Effect of Some Meteorological Phenomena on the Wind Potential of Baja California

Zamora, Marlene; Arista, Alejandro Adolfo Lambert; Montero, Gisela

doi:10.1016/j.egypro.2014.10.086

Cited by 7 publications

(11 citation statements)

References 6 publications

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“…Furthermore, for months with an average V 2 of about 8-9 m/s the exergetic efficiency is smaller, as in April and May. This corresponds to the normal behavior of the wind speed (V 1 ) in the region, being lower in the summer and higher in spring and autumn [22,23].…”

Section: Resultsmentioning

confidence: 99%

Exergetic Analysis of La Rumorosa-I Wind Farm

Lopez¹,

Lambert²,

Jaramillo³

et al. 2017

J Fundam Renewable Energy Appl

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Section: Resultsmentioning

confidence: 99%

Exergetic Analysis of La Rumorosa-I Wind Farm

Lopez¹,

Lambert²,

Jaramillo³

et al. 2017

J Fundam Renewable Energy Appl

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“…For Zamora et al [18], the Santa Ana winds are associated with a change in relative humidity with drops of less than 10%, the direction in the northeast quadrant corresponding to 0 • to 90 • , and speeds greater than or equal to 20 mph (8.9411 m/s).…”

Section: Santa Anamentioning

confidence: 98%

“…A criterion for evaluating a Santa Ana event occurrence associated with wind speed was established at ≥4.5 m/s; the value at which the blaze remains from a fire could accelerate its spread in the air [34]. Consequently, days considered with Santa Ana winds are linked to fires; for the wind direction, the criterion was established in winds from the first quadrant [4,5,18]. The days that meet the two previous conditions are considered days on which a Santa Ana event occurred, the days that do not meet these criteria are considered days on which a Santa Ana wind did not occur.…”

Section: Santa Anamentioning

confidence: 99%

“…Relative humidity on those days did not fall to the above limit, so the criteria were reduced only to the speed and direction of the wind. Other research has also ruled out this parameter [1,18]. From the time series resulting from the criteria established above, the Hurst exponents and fractal dimensions were calculated for days with Santa Ana winds and days without Santa Ana winds (Table 3).…”

Section: Hurst Exponentmentioning

confidence: 99%

“…Similarly, Álvarez and Carbajal [1] studied the behavior of meteorological variables and sand storms in Northwestern Mexico during the Santa Ana winds occurrence, based on the simulation of an extreme event in October 2007. Other investigations carried out in this area correspond to a whirl ascension due to the winds [16], to the analysis of the evolution and extension of the Santa Ana winds for an event from February 2002 using the QuickScat satellite [17], the determination of the wind potential generated by these winds on the Baja California coast [18], and the evaluation of the winds' effect on the bio-optical properties [19].…”

Section: Introductionmentioning

confidence: 99%

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Santa Ana Winds: Fractal-Based Analysis in a Semi-Arid Zone of Northern Mexico

et al. 2021

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A fractal analysis based on the time series of precipitation, temperature, pressure, relative humidity, and wind speed was performed for 16 weather stations located in the hydrographic basin of the Guadalupe River in Baja California, Mexico. Days on which the phenomenon known as Santa Ana winds occurs were identified based on the corresponding criteria of wind speed (≥4.5 m/s) and wind direction (between 0° and 90°). Subsequently, the time series was formed with data representing the days on which this phenomenon occurs in each of the analyzed weather stations. A time series was additionally formed from the days in which the Santa Ana winds condition does not occur. Hurst exponents and fractal dimension were estimated applying the rescaled range method to characterize the established time series in terms of characteristics of persistence, anti-persistence, or randomness along with the calculation of the climate predictability Index. This enabled the behavior and correlation analysis of the meteorological variables associated with Santa Ana winds occurrence. Finally, this type of research study is instrumental in understanding the regional dynamics of the climate in the basin, and allows us to establish a basis for developing models that can forecast the days of occurrence of the Santa Ana winds, in such a way that actions or measures can be taken to mitigate the negative consequences generated when said phenomenon occurs, such as fires and droughts.

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Evaluation of orography and roughness model inputs and deep neural network regression for wind speed predictions

2022

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Many wind energy projects start with resource assessments based on outputs from numerical models followed by meteorological campaigns. These models are imperfect and have a substantial input parameter space. It is difficult to discern how the inputs affect the simulation results. Surface roughness is generally crudely represented in mesoscale models. In this work, we use the Weather Research and Forecast model to simulate winds at a meteorological mast in Mexico with wind observations at 80 m. The model sensitivity to changes in surface roughness is contrasted with permissively perturbed orography fields to gauge the observed changes' relative importance. Changes in surface roughness affect the root mean square error of the simulated 80‐m winds compared to the observations, in the same order of magnitude as orography perturbations. Using a roughness field derived from a synthetic aperture radar improved the wind speed bias. Wind speed predictions using high‐resolution (185 m) simulations sometimes showed excellent agreement with the observations, but there were several instances when medium‐resolution (1.6 km) simulations performed better. A wind speed time series with high temporal (10 min) resolution from a low spatial (6 km) resolution simulation was used to train a deep neural network regression (DNNR) model. The trained DNNR reduced the wind speed error the most compared to any of the other simulations performed in this study. The second‐best model performance was obtained using the radar roughness‐derived field. The results of the simulations at medium resolution with perturbed orography were very similar to those using different roughness fields inputs.

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Effect of Some Meteorological Phenomena on the Wind Potential of Baja California

Cited by 7 publications

References 6 publications

Exergetic Analysis of La Rumorosa-I Wind Farm

Exergetic Analysis of La Rumorosa-I Wind Farm

Santa Ana Winds: Fractal-Based Analysis in a Semi-Arid Zone of Northern Mexico

Evaluation of orography and roughness model inputs and deep neural network regression for wind speed predictions

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