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PALM Model System v 6.0 input and configuration files for coupled large eddy simulations of land surface heterogeneity effects and diurnal evolution of late summer and early autumn atmospheric boundary layers during the CHEESEHEAD19 field campaign
<p>Namelist, configuration and forcing files for the PALM Model System 6.0 revision number 21.10-rc.2 used for the numerical simulations Coupled Large Eddy Simulations of land surface heterogeneity induced atmospheric boundary layer response during the CHEESEHEAD19 field campaign.</p>
Output of the Land Surface Model ORCHIDEE over river catchments in Europe, run with GSWP3 and synthetic forcings where the precipitation is modified
<p># Description of the data file</p> <p>This dataset contains the main outputs used for the results of the article: "Budyko framework based analysis of the effect of climate change on watershed evaporation efficiency and its impact on discharge over Europe", by Julie Collignan, Jan Polcher, Sophie Bastin, Pere Quintana-Segui, accepted by the journal *Water Resources Research*.</p> <p>This study uses the outputs of a land surface model (LSM), forced with differentan atmospheric datasets from 1901 to 2010. The atmospheric dataset are based on GSWP3 (Hyungjun, K. (2017), doi: 10.20783/DIAS.501) and was modified to create synthetic forcings with different precipitation characteristics (annual average, intra-annual distribution). The LSM was run with all synthetic forcings, and the outputs (precipitation, evapotranspiration, potential evapotranspiration, discharge) were integrated at the level of each river basin which are sampled by gauging stationss. These outputs are used to fit a parametric equation of the Budyko framework to decompose the partial trends in discharge and the relative weight of the different climatic components.</p> <p># Details of the variables and attribute of the file</p> <p>This study was led over 2196 river basins over Europe. </p> <p>For each catchment used in the study, it gathers:<br> - name of the station at the outlet *name*<br> - name of the river associated *rivers*<br> - lat/lon of the station *Localisation*<br> - upstream area of the catchment *upstream*<br> - Observed discharge (data not used in the article) *DisObs*<br> These data come from three different sources:<br> * *Global Runoff Data Centre (GRDC)*, https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html ;<br> * *Ministere de lenergie* (February 2021), https://www.hydro.eaufrance.fr/" ;<br> * *Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico*, 2020</p> <p>Each catchment was projected on the grid of the LSM ORCHIDEE (*IPSL, https://orchidee.ipsl.fr/*) during the construction of its river routing system. More details are given in the associated article.</p> <p>The results for four different run of the LSM are included in this dataset. This dataset gathers for each run:<br> * Precipitation *P*<br> * Evapotranspiration *E*<br> * Potential evapotranspiration *PET*<br> * Discharge *DisMod*</p> <p>The different synthetic forcings are:<br> - the reference forcing with the un-modified atmospheric dataset: *the Global Soil Wetness Project Phase 3 (GSWP3)*, Hyungjun, K. (2017), doi: 10.20783/DIAS.501<br> - *f2000*: A forcing where all 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year. Therefore, all components of $P$ (average and intra-annual variations) are set constant.<br> - *cstmean*: A forcing for which we keep the relative intra-annual distribution of $P$ of each year, but where the average $P$ of each year is set constant. The 3h values of $P$ are scaled so the hydrological year average is set to the one of the year 2000 (September 1999 to September 2000).<br> - *cstintravar*: A forcing for which we keep the annual average of $P$ for each year, but where the relative intra-annual distribution of $P$ is set constant. The 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year and then scaled over each hydrological year so the yearly average is set to the one of the corresponding years in the reference forcing.