Data set combines radar, lidar, and radiosonde measurements to give a comprehensive view of cloud boundaries, locations, and temperatures for the SHEBA annual cycle. Information includes: best estimated low cloud base and high cloud top, total cloud thickness, total number of cloud layers, the presence and location of liquid in the atmospheric column and temperatures at all relevant heights.

Detailed land-cover mapping is essential for a range of research issues addressed by sustainability science, especially for questions posed of urban areas, such as those of the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) program. This project provides a 1-meter land-cover mapping of the CAP LTER study area (greater Phoenix metropolitan area and surrounding Sonoran desert). The mapping is generated primarily using 2015 National Agriculture Imagery Program (NAIP) four-band data, with auxiliary GIS data used to improve accuracy. Auxiliary data include the 2015 cadastral parcel data, the 2014 USGS LiDAR data (1-meter), the 2014 Microsoft/OpenStreetMap Building Footprint data, the 2015 Street TIGER/Line, and a previous (2010) NAIP-based land-cover map of the study area (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-cap&identifier=623). Among auxiliary data, building footprints and LiDAR data significantly improved the boundary detection of above-ground objects. Post-classification, manual editing was applied to minimize classification errors. As a result, the land-cover map achieves an overall accuracy of 94 per cent. The map contains eight land cover classes, including: (1) building, (2) asphalt, (3) bare soil and concrete, (4) tree and shrub, (5) grass, (6) water, (7) active cropland, and (8) fallow. When compared to the aforementioned, previous (2010) NAIP-based land-cover map for the study area, buildings and tree canopies are classified more accurately in this 2015 land-cover map.

Drone LiDAR for Property Claims Market Outlook

According to our latest research, the global Drone LiDAR for Property Claims market size reached USD 1.12 billion in 2024, with a robust compound annual growth rate (CAGR) of 17.4% observed between 2024 and 2033. The market is forecasted to attain a value of USD 5.09 billion by 2033, driven by the increasing adoption of advanced surveying technologies and the critical need for rapid, accurate, and cost-effective property assessment solutions. The growth of the Drone LiDAR for Property Claims market is primarily attributed to the rising frequency of natural disasters, the growing complexity of property insurance claims, and the insurance industry's digital transformation initiatives.

One of the principal growth factors for the Drone LiDAR for Property Claims market is the escalating occurrence of extreme weather events and natural disasters globally. As climate change intensifies, insurers and property owners face mounting challenges in accurately assessing damages and processing claims swiftly. Traditional manual inspections are not only time-consuming but also prone to human error, which can lead to disputes and delayed settlements. The integration of drone-mounted LiDAR technology allows for rapid, high-resolution, and precise mapping of affected properties, expediting the claims process while minimizing operational risks and costs. This capability has become a game-changer, particularly in regions frequently impacted by hurricanes, floods, and wildfires, where timely property assessment is crucial for both insurers and policyholders.

Another significant driver fueling the expansion of the Drone LiDAR for Property Claims market is the ongoing digital transformation within the insurance sector. Insurers are increasingly leveraging automation, artificial intelligence, and advanced analytics to streamline claims management and enhance customer experience. Drone LiDAR solutions seamlessly integrate with these digital platforms, providing insurers with accurate, real-time data that supports automated claim validation, fraud detection, and risk assessment. This technological synergy not only reduces the administrative burden but also enables insurers to offer faster, more transparent, and customer-centric services. As regulatory frameworks evolve to support the use of drones and remote sensing in insurance applications, the adoption of Drone LiDAR is expected to accelerate further.

Additionally, the cost-effectiveness and scalability of drone LiDAR solutions are catalyzing their adoption across diverse end-user segments, including insurance companies, property owners, government agencies, and third-party assessors. Unlike traditional aerial surveys that require expensive manned aircraft and lengthy data processing times, drone-based LiDAR systems can be rapidly deployed, cover large areas with minimal logistical overhead, and deliver actionable insights within hours. This operational agility is particularly valuable in post-disaster scenarios, where timely property assessment can significantly impact recovery efforts and financial settlements. As drone technology becomes more affordable and accessible, even small and medium-sized insurers and property owners are beginning to leverage these solutions, expanding the addressable market.

