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1,121 results for “weathering”
Daily weather data from Nizanda, Mexico (2006-2024)
<p>Daily weather data from Nizanda, Oaxaca, Mexico. Weather data are available on a daily basis as minimum time resolution; however, data are available mostly at an hourly time resolution.</p> <p>Weather data comprise precipitation, air temperature, wind speed and direction, air humidity, atmospheric pressure, and other derived climatic variables.</p> <p>For further information about this dataset, contact Jorge A. Meave (jorge.meave@ciencias.unam.mx) and/or Rodrigo Muñoz (rod.munozaviles@gmail.com).</p> <p>The weather station is located at the following geographical coordinates: 16.661000º N, 95.007484º W</p> <p>Weather data were recorded with a Davis Instruments Vantage Pro2 weather station/sensor suite. Data prior to year 2020 was recorded locally in a Davis Vantage Pro2 console (data segments 1-3), while data after 2020 was recorded online on the cloud using a Davis Vantage Connect cellular transmitter together with the WeatherLink.com service (data segment 4).</p>
Replication Materials for "Weather, Credit, and Economic Fluctuations: Evidence from China", JEBO, 2024
<p>Replication Materials for "Weather, Credit, and Economic Fluctuations: Evidence from China" by Zhenzhu Chen, Li Li and Yao Tang</p> <p>All codes were written and run with Matlab R2023a, and require the "Empirical macro toolbox" by Filippo Ferroni and Fabio canova</p> <p>For additional information, please contact Yao Tang at yao.tang.77@gmail.com. </p>
Weather data from Langebaanweg, South Africa, from 2015 to 2022
<p>Weather data from Langebaanweg, South Africa, from 2015 to 2022. The measurement location is latitude -32.9720 and longitude 18.1570. The dataset contains the hourly measurements of the wind speed, the wind direction, and the rainfall. </p>
Extreme Weather Event Real-time Attribution Machine (EWERAM) forecasts for Cyclone Gabrielle
<p>These files contain the hourly precipitation, wind, humidity, and pressure data as well as the land-sea mask, orography, and regional council data supporting the investigation into the human role in Cyclone Gabrielle performed by the EWERAM (Extreme Weather Event Real-time Attribution Machine) consortium. The EWERAM experiment design is outline by Tradowsky and co-authors (2023, 10.1088/2752-5295/acf4b4).</p> <p>Directory and data format follows the conventions of the Climate of the 20th Century Plus Detection and Attribution (C20C+ D&A) Project. Details are provided at https://portal.nersc.gov/c20c/experiment.html and in Stone and co-authors (2019, 10.1016/j.wace.2019.100206).</p>
Data from: The Jive Verification System and its Transformative Impact on Weather Forecasting Operations
<p><span><span>This repository </span><span>contains</span><span> the data used for the analysis </span><span>in</span><span> the paper “</span></span><span><span>The Jive Verification System and its Transformative Impact on Weather Forecasting Operations” Loveday et al. 2024 </span><span>to be </span><span>submitted</span><span> to the Bulletin of the American Meteorological Society (BAMS). </span></span><span> </span></p>
Weathering by mycorrhizal fungi defines a threshold for nutrients in ecosystems along an aridity gradient - Dataset
<p>Dataset for the Statistical Analysis of Hyphal-Covered Mineral Pellets.<br>This dataset originates from a two-year (2016 to 2018) research project conducted in Chile, spanning locations from Nahuelbuta to Pan de Azúcar (Atacama Desert). It includes detailed records of hyphal growth observed on mineral pellets. Comprehensive descriptions of all methods and data will be available in an upcoming journal publication (DOI will be announced soon).</p>
Code and data for: Ladder fuels, not canopy volumes, consistently associated with forest wildfire severity even in extreme topographic-weather conditions
<p>Code and associated metrics for Hakkenberg et.al. 2024. Ladder fuels rather than canopy volumes consistently predict wildfire severity even in extreme topographic-weather conditions</p> <p>Contact chrishakkenberg@gmail.com for all questions and requests</p>
Annual Weather with Forecast
<p>Dataset for 2021 of weather in different locations.</p>
Supplementary data files for JGR 2021JB022729R, Initial results from the Oman Drilling Project Multi-Borehole Observatory: Petrogenesis and ongoing alteration of mantle peridotite in the weathering horizon
<p>These are the four supplementary data files for a paper accepted in the Journal of Geophysical Research (JGR), "Initial results from the Oman Drilling Project Multi-Borehole Observatory: Petrogenesis and ongoing alteration of mantle peridotite in the weathering horizon", 2021JB022729R, which are being uploaded here in conformance with JGR's requirement that data be placed in an open archive. The date of publication is not yet known, but JGR requires that the data be uploaded when the paper is accepted, so we have used the date of acceptance, November 20, 2021.</p>
Weather driven demography and population dynamics of an endemic perennial plant during a 34-year period
