Homogenisation of monthly temperatures of the Swedish observational network 1850-2020

2021 ◽  
Author(s):  
L. Magnus T. Joelsson ◽  
Christophe Sturm ◽  
Johan Södling ◽  
Erik Engtsröm
Keyword(s):  
Time Series ◽  
Data Series ◽  
Data Set ◽  
Observational Network ◽  
Input Parameters ◽  
Break Points ◽  
Data Coverage ◽  
Weather Stations ◽  
Climate Indicator ◽  
Selection Of

<p>Monthly averages of statistical temperature variables (i.e. monthly averages of daily maximum, minimum, and mean temperatures) are homogenised for a large part of the Swedish observational network dataset from 1850 to 2020. Data from 573–587 weather stations (depending on variable) are coupled into 299–303 time series. The coupling of time series is partly performed automatically following a set of criteria of geographical proximity, altitude, proximity to coast line, time series overlap, and correlation of the data series.</p><p>The homogenisation of the data set is performed with the recently developed homogenisation tool Bart. Bart is a fully automatic modification of the homogenisation tool HOMER. Bart uses a set of input parameters to accept or reject potential homogeneity break points suggested by the different functions of HOMER. Bart performs correction and gap filling of the data series according to the accepted homogeneity break points. A rudimentary sensitivity test is performed to examine how sensitive the homogenisation is to the selection of the input parameters assumed most important and to find a optimal set up of these parameters. Other features in Bart include a novel procedure for the selection of reference time series to account for uneven data coverage, and parallel computing to reduce the computational time.</p><p>An important application of the homogenised data set is the calculation of the climate indicator of temperature. The climate indicator of temperature is the average annual mean temperatures of thirty-nine weather stations, carefully selected to represent the climate in Sweden over the last 170 years. The use of homogenised data gives a 1.8 °C (10 a)-1 greater warming than if raw data is used from 1860 to present, the period for which data coverage is sufficient.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.18ad2b13c10069128270161/sdaolpUECMynit/12UGE&app=m&a=0&c=73e0a62bb7be6a58ba81126e3ce2b48e&ct=x&pn=gnp.elif&d=1" alt=""></p>

Long term variations of the hydrochemical composition of deep thermal ground water in the Lower Bavarian Molasse Basin – Causes and Perspectives

2021 ◽  
Author(s):  
Annette Dietmaier ◽  
Thomas Baumann
Keyword(s):  
Time Series ◽  
Real Life ◽  
Data Series ◽  
Fluid Composition ◽  
Molasse Basin ◽  
Deep Groundwater ◽  
Data Set ◽  
Groundwater Exploitation ◽  
Groundwater Aquifers

<p>The European Water Framework Directive (WFD) commits EU member states to achieve a good qualitative and quantitative status of all their water bodies.  WFD provides a list of actions to be taken to achieve the goal of good status.  However, this list disregards the specific conditions under which deep (> 400 m b.g.l.) groundwater aquifers form and exist.  In particular, deep groundwater fluid composition is influenced by interaction with the rock matrix and other geofluids, and may assume a bad status without anthropogenic influences. Thus, a new concept with directions of monitoring and modelling this specific kind of aquifers is needed. Their status evaluation must be based on the effects induced by their exploitation. Here, we analyze long-term real-life production data series to detect changes in the hydrochemical deep groundwater characteristics which might be triggered by balneological and geothermal exploitation. We aim to use these insights to design a set of criteria with which the status of deep groundwater aquifers can be quantitatively and qualitatively determined. Our analysis is based on a unique long-term hydrochemical data set, taken from 8 balneological and geothermal sites in the molasse basin of Lower Bavaria, Germany, and Upper Austria. It is focused on a predefined set of annual hydrochemical concentration values. The data range dates back to 1937. Our methods include developing threshold corridors, within which a good status can be assumed, and developing cluster analyses, correlation, and piper diagram analyses. We observed strong fluctuations in the hydrochemical characteristics of the molasse basin deep groundwater during the last decades. Special interest is put on fluctuations that seem to have a clear start and end date, and to be correlated with other exploitation activities in the region. For example, during the period between 1990 and 2020, bicarbonate and sodium values displayed a clear increase, followed by a distinct dip to below-average values and a subsequent return to average values at site F. During the same time, these values showed striking irregularities at site B. Furthermore, we observed fluctuations in several locations, which come close to disqualifying quality thresholds, commonly used in German balneology. Our preliminary results prove the importance of using long-term (multiple decades) time series analysis to better inform quality and quantity assessments for deep groundwater bodies: most fluctuations would stay undetected within a < 5 year time series window, but become a distinct irregularity when viewed in the context of multiple decades. In the next steps, a quality assessment matrix and threshold corridors will be developed, which take into account methods to identify these fluctuations. This will ultimately aid in assessing the sustainability of deep groundwater exploitation and reservoir management for balneological and geothermal uses.</p>

scholarly journals Stationary and Non-Stationary Frameworks for Extreme Rainfall Time Series in Southern Italy

