national lab for remote sensing and nowcasting dual polarization radar and rainfall nowcasting by...

National Lab for Remote Sensing and Nowcasting

Dual Polarization Radar and Rainfall Nowcasting

• by Mark Alliksaar


Dual Polarization can potentially improve rainfall nowcasting in three ways:

1. Radar attenuation can be corrected using the polarimetric parameter Φdp. This improves rain rate estimates and QPE derived from reflectivity factor Z.

2. Rain rate and QPE can also be derived directly from Kdp instead of Z.

3. Hail identification. In conventional QPE estimates, hail contamination is always a possibility in convective situations.


Rain rate estimation from Kdp

• R is rain rate in mm/hr

• b,c are empirical constants

• R derived from Kdp is more accurate because Kdp not subject to attenuation

( ) bdpdp cKKR =


Attenuation correction using Φdp

• ΔZ is attenuation correction

• r is range along radial

• α is an empirical constant

( ) ( )rrZ dpΦ=Δ α

Cloud Physics & Severe Weather Research Division King City

August 2, 2005(day of Air France accident at YYZ)


Corrected Radar Reflectivity0.4° PPI


Enhanced View near YYZ

Corrected Radar ReflectivityRadar Reflectivity


Attenuation Calculation(Radial 200.5°)

Z

φdp

ZcorrCloud Boundary


Validation with Buffalo NEXRADFrequency Histograms of Reflectivity near YYZ


August 19, 2005(flash flood event in North Toronto)


Radar Reflectivity

Corrected Reflectivity ZCORRReflectivity Z


One Hour Precipitation Accumulation (Z)Enhanced View in North Toronto

Rain Accumulation (Z)


One Hour Precipitation Accumulation (Zc)Enhanced View in North Toronto

Rain Accumulation (ZCORR)


Improved QPE Using Zcorr (Location near MSC HQ in Downsview)


Summer Applications: Hail Detection


iParCA (interactive Particle Classification Algorithm)

• developed by Environment Canada, King City research group

• input: 6 polarimetric radar products (Zh, Zdr, ρHV, Kdp as well as standard deviations of Zh and Zdr)

• output: hydrometeor type at each range gate determined by fuzzy logic routines


an example of a fuzzy logic membership function for moderate rain


iParCA GUI interface


Comparison of URP and iParCA Hail AlgorithmsURP Hail Algorithm:• Related to storm structure

• Based on vertical integration of cell’s reflectivity profile

• Disadvantages:– Difficult to quantify

– Exact hail location not specified

iParCA Hail Algorithm:• Measurements directly related

to hail properties

• iParCA Fuzzy Logic Thresholds:

• Z : 50 – 75 dBZ

• ZDR : 0 – 1 dB

• ρHV : 0.80 – 0.90

• φDP : abrupt changes


Grimsby HailstormJuly 23rd 2008 – 0140 Z

Radar Reflectivity Enhanced View



ρHV φdp

VIL



Location 201/94 202/97.5

Zcorr 46.18 (39.35)

50.42

(49.21)

ZDRcorr 1.05 0.14

ρHV 0.93 0.82

1

1

2

2

Hail Pixel Map: iParCA: URP



Hail Pixel Map: iParCA: URP


Detection Statistics of URP vs iParCA Hail Algorithms

• 74 cells examined for 21 days during the summers of 2005-2008.

• Cases were selected by meteorologist M. Leduc targeting those cells which may contain high impact weather based on reflectivity patterns.

iParCA URP

Hit 63 51

Miss 4 16

False Alarm 5 3

Correct Negative 2 4

Total

Skill Scores

74 74

CSI 88 73

Bias 1.5 -19.4

POD 94 76

FAR 7 6


Summary Statistics of URP vs iParCA Hail Algorithms

• 30 cells on 10 of the study days were examined in depth to assess the physical reasoning for the differences in the algorithm performance.

• In 21/30 cells iParCA was subjectively determined to be better in terms of the quality of the information

• Reasons for iParCA superiority:– Geometry/Timing 4 cases– Attenuation Correction 5 cases– Dual Polarization Discrimination 11 cases– Location 5 cases

• iParCA Hail Product superior for 70% of cells studied


Questions?

national lab for remote sensing and nowcasting dual polarization radar and rainfall nowcasting by...

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