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114 results for “disdrometer”
Particle size and velocity distributions from a Thies Clima 3D Stereo disdrometer installed at the Casale Calore site in L'Aquila (Italy), monthly netCDF archive
<p>Disdrometric data from a Thies Clima 3D Stereo disdrometer, with 22 size classes and 20 velocity classes, located at the instrumented site of Casale Calore in L'Aquila (Italy, 42.3831 N, 13.3148 E, 683 m a.s.l.), managed by the University of L'Aquila and the Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS). </p> <p>Mid values and widths of the classes and instrument ancillary data are provided. One-minute spectra are aggregated every 5 minutes and saved in monthly netCDF files.</p> <p>Metadata available at <a href="https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00">https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00</a></p> <p>--------------------------------------------------------------------</p> <p>Example of netCDF file structure:</p> <h2><strong>File "LAQ_3DS_202301_5min.nc"</strong></h2> <pre><strong> dimensions</strong>: <em>diameter </em>= 22; <em>velocity </em>= 20; <em>n_image </em>= 20; <em>y_image </em>= 12; <em>x_image </em>= 12; <em>time </em>= UNLIMITED; // (8741 currently) <strong>variables</strong>: long <em>time_UTC</em>(time=8741); :description = "Measurement time. Timestamp indicates the end of the observation interval, e.g. 01-Mar-2020 00:05:00 represents the particle counts registered between 01-Mar-2020 00:00:01 and 01-Mar-2020 00:05:00."; :time_zone = "UTC"; :units = "Seconds since 1970-01-01 00:00:00 (Unix time)."; :_ChunkSizes = 512U; // uint float <em>diameters</em>(diameter=22); :description = "Mid values of the size classes"; :units = "mm"; float <em>velocities</em>(velocity=20); :description = "Mid values of the velocity classes"; :units = "m s^-1"; float <em>diameters_width</em>(diameter=22); :description = "Width of the size classes"; :units = "mm"; float <em>velocities_width</em>(velocity=20); :description = "Width of the velocity classes"; :units = "m s^-1"; int <em>spectrum</em>(diameter=22, velocity=20, time=8741); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over 5 minutes."; :units = "counts"; :_ChunkSizes = 22U, 20U, 1U; // uint float <em>PSD</em>(diameter=22, time=8741); :description = "Particle size distribution, 5 minutes interval, normalized by the observed volume."; :units = "m^-3 mm^-1"; :_ChunkSizes = 22U, 1U; // uint double <em>monthlySpectrum</em>(diameter=22, velocity=20); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over the entire month."; :units = "counts"; double <em>monthlyPSD</em>(diameter=22); :description = "Particle size distribution for the whole month, normalized by the observed volume."; :units = "m^-3 mm^-1"; int <em>images</em>(x_image=12, y_image=12, n_image=20, time=8741); :description = "Images of samples of the detected precipitating particles. Images are 48x12 pixel maximum, for a max of 4 stacked 12x12 images. Most of the time less than 4 images are provided."; :units = "0-255 pixel values"; :_ChunkSizes = 12U, 12U, 20U, 1U; // uint int <em>image_count</em>(time=8741); :description = "How many images are registred by the instrument in the minute."; :units = "0-4 count"; :_ChunkSizes = 1024U; // uint int <em>precip_type</em>(n_image=20, time=8741); :description = "Precipitation type as classified by the instument based on shape, size, velocity and presence of water, according to the following table with 11 entries (0-10): 0-reserved value, 1-false positive, 2-rain or graupel, 3-drizzle, 4-drizzle with rain, 5-rain, 6-rain with snow, 7-snow, 8-ice prisms, 9-graupel, 10-hail."; :units = "0-10 code"; :_ChunkSizes = 20U, 1U; // uint int <em>particle_diam</em>(n_image=20, time=8741); :description = "Main diameter of the particles shown in the images."; :units = "mm"; :_ChunkSizes = 20U, 1U; // uint //<strong> global attributes</strong>: :<em>title </em>= "Thies Clima 3D Stereo disdrometer data, aggregated to 5min, monthly netCDF archive."; :<em>comment </em>= "Particle counts diveded in 22 size classes and 20 velocity classes. Note that this data has been processed regardless of precipitation type."; :<em>time_label </em>= "Jan 2023"; :<em>institution </em>= "CNR-ISAC, Rome (IT)"; :<em>contact_person </em>= "Luca Baldini, CNR-ISAC, Rome, l.baldini@isac.cnr.it"; :<em>source </em>= "TC 3DS disdrometer at MZS (Antarctica)"; :<em>location </em>= "Mario Zucchelli Station (74°42\'S, 164°07\'E, 15 m a.s.l.)"; :<em>author </em>= "Giacomo Roversi, Ca\' Foscari University, Venice (IT) and CNR-ISAC, Rome (IT), g.roversi@isac.cnr.it"; :<em>creation_date </em>= "23-Oct-2024 11:13:22 UTC"; :<em>coverage </em>= "Monthly coverage (Jan 2023): 100 %"; :<em>time_resolution </em>= "5 minutes"; :<em>history </em>= "Created from raw TC telegram TDD 163, aggregated to 5min temporal resolution with a sum of the 1-minute counts if least 3 out of 5 are not NaN."; </pre> <p> </p> <p> </p>