<p>1. Increased anthropogenic influence on the environment has accentuated the need to assess how climate and other environmental factors drive vital rates and population dynamics of different types of organisms. However, to allow distinction between effects of multiple correlated variables, and to capture the effects of rare and extreme climatic conditions, studies extending over decades are often necessary.</p> <p>2. In this study we used an individual-based dataset collected in three populations of <i>Pulsatilla vulgaris</i> subsp. <i>gotlandica</i> during 34 years, to explore the effects of variation in precipitation and temperature on vital rates and population dynamics.</p> <p>3. Most of the observed conspicuous variation in flowering among years was associated with differences in precipitation and temperature in the previous summer and autumn with a higher incidence of flowering following summers with high precipitation and low temperatures. In contrast, climatic variables had no significant effects on individual growth or survival.</p> <p>4. Although the weather-driven variation in flowering had only moderate absolute effects on the population growth rate, simulated persistent changes in average precipitation and temperature resulted in considerable reductions in population sizes compared with current conditions. Analyses carried out with with subsets of data consisting of 5 and 10 years yielded results that strongly deviated from those based on the full data set.</p> <p>5. Synthesis: The results of this study illustrate the importance of long-term demographic monitoring to identify key climatic variables affecting vital rates and driving population dynamics in long-lived organisms.</p>
Coffee Berry Borer (Hypothenemus hampei Ferrari) trap catch and associated weather variables on Hawaii Island
<p>We sampled flying female CBB adults bi-weekly over a three-year period using red funnel traps baited with an alcohol lure at 14 commercial coffee farms on Hawaii Island to characterize seasonal phenology and the relationship between flight activity and five weather variables. We captured almost 5 million scolytid beetles during the sampling period, with 81-93% of the trap catch comprised of CBB. Of the captured non-target beetles, the majority were tropical nut borer, black twig borer and a species of <i>Cryphalus</i>. Two major flight events were consistent across all three years: an initial emergence from January-April that coincided with early fruit development and a second flight during the harvest season from September-December. A generalized additive mixed model (GAMM) revealed that mean daily air temperature had a highly significant positive correlation with CBB flight; most flight events occurred between 20-26 °C. Mean daily solar radiation also had a significant positive relationship with flight. Flight was positively correlated with maximum daily relative humidity at values below ~94%, and cumulative rainfall up to 100 mm; flight was also positively correlated with maximum daily wind speeds up to ~2.5 m/s, after which activity declined.</p>
Predicting the execution time of COSMO weather forecast models
<p>This data set is the work of E. Di Giacomo Master Thesis at the University of Bologna.</p> <p>Predicting the execution time of a numerical weather forecast model is a complex task. Generally, these models simulate the evolution of atmospheric weather and they are typically used for the production of weather forecasts, one or multiple times a day. Given their computational complexity, they require large computing capabilities, such as High Performance Computing systems. In these systems, job scheduling and resource allocation are carefully managed to optimize the usage of the finite and expensive hardware resources; in particular, several allocation and related pricing decisions are based on estimates of the duration of the application submitted, such as the execution time of weather forecast models.<br> A reliable prediction of execution time allows for a better management of the overall system, an improved planning of the model execution, as well as the identification of possible anomalies during the execution, thus providing great benefits to both system administrators and users.</p> <p>This data set regards a particular weather forecast model, namely the COSMO model, the weather forecasting model used at the the Hydro-Meteo-Climate Structure of Arpae Emilia-Romagna. The data set contains many execution times of the COSMO meteorological model run under a variety of different scientific parameters and parallelization levels.</p>
Data from the article "Modulation of wintertime canopy Urban Heat Island (CUHI) intensity in Beijing by synoptic weather pattern in planetary boundary layer"
<p>The link includes four datasets, "pcttype" is weather typing data, "pblh" is PBLH data, "uhii-UV" is the mean value of CUHII and wind direction UV of all urban stations, and "uhii-sws" is the value of CUHII, wind speed and wind direction of all urban stations.</p>
Code for JAMES publication - Implementation of prognostic cloud ice number concentrations for the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme.