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1477 ◽  
Author(s):  
Davide De Luca ◽  
Luciano Galasso
Keyword(s):  
Time Series ◽  
Climate Models ◽  
Southern Italy ◽  
Regional Climate ◽  
Data Series ◽  
Rainfall Time Series ◽  
Annual Maximum ◽  
Data Set ◽  
Maximum Rainfall ◽  
Rainfall Events

This study tests stationary and non-stationary approaches for modelling data series of hydro-meteorological variables. Specifically, the authors considered annual maximum rainfall accumulations observed in the Calabria region (southern Italy), and attention was focused on time series characterized by heavy rainfall events which occurred from 1 January 2000 in the study area. This choice is justified by the need to check if the recent rainfall events in the new century can be considered as very different or not from the events occurred in the past. In detail, the whole data set of each considered time series (characterized by a sample size N > 40 data) was analyzed, in order to compare recent and past rainfall accumulations, which occurred in a specific site. All the proposed models were based on the Two-Component Extreme Value (TCEV) probability distribution, which is frequently applied for annual maximum time series in Calabria. The authors discussed the possible sources of uncertainty related to each framework and remarked on the crucial role played by ergodicity. In fact, if the process is assumed to be non-stationary, then ergodicity cannot hold, and thus possible trends should be derived from external sources, different from the time series of interest: in this work, Regional Climate Models’ (RCMs) outputs were considered in order to assess possible trends of TCEV parameters. From the obtained results, it does not seem essential to adopt non-stationary models, as significant trends do not appear from the observed data, due to a relevant number of heavy events which also occurred in the central part of the last century.

Identifying anthropogenic effects into Doñana aquifer (SW Spain) through hydrogram clustering of piezometric database

2020 ◽  
Author(s):  
Carolina Guardiola-Albert ◽  
Nuria Naranjo-Fernández ◽  
Héctor Aguilera ◽  
Esperanza Montero-González
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Groundwater Resources ◽  
Data Series ◽  
Time Series Clustering ◽  
Missing Data Imputation ◽  
Groundwater Levels ◽  
Aquifer Systems ◽  
Visual Clustering ◽  
Selection Of

<p>Nowadays, the application of time series clustering is increasing in hydrogeology works. Groundwater level long data series provides a useful record to identify different hydrological behaviors and to validate the conceptual model of groundwater flow in aquifer systems. Piezometers also register the response to any changes that directly affect the amount of available groundwater resources (recharge or exploitation).</p><p>What are the expected variations of groundwater levels in an aquifer under high exploitation pressure? In this work, groundwater level time series from 160 piezometers in the hydrological years from 1975 to 2016 were analyzed. Especially, 24 piezometers are deeply studied. Data were preprocessed and transformed: selection of points, missing data imputation and data standardization. Visual clustering, k-means clustering and time series clustering were applied to classify groundwater level hydrographs using the available database. Six and seven groups of piezometers were identified to be associated with the different hydrofacies and extraction rates. Time series clustering was found to be the best method to analyze the studied piezometric database. Moreover, it was possible to characterize actual hydrodynamics, which will be useful for groundwater managers to make sustainable decisions.</p>

scholarly journals A pragmatic re-engineering approach to extend short observation time series by climate anomaly gridding

2021 ◽  
Author(s):  
Ole Einar Tveito
Keyword(s):  
Time Series ◽  
Mean Value ◽  
Observation Time ◽  
Data Series ◽  
Climate Anomaly ◽  
Data Set ◽  
Actual Problem ◽  
Climate Gradients ◽  
Observation Series

