Diurnal cycle and multi-decadal trend of formaldehyde in the remote atmosphere near 46° N

Abstract. Only very few long-term records of formaldehyde (HCHO) exist that are suitable for trend analysis. Furthermore, many uncertainties remain as to its diurnal cycle, representing a large short-term variability superimposed on seasonal and inter-annual variations that should be accounted for when comparing ground-based observations to, e.g., model results. In this study, we derive a multi-decadal time series (January 1988–June 2015) of HCHO total columns from ground-based high-resolution Fourier transform infrared (FTIR) solar spectra recorded at the high-altitude station of Jungfraujoch (Swiss Alps, 46.5° N, 8.0° E, 3580 m a. s. l. ), allowing for the characterization of the mid-latitudinal atmosphere for background conditions. First we investigate the HCHO diurnal variation, peaking around noontime and mainly driven by the intra-day insolation modulation and methane (CH4) oxidation. We also characterize quantitatively the diurnal cycles by adjusting a parametric model to the observations, which links the daytime to the HCHO columns according to the monthly intra-day regimes. It is then employed to scale all the individual FTIR measurements on a given daytime in order to remove the effect of the intra-day modulation for improving the trend determination and the comparison with HCHO columns simulated by the state-of-the-art GEOS-Chem v9-02 chemical transport model. Such a parametric model will be useful to scale the Jungfraujoch HCHO columns on satellite overpass times in the framework of future calibration/validation efforts of space-borne sensors. GEOS-Chem sensitivity tests suggest then that the seasonal and inter-annual HCHO column variations above Jungfraujoch are predominantly led by the atmospheric CH4 oxidation, with a maximum contribution of 25 % from the anthropogenic non-methane volatile organic compound precursors during wintertime. Finally, trend analysis of the so-scaled 27-year FTIR time series reveals a long-term evolution of the HCHO columns in the remote troposphere to be related to the atmospheric CH4 fluctuations and the short-term OH variability: +2.9 % year−1 between 1988 and 1995, −3.7 % year−1 over 1996–2002 and +0.8 % year−1 from 2003 onwards.

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Diurnal cycle and multi-decadal trend of formaldehyde in the remote atmosphere near 46° N

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-31287-2015 ◽

2015 ◽

Vol 15 (21) ◽

pp. 31287-31333

Author(s):

B. Franco ◽

E. A. Marais ◽

B. Bovy ◽

W. Bader ◽

B. Lejeune ◽

...

Keyword(s):

Time Series ◽

Diurnal Cycle ◽

Transport Model ◽

Parametric Model ◽

Swiss Alps ◽

Chemical Transport Model ◽

Ch4 Oxidation ◽

Short Term ◽

Annual Variations

Abstract. Only very few long-term trends of formaldehyde (HCHO) exist. Furthermore, many uncertainties remain as to its diurnal cycle, representing a large short-term variability superimposed on seasonal and inter-annual variations that should be accounted for when comparing ground-based observations to e.g., model results. In this study, we derive a multi-decadal time series (January 1988–June 2015) of HCHO total columns from ground-based high-resolution Fourier transform infrared (FTIR) solar spectra recorded at the high-altitude station of Jungfraujoch (Swiss Alps, 46.5° N, 8.0° E, 3580 m a.s.l.), allowing for the characterization of the mid-latitudinal atmosphere for background conditions. First we investigate the HCHO diurnal variation, peaking around noontime and mainly driven by the intra-day insolation modulation and methane (CH4) oxidation. We also characterize quantitatively the diurnal cycles by adjusting a parametric model to the observations, which links the daytime to the HCHO columns according to the monthly intra-day regimes. It is then employed to scale all the individual FTIR measurements on a given daytime in order to remove the effect of the intra-day modulation for improving the trend determination and the comparison with HCHO columns simulated by the state-of-the-art chemical transport model GEOS-Chem v9-02. Such a parametric model will be useful to scale the Jungfraujoch HCHO columns on satellite overpass times in the framework of future calibration/validation efforts of space borne sensors. GEOS-Chem sensitivity tests suggest then that the seasonal and inter-annual HCHO column variations above Jungfraujoch are predominantly led by the atmospheric CH4 oxidation, with a maximum contribution of 25 % from the anthropogenic non-methane volatile organic compound precursors during wintertime. Finally, trend analysis of the so-scaled 27 year FTIR time series reveals a long-term evolution of the HCHO columns in the remote troposphere to be related with the atmospheric CH4 fluctuations and the short-term OH variability: +2.9 % yr−1 between 1988 and 1995, −3.7 % yr−1 over 1996–2002 and +0.8 % yr−1 from 2003 onwards.

