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

Contents Introduction Algorithm Examples Performance Plans

Problem overview: NWCSAF approach: 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 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

three classes of precipitation intensity from 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

Data sets for algorithm development Colocated sets of: AVHRR NWP Tsurface (HIRLAM) radar reflectivities (dBZ), gauge adjusted, of the BALTRAD Radar Data Centre BRDC (Michelsson et.al. 2000) No quantitative tuning to MSG performed for version 1.0 which is presented here!

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

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

Correlation of Spectral features with rain Correlation with class, all potentially raining cloudtypes T11 -0.24 Tsurf - T11 0.26 T11-T11 -0.16 R0.6 0.18 R3.7 -0.18 ln(R0.6/R3.7) 0.26 R0.6/R1.6 0.42 3.7m day algorithm, all 0.35 1.6m day algorithm, all 0.44 night algorithm, all 0.30

Probability distribution, all raining Cloudtypes Night algorithm 3.7 Day algorithm 1.6 Day algorithm

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

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)

Cloud type and total precipitation likelihood (day), March 2004, 12UTC 100% - 70% 60% 50% 40% 30% 20% 10% 0% Cloud type and total precipitation likelihood (day), March 2004, 12UTC

Night algorithm, courtesy of M Night algorithm, courtesy of M. Putsay, Hungarian Meteorological Service

Day algorithm, 20031014, 1045

05:30 06:30 07:30 Upper:PC1, lower:PC2, 20031014

30 min. sampling 10 min. sampling

high+ very high opaque medium level Cirrus moderate-thick Ci over lower cloud

Day 20% No Rain MSG Rain (30 min) 84.1% 15.9% 24.0% 76.0% Day 20% Hungary,gauges march-june 2004 No Rain MSG Rain (30 min) 84.1% 15.9% 24.0% 76.0% Day 20% Hungary,gauges march-june 2004 No Rain MSG Rain (10 min) 82.9% 17.1% 21.5% 78.5% 20% likelihood threshold N=36466 Rain: 7.1% (30min) 4.9% (10min) 20% POD= 0.76 FAR= 0.73 PODF= 0.16 HK= 0.60 BIAS= 2.85 ACC= 0.84 30% POD= 0.58 FAR= 0.65 PODF= 0.08 HK= 0.50 BIAS= 1.66 ACC= 0.89 POD= 0.78 FAR= 0.81 PODF= 0.17 HK= 0.61 BIAS= 4.13 ACC= 0.83 POD= 0.62 FAR= 0.74 PODF= 0.09 HK= 0.52 BIAS= 2.42

Day 20% No Rain MSG Rain (30 min) 78.2% 14.7% 1.7% 5.4% Day 20% Hungary,gauges march-june 2004 No Rain MSG Rain (30 min) 78.2% 14.7% 1.7% 5.4% Day 20% Hungary,gauges march-june 2004 No Rain MSG Rain (10 min) 78.9% 16.3% 1.0% 3.8% 20% likelihood threshold N=36466 Rain: 7.1% (30min) 4.9% (10min) Percent of total number  Percent of gauge class  Day 20% Hungary,gauges march-june 2004 No Rain MSG Rain (30 min) 84.1% 15.9% 24.0% 76.0% Day 20% Hungary,gauges march-june 2004 No Rain MSG Rain (10 min) 82.9% 17.1% 21.5% 78.5%

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)

Validation of MSG PC product Day, 45 N – 55 N, Total data points : 12123 (4.6 % raining) Likelihood of precipitation agrees well with synop

Validation of MSG PC product Night, 45 N – 55 N, Total data points : 12123 (4.6 % raining) Likelihood of precipitation agrees well with synop

Validation of MSG PC product Day, 30 N – 45 N, Total data points : 7218 (2.5% raining) Likelihood of precipitation is over-estimated by the PC product

Validation of MSG PC product Night, 30 N – 45 N, Total data points : 7218 (2.5 % raining) Likelihood of rain is over-estimated by the PC product

Day 30N 45N No Rain MSG Rain Synop ww 84.2% 15.8% 9.8% 90.2% Night 81.3% 18.7% Rain Synop ww 11.4% 88.6% 20% likelihood threshold N=12123 4.6% raining 20% POD= 0.90 FAR= 0.78 PODF= 0.16 HK= 0.74 BIAS= 4.12 ACC= 0.84 POD= 0.88 HK= 0.72 BIAS= 4.17

Day 30N 45N No Rain MSG Rain Synop ww 87.4% 12.6% 7.8% 92.2% Night 85.5% 14.5% 9.8% 91.1% 20% likelihood threshold N=7218 2.5% raining 20% POD= 0.92 FAR= 0.84 PODF= 0.13 HK= 0.79 BIAS= 5.78 ACC= 0.86 POD= 0.91 FAR= 0.86 PODF= 0.14 HK= 0.77 BIAS= 6.51

Score summary for MSG hardclustering threshold 20% PC Product POD FAR HK BIAS Details AMSU LAND 0.89 0.83 0.47 Against BALTRAD radar AMSU skill to resolve intensity not considered here AMSU 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 WW MSG night 45-55 0.72 4.17 MSG day 30-45N 0.92 0.84 0.79 5.78 30-45N against Synop WW MSG 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 0.81 0.61 4.13

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?

Algorithm Performance – Summary  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  Discontinueties between day and night algorithm  Precipitation likelihood fairly correct between 45-55N  South of 45N precipitation likelihood overestimated

What is next? Status: ongoing  tuning against European synop, covering a years cycle Status: ongoing while tuning, try to decrease discontinuaty between day and night algorithm, especially for PC2 need more gauge data for PC2 tuning  later: investigate usefulness of additional channels