Strong activity of the Orinoco Low-Level Jet and its association with moisture transport in northern South America

2021 ◽  
Author(s):  
Alejandro Builes-Jaramillo ◽  
Johanna Yepes ◽  
Salas Hernán D.
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Wind Speed ◽  
South America ◽  
Water Transport ◽  
Composite Analysis ◽  
Strong Activity ◽  
Easterly Wind ◽  
Low Level Jet ◽  
Northern South America

<p>We classified events of extreme Orinoco Low-Level Jet (OLLJ) activity using the ERA5 time series of daily winds at 925 hPa averaged over the 6°S–8°N/67°W–69°W area for the period 1981-2019. This area exhibits an overall mean of 3.7 m/s easterly wind speed and an overall standard deviation of 3.5 m/s. Then, during December-January-February (June-July-August), the season of strong (weak) OLLJ activity, we defined the events below (above) one standard deviation from the overall mean. Hence, days with easterly wind speeds higher than 7.2 m/s are considered events with strong activity during DJF. In contrast, days with westerly wind speed higher than 0.2 m/s are the events with weak activity during JJA. A composite analysis of precipitation from CHIRPS dataset during the days classified as strong or weak OLLJ activity showed that during the most active period (DJF), daily precipitation values are close to 0 mm/day; except for increased precipitation in the border between Colombia, Ecuador, Peru, and Brazil. In contrast, precipitation composites during the period of non-activity of the OLLJ (JJA), showed that precipitation increases in the range 5–10 mm/day along the OLLJ corridor. A detailed analysis of the precipitation time series used for composite analysis indicates that the probability of precipitation during DJF (JJA) is less (more) than 20% (80%) over Venezuela and the Guianas. In terms of advective water transport (qV) during the most active events of the OLLJ water is transported from the Tropical Atlantic towards northern South America through the OLLJ corridor, whilst during the less active events water transport along the OLLJ corridor comes from the north Amazon basin towards northern South America. In conclusion, during DJF the OLLJ is associated with the northerly cross-equatorial flow and dry season, whereas during JJA the southerly cross-equatorial flow from the Amazon river basin predominates, which contributes to the rainy season over the Orinoco region.</p>

The Orinoco Low-Level Jet and its association with the hydroclimatology of northern South America

2021 ◽  
Keyword(s):  
Wind Speed ◽  
South America ◽  
Moisture Flux ◽  
Spatial And Temporal Variability ◽  
Frequency Of Occurrence ◽  
Flux Divergence ◽  
Vapor Flux ◽  
Low Level ◽  
Low Level Jet ◽  
Northern South America

Abstract We investigated the relationship between the frequency of occurrence of the Orinoco Low-Level Jet (OLLJ) and hydroclimatic variables over northern South America. We use data from the ERA5 atmospheric reanalysis to characterize the spatial and temporal variability of the OLLJ in light of the LLJ-classification criteria available in the literature. An index for the frequency of occurrence of an LLJ was used, based on the hourly maxima of wind speed. The linkages among the OLLJ, water vapor flux, and precipitation were analyzed using a composite analysis. Our results show that during December–January–February (DJF), the OLLJ exhibits its maximum wind speed, with values around 8–10 m/s. During DJF, the analysis shows how the OLLJ transports atmospheric moisture from the Tropical North Atlantic Ocean. During this season, the predominant pathway of the OLLJ is associated with an area of moisture flux divergence located over northeastern South America. During JJA, an area of moisture flux convergence associated with the northernmost location of the ITCZ inhibits the entrance of moisture from northerlies. We also show that the occurrence of the OLLJ is associated with the so-called cross-equatorial flow. During DJF, the period of strongest activity of the OLLJ is associated with the northerly cross-equatorial flow and dry season, whereas during JJA the southerly cross-equatorial flow from the Amazon river basin predominates and contributes to the rainy season over the Orinoco region.

Weibull Parameter Estimation — A Comparison of Different Methods

2005 ◽  
Vol 29 (3) ◽  
pp. 309-315 ◽  
Author(s):  
H Basumatary ◽  
E Sreevalsan ◽  
K K Sasi
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Standard Deviation ◽  
Wind Speed ◽  
Probability Function ◽  
Time Series Data ◽  
Series Data ◽  
Weibull Parameter ◽  
Wind Regime ◽  
Wind Regimes

The Weibull probability function is a widely accepted tool to model wind regimes. This paper presents a comparative study of different methods used to estimate Weibull parameters of a wind regime. Five different methods are described and used for the estimation. Time series data of wind speed over a whole year for two sites have been used for the study. The results obtained as a plot of error versus wind speed are similar in all the five methods, yet the method of standard deviation gives the best results.

scholarly journals Precipitation over Northern South America and Its Seasonal Variability as Simulated by the CMIP5 Models

2015 ◽  
Vol 2015 ◽  
pp. 1-22 ◽  
Author(s):  
Juan P. Sierra ◽  
Paola A. Arias ◽  
Sara C. Vieira
Keyword(s):  
South America ◽  
Cmip5 Models ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Precipitation Pattern ◽  
Socioeconomic Impacts ◽  
Low Level ◽  
Low Level Jets ◽  
Low Level Jet ◽  
Northern South America