From a regional perspective, North America currently dominates the Drone LiDAR for Property Claims market, accounting for over 38% of the global revenue in 2024. The region's leadership is underpinned by a mature insurance industry, high rates of natural disaster occurrences, and a supportive regulatory environment for drone operations. Europe follows closely, driven by stringent insurance regulations and increasing investments in digital infrastructure. Meanwhile, the Asia Pacific region is poised for the fastest growth, with a projected CAGR of 19.2% over the forecast period, fueled by urbanization, climate change impacts, and government initiatives promoting smart city and disaster management solutions. Latin America and the Middle East & Africa are also witnessing gradual adoption, albeit at a slower pace, as awareness and regulatory frameworks continue to evolve.

The ABLE 2A and 2B (Atmospheric Boundary Layer Experiments) data consists of estimates of the rate of exchange of a wide variety of aerosols and gases between the Amazon Basin and its atmospheric boundary layer, and the processes by which these aerosols and gases are moved between the boundary layer and the free troposphere. The data are presented in gzipped ASCII text files in Global Tropospheric Experiment (GTE) format.

The ABLE-2 project consisted of two expeditions: the first in the Amazonian dry season (ABLE-2A, July-August 1985); and the second in the wet season (ABLE-2B, April-May 1987). The ABLE-2 core research data were gathered by NASA Electra aircraft flights that stretched from Belem, at the mouth of the Amazon River, west to Tabatinga, on the Brazil-Colombia border, from a base at Manaus in the heart of the forest. See Figure 1. These observations were supplemented by ground based chemical and meteorological measurements in the dry forest, the Amazon floodplain, and the tributary rivers through use of enclosures, an instrumented tower in the jungle, a large tethered balloon, and weather and ozone sondes.

This study showed air above the Amazon jungle to be extremely clean during the wet season but air quality deteriorated dramatically during the dry season as the result of biomass burning, performed mostly at the edges of the forest. Biomass burning is also a source of greenhouse gases carbon dioxide and methane, as well as other pollutants (carbon monoxide and oxides of nitrogen). Amazonian ozone deposition rates were found to be 5 to 50 times higher than those previously measured over pine forests and water surfaces. The Amazon River floodplain is a globally significant source of methane, supplying about 12% of the estimated worldwide total from all wetlands sources. Over Amazonia, carbon monoxide is enhanced by factors ranging from 1.2 to 2.7 by comparison with adjacent regions due to isoprene oxidation and biomass burning. Over the rainforest individual convective storms transport 200 megatons of air per hour, of which 3 megatons is water vapor that releases 100,000 megawatts of energy into the atmosphere through condensation into rain.

The ABLE was a collaboration of U.S. and Brazilian scientists sponsored by NASA and Instituto Nacional de Pesquisas Espaciais (INPE) and supported by the Global Tropospheric Experiment (GTE) component of the NASA Tropospheric Chemistry Program.

CAL_LID_L2_01kmCLay-Standard-V4-51 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 1 km Cloud Layer, Version 4-51 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Within this layer product are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The lidar layer products contain column descriptors associated with several layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., aerosol layers) identified within the column. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. From June 13, 2006, to September 13, 2018, CALIPSO was part of the A-Train constellation for coincident Earth Observations. After September 13, 2018, the satellite was lowered from 705 to 688 km to resume flying in formation with CloudSat, called the C-Train.

