Mending and extending observational temperature series by linear and nonlinear regression

2021 ◽  
Author(s):  
Jiří Mikšovský ◽  
Petr Štěpánek
Keyword(s):  
Nonlinear Regression ◽  
Temperature Series ◽  
The Czech Republic ◽  
Improve Accuracy ◽  
Potential Benefits ◽  
Regression Techniques ◽  
Weather Stations ◽  
Temperature Records ◽  
Daily Temperature Series ◽  
Target Locations

<p>While time series of meteorological measurements from land-based weather stations still represent one of the basic types of data employed in climate research, it not uncommon for these records to be incomplete, interrupted by periods of missing or otherwise compromised values. Such gaps typically need to be filled before a subsequent analysis can be performed, and records from other nearby measuring sites are frequently used for this purpose. In this presentation, results of central European daily temperatures estimation from other concurrent measurements by various statistical methods are showcased, with a particular emphasis on assessing potential benefits of application of nonlinear regression techniques. Using multi-decadal daily temperature series originating from a dense network of weather stations covering the territory of the Czech Republic, we show that while nonlinear regression does not always outperform its linear counterpart, it can substantially improve accuracy of temperature estimates for some target locations. The gain is shown to be especially prominent for sites exhibiting atypical behavior compared to their local geographic neighborhood, such as isolated mountain-based stations. In addition to regression-based restoration of compromised segments in the temperature records, use of this methodology for extending the temperature records beyond their original period of measurements is also discussed, as well as its potential for homogeneity testing.</p>

Long-period weather observations elsewhere in the British Isles and Europe

2019 ◽  
pp. 56-68
Author(s):  
Stephen Burt ◽  
Tim Burt
Keyword(s):  
Temperature Series ◽  
Daily Temperature ◽  
Continuous Series ◽  
British Isles ◽  
Long Period ◽  
Weather Observations ◽  
Central England Temperature ◽  
Weather Stations ◽  
Temperature Records

This chapter summarises other long-period weather observations from both the British Isles and Europe. The Radcliffe Observatory possesses the longest continuous series of weather records in Britain for one site: the first observations date from the mid-1760s, with unbroken daily temperature records since November 1813. It includes references to Gordon Manley’s Central England Temperature series. There are brief descriptions of the longest-running weather stations in Europe, including Uppsala and Stockholm in Sweden, Padua and Milan in Italy, Hohenpeissenberg in Germany, and the British observatories at Kew, Armagh and Durham, many (like Oxford) starting life as astronomical observatories in the eighteenth or early nineteenth centuries. The chapter ends with a brief comment as to why such long weather records remain important in the present day.

scholarly journals Comparison of homogenization methods for daily temperature series against an observation-based benchmark dataset

2020 ◽  
Vol 140 (1-2) ◽  
pp. 285-301 ◽  
Author(s):  
Antonello Angelo Squintu ◽  
Gerard van der Schrier ◽  
Petr Štěpánek ◽  
Pavel Zahradníček ◽  
Albert Klein Tank
Keyword(s):  
Temperature Series ◽  
Benchmark Dataset ◽  
Daily Temperature ◽  
Reference Series ◽  
The Czech Republic ◽  
Benchmark Datasets ◽  
Homogenization Methods ◽  
Daily Temperature Series ◽  
Almost All ◽  
Station Density

AbstractHomogenization of daily temperature series is a fundamental step for climatological analyses. In the last decades, several methods have been developed, presenting different statistical and procedural approaches. In this study, four homogenization methods (together with two variants) have been tested and compared. This has been performed constructing a benchmark dataset, where segments of homogeneous series are replaced with simultaneous measurements from neighboring homogeneous series. This generates inhomogeneous series (the test set) whose homogeneous version (the benchmark set) is known. Two benchmark datasets are created. The first one is based on series from the Czech Republic and has a high quality, high station density, and a large number of reference series. The second one uses stations from all Europe and presents more challenges, such as missing segments, low station density, and scarcity of reference series. The comparison has been performed with pre-defined metrics which check the statistical distance between the homogenized versions and the benchmark. Almost all homogenization methods perform well on the near-ideal benchmark (maximum relative root mean square error (rRMSE): 1.01), while on the European dataset, the homogenization methods diverge and the rRMSE increases up to 1.87. Analyses of the percentages of non-adjusted inhomogeneous data (up to 39%) and substantial differences in the trends among the homogenized versions helped identifying diverging procedural characteristics of the methods. These results add new elements to the debate about homogenization methods for daily values and motivate the use of realistic and challenging datasets in evaluating their robustness and flexibility.

