Markus Gross

We present a review of wind energy development in Mexico, factors hampering this development, and proposals for solutions to address this hampering. This review is relevant in the context of climate change mitigation strategies and the achievement of the United Nations’ sustainable development goals. Wind energy can be harvested at competitive costs to solve society’s energy poverty and climate change problems. Firstly, we present the current wind energy installed capacity and wind power generation status globally and in Mexico and discuss why Mexico is lagging behind, particularly since 2020. Despite this lag, several state governors are still considering wind energy developments. The current economic context is then considered, with community wind energy as a solution forward for wind energy development, using a successful case study from the UK that has addressed energy poverty and provided an additional income source for an island community. Any community energy project using wind as its main energy resource relies on accurate wind energy assessment in its feasibility analysis. Thus, an evaluation of different wind energy atlases for Mexico was performed, which showed that models considering microscale processes could lead to a relative difference of more than 50% when compared to those that do not consider them. This led to the conclusion that microscale effects must be considered in wind energy characterization models. Furthermore, it is acknowledged that wind faces other challenges, such as the effect of future climate change scenarios, grid planning, and vulnerability and risk associated with tropical storms, which can be substantial in Mexico. Solutions are proposed in the form of possible wind power generation scenarios, planning and implementation of centralized and distributed transmission lines, and possible wind siting and technological choices to reduce the vulnerability and risk to tropical storms. Finally, we close with some future perspectives for researchers and decision-makers. The main conclusions are that sustainable growth can only be compatible with a transition to renewable sources of energy, energy community projects can address energy poverty and achieve sustainable development goals, wind energy feasibility studies need to include microscale effects, return of investment can be improved by siting the wind farms in regions of low vulnerability and risk to extreme events, and high-voltage transmission lines are crucial for sustainable development, even with the important role that distributed systems play. Finally, turbine growth and materials recycling, among other factors, must be considered when assessing the environmental impacts of wind farm decommissioning.

The effect of Arctic sea ice loss on the boreal winter regional trends of wind speed and latitudinal position of the Northern Hemisphere subtropical jet stream (STJ) in 1980–2012 is investigated. Two sets of global simulations with reduced Arctic sea ice extent are analyzed: simulations that, south of 70 N, use a climatological annual cycle of the sea surface temperature (SST) and a second set that uses full SST variability. Results with the climatological SST have a significant but weak response of the STJ wind speed and latitudinal position to the warmer Arctic: the wind speed generally decreases and the jet core is displaced equatorward. However, in the realistic SST simulations, the effect of Arctic warming is only slightly evident in a small equatorward shift of the jet over the Atlantic basin. Over the Pacific basin the STJ is mostly driven by tropical and mid-latitude SST variability, with little influence from the Arctic region. A weakening and poleward shift of the STJ that is observed in the realistic SST simulations over the Pacific basin is attributed to negative SST trends in the tropical Pacific and the consequent weakening of the mid-latitude meridional gradient of geopotential height in the upper troposphere.

Because of the need to diversify the renewable energy matrix and because hydrokinetic tidal energy technologies are mature, many in-stream tidal energy resource studies are available globally. Still, there are many questions regarding the effect of seabed changes on tidal energy resources. For coastal regions in particular, where the seabed is generally more mobile than in deep waters, bathymetric evolution could significantly affect tidal energy production. Here, two models are used to analyse the potential effect of natural morphodynamic change on tidal energy resources at two macro-tidal sandy bays, Adaír Bay and San Jorge Bay, in the Upper Gulf of California, Mexico. One of the models is (purely) hydrodynamic, and the other is a morphodynamic model (with hydrodynamic–morphodynamic coupling). The models are validated against tidal current observations obtained with acoustic Doppler current profilers in the region of interest, using three different error statistics, which showed good agreement between models and observations. The results also showed that the most significant bed changes and the largest renewable energy resources are located near the shore. Moreover, there was a good correlation between (a) regions with the most significant depth changes and (b) the areas where the difference in annual energy production with and without depth change was largest. Finally, a two-year simulation with the morphodynamic model permitted to analyse the seabed evolution of a zonal profile off Punta Choya, the headland between the two bays. This profile evolved towards a featureless equilibrium, as expected from the morphological classification for macro-tidal sandy environments under a dominant tidal forcing. However, most importantly, this natural evolution would not be detrimental to tidal energy exploitation at the site.

