In atmospheric modeling, an accurate representation of land cover is required because such information impacts water and energy budgets and, consequently, the performance of models in simulating regional climate. This study analyzes the impact of the land cover data on an operational weather forecasting system using the Weather Research and Forecasting (WRF) model for central Mexico, with the aim of improving the quality of the operative forecast. Two experiments were conducted using different land cover datasets: a United States Geological Survey (USGS) map and an updated North American Land Change Monitoring System (NALCMS) map. The experiments were conducted as a daily 120 h forecast for each day of January, April, July, and September of 2012, and the near-surface temperature, wind speed, and hourly precipitation were analyzed. Both experiments were compared with observations from meteorological stations. The statistical analysis of this study showed that wind speed and near-surface temperature prediction may be further improved with the updated and more accurate NALCMS dataset, particularly in the forecast covering 48 to 72 h. The Root Mean Square Error (RMSE) of the average wind speed reached a maximum reduction of up to 1.2 m s⁻¹, whereas for the near-surface temperature there was a reduction of up to 0.6 °C. The RMSE of the average hourly precipitation was very similar between both experiments, however the location of precipitation was modified.

Mexico City constitutes one of the largest concentrations of population on the planet and is settled in a valley that, before the 16th century, had a lake system. The lakes were desiccated artificially, and currently, only small lakes remain. The impact of the lake system desiccation on precipitation was studied by performing numerical experiments: with the ancient lake system and without it. The experiments were carried out with the Weather Research and Forecasting (WRF) model coupled with a lake model for two months, using identical initial and boundary conditions, where only the system and lake physics were changed. The mean daily accumulated precipitation reduced when the system was removed. Additionally, the hourly distribution of rainfall changed from a relatively small diurnal variability when there was a lake system to a larger variability with a peak in the afternoon when the system was removed. Extreme precipitation events became more intense in the simulations with lakes. When the lakes were removed, the diurnal temperature range increased, and the boundary layer height became more variable, with a higher daily maximum. The results presented here show that the WRF-Lake model leads to opposite results compared to those with a non-coupled lake.

Land use land cover (LULC) significantly impacts local circulation in the Mexico Basin, particularly wind field circulations such as gap winds, convergence lines, and thermally induced upslope/downslope wind. A case study with a high-pressure system over the Mexico Basin isolates the influence of large-scale synoptic forcing. Numerical simulations reveal a wind system composed of meridional circulation and a zonal component. Thermal pressure gradients between the Mexico basin and its colder surroundings create near-surface convergence lines as part of the meridional circulation. Experiments show that the intensity and organization of meridional circulations and downslope winds increase when LULC changes from natural and cultivated land to urban. Zonal circulation exhibits a typical circulation pattern with the upslope flow and descending motion in the middle of the basin. Large values of moist static energy are near the surface where air parcels pick up energy from the surface either as fluxes of enthalpy or latent heat. Surface heat fluxes and stored energy in the ground are drivers of local circulation, which is more evident in zonal circulation patterns.

Urbanization impacts the surface temperature fields increasing the vulnerability of urban residents to heat exposure. Identifying vulnerable urban populations to extreme heat exposure is crucial to develop mitigation and adaptation strategies towards sustainability. We used an urban growth model (SLEUTH) to simulate emerging urban areas in Mexico City Metropolitan Area (MCMA) under a hypothetical land-use policy scenario projected to 2060 in which no restrictions were posed to urban growth. SLEUTH outputs were used in the numerical model Weather Research and Forecasting (WRF) to quantify expected changes in near-surface temperature within the MCMA. We calculated and mapped heat exposure as differences in average (Tmean), maximum (Tmax) and minimum (Tmin) temperatures over the diurnal cycle between future and current land cover conditions. Population vulnerability to projected increases in heat exposure was determined using a set of socioeconomic indicators. SLEUTH simulations showed an urban area expansion of nearly 4,790 km² by 2060. Overall, changes in Tmin were greater than changes observed for Tmax and Tmean. Tmean, Tmax and Tmin increases up to 0.6°C, 1.3°C and 2.6°C, respectively, were recorded for the MCMA with greatest temperature changes observed in the State of Mexico. Results suggested the presence of socioeconomic disparities in the projected spatial exposure of urban-induced heat in MCMA. We argue that our results could be used to inform and guide locally tailored actions aimed at reducing exposure and increasing population´s capacities to cope and adapt to future threats.

In Markov random field (MRF) models, parameters such as internal and external reference fields are used. In this paper, the influence of these parameters in the segmentation quality is analyzed, and it is shown that, for image segmentation, a MRF model with a priori energy function defined by means of non-homogeneous internal and external field has better segmentation quality than a MRF model defined only by a homogeneous internal reference field. An analysis of the MRF models in terms of segmentation quality, computational time and tests of statistical significance is done. Significance tests showed that the segmentations obtained with MRF model defined by means of non-homogeneous reference fields are significant at levels of 85% and 75%.

The Metropolitan Zone of Mexico City, as well as the associated basin, includes the territories of Mexico City, some municipalities of the State of Mexico and the state of Hidalgo. In addition, this area is the most densely populated in Mexico. The region is influenced by mid-latitude and tropical weather systems and is vulnerable to extreme hydrometeorological events. In this context, we developed a dataset from the records of 136 geolocated sites that includes daily precipitation data from the CLImate COMputing (CLICOM) project and the Mexico City Water System. The data spans the period from 1930 to 2015 for the rainy months (June–October) from stations with records of 20 or more years. In each recording site, automatic and manual data quality control were performed to verify the consistency of the daily precipitation data. We believe that our highly dense precipitation dataset will be useful for climate, trend and extreme events analysis. Additionally, the data will allow validating simulations of numerical atmospheric models. The dataset is public, and it was previously used in other research to determine areas susceptible to flooding due to heavy rain events and to develop a web mapping application of daily precipitation data.