Find research datasets worth reusing

opencc-by-nc-sa-4.0Mar 2022View details →

Data archive for "Seamless lightning nowcasting with recurrent-convolutional deep learning"

This dataset contains the machine learning training data files, pretrained model weights and precomputed results for the paper "Seamless lightning nowcasting with recurrent-convolutional deep learning" published in: Leinonen, J., Hamann, U., & Germann, U. (2022). Seamless Lightning Nowcasting with Recurrent-Convolutional Deep Learning, Artificial Intelligence for the Earth Systems, 1(4), e220043, doi:<a href="https://doi.org/10.1175/AIES-D-22-0043.1">10.1175/AIES-D-22-0043.1</a>. A preprint of the paper can be found at <a href="https://arxiv.org/abs/2203.10114">https://arxiv.org/abs/2203.10114</a>. The ML code can be found at <a href="https://github.com/MeteoSwiss/c4dl-lightningdl">https://github.com/MeteoSwiss/c4dl-lightningdl</a>. Download all the files here and extract the contents to the following subdirectories in the ML code directory: <ul> <li>Training data (c4dl-patches-*.zip) -> data/2020/</li> <li>Results (<a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-results-lightningdl.zip?versionId=54046830-4c7e-48c6-af42-d6d5606af86b">c4dl-results-lightningdl.zip</a>) -> results/</li> <li>Pretrained models (<a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-models-lightningdl.zip?versionId=364bca7c-e6ad-4ed9-9264-57c759ea0ac6">c4dl-models-lightningdl.zip</a>) -> models/</li> </ul> Additionally, the file <a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-randomexamples-lightningdl.zip?versionId=426ca113-950f-4a50-8ef0-8e5d12afe697">c4dl-randomexamples-lightningdl.zip</a> contains the randomly selected examples complementing Figs. 7–9 of the paper, and the file <a href="https://zenodo.org/api/files/939609f2-6699-4f56-9428-391ebe78e010/c4dl-inputsamples-lightningdl.zip">c4dl-inputsamples-lightningdl.zip</a> contains figures showing samples of all the input variables for the three cases shown in Figs. 7–9.

opencc-by-4.0May 2024View details →

Data and analysis scripts for the submission "Seamlessly Scaling Applications with DAPHNE"

To regenerate the plots: You will need to install `R` (4.3.2) and the `tidyverse` package (2.0.0) We give a Nix flake that captures this environment (Install Nix: https://nixos.org/download/ and activate the flake feature: https://nixos.wiki/wiki/Flakes#Other_Distros.2C_without_Home-Manager) With Nix: `nix develop --command Rscript analysis_compas.R` Without Nix: `Rscript analysis_compas.R`

opencc-by-4.0Aug 2021View details →

Seamless Projections of Global Storm Surge and Ocean Waves under a Warming Climate

This is dataset of global ocean waves and storm surges used in the paper "Seamless Projections of Global Storm Surge and Ocean Waves under a Warming Climate" by Shimura et al. (2022, Geophysical Research Letters). AnnualMaxSSH_SeamlessExperiment.nc contains the global annual maximum storm surge in the seamless experiment. AnnualMaxHs_SeamlessExperiment.nc contains the global annual maximum significant wave heights in the seamless experiment. AnnualMaxHs_TimesliceExperiment_Historical.nc contains the global annual maximum significant wave heights in the time-slice experiment (the historical climate simulations). AnnualMaxHs_TimesliceExperiment_ProjectedFuture.nc contains the global annual maximum significant wave heights in the time-slice experiment (the projected future climate simulations).

opencc-by-4.0Dec 2022View details →

Global long-term (2010-2020) daily seamless fused XCO2 and XCH4 from GOSAT, OCO-2, and CAMS-EGG4

A novel spatiotemporally self-supervised fusion method is proposed to establish long-term daily seamless XCO2 and XCH4 products from 2010 to 2020 over the globe at grids of 0.25°. More details are provided in https://doi.org/10.5194/essd-2023-28.

opencc-by-4.0Nov 2019View details →

ChinaHighPM₁: Daily Seamless 1 km Ground-Level PM₁ Dataset for China (2000–Present)

