Find research datasets worth reusing

opencc-zeroDec 2017View details →

Data from: Severe fire weather and intensive forest management increase fire severity in a multi-ownership landscape

Many studies have examined how fuels, topography, climate, and fire weather influence fire severity. Less is known about how different forest management practices influence fire severity in multi-owner landscapes, despite costly and controversial suppression of wildfires that do not acknowledge ownership boundaries. In 2013, the Douglas Complex burned over 19,000 ha of Oregon & California Railroad (O&C) lands in Southwestern Oregon, USA. O&C lands are comprised of a checkerboard of private industrial and federal forestland (Bureau of Land Management, BLM) with contrasting management objectives, providing a unique experimental landscape to understand how different management practices influence wildfire severity. Leveraging Landsat based estimates of fire severity (Relative differenced Normalized Burn Ratio, RdNBR) and geospatial data on fire progression, weather, topography, pre-fire forest conditions, and land ownership, we asked 1) what is the relative importance of different variables driving fire severity, and 2) is intensive plantation forestry associated with higher fire severity? Using Random Forest ensemble machine learning, we found daily fire weather was the most important predictor of fire severity, followed by stand age and ownership, followed by topographic features. Estimates of pre-fire forest biomass were not an important predictor of fire severity. Adjusting for all other predictor variables in a general least squares model incorporating spatial autocorrelation, mean predicted RdNBR was higher on private industrial forests (RdNBR 521.85 ± 18.67 SE) versus BLM forests (398.87 ± 18.23 SE) with a much greater proportion of older forests. Our findings suggest intensive plantation forestry characterized by young forests and spatially homogenized fuels, rather than pre-fire biomass, were significant drivers of wildfire severity. This has implications for perceptions of wildfire risk, shared fire management responsibilities, and developing fire resilience for multiple objectives in multi-owner landscapes.

opencc-zeroDec 2020View details →

Bird strikes at commercial airports explained by citizen science and weather radar data

1. Aircraft collisions with birds span the entire history of human aviation, including fatal collisions during some of the first powered human flights. Much effort has been expended to reduce such collisions, but increased knowledge about bird movements and species occurrence could dramatically improve decision support and proactive measures to reduce them. Migratory movements of birds pose a unique, often overlooked, threat to aviation that is particularly difficult for individual airports to monitor and predict: the occurrence of birds vary extensively in space and time at the local scales of airport responses. 2. We use two publicly available datasets, radar data from the US NEXRAD network characterizing migration movements and eBird data collected by citizen scientists to map bird movements and species composition with low human effort expenditures but high temporal and spatial resolution relative to other large scale bird survey methods. As a test case we compare these results from weather radar distributions and eBird species composition with detailed bird strike records from three major New York airports. 3. We show that weather radar based estimates of migration intensity can accurately predict probability of bird strikes, with 80% of the variation in bird strikes across the year explained by the average amount of migratory movements captured on weather radar. We also show that eBird based estimates of species occurrence can, using species' body mass and flocking propensity, accurately predict when most damaging strikes occur. 4. Synthesis and applications: Our results highlight the power of federating datasets with movement and distribution data for developing better and more taxonomically and ecologically tuned models of likelihood of strikes occurring and severity of strikes. By better understanding when, and where, different species occur, airports across the world can predict seasonal periods of collision risks with greater temporal and spatial resolution; such predictions include potential to predict when the most severe and damaging strikes may occur.

opencc-zeroAug 2021View details →

Ecological resilience and resistance to extreme weather events - review data

Extreme weather events (EWEs) are expected to increase in stochasticity, frequency, and intensity due to climate change. Documented effects of EWEs, such as droughts, hurricanes, and temperature extremes, range from shifting community stable states to species extirpations. To date, little attention has been paid to how populations resist and/or recover from EWEs through compensatory (behavioural, demographic or physiological) mechanisms; limiting the capacity to predict species responses to future changes in EWEs. Here, we systematically reviewed the global variation in species' demographic responses, resistance to, and recovery from EWEs across weather types, species, and biogeographic regions. Through a literature review and meta-analysis, we tested the prediction that population abundance and probability of persistence will decrease in populations after an EWE and how compensation affects that probability. Across 524 species population responses to EWEs reviewed (27 articles), we noted large variation in responses, such that, on average, the effect of EWEs on population demographics was not negative as predicted. The majority of species populations (80.4%) demonstrated compensatory mechanisms during events to reduce their deleterious effects. However, for populations that were negatively impacted, the demographic consequences were severe. Nearly 20% of the populations monitored experienced declines of over 50% after an EWE, and 6.8% of populations were extirpated. Population declines were reflected in a reduction in survival. Further, resilience was not common, as 80.0% of populations that declined did not recover to before EWE levels while monitored. However, average monitoring time was only two years with over a quarter of studies tracking recovery for less than the study species generation time. We conclude that EWEs have positive and negative impacts on species demography, and this varies by taxa. Species population recovery over short time intervals is rare, but long-term studies are required to accurately assess species resilience to current and future events.

