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 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.

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 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.

scholarly journals Satellite-derived SO<sub>2</sub> flux time-series and magmatic processes during the 2015 Calbuco eruptions

2017 ◽  
Author(s):  
Federica Pardini ◽  
Mike Burton ◽  
Fabio Arzilli ◽  
Giuseppe La Spina ◽  
Margherita Polacci
Keyword(s):  
Time Series ◽  
Melt Inclusion ◽  
Computational Cost ◽  
High Temporal Resolution ◽  
Climatic Impact ◽  
So2 Emissions ◽  
Plume Height ◽  
So2 Flux ◽  
Magmatic Processes ◽  
Average Flux

Abstract. Quantifying time-series of sulphur dioxide (SO2) emissions during explosive eruptions provides insight into volcanic processes, assists in volcanic hazard mitigation, and permits quantification of the climatic impact of major eruptions. While volcanic SO2 is routinely detected from space during eruptions, the retrieval of plume injection height and SO2 flux time-series remains challenging. Here we present a new numerical method based on forward- and backward-trajectory analyses which enable such time-series to be robustly determined. The method is applied to satellite images of volcanic eruption clouds through the integration of the HYSPLIT software with custom-designed Python routines in a fully automated manner. Plume injection height and SO2 flux time-series are computed with a period of ~ 10 minutes with low computational cost. Using this technique, we investigated the SO2 emissions from two sub-Plinian eruptions of Calbuco, Chile, produced in April 2015. We found a mean injection height above the vent of ~ 15 km for the two eruptions, with overshooting tops reaching ~ 20 km. We calculated a total of 300 ± 46 kt of SO2 released almost equally during both events, with 160 ± 30 kt produced by the first event and 140 ± 35 kt by the second. The retrieved SO2 flux time-series show an intense gas release during the first eruption (average flux of 2560 kt day−1), while a lower SO2 flux profile was seen for the second (average flux 560 kt day−1), suggesting that the first eruption was richer in SO2. This result is exemplified by plotting SO2 flux against retrieved plume height above the vent, revealing distinct trends for the two events. We propose that a pre-erupted exsolved volatile phase was present prior to the first event, which could have led to the necessary overpressure to trigger the eruption. The second eruption, instead, was mainly driven by syneruptive degassing. This hypothesis is supported by melt inclusion measurements of sulfur concentrations in plagioclase phenocrysts and groundmass glass of tephra samples through electron microprobe analysis. This work demonstrates that detailed interpretations of sub-surface magmatic processes during eruptions are possible using satellite SO2 data. Quantitative comparisons of high temporal resolution plume height and SO2 flux time-series offer a powerful tool to examine processes triggering and controlling eruptions. These novel tools open a new frontier in space-based volcanological research, and will be of great value when applied to remote, poorly monitored volcanoes, and to major eruptions that can have regional and global climate implications through, for example, influencing ozone depletion in the stratosphere and light scattering from stratospheric aerosols.

scholarly journals Investigation of correlation of the variations in land subsidence (detected by continuous GPS measurements) and methodological data in the surrounding areas of Lake Urmia

2012 ◽  
Vol 19 (6) ◽  
pp. 675-683 ◽  
Author(s):  
K. Moghtased-Azar ◽  
A. Mirzaei ◽  
H. R. Nankali ◽  
F. Tavakoli
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Salt Lake ◽  
Linear Trend ◽  
Groundwater Resources ◽  
Time Interval ◽  
Gps Measurements ◽  
Lake Urmia ◽  
North West ◽  
Continuous Gps

Abstract. Lake Urmia, a salt lake in the north-west of Iran, plays a valuable role in the environment, wildlife and economy of Iran and the region, but now faces great challenges for survival. The Lake is in immediate and great danger and is rapidly going to become barren desert. As a result, the increasing demands upon groundwater resources due to expanding metropolitan and agricultural areas are a serious challenge in the surrounding regions of Lake Urmia. The continuous GPS measurements around the lake illustrate significant subsidence rate between 2005 and 2009. The objective of this study was to detect and specify the non-linear correlation of land subsidence and temperature activities in the region from 2005 to 2009. For this purpose, the cross wavelet transform (XWT) was carried out between the two types of time series, namely vertical components of GPS measurements and daily temperature time series. The significant common patterns are illustrated in the high period bands from 180–218 days band (~6–7 months) from September 2007 to February 2009. Consequently, the satellite altimetry data confirmed that the maximum rate of linear trend of water variation in the lake from 2005 to 2009, is associated with time interval from September 2007 to February 2009. This event was detected by XWT as a critical interval to be holding the strong correlation between the land subsidence phenomena and surface temperature. Eventually the analysis can be used for modeling and prediction purposes and probably stave off the damage from subsidence phenomena.

