# Spatio-Temporal Distribution of Deep Convection Observed along the Trans-Mexican Volcanic Belt
## 5 Conclusions
Furthermore, the study brings to light the possibility of identifying and quantifying region-specific climatological characteristics, such as the \"canicula\", with the use of satellite observations of clouds and precipitation. Knowing that a wide range of dynamical processes controls the formation of deep convective cloud systems and the accompanying variability in characteristics, observational-based approaches can obviously benefit from reinforcement through in-depth studies including extensive numerical weather simulations. In turn, satellite-observed distributions of clouds and precipitation can provide modelers with valuable information about the quality of parameterizations related to convective processes in large-scale models. While the straightforward CTP-COT threshold-based DCC cloud typing method used in this study can provide a first estimate of the frequency of clouds with considerable vertical extent and liquid/ice content, more sophisticated cloud classification schemes should be considered in future studies. For example, k-means clustering methods might provide (deep convective) cloud regimes that are tuned to the particular meteorological/climatological conditions in a study area. Several satellite-based observations can be added to further improve our understanding as well as the monitoring of deep convective processes in Mexico. For example, cloud observations obtained from advanced imagers, with relatively high spatial resolution and high temporal coverage of minutes, mounted on the NOAA Geostationary Operational Environmental Satellite (GOES)-R series [74; 75], can be used to investigate the diurnal cycle of convective clouds in Mexico. Furthermore, satellite observations of, e.g., water vapor and surface temperature, can be used to characterize and study the conditions in the pre-convective and convective initiation environment. The following are available at [[https://www.mdpi.com/2072-4292/13/6/1215/s1](https://www.mdpi.com/2072-4292/13/6/1215/s1)]([https://www.mdpi.com/2072-4292/13/6/1215/s1](https://www.mdpi.com/2072-4292/13/6/1215/s1)). Figure S1: Spatial distribution of temporal correlation computed for MODIS (frequency of deep, convective clouds) and CHIRPS (accumulated precipitation) for four seasons (December-February DJF; March-May MAM; June-August JJA; September-November SON). Highest correlation values are found for SON (domain-average 0.93), especially over larger terrain elevations. For JJA (domain-average 0.81), the correlation values vary much more within the study area, which can at least partly be explained by the generally very high observed variability (std) of DCC frequency for those months (also seen in Figure 8 in the manuscript). For DJF (domain-average 0.79) and MAM (domain-average 0.77), the spatial distribution looks smoother again. Here, DCC frequency and precipitation amounts are generally much lower as well as the variability. However, for MAM, with already increased DCC frequencies in May, higher correlation values can be clearly observed along the TMVB. The maps are added as supplementary material including a short description. Conceptualization, N.C., J.F.L.-C., C.C.H. ; methodology, J.F.L.-C., C.C.H., N.C.; software, J.F.L.-C.; validation, J.F.L.-C., C.C.H., N.C.; formal analysis, J.F.L.-C., C.C.H., N.C.; investigation, J.F.L.-C., C.C.H., N.C.; resources, N.C., J.F.L.-C., C.C.H. ; data curation, J.F.L.-C., C.C.H. ; writing--original draft preparation, J.F.L.-C., C.C.H., N.C.; writing--review and editing, J.F.L.-C., C.C.H., N.C., J.F. ; visualization, J.F.L.-C.; supervision, N.C., J.F. ; project administration, N.C., J.F. ; funding acquisition, N.C., J.F. All authors have read and agreed to the published version of the manuscript. This research was funded by the Consejo Nacional de Ciencia y Tecnologia (CONACYT)--Mexico (Grant Number 298737) and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-FOR 2589. The publication of this article was funded by Freie Universitat Berlin. Publicly available datasets were analyzed in this study.