Oleg Skrynyk

Universidad Rovira i Virgili

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Enric Aguilar

Universidad Rovira i Virgili

Vladyslav Sidenko

Ukrainian Hydrometeorological Institute

Volodymyr Osadchyi

Ukrainian Hydrometeorological Institute

Dmytro Oshurok

Ukrainian Hydrometeorological Institute

Olesya Skrynyk

Ukrainian Hydrometeorological Institute

Dmytro Boichuk

Ukrainian Hydrometeorological Institute

Sergiy Bubin

Nazarbayev University

Luc Yannick Andréas Randriamarolaza

Universidad Rovira i Virgili

Rob Allan

Met Office

José Antonio Guijarro

Agencia Estatal de Meteorología

Cooperative Institutions

Ukrainian Hydrometeorological Institute

University of Bern

Universidad Rovira i Virgili

Oeschger Centre for Climate Change Research

Hungarian Meteorological Service

National Academy of Sciences of Ukraine

Bureau of Meteorology

Central Institution for Meteorology and Geodynamics

Engineering (Italy)

TXT e-solutions (Italy)

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Data quality control and homogenization of daily precipitation and air temperature (mean, max and min) time series of Ukraine

International Journal of Climatology (2023)

Oleg Skrynyk Vladyslav Sidenko Enric Aguilar José Antonio Guijarro Olesya Skrynyk

Abstract In this paper we present the results of quality control and homogenization procedures applied to long time series of daily atmospheric precipitation sums (Rr) and daily mean (Tm), maximum (Tx) and minimum (Tn) air temperature collected in Ukraine. The daily data from 178 meteorological stations covering the period of 1946–2020 were analysed. In order to perform a thorough quality assurance check, we used the R package INQC, while the Climatol homogenization software was used to detect and remove breaks from the time series. The INQC quality assurance tests revealed a relatively small number of erroneous records (around 0.01% for each variable) and suspicious values (up to 0.09%). The application of Climatol resulted in 195, 296, 355 and 359 break points, detected for Rr, Tm, Tx and Tn, respectively. These quantities coincide roughly with the results of the HOMER homogenization procedure applied to monthly time series for the same stations and almost the same period (performed in the previous works of the authors). To verify the homogenization results, statistical comparison of the raw and homogenized time series was performed. The verification demonstrated that the quality control and homogenization procedures detected and removed errors and breaks very well, and air temperature and precipitation fields after the homogenization are more self‐consistent compared to the original raw data.

Homogenization

10.1002/joc.8080

Cite

Citations (4)

Uncertainty of heat wave metric calculations

Oleg Skrynyk Enric Aguilar Caterina Cimolai

Climate/weather extremes such as heat waves (HWs) are of the great interest to study as they have the significant harmful effect on the environment and society. There are many researches dealing with the calculation of HW metrics and their long-term trends on both the global and regional/national spatial scale. In our work based on a case study of Ukraine, we aimed to quantify the uncertainty of HW metric calculations, which might originate from climate input data. To this end, we used a mini statistical ensemble of several gridded data sets of maximum daily air temperature (TX), covering the territory of Ukraine for the period of 1950-2020 (70 years) with the same spatial resolution. The ensemble included ERA5 reanalysis data (remapped by means of the CDO software to the finer grid of 0.1ox0.1o with different interpolation algorithms), ERA5-Land, E-OBS (the ensemble mean) and Ukrainian gridded observation data previously developed for the period of 1946-2020. We defined a HW as an event when conditions (TX in our case) above criteria (90-th percentile calculated based on the WMO standard 1961-1990 reference period) persist at least three consecutive days, with permission of a 1-day time gap. Four HW metrics were considered, namely heat wave number (HWN), duration (HWD), frequency (HWF) and amplitude (HWA). The calculation of yearly time series of the HW metrics was performed by means of the R package heatwaveR for each grid point of the domain and each member of the constructed statistical ensemble. The uncertainty of the HW metrics was defined as a difference between min and max metric’s values calculated for different members of the ensemble. We also calculated the range of the possible variations in long term trends of obtained yearly time series of the HW metrics. Our results showed that depending on climate data used for HW climatology analysis, the calculation results might differ significantly for a particular grid point and year. However, on average (over the whole domain and the period under study), variation of the HW metrics is not so pronounced. Moderate variations are also observed in long-term trends of the metric time series.   This work has received funding through the MSCA4Ukraine project, which is funded by the European Union