The effect of clouds and aerosols on regional and global climate is of great importance. Two longstanding elements of the NASA climate and radiation science program are field studies incorporating airborne remote sensing and in-situ measurements of clouds and aerosols. These projects involve coordination of ground based and satellite measurements with the airborne observations. The goals of the experiments include testing satellite remote sensing retrievals, development of advanced remote sensing techniques and fundamental advances in knowledge of cloud radiation and microphysical properties. Active lidar profiling is especially valuable because the cloud height structure is measured unambiguously, up to the limit of signal attenuation.The Cloud Physics Lidar (successor to the Cloud Lidar System) is an airborne lidar system designed specifically for studying clouds and aerosols using the NASA ER-2 High Altitude Aircraft. Because the ER-2 typically flies at an altitude of 65,000 feet (20 km), its instruments are above 94% of the Earth's atmosphere, thereby allowing ER-2 instruments to function as spaceborne instrument simulators. The Cloud Physics Lidar provides a unique tool for atmospheric profiling and is sufficiently small and low cost to include in multiple instrument missions.The Cloud Physics Lidar provides a complete battery of cloud physics information. Data products include: (1) Cloud profiling with 30 m vertical and 200 m horizontal resolution at 1064 nm, 532 nm, and 355 nm;(2) Aerosol, boundary layer, and smoke plume profiling;(3) Optical depth estimates (column and by layer); and(4) Extinction profiles. The CPL provides information to permit a comprehensive analysis of radiative and optical properties of optically thin clouds. To determine the effects of particulate layers on the radiative budget of the earth-atmosphere system, certain information about the details of the layer and its constituents is required. The effect of clouds is often referred to as cloud radiative forcing. Cloud radiative forcing, in general, is the alteration that the presence of clouds has on the energy budget. The information required to compute the radiative forcing includes the vertical distribution of short wave cross section, a parameter that the CPL provides up to the limits of optical signal attenuation.Using optical depth measurements determined from attenuation of Rayleigh and aerosol scattering, and using the integrated backscatter, the extinction-to-backscatter parameter can be derived. This permits rapid analysis of cloud optical depth since only lidar data is required; there is no need to use other instrumentation. Using the derived extinction-to-backscatter ratio, the internal cloud extinction profile can then be obtained.The CPL uses photon-counting detectors with a high repetition rate laser to maintain a large signal dynamic range. This dramatically reduces the time required to produce reliable and complete data sets.ORNL DAAC has archived CPL quicklook data samples from the SAFARI 2000 Field Campaign. The samples were provided by the CPL group at the NASA Goddard Space Flight Center (GSFC). The actual Cloud Physics Lidar data are stored at the CPL Web Site at NASA GSFC and can be accessed at [http://virl.gsfc.nasa.gov/cpl/safari2000_pass.htm]. Data users are asked to read and abide by the CPL data usage policy found at [http://virl.gsfc.nasa.gov/cpl/cpl_register.htm]. For systems specifications and other information regarding Cloud Physics Lidar, please visit the Cloud Physics Lidar Home Page at [http://cpl.gsfc.nasa.gov/].

DeLiAn is a collection of lidar-derived aerosol intensive optical properties for several aerosol types. The intensive parameters are the particle linear depolarization ratio, the extinction-to-backscatter ratio (lidar ratio) and the Ångström exponent. The data collection is based on globally distributed, long-term, ground-based, multiwavelength, Raman and polarisation lidar measurements.

DeLiAn is available in two data formats: NetCDF and excel workbook. The intensive optical properties are presented at the typical lidar wavelengths, 355, 532 and 1064 nm, for 13 aerosol categories in total. The variables included in the datafiles are listed below. For each variable, a full description is provided in the long_name attribute (applicable for the netCDF file only). The same information is provided in the first excel sheet (“List of variables”).

• ScienceData
  • angstrom_exponent_backscatter_355_532
  • angstrom_exponent_backscatter_532_1064
  • angstrom_exponent_extinction_355_532
  • campaign_rv
  • date
  • error_angstrom_exponent_backscatter_355_532
  • error_angstrom_exponent_backscatter_532_1064
  • error_angstrom_exponent_extinction_355_532
  • error_lidar_ratio_355
  • error_lidar_ratio_532
  • error_particle_linear_depolarization_ratio_355
  • error_particle_linear_depolarization_ratio_532
  • lidar_ratio_355
  • lidar_ratio_532
  • location
  • measurement_type
  • number_samples
  • particle_linear_depolarization_ratio_355
  • particle_linear_depolarization_ratio_532
  • reference
  • system

For any further information or expression of interest with respect to DeLiAn, please contact Athena Augusta Floutsi (floutsi@tropos.de) and/or Holger Baars (baars@tropos.de).

The Version 2 (V2) CALIPSO Lidar Level 2 Polar Stratospheric Clouds (PSC) data product ensemble describes the spatial distribution, optical properties, and composition of PSC layers observed by the CALIPSO lidar (CALIOP). The product contains profiles of PSC presence, composition, optical properties, and meteorological information on a uniform 5-km horizontal x 180-m vertical grid along CALIPSO orbit tracks. Aura Microwave Limb Sounder (MLS) measurements of the primary PSC condensable vapors HNO3 and H2O and a number of parameters from the Aura MLS V2 Derived Meteorological Products (DMPs) are also included in the V2 PSC data product ensemble.

CAL_LID_L2_05kmALay-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 5 km Aerosol Layer Data, Version 4-21 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The version of this product was changed from 4-20 to 4-21 to account for a change in the operating system of the CALIPSO production cluster. Data collection for this product is complete.Within the Lidar Aerosol Layer Product, there are two general classes of data:- Column Properties (including position data and viewing geometry) and Layer Properties. The lidar layer products consist of a sequence of column descriptors, each associated with a variable number of layer descriptors. The column descriptors specify the temporal and geophysical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., aerosol layers) identified within the column. CALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre national d'études spatiales).

