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

2019 ◽  
Vol 12 (6) ◽  
pp. 3383-3394 ◽  
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 for deriving 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 12 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 nonlinear aspects of the long-term trends. Additional implications, advantages and limitations of this approach are discussed.

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 Comparison of GPS tropospheric delays derived from two consecutive EPN reprocessing campaigns from the point of view of climate monitoring

2016 ◽  
Vol 9 (9) ◽  
pp. 4861-4877 ◽  
Author(s):  
Zofia Baldysz ◽  
Grzegorz Nykiel ◽  
Andrzej Araszkiewicz ◽  
Mariusz Figurski ◽  
Karolina Szafranek
Keyword(s):  
Time Series ◽  
Point Of View ◽  
Trend Test ◽  
Data Sets ◽  
Total Delay ◽  
University Of Technology ◽  
Time Period ◽  
Seasonal Signals ◽  
Linear Trends

Abstract. The main purpose of this research was to acquire information about consistency of ZTD (zenith total delay) linear trends and seasonal components between two consecutive GPS reprocessing campaigns. The analysis concerned two sets of the ZTD time series which were estimated during EUREF (Reference Frame Sub-Commission for Europe) EPN (Permanent Network) reprocessing campaigns according to 2008 and 2015 MUT AC (Military University of Technology Analysis Centre) scenarios. Firstly, Lomb–Scargle periodograms were generated for 57 EPN stations to obtain a characterisation of oscillations occurring in the ZTD time series. Then, the values of seasonal components and linear trends were estimated using the LSE (least squares estimation) approach. The Mann–Kendall trend test was also carried out to verify the presence of linear long-term ZTD changes. Finally, differences in seasonal signals and linear trends between these two data sets were investigated. All these analyses were conducted for the ZTD time series of two lengths: a shortened 16-year series and a full 18-year one. In the case of spectral analysis, amplitudes of the annual and semi-annual periods were almost exactly the same for both reprocessing campaigns. Exceptions were found for only a few stations and they did not exceed 1 mm. The estimated trends were also similar. However, for the reprocessing performed in 2008, the trends values were usually higher. In general, shortening of the analysed time period by 2 years resulted in a decrease of the linear trends values of about 0.07 mm yr−1. This was confirmed by analyses based on two data sets.

scholarly journals Analysis of HCl and ClO time series in the upper stratosphere using satellite data sets

2011 ◽  
Vol 11 (11) ◽  
pp. 5321-5333 ◽  
Author(s):  
A. Jones ◽  
J. Urban ◽  
D. P. Murtagh ◽  
C. Sanchez ◽  
K. A. Walker ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Atmospheric Chemistry ◽  
Linear Trend ◽  
Montreal Protocol ◽  
Data Sets ◽  
Sigma Level ◽  
Chlorine Monoxide ◽  
Significant Downward Trend ◽  
The Tropics

Abstract. Previous analyses of satellite and ground-based measurements of hydrogen chloride (HCl) and chlorine monoxide (ClO) have suggested that total inorganic chlorine in the upper stratosphere is on the decline. We create HCl and ClO time series using satellite data sets extended to November 2008, so that an update can be made on the long term evolution of these two species. We use the HALogen Occultation Experiment (HALOE) and the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) data for the HCl analysis, and the Odin Sub-Millimetre Radiometer (SMR) and the Aura Microwave Limb Sounder (Aura-MLS) measurements for the study of ClO. Altitudes between 35 and 45 km and two mid-latitude bands: 30° S–50° S and 30° N–50° N, for HCl, and 20° S–20° N for ClO and HCl are studied. ACE-FTS and HALOE HCl anomaly time series (with QBO and seasonal contributions removed) are combined to produce all instrument average time series, which show HCl to be reducing from peak 1997 values at a linear estimated rate of −5.1 % decade−1 in the Northern Hemisphere and −5.2 % decade−1 in the Southern Hemisphere, while the tropics show a linear trend of −5.8 % per decade (although we do not remove the QBO contribution there due to sparse data). Trend values are significantly different from a zero trend at the 2 sigma level. ClO is decreasing in the tropics by −7.1 % ± 7.8 % decade−1 based on measurements made from December 2001 to November 2008. The statistically significant downward trend found in HCl after 1997 and the apparent downward ClO trend since 2001 (although not statistically significant) confirm how effective the 1987 Montreal protocol objectives and its amendments have been in reducing the total amount of inorganic chlorine.