Data for "Leveraging a Disdrometer Network to Develop a Probabilistic Precipitation Phase Model in Eastern Canada"
<p><a name="_Toc157072893"></a><strong>Abstract</strong>. This study presents a probabilistic model that partitions the precipitation phase based on hourly measurements from a network of radar-based disdrometers in eastern Canada. The network consists of 27 meteorological stations located in a boreal climate for the years 2020-2023. Precipitation phase observations showed a 2-m air temperature interval between 0-4°C where probabilities of occurrence of solid, liquid, or mixed precipitation significantly overlapped. Single-phase precipitation was also found to occur more frequently than mixed-phase precipitation. Probabilistic phase-guided partitioning (PGP) models of increasing complexity using random forest algorithms were developed. The PGP models classified the precipitation phase and partitioned the precipitation accordingly into solid and liquid amounts. PGP_basic is based on 2-m air temperature and site elevation, while PGP_hydromet integrates relative humidity. PGP_full includes all the above data plus atmospheric reanalysis data. The PGP models were compared to benchmark precipitation phase partitioning methods. These included a single temperature threshold model set at 1.5°C, a linear transition model with dual temperature thresholds of –0.38 and 5°C, and a psychrometric balance model. Among the benchmark models, the single temperature threshold had the best classification performance due to a low count of mixed-phase events. The other benchmark models tended to over-predict mixed-phase precipitation in order to decrease partitioning error. All PGP models showed significant phase classification improvement by reproducing the observed overlapping precipitation phases based on 2-m air temperature. In terms of partitioning error, PGP_full had the lowest RMSE and the least variability in performance. The RMSE of the single temperature threshold model was the highest and showed the greatest performance variability. The improvement of mixed-phase prediction remains a challenge. This study establishes a basis for integrating automated phase observations into a hydrometeorological observation network and developing probabilistic precipitation phase models.</p>
Supplementary to "Quantifying the wind-induced bias of rainfall measurements for the Thies optical disdrometer"
<p>Supplementary material for the paper "Quantifying the wind-induced bias of rainfall measurements for the Thies optical disdrometer" submitted to the journal Water Resources Research</p>
Data for "Two months of disdrometer data in the Paris area"
<p>The data set corresponds the data presented in the data paper : “Two months of disdrometer data in the Paris area”, published in 2018 in the Journal “ Earth System Science Data” (https://www.earth-system-science-data.net/).</p> <p>More details can be found in the Read_me_v1.txt file and in the paper.</p>
Precipitation disdrometer at C1 from October 2013 to May 2016
An OTT Parsivel Disdrometer is used to generate detailed precipitation information on the fall speeds and sizes of hydrometeors at the Niwot Ridge C1 site. This disdrometer data aids in the quantification of rain and snowfall microstructure allowing users of the data to investigate the influence that hydrometeor characteristics have on precipitation accumulation rates, streamflow/runoff, precipitation gauge errors, and radar reflectivity-rain/snow rate relationships (these are current uses of the data but there are many more possibilities). Parameters, such as intensity of precipitation, radar reflectivity, and visibility, are derived from the particle size and speed quantities and are complimentary to the other precipitation instrumentation at C1. The type of precipitation is derived from empirical relationships of fall speeds and volume-equivalent diameters and is useful for determining transitions between rain, graupel, snow, etc. The disdrometer at C1 fits into the larger goal of improving our understanding of orographic precipitation and the hydrometeorological dynamics at play on the eastern slopes of the Colorado Front Range.
