Early detection of thunderstorms using satellite, radar and

Transcription

1 Federal Department of Home of Home Affairs Affairs FDHA FDHA Federal Office of of Meteorology and and Climatology MeteoSwiss Early detection of thunderstorms using satellite, radar and Observing convection with satellite, radar, and lightning measurements lightning observations Ulrich Hamann, Lorenzo Clementi, Marco Gabella, Urs Germann, Alessandro M. Hering, Luca Nisi and Marco Sassi

2 Meteosat Second Generation Nowcasting strategy of Meteoswiss Swiss Radar Network Wind Hail COALITION Thunderstorm Radar Tracking Vaisala Lightning Sensors Nowcasting tracking and monitoring, statistical learning, turbulence, cloud physics, climatological data, topography, weather types COALITION Lightning Flash Floods

3 Hering, A. M., Morel, C., Galli, G., Sénési, S., Ambrosetti, P., and Boscacci, M. (2004), Nowcasting thunderstorms in the alpine region using a radar based adaptive thresholding scheme. Proceedings, Third ERAD Conference, Visby, Sweden,

4 Goals Forecast of Severeness Early detection High POD, low FAR Input Satellite, Radar, NWP, lightning, topography 8 modules for VIL or CTT Forecasting method Pseudo kinetic energy approach COALITION First Generation Nisi, L., Ambrosetti, P. and Clementi, L. (2014), Nowcasting severe convection in the Alpine region: the COALITION approach. Q.J.R. Meteorol. Soc., 140: doi: /qj.2249 Pseudo kinetic energy approach

5 COALITION Second Generation Goals Forecast of Severeness Early detection High POD, low FAR Input Satellite, Radar, NWP, lightning, topography Methods Logistic and Linear Regressions Neural Networks Hamann, U., L. Nisi, L. Clementi, J. Figueras i Ventura, M. Gabella, A. M. Hering, I. Sideris, S. Trefalt, U. Germann, 2015, Observing convection with satellite, radar, and lightning measurements, Symposium EGU General Assembly 2015, Vienna, Austria Hamann, U., L. Nisi, A. Hering, L. Clementi, M. Gabella, U. Germann, 2014 Investigating the typical development of thunderstorms using satellite, radar and lightning observations Eumetsat Conference 2014, Geneva, Switzerland Regression models Neural Networks

6 COALITION Second Generation MSG SEVIRI observations 12 SEVIRI channels, channel differences, cloud top structure, temporal derivations NWC-SAF products Cloud Mask, Cloud Type, Cloud Phase, Cloud Top Temperature /Height/Pressure, precipitable water (total, boundary, mid, high layer), CAPE, instability indices (KI, LI, SHW) Parallax Correction Cloud Top Height Temporal Derivatives consider Advection SEVIRI High Resolution Winds COSMO CAPE, CIN, precipitable water, tropopause height, frezing level, surface temperature etc. Radar observations maximum reflection, precipitation, CombiPrecip, Probability of Hail, Maximum Expected Hail Size, EchoTops, Hydrometeors Temporal Derivatives consider Advection Radar Winds (Maple) co-located multi sensor database Lightning observations Lightning density, Lightning Current, Polarization, Cloud- Ground Intra-Cloud

7 COALITION Second Generation co-located multi sensor database Logistical Regression Linear Regression or Neural Networks Probability maps for Hail Lighting Precipitation VIL etc. Analysis or Forecast Intensity estimate for Hail Lighting Precipitation VIL etc. Analysis or Forecast

8 MSG SEVIRI observations 12 SEVIRI channels, channel differences, cloud top structure, temporal derivations NWC-SAF products Cloud Mask, Cloud Type, Cloud Phase, Cloud Top Temperature /Height/Pressure, precipitable water (total, boundary, mid, high layer), CAPE, instability indices (KI, LI, SHW) COSMO CAPE, CIN, precipitable water, tropopause height, frezing level, surface temperature etc. COALITION Second Generation Processing with PyTROLL Long term archive 7 summer seasons 18 months of data Parallax Correction Cloud Top Height Location: Switzerland and Alpine Region Temporal Derivatives consider Advection SEVIRI High Resolution Winds co-located multi sensor database 2 months of data in progress Radar observations maximum reflection, precipitation, CombiPrecip, Probability of Hail, Maximum Expected Hail Size, EchoTops, Hydrometeors Long term archive Temporal Derivatives consider Advection Radar Winds (Maple) Long term archive Long term archive Lightning observations Lightning density, Lightning Current, Polarization, Cloud- Ground Intra-Cloud

9 Tests for Convective Initiation Mecikalski, J. R., Wayne M. MacKenzie Jr., Marianne König, and Sam Muller, (2010), Cloud-Top Properties of Growing Cumulus prior to Convective Initiation as Measured by Meteosat Second Generation. Part II: Use of Visible Reflectance. J. Appl. Meteor. Climatol., 49,

10 19UTC 17UTC 15UTC 13UTC Radar instantaneous precipitation rate Tests for Convective Initiation according to Mecikalsky TRT cell detection WV062-IR108 Number of CI tests Cloud Type

11 Radar Radar Combined satellite, radar, and lightning observations for 23 rd July UTC 09UTC 12UTC 15UTC 18UTC 21UTC 24UTC MSG SEVIRI MSG SEVIRI Lightning 06UTC 09UTC 12UTC 15UTC 18UTC 21UTC 24UTC 06UTC 09UTC 12UTC 15UTC 18UTC 21UTC 24UTC 11 06UTC 09UTC 12UTC 15UTC 18UTC 21UTC 24UTC

12 Summary Nowcasting Algorithms at MeteoSwiss Thunderstorm Radar Tracking COALITION First Generation COALITION Second Generation (in preparation) Goals of COALITION Input datasets: Radar, satellite, lightning, NWP Increase lead-time for warnings Forecast of intensification of thunderstorms COALITION SG Replacing pseudo-kinetic energy with regression and neural network technics Making use of long term statistics Multi-sensor analysis Satellite: Brightness temperatures, reflectivity, channel differences, temporal tendencies Radar: Maximum reflectivity, ECHOTOPS, MESH, POH Lightning: lightning density, cloud-ground, intra-cloud, current COSMO: CAPE, CIN, wind shear, tropopause height, freezing level