<br> \end{itemize}</p> <p>## ncdump -h Filename.nc</p> <p>```<br> dimensions:<br> basins = 2196 ;<br> years = UNLIMITED ; // (110 currently)<br> loc = 2 ;<br> lenstr = 57 ;<br> variables:<br> short years(years) ;<br> years:long_name = "Years" ;<br> years:units = "year" ;<br> char names(basins, lenstr) ;<br> names:long_name = "Name of the station at the catchment output" ;<br> char rivers(basins, lenstr) ;<br> rivers:long_name = "Name of the river where the station is positioned" ;<br> float upstream(basins) ;<br> upstream:long_name = "Upstream area of the catchment" ;<br> upstream:units = "km^2" ;<br> float Localisation(basins, loc) ;<br> Localisation:long_name = "Position of each catchment: (Lon, Lat)" ;<br> Localisation:units = "degrees_east, degrees_north" ;<br> float DisObs(years, basins) ;<br> DisObs:long_name = "Discharge observation at the outlet of the catchment" ;<br> DisObs:units = "m3/s" ;<br> float E_ref(years, basins) ;<br> E_ref:long_name = "Modeled average annual evaporation with forcing ref" ;<br> E_ref:units = "m3/s" ;<br> float P_ref(years, basins) ;<br> P_ref:long_name = "Average annual precipitation for forcing ref" ;<br> P_ref:units = "m3/s" ;<br> float PET_ref(years, basins) ;<br> PET_ref:long_name = "Modeled average annual potential evaporation with forcing ref" ;<br> PET_ref:units = "m3/s" ;<br> float DisMod_ref(years, basins) ;<br> DisMod_ref:long_name = "Modeled Discharge with forcing ref" ;<br> DisMod_ref:units = "m3/s" ;<br> float E_f2000(years, basins) ;<br> E_f2000:long_name = "Modeled average annual evaporation with forcing f2000" ;<br> E_f2000:units = "m3/s" ;<br> float P_f2000(years, basins) ;<br> P_f2000:long_name = "Average annual precipitation for forcing f2000" ;<br> P_f2000:units = "m3/s" ;<br> float PET_f2000(years, basins) ;<br> PET_f2000:long_name = "Modeled average annual potential evaporation with forcing f2000" ;<br> PET_f2000:units = "m3/s" ;<br> float DisMod_f2000(years, basins) ;<br> DisMod_f2000:long_name = "Modeled Discharge with forcing f2000" ;<br> DisMod_f2000:units = "m3/s" ;<br> float E_cstmean(years, basins) ;<br> E_cstmean:long_name = "Modeled average annual evaporation with forcing cstmean" ;<br> E_cstmean:units = "m3/s" ;<br> float P_cstmean(years, basins) ;<br> P_cstmean:long_name = "Average annual precipitation for forcing cstmean" ;<br> P_cstmean:units = "m3/s" ;<br> float PET_cstmean(years, basins) ;<br> PET_cstmean:long_name = "Modeled average annual potential evaporation with forcing cstmean" ;<br> PET_cstmean:units = "m3/s" ;<br> float DisMod_cstmean(years, basins) ;<br> DisMod_cstmean:long_name = "Modeled Discharge with forcing cstmean" ;<br> DisMod_cstmean:units = "m3/s" ;<br> float E_cstintravar(years, basins) ;<br> E_cstintravar:long_name = "Modeled average annual evaporation with forcing cstintravar" ;<br> E_cstintravar:units = "m3/s" ;<br> float P_cstintravar(years, basins) ;<br> P_cstintravar:long_name = "Average annual precipitation for forcing cstintravar" ;<br> P_cstintravar:units = "m3/s" ;<br> float PET_cstintravar(years, basins) ;<br> PET_cstintravar:long_name = "Modeled average annual potential evaporation with forcing cstintravar" ;<br> PET_cstintravar:units = "m3/s" ;<br> float DisMod_cstintravar(years, basins) ;<br> DisMod_cstintravar:long_name = "Modeled Discharge with forcing cstintravar" ;<br> DisMod_cstintravar:units = "m3/s" ;</p> <p>// global attributes:<br> :author = "Julie Collignan, julie.collignan@lmd.ipsl.fr" ;<br> :model = "ORCHIDEE, IPSL, https://orchidee.ipsl.fr/" ;<br> :source_stations1 = "Global Runoff Data Centre (GRDC), https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html" ;<br> :source_stations2 = "Ministere de lenergie (February 2021), https://www.hydro.eaufrance.fr/" ;<br> :source_stations3 = "Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico, 2020" ;<br> :atmospheric_dataset = "GSWP3, Hyungjun, K. (2017), doi: 10.20783/DIAS.501" ;<br> :date = "28/07/2023";<br> }<br> ```</p>
SinoLC-1: the first 1-meter resolution national-scale land-cover map of China created with the deep learning framework and open-access data (User guide V2.4)