Heat wave

10.5194/egusphere-egu24-16497

Cite

Citations (0)

Rescue of Ukrainian early historical climatological data

Dmytro Boichuk Jürg Luterbacher Rob Allan Olesya Skrynyk Vladyslav Sidenko

Modern climate applications and climate services are seeing the need for more data and information (including its historical part) on climate variability at high temporal and spatial resolution. Therefore, daily or even sub-daily meteorological data are required increasingly to feel this gap and provide the basis for climate research, extreme events analysis and impact studies.The main objective of our work is to present information on results of data rescue (DARE) activity conducted recently in the Ukrainian Hydrometeorological Institute (UHMI, Kyiv, Ukraine) in close collaboration with several national and international partners. Our DARE activity was concentrated mainly on the original sub-daily, pre-1850 meteorological observations conducted at eight meteorological stations located in the territory of modern Ukraine, namely Kyiv, Kharkiv, Poltava, Kamyanets-Podilsky, Lugansk, Dnipro, Kherson and Odesa. These eight stations are the only ones, whose pre-1850 data have been found in an archive of the Central Geophysical Observatory (CGO), an observation institution of the Ukrainian Weather Service.The data are contained in 38 special hard copy books. Before digitization, the book pages were photocopied to create a database of the images of all the paper sources. Its two copy versions are now stored at the UHMI and CGO, respectively. After the creation of the images database, the data were digitized manually by the authors. In total 291&#160;103 values were digitized. These include 165&#160;980 air temperature records (~57% of the total), 124&#160;376 atmospheric pressure measurements (~42.7%) and 747 precipitation totals (~0.3%).Quality control of the digitized data was conducted, including intercomparisons between the stations as well as comparisons with monthly temperature data that were digitized previously from other sources. The quality control procedures revealed a fairly good agreement among the rescued time series on the monthly time scale as well as a good accordance with the monthly data from other sources. However, several periods at some stations should be used with caution, due to relatively large discrepancies revealed. The rescued digital dataset can be used for different meteorological and climatological purposes, including the analysis of extreme events for the pre-1850 period in comparison with today&#8217;s climate, regional climatological studies, etc. The dataset is an important supplement to existing digitized archives of meteorological measurements that were performed in the first half of the 19th century.

Ukrainian

Hydrometeorology

Digitization

10.5194/egusphere-egu2020-17698

Cite

Citations (1)

Gridded data of daily atmospheric precipitation and minimum, mean and maximum air temperature for Ukraine, 1946-202

Volodymyr Osadchyi Oleg Skrynyk Vladyslav Sidenko Enric Aguilar José Antonio Guijarro

In this contribution, we present the results of the development of long gridded climate time series, which cover the territory of Ukraine for the period of 1946-2020 (75 years). The spatial resolution of the developed data is 0.1o×0.1o (approximately 10 km in both longitude and latitude directions), while their time discreteness is 1 day. Four essential climate variables are included in the dataset, namely daily sums of atmospheric precipitation and daily minimum, mean and maximum air temperature. The created gridded product is based on the complete collection of weather measurements, performed at 178 meteorological stations of Ukraine, which constitute the modern national observation network. Quality assurance check, homogenization and gridding of the station time series were performed by means of widely used and well approved climatological software, i.e. INQC, Climatol and MISH, respectively. The produced gridded time series were statistically compared on the monthly and daily time scales with several existing data sets, which have the same spatial resolution (i.e., previously developed gridded monthly data of Ukraine, ERA5-Land, E-OBS). The comparison showed good accordance with UA monthly data (partly obtained from other paper sources than the daily data) and acceptable agreement with ERA5-Land and E-OBS data. The developed long gridded time series are of great importance as they were built with the involvement of as many real weather measurements as possible. Therefore, they can be used as a reference for a wide variety of climatological applications for the territory of Ukraine.   Oleg Skrynyk acknowledges the support from the MSCA4Ukraine fellowship program, which is funded by the European Union.

