Investigating ground-based radar and spaceborne infrared radiometer synergy for lightning areal prediction in complex orography

Abstract

A new multi-sensor approach, named PoLCast (Probability of Lightning foreCast), for predicting the lightning activity in a complex orography geographical area is proposed and discussed. The PoLCast input information are the ground-based weather radar horizontally polarized reflectivity factor and the atmospheric instability indexes, derived from the Spin Enhanced Visible Infrared Imager (SEVIRI) radiometer onboard the Meteosat Second Generation (MSG) satellite. The weather radar data are used to calculate the probability of lightning following a direct relation between the maximum values of the reflectivity factor and the lightning occurrence, whereas the atmospheric instability indexes from SEVIRI are used to constrain the probability of lighting, derived from weather radar data, and enhance such probability in cases of more unstable troposphere. To test the PoLCast methodology, the output of the Blitzortung and the “Sistema Italiano Rilevamento Fulmini” (SIRF) ground-based lightning sensor networks are used together with the C-band Mt. Midia weather radar within its 180 km diameter coverage over the Central Italy area. Both satellite and radar data are pre-processed into PoLCast to obtain a single time series in terms of the areal probability of lightning (PoL). PoLCast performances are evaluated in terms of statistical scores using 12 heterogeneous case studies over Central Italy. Even though the number of available cases is relatively limited, quantitative results show high areal PoL (from 0 to 100%) with a case-by-case variability of false alarm rate from 3 to 72%. The advantage of a multi-sensor technique, such as PoLCast, with respect to an approach using weather radar data only, becomes more evident when lightning activity is not present and the leading time of lightning forecast exceeds 2.5 h.

Appendix A: Atmospheric instability indexes

In this appendix, a brief summary of the instability indexes is given. The instability indexes considered in this work are the K-Index (KI), maximum buoyancy index (MB), and the lifted index (LI). They can be potentially used for the prediction of severe weather events, as they provide an indication of the potential convective triggering and development in clear air well in advance with respect to the time when the thunderstorm will occur. The main limitation of the instability products is that their computation cannot be performed in cloudy areas, as well explained in the EUMETSAT Global Instability Index Factsheet (Conte et al. 2011). They are usually distributed as Global Instability Index (GII) at a coarse horizontal resolution of about 60–70 km over the Mediterranean area every 15 min. Instead, regional instability index (RII), used in this paper, are distributed with a spatial resolution of 25 km over the area of central Italy every 5 min. Validation of instability indexes from satellite against independent measurement, such as sounding data, is performed at EUMETSAT (König 2002; De Coning et al. 2010), though a systematic study is not available to our knowledge.

LI depends on the temperature at the vertical level of 500 mb, which corresponds to 5.5 km in standard atmosphere conditions, compared with the temperature of an air parcel adiabatically lifted up. Lower values of LI likely correspond to higher probability of thunderstorms. Table 5 summarizes typical values of LI. KI is a function of temperature and humidity at pressure levels of 850, 700, and 500 mb. Larger values of KI usually predict thunderstorms. Table 6 describes KI typical values. MB index was developed to identify areas for potential instability. Larger values of MB indicate higher probability of thunderstorms, as indicated in Table 7.

Table 5 Lifted index definition and typical values

Table 6 K Index definition and typical values

Table 7 Maximum buoyancy index definition and typical values