GV for DPR radar algorithm development

1. GV for DPR radar algorithm development Presented by Prof. Kenji Nakamura for Toshio Iguchi (NICT, Tokyo, Japan) GPM GV Meeting September 2005, Taipei, Taiwan

2. General Characteristics of Radar Rainfall Retrieval Real precipitation has more parameters than any radar can observe directly. I.e., Rain retrieval is an ill-posed problem. Assumptions about hydrometeor properties as well as correlations among variables are necessary to generate a rainfall product. Some of the assumptions are based on the past experience, physical intuition and the results from Cloud Resolving Models. To what extent GV can improve the assumptions is an issue. (The first two bullets are based on Dr. Kummerow's slide)

3. Special Concerns in Rain Profiling Algorithms for Spaceborne Radar • Attenuation correction is essential • Use of surface reference technique (SRT) • SRT not always usable • k-Z relation for rain attenuation (H-B solution) • Attenuation by CLW and WV is not negligible • In particuar, Ka-band radar • Type of particles (rain, snow, graupel, etc.) and their physical and electromagnetic properties need to be known (or assumed). • Inhomogeneity of rain within IFOV • Entangled with apparent attenuation, etc.

4. Magnitude of possible biases in the TRMM PR algorithm • Attenuation by cloud, water vapor, and O2. • CLW: Att(Ka) = 10 * Att(Ku), up to 5 dB • WV: Att(Ka) = 5 * Att(Ku), up to 1.5 dB near surface • O2: Att(Ka) = 5 * Att(Ku), 0.4 dB near surface

5. Reduction of uncertainties with DPR • Two major error sources are DSD and radar calibration (attenuation to the first range gate). • DF algorithm (Meneghini's Ze-ratio method) can estimate two DSD parameters at each range bin. • DF algorithms may mitigate the issue with unreliable PIA, and unknown attenuation by CLW, H2O, BB, etc. • Dual-frequency Hitschfeld-Bordan (DFHB) method can estimate the attenuation to the first range gate (DSD model with a single parameter is assumed. Needs enough attenuation over a path). • NUBF issues remain.

6. New Factors in DPR Algorithm Compared with TRMM PR, DPR will provide • More accurate estimates with higher sensitivity • Increased number of output variables • E.g. Two DSD parameters at each range bin. by assuming • More detailed microphysical models and using • More complicated algorithm • Combination of different algorithms • Optimum weights and combination among Zm(Ku), Zm(Ka), SRT(Ka) and SRT(Ku) depend on region, height, rain rate, etc.

7. Validation of DPR algorithm We need to validate both the end products and the assumptions in the algorithm. We need to validate • increased number of output variables and their error bounds with better accuracy • How can we validate instantaneous N0(r) and D0(r)? • the way of combining different pieces of information • in different rain intensity ranges • in different types of precipitation • in different background surface types. • microphysical models • DSD, solid precipitation types, inhomogeneity • effect of beam mismatching and correction for it.

8. Some specific algorithm issues related to GV • Algorithm for R<1 mm/h • DF algorithm cannot be used (not enough sensitivity at Ku band) • SRT is not reliable even at Ka band (not enough attenuation) • Attenuation by cloud water and water vapor cannot be ignored • DSD assumption is crucial. Can we validate DSD parameters for R<1mm/h? (N.B. Disdrometer data show a large uncertainty.) • No good models of solid particles (snow, graupel, hail) are available • DSD, density, dielectric constant of each particle • Spatial distribution • Can GV give a better model and narrow the range of uncertainty? • Development storm structure models • Evaluation and correction of NUBF effect • Effect of NUBF on DSD estimation. Reliability of DF algorithm. • Effect of beam-mismatch • A profile between the surface and the lowest observable data point must be assumed. • Statistical characteristics of vertical profiles near surface are useful.

9. Summary • Any GV data that validate the assumptions used in the DPR algorithm or characterize the storm to narrow the range of parameters will help reduce the uncertainties of the estimates. • Major uncertainties (DSD and calibration) in SF algorithm can be reduced with DF algorithms, especially when SRT is applicable. • Can GV validate the instantaneous products in most of conditions? • Attenuation correction, DSD parameter retrievals, beam mismatching, and NUBF corrections are all entangled. How to disentangle each effect is a challenge. • More simulation studies are required to evaluate each effect and to reveal how they are coupled. • Correlation information among parameters from GV may be valuable.