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

Atmosphere ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 48
Author(s):  
Yeraldin Serpa-Usta ◽  
Alvaro Alberto López-Lambraño ◽  
Dora-Luz Flores ◽  
Ena Gámez-Balmaceda ◽  
Luisa Martínez-Acosta ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Arid Zone ◽  
Negative Consequences ◽  
Santa Ana ◽  
Regional Dynamics ◽  
Rescaled Range ◽  
Santa Ana Winds ◽  
Guadalupe River ◽  
Weather Stations

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.

scholarly journals Santa Ana winds and predictors of wildfire progression in southern California

2014 ◽  
Vol 23 (8) ◽  
pp. 1119 ◽  
Author(s):  
Michael Billmire ◽  
Nancy H. F. French ◽  
Tatiana Loboda ◽  
R. Chris Owen ◽  
Marlene Tyner
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Southern California ◽  
Burned Area ◽  
Strong Positive Correlation ◽  
Fuel Temperature ◽  
Fuel Loading ◽  
Fire Event ◽  
Santa Ana ◽  
Santa Ana Winds

Santa Ana winds have been implicated as a major driver of large wildfires in southern California. While numerous anecdotal reports exist, there is little quantitative analysis in peer-reviewed literature on how this weather phenomenon influences fire progression rates. We analysed fire progression within 158 fire events in southern California as a function of meteorologically defined Santa Ana conditions between 2001 and 2009. Our results show quantitatively that burned area per day is 3.5–4.5 times larger on Santa Ana days than on non-Santa Ana days. Santa Ana definition parameters (relative humidity, wind speed) along with other predictor variables (air temperature, fuel temperature, 10-h fuel moisture, population density, slope, fuel loading, previous-day burn perimeter) were tested individually and in combination for correlation with subsets of daily burned area. Relative humidity had the most consistently strong correlation with burned area per day. Gust and peak wind speed had a strong positive correlation with burned area per day particularly within subsets of burned area representing only the first day of a fire, >500 ha burned areas, and on Santa Ana days. The suite of variables comprising the best-fit generalised linear model for predicting burned area (R2 = 0.41) included relative humidity, peak wind speed, previous-day burn perimeter and two binary indicators for first and last day of a fire event.

scholarly journals Santa Ana Winds: A Descriptive Climatology

2019 ◽  
Vol 34 (2) ◽  
pp. 257-275 ◽  
Author(s):  
Tom Rolinski ◽  
Scott B. Capps ◽  
Wei Zhuang
Keyword(s):  
Time Series ◽  
Los Angeles ◽  
San Diego ◽  
Extended Period ◽  
Time Of Day ◽  
Synoptic Scale ◽  
Santa Ana ◽  
Santa Ana Winds ◽  
The Impact

Abstract The criteria used to define Santa Ana winds (SAWs) are dependent upon both the impact of interest (e.g., catastrophic wildfires) and the location and/or time of day examined. We employ a comprehensive definition and methodology for constructing a climatological SAW time series from 1981 through 2016 for two Southern California regions, Los Angeles and San Diego. For both regions, we examine SAW climatology, distinguish SAW-associated synoptic-scale atmospheric patterns, and detect long-term, significant SAW trends. San Diego has 30% fewer SAW days compared to Los Angeles with 80% of SAW events starting in Los Angeles first. Further, 45% of San Diego SAW events are single-day events compared to 35% for Los Angeles. The longest duration event spanned 16 days for Los Angeles (27 November–12 December 1988) and 8 days for San Diego (9–16 January 2009). Although SAW-driven fires can be large and devastating, these types of fires occurred on only 6% and 5% of SAW days for the Los Angeles and San Diego regions, respectively. Finally, we find and investigate an extended period of elevated SAW day count occurring after 2005. This new climatology will allow us to produce month- and season-ahead forecasts of SAW days, which is useful for planning end-of-year staffing coverage by the local, state, and federal fire agencies.

scholarly journals Estimation of seasonal changes in the frequency characteristics of wind speed and direction in coastal Dagestan, Russia

2021 ◽  
Vol 15 (4) ◽  
pp. 152-160
Author(s):  
D. N. Kobzarenko ◽  
A. M. Kamilova ◽  
B. D. Pashtaev
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Power ◽  
Seasonal Changes ◽  
Wavelet Function ◽  
Frequency Characteristics ◽  
Point Of View ◽  
Mathematical Research ◽  
Weather Stations ◽  
Complex Morlet Wavelet

