Nowcasting thunderstorms for aeronautical end-users

Transcription

1 Nowcasting thunderstorms for aeronautical end-users Jean-Marc Moisselin Météo-France, Nowcasting Department co-authors: Céline Jauffret (Météo-France)

2 Overview Introduction SAT RADAR NWP image crédit: ESA Summary and future works

3 Convection and aviation AeroMeteo Twitter Instagram

4 Convection and aviation Thunderstorms are one of the most hazardous conditions for air navigation and aeronautical operations. Indeed, those meteorological systems can produce severe turbulence, low level wind shear and downbursts, icing, low ceilings and poor visibilities, hail and lightning. What are the detection and nowcast tools?

5 Overview Introduction SAT RADAR NWP image crédit: ESA Summary and future works

6 Rapidly Developing Thunderstorm (RDT) is a story......of SCIENCE convection features (overshooting tops, convection Yes/No diagnosis, new satellites), HAIC project...of SOFTWARE in the framework of NWCSAF...of OPERATION For example by Météo-France for forecasters or aviation end-users

7 RDT: data fusion for description of convection INPUT DATA: MULTISOURCE NWP data NWCSAF products PGE RDT GEO data (5 IR channels + VIS) Lightning Data OUTPUT: MULTILEVEL DESCRIPTION OF CONVECTION PGE11 ->RDT Main description of cell: Yes/No convection diagnosis, cell-development phase, position, surface, T, gap to tropopause, cloud type and phase, cloud top pressure. Displacement Relevant trends are calculated Overshooting Tops, Lightning Activity, Convective Index, Rainfall Activity

8 4-steps algorithm of RDT STEP1: 10.8 µm detection - In order to detect cells - Vertical extension: at least 6 C STEP2: Tracking - In order to recognize each cell in the previous slot) - Trends calculation is then allowed ② ① STEP3: Discrimination - In order to identify convective cells - Statistical process ③ - STEP4: Forecast (v2016) No creation, no dissipation of cells Improvement of tracking (NWP, HRW) ④

9 Main Validation Results over EUROPE POD>70% for convective seasons. Acceptable (65%) when other seasons included FAR highly dependent of the verification method. Between 14 and 34%. High flash tolerance (35 km), full period, trajectory approach : FAR=22% Cumulative Density Early diagnosis: 25% of RDT delivers a yes convection diagnosis BEFORE lightning activity occurs. RDT diagnosis before 1st flash Env. 25% Age of 1st stroke

10 Overshooting Tops (OT) Detection inside each cell At first step, selection of a pixel of interest: BT 10.8, BTD WV6.2-IR10.8, WBTD WV6.2-WV7.3, highest VIS0.6 reflectance Then criteria to be verified concern * WV6.2-IR10.8 * VIS0.6 * Difference between temperature of OT candidate and the mean temperature of the cloud cell * Difference between OT candidate temperature and NWP tropopause temperature * Morphologic criteria to confirm a spot of cold temperatures to determine the pixels that belong to an OT HRV for tuning/validation

11 RDT Productions at Météo-France GOES-W 135 W 30min GOES-E 75 W MSG 0 MSG1 40.5E Himawari 140 E 30min 15min 15min 20min For each satellite multiple parallel productions on several subdomains merge of cloud cell sets in a single product xml files and few attributes: OK for UPLINK

12 RDT on-board ewas Solution From ewas User Forum, Barcelona, 17th November 2016 GTD Lecture

13 Overview Introduction SAT RADAR NWP image crédit: ESA Summary and future works

14 ASPOC and ASPOC3D for ATC The ASPOC (Application de suivi et prévision des orages pour le contrôle aérien) application for thundestorm warning is already provided to air-traffic controller (forecast of +30 minutes). A new version, ASPOC3D, which provides an estimated top altitude of each convective cloud as supplementary information, has been developed by Météo-France and is currently under implementation at French enroute and approach air traffic control centres (spring 2018).

15 satellite cloud top to enhance radar-based convection diagnosis A radar-sat mixed product used for aeronatical end-users. Also used for SESAR convection nowcast consolidated product

16 Extrapolation of radar data 1-Rainy cells identification in the observed image : 2-Determination of each cell displacement using the previous image : 3-Identification/estimation of displacement are repeated 4-Interpolation of all the cells displacement (vector) successive 5 min advections with the same motion field / observation H forecast H+5 forecast H+10 You can apply the field on QPE or reflectivities +5 / forecast H+60

17 Overview Introduction SAT RADAR NWP image crédit: ESA Summary and future works

18 AROME-NWC characteristics AROME-NWC=AROME France built for nowcasting Same Physics, dynamics, coupled model, domain, mesh and assimilation method AROME AROME-NWC Assimilation Cut off variable (1h30 for production) Cut off 10 minutes runs (/day) 8 24 Max. Forecast range up to 42h 6h Forecast range sample 1h 15 minutes Availability H+2h to H+4h H+30 minutes

19 AROME-NWC verification ARO 15 NWC 15 ARO 12 NWC 16 NWC 17 NWC 18 HSS (Heidke Skill Score) for 1h accumulated precipitations (2 mm/h threshold). From [Brousseau, 2016].

20 Some forecaster remarks The last AROME-NWC run is not necessarily/systematically the more accurate 17 UTC run +1h forecast OBS 14 UTC run +4h forecast 13 UTC run +5h forecast Correct forecast of general features of reflectivity fields but +1 hour: correct dry area eastward high reflectivity line +4 and +5 hours: correct high reflectivity patterns in the South

21 A web dashboard for forecasters 1) To help forecasters to quickly identify the met. situation and the parameters to watch. 2) To provide a synthetic representation of information

22 Overview Introduction SAT RADAR image crédit: ESA Summary and future works

23 Fusion Extrapolation and NWP Arome-NWC Extrapolation Data Fusion

24 Fusion: Adaptive and Self-Confident Algorithms See for example Auer, P., Cesa-Bianchi, N., & Gentile, C., Adaptive and self-confident on-line learning algorithms. J. of Computer and System Sciences, 64, p Two predictors for France domain * QPE Extrapolation (up to 3 hours!, refreshed every 5 minutes). 5 resolution of forecasts * The last Arome-NWC available (refreshed hourly). 5 resolution of forecasts Fusion = α Extrapolation + (1- α) Arome-NWC Application Alpha: forecast range dependent but the same for all grid points. Alpha defined by dynamical statistical training. Every 5 minutes! Verification and training: radar QPE Strategy for minimizing the regret: to be better than best expert (or not so far away)

25 The weights in data fusion Fusion = α Extrapolation + (1- α) Arome-NWC Jan/30 F F F C C F : frontal precipitations Feb/13 FC FC C C C F FC C : convective precipitations Alpha value Forecast range (h) 3

26 Overview Introduction SAT RADAR image crédit: ESA Summary and future works

27 Summary and future works Radar-based products: high accuracy due to radar input data Geostationary-based products: global coverage Nowcasting: numerical prediction usable forecasts with shorter deadlines are Methods of fusion between extrapolation and numerical prediction. Satellite based products future improvements - Lightning Jump in RDT v2018 (a proxy for Hail) - New generation of satellite (GOES-R, MTG) - Improvement of Convection Initiation (v2016->v2018) Radar based products future improvements - Toward a unified European product (SESAR IP-068_ Adverse Weather) - To exploit the full radar-measurement on the vertical and double polarisation Data Fusion - Reflectivity and QPE - Weights defined for sub-regions instead as for whole domain

28 Thank you