# Presentation: 436155

## GV for DPR radar algorithm development 

- Presented by Prof. Kenji Nakamura
- for Toshio Iguchi
- (NICT, Tokyo, Japan)
- GPM GV Meeting
-  September 2005, Taipei, Taiwan

## 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)

## 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.

## 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**

## 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.

## 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 *Z**m*(Ku), *Z**m*(Ka), SRT(Ka) and SRT(Ku) depend on region, height, rain rate, etc.

## 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.

## 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.

## 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.