This LiDAR data was captured by Blom Aerofilms Ltd (now part of Cyient Europe Limited) for Historic Environment Scotland (HES) as part of the Rae Project, the 3D Digital Documentation of HES’ Properties in Care and other HES Projects. The LiDAR data was captured in 2016/17 of the sites and their immediate landscape and processed in LAS/LAZ format to 0.25m resolution for the following sites: • Inchcolm Abbey; • Kilmartin Glen* – Covering HES Properties in Care; Achnabreck Cup and Mark Rocks; Ballygowan Cup and Mark Rocks; Baluachraig Cup and Mark Rocks; Cairnbaan Cup and Mark Rocks; Carnasserie Castle; Dunadd Hill Fort; Dunchraigaig Cairn; Kilmartin Crosses; Kilmartin Glebe Cairn; Kilmartin Sculptured Stones; Kilmichael Glassary Cup and Ring Mark Rock; Nether Largie Mid Cairn; Nether Largie North Cairn; Nether Largie South Cairn; Ri Cruin Cairn; Temple Wood Stone Circles • Eileach-an-Naoimh Christian Settlement; • Eliean Mor Chapel and Stones; • Inchkenneth Chapel, Isle of Mull; • Inchmahome Priory; • Iona Abbey; • Iona Nunnery; and • Holyrood Park Please Note: Kilmartin Glen data is one complete area encompassing all HES sites here denoted as “Kilmartin Glen” in the above list including the contextual landscape of those sites as defined in the project. Data was supplied as Raw data in .laz format and 0.25m .las format and in ascii format as DTM (Terrain) and DSM (Surface) data. Data is distributed here on the Scottish Remote Sensing Portal as GeoTiff and LAZ. Each site (data area) boundary is included in a GIS shapefile depicting the location of the data, key attribute information and boundaries of other HES LiDAR datasets It is important to note that only areas required for the specific purposes of the HES project were procured

