Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
95
datasets available to search
ShareScore release 0.9.0
Dataset results
95 results for “Ceilometer”
NOAA PSL CL31 Ceilometer Backscatter and Cloud Base Height Data for SPLASH
<p>This dataset contains daily files from a CL31 ceilometer manufactured by Vaisala that was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. </p> <p>The files contain backscatter profiles, cloud base heights, and visibility. The ceilometer measures vertical profiles of backscatter using laser technology. From the backscatter profiles, cloud base height and vertical visibility are determined using with the Vaisala software CL-view. For details on the instrument specifics and the methods, see the manufacturer manual (cl31usersguide.pdf). </p> <p>The file format is the original Vaisala format (.DAT). For a description of the format see the manufacturer manual. To convert the .DAT file format to netcdf format, the open source command line Python program ‘cl2nc’ (https://github.com/peterkuma/cl2nc) can, for example, be used. </p> <p>The file naming conventions for the .DAT files are as follows:</p> <p>NOAA_PSL_CL31_Roaring Judy_yyyymmdd_HH.DAT</p> <p>with</p> <p>yyyy: Year</p> <p>mm: Month</p> <p>dd: Day</p> <p>HH: Hour when the first sample was written to the file</p> <p>The time stamp of all data is in UTC.</p>
Instrument in pills: Automatic lidars and ceilometers (ALC)
<p>In this 10 minutes video, Simone Kotthaus tells everything about automatic lidars and ceilometers, often named with the acronym ALC. </p>
Evaluating Cloud Properties Across New Zealand: Ceilometer Network Data
<p><span>A network of 16 Vaisala CL31 ceilometers operated by MetService at airports across New Zealand had data available and were processed using ALCF with a cloud threshold mask of 6e-6. Nine sites were in Te Ika-a-Māui / North Island, six in Te Waipounamu / South Island and one site on Chatham Island. Metadata for the 16 sites are available in the Processed Ceilometer Medata.csv file.<br></span></p>
Aerosol products presented in "ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications"
<p>ALICENET output products on aerosol optical and physical properties and vertical layering presented in “Bellini, A., Diémoz, H., Di Liberto, L., Gobbi, G. P., Bracci, A., Pasqualini, F., and Barnaba, F.: Alicenet – An Italian network of Automated Lidar-Ceilometers for 4D aerosol monitoring: infrastructure, data processing, and applications, AMT, https://doi.org/10.5194/egusphere-2024-730, 2024”.</p> <p>The aod*.txt files include the following information:</p> <p>- date: date in UTC<br>- AOD_ALICENET: AOD as retrieved by ALICENET at 1064 nm<br>- AOD_AERONET/SKYNET: AOD measured by a co-located photometer from AERONET/SKYNET (level 2) at 1020 nm<br>- AE: Angstrom Exponent from AERONET/SKYNET (level 2)</p> <p>The contiunous.aerosol.layer.rome.txt file includes the following information:</p> <p>- date: date in CET<br>- continuous_aerosol_layer: Continous Aerosol Layer heights as retrieved by ALICENET</p> <p>The mixed.aerosol.layer.rome.txt file includes the following information:</p> <p>- date: date in CET<br>- mixed_aerosol_layer: Mixed Aerosol Layer heights as retrieved by ALICENET</p> <p>This work received partial financial support from the EC H2020 Project RI-URBANS (GA No 101036245), and benefited from work done within the Action PROBE (CA18235), supported by COST (European Cooperation in Science and Technology).</p>
TEAMx-PC22 (TEAMx pre-campaing 2022) – GeoSphere Austria ceilometer data
