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21 results for “complex terrain”
High-resolution surface wind observations over complex terrain: Big Southern Butte, Salmon River Canyon, Birch Creek
<p>This dataset contains high-resolution wind observations from three field campaigns that took place during 2010-2014 at Big Southern Butte, Salmon River Canyon, and Birch Creek, Idaho. There are three SQLite databases containing 30-s averaged 3-m wind speed, wind direction, and wind gust data from 30-90 cup-and-vane anemometers over a period of 2-4 months at each field site.</p>
A Scene-Level Method for Estimating Small River Widths in Complex Terrain Using Remote Sensing
<p><strong>Files and Descriptions:</strong></p> <p>1. <strong>TP_Lake.csv</strong>: This CSV file contains identified lakes in the Tibetan Plateau.</p> <p>2. <strong>TP_River_Monthly_Statistics.csv</strong>: Monthly statistics for river data on the Tibetan Plateau, including estimations like active channel percentage and width for different river orders.</p> <p>3. <strong>S2RiverWidth.py</strong>: The Python script that contains the main code for river width estimation model. This script includes the functions for preprocessing Sentinel-2 images and predicting river widths.</p> <p>4. <strong>best_model_vCloud10.pth</strong>: The pre-trained deep learning model weights used for river width estimation. This model is a ResNeXt model fine-tuned on our dataset. It accepts Sentinel-2 TOA image with cloud percentage <10% (SCL).</p> <p>5. <strong>Example.tif</strong>: A Sentinel-2 TOA image in .tif format, used for testing the river width estimation model.</p> <p><strong>Model Input Requirements:</strong><br>The model requires a Sentinel-2 TOA image in .tif format as input. The image should be scaled by a factor of 10,000 (with reflectance values range from 0 to 1). The `get_model_input` function automatically crops the image to 224x224 pixels around the center to fit the model's input requirements.</p>
Predictors and predictands for "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain"
<p>Predictors and predictands for "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain". Training predictors from the ERA5 reanalysis, projecting predictors from CORDEX EUR11, and predictand from ReKIS (https://rekis.hydro.tu-dresden.de). Data is saved in ".rds" format, to be read from R, except for CORDEX files in NetCDF.</p>
Considerations for high-resolution regional meteorological wind modelling over complex terrain: a typhoon case study for assessing forestry damage (data)
<p>This is the experiment data.</p> <p>The Weather Research and Forecasting (WRF) model is a popular and easily used as a numerical weather prediction (NWP) model, but configuring WRF to produce accurate results can be time-consuming. This is especially so when simulating extreme events, over complex terrain, or at high resolutions. In this study, a strong wind event from Tropical Cyclone (TC) Thad in year 1981 was simulated at 200 m resolution over an experiment forest in a mountainous region of Hokkaido island, Japan. The simulation configuration is challenging, in order to cover a larger area to produce a TC with appropriate track and intensity, and at the same time to resolve the smallest domain of sub-km grid spacing with computational stability. A mixed nesting method was applied with two-way nesting up for the first three domains, followed a separate simulation over the smallest domain. The mixed method could produce 10 min wind speed distributions similar to that of the full simulation with two-way nesting of all four domains, if a 30-minute boundary update interval was used for the separate simulation. Mixed nesting improves the efficiency of the simulation process, since the larger phenomenon scale and smaller human impact scale can be tuned separately. </p>
Data accompanying the paper titled: Evaluation of a forest parameterization to improve boundary layer flow simulations over complex terrain.
