NWCSAF Convection products: v2018 improvements, validation and adaptability to end-users Eumetsat 2019 October, 3 rd , 2019 Claudon, M., Moisselin, J.-M., Autonès, F.
NWCSAF GEO Products: storms monitoring at different development stages. A portfolio for convection Courtesy NWCSAF LE Developing convective storm Convection Initiation Pre-convective environment RDT-CW (Rapidly Developing CI (convection initiation) iSHAI (imaging Thunderstorm) Satellite Humidity and Instability) Precipitation products Time Any time Cloud products ( CMA , CT , CTTH , CMIC ), High Resolution Winds ( HRW ), ASII Eumetsat Conference, Boston 2019
Overview 1.CI : Convection Initiation 2. RDT: Rapidly Developing Thunderstorm 3. Conclusion Eumetsat Conference, Boston 2019
Convection Initiation at a glance Probability for a pixel to become a thunderstorm First version: v2016 adaptation of SATCAST methodology ( Best Practice Document, 2013, for EUMETSAT Convection Working Group, Eds J.Mecikalski, K. Bedka and M. König ) Visiting Scientist Activity ( Karagiannidis, A., 2016, Final Report on Visiting Scientist Activity for the validation and improvement of the Convection Initiation (CI) product of NWC SAF v2016 and v2018, Visiting Scientist Activity followed in Nowcasting Department of Météo France, Toulouse, France Period June-December 2016) Now : v2018 (PRE-OPERATIONAL status) Input: Satellite data (multiple channels), Numerical Weather Prediction data, NWC SAF products: Cloud ● Products (CT, CMIC), HRW Output: NetCDF Pixel-based product, with 4 classes of probability (very low, low, medium, high) and 3 forecast ● periods (30, 60 and 90 minutes) V2018 improvements: ● Use of CMIC and Cloud Type for the identification of areas of interest ● New tuning, including a mode for daytime and nighttime ● Tracking improvement, Forecast horizon extension, Parallelization capabilities ● Validation improvement (AS work by TROPOS from Leibnitz Institute) Validation improvement (AS work by TROPOS from Leibnitz Institute) Eumetsat Conference, Boston 2019
CI product validation process 2018 NWC SAF Validation Report for Convection products* V2018.1 patch FUTURE MSG MSG TROPOS GOES-16 Quantitative Qualitative Quantitative Qualitative Validation Validation Validation Validation Radar data as Radar data as Radar-derived Radar-derived ground truth ground truth convective objects as convective ground truth objects as Pixels with Newly developed ground truth reflectivity over objects with Tracked radar threshold (30 or reflectivity over convective cells 35 dBZ) threshold (35 dBZ) Use of parameters (age, lightning pairing, cloud top pressure) * http://www.nwcsaf.org/Downloads/GEO/2018/Documents/Scientific_Docs/NWC-CDOP3-GEO-MF-PI-SCI-VR-Convection_v1.0.pdf Eumetsat Conference, Boston 2019
CI v2018 - Validation on MSG case studies Radar Ground Truth CI classes GT Generally relevant, even if all cases encountered: ● Good Detection (GD) ● False Alarms (FA) ● Misses (MI) ● Double penalties The relevancy is clearly to analyse regarding the situation (isolated, embedded, edge of cloud systems, etc.) MSG-IR10.8 + CI probability [0’-30’] product 13h00Z + Ground Truth = radar > 30dBZ [13Z-13h30Z] Eumetsat Conference, Boston 2019
CI v2018 - Validation on case studies - Summary FAR problem seems the main one - Sometimes explained by spatial double penalty as CI not so far away from new convective clouds - Sometimes explained by delayed convection (CI [0-30’] should have been CI [0- 60’]) Less relevant in cold-air mass. Explanations: - Threshold to be tuned - Fractioned cloud type excluded of CI calculation - Movement field more difficult to assess in that case Useful signal for forecasters or other experienced users. As additional information (rather than replacing other ones) Eumetsat Conference, Boston 2019
CI v2018 - Quantitative validation methodology Cloud Object Radar Object ✗ HRV field ✗ Newly filtered using a developing Gaussian filter convective cells ✗ Minimum life time ✗ Reflectivity = 30 minutes threshold at 35 ✗ Minimum object dBZ size = 10 pixels ✗ Minimum life ✗ Connectivity-type time = 30 = 8 pixels minutes ✗ Parallax- corrected tracks ✗ Motion of cloud CI level of probability POD FAR fields using the Level 2 (low prob) 0.52 0.76 TV-L1 optical flow algorithm Level 4 (high prob) 0.28 0.50 from TROPOS AS report http://www.nwcsaf.org/aemetRest/downloadAttachment/5278 Eumetsat Conference, Boston 2019
