Ukrainian early (pre‐1850) historical weather observations

Skrynyk, Oleg; Luterbacher, Jürg; Allan, Rob; Boichuk, Dmytro; Sidenko, Vladyslav; Skrynyk, Olesya; Palarz, Angelika; Oshurok, Dmytro; Xoplaki, Elena; Osadchyi, Volodymyr

doi:10.1002/gdj3.108

Cited by 5 publications

(8 citation statements)

References 32 publications

(37 reference statements)

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“…Slivinski et al (2019a) investigated how well the 20CRv3 system represents the 1915 Galveston Hurricane, and found that 20CRv3 has the strongest intensity (i.e., lowest central pressure) of the four historical reanalyses considered there: 20CRv2c, 20CRv3, ERA-20C, and CERA-20C. Here, we investigate an extratropical winter storm that impacted North America in the nineteenth century and has been the focus of several previous studies (Kocin 1983(Kocin , 1988Michaelis and Lackmann 2013), but 20CRv3 could be used to study a variety of weather extremes back to 1806 (e.g., the 1816 year without a summer; Brugnara et al 2015;Skrynyk et al 2021).…”

Section: Case Study: the Great Blizzard Of 1888mentioning

confidence: 99%

An Evaluation of the Performance of the Twentieth Century Reanalysis Version 3

et al. 2021

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The performance of a new historical reanalysis, the NOAA-CIRES-DOE 20th Century Reanalysis Version 3 (20CRv3), is evaluated via comparisons with other reanalyses and independent observations. This dataset provides global, 3-hourly estimates of the atmosphere from 1806 to 2015 by assimilating only surface pressure observations and prescribing sea surface temperature, sea ice concentration, and radiative forcings. Comparisons with independent observations, other reanalyses, and satellite products suggest that 20CRv3 can reliably produce atmospheric estimates on scales ranging from weather events to long-term climatic trends. Not only does 20CRv3 recreate a “best estimate” of the weather, including extreme events, it also provides an estimate of its confidence through the use of an ensemble. Surface pressure statistics suggest that these confidence estimates are reliable. Comparisons with independent upper-air observations in the Northern Hemisphere demonstrate that 20CRv3 has skill throughout the 20th century. Upper-air fields from 20CRv3 in the late 20th century and early 21st century correlate well with full-input reanalyses, and the correlation is predicted by the confidence fields from 20CRv3. The skill of analyzed 500hPa geopotential heights from 20CRv3 for 1979-2015 is comparable to that of modern operational 3- to 4-day forecasts. Finally, 20CRv3 performs well on climate timescales. Long time series and multidecadal averages of mass, circulation, and precipitation fields agree well with modern reanalyses and station- and satellite-based products. 20CRv3 is also able to capture trends in tropospheric layer temperatures that correlate well with independent products in the 20th century, placing recent trends in a longer historical context.

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Section: Case Study: the Great Blizzard Of 1888mentioning

confidence: 99%

An Evaluation of the Performance of the Twentieth Century Reanalysis Version 3

et al. 2021

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“…Nonetheless, in recent years, there has been a renewed interest in tracking down and digitizing station history metadata for many countries and individual stations in the recognition that this can help identify genuine non-climatic breakpoints, e.g., [46][47][48][49][50][51][52][53][54][55][56][57][58]. Unfortunately, the levels of detail and comprehensiveness of the station history metadata currently available vary substantially between countries.…”

Section: A Case Study Of the Ukrainian Resultsmentioning

confidence: 99%

“…Therefore, if we are interested in using these records to study regional, hemispheric or global temperature trends, it is important to accurately account for these biases. Indeed, many of us are actively engaged in developing and/or improving the reliability of the available temperature records for Europe [18,19,35,[44][45][46]49,[56][57][58][59][69][70][71][75][76][77][78][79] and elsewhere [18,19,38,39] through various homogenization techniques and/or the collection of more data.…”