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Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽

10.3390/electronics10101151 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1151

Author(s):

Carolina Gijón ◽

Matías Toril ◽

Salvador Luna-Ramírez ◽

María Luisa Marí-Altozano ◽

José María Ruiz-Avilés

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Analysis ◽

Data Traffic ◽

Medium Term ◽

Short Term ◽

Traffic Forecasting ◽

Series Analysis ◽

Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

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An Efficient Prediction Model for Water Discharge in Schoharie Creek, NY

Journal of Climatology ◽

10.1155/2014/284137 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 6

Author(s):

Katerina G. Tsakiri ◽

Antonios E. Marsellos ◽

Igor G. Zurbenko

Keyword(s):

New York ◽

Time Series ◽

Water Discharge ◽

Winter Period ◽

Statistical Methodology ◽

Summer Period ◽

Short Term ◽

Efficient Prediction ◽

Climatic Time Series

Flooding normally occurs during periods of excessive precipitation or thawing in the winter period (ice jam). Flooding is typically accompanied by an increase in river discharge. This paper presents a statistical model for the prediction and explanation of the water discharge time series using an example from the Schoharie Creek, New York (one of the principal tributaries of the Mohawk River). It is developed with a view to wider application in similar water basins. In this study a statistical methodology for the decomposition of the time series is used. The Kolmogorov-Zurbenko filter is used for the decomposition of the hydrological and climatic time series into the seasonal and the long and the short term component. We analyze the time series of the water discharge by using a summer and a winter model. The explanation of the water discharge has been improved up to 81%. The results show that as water discharge increases in the long term then the water table replenishes, and in the seasonal term it depletes. In the short term, the groundwater drops during the winter period, and it rises during the summer period. This methodology can be applied for the prediction of the water discharge at multiple sites.

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Prediction of long-term cumulative incidences based on short-term parametric model for competing risks: application in early breast cancer

Breast Cancer Research and Treatment ◽

10.1007/s10549-016-3789-9 ◽

2016 ◽

Vol 156 (3) ◽

pp. 577-585 ◽

Cited By ~ 2

Author(s):

B. Cabarrou ◽

L. Belin ◽

S. M. Somda ◽

M. C. Falcou ◽

J. Y. Pierga ◽

...

Keyword(s):

Breast Cancer ◽

Early Breast Cancer ◽

Competing Risks ◽

Parametric Model ◽

Short Term

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Regional and intercontinental pollution signatures on modeled and measured PAN at northern mid-latitude mountain sites

10.5194/acp-2018-90 ◽

2018 ◽

Author(s):

Arlene M. Fiore ◽

Emily V. Fischer ◽

Shubha Pandey Deolal ◽

Oliver Wild ◽

Dan Jaffe ◽

...