<p>In this repository, we include the source codes for WRF microphysics parameterization used in the JAMES publication "Implementation of prognostic cloud ice number concentrations for the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme"</p> <p> </p> <p>The revised WDM6 code, which predicts the number concentration of cloud ice, and the original WDM6 code are uploaded. Each code is modified so that detailed microphysical processes can be found in wrfout.</p> <p> </p> <p>Namelist files shows the namelist.input for each case.</p> <p> </p> <p>Scripts file for figures in this manuscript are uploaded</p>
Chemical compositions data for "Space weathering of the Chang'e-5 lunar sample from a mid-high latitude region on the Moon"
<p>Data for “Space weathering of the Chang’e-5 lunar sample from a mid-high latitude region on the Moon”</p>
Data for "An Ensemble-Based Statistical Methodology to Detect Differences in Weather and Climate Model Executables" Part 1/2
<p>Ensemble simulations from the weather and climate model COSMO. The data has been used for model verification cases in the corresponding paper (https://doi.org/10.5194/gmd-2021-248).</p> <p>The data is partitioned into the following parts:</p> <ol> <li>gpu_dycore.tar.gz<br> 5-day ensemble (600 members) produced with COSMO 5.09 GPU version in double precision.</li> <li>cpu_nodycore.tar.gz<br> 5-day ensemble (200 members) produced with COSMO 5.09 CPU version in double precision.</li> <li>gpu_dycore_sp.tar.gz<br> 5-day ensemble (200 members) produced with COSMO 5.09 GPU version in single precision.</li> </ol> <p>The second part of the dataset with the diffusion ensembles can be found here: https://doi.org/10.5281/zenodo.6355647</p>
Variation in incubation length and hatching asynchrony in Eastern Kingbirds: weather eclipses female effects
<p>Incubation length and hatching asynchrony are integral elements of the evolved reproductive strategies of birds and here we here quantify intra- and interpopulation variation in both traits for Eastern Kingbird (<i>Tyrannus tyrannus</i>) populations from New York (NY), Kansas (KS), and Oregon (OR). Multiple samples from a subset of females demonstrated that neither incubation length nor hatching asynchrony was repeatable, that both traits varied with a female trait (clutch size), but that variation in both incubation length and hatching asynchrony was to a substantial degree linked to weather, namely ambient temperature and precipitation. Incubation length exhibited the same median (15 days) and range (13 to 17 days) at all sites, and model selection results indicated that incubation periods were longer in NY than KS, among the smallest and largest clutches, when rain was frequent throughout the period when eggs were in the nest, in replacement nests, and likely when ambient temperatures were low during egg-laying. Full hatching usually required 2 days (but up to 3), and more synchronous hatching was associated with small clutch size, short incubation periods, frequent rain during the egg-laying period, and low ambient temperatures during the first half of incubation. Only 5% to 9% of nestlings starved and starvation was not associated with greater hatching asynchrony, indicating the nestling starvation was likely not a cost of hatching asynchrony. Thus, although clutch size, a female trait, contributed to variation in incubation length and hatching asynchrony, weather was a greater source of variation, especially for incubation length. Weather thus influences length of exposure of eggs to destructive forces (predators and severe weather), and as a consequence, climate change has the potential to influence reproductive success.</p>
The role of aerosol-radiation interaction in the meteorology prediction at the weather scale