opencc-zeroSep 2021View details →

Data for: Fire history and weather interact to determine extent and synchrony of mast-seeding in rhizomatous scrub oaks of Florida

In disturbance-prone ecosystems, fitness consequences of plant reproductive strategies are often determined by the relative timing of seed production and disturbance events, but the role of disturbances as proximate drivers of seed production has been overlooked. We use long-term data on seed production in Quercus chapmanii, Q. geminata, and Q. inopina, rhizomatous oaks found in Southcentral Florida's oak scrub, to investigate the role of fire history and its interaction with weather in shaping acorn production and its synchrony. Acorn production increased with the time since last fire, combined with additive or interactive effects of spring precipitation (+) or drought (–). Furthermore, multiple matrix regression models revealed that ramet pairs with shared fire history were more synchronous in seed production than ones that burned in different years. Long-term trends suggest that increasingly drier spring weather, in interaction with fire frequency, may drive a decline of seed production. Such declines could affect the community of acorn-reliant vertebrates in the Florida scrub, including endangered Florida scrub-jays (Aphelocoma coerulescens). These results illustrate that fire can function as a proximate driver of seed production in mast-seeding species, highlighting the increasingly recognized importance of interactions among reproductive strategies and disturbance regimes in structuring plant populations and communities.

opencc-zeroSep 2021View details →

Bedrock weathering controls on terrestrial carbon-nitrogen-climate interactions

Anthropogenic nitrogen deposition is widely considered to increase CO2 sequestration by land plant communities on a global scale. Here, we suggest that bedrock nitrogen weathering contributes significantly more to nitrogen-carbon interactions than anthropogenic nitrogen deposition. This working hypothesis is based on the application of empirical results into a global biogeochemical simulation model from the mid-1800s to the end of the 21st century. We demonstrate that rock nitrogen inputs have contributed roughly 2 to 11 times more to net primary productivity gains than nitrogen deposition since pre-industrial times. Projections based on RCP 8.5 show that rock nitrogen inputs and biological nitrogen fixation contribute 2 to 5 times more to terrestrial carbon uptake than anthropogenic nitrogen deposition through year 2101. The enhancement of carbon uptake via rock nitrogen weathering partially resolves nitrogen-carbon discrepancies in Earth system models and offers an alternative explanation for lack of progressive nitrogen limitation in the terrestrial biosphere. We conclude that natural N inputs impart major control over terrestrial CO2 sequestration in Earth's ecosystems.

opencc-by-4.0Oct 2021View details →

The star-planet interaction by combining asteroseismic and space weather techniques

The characterization of exoplanets conditions cannot be based solely on the knowlegde of the planetary main parameters, since the properties and the activity level of the host star, as well as the effects of extreme space weather phenomena, need also to be considered. Here we propose a synergic strategy based both on an asteroseismic approach and a space weather/space climate analysis. By studying the oscillation spectra it is possible to derive the host star fundamental parameters, including a precise determination of the age. Combining this informations with those coming from observations in the UV spectrum of the star (Ca II H & K lines), and by using relations which we have already calibrated on the Sun (Reda et al. 2021 submitted), we are able to estimate the mean stellar wind acting on the exoplanets, enabling to estimate the erosion of their atmospheres. The best targets for this approach consists of terrestrial planets orbiting around solar-like stars, which are exactly the primary target of the PLATO mission.

opencc-by-4.0Oct 2021View details →

Global Fire Weather Indices - supporting data for Jain et al. 2021, Nature Climate Change