ChinaHighPM1 is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution. Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM1 dataset for China from 2000 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.83, a root-mean-square error (RMSE) of 9.50 µg m-3, and a mean absolute error (MAE) of 6.17 µg m-3 on a daily basis. If you use the ChinaHighPM1 dataset in your scientific research, please cite the following reference (Wei et al., EST, 2019): <ul> <li> Wei, J., Li, Z., Guo, J., Sun, L., Huang, W., Xue, W., Fan, T., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-EST-2019.pdf">Satellite-derived 1-km-resolution PM1 concentrations from 2014 to 2018 across China</a>. Environmental Science & Technology, 2019, 53(22), 13265–13274. https://doi.org/10.1021/acs.est.9b03258 </li> </ul> More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a>

opencc-by-4.0Jun 2023View details →

The Long-term, High-accuracy and Seamless Soil Moisture (LHS-SM) dataset over the Qinghai-Tibet Plateau: part 2 (2011-2020)

Soil moisture (SM) is a vital variable in the water-energy cycle and characterizing its spatiotemporal dynamics is crucial for understanding the impacts of climate change. Although substantial efforts have been devoted to derive SM data at fine scale, there is still a research gap in obtaining the long-term, high-accuracy and high-resolution SM data over the Qinghai-Tibet Plateau (QTP) due to its complex topography. Therefore, this study generated the long-term, high-accuracy and seamless soil moisture (LHS-SM) dataset over the QTP during 2001-2020 using a two-step downscaling method. First the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1km utilizing five machine learning approaches. Then a dynamic data merging method that considers the spatiotemporal nonstationary error was applied to derive the final LHS-SM data. Results indicated that LHS-SM data exhibited satisfying accuracy (mean R = 0.55, ubRMSE = 0.049 m³/m³) and certain improvement to the ESA CCI SM data both at station and network scales. The dataset can be used for various regional hydrology, meteorology, ecological analysis and modeling.

opencc-by-4.0Jun 2022View details →

ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2017)

The Essential thermaL Infrared remoTe sEnsing (ELITE) 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>). 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. This is the ELITE seamless LST product in 2017. Please <a href="https://zenodo.org/record/7306248">click here</a> to download the ELITE LST product in 2016 and <a href="https://zenodo.org/record/8256087">click here</a> to download the ELITE LST product in 2018. Dataset Characteristics: <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2017</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> Citation (Please cite these papers when using the data): <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> If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).

opencc-by-4.0Jul 2011View details →

Building bridges: mapping diverse classifications for a seamless user navigation experience

This paper describes a BBC project to unify Archive and Production workspaces, during which numerous issues with managing different types of metadata and Knowledge Organi- sation Systems (KOSs) were encountered. Integrating diverse content silos requires bringing together not simply the assets, but also the metadata used to manage those assets. The paper summarises the theoretical background to the project, the BBC’s ‘information ecosystem’, and the user research and requirements-gathering exercises undertaken. Much work on developing metadata crosswalks has been at the heading or label level, and not based on semantic analysis of the content of the labelling or description. However, such semantic analysis needs to be undertaken when mapping diverse taxonomies, thesauri, and keyword lists and, in practice, often needs to balance preservation of local or specialised terminology with accessibility for general users. Just as metadata about content permits the organization of that content, so metadata about metadata (parametadata, or meta-metadata) permits the organization of metadata, enabling end users to make informed browse and navigation choices. Increasingly, in order to integrate content, different KOSs, such as taxonomies and ontologies, need to be related. The paper concludes by summarising the ways in which problems that arose during the integration project were resolved, and how policies for managing parametadata, subjective metadata, and semantic-level mapping were developed.

opencc-by-4.0Aug 2023View details →

ELITE land surface temperature: seamless 1km LST over China (2002)

