Geoscience Data Journal最新文献

{"title":"High-resolution climate projection dataset over India using dynamical downscaling","authors":"Anasuya Barik,&nbsp;Sanjeeb Kumar Sahoo,&nbsp;Sarita Kumari,&nbsp;Somnath Baidya Roy","doi":"10.1002/gdj3.266","DOIUrl":"10.1002/gdj3.266","url":null,"abstract":"<p>High-resolution climate projections are valuable resources for understanding the regional impacts of climate change and developing appropriate adaptation/mitigation strategies. In this study, we developed a 10-km gridded hydrometeorological dataset over India by dynamic downscaling of the bias-corrected Community Earth System Model (CESMv1) climate projections under RCP8.5 scenario using the state-of-the-art Weather Research and Forecasting (WRF) model. The downscaled CESM dataset (DSCESM) is archived in the World Data Center for Climate (WDCC) portal at three temporal resolutions (daily, monthly and monthly climatology) for current (2006–2015), mid-century (2041–2050) and end-century (2091–2100) periods. The dataset includes 2-m air temperature, total accumulated precipitation, wind speed, relative humidity, sensible and latent heat fluxes, along with surface shortwave and outgoing longwave radiation. All the DSCESM variables were evaluated against reanalysis data and station observations for the period 2006–2015. This dataset can help us quantitatively understand regional climate change in India. It can also be used in conjunction with agricultural, hydrological, fire and other application models for climate change impact assessment on various sectors to help develop effective adaptation/mitigation strategies.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"921-935"},"PeriodicalIF":3.3,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.266","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141609052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS-M6)","authors":"Stephen R. Sobie,&nbsp;Dhouha Ouali,&nbsp;Charles L. Curry,&nbsp;Francis W. Zwiers","doi":"10.1002/gdj3.257","DOIUrl":"10.1002/gdj3.257","url":null,"abstract":"<p>Canada-wide, statistically downscaled simulations of global climate models from the Sixth Coupled Model Inter-comparison Project (CMIP6) have been made available for 26 models using a new multivariate approach and an improved observational target dataset. These new downscaled scenarios comprise daily simulations of precipitation, maximum temperature, and minimum temperature at 1/12<i>°</i> resolution across Canada. Simulations from each of the 26 downscaled global climate models span a historical period (1950–2014), and three future Shared Socio-economic Pathways (SSPs) representing low (SSP1 2.6), moderate (SSP2 4.5) and high (SSP5 8.5) future emissions from 2015 to 2100. Results from an evaluation of the multivariate downscaling method over Canada yield improved performance in replicating multivariate and compound climate indices compared to previously used univariate downscaling methods. This Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS-M6) dataset is intended to facilitate climate impacts assessments, hydrologic modelling, and analysis tools for presenting climate projections.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"806-824"},"PeriodicalIF":3.3,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.257","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141609054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Meteorological data from Badwater, Death Valley National Park 1998 to 2019","authors":"Christopher P. McKay","doi":"10.1002/gdj3.264","DOIUrl":"10.1002/gdj3.264","url":null,"abstract":"<p>We installed a meteorological recording system at Badwater (elev. −75 m), the lowest point in Death Valley, California and recorded data over the period 1998–2019. A second station (the Outhouse Station) was established nearby from 2014 to 2019. Here, we report on and publicly archive the data from these two stations. Of interest was the comparison between two air temperature measurements at the Badwater Station, the first with an aspirated platinum resistance temperature device and the second with a thermistor probe in a passive sun shield. During the hottest periods of the summer when temperatures were typically between 30°C at night and 50°C daily peak, the passively shielded sensor indicated up to 0.5°C warmer than the aspirated temperature sensor due to radiative effects. The data suggest a correction for radiative heating of (<i>T–</i>35)/30, for <i>T</i> &gt; 35°C, where, <i>T</i>, is the uncorrected temperature reading of a passively shielded sensor subtracted after any calibration at lower temperatures. Our station was the first precision temperature measurements at Badwater. A longer record exists for the reporting station near the visitor's centre at the Furnace Creek. The summer temperature maxima at the Badwater site correlate well with the values the same day from the Furnace Creek site. The daily maximum temperatures in winter at the Badwater site appear to be about 1°C lower than at the Furnace Creek site. The largest differences are in the minimum temperatures for which the Badwater site averages about 2–3°C warmer than the Furnace Creek site.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"896-901"},"PeriodicalIF":3.3,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.264","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141523894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new, high-resolution atmospheric dataset for southern New Zealand, 2005–2020","authors":"Elena Kropač,&nbsp;Thomas Mölg,&nbsp;Nicolas J. Cullen","doi":"10.1002/gdj3.263","DOIUrl":"10.1002/gdj3.263","url":null,"abstract":"<p>The regional climate of New Zealand's South Island is shaped by the interaction of the Southern Hemisphere westerlies with the complex orography of the Southern Alps. Due to its isolated geographical setting in the south-west Pacific, the influence of the surrounding oceans on the atmospheric circulation is strong. Therefore, variations in sea surface temperature (SST) impact various spatial and temporal scales and are statistically detectable down to temperature anomalies and glacier mass changes in the high mountains of the Southern Alps. To enable future studies on the processes that govern the link between large-scale SST and local-scale high-mountain climate, we utilized dynamical downscaling with the Weather Research and Forecasting (WRF) model to produce a regional atmospheric modelling dataset for the South Island of New Zealand over a 16-year period between 2005 and 2020. The 2 km horizontal resolution ensures realistic representation of high-mountain topography and glaciers, as well as explicit simulation of convection. The dataset is extensively evaluated against observations, including weather station and satellite data, on both regional (in the inner domain) and local (on Brewster Glacier in the Southern Alps) scales. Variability in both atmospheric water content and near-surface meteorological conditions is well captured, with minor seasonal and spatial biases. The local high-mountain climate at Brewster Glacier, where land use and topographic model settings have been optimized, yields remarkable accuracy on both monthly and daily time scales. The data provide a valuable resource to researchers from various disciplines studying the local and regional impacts of climate variability on society, economies and ecosystems in New Zealand. The model output from the highest resolution model domain is available for download in daily temporal resolution from a public repository at the German Climate Computation Center (DKRZ) in Hamburg, Germany (Kropač et al., 2023; 16-year WRF simulation for the Southern Alps of New Zealand, World Data Center for Climate (WDCC) at DKRZ [data set]. https://doi.org/10.26050/WDCC/NZ-PROXY_16yrWRF).</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"873-895"},"PeriodicalIF":3.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.263","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RDD2022: A multi-national image dataset for automatic road damage detection","authors":"Deeksha Arya,&nbsp;Hiroya Maeda,&nbsp;Sanjay Kumar Ghosh,&nbsp;Durga Toshniwal,&nbsp;Yoshihide Sekimoto","doi":"10.1002/gdj3.260","DOIUrl":"10.1002/gdj3.260","url":null,"abstract":"<p>The data article describes the Road Damage Dataset, RDD2022, encompassing of 47,420 road images from majorly six countries, Japan, India, the Czech Republic, Norway, the United States, and China. The dataset incorporates over 55,000 instances of road damage, specifically longitudinal cracks, transverse cracks, alligator cracks, and potholes. Designed to facilitate the development of deep learning methodologies for automated road damage detection and classification, RDD2022 was unveiled as part of the Crowd sensing-based Road Damage Detection Challenge (CRDDC'2022), with a major contribution from the challenge winners. This challenge garnered global participation, urging researchers to propose solutions for automatic road damage detection in multiple countries. A noteworthy outcome of CRDDC'2022 was the emergence of a top-performing model achieving a remarkable F1 Score of 76.9% for road damage detection in all six countries using RDD2022. This success underscores the dataset's practical applicability for municipalities and road agencies, enabling low-cost, automatic monitoring of road conditions. Beyond its immediate utility, RDD2022 stands as a valuable benchmark for researchers in computer vision, geoscience, and machine learning, offering a rich resource for algorithmic evaluation in diverse image-based applications, including classification and object detection. The latest big data cup, Optimized Road Damage Detection Challenge (ORDDC'2024), is also based on RDD2022, underscoring its continued relevance and pivotal role in current research and development endeavors.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"846-862"},"PeriodicalIF":3.3,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.260","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141530174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The accurate digitization of historical sea level records","authors":"Patrick J. McLoughlin,&nbsp;Gerard D. McCarthy,&nbsp;Glenn Nolan,&nbsp;Rosemarie Lawlor,&nbsp;Kieran Hickey","doi":"10.1002/gdj3.256","DOIUrl":"https://doi.org/10.1002/gdj3.256","url":null,"abstract":"<p>Understanding regional sea level variations is crucial for assessing coastal vulnerability, with accurate sea level data playing a pivotal role. Utilizing historical sea level marigrams can enhance datasets, but current digitization techniques face challenges such as bends and skews in paper charts, impacting sea level values. This study explores often-overlooked issues during marigram digitization, focusing on the case study of Dún Laoghaire in Ireland (1925–1931). The methodology involves digitizing the original marigram trace and underlying grid to assess offsets at the nearest ft (foot) interval on the paper chart, corresponding to changes in the water level trace for each hour interval. Subtracting the digitized value from the known value (the actual measurement) allows for the determination of differences, which are then subtracted from each hourly trace value. After adjusting for offsets ranging from −3.962 to 13.716 mm (millimetres), the study improves the final accuracy of sea level data to approximately the 10 mm level. Notably, data from 1926 and 1931 exhibit modest offsets (&lt;7 mm), while other years show more substantial offsets (&gt;9–14 mm), emphasizing the importance of adjustments for accuracy. Such 10 mm accuracy is compatible with requirements of the Global Sea Level Observing System. Comparing the adjusted digitized data with other survey data shows similar amplitudes and phases for Dún Laoghaire in both the historical and modern datasets, and there is an overall mean sea level rise of 1.5 mm/year when combined with the available data from the Dublin region.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"790-805"},"PeriodicalIF":3.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.256","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142435448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An industrial heat source dataset based on remotely sensed active fire/hotspot detection in China from 2012 to 2021","authors":"Caihong Ma,&nbsp;Xin Sui,&nbsp;Linlin Guan,&nbsp;Yanmei Xie,&nbsp;Tianzhu Li,&nbsp;Pengyu Zhang,&nbsp;Yubao Qiu,&nbsp;Weimin Huang","doi":"10.1002/gdj3.259","DOIUrl":"10.1002/gdj3.259","url":null,"abstract":"<p>The distribution of industrial heat sources (IHSs) is a crucial indicator for evaluating energy consumption and air pollution levels. However, there is a notable lack of IHS datasets in China that are frequently updated, span long periods, contain detailed characteristic information, have been individually validated and are publicly available. In this study, IHS datasets from China between 2012 and 2021 were constructed using the Visible Infrared Imaging Radiometer Suite (VIIRS) I Band 375 m NRT Active Fire/Hotspots (ACF) Product (VNP14IMGTDL_NRT) to monitor and analyse large-scale IHSs. First, a density segmentation method based on an improved K-means algorithm using ACF data and spatial topological correlation analysis was conducted to construct the IHS. Then, 4410 records covering China between 2012 and 2021, with 21 attributes, were obtained and verified, with an individual identification precision of 95.08% via manual verification based on high-resolution remote-sensing images and point of interest (POI) data. Finally, the trend of the spatiotemporal variation in IHSs was analysed using a long time series. The results showed that the spatial distribution of IHSs in China from 2012 to 2021 exhibited local aggregation and a gradual shift from east to west. In addition, the number of IHSs in China showed an initial increasing trend from 2012 to 2014, followed by a decrease since 2014, consistent with national energy reform-related policies. The results of this study indicate the temporal variation in IHSs, enhance the precision of identifying fire location categories and demonstrate the potential for improving energy efficiency, reducing emissions and ensuring sustainable development in China.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"833-845"},"PeriodicalIF":3.3,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141340048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geochemistry of forty-one eclogitic and pyroxenitic mantle xenoliths from the Central Slave Craton, Canada (Ekati Diamond Mine)","authors":"D. E. Jacob,&nbsp;A. Fung","doi":"10.1002/gdj3.258","DOIUrl":"10.1002/gdj3.258","url":null,"abstract":"<p>This article describes a novel dataset on non-diamondiferous eclogite and garnet pyroxenite xenoliths from four kimberlite pipes of the Ekati Diamond Mine (Central Slave Craton, Canada). Xenoliths brought to the surface by kimberlite eruptions are direct sources of information on the composition and evolution of the Earth's mantle. Eclogite and garnet pyroxenite xenoliths, specifically, are testimony of subduction into, and metasomatism of, the mantle beneath cratons. Furthermore, these rocks are major hosts for diamond and thus an important part of the deep carbon cycle. The sample suite consists of 41 small xenoliths (2–5 cm) recovered from drill cores. The dataset includes major and trace element concentrations for garnet, clinopyroxene and ilmenite, as well as stable oxygen isotope compositions of garnets. Strontium and neodymium isotopic compositions are reported for garnet and clinopyroxene for four samples which were large enough to allow for analysis. Overall, this dataset significantly expands and complements existing datasets on diamondiferous and non-diamondiferous xenoliths from the Slave Craton in Canada, furthering our understanding of the composition of the Slave subcratonic lithosphere. The dataset includes several samples with rare mineral assemblages, including an olivine-bearing eclogite as well as ilmenite and apatite-bearing garnet-pyroxenites, and thus provides data shedding light on rarely reported compositional nuances in xenolith suites found in kimberlites.