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

Abstract

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.

Indices for daily temperature and precipitation in Madagascar, based on quality‐controlled and homogenized data, 1950–2018

“…Moreover, annual and seasonal regional trends were almost consistent and significant at the 0.05 level. Climatol homogenization improved the accuracy and reliability of indices' trends, as Skrynyk et al (2021) concluded. Therefore, climate indices were calculated using the daily homogenized series with Climatol.…”

“…Climatol homogenization improved the accuracy and reliability of indices' trends, as Skrynyk et al . (2021) concluded. Therefore, climate indices were calculated using the daily homogenized series with Climatol.…”

“…This paper reveals new features in changes (e.g., appearance of very extreme cold nights and warm days, a shift in precipitation magnitude and ever‐increasing consecutive drought events) in the climate of Madagascar. The next step in this research is to delve further into an analysis of the findings by continuous improvement in the data set through Data Rescue (DARE) and homogenization, in order to reduce uncertainty (Skrynyk et al ., 2021). For example, elements (e.g., solar radiation, wind speed, etc.)…”

“…Madagascar houses endemic fauna and flora (Goodman et al 2005 and homogenization, in order to reduce uncertainty (Skrynyk et al 2021). For example, elements (e.g.…”

Developing a high-resolution gridded rainfall product for Bangladesh during 1901–2018

“…Skrynyk, et al . 50 evaluated the uncertainty associated with Climatol’s adjustment algorithm and reported a better capacity of Climatol to remove systematic errors related to jumps in the means. Domonkos, et al .…”