Daily fire weather indices (FWI and ISI, outputs of the Canadian Fire Weather Index System) from 1979-2020 at 0.25 deg resolution. This data supports the analysis in "Observed increases in extreme fire weather driven by atmospheric humidity and temperature", Jain et al. 2021, accepted for publication in Nature Climate Change. Variables were processed using inputs from the ERA5 Reanalysis (hourly surface data from 1979–2020, available from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a>). FWI System indices were calculated using the CFFDRS R package using the overwintering procedure outlined in McElhinny et al. 2020.  References McElhinny, M., Beckers, J. F., Hanes, C., Flannigan, M., and Jain, P.: A high-resolution reanalysis of global fire weather from 1979 to 2018 – overwintering the Drought Code, Earth Syst. Sci. Data, 12, 1823–1833, https://doi.org/10.5194/essd-12-1823-2020, 2020.

opencc-zeroOct 2021View details →

Data from: Weather and topography regulate the benefit of a conditionally helpful parasite

Heat-induced mass mortalities involving ecosystem engineers may have long-lasting detrimental effects at the community level, eliminating the ecosystem services they provide. Intertidal mussels are ecologically and economically valuable with some populations facing unprecedented heat-induced mass mortalities. Critically, mussels are also frequently infested by endolithic parasites that modify shell albedo, hence reducing overheating and mortality rates under heat stress. Using a biophysical model, we explored the topographical and meteorological conditions under which endolithically-driven thermal buffering becomes critical to survival. Based on meteorological data from a global climate analysis, we modelled body temperatures of infested and non-infested mussels over the last decade (2010-2020) at nine sites spread across ca. 20° of latitude. We show that thermal buffering is enhanced where and when heat stress is greatest, that is, on sun-exposed surfaces under high solar radiation and high air temperature. These results suggest that new co-evolutionary pathways are likely to open for these symbiotic organisms as climate continues to change, potentially tipping the balance of the relationship from a parasitic to a more mutualistic one. However, endolithically-driven reductions in body temperatures can also occur at or below optimal temperatures, thereby reducing the host's metabolic rates and making the interplay of positive and negative effects complex. In parallel, we hindcasted body temperatures using empirical data from nearby weather stations and found that predictions were very similar with those obtained from two global climate reanalyses (i.e., NCEP-DOE Reanalysis 2 and ECMWF Reanalysis v5). This result holds great promise for modelling the distribution of terrestrial ectotherms at ecologically relevant spatiotemporal scales, as it suggests we can reasonably bypass the practical issues associated with weather stations. For intertidal ectotherms, however, the challenge will be incorporating body temperatures over the full tidal cycle.

opencc-by-4.0Nov 2022View details →

Dataset for Weather drives variation in COVID-19 transmission and detection

This dataset contains the input data for the panel regression and SEIR analyses for the paper "Weather drives variation in COVID-19 transmission and detection", as well as the results of the SEIR calibrations. Under the `inputs` directory, the `panel_all.csv` data provides the compiled data for the core analyses. For information on COVID cases, we use daily reports from the John Hopkins University's COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) (Dong et al. 2020). We complement these data with a variety of online sources on confirmed cases at the subnational level. For information on weather, we use ERA 5 reanalysis (Hersbach et al. 2018). The data were downloaded from the Copernicus Climate Change Service (C3S) Climate Data Store in January 2021. The governance data (in `inputs/governance`) is drawn from <a href="https://info.worldbank.org/governance/wgi/">https://info.worldbank.org/governance/wgi/</a> for the year 2019. The mapping is done using GADM 3.6, simplified to reduce its size, and stored in `inputs/gadm36_levels_simple`. The result data in the `results` directory represents calibrations of the Bayesian SEIR model:  - Files of the form `epimodel-meta-0314...-nodel.csv` include parameter estimates both as initially calibrated (group = "Raw") and after the meta-analysis (group = "Combined"). Paramater estimates are represented by their mean, standard deviation, and five quantiles. Different files of this form reflect different assumptions: (1) `-noweather` exclude weather effects; `-noprior` exclude panel regression priors, and `-full3` include weather and priors; (2) `-all` were estimated using all observations; `-mobile` were estimated using only region/observations with mobility data; (3) `-nobs` include meta-analyzed data weighted by the number of observations; `-pop` are weighted by population; and `-region` are weighted by region.  - The `global-0314.RData` file contains calibrations treating the whole world as one region.  - The `pairwise.csv` and `pairwise-all.csv` compare data from regions that have valid no-prior (suffix 1) and no-weather (suffix 2) projections. `pairwise.csv` has summary statistics while `pairwise-all.csv` includes every region. Acknowledgements: The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. Bibliography: Dong E, Du H, Gardner L (2020): An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. 20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1 Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed in January 2021), 10.24381/cds.adbb2d47

opencc-zeroDec 2022View details →

Woody phenology and weather data related to Trelease Woods, Urbana, IL, USA 1993-2022