Overview The dataset includes data collected during the ATMO-ACCESS Trans-National Access project "Industrial Pollution Sensing with synergic techniques (IPOS TNA)" that has been conducted from June 8 to June 24, 2024 at the Cabauw Experimental Site for Atmospheric Research (CESAR, 51°58'03''N, 4°55'47"E, 3 m.a.s.l.) of the Royal Netherlands Meteorological Institute (KNMI). The IPOS TNA was supporting the 3rd Intercomparison Campaign of UV-VIS DOAS Instruments (CINDI-3).The observations were taken with use of three instruments:ESA Mobile Raman Lidar (EMORAL). Lidar emits pulses at fixed wavelengths (355, 532 and 1064 nm), simultaneously with the pulse repetition rate of 10 Hz and pulse duration of 5-7 ns. The backward scattered laser pulses are detected at 5 Mie narrow-band channels (355p,s 532p,s and 1064 nm) and 3 Raman narrow-band channels (for N2 at 387, 607 nm and H2O at 408nm) as well as broad-band fluorescence channel (470 nm). The temporal resolution was set at 1 min and and spatial resolution to 3.75 m. The overlap between the laser beam and the full field of view of the telescope was at ~250 m a.g.l. EMORAL lidar is a state-of-the-art lidar system developed through a collaborative effort involving the University of Warsaw (UW, Poland; leader and operator), Ludwig Maximilian University of Munich (LMU, Germany), National Observatory of Athens (NOA, Greece), Poznan University of Life Sciences (PULS, Poland), and companies Raymetrics (Greece; core manufacturer), Licel (Germany), and InnoLas Laser (Germany). This complex instrument, part of ESA’s Opto-Electronics section (TEC-MME) at the European Space Research and Technology Centre (ESA-ESTEC, The Netherlands), is designed to perform precise atmospheric measurements. EMORAL lidar was validated by the ACTRIS Centre for Aerosol Remote Sensing (CARS) at the Măgurele Center for Atmosphere and Radiation Studies (MARS) of National Institute of R&D for Optoelectronics (INOE, Romania).PM counter GrayWolf PC-3500, GRAYWOLF Graywolf Sensing Solutions (USA) https://graywolfsensing.com/wp-content/pdf/GrayWolfPC-3500Brochure-818.pdf (last access 25/2/2025)Model 540 Microtops II® Sunphotometer, Solar Light Company, LLC (USA) https://www.solarlight.com/product/microtops-ii-sunphotometer (last access 25/2/2025)The dataset contain following items:1) EMORAL lidar data files The data contain of two files LiLi_IPOS.zip and LiLi_IPOS_quicklooks.zip. Both are described in detail below.The LiLi_IPOS.zip file is a folder that contains the high-resolution data obtained using the Lidar, Radar, Microwave radiometer algorithm (LiRaMi; more in Wang et al., 2020). The results were obtained only from the lidar data (referred to as Limited LiRaMi, i.e. LiLi algorithm version). The folder contains files in netcdf4 format for each day of observations. The data products are calculated from the analog channels only.Each of the .nc file has a structure, which contains Variables:Location (string)Latitude (size: 1x1 [deg])Longitude (size: 1x1 [deg])Altitude (size: 1x1 [m a.g.l.])time vector (size: 1 x time, [UTC])range vector (size: range x 1, [m])RCS532p matrix (size: range x time, [V m2]), which contains the data of the range-corrected signal at 532nm, parallel polarizationRCS532s matrix (size: range x time, [V m2]), which contains the data of the range-corrected signal at 532nm, perpendicular polarizationRCS1064 matrix (size: range x time, [V m2]), which contains the data of the range-corrected signal at 1064nmSR532 matrix (size: range x time, [unitless]), which contains the data of the scattering ratio at 532nmATT_BETA532 matrix (size: range x time, [m2/sr]), which contains the data of the attenuated backscatter coefficient at 532nm, parallel polarizationC532 constant (size: 1x1, [V sr]), which is the instrumental factor for 532nmSR1064 matrix (size: range x time, [au]), which contains the data of the scattering ratio at 1064nmATT_BETA1064 matrix (size: range x time, [m2/sr]), which contains the data of the attenuated backscatter coefficient at 1064nmC1064 constant (size: 1x1, [V sr]), which is the instrumental factor for 1064nmCOLOR_RATIO matrix (size: range x time, [au]), which contains the data of color ratio of 532nm and 1064nm.PARTICLE_DEPOLARIZATIO_RATIO matrix (size: range x time, [au]), which contains the data of particle depolarization ratio at 532nmC constant (size: 1x1, [au]), which is the depolarization constant for 532nm.The LiLi_IPOS_quicklooks.zip file contains high-resolution figures representing the data in the form of quicklooks of following parameters:Range-corrected signal at 1064nmScattering ratio at 532nmColor ratio of 532 and 1064nmParticle depolarization ratio at 532nmAerosol target classification from LiLi algorithmWang, D., Stachlewska, I. S., Delanoë, J., Ene, D., Song, X., and Schüttemeyer D., (2020). Spatio-temporal discrimination of molecular, aerosol and cloud scattering and polarization using a combination of a Raman lidar, Doppler cloud