Section: Discussionmentioning

confidence: 99%

“…Still, over the last decade or so, particularly in Europe, there has been an increasing realization of the importance of not only tracking down and digitizing early historical weather records [44][45][46][47][48][49][50][51][52][53], but also identifying and digitizing the relevant station history metadata associated with temperature records [46][47][48][49][50][51][52][53][54][55][56][57][58]. Therefore, for quite a few of the GHCN stations, relevant history metadata are now available.…”

Section: Time Series Ghcn (Raw) Ghcn (Homogenized) Extra Homogenizationmentioning

confidence: 99%

“…Some of these station history metadata have been published in the scientific literature, e.g., [52,54,[58][59][60][61][62][63][64][65], while others are archived by various organizations such as national meteorological services, e.g., [51,[66][67][68]. However, much of the collected station metadata are part of ongoing digitizing projects, e.g., [46][47][48][49][50][55][56][57][69][70][71], and are not yet publicly archived. In addition, analyzing and interpreting the relevance of historical metadata for evaluating homogenization adjustments can be somewhat subjective.…”

Section: Time Series Ghcn (Raw) Ghcn (Homogenized) Extra Homogenizationmentioning

confidence: 99%

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Evaluation of the Homogenization Adjustments Applied to European Temperature Records in the Global Historical Climatology Network Dataset

O’Neill

Connolly²,

Connolly³

et al. 2022

Atmosphere

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The widely used Global Historical Climatology Network (GHCN) monthly temperature dataset is available in two formats—non-homogenized and homogenized. Since 2011, this homogenized dataset has been updated almost daily by applying the “Pairwise Homogenization Algorithm” (PHA) to the non-homogenized datasets. Previous studies found that the PHA can perform well at correcting synthetic time series when certain artificial biases are introduced. However, its performance with real world data has been less well studied. Therefore, the homogenized GHCN datasets (Version 3 and 4) were downloaded almost daily over a 10-year period (2011-2021) yielding 3689 different updates to the datasets. The different breakpoints identified were analyzed for a set of stations from 24 European countries for which station history metadata were available. A remarkable inconsistency in the identified breakpoints (and hence adjustments applied) was revealed. Of the adjustments applied for GHCN Version 4, 64% (61% for Version 3) were identified on less than 25% of runs, while only 16% of the adjustments (21% for Version 3) were identified consistently for more than 75% of the runs. The consistency of PHA adjustments improved when the breakpoints corresponded to documented station history metadata events. However, only 19% of the breakpoints (18% for Version 3) were associated with a documented event within 1 year, and 67% (69% for Version 3) were not associated with any documented event. Therefore, while the PHA remains a useful tool in the community’s homogenization toolbox, many of the PHA adjustments applied to the homogenized GHCN dataset may have been spurious. Using station metadata to assess the reliability of PHA adjustments might potentially help to identify some of these spurious adjustments.

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Digitizing UK analogue magnetogram records from large geomagnetic storms of the past two centuries

Beggan

Eaton

Maume

et al. 2022

Geoscience Data Journal

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Continuous geomagnetic records of the strength and direction of the Earth's field at the surface extend back to the 1840s. Over the past two centuries, eight observatories have existed in the United Kingdom, which measured the daily field variations using light‐sensitive photographic paper to produce analogue magnetograms. Around 350,000 magnetograms have been digitally photographed at high resolution. However, converting the traces to digital values is difficult and time consuming as the magnetograms can have over‐lapping lines, low quality recordings and obscure metadata for conversion to SI units. We discuss our approach to digitizing the traces from large geomagnetic storms and highlight some of the issues to be aware of when capturing magnetic information from analogue measurements. These include cross‐checking the final digitized values with the recorded hourly mean values from observatory year books and comparing several observatory records for the same storm to catch errors such as sign inversions or incorrect ‘wrap‐around’ of data on the paper records.

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Ukrainian early (pre‐1850) historical weather observations

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 5 publications

References 32 publications

An Evaluation of the Performance of the Twentieth Century Reanalysis Version 3

An Evaluation of the Performance of the Twentieth Century Reanalysis Version 3

Evaluation of the Homogenization Adjustments Applied to European Temperature Records in the Global Historical Climatology Network Dataset

Digitizing UK analogue magnetogram records from large geomagnetic storms of the past two centuries

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