The Essential thermaL Infrared remoTe sEnsing (ELITE) 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>). This dataset is the ELITE seamless 1km LST  over China landmass (2002-2020). Firstly, a look-up-table-based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled using the geographically weighted regression to obtain 1km LST. Finally, the multi-scale kalman filter is used to fuse MODIS LST and AMSR-E/AMSR2 LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST. This is the seamless LST dataset in 2002. Please <a href="https://zenodo.org/record/8271722">click here</a> to download the ELITE LST product in 2003. Dataset Characteristics: <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2002</li> <li>Spatial Resolution: 1 KM</li> <li>Temporal Resolution: 2 times per day</li> <li>Data Format: hdf</li> <li>Scale: 0.02</li> </ul> Citation (Please cite these papers when using the data): <ol> <li>Xu, S., & Cheng, J. (2021). A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering. Remote Sensing of Environment, 254, 112256</li> <li>Zhang, Q., Wang, N., Cheng, J., & Xu, S. (2020). A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5669-5681 </li> <li>Zhang, Q., & Cheng, J. (2020). An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR-E Data Considering the Comprehensive Effects of Environmental Variables. Earth and Space Science, 7, e2019EA001006. https://doi.org/10.1029/2019EA001006 </li> </ol>   If you have any questions, please contact Prof. Jie Cheng (<a href="mailto:Jie_Cheng@bnu.edu.cn">Jie_Cheng@bnu.edu.cn</a>).

restrictedcc-by-4.0Jan 2022View details →

ChinaHighPMC: Daily Seamless 1 km Ground-Level PM₂.₅ Composition Dataset for China (2000–Present)

ChinaHighPMC is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution. Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM2.5 chemical composition (i.e., SO42-, NO3-, NH4+, and Cl-) dataset for Eastern China from 2013 to 2020. This dataset exhibits high quality, with cross-validation coefficients of determination (CV-R2) of 0.74, 0.75, 0.71, and 0.66, and root-mean-square errors (RMSEs) of 6.0, 6.6, 4.3, and 2.3 µg m-3 for SO42-, NO3-, NH4+, and Cl-, respectively, on a daily basis. If you use the ChinaHighPMC dataset in your scientific research, please cite the following reference (Wei et al., EST, 2023): <ul> <li>Wei, J., Li, Z., Chen, X., Li, C., Sun, Y., Wang, J., Lyapustin, A., Brasseur, G., Jiang, M., Sun, L., Wang, T., Jung, C., Qiu, B., Fang, C., Liu, X., Hao, J., Wang, Y., Zhan, M., Song, X., and Liu, Y. <a href="https://weijing-rs.github.io/publications/Wei_et_al-EST-2023.pdf">Separating daily 1 km PM2.5 inorganic chemical composition in China since 2000 via deep learning integrating ground, satellite, and model data</a>. Environmental Science & Technology, 2023, 57(46), 18282–18295. https://doi.org/10.1021/acs.est.3c00272</li> </ul> More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a>

opencc-by-4.0Nov 2023View details →

Global seamless and high-resolution temperature dataset (GSHTD), 2001–2020

Reference: Yao et al., 2023. Global seamless and high-resolution temperature dataset (GSHTD), 2001–2020. Remote Sensing of Environment 286, 113422.

opencc-by-4.0May 2023View details →

USHAP: Big Data Seamless 1 km Ground-level Black Carbon Dataset for the United States

USHAP (USHighAirPollutants) is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for the United States. It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level Black Carbon (BC) dataset in the United States from 2000 to 2020. Our daily BC estimates agree well with ground measurements with an average cross-validation coefficient of determination (CV-R2) of 0.80 and normalized root-mean-square error (NRMSE) of 0.60, respectively.All the data will be made public online once our paper is accepted, and if you want to use the USHighBC dataset for related scientific research, please contact us (Email: weijing_rs@163.com; weijing@umd.edu).<ul><li>Wei, J., Wang, J., Li, Z., Kondragunta, S., Anenberg, S., Wang, Y., Zhang, H., Diner, D., Hand, J., Lyapustin, A., Kahn, R., Colarco, P., da Silva, A., and Ichoku, C. <a href="https://weijing-rs.github.io/publications/Wei_et_al-LPH-2023.pdf">Long-term mortality burden trends attributed to black carbon and PM2.5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study</a>. The Lancet Planetary Health, 2023, 7, e963–e975. https://doi.org/10.1016/S2542-5196(23)00235-8</li></ul>More air quality datasets of different air pollutants can be found at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a>

opencc-by-4.0Feb 2024View details →

ELITE land surface temperature: FY-4A/AGRI hourly 4km seamless LST (2023.1-2023.5)