Aim. To analyse seasonal changes in the frequency characteristics of wind speed and direction in coastal Dagestan, namely the urban districts of the cities of Makhachkala and Derbent, from the point of view of wind power potential.Material and Methods. The research was based on time series of wind speed and direction for the period 2011-2018, obtained as a result of observations at the Makhachkala and Derbent weather stations. As a mathematical research tool, a continuous wavelet transform with a complex Morlet wavelet function was used.Results. According to the results of analysis, it was found that the main frequency of fluctuations in the time series is one day and one-day periodicity in the time series has pronounced seasonal changes. Also, differences in seasonal changes of one-day periodicity for wind speed and direction between the regions of Makhachkala and Derbent were established and described.Conclusion. The parameters considered in assessing seasonal changes in the dynamics of wind speed and direction can be used as additional parameters for the classification and clustering of regions to identify the best areas of wind power potential. 

Prediction of Atmospheric Turbulence Characteristics for Surface Boundary Layer using Empirical Spectral Methods

2020 ◽  
Author(s):  
Yagya Dutta Dwivedi ◽  
Vasishta Bhargava Nukala ◽  
Satya Prasad Maddula ◽  
Kiran Nair
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Surface Roughness ◽  
Wind Speed ◽  
Atmospheric Turbulence ◽  
Surface Boundary ◽  
Low Frequencies ◽  
Surface Boundary Layer ◽  
Power Spectral ◽  
Mean Wind Speed

Abstract Atmospheric turbulence is an unsteady phenomenon found in nature and plays significance role in predicting natural events and life prediction of structures. In this work, turbulence in surface boundary layer has been studied through empirical methods. Computer simulation of Von Karman, Kaimal methods were evaluated for different surface roughness and for low (1%), medium (10%) and high (50%) turbulence intensities. Instantaneous values of one minute time series for longitudinal turbulent wind at mean wind speed of 12 m/s using both spectra showed strong correlation in validation trends. Influence of integral length scales on turbulence kinetic energy production at different heights is illustrated. Time series for mean wind speed of 12 m/s with surface roughness value of 0.05 m have shown that variance for longitudinal, lateral and vertical velocity components were different and found to be anisotropic. Wind speed power spectral density from Davenport and Simiu profiles have also been calculated at surface roughness of 0.05 m and compared with k−1 and k−3 slopes for Kolmogorov k−5/3 law in inertial sub-range and k−7 in viscous dissipation range. At high frequencies, logarithmic slope of Kolmogorov −5/3rd law agreed well with Davenport, Harris, Simiu and Solari spectra than at low frequencies.

scholarly journals The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters

2018 ◽  
Vol 7 (2) ◽  
pp. 139-150 ◽  
Author(s):  
Adekunlé Akim Salami ◽  
Ayité Sénah Akoda Ajavon ◽  
Mawugno Koffi Kodjo ◽  
Seydou Ouedraogo ◽  
Koffi-Sa Bédja
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Series Data ◽  
Empirical Method ◽  
Likelihood Method ◽  
Series Data ◽  
New Approach ◽  
Weibull Parameters ◽  
Distribution Histogram ◽  
Mean Wind Speed

In this article, we introduced a new approach based on graphical method (GPM), maximum likelihood method (MLM), energy pattern factor method (EPFM), empirical method of Justus (EMJ), empirical method of Lysen (EML) and moment method (MOM) using the even or odd classes of wind speed series distribution histogram with 1 m/s as bin size to estimate the Weibull parameters. This new approach is compared on the basis of the resulting mean wind speed and its standard deviation using seven reliable statistical indicators (RPE, RMSE, MAPE, MABE, R2, RRMSE and IA). The results indicate that this new approach is adequate to estimate Weibull parameters and can outperform GPM, MLM, EPF, EMJ, EML and MOM which uses all wind speed time series data collected for one period. The study has also found a linear relationship between the Weibull parameters K and C estimated by MLM, EPFM, EMJ, EML and MOM using odd or even class wind speed time series and those obtained by applying these methods to all class (both even and odd bins) wind speed time series. Another interesting feature of this approach is the data size reduction which eventually leads to a reduced processing time.Article History: Received February 16th 2018; Received in revised form May 5th 2018; Accepted May 27th 2018; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. , Ouedraogo, S. and Bédja, K. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150.https://doi.org/10.14710/ijred.7.2.139-150