<p>The present repository contains the namelists, output data from the Weather Research and Forecasting (WRF) numerical simulations and observations described in the article "Evaluation of a forest parameterization to improve boundary layer flow simulations over complex terrain" submitted to the Geosciences Model Development journal.</p> <p>The numerical simulations were developed to evaluate the influence of a forest parametrization on the simulation of the boundary layer flow over moderate complex terrain in the context of the Perdigão 2017 field campaign. The numerical simulations used WRF large eddy simulation mode (WRF-LES). The short-term high resolution (40 m horizontal grid spacing) and long-term (200 m horizontal grid spacing) WRF-LES were run for an integration time of 12 hours and 1.5 months, respectively, with and without forest parameterization. The short-term simulations focus on low-level jet events over the valley, while the long-term simulations cover the whole intensive observation period (IOP) of the field campaign. The results are validated using lidar and meteorological tower observations.</p> <p>The files in the repository are organized as follows:</p> <p>Namelists used to run the WRF model are located in the 'namelists' sub-directory.</p> <p>Data slices of domains d03 and d04 covering the location of the three meteorological towers and LIDAR for the whole integration time are located in the 'reduced_domain' sub-directory.</p> <p>Direct WRF output for selected periods (representing the low-level-jet episodes observed) for domains d03 and d04 are located in the 'files_select_time' sub-directory.</p> <p>The observations used to validate the model are located in the 'observations' sub-directory. The complete dataset of observations from the field campaign can be found in the official campaign data repository: re3data.org: Perdigao Field Experiment; editing status 2020-03-20; re3data.org - Registry of Research Data Repositories. http://doi.org/10.17616/R31NJMN4 last accessed: 2021-10-13. The WRF model can be downloaded from https://www2.mmm.ucar.edu/wrf/users/download/get_sources_new.php.</p>
Multivariate projected ensemble of "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain"
<p>Multivariate statistically downscaled projected ensemble for different combinations of GCM-RCMs of both stochastic and deterministic runs for eight historical runs, eight RCP85 runs and one RCP26 run. Selection of good perfoming GCM-RCM combinations in NetCDF format. Variables: precipitation, water vapour pressure, radiation, wind speed, and, maximum, mean and minimum temperature.</p>
zEPHYR - Complex Terrain Benchmark
<p>This repository contains some of the scripts and datasets used for the zEPHYR Complex terrain benchmark<br> </p>
Video supplement for publication "The impact of mesh size and microphysics scheme on the representation of mid-level clouds in the ICON model in hilly and complex terrain"
<p>This is the video supplement for the manuscript "The impact of mesh size and microphysics scheme on the representation of mid-level clouds in the ICON model in hilly and complex terrain". It includes compiled videos from allsky cameras at two different field sites (CLOUDLAB (hilly terrain, HT) and CROSSINN (complex terrain, CT)) for in total four case studies (named by their dates). For each case study, we compiled a video of the cloud cover from the model for the 1 km and 65 m resolution, both with a one-moment (1M) and two-moment (2M) microphysics scheme. </p> <p> </p>
Datasets of paper "Improving the performance of a reduced-order mass-consistent model for urban environments and complex terrain with a higher-order geometrical representation"
<p>These are the datasets, processing scripts, and plots that are used in the paper titled "Improving the performance of a reduced-order mass-consistent model for urban environments and complex terrain with a higher-order geometrical representation" submitted to the JAMES.</p>
A gravity current flows over a wind farm in complex terrain
<p>A gravity current simulated with the Weather Research and Forecasting model flows over topography where a wind farm is located. It produces a nocturnal jet and downstream flow acceleration that enhances the performances of turbines in the back rows. This simulation is related to case 3 (transect 3) of the paper <em>Nocturnal jets over wind farms in complex terrain </em>that will be submitted to <em>Applied Energy.</em></p>
Data from: Microclimate-based species distribution models in complex terrain indicate widespread cryptic refugia under climate change
<p class="MsoNoSpacing"><i>Aim: </i>Species' climatic niches may be poorly predicted by regional climate estimates used in species distribution models (SDMs) due to microclimatic buffering of local conditions. Here, we compare SDMs generated using a locally validated below-canopy microclimate model to those based on interpolated weather station data at two spatial scales to determine the effects of scale, topography, and forest cover on potential future ground-level warming and species distributions.</p> <p class="MsoNoSpacing"><i>Location:</i> Great Smoky Mountains National Park (2090 km<sup>2</sup>; NC, TN, USA)</p> <p class="MsoNoSpacing"><i>Time period: </i>1970 – 2006</p> <p class="MsoNoSpacing"><i>Major taxa:</i> Vascular plant species of the Southern Appalachians</p> <p class="MsoNoSpacing"><i>Methods:</i> We compared the fit and predictions of SDMs generated using a database of plant occurrences and three climate models: macroclimate (1 km, WorldClim), fine-scale (30 m) interpolation of macroclimate with elevation, and fine-scale below-canopy microclimate from a ground-level sensor network.