<p>The<strong> User Guide V2.4 </strong>of the SinoLC-1 land-cover product. The SinoLC-1 was created by the Low-to-High Network (L2HNet), which can be found at: <strong><a href="https://doi.org/10.1016/j.isprsjprs.2022.08.008">L2HNet</a></strong>. A more detailed description of the data can be found in the<strong> <a href="https://doi.org/10.5194/essd-15-4749-2023">paper</a>.</strong> More related work can be found at my <strong><a href="https://lizhuohong.github.io/lzh/">homepage</a>.</strong></p> <p><a href="https://zenodo.org/search?q=parent.id%3A7707461&f=allversions%3Atrue&l=list&p=1&s=10&sort=version"><strong>Click to check all the data versions and download the data (点击查看/下载所有数据版本)</strong></a></p> <p><strong>NOTE: If you have any data needs, questions, or technical issues, contact us at </strong><a href="http://ashelee@whu.edu.cn"><strong>ashelee@whu.edu.cn</strong></a><strong> (Zhuohong Li, 李卓鸿).</strong></p> <p>The land-cover mapping method with Python code is open-access at <a href="https://github.com/LiZhuoHong/Paraformer/"><strong>Code link</strong></a>. You can now update the high-resolution land-cover map by yourself with the code! The updated method is accepted by CVPR 2024 (<strong><a href="https://arxiv.org/abs/2403.02746">Paper link</a></strong>).</p> <p><strong>我们的最新制图算法被计算机视觉顶会CVPR2024接收(<a href="https://arxiv.org/abs/2403.02746">Paper link</a>),代码开源在:<a href="https://github.com/LiZhuoHong/Paraformer/">Code link</a>,您可以利用该代码高效地更新自己数据集的高分土地覆盖图。</strong></p> <p><strong>Citation format of the paper:</strong><br>Li, Z., He, W., Cheng, M., Hu, J., Yang, G., and Zhang, H.: SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data, Earth Syst. Sci. Data, 15, 4749–4780, 2023. </p> <p>Li, Z., Zhang, H., Lu, F., Xue, R., Yang, G. and Zhang, L.: Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels, <em>ISPRS Journal of Photogrammetry and Remote Sensing</em>. <em>192</em>, pp.244-267, 2022.</p> <p><strong>BibTex format of the paper:</strong></p> <blockquote> <pre>@article{li2023sinolc, title={SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data}, author={Li, Zhuohong and He, Wei and Cheng, Mofan and Hu, Jingxin and Yang, Guangyi and Zhang, Hongyan}, journal={Earth System Science Data}, volume={15}, number={11}, pages={4749--4780}, year={2023}, publisher={Copernicus Publications G{\"o}ttingen, Germany} }</pre> <pre>@article{li2022breaking, title={Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels}, author={Li, Zhuohong and Zhang, Hongyan and Lu, Fangxiao and Xue, Ruoyao and Yang, Guangyi and Zhang, Liangpei}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, volume={192}, pages={244--267}, year={2022}, publisher={Elsevier} }</pre> </blockquote>
Exploring the Relationship Between Land Cover Classifications and Urban Heat Island Intensity
<p>This dataset is a collection of data and results from a research project conducted by NASA SEES Interns. The research project aimed to study urban heat islands and their relationship with land cover observations. This dataset upload consists of 12 files. One file is a poster pdf that includes all the information needed about the project. The other 11 files are png images of heatmaps, bar graphs, scatter plots, and tables used in the analysis of our project. For quick reference, the abstract to this project is below:</p> <p><strong>The urban heat island (UHI) effect refers to the phenomenon in which urban areas experience higher temperatures compared to their rural counterparts. This research aims to quantify and examine the UHI effect within three areas of interest (AOIs) by utilizing LANDSAT imagery. In addition, this study seeks to explore the relationship between land cover classifications, which represent the most green (rural) and the most urban areas, and the intensity of the UHI effect. To achieve this, temperature data from local weather stations are analyzed, and statistical methods are employed to determine whether a correlation exists between the difference in land cover classifications and the intensity of the UHI effect, as determined by the average temperature difference between urban and rural areas. Google Earth Engine is used to visualize LANDSAT data from 2013 to 2022 in the months of July and August for each AOI. Subsequently, the data is