Time series analysis of long-term data sets of atmospheric mercury concentrations

2004 ◽  
Vol 380 (3) ◽  
pp. 493-501 ◽  
Author(s):  
Christian Temme ◽  
Ralf Ebinghaus ◽  
J�rgen W. Einax ◽  
Alexandra Steffen ◽  
William H. Schroeder
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Atmospheric Mercury ◽  
Data Sets ◽  
Series Analysis ◽  
Term Data

scholarly journals Validation of Precipitable Water Vapor within the NCEP/DOE Reanalysis Using Global GPS Observations from One Decade

2010 ◽  
Vol 23 (7) ◽  
pp. 1675-1695 ◽  
Author(s):  
Sibylle Vey ◽  
Reinhard Dietrich ◽  
Axel Rülke ◽  
Mathias Fritsche ◽  
Peter Steigenberger ◽  
...  
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Seasonal Cycle ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Atmospheric Temperature ◽  
Interannual Variations ◽  
Seasonal Signals ◽  
The Tropics

Abstract In contrast to previous studies validating numerical weather prediction (NWP) models using observations from the global positioning system (GPS), this paper focuses on the validation of seasonal and interannual variations in the water vapor. The main advantage of the performed validation is the independence of the GPS water vapor estimates compared to studies using water vapor datasets from radiosondes or satellite microwave radiometers that are already assimilated into the NWP models. Tropospheric parameters from a GPS reanalysis carried out in a common project of the Technical Universities in Munich and Dresden were converted into precipitable water (PW) using surface pressure observations from the WMO and mean atmospheric temperature data from ECMWF. PW time series were generated for 141 globally distributed GPS sites covering the time period from the beginning of 1994 to the end of 2004. The GPS-derived PW time series were carefully examined for their homogeneity. The validation of the NWP model from NCEP shows that the differences between the modeled and observed PW values are time dependent. In addition to establishing a long-term mean, this study also validates the seasonal cycle and interannual variations in the PW. Over Europe and large parts of North America the seasonal cycle and the interannual variations in the PW from GPS and NCEP agree very well. The results reveal a submillimeter accuracy of the GPS-derived PW anomalies. In the regions mentioned above, NCEP provides a highly accurate database for studies of long-term changes in the atmospheric water vapor. However, in the Southern Hemisphere large differences in the seasonal signals and in the PW anomalies were found between GPS and NCEP. The seasonal signal of the PW is underestimated by NCEP in the tropics and in Antarctica by up to 40% and 25%, respectively. Climate change studies based on water vapor data from NCEP should consider the large uncertainties in the analysis when interpreting these data, especially in the tropics.

scholarly journals Analysing Hydrometeorological Time Series for Evidence of Climatic Change

1993 ◽  
Vol 24 (2-3) ◽  
pp. 135-150 ◽  
Author(s):  
Geoff Kite
Keyword(s):  
Time Series ◽  
Climatic Change ◽  
Data Sets ◽  
Large Lakes ◽  
Hydrologic Time Series ◽  
Natural Time ◽  
Streamflow Data ◽  
Scientific Attention ◽  
Linear Trends

Considerable scientific attention has been focused on a measured increase in atmospheric CO2 and a suspected corresponding change in climate. Such a change in climate, if it occurred, might be expected to have a magnified effect on hydrologic time series and, indeed, projections have been made of major changes in water resources. If the climatic changes are indeed magnified in hydrologic time series then, by detecting trends in such series, it should be possible to work backwards and identify the causative climatic change. This paper looks at two data sets: 1) long-term temperature, precipitation and streamflow data from sites across Canada and 2) long-term levels of large lakes in Africa and North America. The study assumes that time series may be modelled by trend, periodic, autoregressive and random residual components. The trend component of a time series is generally associated with changes in the structure of the time series caused by cumulative natural or manmade phenomena. Periodicities in natural time series are usually due to astronomical cycles such as the earth's rotation around the sun. Autoregressive components reflect the tendency for an event to be dependent on the magnitude of the previous event(s), a memory effect. The analyses of temperature, precipitation and streamflow data show some significant linear trends but no pattern is apparent. The analyses of longterm lake levels also identify linear trends but these are all explainable without invoking climate change due to greenhouse gases.

scholarly journals The SPARC Data Initiative: comparisons of CFC-11, CFC-12, HF and SF<sub>6</sub> climatologies from international satellite limb sounders

2016 ◽  
Vol 8 (1) ◽  
pp. 61-78 ◽  
Author(s):  
S. Tegtmeier ◽  
M. I. Hegglin ◽  
J. Anderson ◽  
B. Funke ◽  
J. Gille ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Lower Stratosphere ◽  
Atmospheric Transport ◽  
Temporal Structure ◽  
Data Sets ◽  
Upper Troposphere ◽  
Mean Fields ◽  
Mean State

