scholarly journals SO<sub>2</sub> and BrO emissions of Masaya volcano from 2014 to 2020

2021 ◽  
Vol 21 (12) ◽  
pp. 9367-9404
Author(s):  
Florian Dinger ◽  
Timo Kleinbek ◽  
Steffen Dörner ◽  
Nicole Bobrowski ◽  
Ulrich Platt ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Linear Trend ◽  
Lava Lake ◽  
Emission Flux ◽  
Plume Height ◽  
So2 Emission ◽  
Molar Ratios ◽  
Emission Fluxes ◽  
Time Periods

Abstract. Masaya (Nicaragua, 12.0∘ N, 86.2∘ W; 635 m a.s.l.) is one of the few volcanoes hosting a lava lake, today. This study has two foci: (1) discussing the state of the art of long-term SO2 emission flux monitoring with the example of Masaya and (2) the provision and discussion of a continuous data set on volcanic gas data with a large temporal coverage, which is a major extension of the empirical database for studies in volcanology as well as atmospheric bromine chemistry. We present time series of SO2 emission fluxes and BrO/SO2 molar ratios in the gas plume of Masaya from March 2014 to March 2020 – covering the three time periods (1) before the lava lake appearance, (2) a period of high lava lake activity (November 2015 to May 2018), and (3) after the period of high lava lake activity. For these three time periods, we report average SO2 emission fluxes of (1000±200), (1000±300), and (700±200) t d−1 and average BrO/SO2 molar ratios of (2.9±1.5)×10-5, (4.8±1.9)×10-5, and (5.5±2.6)×10-5. Our SO2 emission flux retrieval is based on a comprehensive investigation of various aspects of spectroscopic retrievals, the wind conditions, and the plume height. We observed a correlation between the SO2 emission fluxes and the wind speed in the raw data. We present a partial correction of this artefact by applying dynamic estimates for the plume height as a function of the wind speed. Our retrieved SO2 emission fluxes are on average a factor of 1.4 larger than former estimates based on the same data. Further, we observed different patterns in the BrO/SO2 time series: (1) an annual cyclicity with amplitudes between 1.4 and 2.5×10-5 and a weak semi-annual modulation, (2) a step increase by 0.7×10-5 in late 2015, (3) a linear trend of 1.4×10-5 per year from November 2015 to March 2018, and (4) a linear trend of -0.8×10-5 per year from June 2018 to March 2020. The step increase in 2015 coincided with the lava lake appearance and was thus most likely caused by a change in the magmatic system. We suggest that the cyclicity might be a manifestation of meteorological cycles. We found an anti-correlation between the BrO/SO2 molar ratios and the atmospheric water concentration (correlation coefficient of −0.47) but, in contrast to that, neither a correlation with the ozone mixing ratio (+0.21) nor systematic dependencies between the BrO/SO2 molar ratios and the atmospheric plume age for an age range of 2–20 min after the release from the volcanic edifice. The two latter observations indicate an early stop of the autocatalytic transformation of bromide Br− solved in aerosol particles to atmospheric BrO.

scholarly journals SO<sub>2</sub> and BrO emissions of Masaya volcano from 2014–2020

2020 ◽  
Author(s):  
Florian Dinger ◽  
Timo Kleinbek ◽  
Steffen Dörner ◽  
Nicole Bobrowski ◽  
Ulrich Platt ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Trend ◽  
Lava Lake ◽  
Emission Flux ◽  
Plume Height ◽  
So2 Emission ◽  
Molar Ratios ◽  
Masaya Volcano ◽  
Emission Fluxes ◽  
Time Periods