radar and microwave radiometer, Opt. Express 28, 20117-20134 (2020).2) PM counterThe PM_counter.zip file contains a folder with data from measurements of atmospheric particulate matter collected using the GrayWolf PC-3500 particle counter from June 15 (16:16:21 CEST) to June 20 (07:06:21 CEST), 2024, at the CESAR station (51°58'04.0"N, 4°55'46.4"E). The data were processed using WolfSense PC software for validation and analysis. The final dataset, provided in XLSX format, includes temporal evaluation in particle concentration from 0.3 to 10.0 µm (6 size ranges). The data is divided into three levels:[1] Level 0: Raw data in XLSX format with measurement data in 4 units (µg/m3, cnts/m3, cnts dif, cnts cum).File structure:Line 1: headers describing columns,Line 2-6646: concentration of PM,Column 1: date and time in format DD-MMM-YY HH:MM:SS AM/PM,Column 2-7: concentration of specific PM values: 0.3, 0.5, 1.0, 2.5, 5.0, 10.0 µm, respectively,Column 8: Temperature,Column 9: Carbon Dioxide (CO2),Column 10: Total Volatile Organic Compounds (TVOC),Column 11: pressure in measuring chamber,Missing data (Column 8-10) represented as zero value (0).[2] Level 1: Tables with validated data in 4 units (µg/m3, cnts/m3, cnts dif, cnts cum) in XLSX format.File structure:Line 1: headers describing columns,Line 2-6646: concentration of PM,Column 1: date and time in format DD-MMM-YY HH:MM:SS AM/PM,Column 2-7: concentration of specific PM values: 0.3, 0.5, 1.0, 2.5, 5.0, 10.0 µm, respectively,Column 8: pressure in measuring chamber,Column 9: assembly method, where: [1] measurement at a height of 60 cm during rain (instrument protected by the table), [2] measurement at a height of 160 cm when there is no rain.[3] Level 2: Tables with post-processed data in XLSX format, and graphs in PNG format visualizing the received data.XLSX file structure:PM counter - level 2 (daily average concentrations), PM counter - level 2 (hourly average concentrations) sheets: structure of columns same as in level 1.PM counter - level 2 (data comparison) sheet: Column 1 - Date in format DD.MM.YYYY; Column 2 - PM2.5 concentration measured within IPOS; Column 3 - PM10.0 concentration measured within IPOS; Column 4 - PM2.5 concentration measured at Cabauw-Wielsekade (RIVM), Column 5 - PM10.0 concentration measured Cabauw-Wielsekade (RIVM).General information for all level files:Decimal separator: coma (,).3) SunphotometerThe MICROTOPS_IPOS.zip file is a folder that contains data from measurements of aerosol optical thickness at wavelengths 380, 500, 675, 870, and 1020 nm done with Microtops II hand-held sunphotometer. The final, quality assured dataset, provided in XLSX format, consists of measurement data for: temperature, pressure, solar zenith angle, signal strength at different wavelengths (340, 380, 500, 936, 1020 nm), standard deviation at specific wavelengths, ratio between signals at two different wavelengths (340/380, 380/500, 500/936, 936/1020), and atmospheric optical thickness at different wavelengths.During the IPOS TNA campaign, in total 29 measurements were taken. Each measurement is composed of 6 scans, whereas the first one is a dark scan. The days when a measurement took place were: 13, 23, 24, and 25 of June 2024. Level 0 of data means raw data converted from dbf to xslx format file. Level 1 of data mean raw data converted from dbf to xslx file format, without the dark scans.Files structure:Line 1: Headers describing columns,Column 1: Serial number of the instrumentColumn 2-3: Date and Time in format YYYY-MM-DD; HH:MM:SS,Column 4-8: Data desciprtion of the camapign; Location (decimal); Latitude; Longitude (decimal), AltitudeColumn 9-14: Atmospheric Pressure; Solar Zenith Angle; Air Mass; Standard Deviation Correction; Temperature; ID of the measurement, Column 15-24: Signal strength at specific wavelength and Standard Deviation,Column 25-28: Ratio between signals at two different wavelengths,Column 29-33: Atmospheric Optical Thickness,Column 34-39: Columnar Water Vapour and Natural Logarithm of Voltage,Column 40-47: Calibration coefficients,Column 48-49: Pressure offset and Pressure scale factor,READ ME sheet: Describing the file content and measurement location.4) readme fileATTENTION:We offer a free access to this dataset. The user is however encouraged to share the information on the data use by sending an e-mail to rslab@fuw.edu.plIn the case this dataset is used for a scientific communication (publication, conference contribution, thesis) we would like to kindly ask for considering to acknowledge data provision by citing this dataset.------------------------------------PI of IPOS TNA Iwona Stachlewska and IPOS team members Maciej Karasewicz, Anna Abramowicz, Kinga Wiśniewska, Zuzanna Rykowska, and Afwan Hafiz acknowledge that the published dataset was prepared within the Trans-National Access grant (IPOS TNA no. ATMO-TNA-7-0000000056) within the ATMO-ACCESS grant financed by European Commission Horizon 2020 program (G.A.