<p><strong>ABSTRACT</strong></p> <p><a href="https://www.geosphere.at/">GeoSpere Austria</a> is running operationally a ceilometer (<a href="https://www.vaisala.com/en/products/weather-environmental-sensors/ceilometers-CL31-CL51-meteorology">Vaisala CL51</a>) next to the <a href="https://oscar.wmo.int/surface//index.html#/search/station/stationReportDetails/0-20000-0-11130">meteorological station at Kufstein</a>, Austria. Ceilometer data collected during the TEAMx pre-campaign 2022 (TEAMx-PC22) are provided here. Standard meteorological data is available on the <a href="https://data.hub.geosphere.at/">GeoSphere Austria data hub</a>.</p> <p>The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in <a href="https://www.uibk.ac.at/iup/buch_pdfs/10.1520399106-003-1.pdf">Serafin et al. (2020) </a>and in <a href="https://journals.ametsoc.org/view/journals/bams/103/5/BAMS-D-21-0232.1.xml">Rotach et al. (2022)</a>.</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p>The ceilometer (Vaisala CL51) is operated at Kufstein next to the meteorological station (12.1628°E 47.5753°N 490m asl).</p> <p>Provided are monthly NetCDF data sets, without data corrections.</p> <p><strong>Description of variables</strong></p> <table> <tbody> <tr> <td> <p>time_resol </p> </td> <td> <p> "Mean time resolution of profiles"</p> </td> <td> <p> units = "s"</p> </td> </tr> <tr> <td> <p>Mtime_resol</p> </td> <td> <p> "Mean time resolution of ALH/MLH" </p> </td> <td> <p> units = "s"</p> </td> </tr> <tr> <td> <p>range_resol</p> </td> <td> <p> "Range resolution" </p> </td> <td> <p> units = "m"</p> </td> </tr> <tr> <td> <p>zenith_angle</p> </td> <td> <p> "Zenith (Tilt) angle of device" </p> </td> <td> <p> units = "degrees"</p> </td> </tr> <tr> <td> <p>times</p> </td> <td> <p> "Decimal hours since start of data [UTC] (ALH/MLH)" </p> </td> <td> <p> units = "hours since yyy-mm-dd HH:MM:SS"</p> </td> </tr> <tr> <td> <p>Mtimes</p> </td> <td> <p> "Decimal hours since start of data [UTC] (ALH/MLH)" </p> </td> <td> <p> units = "hours since yyy-mm-dd HH:MM:SS"</p> </td> </tr> <tr> <td> <p>range</p> </td> <td> <p> "Range from Telescope to each range gate"</p> </td> <td> <p> units = "m"</p> </td> </tr> <tr> <td> <p>real_range</p> </td> <td> <p> "Projected range from Telescope to each range gate (w.r.t zenith angle)"</p> </td> <td> <p> units = "m"</p> </td> </tr> <tr> <td> <p>displacement</p> </td> <td> <p> "Displacement of each range gate (w.r.t zenith angle and altitude)"</p> </td> <td> <p> units = "m"</p> </td> </tr> <tr> <td> <p>bsp</p> </td> <td> <p> "Attenuated Back Scatter Profile Signal"</p> </td> <td> <p> units = "Mm**-1 sr**-1"</p> </td> </tr> <tr> <td> <p>cbh</p> </td> <td> <p> "List of Cloud-Base-Heights [agl] (lowest to highest)"</p> </td> <td> <p> units = "m"</p> </td> </tr> </tbody> </table> <p>Contact: kathrin.baumann-stanzer@geosphere.at</p>
Evaluating Cloud Properties at Scott Base: Comparing Ceilometer Observations with ERA5, JRA55, and MERRA2 Reanalyses Using an Instrument Simulator (Pre-review)
<p>Due to its remote location and extreme weather conditions, atmospheric measurements are rare in Antarctica. Partially due to this lack of observational constraints, large biases in the representation of clouds have been identified over Southern hemisphere high latitudes in various grenerations of the Coupled Model Intercomparison Project (CMIP) models, numerical weather prediction models and reanalyses. It has been shown in previous studies that ground-based remote sensing measurements of cloud across the region are critical for complementing satellite data sets due to the importance of boundary layer and low-level cloud processes. These processes are poorly sampled by satellite-based measurements which are typically obscured by overlying cloud cover or are prone to ground clutter. Here we provide a dataset which includes CL51 ceilometer observations of low clouds made during the period 14th February 2022 and 31st December 2023 at Scott Base, Antarctica (77.8 S, 166.7 E). Complemeted by ERA5, JRA55 and MERRA2 reanalyses output that has been processed using an instrument simulator. These datasets allow direct comparison between the reanalyses datasets and the ceilometer observations.</p> <p> </p>
Ceilometer measurements dataset