compared with the land cover classifications from Collect Earth Online using statistical models in Microsoft Excel. These tools were used to take data from three pre-selected areas of interest in GLOBE Observer. The data findings from this analysis suggest that the more tree cover and rural an area is according to our classification method, the lower the UHI intensity. On the other hand, the higher the urban area, the higher the UHI intensity. By beginning this research, we have reinforced the validity of land cover classifications, and we now have the capability to generally predict the UHI intensity of locations based on their classifications. Overall, this investigation aims to contribute to a better understanding of the GLOBE land cover classifications and their potential indications of UHI intensity.</strong></p>
Data from: River interlinking alters land-atmosphere feedback and changes the Indian summer monsoon.
<p>The dataset contains post-processed output from two experiments performed for Indian Summer Monsoon (June-September) from 1991-2012 using WRF-CLM4: CTL and IRR. Here, CTL represents WRF-CLM4 simulation with irrigation currently practiced in India. We use a modified irrigation module in CLM4 that better represents the Indian practices of irrigation by incorporating groundwater withdrawal and flood irrigation practiced over paddy fields. The module can be found at <a href="https://github.com/IMMM-SFA/WRF_CLM4_Irrigation">https://github.com/IMMM-SFA/WRF_CLM4_Irrigation</a> and <a href="https://doi.org/10.1029/2019GL083875">https://doi.org/10.1029/2019GL083875</a>. IRR simulation adds additional irrigation to CTL by increasing the percentage of irrigated area to 80% in regions where India's river-interlinking projects target an increase in the culturable command area.</p> <p>The post-processed output contains the following variables:</p> <ol> <li>Mean Daily Temperature</li> <li>Daily Maximum Temperature</li> <li>Latent Heat Flux</li> <li>Sensible Heat Flux</li> <li>Relative Humidity</li> <li>U-Wind at Pressure levels</li> <li>V-Wind at Pressure levels</li> <li>Net-Solar Radiation on Land</li> <li>Soil Moisture</li> </ol> <p>Irrigation input files for CTL and IRR simulations of WRF-CLM4 are also included.</p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2021)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the ELITE hourly seamless 0.02 ° LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the seamless LST dataset in 2021. Please<a href="../records/10668883" target="_blank" rel="noopener"> <strong><em>click here</em></strong></a> to download the ELITE LST product in Januray-June, 2022.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2021</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2020)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the ELITE seamless LST product in 2020. Please <a href="https://zenodo.org/record/8260245"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2019 and <a href="https://zenodo.org/record/8260240"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2021.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2020</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2018)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the ELITE seamless LST product in 2018. Please <a href="https://zenodo.org/record/8266456"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2017 and <a href="https://zenodo.org/record/8260245"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2019.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2018</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2019)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the ELITE seamless LST product in 2019. Please <a href="https://zenodo.org/record/8256087"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2018 and <a href="https://zenodo.org/record/8264798"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2020.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2019</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
Landscape composition and shannon diversity of landuse classes aggregated from CORINE land cover 2012