Northern South America is identified as one of the most vulnerable regions to be affected by climate change. Furthermore, recent extreme wet seasons over the region have induced socioeconomic impacts of wide proportions. Hence, the evaluation of rainfall simulations at seasonal and interannual time scales by the CMIP5 models is urgently required. Here, we evaluated the ability of seven CMIP5 models (selected based on literature review) to represent the seasonal mean precipitation and its interannual variability over northern South America. Our results suggest that it is easier for models to reproduce rainfall distribution during boreal summer and fall over both oceans and land. This is probably due to the fact that during these seasons, incoming radiation and ocean-atmosphere feedbacks over Atlantic and Pacific oceans locate the ITCZ on the Northern Hemisphere, as suggested by previous studies. Models exhibit the worse simulations during boreal winter and spring, when these processes have opposite effects locating the ITCZ. Our results suggest that the models with a better representation of the oceanic ITCZ and the local low-level jets over northern South America, such as the Choco low-level jet, are able to realistically simulate the main features of seasonal precipitation pattern over northern South America.

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 Air quality variations in Northern South America during the COVID-19 lockdown

2020 ◽  
Vol 749 ◽  
pp. 141621 ◽  
Author(s):  
Juan F. Mendez-Espinosa ◽  
Nestor Y. Rojas ◽  
Jorge Vargas ◽  
Jorge E. Pachón ◽  
Luis C. Belalcazar ◽  
...  
Keyword(s):  
Air Quality ◽  
South America ◽  
Northern South America

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

Ground-motion model for subduction earthquakes in northern South America

2021 ◽  
pp. 875529302110275
Author(s):  
Carlos A Arteta ◽  
Cesar A Pajaro ◽  
Vicente Mercado ◽  
Julián Montejo ◽  
Mónica Arcila ◽  
...  
Keyword(s):  
South America ◽  
Ground Motion ◽  
Ground Motions ◽  
Strong Motion ◽  
Motion Prediction ◽  
Depth Range ◽  
Ground Motion Prediction ◽  
Natural Period ◽  
Nazca Plate ◽  
Northern South America

Subduction ground motions in northern South America are about a factor of 2 smaller than the ground motions for similar events in other regions. Nevertheless, historical and recent large-interface and intermediate-depth slab earthquakes of moment magnitudes Mw = 7.8 (Ecuador, 2016) and 7.2 (Colombia, 2012) evidenced the vast potential damage that vulnerable populations close to earthquake epicenters could experience. This article proposes a new empirical ground-motion prediction model for subduction events in northern South America, a regionalization of the global AG2020 ground-motion prediction equations. An updated ground-motion database curated by the Colombian Geological Survey is employed. It comprises recordings from earthquakes associated with the subduction of the Nazca plate gathered by the National Strong Motion Network in Colombia and by the Institute of Geophysics at Escuela Politécnica Nacional in Ecuador. The regional terms of our model are estimated with 539 records from 60 subduction events in Colombia and Ecuador with epicenters in the range of −0.6° to 7.6°N and 75.5° to 79.6°W, with Mw≥4.5, hypocentral depth range of 4 ≤  Zhypo ≤ 210 km, for distances up to 350 km. The model includes forearc and backarc terms to account for larger attenuation at backarc sites for slab events and site categorization based on natural period. The proposed model corrects the median AG2020 global model to better account for the larger attenuation of local ground motions and includes a partially non-ergodic variance model.

scholarly journals The Systematic Bias of Entropy Calculation in the Multi-Scale Entropy Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 659
Author(s):  
Jue Lu ◽  
Ze Wang
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Coarse Graining ◽  
Sample Entropy ◽  
Systematic Bias ◽  
Original Time Series ◽  
Multi Scale ◽  
Calculation Results ◽  
Surrogate Measure ◽  
Original Time

Entropy indicates irregularity or randomness of a dynamic system. Over the decades, entropy calculated at different scales of the system through subsampling or coarse graining has been used as a surrogate measure of system complexity. One popular multi-scale entropy analysis is the multi-scale sample entropy (MSE), which calculates entropy through the sample entropy (SampEn) formula at each time scale. SampEn is defined by the “logarithmic likelihood” that a small section (within a window of a length m) of the data “matches” with other sections will still “match” the others if the section window length increases by one. “Match” is defined by a threshold of r times standard deviation of the entire time series. A problem of current MSE algorithm is that SampEn calculations at different scales are based on the same matching threshold defined by the original time series but data standard deviation actually changes with the subsampling scales. Using a fixed threshold will automatically introduce systematic bias to the calculation results. The purpose of this paper is to mathematically present this systematic bias and to provide methods for correcting it. Our work will help the large MSE user community avoiding introducing the bias to their multi-scale SampEn calculation results.

scholarly journals Distance variable improvement of time-series big data stream evaluation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ari Wibisono ◽  
Petrus Mursanto ◽  
Jihan Adibah ◽  
Wendy D. W. T. Bayu ◽  
May Iffah Rizki ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Standard Method ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Traffic Demand ◽  
Incremental Model ◽  
Chernoff Bound ◽  
The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

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.

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