<p>For many purposes, including the estimation of climate normals, requires long, continuous  and preferably homogeneous time series. Many observation series do not meet these requirements, especially due to modernisation and automation of the observation network. Despite the lack of long series there is still a need to provide climate parameters representing a longer time period than available. An actual problem is the calculation of new standard climate normals for the 1991-2020 period, where normal values need to be assigned also for observation series not meeting the requirements of WMO to estimate climate normals from observations. </p><p>One possible approach to estimate monthly time series is to extract value from gridded climate anomaly fields. In this study this approach is applied to complete time series that will be the basis for calculation of long term reference values.</p><p>The calculation of the long term time series is a two step procedure. First monthly anomaly grids based on homogenised data series are produced. The homogenized series provide more stable and reliable spatial estimates than applying non homogenised data. The homogenised data set is also complete ensuring a spatially consistent input throughout the analysis period 1991-2020.</p><p>The monthly anomalies for the location of the series to be complete are extracted from the gridded fields. By combining the interpolated anomalies with the observations the long term mean value can be estimated. The study shows that this approach provides reliable estimates of long term values, even with just a few events for calibration. The precision of the estimates depend more on the representativity of the grid estimates than length of the observation series. At locations where the anomaly grids represent the spatial climate variability well, stable estimates are achieved. On the other hand will the estimates at locations where the anomaly grids are less accurate due to sparse data coverage or steep climate gradients lead to estimates with a larger variability, and  thus more uncertain estimates. </p>

scholarly journals An update on multivariate return periods in hydrology

2016 ◽  
Vol 373 ◽  
pp. 175-178 ◽  
Author(s):  
Benedikt Gräler ◽  
Andrea Petroselli ◽  
Salvatore Grimaldi ◽  
Bernard De Baets ◽  
Niko Verhoest
Keyword(s):  
Time Series ◽  
Return Period ◽  
Simulated Data ◽  
Time Span ◽  
Return Periods ◽  
Data Set ◽  
Univariate Case ◽  
Definition Of ◽  
Selection Of

Abstract. Many hydrological studies are devoted to the identification of events that are expected to occur on average within a certain time span. While this topic is well established in the univariate case, recent advances focus on a multivariate characterization of events based on copulas. Following a previous study, we show how the definition of the survival Kendall return period fits into the set of multivariate return periods.Moreover, we preliminary investigate the ability of the multivariate return period definitions to select maximal events from a time series. Starting from a rich simulated data set, we show how similar the selection of events from a data set is. It can be deduced from the study and theoretically underpinned that the strength of correlation in the sample influences the differences between the selection of maximal events.

scholarly journals Investigating bias in the application of curve fitting programs to atmospheric time series

2014 ◽  
Vol 7 (7) ◽  
pp. 7085-7136 ◽  
Author(s):  
P. A. Pickers ◽  
A. C. Manning
Keyword(s):  
Time Series ◽  
Curve Fitting ◽  
Data Sets ◽  
Mathematical Functions ◽  
Data Set ◽  
Zero Crossing ◽  
Sensitivity Tests ◽  
Input Parameters ◽  
Fitting Procedures ◽  
Gas Measurement

Abstract. The decomposition of an atmospheric time series into its constituent parts is an essential tool for identifying and isolating variations of interest from a data set, and is widely used to obtain information about sources, sinks and trends in climatically important gases. Such procedures involve fitting appropriate mathematical functions to the data, however, it has been demonstrated that the application of such curve fitting procedures can introduce bias, and thus influence the scientific interpretation of the data sets. We investigate the potential for bias associated with the application of three curve fitting programs, known as HPspline, CCGCRV and STL, using CO2, CH4 and O3 data from three atmospheric monitoring field stations. These three curve fitting programs are widely used within the greenhouse gas measurement community to analyse atmospheric time series, but have not previously been compared extensively. The programs were rigorously tested for their ability to accurately represent the salient features of atmospheric time series, their ability to cope with outliers and gaps in the data, and for sensitivity to the values used for the input parameters needed for each program. We find that the programs can produce significantly different curve fits, and these curve fits can be dependent on the input parameters selected. There are notable differences between the results produced by the three programs for many of the decomposed components of the time series, such as the representation of seasonal cycle characteristics and the long-term growth rate. The programs also vary significantly in their response to gaps and outliers in the time series. Overall, we found that none of the three programs were superior, and that each program had its strengths and weaknesses. Thus, we provide a list of recommendations on the appropriate use of these three curve fitting programs for certain types of data sets, and for certain types of analyses and applications. In addition, we recommend that sensitivity tests are performed in any study using curve fitting programs, to ensure that results are not unduly influenced by the input smoothing parameters chosen. Our findings also have implications for previous studies that have relied on a single curve fitting program to interpret atmospheric time series measurements. This is demonstrated by using two other curve fitting programs to replicate work in Piao et al. (2008) on zero-crossing analyses of atmospheric CO2 seasonal cycles to investigate terrestrial biosphere changes. We highlight the importance of using more than one program, to ensure results are consistent, reproducible, and free from bias.

