This study evaluates the precipitation product of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) over the Mexican region during the period between April 2014 and October 2015 using three different time scales for cumulative precipitation (hourly, daily and seasonal). Also, the IMERG data have been analyzed as a function of elevation given the rain gauges from the automatic meteorological stations network, located within the area of study, which are used as a reference. In the present study, continuous and categorical statistics are used to evaluate IMERG. It was found that IMERG showed better performance at the daily and seasonal time scale resolutions. While hourly precipitation estimates reached a mean correlation coefficient of 0.35, the daily and seasonal precipitation estimates achieved correlations over 0.51. In addition, the IMERG precipitation product was able to reproduce the diurnal and daily cycles of the average precipitation with a trend towards overestimating rain gauges. However, extreme precipitation events were highly underestimated, as shown by relative biases of −61% and −46% for the hourly and daily precipitation analysis, respectively. It was also found that IMERG tends to improve precipitation detection and to decrease magnitude errors over the higher terrain elevations of Mexico.

This paper evaluates the sensitivity to cumulus and microphysics schemes, as represented in numerical simulations of the Weather Research and Forecasting model, in characterizing a deep convection event over the Cuban island on 1 May 2012. To this end, 30 experiments combining five cumulus and six microphysics schemes, in addition to two experiments in which the cumulus parameterization was turned off, are tested in order to choose the combination that represents the event precipitation more accurately. ERA Interim is used as lateral boundary condition data for the downscaling procedure. Results show that convective schemes are more important than microphysics schemes for determining the precipitation areas within a high-resolution domain simulation. Also, while one cumulus scheme captures the overall spatial convective structure of the event more accurately than others, it fails to capture the precipitation intensity. This apparent discrepancy leads to sensitivity related to the verification method used to rank the scheme combinations. This sensitivity is also observed in a comparison between parameterized and explicit cumulus formation when the Kain-Fritsch scheme was used. A loss of added value is also found when the Grell-Freitas cumulus scheme was activated at 1 km grid spacing.

This paper uses rawinsondes and pilot balloon data from the 2004 North American Monsoon (NAM) Experiment, as well as satellite‐based products and reanalysis datasets that span 1982–2018, to analyze the mixing mechanisms responsible for the temporal and spatial variations of the Gulf of California (GoC) boundary layer during the NAM onset in the core monsoon region. We show that the regional diurnal cycle is strongly affected by low‐level convergence and divergence associated with local land‐sea breezes and by the presence of a thermal inversion over the gulf. Earlier starting monsoons have less moisture available for precipitation than those starting later in the calendar year. Therefore, early onset monsoons have less rainfall during their first month, which is a result that is in contrast with previous studies that have analyzed the timing of the NAM but only reported seasonal precipitation totals. The GoC boundary layer height at the time of monsoon onset, found to be controlled by the gulf’s surface temperature, has a significant impact on the precipitation over Sonora, Sinaloa, and southern Arizona. After the erosion of the thermal inversion over the GoC that coincides with the NAM onset, wind shear produced by the region’s unique geographic and topographic features is the largest source of turbulence for the mixing of the boundary layer. Our results suggest that a numerical model used to forecast or analyze NAM precipitation must have enough spatial resolution to adequately reproduce the effects that the GoC’s features have on its complex diurnal circulation systems.

To study the air quality in the Guadalajara Metropolitan Area (GMA), concentrations of suspended particles (PM 10) and ozone (O 3) reported by eight monitoring stations were analyzed. Also, six commonly found types of synoptic situations (TSS) during 1996-2016 were identified using an atmospheric pattern correlation method on the mean sea level pressure and geopotential heights (850 hPa, 500 hPa, and 200 hPa) of fields given by the North American Regional Reanalysis (NARR) database. Overall, 75% of the period of study was classified as one of the six TSS. Afterward, statistical significance tests (confidence level 95%) were applied to determine whether the TSS affected PM 10 and O 3 concentrations locally in the GMA. PM 10 maximum hourly concentrations (~76.7 µg/m 3) occurred around 8 am local time, while that of ozone (~0.054 ppm) occurred between 1-4 pm local time. Meanwhile, PM 10 monthly levels were higher between December and May, and the highest O 3 concentrations occurred between April and June. Average annual levels of PM 10 have decreased through the years, while the annual trend of mean O 3 concentrations seemed to respond to the 11-year solar cycle. It was also found that during "convective-allowing situations" (TSS VI) and "thermal low over California" (TSS I), PM 10 concentrations remained low in the GMA, and O 3 concentrations rose under the influence of a "low-pressure system over the United States (USA)" (TSS II). Further research is suggested to address the effect of the local circulation in the GMA linked to the TSS on O 3 and PM 10 concentrations.

Convective ensembles promise to increase forecast accuracy while at the same time providing information on the probability of the forecast. A vast number of different methods of ensemble creation have been developed over time. Here, initial conditions and model error uncertainties are represented by a convective-allowing ensemble with more than 50 members. The results are analyzed using one case study with relatively high precipitation over Ensenada, Baja California, Mexico. The ensemble members are perturbed using random initial perturbations, breeding, and the Stochastic Kinetic Energy Backscatter parameterization (SKEBS) within the Weather Research and Forecasting (WRF) model. The aim is to improve the high-resolution ensemble design provided in a previous study for the same region by maximizing the spread of an ensemble with low member count. To this end, a comparative analysis of the members is performed using perturbation growth rates and information entropy. In addition, a comparative verification is performed using observations from one automatic meteorological station and satellite-derived precipitation data. It was found that the growth rates and the one-dimensional power spectral density of the initial perturbation fields are clustered depending on each member’s origin and the methods used to generate the breeding members. An inverse relationship was observed between these two variables, which can be useful for selecting appropriate initial condition perturbations. The dynamical injections of energy, introduced as perturbations to the numerical fields by the SKEBS method, were essential to maintain positive growth rates during the simulation period. Evaluation of the information entropy suggests that a selection of a set of members generated by the SKEBS method is best for increasing the ensemble spread while saving computer resources.