Weather Radar Data
description
Transcript of Weather Radar Data
Weather Radar Data
Doppler Spectral Moments Reflectivity factor Z Mean Velocity v Spectrum width v
Polarimetric Variables Differential Reflectivity ZDR
Specific Differential Phase Correlation Coefficient hv
Linear Depolarization Ratio LDR
Contributors to Measurement Errors
*1) Widespread spatial distribution of scatterers(range ambiguities)
*2) Large velocity distribution (velocity ambiguities) 3) Antenna sidelobes 4) Antenna motion*5) Ground clutter (regular and anomalous
propagation)*6) Non meteorological scatterers (birds, etc.)*7) Finite dwell time 8) Receiver noise*9) Radar calibration *--- these can be somewhat mitigated
7 Scans
5 Scans
Mitigation of Range Ambiguities
Uniform PRTs
Alternate batches of long (for Z) and short(for velocity) PRTS.
4 Scans
El = 19.5o
= 4.3= 5.25
= 2.4= 1.45= 0.5
Long PRTs (first PPI scan) for reflectivity ra>460 km;Short PRTs (second PPI scan) for velocity, ra <200km; typically 150 km
Reflectivity Field of
Widespread Showers
(Data displayed to
460 km)
Velocity Field: Widespread Showers (5dB overlaid threshold; data displayed to 230 km)
Spectrum Width Field: Widespread Showers (20 dB threshold)
Echoes from Birds leaving a Roost; Spectrum Width Field
Measurements of Rain
¶ R(Z) relations
¶ Error sources
¶ Procedure on the WSR-88D
Reflectivity Factor Rainfall Rate Relations
Marshall-Palmer:Z = 200 R1.6 Z(mm6 m-3); R(mm h-1)
For WSR-88D: Z = 300 R1.4 - convective rain Z = 200 R1.2 - tropical rain
Rain Rate Error Sources
*1) Radar calibration
2) Height of measurements
*3) Attenuation
4) Incomplete beam filling
*5) Evaporation
*6) Beam blockage
7) Gradients of rain rate
8) Vertical air motions
*9) Variability in DSD
DSDs, R(Z), and R(disdrometer)
Sep 11, 1999
Log(
N)
Log(
N)
DSD’s, R(Z), and R(disrometer)
Dec 3, 1999
Log(
N)
Locations of Z Data used in the WSR-88D for Rain Measurement
HEIG
HT
0 2035 230 km
RANGE
3.5°
2.4°
1.5°
0.5°
135
Applications of Polarization
♪ Polarimetric Variables
♪ Measurements of Rain
♪ Measurements of Snow
♪ Classification of Precipitation
Polarimetric Variables
Quantitative - Zh, ZDR, KDP
Qualitative - |hv(0)|, , LDR, xv, hv
Are not independent Are related to precipitation parameters Relations among hydrometeor
parameters allow retrieval of bulk precipitation properties and amounts
Rainfall Relation R(KDP, ZDR)
R(KDP, ZDR) = 52 KDP0.96 ZDR
-0.447 - is least sensitive to the variation of the median drop diameter Do - is valid for a 11 cm wavelength
Scatergrams: R(Z) and
R(KDP, ZDR)vs Rain Gauge
Sensitivity to Hail
R(g
au
ge
s)
R(g
au
ge
s)-
R(Z
)R
(ga
ug
es
)-R
(KD
PZ
DR)
Area Mean Rain Rate and Bias
R(gauges)-R(radar)
Fundamental Problems in Remote Sensing of Precipitation
♥Classification - what is where?
♥Quantification - what is the amount?