Maximum temperature

Mean radiant temperature

10.5194/egusphere-egu24-15129

Cite

Citations (0)

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

International Journal of Climatology (2021)

Luc Yannick Andréas Randriamarolaza Enric Aguilar Oleg Skrynyk Sergio M. Vicente‐Serrano Fernando Domínguez‐Castro

Abstract This study updates knowledge on climate evolution in Madagascar from 1950 to 2018. Changes were analyzed using annual and seasonal climate indices at regional and station level. The original daily series of minimum and maximum temperature and precipitation obtained from 28 meteorological stations were quality controlled and homogenized. Thirty‐seven (37) climate indices were obtained from the daily series. The results show that changes in temperature had a higher degree of spatial coherence than changes in precipitation. Trends for temperature indices were mostly significant at 0.05 level and compatible with warming. Changes in minimum temperatures were greater than those for the maximum, leading to a significant decrease in the diurnal temperature range (DTR). Warm nights increased more than warm days, (0.70 days⋅decade –1 ) and cold nights decreased more than cold days, (0.21 days⋅decade –1 ). In addition, we found more stations with significant trends for very cold nights (92.60%) than for very warm days (51.80%) but they progressed differently (decrease and increase, respectively). Station‐by‐station precipitation index trends were mostly non‐significant at 0.05 level, and most regional precipitation index showed decreasing trends. A shift in precipitation magnitude was observed around 2000–2018, a period of intensified drying (where 70.40% of stations recorded non‐significant decreasing trends). An analysis of drought characteristics (i.e., intensity, magnitude and duration) highlighted the situation, especially in the south‐east at an annual timescale.

Diurnal temperature variation

Maximum temperature

Climate extremes

10.1002/joc.7243

Cite

Citations (22)

АPPLICATION OF THE HOMER SOFTWARE TO QUALITY CONTROL AND HOMOGENIZE TIME SERIES OF MONTHLY PRECIPITATION SUMS

Hydrology hydrochemistry and hydroecology (2023)

L.V. Palamarchuk Volodymyr Osadchyi V.I. Skrynyk Z. Kyreieva Vladyslav Sidenko

Palamarchuk L.V., Ukrainian Hydrometeorological Institute of the National Academy of Sciences of Ukraine and the State Service Emergencies of Ukraine, KyivOsadchyi V.I., Ukrainian Hydrometeorological Institute of the National Academy of Sciences of Ukraine and the State Service Emergencies of Ukraine, KyivSkrynyk O.A., Ukrainian Hydrometeorological Institute of the National Academy of Sciences of Ukraine and the State Service Emergencies of Ukraine, Kyiv, National University of Bioresources and Nature Management, Kyiv Kyreieva Z.M., Ukrainian Hydrometeorological Institute of the National Academy of Sciences of Ukraine and the State Service Emergencies of Ukraine, Kyiv, Taras Shevchenko National University of Kyiv Sidenko V.P., Ukrainian Hydrometeorological Institute of the National Academy of Sciences of Ukraine and the State Service Emergencies of Ukraine, KyivOshurok D.O., Ukrainian Hydrometeorological Institute of the National Academy of Sciences of Ukraine and the State Service Emergencies of Ukraine, KyivSkrynyk O.Y., Ukrainian Hydrometeorological Institute of the National Academy of Sciences of Ukraine and the State Service Emergencies of Ukraine, Kyiv In our work, we present a digital dataset of monthly atmospheric precipitation sums collected at 177 meteorological stations and 47 precipitation posts in Ukraine during the period of 1946-2020. Quality control check and homogenization of the time series were performed by means of the HOMER software. The quality control procedure revealed 1316 anomaly values (outliers), which constitute 0.7% of the total amount of considered precipitation measurements. A significant part of the detected outliers (465) was identified as rough errors, which were corrected after analysis of original paper sources. Simultaneous use of the observation data from meteorological stations and precipitation posts allowed to improve accuracy/quality of the latter (by comparing them with corresponding measurements from the meteorological stations) and make precipitation fields more consistent. The homogenization procedure detected 265 breaks. Such breaks are usually considered as moments of time when abrupt shifts in time series evolution are happened. The main reasons for breaks are station/post relocations, replacement of measurement devices etc., namely any non-climatic factors. According to the WMO recommendations, the obtained homogenized time series can be applied to study regional climate including its variability and persistent change.

