Identifying Rogue Air Temperature Stations Using Cluster Analysis of Percentile Trends

2005 ◽  
Vol 18 (8) ◽  
pp. 1275-1287 ◽  
Author(s):  
Scott M. Robeson ◽  
Jeffrey A. Doty
Keyword(s):  
Cluster Analysis ◽  
Air Temperature ◽  
Large Body ◽  
Large Temperature ◽  
Daily Maximum ◽  
Climatic Data ◽  
Single Linkage ◽  
Frequency Distributions ◽  
Temperature Trends ◽  
Average Linkage

Abstract A new and efficient method for identifying “rogue” air temperature stations—locations with unusually large air temperature trends—is presented. Instrumentation problems and spatially unrepresentative local climates are sometimes more apparent in air temperature extremes, yet can have more subtle impacts on variations in mean air temperature. As a result, using data from over 1300 stations in North America, the tails of daily air temperature frequency distributions were examined for unusual trends. In particular, linear trends in the 5th percentile of daily minimum air temperature during the winter months and the 95th percentile of daily maximum air temperature during the summer were analyzed. Cluster analysis then was used to identify stations that were distinct from other locations. Both single- and average linkage clustering were evaluated. By identifying individual stations along the entire periphery of the percentile trend space, single-linkage clustering appears to produce better results than that of average linkage. Average linkage clustering tends to group together several stations with large trends; however, only a handful of these stations appear distinctly different from the large body of trends toward the center of the percentile trend space. Maps of the rogue stations show that most are in close proximity to numerous other stations that were not grouped into the rogue cluster, making it unlikely that the unusually large temperature trends were due to regional climatic variations. As with all approaches for evaluating data quality, time series plots and station history information also must be inspected to more fully understand inhomogeneous variations in historical climatic data.

scholarly journals PENGELOMPOKAN DESA/KELURAHAN DI KOTA DENPASAR MENURUT INDIKATOR PENDIDIKAN

2016 ◽  
Vol 5 (2) ◽  
pp. 38
Author(s):  
NI WAYAN ARIS APRILIA A.P ◽  
I GUSTI AYU MADE SRINADI ◽  
KARTIKA SARI
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
The Other ◽  
Single Linkage ◽  
Complete Linkage ◽  
Hierarchical Method ◽  
Average Linkage ◽  
Different Characteristics

Cluster analysis is one of data analysis used to classify objects in clusters which has objects with the same characteristics, whereas the other cluster has different characteristics. One part of the method of analysis cluster is hierarchy method. In a hierarchical method there are methods of linkage in the form of incorporation. Generally, methods of linkage is divided into 5 methods: single linkage, complete linkage, average linkage, Ward and centroid.  The purpose of this study was to determine the best method of linkage among the method of single linkage, complete linkage, average linkage, and Ward, using Euclidean and Pearson proximity distance. Base on the smallest value of CTM (Cluster Tightness Measure), the best method of linkage as a result of this research was average linkage in Pearson distance.

scholarly journals Validity studies among hierarchical methods of cluster analysis using cophenetic correlation coefficient

2019 ◽  
Vol 7 (2A) ◽  
Author(s):  
Priscilla Ramos Carvalho ◽  
Casimiro Sepúlveda Munita ◽  
André Luiz Lapolli
Keyword(s):  
Cluster Analysis ◽  
Correlation Coefficient ◽  
Single Linkage ◽  
Cophenetic Correlation ◽  
Data Set ◽  
Archaeological Data ◽  
Average Linkage ◽  
Linkage Method ◽  
Cophenetic Correlation Coefficient ◽  
Statistical Program

The literature presents many methods for partitioning of data set, and is difficult choose which is the most suitable, since the various combinations of methods based on different measures of dissimilarity can lead to different patterns of grouping and false interpretations. Nevertheless, little effort has been expended in evaluating these methods empirically using an archaeological data set. In this way, the objective of this work is make a comparative study of the different cluster analysis methods and identify which is the most appropriate. For this, the study was carried out using a data set of 45 samples of ceramic fragments, analyzed by instrumental neutron activation analysis (INAA). The methods used for this study were: Single linkage, Complete linkage, Average linkage, Centroid and Ward. The validation was done using the cophenetic correlation coefficient and comparing these values the average linkage method obtained better results. A script of the statistical program R with some functions was created to obtain the cophenetic correlation. By means of these values was possible to choose the most appropriate method to be used in the data set.

scholarly journals Comparative Study of Hydrochemical Classification Based on Different Hierarchical Cluster Analysis Methods