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

scholarly journals Hot and cold flavors of southern California’s Santa Ana winds: their causes, trends, and links with wildfire

2021 ◽  
Author(s):  
Alexander Gershunov ◽  
Janin Guzman Morales ◽  
Benjamin Hatchett ◽  
Kristen Guirguis ◽  
Rosana Aguilera ◽  
...  
Keyword(s):  
Great Basin ◽  
Heat Waves ◽  
Early Warning Systems ◽  
High Amplitude ◽  
Warming Trend ◽  
Snow Accumulation ◽  
Burned Area ◽  
Rossby Wave Breaking ◽  
Santa Ana ◽  
Santa Ana Winds

AbstractSanta Ana winds (SAWs) are associated with anomalous temperatures in coastal Southern California (SoCal). As dry air flows over SoCal’s coastal ranges on its way from the elevated Great Basin down to sea level, all SAWs warm adiabatically. Many but not all SAWs produce coastal heat events. The strongest regionally averaged SAWs tend to be cold. In fact, some of the hottest and coldest observed temperatures in coastal SoCal are linked to SAWs. We show that hot and cold SAWs are produced by distinct synoptic dynamics. High-amplitude anticyclonic flow around a blocking high pressure aloft anchored at the California coast produces hot SAWs. Cold SAWs result from anticyclonic Rossby wave breaking over the northwestern U.S. Hot SAWs are preceded by warming in the Great Basin and dry conditions across the Southwestern U.S. Precipitation over the Southwest, including SoCal, and snow accumulation in the Great Basin usually precede cold SAWs. Both SAW flavors, but especially the hot SAWs, yield low relative humidity at the coast. Although cold SAWs tend to be associated with the strongest winds, hot SAWs tend to last longer and preferentially favor wildfire growth. Historically, out of large (> 100 acres) SAW-spread wildfires, 90% were associated with hot SAWs, accounting for 95% of burned area. As health impacts of SAW-driven coastal fall, winter and spring heat waves and impacts of smoke from wildfires have been recently identified, our results have implications for designing early warning systems. The long-term warming trend in coastal temperatures associated with SAWs is focused on January–March, when hot and cold SAW frequency and temperature intensity have been increasing and decreasing, respectively, over our 71-year record.

scholarly journals Enhancing Wind Turbine Power Forecast via Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 261
Author(s):  
Tianyang Liu ◽  
Zunkai Huang ◽  
Li Tian ◽  
Yongxin Zhu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Rapid Development ◽  
Superior Performance ◽  
Learning Platform ◽  
Novel Approach ◽  
Wind Power Forecast ◽  
Transformation Methods

The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.

scholarly journals Dust Deposition on the Gulf of California Caused by Santa Ana Winds

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 275 ◽  
Author(s):  
Christian A. Álvarez ◽  
José N. Carbajal ◽  
Luis F. Pineda-Martínez ◽  
José Tuxpan ◽  
David E. Flores
Keyword(s):  
Gulf Of California ◽  
Dust Emission ◽  
Positive Influence ◽  
Dust Particles ◽  
Weather Research And Forecasting ◽  
Western Coast ◽  
Emission Rates ◽  
Dust Deposition ◽  
Santa Ana ◽  
Santa Ana Winds

Numerical simulations revealed a profound interaction between the severe dust storm of 2007 caused by Santa Ana winds and the Gulf of California. The weather research and forecasting model coupled with a chemistry module (WRF-CHEM) and the hybrid single-particle Lagrangian integrated trajectory model (HYSPLIT) allowed for the estimation of the meteorological and dynamic aspects of the event and the dust deposition on the surface waters of the Gulf of California caused by the erosion and entrainment of dust particles from the surrounding desert regions. The dust emission rates from three chosen areas (Altar desert, Sonora coast, and a region between these two zones) and their contribution to dust deposition over the Gulf of California were analyzed. The Altar Desert had the highest dust emission rates and the highest contribution to dust deposition over the Gulf of California, i.e., it has the most critical influence with 96,879 tons of emission and 43,539 tons of dust deposition in the gulf. An increase of chlorophyll-a concentrations is observed coinciding with areas of high dust deposition in the northern and western coast of the gulf. This kind of event could have a significant positive influence over the mineralization and productivity processes in the Gulf of California, despite the soil loss in the eroded regions.

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