The Essential thermaL Infrared remoTe sEnsing (ELITE) 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>). This dataset is the ELITE hourly seamless 4 km LST dataset covering the FY-4A/AGRI nominal fixed disc (80.6°N-80.6°S, 24.1°E-174.7°W).  First, an improved temperature and emissivity separation algorithm was used to obtain the clear-sky LST. Then, under the framework of the SEB theory, a unique way was proposed to solve the temperature difference between the cloudy-sky LST and hypothetical clear-sky LST caused by cloud radiative effects. The in situ validation results show that the bias (RMSE) of the AGRI hourly seamless LST is 0.02 K (2.84 K). The temporal resolution and spatial resolution of this dataset are 1 hour and 4 km, respectively. This is the ELITE FY-4A/AGRI seamless LST product in 2023. Please <a href="../records/10595576">click here</a> to download the ELITE LST product in 2022. Dataset Characteristics: <ul> <li>Spatial Coverage: AGRI nominal fixed disc (80.6°N-80.6°S, 24.1°E-174.7°W)</li> <li>Temporal Coverage: 2023.1-2023.5</li> <li>Spatial Resolution: 4 km (subsatellite point)</li> <li>Temporal Resolution: one hour</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> </ul> Citation (Please cite these papers when using the data): <ol> <li>Liu, W., Cheng, J. & Wang, Q. (2023). Estimating Hourly All-Weather Land Surface Temperature From FY-4A/AGRI Imagery Using the Surface Energy Balance Theory. IEEE Transactions on Geoscience and Remote Sensing, 61, 5001518</li> </ol> If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).

opencc-by-4.0Feb 2024View details →

ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2022.7-2022.12)

The Essential thermaL Infrared remoTe sEnsing (ELITE) 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>). 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. This is the seamless LST dataset in 2022.7-2022.12. Please <a href="../records/8260240">click here</a> to download the ELITE LST product in 2021. Dataset Characteristics: <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2022.7-2022.12</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> Citation (Please cite these papers when using the data): <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> If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).

Global oceanic seamless POC concentration products derived from MODIS-Aqua and Terra

The dataset integrates seamless POC concentration daily products for the global ocean, derived from MODIS-Aqua and Terra XGBoost satellite retrieval products. It covers the time span from 2017 to 2020. The POC concentration in the product used int32 to integer data, specifically, the raw POC concentration (mg m-3) is first multiplied by 10000 and then rounded using int32.

Global oceanic seamless POC concentration products derived from MODIS-Terra

The dataset integrates seamless POC concentration daily products for the global ocean from 2009 to 2016, derived from MODIS-Terra‘s XGBoost satellite retrieval products.  The POC concentration in the product used int32 to integer data, specifically, the raw POC concentration (mg m-3) is first multiplied by 10000 and then rounded using int32.

Global oceanic seamless POC concentration products derived from MODIS-Aqua and Terra

The dataset integrates seamless POC concentration daily products for the global ocean from 2021 to 2022 with a 9-km resolution, derived from MODIS-Aqua and Terra XGBoost satellite retrieval products. The POC concentration in the product used int32 to integer data, specifically, the raw POC concentration (mg m-3) is first *10000 and then rounded using int32.

