Quality control and homogenization of monthly extreme air temperature of Ukraine

Reliable long‐term observations from precipitation stations are often required for climatological studies but are strongly limited in many regions of the world. To improve this limitation for West Africa, we compiled daily and monthly observations from more than 20 national, continental and global databases, to establish a historical precipitation archive with a focus on four countries (Burkina Faso, Ghana, Benin and Togo). The new archive contains long‐term daily and monthly precipitation measurements from 1819 to 2013 for more than 1,000 sites. It is, therefore, the most comprehensive historical dataset with daily and monthly precipitation observations for this region. To produce a quality‐controlled and harmonized precipitation dataset for the focal region, various statistical algorithms have been implemented. These algorithms rely on straightforward geostatistical approaches (e.g., spatial correlograms) and corresponding statistical tests for identification and elimination of unreliable time series, in addition to various standard approaches used by global data centers. Although the quality control revealed various data errors and uncertainties for measurements and meta‐information (e.g., unit conversion errors, temporal offsets, frequent and long data gaps), a spatial interpolation using the quality‐controlled and harmonized dataset produced relatively reliable precipitation patterns for different target variables (e.g., monthly precipitation amount and daily precipitation probability). A major remaining challenge is providing free access to this database for research and other noncommercial purposes, due to national data protection regulations. However, several further tasks have been initiated and implemented (e.g., free provision of gridded precipitation datasets and point statistics) to improve the access and availability of station‐based precipitation observations and related data products for this climatologically challenging region.

Section: Introductionmentioning

confidence: 99%

Towards a historical precipitation database for West Africa: Overview, quality control and harmonization

Bliefernicht

Salack

Waongo

et al. 2021

“…and approximate the data to the real climate signal, that took place in some area. Usually the homogenization procedure allows to improve the consistency of the data, which can be seen in the process of a statistical comparison of the raw and homogenized time series (e.g., Mamara et al ., 2014; Prohom et al ., 2016; Osadchyi et al ., 2018; Yosef et al ., 2018; Fioravanti et al ., 2019; Skrynyk et al ., 2019; Dumitrescu et al ., 2020). However, the question that may remain unclear is: how far are the homogenized data from the true climate signal?…”

Section: Introductionmentioning

confidence: 99%

Uncertainty evaluation of Climatol's adjustment algorithm applied to daily air temperature time series

Skrynyk

Aguilar

Guijarro

et al. 2020

Self Cite

The present study investigated the uncertainty associated with Climatol's adjustment algorithm applied to daily minimum and maximum air temperature. The uncertainty quantification was performed based on several numerical experiments and the benchmark data, that were created in the frame of the INDECIS project. Using a complex approach, the uncertainty was evaluated on different levels of detail (day-today evaluation through formalism of random functions and through six statistical metrics) and time resolution (daily and yearly). However, only the main source of potential residual errors was considered, namely station signals introduced into a raw data set to be homogenized/adjusted. Other influencing factors were removed from the analysis or kept almost unchanged. According to our calculations, the Climatol's adjustment uncertainty, evaluated on the daily scale, varies in time. The width of the residual errors distribution in summer months is substantially less compared to wintertime. The slight seasonality is also observed in the means of the residual errors. The uncertainty evaluation based on the statistical metrics usually neglect such nonstationarity of the residual errors providing just averaged in time assessments. On the other hand, metrics provide detailed information regarding both types of the residual errors, systematic and scatter. The metrics values showed good capability of the Climatol software to remove the systematic errors related to jumps in the means, while the scatter errors are removed from the raw time series with less efficiency. On yearly scale, the uncertainty evaluation was performed for yearly temperature data and several climate extreme indices. The both types of the errors are removed well in yearly time series of the air temperature and the extreme indices. The metrics values also showed significant reduction of the adjustment uncertainty of Climatol's adjustment. Substantial decreasing of linear trend errors in yearly time series can also be reported.

“…At the same time, possible inhomogeneities in time series and their influence on the changes revealed have not been studied at all, to the best of our knowledge. Perhaps, the only exception can be found in (Osadchyi et al ., ; Skrynyk et al ., ) where some initial attempts to homogenize the Ukrainian monthly temperature data were made. Nevertheless, those results cannot be considered complete due to either the limited number of stations (33) used or the period they span (1961–2010).…”

Section: Introductionmentioning

confidence: 99%

Homogenization of Ukrainian air temperature data

Osadchyi

Skrynyk

Radchenko

et al. 2017

Self Cite

A database of monthly mean air temperature data collected in Ukraine has been created. The database contains records of 514 stations, which were working at one time or another during the period 1812-2014. Metadata have also been collected using the available descriptions of the stations' histories. Regular observations at a sufficiently large number of stations contained in the database began in the early 1880s. However, due to the disruptions that occurred during World Wars I and II and resulted in a lot of missing data, a reliable homogenized data set can be obtained only for the period 1946-2014. For this period, we have homogenized the data from 178 stations by means of the HOMER software. The total number of breaks that we have identified is 287. However, only approximately 31% of them can be explained by the station relocations or other reasons. The shift magnitudes in annual series do not exceed ±1 ∘ C with the small negative mean value. Therefore, on average, the inhomogeneities of the time series have an effect of increasing the past temperature slightly over the territory of Ukraine and, consequently, decrease the rate of the temperature rise. The trend analysis of the raw and homogenized time series confirms this conclusion. The homogenized data show a more realistic pattern of the spatial distribution of the temperature tendencies, in contrast to many artificial spots of higher or lower trend values in the raw data. After the homogenization, the areas with insignificant trends were considerably reduced.