Abstract. Masaya volcano (Nicaragua, 12.0° N, 86.2° W, 635 m a.s.l.) is one of the few volcanoes hosting a lava lake, today. We present continuous time series of SO2 emission fluxes and BrO / SO2 molar ratios in the gas plume of Masaya from March 2014 to March 2020. This study has two foci: (1) discussing the state of the art of long-term SO2 emission flux monitoring on the example of Masaya and (2) the provision and discussion of a continuous dataset on volcanic gas data unique in its temporal coverage, which poses a major extension of the empirical data base for studies on the volcanologic as well as atmospheric bromine chemistry. Our SO2 emission flux retrieval is based on a comprehensive investigation of various aspects of the spectroscopic retrievals, the wind conditions, and the plume height. Our retrieved SO2 emission fluxes are on average a factor of 1.4 larger than former estimates based on the same data. We furthermore observed a correlation between the SO2 emission fluxes and the wind speed when several of our retrieval extensions are not applied. We make plausible that such a correlation is not expected and present a partial correction of this artefact via applying dynamic estimates for the plume height as a function of the wind speed (resulting in a vanishing correlation for wind speeds larger than 10 m/s). Our empirical data set covers the three time periods (1) before the lava lake elevation, (2) period of high lava lake activity (December 2015–May 2018), (3) after the period of high lava lake activity. For these three time periods, we report average SO2 emission fluxes of 1000 ± 200 t d−1, 1000 ± 300 t d−1, and 700 ± 200 t d−1 and average BrO / SO2 molar ratios of (2.9 ± 1.5) × 10−5, (4.8 ± 1 : 9) ×10−5, and (5.5 ± 2–6) × 10−5. These variations indicate that the two gas proxies provide complementary information: the BrO / SO2 molar ratios were susceptible in particular for the transition between the two former periods while the SO2 emission fluxes were in particular susceptible for the transition between the two latter time periods. We observed an extremely significant annual cyclicity for the BrO / SO2 molar ratios (amplitudes between 1–4–2–6 × 10−5) with a weak semi-annual modulation. We suggest that this cyclicity might be a manifestation of meteorological cycles. We found an anti-correlation between the BrO / SO2 molar ratios and the atmospheric water concentration (correlation coefficient of −47 %) but in contrast to that neither a correlation with the ozone mixing ratio (+21 %) nor systematic dependencies between the BrO / SO2 molar ratios and the atmospheric plume age for an age range of 2–20 min after the release from the volcanic edifice. The two latter observations indicate an early stop of the autocatalytic partial transformation of bromide Br− solved in aerosol particles to atmospheric BrO. Further patterns in the BrO / SO2 time series were (1) a step increase by 0.7 × 10−5 in late 2015, (2) a linear trend of 1.2 × 10−5 per year from December 2015 to March 2018, and (3) a linear trend of −0.8 × 10−5 per year from June 2018 to March 2020. The step increase in 2015 coincided with the 55 elevation of the lava lake and was thus most likely caused by a change in the magmatic system. The linear trend between late 2015 and early 2018 may indicate the evolution of the magmatic gas phase during the ascent of juvenile gas-rich magma whereas the linear trend from June 2018 on may indicate a decreasing bromine abundance in the magma.

scholarly journals State of the art in wind speed in England using BATS ,TBATS , Holt’s Linear and ARIMA model

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 129-138
Author(s):  
Mostafa Abotaleb ◽  
Tatiana Makarovskikh ◽  
Aynur Yonar ◽  
Amr Badr ◽  
Pradeep Mishra ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Energy ◽  
Linear Trend ◽  
Renewable Energy Sources ◽  
Arima Model ◽  
Training Data ◽  
Time Series Models ◽  
Testing Data ◽  
Mean Square Errors

Wind energy is one of the most important renewable energy sources in the world. Hence, the prediction of wind speed is a highly significant subject with respect to both protecting the environment and economic development. England is among the countries with an increasing interest in the potential for wind energy systems. In this study, various time series models, including BATS, TBATS, Holt’s Linear Trend, and ARIMA models were applied for wind speed prediction in England, and their performance was compared. The available wind speed data between 1994-07-07 and 2015-12-31 were divided into two parts: training data that is used to build up the models and testing data that is used to measure the validity of a model forecast. The results of the testing data indicate that the BATS and ARIMA outperform the other time series models according to the root mean square errors.

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

A Contextual Evaluation of Composite Forecasts of Annual Earnings

2016 ◽  
Vol 19 (03) ◽  
pp. 1650014 ◽  
Author(s):  
Pieter T. Elgers ◽  
May H. Lo ◽  
Wenjuan Xie ◽  
Le Emily Xu
Keyword(s):  
Time Series ◽  
Firm Size ◽  
Predictive Accuracy ◽  
Relative Accuracy ◽  
Regulation Fair Disclosure ◽  
Fair Disclosure ◽  
Time Periods ◽  
Time Specific ◽  
Annual Earnings ◽  
The Impact

This study addresses the impact of firm- and time-specific attributes on the accuracy of composite forecasts of annual earnings, constructed from time-series, price-based, and analysts' forecasts. The attributes examined include firm size, analysts' coverage, and time periods pre-dating and following the implementation of regulation fair disclosure. Our results indicate that the relative accuracy of the composite forecasts is time-specific. In the pre-regulation fair disclosure period, composite forecasts significantly outperform each of the three individual forecast sources. Moreover, the extent of improvement in accuracy of composite forecasts is significantly higher for the smaller and lightly-covered firms. Collectively, these results suggest that the predictive accuracy of composite forecasts is contextual.

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 Benchmarking homogenization algorithms for monthly data

2012 ◽  
Vol 8 (1) ◽  
pp. 89-115 ◽  
Author(s):  
V. K. C. Venema ◽  
O. Mestre ◽  
E. Aguilar ◽  
I. Auer ◽  
J. A. Guijarro ◽  
...  
Keyword(s):  
Time Series ◽  
Performance Metrics ◽  
Linear Trend ◽  
Simulated Data ◽  
Integrated Approach ◽  
Global Network ◽  
Temperature And Precipitation ◽  
Skill Scores ◽  
Simulated Network ◽  
The Individual

Abstract. The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training the users on homogenization software was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that automatic algorithms can perform as well as manual ones.

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