Space–time modeling of vertical ozone profiles

Lee, Jae Yong; Berger, James O.

doi:10.1002/env.608

Cited by 4 publications

(3 citation statements)

References 15 publications

(19 reference statements)

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“…The Kalman Filter uses past and current observations to predict the current states; an extension of this is the Kalman Smoother, which uses all available observations at all times to generate the posterior density given all observations p( θ t |y 1 :n ). Despite its simplicity, this formulation has many applications in real environmental problems [e.g., Lee and Berger , ; Kurtenbach et al ., ; Laine et al ., ]. However, all the strength of this methodology relies on the linear‐Gaussian properties that do not always hold, especially when extreme events are concerned.…”

Section: Methodsmentioning

confidence: 99%

Long‐term variations in global sea level extremes

et al. 2015

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Decadal to multidecadal variations in sea level extremes unrelated to mean sea level changes have been investigated using long tide gauge records distributed worldwide. A state space approach has been applied that provides robust solutions and uncertainties of the time evolving characteristics of extremes, allowing for data gaps and uneven sampling, both common features of historical sea level time series. Two different models have been formulated for the intensity and for the occurrence of extreme sea level events and have been applied independently to each tide gauge record. Our results reveal two key findings: first, the intensity and the frequency of occurrence of extreme sea levels unrelated to mean sea level vary coherently on decadal scales in most of the sites examined (63 out of 77) and, second, extreme sea level changes are regionally consistent, thus pointing toward a common large‐scale forcing. This variability of extremes associated with climate drivers should be considered in the framework of climate change studies.

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Section: Methodsmentioning

confidence: 99%

Long‐term variations in global sea level extremes

et al. 2015

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“…One may then assign discriminatory SO 2 exposure to the different at-risk populations in the study region of a vast land-base. This could substantially improve on those methods in which measurements from a (few) centrally located stationary monitoring station(s) are used for the entire population (see examples in [21,26,30,42]). A primary concern of this latter approach is that it does not take into account the spatial and temporal variability of the air pollutants and the relationship of concentrations between the ambient environment of the monitoring stations and the specific personal breathing zone [44].…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Modeling of Ambient Sulfur Dioxide Concentrations in Rural Western Canada

Fan

Burstyn

2008

Environ Model Assess

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“…A recent study that reviews the challenges and problems in trend analysis of climatic time series was published by Bates et al (2012), and a general trend analysis reference is Chandler and Scott (2011). For state space and functional analysis of atmospheric time series of similar type to that performed here, see Lee and Berger (2003) and Meiring (2007). This paper studies the feasibility and practical implementation of a state space approach for atmospheric time series analysis by defining a dynamic linear model (DLM) for stratospheric ozone time series.…”

Section: Laine Et Al: Time-varying Trends In Stratospheric Ozonementioning

confidence: 99%

Analysing time-varying trends in stratospheric ozone time series using the state space approach

2014

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Abstract. We describe a hierarchical statistical state space model for ozone profile time series. The time series are from satellite measurements by the Stratospheric Aerosol and Gas Experiment (SAGE) II and the Global Ozone Monitoring by Occultation of Stars (GOMOS) instruments spanning the years 1984-2011. Vertical ozone profiles were linearly interpolated on an altitude grid with 1 km resolution covering 20-60 km. Monthly averages were calculated for each altitude level and 10 • wide latitude bins between 60 • S and 60 • N. In the analysis, mean densities are studied separately for the 25-35, 35-45, and 45-55 km layers. Model variables include the ozone mean level, local trend, seasonal oscillations, and proxy variables for solar activity, the Quasi-Biennial Oscillation (QBO), and the El Niño-Southern Oscillation (ENSO). This is a companion paper to Kyrölä et al. (2013), where a piecewise linear model was used together with the same proxies as in this work (excluding ENSO). The piecewise linear trend was allowed to change at the beginning of 1997 in all latitudes and altitudes. In the modelling of the present paper such an assumption is not needed as the linear trend is allowed to change continuously at each time step. This freedom is also allowed for the seasonal oscillations whereas other regression coefficients are taken independent of time. According to our analyses, the slowly varying ozone background shows roughly three general development patterns. A continuous decay for the whole period 1984-2011 is evident in the southernmost latitude belt 50-60 • S in all altitude regions and in 50-60 • N in the lowest altitude region 25-35 km. A second pattern, where a recovery after an initial decay is followed by a further decay, is found at northern latitudes from the equator to 50 • N in the lowest altitude region (25-35 km) and between 40 • N and 60 • N in the 35-45 km altitude region. Further ozone loss occurred after 2007 in these regions. Everywhere else a decay is followed by a recovery. This pattern is shown at all altitudes and latitudes in the Southern Hemisphere (10-50 • S) and in the 45-55 km layer in the Northern Hemisphere (from the equator to 40 • N). In the 45-55 km range the trend, measured as an average change in 10 years, has mostly turned from negative to positive before the year 2000. In those regions where the "V" type of change of the trend is appropriate, the turning point is around the years 1997-2001. To compare results for the trend changes with the companion paper, we studied the difference in trends between the years from 1984 to 1997 and from 1997 to 2011. Overall, the two methods produce very similar ozone recovery patterns with the maximum trend change of 10 % in 35-45 km. The state space method (used in this paper) shows a somewhat faster recovery than the piecewise linear model. For the percent change of the ozone density per decade the difference between the results is below three percentage units.

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Space–time modeling of vertical ozone profiles

Cited by 4 publications

References 15 publications

Long‐term variations in global sea level extremes

Long‐term variations in global sea level extremes

Spatiotemporal Modeling of Ambient Sulfur Dioxide Concentrations in Rural Western Canada

Analysing time-varying trends in stratospheric ozone time series using the state space approach

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