CI v2018 – GOES-16 qualitative validation Definition of radar GT (ground truth) NEW RADAR 2018-10-02T19:30:00Z ALL RADAR CONVECTIVE CONVECTIVE OBJECTS OBJECTS French Guiana IR satellite imagery superimposed with radar reflectivity (threshold 32dBZ, green / yellow) and radar convective objects (red contours) Ground Truth NEW RADAR CONVECTIVE OBJECTS PAIRED WITH LIGHTNING, ‘WARM’ CLOUDS Spatial Tolerance 0.1° Eumetsat Conference, Boston 2019
CI v2018 – GOES-16 validation - Results CI at 19:05:00Z, GT birth = 19:30:00Z Miss Infrared satellite imagery superimposed with: Already existing radar convective objects • (threshold at 32 dBZ, cyan contours) New radar convective object = Ground truth • (threshold at 32 dBZ, green contours). Birth at 19:30:00Z CI forecast [0;30min]. Yellow to magenta • CI at 19:35:00Z, GT birth = 19:30:00Z 2018-10-02 Case study summary ➢ CI Radar signature appears often before satellite one. ➢ When taking into account 0.1° spatial tolerance around GT, there are lots of late good detections (80 %). ➢ In CI production, use of IR10.3 is similar to IR11.2, but produces significantly less false alarms (not Late shown). good detection Eumetsat Conference, Boston 2019
Overview 1. CI: Convection Initiation 2.RDT: Rapidly Developing Thunderstorm 3. Conclusion Eumetsat Conference, Boston 2019
v2018 RDT - A well-known product Detection, tracking, characterisation and nowcast of convective cloud systems └ (stage, severity, overshooting tops detection overshooting tops detection …) ✔ Labelled as Operational (Eumetsat sense) ✔ V 2018 improvements : ● Improved and configurable detection (thanks to users’ feedback) limit for upper cloud cells’ contours with tropopause and/or fixed T° ● Lightning jump detection Lightning jump detection rapid increase of lightning trend correlated to - and sometimes precursor of - severe weather (hail ...) analysis of trends at lightning data time scale linked to lightning network performances (Cloud-to-Ground and IntraCloud) input to severity attribute ● Additional Discrimination scheme adapted to and tuned with current satellite configurations v2018.1 adapted to GOES16 adapted to GOES16 ● Other (high altitude Ice Crystal calculation, new lightning pairing rules ...) Eumetsat Conference, Boston 2019
Validation of Overshooting Tops (OT) Detection within RDT (1/3) Expertised CHMI 1 OT database (published by CWG 2 ) - 2.5’ experimental MSG1 scan 20130620 [09h-19h30] and 20130729 [13h-18h30] Automatic OTD within Reprocessed RDT - 1800 OT identified 4 configurations : - limited area over central Europe - FDSS-15’ and RSS-5’ ( 1 )Czech HydroMeteorological Institute - v2018 and dev t version with use of HRV ( 2 )Convection Working Group Pairing method between CHMI-OT and RDT-OT ● - Time synchronisation : area scanned approximatively +11’ for MSG/FDSS, 3’ for MSG/RSS, ~ 1’ for MSG-2.5’ ● - Time tolerance: maximum 5’ (RSS) or 15’ (FDSS) between RDT-OT and CHMI-OT ● - Spatial tolerance: 20 km maximum distance (~ mean OT size) ● - Score calculation: ✔ HIT: at least one RDT-OT associated to a CHMI-OT ✔ MISS: CHMI-OT without associated RDT-OT ✔ FA: RDT-OT without associated CHMI-OT Eumetsat Conference, Boston 2019
RDT-OT vs CHMI-OT (2/3) – 20130620 case study 11h45Z(+11’) 11h57Z 12h07Z FA ? FA ? MI ? 12h10Z RDT-OTD MI ? 12h00Z (+11’) HI CHMI-OT 12h12Z Expert CHMI-OTs: MI ● much more numerous ● high space and time variability of OTs from one slot to the other RDT-OTD: ● seems subjectively more or less OK, ● lot of misses (or FA?) due to scan rate differences ● correspondance not so obvious Eumetsat Conference, Boston 2019
RDT-OT vs CHMI-OT (3/3) – Quantitative Results ● Large number of misses, due to large scan rate differences (~1800 expert OTs vs ~700 RDT-OT RSS and ~200 RDT-OT FDSS) Expected low POD = HI/(HI+ MI ) or TS=HI/(HI+FA+ MI ) Focus on FAR=FA/(HI+FA) RDT-OTD vs CHMI-OT ➔ Globally better results with RSS mode for RDT-OT High frequency scan rate allow easier detection of short-lived OT ( for MTG ) ➔ Use of HRV (reflectance+gradient) slightly improves scores seems to lower FA on some cases, to increase HI on others, but limited impact further studies needed (HRV texture) ➔ Results dependent on mode and day better scores on 20130729, especially with RSS mode Signatures of RDT-OTs’ parameters (BTD, BT, reflectance) differ from one day to the other Eumetsat Conference, Boston 2019
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