<p>This dataset contains images obtained by appropriate transformation of cloud detections from a ceilometer located in San Giovanni La Punta (Catania, Italy), from January 2023 to mid-March 2023. We used a Lufft CHM 15k ceilometer that leverages Light Detection and Ranging (LiDAR) technology. The ceilometer took measurements every 15 seconds quantifying the concentration of particles in the atmosphere. By taking advantage of the reflection, it is possible to determine the cloud layer coverage. Once raw data was collected, the variables of interest were scaled using a normalisation factor of the lidar-based ceilometer. All the variables are used to generate backscatter profiles. Specifically, we plot the time on the x-axis and the height of the measured particles (contained in the backscatter coefficient) on the y-axis. The colour of the plot depends on the intensity of the measured particle: intense blue means absence of particulate; red means intense presence of particulate. Numerically, the scale goes from the value 0 to the value of 5 · 10−6. We generated a backscatter profile for each day of measurements and each of these were further divided for every hour. In this way, we obtained 24 backscatter profiles for each day of observation. </p> <p>The file uploaded is a file with '. ZIP' extension. Once extracted, there are two folders: train and test. For each folder, there are two classes of images: true and false. We suggest using 'ImageFolder' to use the dataset (see reference: <a href="https://pytorch.org/vision/main/generated/torchvision.datasets.ImageFolder.html" target="_blank" rel="noopener">https://pytorch.org/vision/main/generated/torchvision.datasets.ImageFolder.html</a>)</p> <p>For an example of using the dataset, please visit the following authors' repository: <a href="https://github.com/alessiochisari/CeilometerDatasetBenchmark">https://github.com/alessiochisari/CeilometerDatasetBenchmark</a></p> <p>The repository contains all the Jupyter Notebooks used by the authors to benchmark five state-of-the-art models on this new dataset.</p> <p>Please cite our work for any use of the dataset or code in this repository using this citation:</p> <p>@inproceedings{chisari2024, author = {Alessio Barbaro Chisari and others}, title = {On the Cloud Detection from Backscattered Images Generated From a Lidar-Based Ceilometer: Current State and Opportunities}, booktitle = {{IEEE} {ICIP} 2024, Abu Dhabi, UAE, October 27-30, 2024}, year = {2024} }</p>
BOREAS TF-08 NSA-OJP and SSA-OBS Ceilometer Data
The BOREAS TF-08 team used ceilometers to collect data on the fraction of the sky covered with clouds and the cloud height. Included with these data is the surface-based lifting condensation level, derived from temperature and humidity values acquired at the flux tower at the NSA-OJP site. Ceilometer data were collected at the NSA-OJP site in 1994 and at the NSA-OJP and SSA-OBS sites in 1996.
GPM Ground Validation Ceilometers UConn
The GPM Ground Validation Ceilometers UConn dataset includes ceilometer cloud height measurements. These data were collected during the GPM Ground Validation Field Campaign at the University of Connecticut (UConn), which provided an unprecedented set of wintry precipitation observations from multiple traditional and novel precipitation measurement instruments that can be used for validating ground-based and satellite remote sensing observations. The ceilometer dataset files are available from December 15, 2023, through May 21, 2024, in netCDF-4 format, with associated browse data available in PNG format.
GPM GROUND VALIDATION ENVIRONMENT CANADA (EC) VAISALA CEILOMETER GCPEX V1
The GPM Ground Validation Environment Canada (EC) VAISALA Ceilometer GCPEx dataset was collected during the GPM Cold-season Precipitation Experiment (GCPEx) in Huronia, Canada from January 15, 2012 through March 1, 2012. The GPM Cold-season Precipitation Experiment (GCPEx) occurred in Ontario, Canada during the winter season of 2011-2012. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. The CT25K ceilometer uses pulsed diode laser LIDAR technology to derive backscatter profiles, cloud heights and vertical visibilities. It is also able to detect 3 cloud layers simultaneously.