scholarly journals A Synthesis of the Information Given by Temporal Data Series: The Representative Day

2021 ◽  
Author(s):  
Tiziano Tirabassi ◽  
Daniela Buske
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Least Squares ◽  
Data Series ◽  
Data Sets ◽  
Temporal Data ◽  
Concentration Data ◽  
Data Set ◽  
Meaningful Information ◽  
And Control

The recording of air pollution concentration values involves the measurement of a large volume of data. Generally, automatic selectors and explicators are provided by statistics. The use of the Representative Day allows the compilation of large amounts of data in a compact format that will supply meaningful information on the whole data set. The Representative Day (RD) is a real day that best represents (in the meaning of the least squares technique) the set of daily trends of the considered time series. The Least Representative Day (LRD), on the contrary, it is a real day that worst represents (in the meaning of the least squares technique) the set of daily trends of the same time series. The identification of RD and LRD can prove to be a very important tool for identifying both anomalous and standard behaviors of pollutants within the selected period and establishing measures of prevention, limitation and control. Two application examples, in two different areas, are presented related to meteorological and SO 2 and O 3 concentration data sets.

scholarly journals Long-term in situ measurements of NO<sub>x</sub> and NO<sub>y</sub> at Jungfraujoch 1998–2009: time series analysis and evaluation

2011 ◽  
Vol 11 (8) ◽  
pp. 21835-21875
Author(s):  
S. Pandey Deolal ◽  
D. Brunner ◽  
M. Steinbacher ◽  
U. Weers ◽  
J. Staehelin
Keyword(s):  
Time Series ◽  
Warm Season ◽  
Data Series ◽  
Data Set ◽  
Long Term Trends ◽  
Investigation Period ◽  
Transport And Mixing ◽  
Time Required

Abstract. We present an analysis of the NOy (NOx + other oxidized species) measurements at the high alpine site Jungfraujoch (JFJ, 3580 m a.s.l.) for the period 1998–2009, which is the longest continous NOy data set reported from the lower free troposphere worldwide. Due to stringent emission control regulations, nitrogen oxides (NOx) emissions have been reduced significantly in Europe since the late 1980s as well as during the investigation period. However, the time series of NOy at JFJ does not show a consistent trend but a maximum during 2002 to 2004 and a decreasing tendency thereafter. The seasonal cycle of NOy exhibits a maximum in the warm season and a minimum in the cold months, opposite to measurements in the PBL, reflecting the seasonal changes in vertical transport and mixing. Except for summer, the seasonal mean NOx concentrations at JFJ show a high year-to-year variability which is strongly controlled by short episodic pollution events obscuring any long-term trends. The low variability in mean and median NOx values in summer is quite remarkable indicating rapid photochemical conversion of NOx to higher oxidized species (NOz) of the NOy family on a timescale shorter than the time required to transport polluted air from the boundary layer to JFJ. In order to evaluate the quality of the NOy data series, an in-situ intercomparison with a second collocated NOy analyzer with a separate inlet was performed in 2009–2010 which showed an agreement within 10 % including all uncertainties and errors.

scholarly journals Investigating bias in the application of curve fitting programs to atmospheric time series

2015 ◽  
Vol 8 (3) ◽  
pp. 1469-1489 ◽  
Author(s):  
P. A. Pickers ◽  
A. C. Manning
Keyword(s):  
Time Series ◽  
Curve Fitting ◽  
Data Sets ◽  
Mathematical Functions ◽  
Data Set ◽  
Zero Crossing ◽  
Sensitivity Tests ◽  
Input Parameters ◽  
Fitting Procedures ◽  
Gas Measurement

