# Spatio-Temporal Distribution of Deep Convection Observed along the Trans-Mexican Volcanic Belt
## 1 Introduction
In [PERSON] and [PERSON] [18], data from a surface station network are used to document the occurrence and spatial distribution of severe weather phenomena associated with deep, moist convection over southeastern South America. In [PERSON] and [PERSON] [19] reflectivity measurements from C-band weather radar are used to study the characteristics of convective storms in Belgium, such as the probability of occurrence of a storm and its correlation with orographic variations. In addition to using model, sounding and ground-based data, several studies regarding severe weather research focus on the use of satellite observations. One of the main advantages of satellite data is the large-scale spatial coverage as well as high temporal coverage, depending on the type of satellite and on-board instruments. In countries like Mexico, where, due to the lack of ground-based instruments such as weather radars for many regions, no wide-scale severe weather detection and monitoring exists, the use of satellite data turns into an affordable alternative. A broad spectrum of measurements from several passive and active instruments mounted on different types of satellites and the large set of atmospheric parameters that can be subsequently inferred is available. For example, [PERSON] and [PERSON] [20] analyzes, from a climatic perspective, the spatial distribution of deep convection over India using measurements from the Advanced Very-High Resolution Radiometer (AVHRR) and cloud datasets obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) mounted on various polar-orbiting satellites. [PERSON] and [PERSON] [18] explores the relationship of severe weather documented with the surface station network to mesoscale convective systems (MCS) observed with measurements from the Geostationary Operational Environmental Satellite-12 Infrared (GOES-IR). [PERSON] and [PERSON] [21] utilizes a joint analysis of cloud profiling radar (CPR) observations from CloudSat and geostationary satellite data to characterize tropical deep convective clouds. In [PERSON] et al. [22], a combination of radar measurements and a set of cloud physical properties retrieved from measurements from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on the Meteosat Second Generation (MSG) geostationary satellites is exploited to investigate the possibility of automatic detection of deep convective clouds in the Netherlands. [PERSON] et al. [23] presents a conceptual model for the detection of severe convective storms, producing tornadoes and large hail, based on satellite-retrieved vertical profiles of cloud top temperature and particle effective radius relations from AHVRR data and geostationary satellites. Although many other studies are dedicated to this type of research, those mentioned above give an idea of the multiple applications that satellite data can have for the study of deep convection-associated severe weather and the environments in which it forms. In Mexico, there is little documentation of severe convective storms and their multiple manifestations. Recently, [PERSON] [24] explored several characteristics of MCSs, and [PERSON] and [PERSON] [25] analyzed extreme precipitation events. Some characteristics of the convective storms and their relationship to the cloud-to-ground lightning have been studied as well [26; 27]. Moreover, supercell storms and tornadoes in the northeastern part of Mexico have been studied [28; 29]. Given that tornadoes are one of the most violent manifestations of severe weather, they can be used to delimit the areas where extreme meteorological events occur. In this sense, the TMVB stands out as a region of great importance. Previous studies highlight the influence of the environmental and topographic characteristics of the TMVB in the generation of non-supercell tornadogenesis [30]. This area in central Mexico experiences about 40% of total tornadic activity recorded in the country [31; 32] (Figure 1a).