Hydrometeorology

Ukrainian

10.17721/2306-5680.2023.1.7

Cite

Citations (1)

The Chernobyl nuclear accident 137 Cs cumulative depositions simulated by means of the CALMET/CALPUFF modelling system

Atmospheric Pollution Research (2017)

Dario Giaiotti Dmytro Oshurok Oleg Skrynyk

Deposition

10.1016/j.apr.2017.11.007

Cite

Citations (28)

Influence of inhomogeneity of temperature time series on the estimation of regional climate changes

Reports of the National Academy of Sciences of Ukraine (2017)

Volodymyr Osadchyi Olesya Skrynyk Oleg Skrynyk

10.15407/dopovidi2017.07.043

Cite

Citations (1)

Uncertainty of Climatol adjustment algorithm for daily time series of additive climate variables

Oleg Skrynyk Enric Aguilar José Antonio Guijarro Sergiy Bubin

Before using climatological time series in research studies, it is necessary to perform their quality control and homogenization in order to remove possible artefacts (inhomogeneities) usually present in the raw data sets. In the vast majority of cases, the homogenization procedure allows to improve the consistency of the data, which then can be verified by means of the statistical comparison of the raw and homogenized time series. However, a new question then arises: how far are the homogenized data from the true climate signal or, in other words, what errors could still be present in homogenized data?The main objective of our work is to estimate the uncertainty produced by the adjustment algorithm of the widely used Climatol homogenization software when homogenizing daily time series of the additive climate variables. We focused our efforts on the minimum and maximum air temperature. In order to achieve our goal we used a benchmark data set created by the INDECIS* project. The benchmark contains clean data, extracted from an output of the Royal Netherlands Meteorological Institute Regional Atmospheric Climate Model (version 2) driven by Hadley Global Environment Model 2 - Earth System, and inhomogeneous data, created by introducing realistic breaks and errors.The statistical evaluation of discrepancies between the homogenized (by means of Climatol with predefined break points) and clean data sets was performed using both a set of standard parameters and a metrics introduced in our work. All metrics used clearly identifies the main features of errors (systematic and random) present in the homogenized time series. We calculated the metrics for every time series (only over adjusted segments) as well as their averaged values as measures of uncertainties in the whole data set.In order to determine how the two key parameters of the raw data collection, namely the length of time series and station density, influence the calculated measures of the adjustment error we gradually decreased the length of the period and number of stations in the area under study. The total number of cases considered was 56, including 7 time periods (1950-2005, 1954-2005, &#8230;, 1974-2005) and 8 different quantities of stations (100, 90, &#8230;, 30). Additionally, in order to find out how stable are the calculated metrics for each of the 56 cases and determine their confidence intervals we performed 100 random permutations in the introduced inhomogeneity time series and repeated our calculations With that the total number of homogenization exercises performed was 5600 for each of two climate variables.Lastly, the calculated metrics were compared with the corresponding values, obtained for raw time series. The comparison showed some substantial improvement of the metric values after homogenization in each of the 56 cases considered (for the both variables).-------------------*INDECIS is a part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462). The work has been partially supported by the Ministry of Education and Science of Kazakhstan (Grant BR05236454) and Nazarbayev University (Grant 090118FD5345).

Homogenization

Benchmark (surveying)

10.5194/egusphere-egu2020-5365

Cite

Citations (2)

Studies of spottiness of area pollution by strong high-altitude finite source on the base of simple analytical models of atmospheric diffusion

Geofizicheskiy Zhurnal (2010)

Oleg Skrynyk

Using analytical solutions of simple model diffusion problems, we have studied the process of formation of a large-scale spot-like pollution structure of the underlying surface by a powerful high-altitude finite source. We have established a criterion for the appearance of a spot-like pollution structure in case of presence such a source which is defined by the ratio of time of source activity to characteristic time of periodic change of intensity of vertical turbulent mixing in the atmospheric boundary layer.

Turbulent diffusion

Source model

Intensity

10.24028/gzh.0203-3100.v32i3.2010.117539

Cite

Citations (0)