ATL09 contains calibrated, attenuated backscatter profiles (CAB), layer-integrated attenuated backscatter, blowing snow, and other parameters including cloud and aerosol layer height and atmospheric characteristics. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.

This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.

Overview

The database includes text files containing the scattering amplitude matrices for single spherical/nonspherical particles for radar and lidar. They are the lookup tables used for calculating radar and lidar observables in the Cloud-Resolving Radar Simulator (Oue et al. 2020). The radar scattering properties were calculated for several hydrometeor categories using a T-matrix method proposed by Mishchenko (2000) accounting for incident angles, scattering direction (forward and backward), polarimetry (horizontally (H) and vertically (V) polarized waves), particle aspect ratio, phase (liquid or ice), bulk density, temperature, particle size, and radar frequency. The lidar scattering properties at a vertical incidence were calculated for spherical liquid or ice particles using the BHMIE Mie code (Bohrean and Hyffman,1998) accounting for lidar wavelength, temperature, and bulk density. The hydrometeor categories are commonly used for cloud resolving models employing bulk microphysical schemes (e.g., cloud, rain, ice cloud, snow aggregates, and graupel). Detailed descriptions are also available in the CR-SIM user guide (https://github.com/marikooue/CR-SIM/releases/tag/crsim-v3.34).

Data structure

The data files are arranged and zipped every hydrometeor types. The names of the tar-zipped directories under the top directory LLUT3 represents the hydrometer type.
For lidar scattering, the following directories are included:
ceilo: Ceilometer lidar backscatter properties at a wavelength of 905 nm
mpl: Micropulse lidar (MPL) backscatter properties at wavelengths of 353 and 532 nm

For radar scattering, the following hydrometer types are included:
cloud: Radar scattering for liquid cloud droplets (spherical shape)
raina: Radar scattering for raindrops with the aspect ratio model proposed by Andsager et al. (1999)
rainb: Radar scattering for raindrops with the aspect ratio model proposed by Brandes et al (2002)
ice_ar0.90: Radar scattering for cloud ice with an aspect ratio of 0.9
ice_ar0.20: Radar scattering for cloud ice with an aspect ratio of 0.2
smallice: Radar scattering for spherical cloud ice particles
snow_ar0.60: Radar scattering for snowflakes with an aspect ratio of 0.6
graupel_ar0.60: Radar scattering for graupel particles with an aspect ratio of 0.6
graupel_ar0.80: Radar scattering for graupel particles with an aspect ratio of 0.8
graupel: Radar scattering for spherical graupel particles
gh_ryzh: Radar scattering for graupel particles with the graupel aspect ratio model proposed by Ryzhkov et al (2011)
unrimedice_ar0.40: Radar scattering for unrimed ice particles with an aspect ratio of 0.4
unrimedice_ar0.60: Radar scattering for unrimed ice particles with an aspect ratio of 0.6
unrimedice_ar0.80: Radar scattering for unrimed ice particles with an aspect ratio of 0.8
unrimedice: Radar scattering for spherical unrimed ice particles
partrimedice_ar0.40: Radar scattering for partially rimed ice particles with an aspect ratio of 0.4
partrimedice_ar0.60: Radar scattering for partially rimed ice particles with an aspect ratio of 0.6
partrimedice_ar0.80: Radar scattering for partially rimed ice particles with an aspect ratio of 0.8
partrimedice: Radar scattering for partially rimed spherical ice particles

The file name convention

For lidar scattering data, each file name has the following format:
[hydrometeor type]_[instrument name]_ [wavelength in nm]_[phase ID]_d[bulk density in kg m-3].dat
The hydrometeor type shows: 1) ‘cld’ for liquid cloud droplets, and 2) ‘ice’ for ice particles. The phase ID shows: 1) ‘p25’ for ceilometer liquid cloud, 2) ‘p20’ for MPL lidar liquid cloud, and 3) ‘m30’ for MPL lidar ice.

For radar scattering data, each file name has the following format.
[hydrometeor type]_fr[frequency in GHz]GHz_t[temperature in K]_rho[bulk density in kg m-3]_el[elevation angle in degree].dat
The hydrometeor type follows the directory name presented above.

Format of the data files

Line 1: Wavelength in mm
Line 2: Temperature in K
Line 3: Refractive index (real and imaginary)
Line 4: Number of radii calculated and number of elevation angles
Line 6: Incident angle and scattered angle in degrees
Line 7: Radius in mm and aspect ratio
Line 8: Forward scattering amplitude for co-polarization VV and HH (complex number)
Line 9: Backward scattering amplitude for co- and cross polarizations VV, VH, HV, HH (complex number)
Line 10 to the end of file: Repeat Line 7 to Line 9 with different radii until the maximum radius.

ATL09QL is the quick look version of ATL09. Once final ATL09 files are available the corresponding ATL09QL files will be removed. ATL09 contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.

Daily NetCDF files from the Arctic High Spectral Resolution Lidar deployed at Eureka, Nunavut, Canada. Fields include aerosol backscatter and circular depolarization ratio. This dataset is an ongoing collection and is updated daily. View the near-realtime data at the Home Page link below. Instrument deployed by Ed Eloranta at the University of Wisconsin. As additional resources for cloud properties derived from a combination of Millimeter Cloud Radar (MMCR), High Spectral Resolution Lidar (HSRL), and Atmospheric Emitted Radiance Interferometer (AERI) data, go to the following locations within the CADIS Portal: Atmosphere/Cloud Properties Across the Arctic Basin from Surface and Satellite Measurements/Cloud occurrence and layering at Arctic atmospheric observatories AND Atmosphere/Development of Data Products for the University of Wisconsin High Spectral Resolution Lidar. Contact Ed Eloranta at eloranta@lidar.ssec.wisc.edu for questions regarding the lidar data. This is an International Arctic Systems for Observing the Atmosphere (IASOA) data set.