In this study, weekly phenological observations throughout spring and autumn were made annually over 30 years for 22 woody plant taxa of multiple growth forms in Trelease Woods, a mature old-growth deciduous forest remnant near Urbana, Illinois, USA. The growth forms included canopy trees, saplings, subcanopy treelets, shrubs, and vines. Data collection began in the spring of 1993 and continued through the end of autumn 2022. The phenological data set is paired with weather data collected from a nearby weather station in Champaign, Illinois. These two data sets were used to determine if dates of phenological events and durations of phenophases were changing over time, possibly in connection with changing weather related to global climate change. To supplement these phenological data, two older phenological data sets are made available here. First, Charles Smith, Woods Custodian at the University of Illinois, made comparable observations from 1949 to 1964 of seven of the same phenological events on six of the same woody species. Data were collected from Trelease Woods and Brownfield Woods. The latter is an old-growth forest separated by 2 km from Trelease Woods, and the two sites were connected in a larger forest before the mid-19th century. Second, Thomas Mikesell, a farmer from Ohio, USA, made comparable observations from 1883 to 1912 of six of the same phenological events on 11 of the same woody species. Mikesell's data were collected in a deciduous forest near Wauseon, Ohio, USA. Because of the distance between the Mikesell study site and the site of the current study in Urbana, Illinois, in order to compare the two data sets, adjustments were made to the event dates in the Mikesell data set based on bioclimatic laws. Comparison of the current data to those collected by Charles Smith and Thomas Mikesell provides evidence as to whether the trends in phenological event dates observed from 1993 to 2021 held consistent when investigated at longer time scales.

opencc-zeroJan 2023View details →

Does weather drive habitat use and movement of a non-migratory bird?

Climate change is predicted to increase the intensity and frequency of weather extremes (e.g., extreme heat and drought), which will likely affect wildlife species in significant ways. Maintaining landscape heterogeneity has been suggested as a potential conservation strategy to buffer animals from weather extremes. Because animal movement influences survival and population connectivity, understanding animal space use and movement in response to shifts in weather is useful for the conservation of wildlife. Non-migratory species are predicted to be more negatively affected by climate change because they have a limited ability to disperse to find resources. We studied the Northern Bobwhite (Colinus virginianus; hereafter, bobwhite) to understand how weather alters habitat use and movement of a non-migratory animal across a landscape. We collected telemetry locations on bobwhite across western Oklahoma during 2019–2020 and paired these data with meteorological and vegetation data. We analyzed the data at 2 temporal scales – hourly locations and 12-hour paths. At the hourly scale, we analyzed tree cover use, shrub cover use, and normalized difference vegetation index (NDVI) use using generalized or linear-mixed models. At the 12-hour scale, we calculated three different movement metrics: cumulative distance, net displacement, and sinuosity, and analyzed each movement metric separately using generalized linear-mixed models. We found that bobwhite used denser tree cover and higher NDVI values as air temperature increased in conjunction with solar radiation. Bobwhite used denser shrub cover as air temperature increased and average wind speed was high as well as when air temperature and average wind speed were low. The interaction between variability in vegetation and climate influenced bobwhite movement. Bobwhite in areas with higher NDVI variance moved farther when mean air temperatures were > 0°C than bobwhite in areas with low NDVI variance. When mean air temperatures were ≤ 0°C, NDVI variance had little effect on the cumulative distance moved of bobwhite. Our findings show that some animals use different vegetation types during different weather conditions and that variability in vegetation on the landscape and weather together alters animal movement. Managing for landscape heterogeneity is a solution that may buffer animals from increased climate variability in the future.

A dataset of Korean weather with anomaly score from 2010 to 2020

This dataset describes the weather data of 64 cities in Korea for each day and the weather anomaly scores for each day from 2010 to 2020. The dataset includes city name, dates, temperature, humidity, vapor pressure, dew point temperature, sea level pressure, ground pressure, ground temperature, LOF anomaly score, IF anomaly score, COPOD anomaly score, ABOD anomaly score, HBOS anomaly score, SOD anomaly score and ROD anomaly score. In the dataset, the weather data and the weather anomaly score of each day for 64 Korean cities from 2010 to 2020 are stroed into 64 csv files. Each csv file in the dataset represents each city. The 64 cities include Seoul, the capital of Korea, and the 6 metropolitan cities of Busan, Daegu, Incheon, Gwangju, Daejeon, and Ulsan. In addition, the weather data and weather anomaly scores for 19 coastal cities and 4 islands in Korea are included in the dataset.