Data for journal article: "Automated precipitation monitoring with the Thies disdrometer: Biases and ways for improvement"
<p>This dataset contains data used for the journal article" Automated precipitation monitoring with the Thies disdrometer: Biases and ways for improvement" submitted to Atmospheric Measurement Techniques.</p>
Laser Disdrometer Particle Size and Velocities Distributions Raw data (2014-2017) for Melbourne, Australia
<p>This dataset includes raw data obtained from Laser Disdrometers installed in the proximity of Melbourne, Australia. It includes data from 2 Thies LPM and 2 OTT Parsivel. The description of this dataset as well as a first step in the processing are described in Guyot et al. (under review) at Hydrology and Earth Sciences Systems (publisher EGU) under an open-source Discussion format. </p>
Comparison of three types of laser optical disdrometers under natural rainfall conditions
<p>This dataset contains the raw files of one rain gauge and four disdrometers used for a comparative study of rainfall characteristics measured by optical laser-based disdrometers.</p> <p>Please see data description file for further information.</p>
Database of the Italian disdrometer network (V04)
<p><span lang="EN-US">This is the Version 04 (V04) of the GID database that includes the data collected by the GID disdrometer network along the Italian peninsula. The main upgrade of V04 with respect to the V03 is the higher number of disdrometer data due to the presence of new sensors in the GID network (in the V03 there were 19 disdrometers, while in the V04 there are 23 disdrometers) and the availability of more years of measurements (i.e. with respect to V03 we added data until 31 December 2024). <span> </span></span></p> <p><span lang="EN-US">The disdrometers belong to several Italian institutions that are part of the Italian Group of Disdrometry (in Italian it reads: Gruppo Italiano Disdrometria, GID, </span><span><a href="https://www.gid-net.it/"><span lang="EN-US">https://www.gid-net.it/</span></a></span><span lang="EN-US">). </span></p> <p><span lang="EN-US">In particular, the GID database contains the 1-minute Drop Size Distributions (DSD) obtained from the data of the laser disdrometers of the GID network during rainy minutes. For each disdrometer, data are available up to 31 December 2024. The database is structured in 23 sub-folders (one for each disdrometer), the name of these subfolders is the disdrometer ID in five digits (for more information see </span><span><a href="https://www.gid-net.it/network/"><span lang="EN-US">https://www.gid-net.it/network/</span></a></span><span lang="EN-US">). In each of these folders, there is one .xlsx file for each year of measurement. The latter file reports the time and the DSDs collected by the selected disdrometer during a given year. Following there are the mean value of class diameter (in mm) and the diameter class width (in mm) used to compute the DSD for the two types of laser disdrometers available in the GID network: </span></p> <p><span lang="EN-US"><span>-<span> </span></span></span><span lang="EN-US">Thies Clima Laser Precipitation Monitor disdrometer (TC):</span></p> <p><span lang="EN-US"><span>·<span> </span></span></span><span lang="EN-US">Mid-value of class diameter (mm): [0.1875, 0.3125, 0.4375, 0.625, 0.875, 1.125, 1.375, 1.625, 1.875, 2.25, 2.75, 3.25, 3.75, <span> </span>4.25, 4.75, 5.25, 5.75, 6.25, 6.75, 7.25, 7.75, 9];</span></p> <p><span lang="EN-US"><span>·<span> </span></span></span><span lang="EN-US">Diameter class width (mm): [0.125, 0.125, 0.125, 0.250, 0.250, 0.250, 0.250, 0.250, 0.250, 0.500, 0.500, 0.500, 0.500, 0.500, <span> </span>0.500, 0.500, 0.500, 0.500, 0.500, 0.500, 0.500, 1.25];</span></p> <p><span lang="EN-US"><span>-<span> </span></span></span><span lang="EN-US">OTT Parsivel 2 disdrometer (P2)</span></p> <p><span lang="EN-US"><span>·<span> </span></span></span><span lang="EN-US">Mid-value of class diameter (mm): [0.062, 0.187, 0.312, 0.437, 0.562, 0.687, 0.812,0.937, 1.062, 1.187, 1.375, 1.625, 1.875, 2.125,2.375, 2.750, 3.250, 3.750, 4.250, 4.750, 5.500,6.500, 7.500, 8.500, 9.500, 11.00, 13.00, 15.00,17.00, 19.00, 21.50, 24.50];</span></p> <p><span lang="EN-US"><span>·<span> </span></span></span><span lang="EN-US">Diameter class width (mm): [0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125,0.125, 0.125, 0.125, 0.250, 0.250, 0.250, 0.250,0.250, 0.500, 0.500, 0.500, 0.500, 0.500, 1.000,1.