CAL_lID_L2_333mCLay-ValStage1-V3-01 data are Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 2 1/3km (333m) cloud layer data. Version 3.01 of the Lidar Level 2 data products is a significant improvement over previous versions. Major code and algorithm improvements include:- the elimination of a bug in the cloud clearing code that caused a substantial overestimate of low cloud fraction in earlier data releases- enhancements to the cloud-aerosol discrimination algorithm that increase the number of diagnostic parameters used to make classification decisions- improved daytime calibration procedures, resulting in more accurate estimates of layer spatial and optical properties- an entirely new algorithm for assessing cloud thermodynamic phase. Within the Lidar Cloud Layer Product there are two general classes of data: Column Properties (including position data and viewing geometry) and Layer Properties. The lidar layer products consist of a sequence of column descriptors, each one of which is associated with a variable number of layer descriptors. The column descriptors specify the temporal and geo-physical location of the column of the atmosphere through which a given lidar pulse travels. Also included in the column descriptors are indicators of surface lighting conditions, information about the surface type, and the number of features (e.g., cloud and/or aerosol layers) identified within the column. For each feature within a column, a set of layer descriptors is reported. The layer descriptors provide information about the spatial and optical characteristics of a feature, such as base and top altitudes, integrated attenuated backscatter, and optical depth. New parameters for the V3.01 product include: column optical depths, layer top pressure, layer base pressure, layer mid-point pressure, layer top temperature, and layer base temperature. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES.

Abstract: The spatiotemporal distribution of aerosol particles in the atmosphere has a great impact on radiative transfer, clouds, and air quality. Modern remote sensing methods, as well as airborne in situ measure- ments by unpiloted aerial vehicles (UAV) or balloons, are suitable tools to improve our understanding of the role of aerosol particles in the atmosphere. To validate the measurement capabilities of three relatively new measurement systems and to bridge the gaps that are often encountered between remote sensing and in situ ob- servation, as well as to investigate aerosol particles in and above the boundary layer, we conducted two measure- ment campaigns and collected a comprehensive dataset employing a scanning aerosol lidar, a balloon-borne ra- diosonde with the Compact Optical Backscatter Aerosol Detector (COBALD), an optical particle counter (OPC) on a UAV, and a comprehensive set of ground-based instruments. The extinction coefficients calculated from near-ground-level aerosol size distributions measured in situ are well correlated with those retrieved from lidar measurements, with a slope of 1.037 ± 0.015 and a Pearson correlation coefficient of 0.878, respectively. Verti- cal profiles measured by an OPC-N3 on a UAV show similar vertical particle distributions and boundary layer heights to lidar measurements. However, the sensor, OPC-N3, shows a larger variability in the aerosol backscat- ter coefficient measurements, with a Pearson correlation coefficient of only 0.241. In contrast, the COBALD data from a balloon flight are well correlated with lidar-derived backscatter data from the near-ground level up to the stratosphere, with a slope of 1.063 ± 0.016 and a Pearson correlation coefficient of 0.925, respectively. This consistency between lidar and COBALD data reflects the good data quality of both methods and proves that lidar can provide reliable and spatial distributions of aerosol particles with high spatial and temporal resolutions. This study shows that the scanning lidar has the capability to retrieve backscatter coefficients near the ground level (from 25 to 50 m above ground level) when it conducts horizontal measurement, which is not possible for verti- cally pointing lidar. These near-ground-level retrievals compare well with ground-level in situ measurements. In addition, in situ measurements on the balloon and UAV validated the scanning lidar retrievals within and above the boundary layer. The scanning aerosol lidar allows us to measure aerosol particle distributions and profiles from the ground level to the stratosphere with an accuracy equal to or better than in situ measurements and with a similar spatial resolution. TechnicalRemarks: Data of publication "Comparison of scanning aerosol LIDAR and in situ measurements of aerosol physical properties and boundary layer heights"

AECL (Airborne Elastic Cloud Lidar) data from two AECL instruments onboard the NRC Convair 580 during the ICICLE (In-Cloud ICing and Large-drop Experiment) field campaign based out of Rockford, Illinois (USA) from 18 January - 8 March 2019. The AECL is a compact single wavelength airborne elastic lidar operating at 355nm used for the retrieval of vertical profiles of atmospheric properties, such as scattering and extinction properties of clouds and aerosols.

CAL_LID_L2_01kmCLay-Standard-V4-10 data are CALIPSO Lidar Level 2 1km cloud layer data. ........ Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES.