An algorithm for daily temperature comparison: Co.Temp - comparing series of temperature (original) (raw)
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Homogenization of daily temperature series in the European Climate Assessment & Dataset
International Journal of Climatology
The daily maximum and minimum temperature series of the European Climate Assessment & Dataset are homogenised using the quantile matching approach. As the dataset is large and the detail of metadata is generally missing, an automated method locates breaks in the series based on a comparison with surrounding series and applies adjustments which are estimated using homogeneous segments of surrounding series as reference. A total of 6500 series have been processed and after removing duplicates and short series, about 2100 series have been have been adjusted. Finally, the effect of the homogenization of daily maximum and minimum temperature on trend estimation is shown to produce a much more spatially homogeneous and then plausible picture.
Temperature as an Indicator of Climate Variation at a Local Weather Station
2012
This study assessed temperature data from Chichiri weather station in Blantyre, Malawi for the possibility of warming and variation over a nine year (2000-2009) period. Blantyre was chosen because it is the industrial and commercial capital of Malawi whereas Chichiri is close to two industrial sites (Makata and Maselema). The data used was for mean monthly minimum temperature and mean monthly maximum temperature. The data was analyzed using Statistical Package for Social Scientists (SPSS) and Microsoft Excel. The results showed that there were no significant differences (p > 0.05) between yearly mean minimum temperatures which was also the case for the mean maximum temperature. However there were significant differences between mean monthly minimum and maximum temperatures (p < 0.05). Graphical presentation of the data showed increase in temperature over the years, especially between 2004 -2005, which is in agreement with literature. This study has shown that temperatures over Chichiri are increasing which may be indicative of variation and sequential warming.
Journal of Climate, 2018
Traditionally, the daily average air temperature at a weather station is computed by taking the mean of two values, the maximum temperature (Tmax) and the minimum temperature (Tmin) over a 24-hour period. These values form the basis for numerous studies of long-term climatologies (e.g., 30-year normals) and recent temperature trends and changes. However, many first-order weather stations-- such as those at airports-- also record hourly temperature data. Using an average of the 24 hourly temperature readings to compute daily average temperature has been shown to provide a more precise and representative estimate of a given day’s temperature. This study assesses the spatial variability of the differences in these two methods of daily temperature averaging (i.e., [Tmax + Tmin]/2, average of 24 hourly temperature values) for 215 first-order weather stations across the conterminous United States (CONUS) the 30-year period 1981-2010. A statistically significant difference is shown between the two methods, as well as consistent overestimation of temperature by the traditional method ([Tmax + Tmin]/2), particularly in southern and coastal portions of the CONUS. The explanation for the long-term difference between the two methods is the underlying assumption for the twice- daily method that the diurnal curve of temperature is symmetrical. Moreover, this paper demonstrates a spatially-coherent pattern in the difference compared to the most recent part of the temperature record (2001-2015). The spatial and temporal differences shown have implications for assessments of the physical factors influencing the diurnal temperature curve, as well as the exact magnitude of contemporary climate change.
A daily high-resolution gridded climatic data set for Emilia-Romagna, Italy, during 1961-2010
International Journal of Climatology, 2015
A daily high-resolution gridded climatic data set is presented for Emilia-Romagna, Italy, covering the period 1961-2010. Time series of precipitation and temperatures, from 254 and 60 locations, respectively, were first checked for quality, temporal homogeneity and synchronicity, then interpolated on a grid. For temperature, a daily best-performing detrending procedure was used, followed by the interpolation of regression residuals by means of a modified inverse distance scheme, accounting for orographic barriers. Elevation, urban fraction and topographic position are the geographical proxy parameters used for detrending. The same scheme, without detrending, was used for daily precipitation. All data were spatially interpolated on a high-resolution digital elevation model, and then averaged on a triangulated irregular grid with variable resolution depending on topography. Interpolation determined average errors between 1.0 and 1.5 ∘ C, with higher values for minimum temperatures, in winter and for years prior to 2000. Precipitation is on average underestimated, up to 25% for intense and heavy precipitation in the summer semester. Multiple detrending improves minimum temperature estimation, while the modified distance scheme reduces interpolation errors for temperature and precipitation. The data set is mainly addressed to users and applications requiring time-averaged temperature and precipitation fields. Its main limitations concern precipitation underestimation, winter minimum temperature unexplained variance, unresolved pattern scales, station density and undetected asynchronicities. The data set shows a significant increase in mean annual temperatures all over the region, with trend values up to 0.5 ∘ C decade −1. An average, locally significant, decrease in annual precipitation is also detectable, mostly over the western mountains (−100 mm decade −1), while significant increases are identified in some areas close to the Po River Delta. Local spatial patterns may, however, be susceptible to large errors, especially in low trend areas.
Development of an automated climatic data scraping, filtering and display system
Computers and Electronics in Agriculture, 2010
The following paper compares two methods for identifying warm and cold waves, representing different methodological approaches: the 'relative' approach, i.e. wave identification based on the standard deviation, and the 'arbitrary' approach, i.e. wave identification based on a specified thermal threshold. The 1981-2010 comparison is based on data from eleven selected large cities of the world. The cities are located in zones C and D according to the Köppen climatic classification. More of the thermal waves and their parameters (number of waves, number of days in waves, their durations, and number of warm and cold days) were determined by means of the relative method than the arbitrary method. Cold waves and cold days distinguished by means of both methods, predominated over warm days and warm waves in a given period, whereas the number and duration of warm waves and warm days increased.
Similarity assessment and adjustment of integrated temperature databases. Cuyo region, Argentina
Investigaciones Geográficas, 2021
Given the frequent spatial-temporal limitations and deficiencies of instrumental meteorological records, the use of alternative information sources, such as integrated databases, are important for analyses and studies of diverse nature. The research aim was to evaluate the accuracy of integrated databases of monthly temperature, belonging to Climate Research Unit, University of Delaware and Global Historical Climatology Network, gridded with a pixel size of 3,098.01 km2 (0.5º x 0.5º), surface area of 151,802.5 km2 and temporary length of 22 years (1993-2014), through the modified structural similarity index (mSSIM). The study area is located in central-western Argentina (between 30º and 35º S, and 71º and 66º W). The University of Delaware grid showed the best fit of the data series from 10 weather stations located in the study area. Therefore, a proposal was presented to increase similarity indices, especially for those cells without instrumental reference information. The study de...
Acta Geophysica, 2018
The increase of air surface temperature at global scale is a fact with values around 0.85°C since the late nineteen century. Nevertheless, the increase is not equally distributed all over the world, varying from one region to others. Thus, it becomes interesting to study the evolution of temperature indices for a certain area in order to analyse the existence of climatic trend in it. In this work, monthly temperature time series from two Mediterranean areas are used: the Umbria region in Italy, and the Guadalquivir Valley in southern Spain. For the available stations, six temperature indices (three annual and three monthly) of mean, average maximum and average minimum temperature have been obtained, and the existence of trends has been studied by applying the non-parametric Mann-Kendall test. Both regions show a general increase in all temperature indices, being the pattern of the trends clearer in Spain than in Italy. The Italian area is the only one at which some negative trends are detected. The presence of break points in the temperature series has been also studied by using the nonparametric Pettit test and the parametric standard normal homogeneity test (SNHT), most of which may be due to natural phenomena.