RECENT PROGRESS IN CONVECTIVE PHENOMENA MONITORING AND FORECASTING AT THE INM F. Martín, F. Elizaga, I. San Ambrosio and J. M. Fernández Servicio de Técnicas de Análisis y Predicción, STAP (Forecasting and Analysis Techniques Service) Instituto Nacional de Meteorología, INM RECENT PROGRESS IN CONVECTIVE PHENOMENA MONITORING AND FORECASTING AT THE INM F. Martín, F. Elizaga, I. San Ambrosio and J. M. Fernández Servicio de Técnicas de Análisis y Predicción, STAP (Forecasting and Analysis Techniques Service) Instituto Nacional de Meteorología, INM

Summary of the presentation –Outlook of convection monitoring at INM –Integration of remote sensing data and NWP output: Regional level: hail moduleRegional level: hail module National levelNational level – Doppler radar-based products –Specific products for end-users –Conclusions

Convection monitoring at INM: Basic approach Specific oriented NWP output for deep moist convection: CAPE, CIN, SRH,… Maps from ECMWF and HIRLAM models Pseudo sounding derived from NWP models and thunderstorm oriented parameters MSG imagery and Nowcasting SAF products Integration of remote sensing data: –Regional level –National level

Specific oriented NWP output for deep moist convection: CAPE, CIN, SRH,.. maps from ECMWF and HIRLAM models: One example Lift Index & Wet bulb Potential Temperat ure at 850 hPa Convective Precipitation Low Level Winds & CAPE Storm Relative Helicity & Potential Instability at 700 hPa

Integration of CG lightning and radar data Data and methodology Data –Regional radar data : 1 PPI + 12 CAPPIs ( km) + derived radar products (Echotop, VIL, ZMAX,..) in non Doppler mode, every 10 min., 2 Km x2 Km. Other data in Doppler mode –National composite radar data : PPI, CAPPI-2.5 Km height, VIL, Echotop, ZMAX … en non Doppler mode, every 10 min., 2 Km x2 Km. Other data in Doppler mode –CG data, MSG-MET8 imagery and HIRLAM/ECMWF model output Procedures for radar-based convective identification Two procedures have been adapted for monitoring and tracking of radar-based convective storms, taking to account the INM radar data and facilities: –Bidimensional procedure, 2D, is applied on lowest radar elevation on PPI/CAPPI/ZMAX images: Steiner-Youter-Houze, SYH, technique ( regional and national data!!! ) –Three dimensional procedure, 3D, is applied on the 12 CAPPIs: SCIT algorithm (“Storm Cell Identification and Tracking”), developed by Johnson et al. (1998). At regional level!!!.

Integration of lightning and radar data: (I) Radar and lighting data fusion –2D. PPI (CAPPI o ) (t) + lightning data (t-10 min., t) are combined. Radar-based convective objects + CG strikes –Spatial integration at “t” and backward movement of 2D convective structure up to t-10 min., for a temporal integration –Linear extrapolation of lightning and 2D convective structures up to 60 min Cluster analysis –Non radar-combined “CG” strikes are clustered by just distance criterion –Tracking and linear extrapolation of lightning clusters are not applied in the operational procedure

Integration of lightning and radar data Regional and National levels: Flow Chart (II) MSG imagery as a background image (2005) IR10.8 at night HRVIS daytime

Integration of lightning and radar data: (III) Identification of convective structures, 2D: –Radar data to use: Regional level: The lowest PPI (or a low CAPPI) National level: ZMAX composite image –SYH, procedure for convective – stratiform separation (2D) – SYH convective criteria: –Intensity criterion –Peakedness or gradient criterion –Surrounding area criterion

Integration of lightning and radar data: regional level (IV)

Cluster Procedure (I) Data –CG strikes, which have not been combined with convective radar structures, are clustered Procedure –A lightning cluster is a set of CG flashes if for any lightning “i” exists at least other “j” and the distance D ij ≤10 km –A cluster is analysed if its CG number is superior or equal to 10 strikes (number of positive and negative strikes, centroid location, maximum and minimum distances among strikes) –None extrapolation is performed

Cluster Procedure (II)

Examples at regional level (I) Radar ambiguities for long distances and mountainous areas Example using Version 1.0

Examples at regional level (II) CG clusters at mountainous areas (radar screening) Anomalous CG+ /PSD (Positive Strike Dominated) supercell

Integration of lightning and radar data: national level (I) Identification of convective structures ZMAX –Radar image to use: A national composite image of maximum of reflectivity from regional radar data, ZMAX, every 10 min., 2 x 2 km maximum value of ZMAX is selected –When a national pixel is covered by different radars, the maximum value of ZMAX is selected –SYH procedure for convective – stratiform separation is applied – The same procedure of assignment of CG and convective radar- based structure data is applied at national level –Clustering procedure is applied when CG lightings have not been assigned.

Integration of lightning and radar data: national level (II) Example: Thunderstorms over the Iberian Peninsula and airports graphical warning Airports

Identification of 3D convective cell -SCIT: “Storm Cell Identification and Tracking” procedure, developed by Johnson et al. (1998), has been adapted at INM using the 12 CAPPIs from regional radar data every 10 minutes. - 3D Cell properties, extrapolation and tracking are performed

Monitoring of deep convection at regional level (I): hail module 2D Analysis: PPI+IR MSG +Lightning + NWP data 3D Analysis: 12 CAPPI + NWP data + hail Module Hail

Monitoring of deep convection at regional level (II): Close up view Alicante supercell Hail output: “G” denotes severe hail potential

Doppler radar-based products VAD (Velocity Azimuth Display) Identification of mesocyclone (Version 0.1 non operational)

VAD products

Identification of mecocyclone Data. Wind radial velocity data of one Doppler PPI (Winr) Methodology. Identification of special patterns with two well-defined and opposite maxima Special patterns HRVIS Doppler radial velocity image Severe convection

Specific products for end-users Up to forty special hot spots (airports, cities), where special attention is needed, may be selected at each regional and national levels (i.e. aeronautical authorities) Radius of surveillance are defined at each hot spot. When a CG lightning strikes are into these circular areas or are likely to move into them, warning messages are issued.

Conclusions Objective procedures have been developed at INM to integrate different types of data at national and regional levels for monitoring deep moist convection. Graphical and text format products are generated automatically for helping forecasters External end-users are requiring special tailored products associating remote sensing information such as CG products. Doppler radar-based products will be developed in the next future for monitoring convective wind velocity patterns (mesocyclone, intense convergence and divergence). In the next future, GIS information will be included in the automatic procedures to enhance all remote sensing information.