Abstract. A quality assessment of the CFC-11 (CCl3F), CFC-12 (CCl2F2), HF, and SF6 products from limb-viewing satellite instruments is provided by means of a detailed intercomparison. The climatologies in the form of monthly zonal mean time series are obtained from HALOE, MIPAS, ACE-FTS, and HIRDLS within the time period 1991–2010. The intercomparisons focus on the mean biases of the monthly and annual zonal mean fields and aim to identify their vertical, latitudinal and temporal structure. The CFC evaluations (based on MIPAS, ACE-FTS and HIRDLS) reveal that the uncertainty in our knowledge of the atmospheric CFC-11 and CFC-12 mean state, as given by satellite data sets, is smallest in the tropics and mid-latitudes at altitudes below 50 and 20 hPa, respectively, with a 1σ multi-instrument spread of up to ±5 %. For HF, the situation is reversed. The two available data sets (HALOE and ACE-FTS) agree well above 100 hPa, with a spread in this region of ±5 to ±10 %, while at altitudes below 100 hPa the HF annual mean state is less well known, with a spread ±30 % and larger. The atmospheric SF6 annual mean states derived from two satellite data sets (MIPAS and ACE-FTS) show only very small differences with a spread of less than ±5 % and often below ±2.5 %. While the overall agreement among the climatological data sets is very good for large parts of the upper troposphere and lower stratosphere (CFCs, SF6) or middle stratosphere (HF), individual discrepancies have been identified. Pronounced deviations between the instrument climatologies exist for particular atmospheric regions which differ from gas to gas. Notable features are differently shaped isopleths in the subtropics, deviations in the vertical gradients in the lower stratosphere and in the meridional gradients in the upper troposphere, and inconsistencies in the seasonal cycle. Additionally, long-term drifts between the instruments have been identified for the CFC-11 and CFC-12 time series. The evaluations as a whole provide guidance on what data sets are the most reliable for applications such as studies of atmospheric transport and variability, model–measurement comparisons and detection of long-term trends. The data sets will be publicly available from the SPARC Data Centre and through PANGAEA (doi:10.1594/PANGAEA.849223).

An integrated deterministic–stochastic approach for forecasting the long-term trajectories of COVID-19

2021 ◽  
pp. 2141001
Author(s):  
Indrajit Ghosh ◽  
Tanujit Chakraborty
Keyword(s):  
Time Series ◽  
Stochastic Approach ◽  
World Population ◽  
Data Sets ◽  
Univariate Time Series ◽  
Reproduction Numbers ◽  
The World ◽  
Long Term Forecasting ◽  
Ar Models

The ongoing coronavirus disease 2019 (COVID-19) pandemic is one of the major health emergencies in decades that affected almost every country in the world. As of June 30, 2020, it has caused an outbreak with more than 10 million confirmed infections, and more than 500,000 reported deaths globally. Due to the unavailability of an effective treatment (or vaccine) and insufficient evidence regarding the transmission mechanism of the epidemic, the world population is currently in a vulnerable position. The daily cases data sets of COVID-19 for profoundly affected countries represent a stochastic process comprised of deterministic and stochastic components. This study proposes an integrated deterministic–stochastic approach to forecast the long-term trajectories of the COVID-19 cases for Italy and Spain. The deterministic component of the daily-cases univariate time series is assessed by an extended version of the SIR [Susceptible–Infected–Recovered–Protected–Isolated (SIRCX)] model, whereas its stochastic component is modeled using an autoregressive (AR) time series model. The proposed integrated SIRCX-AR (ISA) approach based on two operationally distinct modeling paradigms utilizes the superiority of both the deterministic SIRCX and stochastic AR models to find the long-term trajectories of the epidemic curves. Experimental analysis based on the proposed ISA model shows significant improvement in the long-term forecasting of COVID-19 cases for Italy and Spain in comparison to the ODE-based SIRCX model. The estimated Basic reproduction numbers for Italy and Spain based on SIRCX model are found to be [Formula: see text] and [Formula: see text], respectively. ISA model-based results reveal that the number of cases in Italy and Spain between 11 May, 2020–9 June, 2020 will be 10,982 (6383–15,582) and 13,731 (3395–29,013), respectively. Additionally, the expected number of daily cases on 9 July, 2020 for Italy and Spain is estimated to be 30 (0–183) and 92 (0–602), respectively.

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