</p> <p class="MsoNoSpacing"><i>Results: </i>We found that, although SDM fit was similar across models, microclimate-derived SDMs predicted substantially greater species persistence with 4 °C of regional warming, with a difference of 50% of the species pool in some areas. Microclimate SDMs predicted higher stability of mid-elevation species, particularly in thermally buffered areas near streams, and critically, less change in species composition at high elevation. In contrast, predictions of macroclimate and interpolation models were similar despite improved resolution.</p> <p class="MsoNoSpacing"><i>Main conclusions:</i> Our results demonstrate that careful selection of climate drivers, including local near-ground validation rather than interpolation, is critical for projecting distributions. They also suggest that some species at risk from climate change might persist, even with 4 °C of macroclimate warming, in cryptic refugia buffered by microclimate, pointing to the roles of forest cover and topography in explaining slower-than-expected changes in understory communities. However, certain species, such as those currently occurring on low-elevation ridges that are sensitive to atmospheric changes, may be at more risk than macroclimate or interpolated SDMs suggest.</p> <p class="MsoNoSpacing"> </p>
High quality figures of "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain"
<p>This repository provides the figures for the publication "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain" in their original resolution, ensuring clarity and high-quality visual representations for readers.</p>
Data from: Microclimate-based species distribution models in complex terrain indicate widespread cryptic refugia under climate change
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High-speed control and navigation for quadrupedal robots on complex and discrete terrain
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The diurnal cycle of the horizontal wind field over complex terrain detected with coplanar Doppler lidar scans
<p>This data set contains a 24-h movie (21:00 UTC 23 July to 21:00 UTC 24 July) of the horizontal wind field in a horizontal plane of about 5 km x 5 km in size about 62 m above the city of Stuttgart in south-western Germany. The horizontal wind fields are retrieved from coplanar horizontal scans from three Doppler lidars positioned on opposing slopes and are available with 1-min temporal resolution and 100 m horizontal resolution. The data shows the diurnal cycle of the horizontal wind. During nighttime, downvalley wind establishes in the Neckar Valley and during daytime convective cells moving downstream are visible.<br> The measurements were conducted within the the framework of the Urban Climate under Change [UC]^2 program.</p>
Data from: Humans exploit the biomechanics of bipedal gait during visually guided walking over complex terrain
How do humans achieve such remarkable energetic efficiency when walking over complex terrain such as a rocky trail? Recent research in biomechanics suggests that the efficiency of human walking over flat, obstacle-free terrain derives from the ability to exploit the physical dynamics of our bodies. In this study, we investigated whether this principle also applies to visually guided walking over complex terrain. We found that when humans can see the immediate foreground as little as two step lengths ahead, they are able to choose footholds that allow them to exploit their biomechanical structure as efficiently as they can with unlimited visual information. We conclude that when humans walk over complex terrain, they use visual information from two step lengths ahead to choose footholds that allow them to approximate the energetic efficiency of walking in flat, obstacle-free environments.
A nocturnal jet flows over a wind farm in complex terrain
<p>A nocturnal jet simulated with the Weather Research and Forecasting model flows over topography where a wind farm is located. It produces downstream flow acceleration that enhances the performances of turbines in the back rows. This simulation is related to the case 3 (transect 1) of the paper <em>Nocturnal jets over wind farms in complex terrain </em>that will be submitted to <em>Applied Energy.</em></p>
Data from: Humans exploit the biomechanics of bipedal gait during visually guided walking over complex terrain
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data for the paper 'Improved estimates of the water table in complex terrains with multiple k-folded Collocated Cokriging'
<p>dataset for the paper 'Improved estimates of the water table in complex terrains with multiple k-folded Collocated Cokriging'.</p> <p>Columns are for monitored wells: X, Y, Z (local terrain elevation), average 2019 water table (measured at well locations)</p> <p>All these information have been retrieved from the free database (ARPAV): </p> <p><span><a href="https://www.arpa.veneto.it/dati-ambientali/open-data/idrosfera/acque-sotterranee/acque-sotterranee-livello-piezometrico-delle-falde"><span>https://www.arpa.veneto.it/dati-ambientali/open-data/idrosfera/acque-sotterranee/acque-sotterranee-livello-piezometrico-delle-falde</span></a></span></p>
Data for publication "The impact of mesh size and microphysics scheme on the representation of mid-level clouds in the ICON model in hilly and complex terrain"
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.