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000,2.000, 2.000, 3.000, 3.000];</span></p> <p><span lang="EN-US">To obtain uniform and high-quality DSD, the same processing has been adopted to all the disdrometer raw data (i.e. the V02 of the GID processing). The description of V02 GID processing, along with more information on the GID database structure are available in the data paper "Database of the Italian disdrometer network" published on June 2023 in the Journal "Earth System Science Data" (DOI: </span><span><a href="https://doi.org/10.5194/essd-15-2417-2023"><span lang="EN-US">https://doi.org/10.5194/essd-15-2417-2023</span></a></span><span lang="EN-US">). For further information contact the GID team at <strong>gid.info@gid-net.it</strong>.</span></p>
Evaluation of GPM DPR rain parameters with north Taiwan disdrometers
<p>The attached files contain the rainfall and raindrop size distribution parameters from the North Taiwan disdrometers and GPM DPR.</p>
The Observed Disdrometer Heavy Rainfall Period Datasets in East China during the Meiyu Season
<p>The attached file includes all the 1757 Parsivel disdrometer heavy rainfall periods (HRPs) in East China during the 2019-2022 Meiyu season. The files are the XXX.mat format and can be directly opened with MATLAB.</p>
Data for "Disdrometer measurements under Sense-City rainfall simulator"
<p>The data set corresponds the data presented in the data paper : “Disdrometer measurements under Sense-City rainfall simulator“ which is published in Earth System Science Data” (https://www.earth-system-science-data.net/).</p> <p>More details can be found in the Read_me.txt file and in the paper.</p>
EPFL EPFL_2009 32 disdrometer station data
Disdrometer measurements of the EPFL EPFL_2009 32 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/EPFL_2009/metadata/32.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL EPFL_2009 42 disdrometer station data
Disdrometer measurements of the EPFL EPFL_2009 42 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/EPFL_2009/metadata/42.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL EPFL_ROOF_2008 03 disdrometer station data
Disdrometer measurements of the EPFL EPFL_ROOF_2008 03 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/EPFL_ROOF_2008/metadata/03.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL EPFL_2009 21 disdrometer station data
Disdrometer measurements of the EPFL EPFL_2009 21 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/EPFL_2009/metadata/21.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL EPFL_2009 22 disdrometer station data
Disdrometer measurements of the EPFL EPFL_2009 22 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/EPFL_2009/metadata/22.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL DAVOS_2009_2011 60 disdrometer station data
Disdrometer measurements of the EPFL DAVOS_2009_2011 60 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/DAVOS_2009_2011/metadata/60.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL EPFL_2009 41 disdrometer station data
Disdrometer measurements of the EPFL EPFL_2009 41 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/EPFL_2009/metadata/41.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
EPFL EPFL_ROOF_2008 40 disdrometer station data
Disdrometer measurements of the EPFL EPFL_ROOF_2008 40 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/EPFL_ROOF_2008/metadata/40.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
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