<p class="MsoNormal"><a name="OLE_LINK53"></a><span>The current capabilities of the numerical weather </span><span><span>prediction (NWP)</span></span><span><span> model to simulate aerosol-radiation interaction (ARI) impacts on weather prediction </span></span><span><span>are still rarely considered compared with climate models</span></span><span><span>. Here, the </span></span><span><span>NWP</span></span><span><span> model GRAPES_CUACE is used to evaluate the role of the ARI in meteorology prediction at the weather scale in China.</span></span><span><span> </span></span><span><span>The results show that </span></span><span><span class="jlqj4b"><span>the online calculation of ARI in the model can <a name="OLE_LINK99"></a>extensively improve the meteorology prediction accuracy involving temperature, wind, and pressure at most vertical <a name="OLE_LINK12"></a><span>levels</span><span> in relatively clean, light, and heavy pollution stages. This improvement significantly occurs in the meteorology factors below 950 hPa prediction such as temperature at 2 m, 1000, and 950 hPa, and mean sea level pressure, particularly in heavy pollution areas and stages.</span></span></span><span> </span></span><a name="OLE_LINK13"></a><span><span><span class="jlqj4b"><span>Unlike</span></span></span></span><span><span><span class="jlqj4b"><span> temperature, the improvement of ARI in the predicted wind <a name="OLE_LINK52"></a>at the height of the boundary layer is more significant than near-surface. </span></span></span></span><span><span>However, this improvement declines when the low-cloud exists.</span></span></p>
Enhanced Weathering Using Basalt Rock Powder: Carbon Sequestration, Co-benefits and Risks in a Mesocosm Study With Solanum tuberosum data
<p>Dataset used in the work: Enhanced Weathering Using Basalt Rock Powder: Carbon Sequestration,Co-benefits and Risks in a<br> Mesocosm Study With Solanum tuberosum</p> <p><br> authors: <br> Arthur Vienne, Silvia Poblador , Miguel Portillo-Estrada, Jens Hartmann,<br> Samuel Ijiehon, Peter Wadeand Sara Vicca</p>
Data on the Weather and Habitat and Representative results of qPCR and PCR
<p>We used weather and habitat characteristics, which collected from studied 49 sampling sites, to know how those affect ranavirus infection rate in amphibian larvae of Dryophytes japonicus (Japanese tree frog), Pelophylax nigromaculatus (Black-spotted pond frog), and Lithobates catesbeianus (American bullfrog) in South Korea.</p> <p>Description on the method section: To investigate the relationship between the ranavirus infection rate and habitat characteristics, we analyzed 16 habitat characteristics at each sampling location. The land cover rate (urban area, agricultural area, forest area, grass area, wetland area, bare area, hydrosphere area) within a radius of 3 km2 from the mid-sampling point was calculated on the 2020 South Korean land cover map(http://egis.me.go.kr ). From the land cover data, greenhouse cultivation areas within agricultural areas, salt fields and tidal flat areas within wetland areas, and sea areas within hydrosphere areas were excluded during data handling because these areas are inhabitable for amphibians. Additionally, the shortest distance from the mid-sampling point to agricultural land, mountains, water bodies, and urban areas was calculated in units of 1 m. When a sampling point was located within a specific coverage area, the distance was set to 2 m. All land cover rates, distance data, and altitude values of each sampling point were calculated using QGIS (ver. 3.4.7, QGIS.org 2021). Furthermore, the average air temperature, lowest air temperature, highest air temperature, and average precipitation for the immediately preceding quarter, based on the sampling date of the tadpoles used in the qPCR experiment, were obtained from the local meteorological station closest to the sampling point.</p> <p>We also provided the igures of representative positive qPCR results of D. japonicus (A, B) collected at Haenam, P. nigromaculatus (C, D) collected at Pohang, and L. catesbeianus (E, F) collected at Gmchoen, which showing positive ranavirus detection from the samples, and the Photographs of representative gel electrophoresis of the MCP fragment (~500bp), which confirmed the qPCR result again by sequencing the fragment (G, H) in the further study.</p>
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