lecture26 27april

Hydrometeor Classification Using Polarimetric Radar: 

Hydrometeor Classification Using Polarimetric Radar

Outline: 

Outline Brief review of polarimetric variables. Combining these variables to get information about hydrometeor types Methods of combining these variables Fuzzy logic description Examples from 29 June 2000 supercell

Polarimetric Variables: 

Polarimetric Variables ZH – Size, concentration ZDR—Shape, orientation, (liquid or solid) KDP—Amount of liquid water, size of drops LDR—Orientation, canting, melting (wet hail) HV—Correlation (mixture of types/melting)

Hydrometeor Identification: 

Hydrometeor Identification Combine variables and define sub-ranges over which specific hydro types are expected Straka et al. (2000)

Hydrometeor Identification: 

Hydrometeor Identification Extend this concept to the six-dimensional space (5 radar variables plus temperature) Straka et al. (2000)

Combining the variables: 

Combining the variables In a nutshell: We have inputs (radar variables) We have some decision process We get a result (hydrometeor type) Basically, we want the hydrometeor type that best fits the inputs.

Combining the variables (the old way): 

Combining the variables (the old way) Look-up table (decision tree), e.g.,: IF ZH>55 AND (-1<ZDR<0.5) AND (-0.5<KDP<1) AND (LDR>-24) ... THEN hydrometeor = Large Hail But this is not a good way to do it because Sub-ranges for each hydro type are not mutually exclusive (i.e., they overlap) Very inefficient and not comprehensive

Combining the variables(the fuzzy logic way): 

Combining the variables (the fuzzy logic way) Define functions for each input variable and each hydrometeor type. These functions describe to what degree each variable is a “member” of the hydrometeor type “family”. Another way to think of this is that each of these functions gives a score to each input variable. The higher the score, the greater the “membership” value of that variable to that hydrometeor type, i.e., the more likely it is that type.

Types of Membership functions: 

Types of Membership functions Trapezoid Membership Function: Zrnic et al. (2001) 2-D TMF for Moderate Rain

Types of Membership functions: 

Types of Membership functions Membership Beta Function: Liu and Chandrasekar (2000) 2-D MBF for rain

Combining the variables: 

Combining the variables So, each variable (x) gets a score () based on where it falls on the membership function for each hydrometeor type. Score = (x,m,a,b) The score is like a probability that the radar measurement is due to that specific hydro type.

Slide12: 

Liu and Chandrasekar (2000)

Slide13: 

Rain! Hail!

Combining the variables: 

Combining the variables Now just combine the scores for all the variables to get a total “membership” or “truth” value (µ) for each hydro type. Combine as a weighted sum: Or as a product: Or some combination of sums and products (which is what is done in the CSU algorithm).

Combining the variables(My method): 

Combining the variables (My method) Take weighted sum of MBFs for polarimetric variables: Then multiply this by MBFs for ZH and temperature to get total “truth” value:

The Final Result: 

The Final Result So, now we have total “truth” values (j) for all hydro types. The hydrometeor type with the highest total “truth” value wins.

Slide17: 

Schematic of Fuzzy Logic Process Liu and Chandrasekar (2000)

Examples from 29 June 2000 Supercell: 

Examples from 29 June 2000 Supercell This storm: Produced large hail F1 tornado Well-defined BWER It was also very well observed by 3 radars (two of which were polarimetric) Also had T28 aircraft penetrations which found hail.

Slide19: 

Height = 3km

Slide20: 

Height = 7km

Slide22: 

Zdr column LDR Cap Big drops

Slide23: 

Hail counts Hail size T28 Comparison

Slide24: 

HID can be used as a quality control “threshold” since MBFs can be defined to include other non-meteorological scatterers. Ryzhkov et al. (2005)

Slide25: 

HID can be applied to discriminate rain from snow in winter storms, and performance has much more promise than current surface T-or Tw- based techniques Ryzhkov et al. (2005)

Slide26: 

Polarimetric radar variables as a severe weather detector? hv may be lowered in strong tornadoes (F3 or greater) due to lofting of debris into the pulse volumes (not Rayleigh-Gans scatters obviously) - Schurr et al. 2004 Consistency in this case with weak echo region often associated with tornadoes However, probably need strong tornado to loft sufficient debris into radar scan, need low level coverage (at close range). May allow improvement on Doppler-based tornado warning capabilities, however, significant damage may have already occurred!