While many in-steam tidal energy resource studies have been carried out globally, very few studies have assessed the effect of seabed changes on tidal energy resources. For coastal regions in particular, where the seabed is generally more mobile than in deep waters, bathymetric evolution could have a significant effect on tidal energy production. Here two high-resolution models, one purely hydrodynamic and one morphodynamic, are used to analyse the potential effect of natural morphodynamic evolution on tidal energy resources at two macro-tidal sandy bays, Adaír Bay and San Jorge Bay, in the Upper Gulf of California, Mexico. The high-resolution models are validated using a low-resolution model and ADCP observations to assess the agreement between model predictions and observations of tides at three ADCP moorings within the domain of interest. The models’ skill is evaluated using several error statistics such as the mean relative error, the root mean square error (RMSE), and the correlation coefficient. It was found that the regions with the largest bed changes, and also the largest renewable energy resources, were near the shore. Moreover, the results indicated a good correlation between a) regions with the most significant depth changes, and b) the regions where the difference in annual energy production with and without depth change was largest. Finally, the morphodynamic model was run for two years, and the evolution of a zonal profile (in the west-east direction) off the coast at the southeastern corner of Adaír Bay was inspected. This profile evolved towards a featureless equilibrium profile, in good agreement with the morphological classification for macro-tidal sandy environments and with the model assumptions. But most importantly, this natural evolution would not be detrimental to tidal energy exploitation at the site.

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.

The Wind Power Density (WPD) is widely used for wind resource characterization. However, there is a significant level of uncertainty associated with its estimation. Here, we analyze the effect of sampling frequencies, averaging periods, and the length of time series on the WPD estimation. We perform this analysis using four approaches. First, we analytically evaluate the impact of assuming that the WPD can simply be computed from the cube of the mean wind speed. Second, the wind speed time series from two meteorological stations are used to assess the effect of sampling and averaging on the WPD. Third, we use numerical weather prediction model outputs and observational data to demonstrate that the error in the WPD estimate is also dependent on the length of the time series. Finally, artificial time series are generated to control the characteristics of the wind speed distribution, and we analyze the sensitivity of the WPD to variations of these characteristics. The WPD estimation error is expressed mathematically using a numerical-data-driven model. This numerical-data-driven model can then be used to predict the WPD estimation errors at other sites. We demonstrate that substantial errors can be introduced by choosing too short time series. Furthermore, averaging leads to an underestimation of the WPD. The error introduced by sampling is strongly site-dependent.

Operational weather forecasts are routinely performed at convection-allowing resolutions, and thus these forecasts generate weather features that appear to be realistic. However, at times the comparison of the forecast to observations is less favorable, particularly at grid scales. This lack of skill is partly due to the chaotic system underlying the weather. Another problem is that it is impossible to evaluate the risk of making decisions based on deterministic forecasts. However, running global high-resolution ensembles involves substantial computational resources. A 555-m resolution WRF ensemble based on stochastic perturbations of a deterministic forecast of the North American Mesoscale model was created. Observations are used to constrain the ensemble and improve the skill. This method increases the skill for forecasting 60-h accumulated precipitation in five standard, statistical metrics: bias, false alarm ratio, threat score, probability of detection, and success ratio. Furthermore, the ensemble continuous ranked probability score (CRPS) will be compared to a poor man’s ensemble. The forecast error is generally smaller in more than 70% of the case studies performed when compared to nine deterministic model forecasts. The ensemble-enhanced mesoscale system presented can help to determine the most likely scenario without the significant computational requirement of global ensembles and is expected to be useful when global high-resolution ensembles are not available.