2020 ◽  
Vol 17 (24) ◽  
pp. 9515
Author(s):  
Jianwei Bu ◽  
Wei Liu ◽  
Zhao Pan ◽  
Kang Ling
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Minimum Variance ◽  
Single Linkage ◽  
Complete Linkage ◽  
Advantages And Disadvantages ◽  
Average Linkage ◽  
Analysis Methods ◽  
Hydrochemical Classification

Traditional methods for hydrochemical analyses are effective but less diversified, and are constrained to limited objects and conditions. Given their poor accuracy and reliability, they are often used in complement or combined with other methods to solve practical problems. Cluster analysis is a multivariate statistical technique that extracts useful information from complex data. It provides new ideas and approaches to hydrogeochemical analysis, especially for groundwater hydrochemical classification. Hierarchical cluster analysis is the most widely used method in cluster analysis. This study compared the advantages and disadvantages of six hierarchical cluster analysis methods and analyzed their objects, conditions, and scope of application. The six methods are: The single linkage, complete linkage, median linkage, centroid linkage, average linkage (including between-group linkage and within-group linkage), and Ward’s minimum-variance. Results showed that single linkage and complete linkage are unsuitable for complex practical conditions. Median and centroid linkages likely cause reversals in dendrograms. Average linkage is generally suitable for classification tasks with multiple samples and big data. However, Ward’s minimum-variance achieved better results for fewer samples and variables.

scholarly journals An Improved QC Process for Temperature in the Daily Cooperative Weather Observations

2007 ◽  
Vol 24 (2) ◽  
pp. 206-213 ◽  
Author(s):  
Kenneth G. Hubbard ◽  
Nathaniel B. Guttman ◽  
Jinsheng You ◽  
Zhirong Chen
Keyword(s):  
Quality Assurance ◽  
Data Quality ◽  
Quality Assessment ◽  
Air Temperature ◽  
Spatial Regression ◽  
Daily Maximum ◽  
Correct Identification ◽  
Climatic Data ◽  
Regression Test ◽  
Minimum Air Temperature

Abstract TempVal is a spatial component of data quality assurance algorithms applied by the National Climatic Data Center (NCDC), and it has been used operationally for about 4 yr. A spatial regression test (SRT) approach was developed at the regional climate centers for climate data quality assurance and was found to be superior to currently used quality control (QC) procedures for the daily maximum and minimum air temperature. The performance of the spatial quality assessment procedures has been evaluated by assessing the rate with which seeded errors are identified. A complete dataset with seeded errors for the year 2003 for the contiguous United States was examined for both the maximum and minimum air temperature. The spatial regression quality assessment component (SRT), originating in the Automated Climate Information System (ACIS), and TempVal, originating in the NCDC database, were applied separately and evaluated through the ratio of identified seeded errors to the total number of seeds. The spatial regression test applied in the ACIS system was found to perform better in identifying the seeded errors. For all months, the relative frequency of correct identification of wrong data is 0.72 and 0.83 for TempVal and SRT, respectively. The goal of the comparison was to evaluate quality assurance techniques that could improve data quality assessment at the NCDC, and the results of the comparison led to the recommendation that the SRT be included in the NCDC quality assessment methodology.

Temperature variations in planting mounds during winter

1995 ◽  
Vol 25 (3) ◽  
pp. 507-515 ◽  
Author(s):  
Anders Lindström ◽  
Erik Troeng
Keyword(s):  
Soil Temperature ◽  
Air Temperature ◽  
Early Spring ◽  
Large Temperature ◽  
Winter Period ◽  
Daily Maximum ◽  
Temperature Changes ◽  
Winter Temperatures ◽  
Dry Conditions ◽  
Soil Temperatures

Soil temperatures were measured at a depth of 8 cm in top, middle, and bottom positions of 30 cm high mineral and organic mounds and at 8 cm depth in scarified patches during winter and spring 1987–1988 and 1988–1989. At low air temperature, frozen mounds without snow cover showed much lower temperatures than snow-covered mounds, the maximum difference being 16 °C. During the coldest period of the two winters, when minimum air temperature was −26 °C, soil temperature in the top of a snowless mineral mound remained within −16 to −10 °C for 3.5 days and −8 to −5 °C in a snowless scarified patch. Minimum temperatures were lower, duration of low temperature freezing was longer, and temperature changes were more rapid in mineral than in organic mounds. Large temperature differences were found between the top and the bottom of mounds. In dry conditions during early spring, the upper part of the mineral mound thawed and froze repeatedly with daily maximum and minimum temperatures of 5 °C and −6 °C. Soil temperature patterns during the winter period are discussed in relation to root freezing tolerance of conifer seedlings. Mounding as a scarification method should be used with care as winter temperatures may injure seedling root systems.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

scholarly journals An updated evaluation of the global mean land surface air temperature and surface temperature trends based on CLSAT and CMST