Keyword(s):

Transport Model ◽

Ozone Production ◽

Anthropogenic Emissions ◽

Chemical Transport Model ◽

Reservoir Species ◽

Remote Troposphere ◽

Global Chemical Transport Model ◽

Peroxy Acetyl Nitrate ◽

Source Receptor Relationships

Abstract. Peroxy acetyl nitrate (PAN) is the most important reservoir species for nitrogen oxides (NOx) in the remote troposphere. Upon decomposition in remote regions, PAN promotes efficient ozone production. We evaluate monthly mean PAN abundances from global chemical transport model simulations (HTAP1) for 2001 with measurements from five northern mid-latitude mountain sites (four European and one North American). The multi-model mean generally captures the observed monthly mean PAN but individual models simulate a factor of ~ 4–8 range in monthly abundances. We quantify PAN source-receptor relationships at the measurement sites with sensitivity simulations that decrease regional anthropogenic emissions of PAN (and ozone) precursors by 20 % from North America (NA), Europe (EU), and East Asia (EA). The HTAP1 models attribute more of the observed PAN at Jungfraujoch (Switzerland) to emissions in NA and EA, and less to EU, than a prior trajectory-based estimate. The trajectory-based and modeling approaches agree that EU emissions play a role in the observed springtime PAN maximum at Jungfraujoch. The signal from anthropogenic emissions on PAN is strongest at Jungfraujoch and Mount Bachelor (Oregon, U.S.A.) during April. In this month, PAN source-receptor relationships correlate both with model differences in regional anthropogenic volatile organic compound (AVOC) emissions and with ozone source-receptor relationships. PAN observations at mountaintop sites can thus provide key information for evaluating models, including links between PAN and ozone production and source-receptor relationships. Establishing routine, long-term, mountaintop measurements is essential given the large observed interannual variability in PAN.

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Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

10.20944/preprints201811.0607.v1 ◽

2018 ◽

Author(s):

Scott D. Chambers ◽

Elise-Andree Guérette ◽

Khalia Monk ◽

Alan D. Griffiths ◽

Yang Zhang ◽

...

Keyword(s):

Air Quality ◽

Diurnal Cycle ◽

Transport Model ◽

Chemical Transport ◽

Chemical Transport Model ◽

Transport Models ◽

Potential Health ◽

Meteorological Model ◽

Model Skill ◽

Chemical Transport Models

We propose a new technique to prepare statistically-robust benchmarking data for evaluating chemical transport model meteorology and air quality parameters within the urban boundary layer. The approach employs atmospheric class-typing, using nocturnal radon measurements to assign atmospheric mixing classes, and can be applied temporally (across the diurnal cycle), or spatially (to create angular distributions of pollutants as a top-down constraint on emissions inventories). In this study only a short (<1-month) campaign is used, but grouping of the relative mixing classes based on nocturnal mean radon concentrations can be adjusted according to dataset length (i.e., number of days per category), or desired range of within-class variability. Calculating hourly distributions of observed and simulated values across diurnal composites of each class-type helps to: (i) bridge the gap between scales of simulation and observation, (ii) represent the variability associated with spatial and temporal heterogeneity of sources and meteorology without being confused by it, and (iii) provide an objective way to group results over whole diurnal cycles that separates ‘natural complicating factors’ (synoptic non-stationarity, rainfall, mesoscale motions, extreme stability, etc.) from problems related to parameterizations, or between-model differences. We demonstrate the utility of this technique using output from a suite of seven contemporary regional forecast and chemical transport models. Meteorological model skill varied across the diurnal cycle for all models, with an additional dependence on the atmospheric mixing class that varied between models. From an air quality perspective, model skill regarding the duration and magnitude of morning and evening “rush hour” pollution events varied strongly as a function of mixing class. Model skill was typically the lowest when public exposure would have been the highest, which has important implications for assessing potential health risks in new and rapidly evolving urban regions, and also for prioritizing the areas of model improvement for future applications.

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Trends in Stratospheric Ozone: Lessons Learned from a 3D Chemical Transport Model

Journal of the Atmospheric Sciences ◽

10.1175/jas3650.1 ◽

2006 ◽

Vol 63 (3) ◽

pp. 1028-1041 ◽

Cited By ~ 85

Author(s):

Richard S. Stolarski ◽

Anne R. Douglass ◽

Stephen Steenrod ◽

Steven Pawson

Keyword(s):