Abstract. The decomposition of an atmospheric time series into its constituent parts is an essential tool for identifying and isolating variations of interest from a data set, and is widely used to obtain information about sources, sinks and trends in climatically important gases. Such procedures involve fitting appropriate mathematical functions to the data. However, it has been demonstrated that the application of such curve fitting procedures can introduce bias, and thus influence the scientific interpretation of the data sets. We investigate the potential for bias associated with the application of three curve fitting programs, known as HPspline, CCGCRV and STL, using multi-year records of CO2, CH4 and O3 data from three atmospheric monitoring field stations. These three curve fitting programs are widely used within the greenhouse gas measurement community to analyse atmospheric time series, but have not previously been compared extensively. The programs were rigorously tested for their ability to accurately represent the salient features of atmospheric time series, their ability to cope with outliers and gaps in the data, and for sensitivity to the values used for the input parameters needed for each program. We find that the programs can produce significantly different curve fits, and these curve fits can be dependent on the input parameters selected. There are notable differences between the results produced by the three programs for many of the decomposed components of the time series, such as the representation of seasonal cycle characteristics and the long-term (multi-year) growth rate. The programs also vary significantly in their response to gaps and outliers in the time series. Overall, we found that none of the three programs were superior, and that each program had its strengths and weaknesses. Thus, we provide a list of recommendations on the appropriate use of these three curve fitting programs for certain types of data sets, and for certain types of analyses and applications. In addition, we recommend that sensitivity tests are performed in any study using curve fitting programs, to ensure that results are not unduly influenced by the input smoothing parameters chosen. Our findings also have implications for previous studies that have relied on a single curve fitting program to interpret atmospheric time series measurements. This is demonstrated by using two other curve fitting programs to replicate work in Piao et al. (2008) on zero-crossing analyses of atmospheric CO2 seasonal cycles to investigate terrestrial biosphere changes. We highlight the importance of using more than one program, to ensure results are consistent, reproducible, and free from bias.

scholarly journals Two decades of satellite observations of AOD over mainland China

2017 ◽  
Author(s):  
Gerrit de Leeuw ◽  
Larisa Sogacheva ◽  
Edith Rodriguez ◽  
Konstantinos Kourtidis ◽  
Aristeidis K. Georgoulias ◽  
...  
Keyword(s):  
Time Series ◽  
Mainland China ◽  
Satellite Observations ◽  
Data Series ◽  
Data Sets ◽  
Data Set ◽  
Modis Aod ◽  
The Difference ◽  
Aerosol Properties ◽  
Better Than

Abstract. The retrieval of aerosol properties from satellite observations provides their spatial distribution over a wide area in cloud-free conditions. As such, they complement ground-based measurements by providing information over sparsely instrumented areas, albeit that significant differences may exist in both the type of information obtained and the temporal information from satellite and ground-based observations. In this paper, information from different types of satellite-based instruments is used to provide a 3-D climatology of aerosol properties over mainland China, i.e. vertical profiles of extinction coefficients from CALIOP, a lidar flying on board the CALIPSO satellite, and the column-integrated extinction (AOD), available from three radiometers: ESA’s ATSR-2, AATSR (together referred to as ATSR) and NASA's MODIS/Terra, together spanning the period 1995–2015. AOD data are retrieved from ATSR using the ADV v2.31 algorithm while for MODIS the Collection 6 (C6) DTDB merged AOD data set is used. These data sets are validated and differences are compared using AERONET version 2 L2.0 AOD data as reference. The results show that, over China, MODIS slightly overestimates the AOD and ATSR slightly underestimates the AOD. Consequently, MODIS AOD is overall higher than that from ATSR, and the difference increases with increasing AOD. The comparison also shows that none of the ATSR and MODIS AOD data sets is better than the other one everywhere. However, ATSR ADV has limitations over bright surfaces where the MODIS DB was designed for. To allow for comparison of MODIS C6 results with previous analyses where MODIS Collection 5.1 (C5.1) data were used, also the difference between the C6 and C5.1 DTDB merged data sets from MODIS/Terra over China is briefly discussed. The AOD data sets show strong seasonal differences and the seasonal features vary with latitude and longitude across China. Two-decadal AOD time series, averaged over the whole mainland China, are presented and briefly discussed. Using the 17 years of ATSR data as the basis and MODIS/Terra to follow the temporal evolution in recent years when ENVISAT was lost requires a comparison of the data sets for the overlapping period to show their complementarity. ATSR precedes the MODIS time series between 1995 and 2000 and shows a distinct increase in the AOD over this period. The two data series show similar variations during the overlapping period between 2000 and 2011, with minima and maxima in the same years. MODIS extends this time series beyond the end of the ENVISAT period in 2012, showing decreasing AOD.

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