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8. Case Study15 May 2014 0620 UTC
Warm-Rain Precipitation over Compex Terrain:A New Classification SchemeLeonardo Porcacchia1, P.E. Kirstetter23, J.J. Gourley3, V. Maggioni1, B.L. Cheong2, M. Anagnostou4
(1) Civil and Environmental Engineering, George Mason University, Fairfax VA; (2) Advanced Radar Research Center, University of Oklahoma, Norman OK; (3) NOAA/National Severe Storms Laboratory, National Weather Center, Norman OK; (4) National Observatory of Athens, Athens Greece.
1. Problem: Complex Terrain Monitoring ● Mountainous regions susceptible to natural hazards:
flash floods, debris flow, landslides● Scarce coverage of ground sensors● Operational radars limited by partial beam blockage
● Forecast high-precipitation systems leading to flash floods and extreme events
● Identify warm-rain precipitating events
3. Goal: Improve QPE over Mountainous Regions
5. Methodology: Polarimetric Parameter Spaces ● Cao-Zhang1 (CZ): Z-ZDR, high concentration of small drops● Kumjian-Ryzhkov3 (KR): Z- ZDR, collision-coalescence
● Precipitation growth due to collision-coalescence between drops in the liquid layer
● High concentration of small drops
● Lead to high-precipitation rates: observed in flash floods events in complex terrain
● Atypical Vertical Profile of Reflectivity (VPR) and Z-R relationship: difficult to identify
2. Threat: Warm-Rain Precipitation
6. Results: Vertical Profiles Warm-Rain Stratiform Convective
7. Validation with Gauges
9. Conclusions and Future Work● Warm-rain precipitation often happens in combination with other processes● The proposed scheme successfully segregates warm-rain from convective
and stratiform precipitation● Need to correctly identify warm-rain precipitation and apply an adequate
rainfall estimation algorithm● Need to generalize warm-rain classification scheme worldwide
Classification
Contact: [email protected]
NOXP in North Carolina (US) collecting data for IPHEx http://blog.nssl.noaa.gov/nsslnews/2014/06/2314/
June 16 2015, Uttarakhand, IndiaXu et al. 20084
http://www.comet.ucar.edu/
Radar Hydrology: Principles, Models and ApplicationsYang Hong, Jonathan J. Gourley
4. Dataset: IPHEx Field Campaign ● Summer 2014 Pigeon River Basin, NC (US)● NOAA X-Pol (NOXP) X-band Dual-Polarized Radar● 20 NASA ground stations: Parsivels, gauges
(1) Cao, Qing, Guifu Zhang, Edward Brandes, Terry Schuur, Alexander Ryzhkov, and Kyoko Ikeda. “Analysis of Video Disdrometer and Polarimetric Radar Data to Characterize Rain Microphysics in Oklahoma.” Journal of Applied Meteorology and Climatology 47, no. 8 (August 1, 2008): 2238–55. doi:10.1175/2008JAMC1732.1.(2) Kalogiros, J., M.N. Anagnostou, E.N. Anagnostou, M. Montopoli, E. Picciotti, and F.S. Marzano. “Evaluation of a New Polarimetric Algorithm for Rain-Path Attenuation Correction of X-Band Radar Observations Against Disdrometer.” IEEE Transactions on Geoscience and Remote Sensing 52, no. 2 (February 2014): 1369–80. doi:10.1109/TGRS.2013.2250979.(3) Kumjian, Matthew R., and Olivier P. Prat. “The Impact of Raindrop Collisional Processes on the Polarimetric Radar Variables.” Journal of the Atmospheric Sciences 71, no. 8 (August 1, 2014): 3052–67. doi:10.1175/JAS-D-13-0357.1.(4) Xu, Xiaoyong, Kenneth Howard, and Jian Zhang. “An Automated Radar Technique for the Identification of Tropical Precipitation.” Journal of Hydrometeorology 9, no. 5 (October 1, 2008): 885–902. doi:10.1175/2007JHM954.1.
D0 slope:-0.017 mm km-1
D0 slope:-0.060 mm km-1
D0 slope:-0.155 mm km-1
Nw slope:-603.1 mm-1 m-3 km-1
Nw slope:-28.0 mm-1 m-3 km-1
Nw slope:324.3 mm-1 m-3 km-1
IPHEx schematics of sensors
WR median: -3.32 mm hr-1
NWR median: -1.10 mm hr-1
Reflectivityele 3.1°
● RR = rainfall rate residual: radar - gauges● SCOP-ME2 algorithm for attenuation correction at X-band
provides radar rainfall rate estimate:○ Biased for both warm-rain (WR) and non warm-rain (NWR)○ It underestimates WR rainfall rate (lower median RR)
