Narragansett Bay Hypoxic Event Characteristics Based on Fixed-Site Monitoring Network Time Series: Intermittency, Geographic Distribution, Spatial Synchronicity, and Interannual Variability

Abstract. The Jet Propulsion Laboratory (JPL) lidars, at the Mauna Loa Observatory, Hawaii (MLO, 19.5° N, 155.6° W) and the JPL Table Mountain Facility (TMF, California, 34.5° N, 117.7° W), have been measuring vertical profiles of stratospheric ozone routinely since the early 1990's and late-1980s respectively. Interannual variability of ozone above these two sites was investigated using a multi-linear regression analysis on the deseasonalized monthly mean lidar and satellite time-series at 1 km intervals between 20 and 45 km from January 1995 to April 2011, a period of low volcanic aerosol loading. Explanatory variables representing the 11-yr solar cycle, the El Niño Southern Oscillation, the Quasi-Biennial Oscillation, the Eliassen–Palm flux, and horizontal and vertical transport were used. A new proxy, the mid-latitude ozone depleting gas index, which shows a decrease with time as an outcome of the Montreal Protocol, was introduced and compared to the more commonly used linear trend method. The analysis also compares the lidar time-series and a merged time-series obtained from the space-borne stratospheric aerosol and gas experiment II, halogen occultation experiment, and Aura-microwave limb sounder instruments. The results from both lidar and satellite measurements are consistent with recent model simulations which propose changes in tropical upwelling. Additionally, at TMF the ozone depleting gas index explains as much variance as the Quasi-Biennial Oscillation in the upper stratosphere. Over the past 17 yr a diminishing downward trend in ozone was observed before 2000 and a net increase, and sign of ozone recovery, is observed after 2005. Our results which include dynamical proxies suggest possible coupling between horizontal transport and the 11-yr solar cycle response, although a dataset spanning a period longer than one solar cycle is needed to confirm this result.

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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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Time Series Forecasting of Tunnel Surrounding Rock Displacement

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.261-263.1789 ◽

2011 ◽

Vol 261-263 ◽

pp. 1789-1793 ◽

Cited By ~ 2

Author(s):

Guang Xiang Mao ◽

Yuan You Xia ◽

Ling Wei Liu

Keyword(s):

Neural Network ◽

Time Series ◽

Bp Neural Network ◽

Time Series Prediction ◽

Surrounding Rock ◽

Tunnel Construction ◽

Rock Stress ◽

Rock Types ◽

Network Time ◽

Tunnel Depth

In the process of tunnel construction, because the rock stress redistribute, the vault and the two groups will generate displacement constantly. This paper adopts the genetic algorithm to optimize the weight and threshold of BP neural network, taking the tunnel depth, rock types and part measured values of displacement as input parameters to construct a neural network time series prediction model of tunnel surrounding rock displacement. The method proposed in the paper has been applied in the Ma Tou Tang tunnel construction successfully, and the results show that the model can predict the displacement of the surrounding rock quickly and accurately.

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Rescue of groundwater level time series: how to identify and treat errors

10.5194/egusphere-egu21-9877 ◽

2021 ◽

Author(s):

Jānis Bikše ◽

Inga Retike ◽

Andis Kalvāns ◽

Aija Dēliņa ◽

Alise Babre ◽

...

Keyword(s):

Time Series ◽

Groundwater Level ◽

Monitoring Network ◽

Observation Data ◽

Automatic Data ◽

Mean Values ◽

Temporal Prediction ◽

National Monitoring ◽

Groundwater Drought

Groundwater level time series are the basis for various groundwater-related studies. The most valuable are long term, gapless and evenly spatially distributed datasets. However, most historical datasets have been acquired during a long-term period by various operators and database maintainers, using different data collection methods (manual measurements or automatic data loggers) and usually contain gaps and errors, that can originate both from measurement process and data processing. The easiest way is to eliminate the time series with obvious errors from further analysis, but then most of the valuable dataset may be lost, decreasing spatial and time coverage. Some gaps can be easily replaced by traditional methods (e.g. by mean values), but filling longer observation gaps (missing months, years) is complicated and often leads to false results. Thus, an effort should be made to retain as much as possible actual observation data.In this study we present (1) most typical data errors found in long-term groundwater level monitoring datasets, (2) provide techniques to visually identify such errors and finally, (3) propose best ways of how to treat such errors. The approach also includes confidence levels for identification and decision-making process. The aim of the study was to pre-treat groundwater level time series obtained from the national monitoring network in Latvia for further use in groundwater drought modelling studies.This research is funded by the Latvian Council of Science, project &#8220;Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change&#8221;, project No. lzp-2019/1-0165.

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