CAMEX-4 MIPS CEILOMETER V1
The CAMEX-4 MIPS Ceilometer dataset was collected by the University of Alabama in Huntsville (UAH) Mobile Integrated Profiling System (MIPS), which is a mobile atmospheric profiling system. It includes a 915 MHz Doppler profiler, lidar ceilometer, 12 channel Microwave Profiling Radiometer (MPR), Doppler Sodar, Radio Acoustic Sounding System (RASS), Field Mills, and surface observing station. This dataset contains 15 minute averaged 3-D wind profiles.The ceilometer gathered backscatter power and up to three cloud base heights.
Data from Automatic Lidar and Ceilometer (ALC) measurements at Paris – Chamant (PACHAM) from 2022-11-25 to 2024-02-22 [RAW]
<p>Original data files from ALC measurements at Chamant (Département 60) in the rural area to the NNE of Greater Paris.</p> <p>Part 3 of 3.</p>
Data from Automatic Lidar and Ceilometer (ALC) intercomparison measurements at Paris – SIRTA Atmospheric Observatory (PASIRT) [RAW]
<p>Original data files from ALC measurements from intercomparison periods at Paris–SIRTA Atmospheric Observatory. Intercomparison periods are when two or more ALC are operated within close proximity within a given measurement station.</p>
Data from Automatic Lidar and Ceilometer (ALC) calibration measurements in the greater Paris area [RAW]
<p>Original data files from ALC measurements from calibration periods. A calibration period is when a calibration termination hood was placed on the ALC, typically interrupting a longer-term standard deployment</p>
Data from Automatic Lidar and Ceilometer (ALC) measurements at Paris – Chamant (PACHAM) from 2022-11-25 to 2024-02-22 [RAW]
<p>Original data files from ALC measurements at Chamant (Département 60) in the rural area to the NNE of Greater Paris.</p> <p>Part 1 of 3.</p>
Data from Automatic Lidar and Ceilometer (ALC) measurements at Paris – SIRTA Atmospheric Observatory (PASIRT) from 2023-08-22 to 2024-02-28 [RAW]
<p>Original data files from ALC measurements at Paris–SIRTA Atmospheric Observatory.</p> <p>Part 2 of 2.</p>
Data from Automatic Lidar and Ceilometer (ALC) measurements at Paris – Roissy (PAROIS) from 2024-03-22 to 2024-07-07 [RAW]
<p>Original data files from ALC measurements at Paris-Roissy Charles de Gaulle Airport.</p> <p>Measurements were taken on the N roof terrace of the Meteo France Building at Roissy Charles de Gaulle Airport / Rue du Moulin (ID 95527001) in the NE of the built-up area of Greater Paris.</p>
Data from Automatic Lidar and Ceilometer (ALC) measurements at Paris–Aunay (PAAUNA) from 2022-10-31 to 2023-02-23 [RAW]
<p>Original data files from ALC measurements at Aunay-sous-Auneau (Département 28) in the rural area to the SW of Greater Paris.</p>
Data from Automatic Lidar and Ceilometer (ALC) measurements at Paris – Aunay (PAAUNA) from 2023-02-23 to 2024-02-19 [RAW]
<p>Original data files from ALC measurements at Aunay-sous-Auneau (Département 28) in the rural area to the SW of Greater Paris.</p> <p>Part 1 of 2.</p>
Data from Automatic Lidar and Ceilometer (ALC) measurements at Paris – Droue-sur-Drouette (PADROU) from 2022-10-31 to 2024-01-30 [RAW]
<p>Original data files from ALC measurements at Droue-sur-Drouette (Département 28) in the rural area to the WSW of Greater Paris.</p>
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.