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"825-832"},"PeriodicalIF":3.3,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.258","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A global four-dimensional gridded dataset of ocean dissolved oxygen concentration retrieval from Argo profiles","authors":"Cunjin Xue,&nbsp;Zhenguo Wang,&nbsp;Linfeng Yue,&nbsp;Chaoran Niu","doi":"10.1002/gdj3.251","DOIUrl":"10.1002/gdj3.251","url":null,"abstract":"<p>Lack of a long-term time series of dataset with a high spatiotemporal resolution at a global scale poses a great challenge to clarify the characteristics of DOC in space and depth, and its trend in time. Thus, there is an urgent need for the development of a global DOC gridded dataset in space, time and depth. The Biogeochemical Argo (BGC-Argo) provides an important data source for obtaining global DOC, but is limited by irregular spatial sampling locations. Besides, BGC-Argo has shorter time series coverage and fewer profiles compared to Core-Argo. Thus, this manuscript aims at reconstructing the DOC profiles based on the Core-Argo and BGC-Argo profiles and then developing a spatial, temporal and depth-specific gridded DOC dataset, named G4D-DOC. Validation results demonstrate that G4D-DOC has a good overall consistency with WOA18 and GLODAPv2 datasets, particularly at depths of 10 dbar and 1000 dbar, where it surpasses consistency at other standard depths. In addition, compared to WOA18, G4D-DOC has achieved a breakthrough in a temporal resolution from a climatological monthly to monthly, and compared to GLODAPv2, G4D-DOC has achieved a breakthrough in space from irregular discrete locations to regular grids. Further, G4D-DOC can be widely used to conduct the characteristics of DOC in space and depth and its trends at global and regional scales. The metadata of G4D-DOC is as follows: four dimensions mean two dimensions in space (longitude and latitude), one in time and one in depth; data format is standard Hierarchical Data Format Version 4 (HDF4) with a spatial resolution of 1 degree and temporal resolutions of annual, seasonal and monthly intervals at 26 standard layers above 2000 dbar in depth; the spatial coverage is global and the time period is from 2005 to 2022.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"775-789"},"PeriodicalIF":3.3,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Millions of digitized historical sea-level pressure observations rediscovered”","authors":"Ed Hawkins,&nbsp;Lisa V. Alexander,&nbsp;Rob J. Allan","doi":"10.1002/gdj3.250","DOIUrl":"10.1002/gdj3.250","url":null,"abstract":"&lt;p&gt;We have revised the dataset associated with the paper “Millions of digitized historical sea-level pressure observations rediscovered” by E. Hawkins et al. (Geoscience Data Journal, 10, 385, doi: 10.1002/gdj3.163, 2023). The dataset includes more than 5 million observations of sea level pressure every 3 hours from April 1919 to December 1960 over the UK &amp; Ireland which were contained in the Daily Weather Reports (DWRs) published by the Met Office.&lt;/p&gt;&lt;p&gt;A dataset user brought a small footnote to our attention which stated that in the original DWR documents for April 1919 to February 1930, the column giving the pressure change over the previous 3 hours was in units of half-millibars rather than whole millibars as we had previously assumed. This means that all pressure observations during this period derived using the ‘Change in last 3 hours’ column required small revisions – around 10% of the total dataset.&lt;/p&gt;&lt;p&gt;The ‘change over last 3 hours’ column was first introduced in the DWRs in May 1911 when the units of both pressure observations and the change in 3 hours were in/Hg using two decimal places. From May 1914 onwards, the pressure units were changed to mb, with half-millibars used for the change in pressure. After February 1930, the change in pressure was given in tenths of mb, and this was correctly used. The pressure observations from the DWRs for January 1911 to March 1919 remain unrescued.&lt;/p&gt;&lt;p&gt;The discussion of Figure 1 should read:&lt;/p&gt;&lt;p&gt;&lt;i&gt;Figure 1 shows an example DWR page from 5th April 1919, showing the stations from which eight sea-level pressure observations per day can be derived. Each station has a listing for 01Z, 07Z, 13Z and 18Z, with a pressure observation converted to sea-level (given to a precision of 0.1 mb) and a change in pressure over the previous 3 hr in units of half-millibars. This allows the pressures for 22Z, 04Z, 10Z and 15Z to be calculated, but with a small uncertainty as the change is only given with a precision of 0.5mb. Note that the rows are not always complete, highlighting missing data, especially for 01Z, and therefore also for 22Z the day before&lt;/i&gt;.&lt;/p&gt;&lt;p&gt;The dataset revision means there are small visual differences in updated versions of Figures 6, 9 &amp; 10, but these are not shown here. A revised version of Figure 7 is shown, and the discussion around Figures 7 and 8 should now read:&lt;/p&gt;&lt;p&gt;&lt;i&gt;For example, the missing observation at Eskdalemuir in southern Scotland at 15Z on 23rd November is 956 mb, with other missing observations in Ireland from Malin Head at 972 mb and Blacksod Point at 984 mb. Recovering such individual missing observations may be worthwhile if analysing case studies of particular severe storms&lt;/i&gt;.&lt;/p&gt;&lt;p&gt;&lt;i&gt;Note one almost certainly erroneous observation in the middle panel of the top row of&lt;/i&gt; Figure 7&lt;i&gt;. The 991 mb observation for Birmingham (south-east of the lowest pressure values) at 15Z on 16th November 1928 has no correction listed in the DWRs and is correctly tr","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 3","pages":"351-353"},"PeriodicalIF":3.3,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.250","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140925163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}