<p>Based on Copernicus <a href="https://land.copernicus.eu/pan-european/corine-land-cover">CORINE land cover data</a> from 2012, we aggregated the original CORINE land use classes into 8 classes: urban, agriculture, grassland, Broad-leaved forest, Coniferous forest, Mixed forest, natural/seminatural vegetation, and water (see clc_legend.txt). We calculated the landscape composition (percentage of each land use type according to corine land type) and shannon diversity for 100 - 5000 meters (100-1000 meter with 100 meter intervals, 1000 - 5000 meter with 500 meter interval) buffer area around Landklif plots.</p> <p>LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html"><strong>Bavarian State Ministry of Science and the Arts</strong></a> within the <a href="https://www.bayklif.de/"><strong>Bavarian Climate Research Network (bayklif)</strong></a><strong>. </strong> Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. <strong>LandKliF</strong>, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
A proposed equation for calculating the total horizontal distance in projectiles of varying launch and landing levels
<p><strong>Abstract</strong></p> <p>The projectile draws a path for the flight of the tool, a horizontal distance can be calculated according to the general law, but only if the two points of the launch and fall of the tool are equal, otherwise we need an equation to be added to the general law to calculate the real distance that was generated due to the difference between the launch and fall points. There are several equations to calculate this, but they are complex and can be simplified, the proposed equation was tested on large samples for three different throwing events from the track and field games, (javelin, shotput, disc). The proposed equation was based on the basics of mathematics and geometry. The equation was tested in terms of the advantage of deleting the height different, if the height is zero, the proposed equation is suitable even for projectiles with equal levels, and the credibility of the proposed equation with the previous equation was tested statistically, and there were no differences between the two equations (p>0.05). and due to the relative ease of access of the proposed equation to very similar results, researcher suggests applying the proposed equation.</p>
High spatial resolution dataset of downscaled LUH2 land use scenarios for Belgium (10 m and 100 m)
<p>This dataset comprises high-resolution land use data downscaled from LUH2 scenarios for Belgium at both 10 m and 100 m resolutions. These datasets were generated based on research conducted by Rashidi et al. in 2023 and published in the Land Journal. We employed the GLOBIO land allocation routine to downscale fractional land use data, originally at a 0.25° resolution (approximately 25 km), into discrete land use maps at 10 m and 100 m resolutions. This process utilized three distinct reference land cover maps: ESA WorldCover at 10 m resolution, ESA WorldCover upscaled to 100 m resolution, and CORINE land cover at 100 m resolution.</p> <p>During the downsizing process, we considered three SSP-RCP scenarios to model land use trends for both the present and the year 2050 on a national scale in Belgium. Key components of the model included regional land use demand, an assessment of grid cells' suitability for various land use types, and a reference land cover map. It's important to note that the classification system used in the reference maps differs from that of LUH2. To ensure comparability for land use simulations, we conducted a reclassification process following the methodologies outlined by Pérez-Hoyos et al. (2012), Dong et al. (2018), and Liao et al. (2020). This reclassification consolidated land use classes, except for water, into seven general categories: 1) urban, 2) cropland, 3) pasture, 4) forestry, 5) secondary vegetation, 6) undefined, and 7) natural.</p> <p>The raw data consists of three folders corresponding to the three reference maps, each containing four TIFF files (.tif), one for each scenario type.</p>
Modeling archive of How does humidity data impact land surface modeling of hydrothermal regimes at a permafrost site in Utqiaġvik, Alaska?
<p>Modeling archive contains the meteorological forcings, model input files, and Jupyter notebooks used to generate model meshes and figures for the paper entitled "How does humidity data impact land surface modeling of hydrothermal regimes at a permafrost site in Utqiaġvik, Alaska?"</p>
ET-WB: water balance-based estimations of terrestrial evaporation over global land and major global basins
<p>This is the readme file for the ET-WB dataset described in the ESSD paper "ET-WB: water balance-based estimations of terrestrial evaporation over global land and major global basins" from Xiong et al. (2023)<br> ET-WB dataset-The monthly water balance data from May 2002-December 2021 for the 168 river basins and global land from 23 precipitation, 29 runoff, and 7 terrestrial water storage changes datasets.<br> The five dimensions (236*169*23*7*29) of the matrix represent the time, regions, precipitation, terrestrial water storage changes, and runoff datasets used respectively.<br> ET-WB is distributed in three kind of formats: Mat (ET-WB.mat), NetCDF (ET_WB.nc), and Shapefile (ET_WB.shp) (only for the ensemble median value). All the formats share the same definitions of dimensions (as below), except for the ArcGIS shapefile that is provided for individual regions (168 river basins and global land excluding Antarctic and Greenland).<br> File shapefile.rar is the geospatial database of the study area that can be opened in ArcGIS software. </p> <p>Please find more details in the Readme file.</p>
Figure 3 A in Integrative systematics reveals the new land-snail genus Taphrenalla (Eupulmonata: Ariophantidae) with a description of nine new species from Thailand
Figure 3 A representative ML tree showing only the Taphrenalla clade. The synopsis of the radial groove on shell sculpture, genitalia showing location of dart apparatus, and live specimens of Taphrenalla spp. Results of species delimitation analyses based on the COI gene using ABGD and PTP. Black circles represent nodes with both ML (when Ē 70%) and BI (when Ē 0.95) support. White circles represent nodes with ML BS support but no BI PP support (when <0.95). Each clade colour refers to the distribution range in fig. 1.
Figure 2 in Integrative systematics reveals the new land-snail genus Taphrenalla (Eupulmonata: Ariophantidae) with a description of nine new species from Thailand
Figure 2 Representative ML tree of the Taphrenalla gen. nov., Macrochlamys, Sarika, Cryptozona, Hemiplecta, Megaustenia, and Durgella based on the combined partial sequences of the COI, 16S, and 28S genes. The code in the parenthesis next to the species name refers to voucher numbers of the analysed samples. Numbers on the branches indicate the ML BS and BI PP values.
Figure 1 in Integrative systematics reveals the new land-snail genus Taphrenalla (Eupulmonata: Ariophantidae) with a description of nine new species from Thailand
Figure 1 Map showing the sampling sites of the 11 Taphrenalla species along Southern Thailand. Each colour of the distribution range follows the phylogenetic tree of all Taphrenalla species in fig. 3.
FIGURE 2 in Trophic niche size and overlap in temperate forest land snails are affected by their lifestyle and body size
FIGURE 2 Variation in SEAc of land snail species from the four studied assemblages in relation to their lifestyle (a) and body size (b). Variation in percentages of overlapping SEAc for pairwise species combinations are compared between study sites (c) and three types of lifestyle (d). Different letters refer to significant differences at p<0.0, tested by GEE (a, b) and GLM-qp (c, d). The central line of each box refers to the median value, box height to the interquartile range, whiskers to the non-outlier range (i.e., 1.5 times the interquartile range at each side), and small circles to outliers.
FIGURE 1 in Trophic niche size and overlap in temperate forest land snails are affected by their lifestyle and body size
FIGURE 1 Isotopic niches represented by Standard ellipse area corrected for small sample size (SEAc) of the land snail species collected in four study sites (A, B, C, D) at least in five individuals per site.
FIGURE 10 in Multigene phylogeny reveals the ribbed shell morphotypes in the land snail genus Sarika (Eupulmonata: Ariophantidae), with description of two new species from Thailand and Myanmar
FIGURE 10 Spermatophore of Sarika costabilis sp. nov. paratype CUMZ 7945. A) General view of spermatophore that was broken into two parts. B) Head filament. C–E) Tail filament. C) Three spines located close to the sperm sac. D) Region with and without branching spines. E) Branching spines on the tip region. Red dashed line indicates a series of ten branching spines near the middle length.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.