Time Series ◽

Solar Cycle ◽

Ultraviolet Radiation ◽

Boundary Conditions ◽

Interannual Variability ◽

Transport Model ◽

Stratospheric Ozone ◽

Chemical Transport ◽

Chemical Transport Model ◽

The Past

Abstract Stratospheric ozone is affected by external factors such as chlorofluorcarbons (CFCs), volcanoes, and the 11-yr solar cycle variation of ultraviolet radiation. Dynamical variability due to the quasi-biennial oscillation and other factors also contribute to stratospheric ozone variability. A research focus during the past two decades has been to quantify the downward trend in ozone due to the increase in industrially produced CFCs. During the coming decades research will focus on detection and attribution of the expected recovery of ozone as the CFCs are slowly removed from the atmosphere. A chemical transport model (CTM) has been used to simulate stratospheric composition for the past 30 yr and the next 20 yr using 50 yr of winds and temperatures from a general circulation model (GCM). The simulation includes the solar cycle in ultraviolet radiation, a representation of aerosol surface areas based on observations including volcanic perturbations from El Chichon in 1982 and Pinatubo in 1991, and time-dependent mixing ratio boundary conditions for CFCs, halons, and other source gases such as N2O and CH4. A second CTM simulation was carried out for identical solar flux and boundary conditions but with constant “background” aerosol conditions. The GCM integration included an online ozonelike tracer with specified production and loss that was used to evaluate the effects of interannual variability in dynamics. Statistical time series analysis was applied to both observed and simulated ozone to examine the capability of the analyses for the determination of trends in ozone due to CFCs and to separate these trends from the solar cycle and volcanic effects in the atmosphere. The results point out several difficulties associated with the interpretation of time series analyses of atmospheric ozone data. In particular, it is shown that lengthening the dataset reduces the uncertainty in derived trend due to interannual dynamic variability. It is further shown that interannual variability can make it difficult to accurately assess the impact of a volcanic eruption, such as Pinatubo, on ozone. Such uncertainties make it difficult to obtain an early proof of ozone recovery in response to decreasing chlorine.

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Track Irregularity Time Series Analysis and Trend Forecasting

Discrete Dynamics in Nature and Society ◽

10.1155/2012/387857 ◽

2012 ◽

Vol 2012 ◽

pp. 1-15 ◽

Cited By ~ 17

Author(s):

Jia Chaolong ◽

Xu Weixiang ◽

Wang Futian ◽

Wang Hanning

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Short Term ◽

Measuring Point ◽

Trend Forecasting ◽

Changing Trend ◽

Track Irregularity ◽

Linear Differential

The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM(1,1)is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changing trend of track irregularity at a fixed measuring point; the stochastic linear AR, Kalman filtering model, and artificial neural network model are applied to predict the short-term changing trend of track irregularity at unit section. Both long-term and short-term changes prove that the model is effective and can achieve the expected accuracy.

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Long Short Term Memory Recurrent Network for Standard and Poor’s 500 Index Modelling

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.15.21365 ◽

2018 ◽

Vol 7 (4.15) ◽

pp. 25 ◽

Cited By ~ 3

Author(s):

Said Jadid Abdulkadir ◽

Hitham Alhussian ◽

Muhammad Nazmi ◽

Asim A Elsheikh

Keyword(s):

Neural Network ◽

Time Series ◽

Recurrent Neural Network ◽

Time Series Data ◽

Short Term Memory ◽

Series Data ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Forecasting time-series data are imperative especially when planning is required through modelling using uncertain knowledge of future events. Recurrent neural network models have been applied in the industry and outperform standard artificial neural networks in forecasting, but fail in long term time-series forecasting due to the vanishing gradient problem. This study offers a robust solution that can be implemented for long-term forecasting using a special architecture of recurrent neural network known as Long Short Term Memory (LSTM) model to overcome the vanishing gradient problem. LSTM is specially designed to avoid the long-term dependency problem as their default behavior. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the forecasted outputs. An evaluation analysis is performed to validate that the LSTM model provides better forecasted outputs on Standard & Poor’s 500 Index (S&P 500) in terms of error metrics as compared to other forecasting models.

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