Post on 11-Jan-2016
description
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The SEVIRI Precipitating Clouds Product of the Nowcasting SAF: First results
15 October 2004
IPWG-2, Monterey
Anke Thoss
Swedish Meteorological and Hydrological Institute
Ralf Bennartz
University of Wisconsin
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Contents
•Introduction•Algorithm •Examples •Performance•Plans
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Problem overview:
•Except for strong convection, VIS/IR features are not strongly correlated with precipitation. likelihood estimates in intensity classes
more appropriate than rain rate retrieval
NWCSAF approach:2 complementary products for Nowcasting
purposes
1. Precipitating Clouds (PC) product gives likelihood of precipitation in coarse intensity classes
2. Convective Rain Rate (CRR) product estimates rain rate for strongly convective situations
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PC product:three classes of precipitation intensity
from co-located radar data
Rain rate
Class 0: Precipitation-free 0.0 - 0.1 mm/h
Class 1: Light/moderate precipitation 0.1 - 5.0 mm/h
Class 2: Intensive precipitation 5.0 - ... mm/h
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Data sets for algorithm development
Colocated sets of: AVHRRNWP Tsurface (HIRLAM)radar reflectivities (dBZ),gauge adjusted, of theBALTRAD Radar Data CentreBRDC (Michelsson et.al. 2000) No quantitative tuning to MSG performed for version 1.0 which is presented here!
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Input:
•NWCSAF Cloud type product
•NWP surface temperature (ECMWF)
•MSG channels : 0.6 m, 1.6 m, 3.9 m, 11 m and 12 m
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Algorithm development:
•Based on Cloud type output
•Correlation of spectral features with precipitation investigated •Special attention to cloud microphysics (day/night algorithms)
•Precipitation Index PI constructed as linear combination of spectral features
•Algorithms cloud type specific
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Correlation of Spectral features with rain
Correlation with class, all potentially raining cloudtypes
T11 -0.24Tsurf - T11 0.26T11-T11 -0.16R0.6 0.18R3.7 -0.18ln(R0.6/R3.7) 0.26R0.6/R1.6 0.42
3.7m day algorithm, all 0.351.6m day algorithm, all 0.44night algorithm, all 0.30
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Probability distribution, all raining Cloudtypes
1.6 Day algorithm
Night algorithm 3.7 Day algorithm
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Precipitation Index
Example AVHRR 3.7 day algorithm, all cloud types:PI=35+0.644(Tsurf-T11)+5.99(ln(R0.7/R3.7))-3.93(T11-T12)
Example AVHRR 1.6 day algorithm, all cloud types: PI = 65 -15*abs(4.45-R0.6 /R1.6)+0.495*R0.6-0.915(T11-T12) +0*Tsurf+0*T11
MSG day algorithm:Blend of 3.7µm day algorithm (applied to 3.9 µm channel)and 1.6 µm algorithm with equal weight, some additional features introduced for later use in quantitative tuning (a8-a10):PI=a0 +a1*Tsurf +a2*T11+a3*ln(R0.6/R3.9)+a4*(T11-T12) +a5*abs(a6-R0.6/R1.6)+a7*R0.6 + a8*R1.6+a9*R3.9+a10*(R1.6/R3.9)
MSG night algorithm still identical to PPS
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Cloud type dependence
Algarithm 0 All precipitating cloud types
Reported 30min. rain frequency at Hungarian gauges
March-June 2004
Algorithm1 Medium level clouds 14.9%
5027 colocations
Algorithm2 High and very high opaque clouds
31.4%
4126 colocations
Algorithm3 Medium to thick cirrus
5.3%
5999 colocations
Thick cirrus most rain
Algorithm4 Cirrus over lower cloud
No Precipitation All cloudfree classes,
low and very low clouds,
thin cirrus, fractional cloud
0.1% for cloudfree (of 9255)
0.9% for nonprecipitating cloud types (of 11459)
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Cloud type and total precipitation likelihood (day), March 2004, 12UTC
10%
20%
40%
30%
50%
60%
100% - 70%
0%
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Night algorithm, courtesy of M. Putsay, Hungarian Meteorological Service
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Day algorithm, 20031014, 1045
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Upper:PC1, lower:PC2, 20031014
06:30 07:3005:30
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dependence on sampling intervall
0
10
20
30
40
50
60
70
0% 10% 20% 30% 40% 50% 60%
likelihood of rain [%]
obs
. rai
n fr
eq.[%
] 30 min. sampling10 min. sampling
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cloud classes, 30 min sampling
0
10
20
30
40
50
60
0% 10% 20% 30% 40% 50%
likelihood of rain [%]
obs
. rai
n fr
eq.[
%]
high+ very high opaquemedium levelCirrus moderate-thickCi over lower cloud
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Day 20%Hungary,gauges
march-june 2004
No Rain MSG
Rain MSG
