Drivers of Extreme Wind Events in Mexico for Wind Power Applications

Abstract

<p>In this talk, we investigate the causes of the strongest and weakest winds observed across Mexico and explore the consequences of these to current and future wind energy production in the country. Using 40 years of the ERA-5 atmospheric reanalysis data, we find that the strongest winds in this region are caused by cold surges, where an anticyclone moves South from the Central United States of America resulting in strong Northerly winds across the Gulf of Mexico which channel through the gap in the mountains to the South of Mexico. Other regions have different drivers for high and low wind speed events. The strongest winds across the East coast of Mexico originate from Easterly trade winds propagating across the Gulf of Mexico, whereas those in Baja California Sur are influenced by the proximity of the North Pacific High. These regions in Mexico have peak (and sustained low) wind speeds at different times of year which suggests that wind farms in different regions could compliment one another to optimise wind power generation. However, all stations but Baja California Sur see the same weather patterns associated with weak wind events, meaning that low wind power production may be unavoidable at these times. The conditions that proceed these sustained periods of strong and weak winds are explored to gain some predictability for wind power applications. The El Nino Southern Oscillation is found to influence wind speeds at some locations across Mexico at sub-seasonal time-scales.</p>

How well do atmospheric reanalyses reproduce observed winds in coastal regions of Mexico?

“…We now compare the daily mean interpolated atmospheric reanalyses and observations for the whole of 2006. Figure 2 shows a tendency for the reanalyses to underestimate the wind speeds at most stations, particularly in the case of ERA5 (red), an observation also noted in Thomas et al (2020). This is particularly clear for Chiapas, Oaxaca and Veracruz, as shown by the histograms on the right-hand side.…”

“…In this study, we utilize a set of anemometric observations commissioned by the UNDP-GEF unit and implemented by Mexico's Instituto de Investigaciones Eléctricas (now Instituto Nacional de Electricidad y Energías Limpias), which took place between 2005 and 2007 UNDP (2012). The selected eight weather stations were those in continuous operation at a high temporal resolution for the whole of 2006, allowing for direct comparisons for the same time period and hence meteorological conditions (see Morales-Ruvalcaba et al, 2020;Thomas et al, 2020). They represent a reasonable spread of geographic locations across the country, as shown in Figure 1.…”

“…ERA-Interim shows the biggest improvement from the 6-hourly resolution to the daily, which is most obvious for the Sinaloa (SI01) station where R changes from approximately 0.4-0.8. Indeed, for the 6-hourly resolution, ERA-Interim has R < 0.6 for all but Chiapas and Oaxaca, situated in the Isthmus of Tehuantepec, which is known for experiencing sustained wind patterns (Thomas et al, 2020) due to the Tehuanos winds that blow from the north through the gap in the mountains (Hurd, 1929;Pr osper et al, 2019;Steenburgh et al, 1998). This is likely due to the fact that ERA-Interim data have a 6-h resolution and so there is only a one-time instance, rather than a mean of several, being compared with the T A B L E 3 Pearson correlation coefficients, R, between the observations and of ERA-Interim (left), ERA5 (centre), and MERRA-2 (right) for data in the time resolutions given in the subscripts of the column headers mean of the observations over that period.…”

“…Recently, reanalyses have begun to be used for studies of wind power in Mexico. Thomas et al (2020) investigated the key wind drivers that drive wind power generation in Mexico using ERA5, and Morales-Ruvalcaba et al ( 2020) used MERRA-2 to deduce capacity factors at different sites across the country. As atmospheric reanalyses are not designed for use on small scales, such studies for individual wind farm locations are very sensitive to how well each reanalysis is able to reproduce observations at these sites.…”

Weather patterns in Southeast Asia: Relationship with tropical variability and heavy precipitation

“…Cluster analysis was then performed on the reduced phase space defined by the leading principal components. This methodology is conceptually similar to that followed by other previous studies (e.g., Cassou, 2008; Sáenz and Durán‐Quesada, 2015; Neal et al ., 2016; 2020; Thomas et al ., 2020). This methodology has been applied ten times to construct three sets of patterns:First to the full 38‐year dataset across the planetary‐scale domain to obtain the eight tier‐1 weather patterns; Then separately to time subsets classified into each of the eight tier‐1 weather patterns restricted to the regional‐scale domain in order to obtain the 51 tier‐2 weather patterns; and Finally to the full dataset across the regional‐scale domain to obtain a corresponding set of 51 flat weather patterns. …”