303
13
Radar Polarimetry
for Rain Estimation
Qing Cao and Guifu Zhang
13.1 INTRODUCTION
For decades, the weather radar has played an important role in quantitative precipitation estimation (QPE). The radar has the advantage of large coverage and short
data-updating intervals. As a result, it has been widely used by the international
meteorological/hydrological community. There are numerous weather radar networks in the world. The largest one is the U.S. Next-Generation Radar (NEXRAD)
network (Fulton et al. 1998), composed of 159 Weather Surveillance Radar-1988
Doppler (WSR-88Ds). Another example is the European Weather Radar Network
(OPERA), consisting of radars in 28 European countries (Holleman et al. 2008).
CONTENTS
13.1 Introduction .................................................................................................. 303
13.2 Polarimetric Radar Measurements ...............................................................304
13.2.1 Radar Variables ................................................................................ 305
13.2.2 Radar Measurements ........................................................................306
13.3 Polarimetric Radar–Rain Estimation ...........................................................308
13.3.1 Empirical Radar–Rain Estimation ...................................................309
13.3.2 DSD-Based Retrievals ..................................................................... 312
13.3.2.1 DSD Models ...................................................................... 312
13.3.2.2 DSD Retrieval ................................................................... 313
13.3.3 Issues in Radar–Rain Estimation ..................................................... 320
13.3.3.1 Measurement Errors .......................................................... 320
13.3.3.2 Clutter Filtering ................................................................. 321
13.3.3.3 Classification ..................................................................... 321
13.3.3.4 Attenuation Correction ...................................................... 322
13.3.3.5 Model Error, System Bias, and Calibration ....................... 322
13.4 Validations and Applications ........................................................................ 323
13.4.1 Disdrometer ...................................................................................... 324
13.4.2 Rain Gauge ....................................................................................... 325
13.5 Conclusions .................................................................................................. 328
Acknowledgment .................................................................................................. 328
References ............................................................................................................. 329
13
Radar Polarimetry
for Rain Estimation
Qing Cao and Guifu Zhang
13.1 INTRODUCTION
For decades, the weather radar has played an important role in quantitative precipitation estimation (QPE). The radar has the advantage of large coverage and short
data-updating intervals. As a result, it has been widely used by the international
meteorological/hydrological community. There are numerous weather radar networks in the world. The largest one is the U.S. Next-Generation Radar (NEXRAD)
network (Fulton et al. 1998), composed of 159 Weather Surveillance Radar-1988
Doppler (WSR-88Ds). Another example is the European Weather Radar Network
(OPERA), consisting of radars in 28 European countries (Holleman et al. 2008).
CONTENTS
13.1 Introduction .................................................................................................. 303
13.2 Polarimetric Radar Measurements ...............................................................304
13.2.1 Radar Variables ................................................................................ 305
13.2.2 Radar Measurements ........................................................................306
13.3 Polarimetric Radar–Rain Estimation ...........................................................308
13.3.1 Empirical Radar–Rain Estimation ...................................................309
13.3.2 DSD-Based Retrievals ..................................................................... 312
13.3.2.1 DSD Models ...................................................................... 312
13.3.2.2 DSD Retrieval ................................................................... 313
13.3.3 Issues in Radar–Rain Estimation ..................................................... 320
13.3.3.1 Measurement Errors .......................................................... 320
13.3.3.2 Clutter Filtering ................................................................. 321
13.3.3.3 Classification ..................................................................... 321
13.3.3.4 Attenuation Correction ...................................................... 322
13.3.3.5 Model Error, System Bias, and Calibration ....................... 322
13.4 Validations and Applications ........................................................................ 323
13.4.1 Disdrometer ...................................................................................... 324
13.4.2 Rain Gauge ....................................................................................... 325
13.5 Conclusions .................................................................................................. 328
Acknowledgment .................................................................................................. 328
References ............................................................................................................. 329