# Presentation: 497764

## Dual-Polarization

**Dual-Polarization**
**Quantitative Precipitation Estimation**
- Group Discussion Report
- Edward A. Brandes
- National Center for Atmospheric Research

## Dual-Polarization Radar

- Impacts:
- ●		Data quality
- ●		Hydrometeor classification
- ●		Microphysics retrieval/understanding
- 		of precipitation processes
- ●		Microphysics parameterization in numerical
- 		models
- ●		Precipitation quantification

## Group Discussion Objectives

- Identify science issues related to quantitative precipitation estimation with polarimetric radar
- Facilitate the transfer from research to operations

## Discussion Topics

- ●		Measurement accuracy/Calibration
- ●		Drop axis ratios/DSD model
- ●		Role of KDP
- ●		Hail
- ●		Estimator variable mix/functional form
- ●		Algorithm testing and verification criteria
- ●		Default algorithms
- ●		New techniques/hardware

**Notes:**
List put together after discussion with most of you
Not much time to address them adequately
Unlikely we could achieve unanimity
Maybe we can identify the principal issues that will be important to operational implementation

## Polarimetric Variables

- Radar Reflectivity:
- Specific Differential Propagation Phase:
- Differential Reflectivity:
- _______________
- Correlation Coefficient:

## Measurement Accuracy

**Parameter****	****  ****	****   Error****	****     Error in R**
**	****Z****	****	****0.5 dB****	****	****10%**
**	****Z****DR****	****	****0.1****−0.2 dB****	****	****5−****25%**
**	****K****DP****	****	****0.2****o**** km****−****1****	****	****5****−****100%**
**Model Error**
**Axis ratio****	****	****Z****DR**** error of 0.2****−0.3 dB**
**DSD form**
**Algorithm****	****	****Simulation or observations**
**Canting angle**

## 		    KDP

**	****	****    ****K****DP**
- Advantages
- ● Immune to calibration problems
- ● Unaffected by attenuation
- ● Unaffected by beam blockage
- ● Insensitive to dry tumbling hail

## (a)

**(a)**

**30**

**60**

**90**

**120**

**150**

**40**

**60**

**80**

**(b)**

**30**

**60**

**90**

**120**

**150**

**40**

**60**

**80**

**60**

## 			   KDP

**	****	****	****   ****K****DP**
- Potential problems
- ● Reflectivity gradients
- ● Differential backscatter phase shift
- ● Sensitivity to mismatched sidelobes
- ● DSD sensitivity
- ● High noise level in ΦDP measurements
- ● Reduced spatial resolution in rainfall estimates

## Slide 10

## Estimator variable mix/functional form

- 	Enhanced Z−R Relations
- 	Power-law/Polynomial Relations
- 	Composite Algorithms
- 	Drop-Size Distribution Retrieval

## Florida-Tuned Power-Law Estimators(ZH and KDP in linear units, ZDR in dB)

** ****Empirical axis ratios:**
***  ******r =***** 0.9951 + 0.02510*****D***** – 0.03644*****D*****2**** + 0.005303*****D*****3**** – 0.0002492*****D*****4**
** ****Radar Reflectivity: **
** ****Specific Differential Phase: **
** ****Specific Diff. Phase/Diff. Reflectivity:**
** ****Reflectivity/Diff. Reflectivity:**

## Summary Results:Tuned Power-Law EstimatorsEmpirical Axis Ratios(388 radar−gauge comparisons)

|  | R(ZH) | R(KDP) | R(KDP,ZDR) | R(ZH,ZDR) |
| --- | --- | --- | --- | --- |
| Mean bias
factor | 0.94 | 0.97 | 0.92 | 0.97 |
| Bias factor
range | 2.53 | 2.57 | 2.38 | 1.79 |
| Correlation
coefficient | 0.87 | 0.86 | 0.89 | 0.92 |
| RMSE (mm) | 7.9 | 8.0 | 7.4 | 6.4 |
| RMSE (bias removed) | 7.7 | 7.8 | 7.1 | 6.3 |

## NSSL Composite Algorithm(linear units)

-     					   *R*(*Z*) < 6 mm h−1
- 						   6 < *R*(*Z*) < 50 mm h−1
- 						   *R*(*Z*) > 50 mm h−1
- Where:

## Polarimetric Rainfall Estimation

- Point Estimates

**Polarimetric Rainfall Estimation**

- Areal Estimates

## Drop-Size Distribution Retrieval with Polarimetric Radar Measurements

**Gamma drop size distribution:**
** **
**N(D)=N****0****D****μ****exp(-ΛD)****	**
** **
- N0**	****drop concentration parameter**
- **	****distribution shape term**
- **	****slope factor**
** **
**Solution:**** **

## Spatial Distribution of Retrieved DSD Parameters

- 17 September 1998
-           Convective rain: 192646 UTC                   Stratiform rain: 222154 UTC

## Comparison: Physical ProcessesConstrained-Gamma and Marshall−Palmer DSD Models

## Recommendations

- ●		Endorse the NSSL composite algorithm
- 		as an initial product
- ●		Update and enhance methods for radar 	calibration
- ●		Conduct a community-wide inter-
- 		comparison study to improve algorithms

## Questions! Comments!