ground Validation? Profilers in combination with disdrometers rain gages and polarimetric scanning radar can be used to improve the quality of QPE on the ground. Profilers in combination with other instruments can be used to provide physical validation to evaluate and improve algorithm performance. Profilers can provide information on the variability of Precipitation parameters on a scale which lies within the pixel size of scanning radars and satellite footprints (relevant to non-uniform beam filling and background error covariance for assimilation into models).

7. Profilers Provide a Reflectivity Standard for other Ground-Based Instruments

8. Reflectivity Time Series in Stratiform Rain from Collocated Profilers during TRMM LBA

9. Precision Statistics of Two Collocated Profilers in Central Florida in 1998 During TEFLUN B Plotted are: a)Pdf of reflectivity difference b)Bias and 95% confidence as a function of profiler reflectivity c)Pdf of profiler reflectivity

10. Profiler and WSR- 88D Reflectivity Comparison during TEFLUN B Top Panel: 915 MHz reflectivity 21 August, 1998 Second Panel: 915 MHz reflectivity degraded to 630 m & 5 minute resolution. Third Panel: WSR-88D reflectivity over the profiler. Bottom Panel: Profiler reflectivity variability at WSR-88D Resolution

11. Reflectivity Difference for all Rain Events (August-September 1998) PDF(Z Profiler ) PDF(ΔZ) Mean +/- 95% C.I. in 2 dBZ intervals 2,062 Obs

12. Vertical Reflectivity Gradients

13. Vertical Structure of Reflectivity at Legan in stratiform and convective precipitation

14. Reflectivity Dependence of Vertical Structure of Reflectivity at TEFLUN B and LBA

15. Top Panel: Scatter Plot of 2835 MHz profiler reflectivities at 202 m compared to calculated JWD disdrometer reflectivities excluding JWD samples with less than 64 drops. Bottom Panel: Reflectivity differences between profiler and disdrometer as a function of disdrometer reflectivity. Calibration of TRMM- LBA profiler, Ji-Pirana, Brazil; January - February 1999

16. Reflectivity Dependent Bias observed at Wallops by Height

17. An Ensemble Approach to DSD Retrieval using Profilers

18. DSD Model Descriptions The DSD retrieval models assume different functional forms of the DSD and are divided into two numerical classes. AGamma N(D)=N o D µ exp[(4+µ)D/D m ] Ulbrich 1983 B Normalized Gamma N(D)=N o * F µ (D/D m ) Testud et al. 2001 CLog-Normal N(D)=N t exp[-ln 2 (D/D m )(2ln 2 σ )] Feingold & Levin 1986 D Discrete DSD no mathematical function Note-For methods A,B, and C, the cost function was minimized in D k space with k = 3, 4, 5, and 6 to produce 4 different solutions. EExponential N(D)=N o exp[-(4)D/D m ] µ=0FGamma N(D)=N o D 2.5 exp[-(6.5)D/D m ] µ=2.5 GGamma N(D)=N o D µ exp[-(4+µ)D/D m ] µ=variable Convolution Techniques Deconvolution Techniques

19. D m & R Statistics at 3.2 km The top panel shows the 920-MHz profiler reflectivity for the precipitation event that passed over the Darwin, Australia, profiler site on 16 February 2003. Panel (b) shows the reflectivity at 3.2 km. The mean and standard deviation of the ensemble D m and R are shown for the 3.2 km altitude in panels (c) and (d).

20. Statistics Relative to the Ensemble Mean The mean mass-weighted diameter, D m, is estimated for each DSD model and the ensemble mean is estimated for each resolution volume (each time-height sample). The statistics relative to the ensemble mean are shown here.

21. Summary of Profiler Contributions to Ground-Based QPE   Reference reflectivity in the column over the profiler   Vertical structure of the precipitation parameters above the surface   Direct estimates of precipitation parameters based on profiler retrieved drop-size distributions

23. GPM Front-Range Reflectivity Comparison: 16-17 June 2004

25. Calibrating Vertically Pointing Profilers using a Surface disdrometer Joss-Waldvogel Disdrometer manufactured by Distromet. Detects and calculates the ‘momentum’ of individual rain drops hitting the head. Converts the momentum of each drop to a raindrop size. Yields the number and size of raindrops hitting the 50 cm 2 head. Aeronomy Lab developed software to record the full diameter resolution (127 bins), [Manufacture’s software provides 20 bin output].

26. 400 MHz Doppler Spectrum Source: Greg Forbes

27. Ground Validation for TRMM and GPM   TRMM Ground Validation utilized rain maps produced by scanning radar at various locations around the globe. However it was found that the uncertainty in the ground validation rain products was large and the ability to validate satellite algorithms was limited.   GPM ground validation will place more emphasis on the physical validation of algorithms in continental and oceanic ‘supersites’. Observations will be made in a multidimensional observing volume to support the use of a Satellite Simulator Model.

28. Reflectivity Time Series from Collocated Disdrometers at Wallops Is. Source: Ali Tokay

29. Use of Profilers in Ground Validation for TRMM The introduction of profilers in TRMM ground validation efforts was accomplished as a research effort and was not part of the originally planned Ground Validation activities for TRMM.

30. Factors Contributing to Poor QPE  Calibration of scanning radar  Reflectivity gradients in the lower atmosphere  Use of unrepresentative Z-R relations

31. Use of Disdrometers and Profilers to Calibrate Scanning Radars Disdrometer

32. Profiler Reflectivities vs. Disdrometer Reflectivities from TRMM LBA

33. Approach to Ground Validation TRMM GV emphasized the statistical validation with surface rainfall maps at a number of locations but these products were not of sufficient accuracy to be very useful and largely ignored the physical validation that would be needed to validate algorithm assumptions and performance.

34. Summary of TRMM EXPERIENCE Based on the experience of their use in TRMM ground validation field campaigns, profilers are becoming well established tools for use in multi- sensor field campaigns. A data archive of reflectivity, Doppler velocity, and spectral width was produced for each field campaign. A calibration procedure was developed utilizing co-located disdrometers and substantial progress was made toward routine retrieval of drop-size distributions from profiler measurements.

35. Drop Size Distributions from Profiler Observations  Two profilers operating at different frequencies to distinguish Bragg and Rayleigh scatter  Single profiler with Bragg and Rayleigh components of the Doppler spectrum present  Single UHF profiler without Bragg component: Sans Air Motion (SAM) Key is to separate air motions from hydrometeor fall velocities.

36. The DSD is the building block used to determine precipitation parameters Examples of Precipitation Parameters: Reflectivity = ∫ N(D) D 6 dD Liquid Water Content ~ ∫ N(D) D 3 dD Rain Rate ~ ∫v(D) N(D) D 3 dD Mean Mass-weighted Diameter, D m D m = ∫ N(D) D 4 dD /∫ N(D) D 3 dD D m = (4 + µ) /  D 6 Dependence – Radars are sensitive to this dimension D 3 Dependence Dependent only on the shape of the DSD. Independent of N o