scholarly journals Flexible approach for quantifying average long-term changes and seasonal cycles of tropospheric trace species

2019 ◽  
Author(s):  
David D. Parrish ◽  
Richard G. Derwent ◽  
Simon O'Doherty ◽  
Peter G. Simmonds
Keyword(s):  
Time Series ◽  
Seasonal Cycle ◽  
Linear Trend ◽  
Power Series Expansion ◽  
Mathematical Representation ◽  
Seasonal Cycles ◽  
Data Sets ◽  
Significant Information ◽  
Long Term Changes

Abstract. We present an approach to derive a systematic mathematical representation of the statistically significant features of the average long-term changes and seasonal cycle of concentrations of trace tropospheric species. The results for two illustrative data sets (time series of baseline concentrations of ozone and N2O at Mace Head, Ireland) indicate that a limited set of seven or eight parameter values provides this mathematical representation for both example species. This method utilizes a power series expansion to extract more information regarding the long-term changes than can be provided by oft-employed linear trend analyses. In contrast, the quantification of average seasonal cycles utilizes a Fourier series analysis that provides less detailed seasonal cycles than are sometimes represented as twelve monthly means; including that many parameters in the seasonal cycle representation is not usually statistically justified, and thereby adds unnecessary noise to the representation and prevents a clear analysis of the statistical uncertainty of the results. The approach presented here is intended to maximize the statistically significant information extracted from analyses of time series of concentrations of tropospheric species regarding their mean long-term changes and seasonal cycles, including non-linear aspects of the long-term trends. Additional implications, advantages and limitations of this approach are discussed.

scholarly journals Time Series Analysis of the Pollutants Imissions in Urban Areas

1970 ◽  
Vol 66 (2) ◽  
Author(s):  
Carmen Leane NICOLESCU ◽  
Daniel DUNEA ◽  
Virgil MOISE ◽  
Gabriel GORGHIU
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Urban Areas ◽  
Linear Trend ◽  
Moving Average ◽  
Quality Parameters ◽  
Temporal And Spatial Distribution ◽  
Simple Exponential Smoothing ◽  
Environmental Air ◽  
Local Agencies

Environmental pollution of urban areas is one of the key factors that local agencies and authorities have to consider in the decision-making process. To succeed a sustainable management of the environment, there is necessary to use different kinds of instruments in order to evaluate and forecast the evolution of the environmental state. Understanding temporal and spatial distribution of air quality is essential in making decisions for regional management. In this paper a model for urban air quality forecasting using time series of monthly averages concentrations is presented. Sedimentable dusts (SD), total suspended particulates (TSP), nitrogen dioxide (NO2), and sulfur dioxide (SO2), imissions, recorded between 1995 and 2008 in the urban area of Târgovişte city are used as inputs in the model. The measured pollutant data from the local Environmental Agency database were statistically analyzed in time series including monthly patterns using the auto-regressive integrated moving average (ARIMA) method, linear trend, simple moving average of three terms and simple exponential smoothing. There was discussed the efficiency of using this method in forecasting the environmental air quality. In general, ARIMA technique scores well in predicting the analysed environmental air quality parameters.

Load Forecasting using Time Series Techniques

2021 ◽  
pp. 11343-11357
Author(s):  
Shahida Khatoon, Ibraheem, Priti, Mohammad Shahid
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Linear Trend ◽  
Moving Average ◽  
Load Forecasting ◽  
Electric Load ◽  
Time Data ◽  
Efficient Operation ◽  
Use Of Time ◽  
Distribution Company

Load Forecasting is of great significance for effective and efficient operation of power system. Use of time series is of much importance in load forecasting. In this study, effectiveness of different time series techniques is identified to gathered valuable information. The objective is to predict electric load efficiently and effectively. This paper analyses the prediction accuracy of variety of time series method in modeling Electric load forecasts. The study examines the time series forecasting methods applied to estimate future electric load, specifically, Moving Average (MA), Linear Trend, the Exponential and Parabolic Trend. A comparison of different forecasting techniques of Time Series is demonstrated on real time data. The data utilized for forecast is made available through a distribution company of India. The traditional linear models and hybrid models along with ANN are developed. These models are appraised for the forecasting capability.