Global oceanic seamless POC concentration monthly products derived from MODIS-Aqua and Terra

The dataset integrates seamless POC concentration daily products with a 9-km resolution for the global ocean from 2003 to 2022, derived from MODIS-Aqua and Terra XGBoost satellite retrieval products. The POC concentration in the product used int32 to integer data, specifically, the raw POC concentration (mg m-3) is first *10000 and then rounded using int32.

opencc-by-4.0Apr 2022View details →

INCA-CH seamless nowcasting system, example data

INCA-CH seamless nowcasting system A new generation of nowcasting systems is in development at MeteoSwiss (since 2025). For this reason only a subset of the current INCA-CH parameters will be available trough our <a href="http://opendatadocs.meteoswiss.ch/">open data provision</a>.  During the transition phase to the new system data push deliveries of all current parameters will be guaranteed only for already existing users. However, a change is to be planned (probably in 2027):  parameters will be renamed, added and/or removed.  Information on this will follow as soon as it becomes available. <table> <tbody> <tr> <td> Parameter </td> <td> Description </td> <td> Units </td> <td> Update frequency </td> <td> Forecast range </td> <td> Availability after obs. time** </td> <td> Output granularity </td> </tr> <tr> <td> RR </td> <td> Precipitation quantitative (based on CombiPrecip) </td> <td> mm/h </td> <td> 10 min </td> <td> 0-6 h </td> <td> 7-10 min </td> <td> 10 min </td> </tr> <tr> <td> RR_ext* </td> <td> Precipitation quantitative extended forecast (based on CombiPrecip) </td> <td>  mm/h </td> <td> 10 min </td> <td> 0-28/33h </td> <td> 8-11 min </td> <td> 10 min </td> </tr> <tr> <td> RP </td> <td> Precipitation qualitative (based on radar only) </td> <td> mm/h </td> <td> 5 min </td> <td> 0-6 h </td> <td> 4-5 min </td> <td> 5 min </td> </tr> <tr> <td> RP_ext* </td> <td> Precipitation qualitative extended forecast (based on radar only) </td> <td> mm/h </td> <td>  5 min </td> <td> 0-28/33h </td> <td> 5-6 min </td> <td> 5 min </td> </tr> <tr> <td> RS </td> <td> Snowfall quantitative (based on CombiPrecip) </td> <td> mm/h </td> <td> 10 min </td> <td> 0-6 h </td> <td> 5-6 min </td> <td> 10 min </td> </tr> <tr> <td> RS_ext* </td> <td> Snowfall quantitative extended forecast (based on CombiPrecip) </td> <td> mm/h </td> <td> 10 min </td> <td> 0-28/33h </td> <td> 6-7 min  </td> <td> 10 min </td> </tr> <tr> <td> PN </td> <td> Snowfall qualitative (based on radar only) </td> <td> mm/h </td> <td> 5 min </td> <td> 0-6 h </td> <td> 5-6 min </td> <td> 5 min </td> </tr> <tr> <td> PN_ext* </td> <td> Snowfall qualitative extended forecast (based on radar only) </td> <td> mm/h </td> <td> 5 min </td> <td> 0-28/33h </td> <td> 6-7 min </td> <td> 5 min </td> </tr> <tr> <td> PT </td> <td> Precipitation type for RR: rain, snow, snow-rain, freezing rain, rain and hail (hail first 30min only) </td> <td> classes (0-5) </td> <td> 10 min </td> <td> 0-6 h </td> <td> 7-10 min </td> <td> 10 min </td> </tr> <tr> <td> PT_ext* </td> <td> Precipitation type for RR_ext, extended forecast: rain, snow, snow-rain, freezing rain, rain and hail (hail first 30min only) </td> <td> classes (0-5) </td> <td> 10 min  </td> <td> 0-28/33h </td> <td> 8-11 min </td> <td> 10 min </td> </tr> <tr> <td> NT </td> <td> Precipitation type for RP: rain, snow, snow-rain, freezing rain, rain and hail (hail first 30min only) </td> <td> classes (0-5) </td> <td> 5 min </td> <td> 0-6 h </td> <td> 4-5 min </td> <td> 5 min </td> </tr> <tr> <td> NT_ext* </td> <td> Precipitation type for RP_ext extended forecast: rain, snow, snow-rain, freezing rain, rain and hail (hail first 30min only) </td> <td> classes (0-5) </td> <td> 5 min  </td> <td> 0-28/33h </td> <td> 5-6 min </td> <td> 5 min </td> </tr> <tr> <td> SH_BK_24, SH_BK_12 </td> <td> New snow accumulation: past 24h or 12h </td> <td> cm </td> <td> 10 min </td> <td> 0 h </td> <td> 15-20 min </td> <td> - </td> </tr> <tr> <td> SH_FC_06 </td> <td> 6 h new snow accumulation forecast </td> <td> cm </td> <td> 10 min </td> <td> 6 h </td> <td> 15-20 min </td> <td> - </td> </tr> <tr> <td> ZS </td> <td> Snowfall line </td> <td> m </td> <td> 10 min </td> <td> 0-6 h </td> <td> 13-16 min </td> <td> 60 min </td> </tr> <tr> <td> Z0 </td> <td> Zero degree isotherm </td> <td> m </td> <td> 10 min </td> <td> 0-6 h </td> <td> 13-16 min </td> <td> 60 min </td> </tr> <tr> <td> FF, WG, DD, UU, VV </td> <td> Wind speed, wind gust and wind direction: hourly mean and hourly max for wind gust </td> <td> m/s, ° </td> <td> 10 min </td> <td> 0-6 h </td> <td> 17-19 min </td> <td> 60 min </td> </tr> <tr> <td> FF_10min, WG_10min, DD_10min, UU_10min, VV_10min </td> <td> Wind speed, wind gust and wind direction: 10 min mean and 10 min max for wind gust </td> <td> m/s, ° </td> <td> 10 min </td> <td> 0-6h </td> <td> 17-19 min </td> <td> 10 min </td> </tr> <tr> <td> TT </td> <td> 2 m temperature </td> <td> °C </td> <td> 10 min </td> <td> 0-6 h </td> <td> 13-16 min </td> <td> 60 min </td> </tr> <tr> <td> TT_ext </td> <td> 2 m temperature extended forecast </td> <td> °C </td> <td> 10 min </td> <td> 0-28/33 h </td> <td> 14-17 min </td> <td> 60 min </td> </tr> <tr> <td> TD, RH </td> <td> 2 m dew point and humidity </td> <td> °C, % </td> <td> 10 min </td> <td> 0-6 h </td> <td> 13-16 min </td> <td> 60 min </td> </tr> <tr> <td> TD_ext, RH_ext </td> <td> 2 m dew point and humidity extended forecast </td> <td> °C, % </td> <td> 10 min </td> <td> 0-28/33 h </td> <td> 14-17 min </td> <td> 60 min </td> </tr> <tr> <td> TG </td> <td> Soil surface temperature </td> <td> °C </td> <td> 10 min </td> <td> 0-6 h </td> <td> 13-16 min </td> <td> 60 min </td> </tr> <tr> <td> CT, CL, CM, CH </td> <td> Cloud cover: total, low, medium, high </td> <td> % </td> <td> 10 min </td> <td> 0-6h  </td> <td> 5-10 min </td> <td> 10 min </td> </tr> <tr> <td> SU </td> <td> Relative sunshine duration </td> <td>  % </td> <td> 10 min </td> <td> 0-6h  </td> <td> 5-10 min </td> <td> 10 min </td> </tr> <tr> <td> LI </td> <td> Lightning counts analysis </td> <td>  #flash/ 7x7 km²/10 min </td> <td> 10 min </td> <td> 0 h </td> <td> 5 min </td> <td>  - </td> </tr> <tr> <td> CN </td> <td> Convective inhibition </td> <td> J/kg </td> <td> 10 min </td> <td> 0 h </td> <td> 14-17 min </td> <td> - </td> </tr> <tr> <td> CP </td> <td> Convective available potential energy </td> <td> J/kg </td> <td> 10 min </td> <td> 0 h </td> <td> 14-17 min </td> <td> - </td> </tr> </tbody> </table> nx = 710 ny = 640 resolution = 1000 [m] Coordinates of lower left pixel in Swiss Coordinates <a href="https://epsg.io/21781">CH1903/LV03</a>: x0 = 255500 [m] y0 = -159500  [m] Example how to read INCA netcdf data:<a href="https://inca-examples.readthedocs.io/"> https://inca-examples.readthedocs.io/ </a> INCA-CH digital elevation Model here: <a href="https://zenodo.org/record/7614565">https://zenodo.org/record/7614565</a>