Classification of polarimetric radar signatures using scatterers' joint amplitude and phase distribution functions

2003 ◽  
Author(s):  
Firooz A. Sadjadi
Keyword(s):  
Phase Distribution ◽  
Distribution Functions ◽  
Polarimetric Radar ◽  
Radar Signatures

Simulation and analysis of polarimetric radar signatures of human gaits

2014 ◽  
Vol 50 (3) ◽  
pp. 2164-2175 ◽  
Author(s):  
James Park ◽  
Joel Johnson ◽  
Ninoslav Majurec ◽  
Mark Frankford ◽  
Kyle Stewart ◽  
...  
Keyword(s):  
Polarimetric Radar ◽  
Radar Signatures

On the use of multi-frequency and polarimetric radar backscatter features for classification of agricultural crops

1994 ◽  
Vol 15 (9) ◽  
pp. 1799-1812 ◽  
Author(s):  
A. FREEMAN ◽  
J. VILLASENOR ◽  
J. D. KLEIN ◽  
P. HOOGEBOOM ◽  
J. GROOT
Keyword(s):  
Agricultural Crops ◽  
Polarimetric Radar ◽  
Radar Backscatter

Classification of Precipitation Types during Transitional Winter Weather Using the RUC Model and Polarimetric Radar Retrievals

2012 ◽  
Vol 51 (4) ◽  
pp. 763-779 ◽  
Author(s):  
Terry J. Schuur ◽  
Hyang-Suk Park ◽  
Alexander V. Ryzhkov ◽  
Heather D. Reeves
Keyword(s):  
Numerical Models ◽  
Radar Data ◽  
Storm Event ◽  
Model Output ◽  
Polarimetric Radar ◽  
Radar Observations ◽  
Precipitation Type ◽  
Microphysical Processes ◽  
Using Data

AbstractA new hydrometeor classification algorithm that combines thermodynamic output from the Rapid Update Cycle (RUC) model with polarimetric radar observations is introduced. The algorithm improves upon existing classification techniques that rely solely on polarimetric radar observations by using thermodynamic information to help to diagnose microphysical processes (such as melting or refreezing) that might occur aloft. This added information is especially important for transitional weather events for which past studies have shown radar-only techniques to be deficient. The algorithm first uses vertical profiles of wet-bulb temperature derived from the RUC model output to provide a background precipitation classification type. According to a set of empirical rules, polarimetric radar data are then used to refine precipitation-type categories when the observations are found to be inconsistent with the background classification. Using data from the polarimetric KOUN Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Norman, Oklahoma, the algorithm is tested on a transitional winter-storm event that produced a combination of rain, freezing rain, ice pellets, and snow as it passed over central Oklahoma on 30 November 2006. Examples are presented in which the presence of a radar bright band (suggesting an elevated warm layer) is observed immediately above a background classification of dry snow (suggesting the absence of an elevated warm layer in the model output). Overall, the results demonstrate the potential benefits of combining polarimetric radar data with thermodynamic information from numerical models, with model output providing widespread coverage and polarimetric radar data providing an observation-based modification of the derived precipitation type at closer ranges.

scholarly journals Polarimetric radar signatures of precipitation at S and C-bands

1991 ◽  
Vol 138 (2) ◽  
pp. 109 ◽  
Author(s):  
V.N. Bringi ◽  
V. Chandrasekar ◽  
P. Meischner ◽  
J. Hubbert ◽  
Y. Golestani
Keyword(s):  
Polarimetric Radar ◽  
Radar Signatures

scholarly journals Classification of Rainfall Types Using Parsivel Disdrometer and S-Band Polarimetric Radar in Central Korea

2020 ◽  
Vol 12 (4) ◽  
pp. 642 ◽  
Author(s):  
Jui Le Loh ◽  
Dong-In Lee ◽  
Mi-Young Kang ◽  
Cheol-Hwan You
Keyword(s):  
Vertical Profile ◽  
Drop Size ◽  
Classification Scheme ◽  
Radar Data ◽  
Number Concentration ◽  
Polarimetric Radar ◽  
Retrieval Method ◽  
Median Volume ◽  
Better Than

Tools to identify and classify stratiform and convective rains at various times of the 12 days from June 2015 to March 2016 in Jincheon, Korea, were developed by using a Parsivel disdrometer and S-band polarimetric (S-POL) radar data. Stratiform and convective rains were identified using three different methods (vertical profile of reflectivity (VPR), the method proposed by Bringi et al. (BR03), and a combination of the two (BR03-VPR)) by using a Parsivel disdrometer for its applications to radar as a reference. BR03-VPR exhibits a better classification scheme than the VPR and BR03 methods. The rain types were compared using the drop size distribution (DSD) retrieved from polarimetric variables and reflectivity only. By using the DSD variables, a new convective/stratiform classification line of the log-normalized droplet number concentration ( log 10 N w ) − median volume diameter ( D 0 ) was derived for this area to classify the rainfall types using DSD variables retrieved from the polarimetric radar. For the radar variables, the method by Steiner et al. (SHY95) was found to be the best method, with 0.00% misclassification of the stratiform rains. For the convective rains, the DSD retrieval method performed better. However, for both stratiform and convective rains, the fuzzy method performed better than the SHY95 and DSD retrieval methods.

Sign in / Sign up

Export Citation Format

Share Document