The SEVIRI Precipitating Clouds Product of the Nowcasting SAF: First results

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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%


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




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march-june 2004

No Rain MSG

Rain MSG

No Rain (30 min)

78.2% 14.7%

Rain (30 min) 1.7% 5.4%


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)



march-june 2004

No Rain MSG

Rain MSG

No Rain (30 min)

84.1% 15.9%

Rain (30 min) 24.0% 76.0%


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




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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




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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

Documents

The SEVIRI Precipitating Clouds Product of the Nowcasting SAF: First results