1. Measurements of raindrop-size distributions from dual-polarization spectral observations Dmitri Moisseev and V. Chandrasekar Colorado State University The research is supported by the National Science Foundation

2. Motivation There is a great importance in mapping naturally occurring drop-size distributions. Being able to do this with scanning radars expands the scope of DSD mapping tremendously because of the inherent large space / time sampling capability of scanning radars. This paper presents a novel approach combining the advantages of a traditional dual- polarization radar and the DSD capabilities of a vertical profiler.

3. Dual-polarization spectral measurements

4. Z dr as a function of elevation angle

5. Dual-polarization slant profile observations If measurements are taken at a high elevation angle, optimally between 30 and 60 deg, both Doppler and dual- polarization measurements can be utilized to retrieve microphysical properties of precipitation

6. Spectral differential reflectivity Spectral differential reflectivity is the ratio of hh and vv power spectra. Spectral differential reflectivity is the ratio of hh and vv power spectra. In absence of spectral broadening, e. g. due to turbulence, and wind the Z dr (v) can directly be related to a ratio of hh, vv radar cross-sections defined as functions of equivolumetric diameter. In absence of spectral broadening, e. g. due to turbulence, and wind the Z dr (v) can directly be related to a ratio of hh, vv radar cross-sections defined as functions of equivolumetric diameter.

7. Spectral differential reflectivity a) Spectral differential reflectivity for different axis ratio relations. b) influence of spectral broadening on Z dr (v).

8. Spectrum model Observed spectrum can be represented as Ambient wind velocity Precipitation spectrumBroadening kernel To retrieve a DSD, N(D), one needs to estimate ambient wind velocity, v 0, and spectrum broadening kernel width,  b

9. Methodology At the first step of the procedure wind velocity component and spectrum broadening are estimated from Z dr (v), by minimizing the following function is obtained from deconvolved Doppler power spectra. represents the quiet air spectral differential reflectivity. W hv (v) is the co-polar coherency spectrum. Here is obtained from deconvolved Doppler power spectra. represents the quiet air spectral differential reflectivity. W hv (v) is the co-polar coherency spectrum. )(vZ dec dr )(/)(DD vvhh 

10. At the second step of the procedure precipitation specrum is calculated. This figure shows estimated precipitation spectrum. Simulation input : N w = 8000 m -3 mm -1 D 0 = 1.2 mm   b = 0.5 m/s v 0 = 0 m/s Example of spectral broadening and deconvolution

11. At the third step of the procedure the N(D) is calculated. Example of the retrieved DSD

12. Error analysis Simulation results. Dependence of the D 0 errors on input D 0 values.

13. Error analysis; effect of a size-shape relation Result of 100 simulations. For simulation axis ration were assumed to follow Andsager et al. (1999). Retrieval was carried out assuming Pruppacher and Beard (1971) relation. N w = 8000  D 0 = 1.2 mm

14. Correction of the Z dr bias was carried out by including a Z dr bias as a free parameter in the optimization procedure. Error analysis; Z dr bias influence N w = 8000  D 0 = 1.2 mm

15. Z dr bias correction Deconvolved Z dr (v) after optimization with and without Z dr bias

16. DSD retrieval from CSU-CHILL measurements (30 degrees)

17. Conclusions A New Dual-polarization spectral methodology to retrieve Non-Parametric DSD from scanning radars. A New Dual-polarization spectral methodology to retrieve Non-Parametric DSD from scanning radars. Retrieval errors are comparable to ones from profiler techniques. Retrieval errors are comparable to ones from profiler techniques. Z dr bias can also be estimated. Z dr bias can also be estimated. Knowledge of underlying raindrop shapes is required for an accurate retrieval. Knowledge of underlying raindrop shapes is required for an accurate retrieval.