Weighting Functions
Partitions in the Zh, ZDR Space into Regions of Hydrometeor Types
Weighting Function forModerate Rain WMR(Zh, ZDR)
Scores for hydrometeor classes
M
ii
ij
M
ii
j
A
YZWAS
1
1),(
Ai = multiplicative factor 1Wj = weighting function of two variables assigned to the class j
Yi = a variable other than reflectivity (T, ZDR, KDP, hv, LDR) j = hydrometeor class, one the following: light rain, moderate rain, rain with large drops, rain/hail mixture, small hail, dry snow, wet snow, horizontal crystals, vertical crystals, other
Class j for which Sj is a maximum is chosen as the correct one
Florida
Florida
Florida
Florida
Florida
Fields of classified Hydrometeors - Florida
Fields of classified Hydrometeor - Florida
Fields of classified Hydrometeors - Florida
Suggestions
Data quality - develop acceptance testsAnomalous Propagation - consider
“fuzzy logic” scheme Classify precipitation into type (snow,
hail, graupel, rain, bright band) even if only Z is available
Calibrate the radar (post operationally, use data, gauges, ..anything)
Specific Differential Phase at short wavelengths (3 and 5 cm)
• Overcomes the effects of attenuation
• Is more sensitive to rain rate
• Is influenced by resonant scattering from large drops
Suggestions for Polarimetric measurements at =3 and 5 cm
Develop a classification scheme Develop a R(KDP, ZDR) or other
polarimetric relation to estimate rain Correct Z for attenuation and ZDR for
differential attenuation (use DP)
Use KDP to calibrate Z
Radar Echo Classifier
• Uses “fuzzy logic” technique• Base data Z, V, W used• Derived fields (“features”) are calculated• Weighting functions are applied to the
feature fields to create “interest” fields• Interest fields are weighted and summed• Threshold applied, producing final
algorithm output
AP Detection Algorithm• Features derived from base data are:
– Median radial velocity– Standard deviation of radial velocity– Median spectrum width– “Texture” of the reflectivity – Reflectivity variables “spin” and “sign”
• Similar to texture
• Computed over a local area
Investigate data “features”
• Feature distributions– AP Clutter
– Precipitation
• Best features have good separation between echo types
ClutterCluttermean V
WeatherWeathermean V
ClutterCluttertexture Z
WeatherWeathertexture Z
AP Weighting Functions
-30
a) Mean Radial Velocity(MVE)
b) Mean Spectrum Width(MSW)
c) Texture of SNR(TSNR)
d) Standard Deviation ofRadial Velocity (SDVE)
e) Vertical Difference ofReflectivity (GDZ)
-2.3 0 2.3 -50 50
0 3.2-30 30
0 0.7 30
0 45
-18 0
1
0
1
0
1
0
1
0
1
0
1000
-100 100
F) Spin
G) Sign
0 50 100
10
1
0
1
0
Median Spectrum Width
“Texture” of Reflectivity
Standard Deviation of Radial Velocity
“Reflectivity Spin”
“Reflectivity Sign”
Median Radial Velocity
-10 -0.6 0 0.6
Radial Velocity
AP Clutter
+3 m/s
Interest Field Radial Velocity
AP Clutter
+3 m/s
Field of Weights for AP ClutterWeighting functions areapplied to the feature field to create an “interest” field
Values scaled between 0-1
0-2.3 2.30
1
For median velocity field,the weighting function is:
Example of APDA using S-Pol data from STEPS
Polarimetric truth fieldgiven by the Particle Identification(PID) output
APDA is thresholded at 0.5
Good agreement between PID clutter and APDA
Reflectivity Radial Velocity
PID APDA
Clutter Rain
20 June 2000, 0234 UTC 0.5 degree elevation
Storm-Scale Prediction
• Sample 4-hour forecast from the Center for Analysis and Prediction of Storms’ Advanced Regional Prediction System (ARPS) – a full-physics mesoscale prediction system
• For the Fort Worth forecast– 4-hour prediction– 3 km grid resolution– Model initial state included assimilation of
• WSR-88D reflectivity and radial velocity data
• Surface and upper-air data
• Satellite and wind profiler data
6 pm 7 pm 8 pmR
adar
For
ecas
t w
/Rad
ar
2 hr 3 hr 4 hr
Fcs
t w
/o R
adar
6 pm 7 pm 8 pmR
adar
2 hr 3 hr 4 hr
R(Z) for Snow and Ice Water Content
Snow fall rate:
Z(mm6m-3) =75R2 ; R in mm h-1 of water
Ice Water Content:
IWC(gr m-3)= 0.446 (m)KDP(deg km-1)/(1-Zv/Zh)
Z
KDP
ZDR
hv
Vertical Cross Sections
In Situ and Pol Measurements
T-28 aircraft