No Rain (30 min)
84.1% 15.9%
Rain (30 min) 24.0% 76.0%
Day 20%Hungary,gauges
march-june 2004
No Rain MSG
Rain MSG
No Rain (10 min) 82.9% 17.1%
Rain (10 min) 21.5% 78.5%
N=36466Rain:7.1% (30min)4.9% (10min)
20%likelihoodthreshold
20%POD= 0.76FAR= 0.73PODF= 0.16HK= 0.60BIAS= 2.85ACC= 0.84
30%POD= 0.58FAR= 0.65PODF= 0.08HK= 0.50BIAS= 1.66ACC= 0.89
20%POD= 0.78FAR= 0.81PODF= 0.17HK= 0.61BIAS= 4.13ACC= 0.83
30%POD= 0.62FAR= 0.74PODF= 0.09HK= 0.52BIAS= 2.42ACC= 0.89
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Day 20%Hungary,gauges
march-june 2004
No Rain MSG
Rain MSG
No Rain (30 min)
78.2% 14.7%
Rain (30 min) 1.7% 5.4%
Day 20%Hungary,gauges
march-june 2004
No Rain MSG
Rain MSG
No Rain (10 min) 78.9% 16.3%
Rain (10 min) 1.0% 3.8%
N=36466Rain:7.1% (30min)4.9% (10min)
20%likelihoodthreshold
Day 20%Hungary,gauges
march-june 2004
No Rain MSG
Rain MSG
No Rain (30 min)
84.1% 15.9%
Rain (30 min) 24.0% 76.0%
Day 20%Hungary,gauges
march-june 2004
No Rain MSG
Rain MSG
No Rain (10 min) 82.9% 17.1%
Rain (10 min) 21.5% 78.5%
Percent of total number
Percent of gauge class
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MSG PC Product validation with surface observations
• Dataset: 15 May – 18 June 2004 12:00 UT:
• MSG data and• Collocated surface
observations of present weather (only ww classes indicating clearly rain or no rain considered)
• PC product without use of cloud type (only a NN based cloud mask)
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Validation of MSG PC productDay, 45 N – 55 N, Total data points : 12123 (4.6 % raining)
Likelihood of precipitation agrees well with synop
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Validation of MSG PC productNight, 45 N – 55 N, Total data points : 12123 (4.6 % raining)
Likelihood of precipitation agrees well with synop
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Validation of MSG PC productDay, 30 N – 45 N, Total data points : 7218 (2.5% raining)
Likelihood of precipitation is over-estimated by the PC product
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Validation of MSG PC productNight, 30 N – 45 N, Total data points : 7218 (2.5 % raining)
Likelihood of rain is over-estimated by the PC product
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Day
30N 45N
No Rain MSG
Rain MSG
No Rain Synop ww
84.2% 15.8%
Rain Synop ww
9.8% 90.2%
Night
30N 55N
No Rain MSG
Rain MSG
No Rain Synop ww
81.3% 18.7%
Rain Synop ww
11.4% 88.6%
N=12123
4.6%raining
20%likelihoodthreshold
20%POD= 0.90FAR= 0.78PODF= 0.16HK= 0.74BIAS= 4.12ACC= 0.84
20%POD= 0.88FAR= 0.78PODF= 0.16HK= 0.72BIAS= 4.17ACC= 0.84
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Day
30N 45N
No Rain MSG
Rain MSG
No Rain Synop ww
87.4% 12.6%
Rain Synop ww
7.8% 92.2%
Night
30N 45N
No Rain MSG
Rain MSG
No Rain Synop ww
85.5% 14.5%
Rain Synop ww
9.8% 91.1%
N=7218
2.5%raining
20%likelihoodthreshold
20%POD= 0.92FAR= 0.84PODF= 0.13HK= 0.79BIAS= 5.78ACC= 0.86
20%POD= 0.91FAR= 0.86PODF= 0.14HK= 0.77BIAS= 6.51ACC= 0.86
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Score summary for MSG hardclustering threshold 20%
PC Product POD
FAR
HK BIAS Details
AMSU LAND 0.89 0.83 0.47 Against BALTRAD radarAMSU skill to resolve intensity not considered hereAMSU SEA 0.88 0.75 0.57
MSG day 45-55N 0.90 0.78 0.74 4.12 Alg.0 (no cloud type), May/June
45-55N against Synop WWMSG night 45-55 0.88 0.78 0.72 4.17MSG day 30-45N 0.92 0.84 0.79 5.78 Alg.0 (no cloud type), May/June
30-45N against Synop WWMSG night 30-45 0.91 0.86 0.77 6.51
MSG day 30min.0.76 0.73 0.60 2.85
Cloud type dependant,
March-June 2004
against Hungarian gauges MSG day 10min 0.78 0.81 0.61 4.13
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Open questions
• Why does verification against SYNOP WW look better than for gauge comparison (POD)?
(parallax adjustment, alg0 better than alg1-alg4, May/June
easier, all difficult ww excluded …)
• Timescale / horizontal scale (real effect or convenient Bias correction?)
• How can false alarms be reduced further?
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Algorithm Performance – Summary
Discontinueties between day and night algorithm
Night algorithm seems OK for strong convection, but overestimates precipitation (extent and intensity) for frontal situations
Day algorithm better in general, but has no skill to class precipitation intensity recommended to display total precipitation likelihood
South of 45N precipitation likelihood overestimated
Precipitation likelihood fairly correct between 45-55N
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What is next?
tuning against European synop, covering a years cycle
Status: ongoingwhile tuning, try to decrease discontinuatybetween day and night algorithm, especially
for PC2need more gauge data for PC2 tuning
later: investigate usefulness of additional channels