WeatherPon: A Weather and Machine Learning-based Coupon Recommendation Mechanism in Digital Marketing

These are the datasets that we used.

opencc-by-4.0Sep 2021View details →

Weather dataset from Vallon de Nant, Switzerland (from 16 August 2016 to 14 October 2018)

Weather data collected in the Vallon de Nant, Switzerland from 16 August 2016 to 14 October 2018 Data collected by the reasearch team of Bettina Schaefli at the Institute of Earth Surface Dynamics (IDYST), Faculty of Geosciences and Environment, University of Lausanne, Lausanne, 1015, Switzerland for further information, please contact: bettina.schaefli@giub.unibe.ch Grant: SNSF PP00P2\_157611

Supporting data for the Eastern equatorial Pacific dust fertilization and source weathering influences on the Eocene to Miocene CO2 decline

This is supporting data for Equatorial Pacific dust fertilization and source weathering influences on Eocene to Miocene global CO2 decline.

opencc-by-4.0Sep 2022View details →

PLEIAData:consumption, HVAC (Heating, Ventilation & Air Conditioning), temperature, weather and motion sensor data for smart buildings applications

This dataset presents detailed building operation data from the three blocks (A, B and C) of the Pleiades building of the University of Murcia, which is a pilot building of the European project PHOENIX. The aim of PHOENIX is to improve buildings efficiency, and therefore we included information of: (i) consumption data, aggregated by block in kWh; (ii) HVAC (Heating, Ventilation and Air Conditioning) data with several features, such as state (ON=1, OFF=0), operation mode (None=0, Heating=1, Cooling=2), setpoint and device type; (iii) indoor temperature  per room; (iv) weather data, including temperature, humidity, radiation, dew point, wind direction and precipitation; (v) carbon dioxide and presence data for few rooms; (vi) relationships between HVAC, temperature, carbon dioxide and presence sensors identifiers with their respective rooms and blocks. Weather data was acquired from the IMIDA (Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario).

A dataset of Korean weather with anomaly score from 2010 to 2020

This dataset describes the weather data of 64 cities in Korea for each day and the weather anomaly scores for each day from 2010 to 2020. The dataset includes city name, dates, temperature, humidity, vapor pressure, dew point temperature, sea level pressure, ground pressure, ground temperature, LOF anomaly score, IF anomaly score, COPOD anomaly score, ABOD anomaly score, HBOS anomaly score, SOD anomaly score and ROD anomaly score. In the dataset, the weather data and the weather anomaly score of each day for 64 Korean cities from 2010 to 2020 are stored into 64 csv files. Each csv file in the dataset represents each city. The 64 cities include Seoul, the capital of Korea, and the 6 metropolitan cities of Busan, Daegu, Incheon, Gwangju, Daejeon, and Ulsan. In addition, the weather data and weather anomaly scores for 19 coastal cities and 4 islands in Korea are included in the dataset.

opencc-by-4.0Apr 2023View details →

Data used for global TEC forecasting for space weather application based on deep learning techniques: a comparative study and considerations for real-time implementation

This dataset contains the measurements of TEC from Global Ionospheric Maps (GIMs), provided by the International GNSS Service (IGS), as the target parameter and the global geomagnetic Kp index as the external input. This data set is composed of samples from 2005 to 2017. The datasets have been curated to obtain the same resolution (2 hs) of the two parameters.

openother-openMay 2023View details →

Datasets associated with the publication: "The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models".

ECMWF HRES and ERA5 datasets for the paper "The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models". Content:  ======================================================================================== Storm Friederike: ECMWF HRES forecast, 18.01.2018 00:00 UTC - 23:00 UTC, hourly, GRIB-format. ECMWF ERA5 reanalysis, 16.01.2018 12:00 UTC - 19.01.2018 00:00 UTC, twelve-hourly data, GRIB-format. ======================================================================================== Storm Vladiana: ECMWF HRES analysis, 23.09.2016 00:00 UTC - 23.09.2016 00:00 UTC, six-hourly data, rotated North Pole (latitude: 51˚, longitude: 160˚), NetCDF-format. ======================================================================================== Storm Egon: ECMWF ERA5 reanalysis, 12.01.2017 00:00 UTC - 13.01.2017 06:00 UTC, six-hourly data, GRIB-format.