scholarly journals Different Nonlinear Regression Techniques and Sensitivity Analysis as Tools to Optimize Oil Viscosity Modeling

Resources ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 99
Author(s):  
Dicho Stratiev ◽  
Svetoslav Nenov ◽  
Dimitar Nedanovski ◽  
Ivelina Shishkova ◽  
Rosen Dinkov ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Nonlinear Regression ◽  
Information Criterion ◽  
Least Square ◽  
Gas Oil ◽  
Oil Viscosity ◽  
Prediction Ability ◽  
Regression Techniques ◽  
Comparable Accuracy ◽  
Relative Errors

Four nonlinear regression techniques were explored to model gas oil viscosity on the base of Walther’s empirical equation. With the initial database of 41 primary and secondary vacuum gas oils, four models were developed with a comparable accuracy of viscosity calculation. The Akaike information criterion and Bayesian information criterion selected the least square relative errors (LSRE) model as the best one. The sensitivity analysis with respect to the given data also revealed that the LSRE model is the most stable one with the lowest values of standard deviations of derivatives. Verification of the gas oil viscosity prediction ability was carried out with another set of 43 gas oils showing remarkably better accuracy with the LSRE model. The LSRE was also found to predict better viscosity for the 43 test gas oils relative to the Aboul Seoud and Moharam model and the Kotzakoulakis and George.

Use of nonlinear regression techniques for describing concentration-response relationships of plant species exposed to contaminated site soils

2000 ◽  
Vol 19 (12) ◽  
pp. 2968-2981 ◽  
Author(s):  
Gladys L. Stephenson ◽  
Nicola Koper ◽  
Glenn F. Atkinson ◽  
Keith R. Solomon ◽  
Richard P. Scroggins
Keyword(s):  
Plant Species ◽  
Nonlinear Regression ◽  
Contaminated Site ◽  
Regression Techniques

scholarly journals The regression Tsetlin machine: a novel approach to interpretable nonlinear regression

2019 ◽  
Vol 378 (2164) ◽  
pp. 20190165 ◽  
Author(s):  
K. Darshana Abeyrathna ◽  
Ole-Christoffer Granmo ◽  
Xuan Zhang ◽  
Lei Jiao ◽  
Morten Goodwin
Keyword(s):  
Nonlinear Regression ◽  
Binary Representation ◽  
Propositional Formula ◽  
New Class ◽  
Text Understanding ◽  
Novel Approach ◽  
Continuous Output ◽  
Regression Techniques ◽  
Regression Problems ◽  
Continuous Input

Relying simply on bitwise operators, the recently introduced Tsetlin machine (TM) has provided competitive pattern classification accuracy in several benchmarks, including text understanding. In this paper, we introduce the regression Tsetlin machine (RTM), a new class of TMs designed for continuous input and output, targeting nonlinear regression problems. In all brevity, we convert continuous input into a binary representation based on thresholding, and transform the propositional formula formed by the TM into an aggregated continuous output. Our empirical comparison of the RTM with state-of-the-art regression techniques reveals either superior or on par performance on five datasets. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.

scholarly journals Commodity chain and strengthening of the agro-food sector competitiveness

2012 ◽  
Vol 48 (No, 7) ◽  
pp. 293-297
Author(s):  
I. Blažková
Keyword(s):  
Vertical Integration ◽  
Food Processing ◽  
Market Force ◽  
Food Chains ◽  
Commodity Chain ◽  
Food Sector ◽  
The Czech Republic ◽  
Potential Benefits ◽  
Vertical Interconnection ◽  
Production Characteristics

In the last decade, the character of agro-food chains functioning has changed significantly. Globalisation elements in the food processing and distribution are changing conditions in agro-food sector and influencing also agrarian markets. Due to higher food finalisation and market force of processing and distribution stages in the agribusiness commodity vertical, farm value share in the final food price has decreased. Increasing competition makes agribusiness firms  look for possibilities to strengthen their competitiveness, which is increasingly determined by the ability to develop successful partnerships within commodity verticals, i.e. vertical integration, eventually co-ordination, enforces. In this study, potential benefits and risks of these forms of vertical interconnection are reviewed with respect on specific market and production characteristics of agro-food chains. The problem is presented on the example of the commodity chain of bakery and pasta production in the Czech Republic. At the end of the paper, main arguments for the interconnection of particular stages of this vertical are derived, especially between mills and bakeries.

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