Weather and climate conditions drive the evolution of tropical glaciers which play an important role as water reservoirs for Peruvian inhabitants in the arid coast and semi-arid Andean region. The scarcity of long-term high-quality observations over Peruvian glaciers has motivated the extensive use of reanalysis data to describe the climatic evolution of these glaciers. However, the representativeness and uncertainties of these reanalysis products over these glaciers are still poorly constrained. This study evaluates the ability of the ERA5-Land reanalysis (ERA5L) to reproduce hourly and monthly 2 m air temperature and relative humidity (T2m and Rh2m, respectively) over several Peruvian glaciers. We compared the ERA5L with data from four on-glacier automatic weather stations (AWS), whose hourly time series were completed with nearby stations, for the period January 2017 to December 2019. Results indicates a better performance of the reanalysis for T2m (r >0.80) than for Rh2m (∼0.4< r <∼0.6) in all four glaciers. Concerning the observations, both parameters show a daily cycle influenced by the presence of the glacier. This influence is more prominent during the dry months when the so-called glacier damping and cooling effects are stronger. On a monthly time scale, the ERA5L validation for both parameters are better in wet outer tropical sites (RMSE between ±0.2°C for T2m and between 3%–7% for Rh2m) rather than in dry outer tropical sites (RMSE between ±0.2°C for T2m and between 3%–7% for Rh2m). Among all sites considered in the study, the Rh2m bias is the highest in the Cavalca glacier (correlation of 0.81; RMSE 13%, MAE 11% and bias 8.3%) and the lowest in Artesonraju glacier (correlation of 0.96; RMSE 3%; MAE 2.3% and bias — 0.8%). Based on certain considerations outlined in this paper, it is appropriate to use ERA5L to characterize T2m and Rh2m conditions on Peruvian glaciers, particularly in the wet outer tropics.

The dispersion of air pollutants and the spatial representation of meteorological variables are subject to complex atmospheric local parameters. To reduce the impact of particulate matter (PM_2.5) on human health, it is of great significance to know its concentration at high spatial resolution. In order to monitor its effects on an exposed population, geostatistical analysis offers great potential to obtain high-quality spatial representation mapping of PM_2.5 and meteorological variables. The purpose of this study was to define the optimal spatial representation of PM_2.5, relative humidity, temperature and wind speed in the urban district in Cartagena, Colombia. The lack of data due to the scarcity of stations called for an ad hoc methodology, which included the interpolation implementing an ordinary kriging (OK) model, which was fed by data obtained through the inverse distance weighting (IDW) model. To consider wind effects, empirical Bayesian kriging regression prediction (EBK) was implemented. The application of these interpolation methods clarified the areas across the city that exceed the recommended limits of PM_2.5 concentrations (Zona Franca, Base Naval and Centro district), and described in a continuous way, on the surface, three main weather variables. Positive correlations were obtained for relative humidity (R² of 0.47), wind speed (R² of 0.59) and temperature (R² of 0.64).