scholarly journals Modeling of COVID-19 Outbreak Indicators in China Between January and June

2020 ◽  
pp. 1-9
Author(s):  
Senol Celik ◽  
Handan Ankarali ◽  
Ozge Pasin
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Bayesian Information Criterion ◽  
Linear Trend ◽  
Growth Curves ◽  
Information Criterion ◽  
Time Series Models ◽  
Cumulative Number ◽  
Mean Square ◽  
Nonlinear Growth

ABSTRACT Objectives: The objective of this study is to compare the various nonlinear and time series models in describing the course of the coronavirus disease 2019 (COVID-19) outbreak in China. To this aim, we focus on 2 indicators: the number of total cases diagnosed with the disease, and the death toll. Methods: The data used for this study are based on the reports of China between January 22 and June 18, 2020. We used nonlinear growth curves and some time series models for prediction of the number of total cases and total deaths. The determination coefficient (R2), mean square error (MSE), and Bayesian Information Criterion (BIC) were used to select the best model. Results: Our results show that while the Sloboda and ARIMA (0,2,1) models are the most convenient models that elucidate the cumulative number of cases; the Lundqvist-Korf model and Holt linear trend exponential smoothing model are the most suitable models for analyzing the cumulative number of deaths. Our time series models forecast that on 19 July, the number of total cases and total deaths will be 85,589 and 4639, respectively. Conclusion: The results of this study will be of great importance when it comes to modeling outbreak indicators for other countries. This information will enable governments to implement suitable measures for subsequent similar situations.

scholarly journals Testing the near-field Gaussian plume inversion flux quantification technique using unmanned aerial vehicle sampling

2020 ◽  
Vol 13 (3) ◽  
pp. 1467-1484 ◽  
Author(s):  
Adil Shah ◽  
Joseph R. Pitt ◽  
Hugo Ricketts ◽  
J. Brian Leen ◽  
Paul I. Williams ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Near Field ◽  
Emission Flux ◽  
Methane Gas ◽  
Emission Fluxes ◽  
Site Access ◽  
Aerial Vehicle ◽  
Gaussian Plume ◽  
Flux Quantification ◽  
Temperature And Pressure

Abstract. Methane emission fluxes from many facility-scale sources may be poorly quantified, potentially leading to uncertainties in the global methane budget. Accurate atmospheric measurement-based flux quantification is urgently required to address this. This paper describes the first test (using unbiased sampling) of a near-field Gaussian plume inversion (NGI) technique, suitable for facility-scale flux quantification, using a controlled release of methane gas. Two unmanned-aerial-vehicle (UAV) platforms were used to perform 22 flight surveys downwind of a point-source methane gas release from a regulated cylinder with a flowmeter. One UAV was tethered to an instrument on the ground, while the other UAV carried an on-board prototype instrument (both of which used the same near-infrared laser technology). Both instruments were calibrated using certified standards to account for variability in the instrumental gain factor, assuming fixed temperature and pressure. Furthermore, a water vapour correction factor, specifically calculated for the instrument, was applied and is described here in detail. We also provide guidance on potential systematic uncertainties associated with temperature and pressure, which may require further characterisation for improved measurement accuracy. The NGI technique was then used to derive emission fluxes for each UAV flight survey. We found good agreement of most NGI fluxes with the known controlled emission flux, within uncertainty, verifying the flux quantification methodology. The lower and upper NGI flux uncertainty bounds were, on average, 17 %±10(1σ) % and 227 %±98(1σ) % of the controlled emission flux, respectively. This range of conservative uncertainty bounds incorporate factors including the variability in the position of the time-invariant plume and potential for under-sampling. While these average uncertainties are large compared to methods such as tracer dispersion, we suggest that UAV sampling can be highly complementary to a toolkit of flux quantification approaches and may be a valuable alternative in situations where site access for tracer release is problematic. We see tracer release combined with UAV sampling as an effective approach in future flux quantification studies. Successful flux quantification using the UAV sampling methodology described here demonstrates its future utility in identifying and quantifying emissions from methane sources such as oil and gas extraction infrastructure facilities, livestock agriculture, and landfill sites, where site access may be difficult.

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