2021 ◽  
Vol 56 (1-2) ◽  
pp. 635-650 ◽  
Author(s):  
Qingxiang Li ◽  
Wenbin Sun ◽  
Xiang Yun ◽  
Boyin Huang ◽  
Wenjie Dong ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Temperature Trends ◽  
Global Mean

Genetic Diversity Assessment in Dolichos Bean (Lablab purpureus L.) Based on Principal Component Analysis and Single Linkage Cluster Analysis

2021 ◽  
Author(s):  
S.R. Singh ◽  
S. Rajan ◽  
Dinesh Kumar ◽  
V.K. Soni
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Single Linkage ◽  
Pod Yield ◽  
Diversity Assessment ◽  
Single Linkage Cluster ◽  
Linkage Cluster

Background: Dolichos bean occupies a unique position among the legume vegetables of Indian origin for its high nutritive value and wider climatic adaptability. Despite its wide genetic diversity, no much effort has been undertaken towards genetic improvement of this vegetable crop. Knowledge on genetic variability is an essential pre-requisite as hybrid between two diverse parental lines generates broad spectrum of variability in segregating population. The current study aims to assess the genetic diversity in dolichos genotypes to make an effective selection for yield improvement.Methods: Twenty genotypes collected from different regions were evaluated during year 2016-17 and 2017-18. Data on twelve quantitative traits was analysed using principal component analysis and single linkage cluster analysis for estimation of genetic diversity.Result: Principal component analysis revealed that first five principal components possessed Eigen value greater than 1, cumulatively contributed greater than 82.53% of total variability. The characters positively contributing towards PC-I to PC-V may be considered for dolichos improvement programme as they are major traits involved in genetic variation of pod yield. All genotypes were grouped into three clusters showing non parallelism between geographic and genetic diversity. Cluster-I was best for earliness and number of cluster/plant. Cluster-II for vine length, per cent fruit set, pod length, pod width, pod weight and number of seed /pod, cluster III for number of pods/cluster and pod yield /plant. Selection of parent genotypes from divergent cluster and component having more than one positive trait of interest for hybridization is likely to give better progenies for development of high yielding varieties in Dolichos bean.

Micro-climatological influences on temperature condition in an old people’s home in Helsinki, Finland, caused by extended heat-waves 

2021 ◽  
Author(s):  
Achim Drebs ◽  
Tim Sinsel ◽  
Kirsti Jylhä
Keyword(s):  
Air Temperature ◽  
Heat Waves ◽  
Daily Maximum ◽  
Old People ◽  
Concrete Walls ◽  
Meteorological Forcing ◽  
Clear Sky ◽  
Wave Event ◽  
Construction Type ◽  
Maximum Air Temperature

&lt;p&gt;In our research we describe the micro-climatological influences of two heat-waves around and the air temperature development in a certain old people&amp;#8217;s home in Helsinki, Finland. The stand-alone six-storey concrete building was erected in the late 1970&amp;#8217;s and represents the prevailing construction type of this area. The building is located on a slightly southwards declining slope.&lt;/p&gt;&lt;p&gt;The first simulation used real meteorological forcing-data from the heat-wave event in summer 2018, which lasted from July, 13&lt;sup&gt;th&lt;/sup&gt; until August, 5&lt;sup&gt;th&lt;/sup&gt;. In this period the daily maximum air temperature reached almost every day 25 &amp;#176;C and more, sometimes even more than 30 &amp;#176;C. All air temperature, wind, humidity, and solar radiation (cloudiness) measurements were conducted at a near-by synoptical weather station.&lt;/p&gt;&lt;p&gt;The second simulation used fourteen-day constructed meteorological forcing-data, based on a clear-sky, slowly increasing air temperature, higher than normal humidity, and low wind conditions assumption starting on July, 13&lt;sup&gt;th&lt;/sup&gt; (day 194 of the year).&lt;/p&gt;&lt;p&gt;We used the holistic ENVI-met simulation soft-ware to simulate the physical environment around the old people&amp;#8217;s home and especially the energy fluxes inside the concrete walls to explain the needs for cooling demands.&lt;/p&gt;&lt;p&gt;The research is part of the HEATCLIM-project financed by the Academy of Finland Science Program CLIHE (2020-2023).&lt;/p&gt;

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