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12 results for “Hydrometeors”
Radar and Lidar scattering lookup tables for atmospheric hydrometeors using a T-Matrix method and a Mie theory
<h2>Overview</h2> <p>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).</p> <h2>Data structure</h2> <p>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.<br>For lidar scattering, the following directories are included:<br>ceilo: Ceilometer lidar backscatter properties at a wavelength of 905 nm<br>mpl: Micropulse lidar (MPL) backscatter properties at wavelengths of 353 and 532 nm</p> <p>For radar scattering, the following hydrometer types are included:<br>cloud: Radar scattering for liquid cloud droplets (spherical shape)<br>raina: Radar scattering for raindrops with the aspect ratio model proposed by Andsager et al. (1999)<br>rainb: Radar scattering for raindrops with the aspect ratio model proposed by Brandes et al (2002)<br>ice_ar0.90: Radar scattering for cloud ice with an aspect ratio of 0.9<br>ice_ar0.20: Radar scattering for cloud ice with an aspect ratio of 0.2<br>smallice: Radar scattering for spherical cloud ice particles<br>snow_ar0.60: Radar scattering for snowflakes with an aspect ratio of 0.6<br>graupel_ar0.60: Radar scattering for graupel particles with an aspect ratio of 0.6<br>graupel_ar0.80: Radar scattering for graupel particles with an aspect ratio of 0.8<br>graupel: Radar scattering for spherical graupel particles<br>gh_ryzh: Radar scattering for graupel particles with the graupel aspect ratio model proposed by Ryzhkov et al (2011)<br>unrimedice_ar0.40: Radar scattering for unrimed ice particles with an aspect ratio of 0.4<br>unrimedice_ar0.60: Radar scattering for unrimed ice particles with an aspect ratio of 0.6<br>unrimedice_ar0.80: Radar scattering for unrimed ice particles with an aspect ratio of 0.8<br>unrimedice: Radar scattering for spherical unrimed ice particles<br>partrimedice_ar0.40: Radar scattering for partially rimed ice particles with an aspect ratio of 0.4<br>partrimedice_ar0.60: Radar scattering for partially rimed ice particles with an aspect ratio of 0.6<br>partrimedice_ar0.80: Radar scattering for partially rimed ice particles with an aspect ratio of 0.8<br>partrimedice: Radar scattering for partially rimed spherical ice particles </p> <h2>The file name convention </h2> <p>For lidar scattering data, each file name has the following format:<br>[hydrometeor type]_[instrument name]_ [wavelength in nm]_[phase ID]_d[bulk density in kg m-3].dat<br>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. </p> <p>For radar scattering data, each file name has the following format.<br>[hydrometeor type]_fr[frequency in GHz]GHz_t[temperature in K]_rho[bulk density in kg m-3]_el[elevation angle in degree].dat<br>The hydrometeor type follows the directory name presented above.</p> <h2>Format of the data files</h2> <p>Line 1: Wavelength in mm<br>Line 2: Temperature in K<br>Line 3: Refractive index (real and imaginary)<br>Line 4: Number of radii calculated and number of elevation angles<br>Line 6: Incident angle and scattered angle in degrees<br>Line 7: Radius in mm and aspect ratio<br>Line 8: Forward scattering amplitude for co-polarization VV and HH (complex number)<br>Line 9: Backward scattering amplitude for co- and cross polarizations VV, VH, HV, HH (complex number) <br>Line 10 to the end of file: Repeat Line 7 to Line 9 with different radii until the maximum radius.</p>
Data for figures in Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_
<p>Tar files containing gridded metrics, domain-wide metric means and confidence intervals, and rain-gauge reports used to generate figures in Kemp et al (2021).<br> <br> Citation:<br> </p> <p>Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_.</p>
Vertical profiles of Doppler spectra of hydrometeors from a Micro Rain Radar recorded during the austral summer of 2016/2017 in the Southern Ocean on the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>This dataset includes vertical profiles of the Doppler spectra and derived quantities of hydrometeors using a Micro Rain Radar (MRR-2) during the Antarctic Circumnavigation Expedition in 2016/2017. The MRR is a frequency-modulated-continuous-wave (FMCW) Doppler radar working at 24 GHz (K band). It measures the Doppler spectrum at vertical incidence with 31 range gates of 100 m. The data are processed to calculate rainfall rates and standard radar moments. For rain events, the drop size distribution and the rainfall rate are derived using the method by Peters et al. (2005). For snow events, the moments are computed from the Doppler spectrum as described in Maahn & Kollias (2012).</p> <p><strong>Dataset contents</strong></p> <ul> <li>RawSpectra/${year}${month}/MRR_ACE_${year}${month}${day}_RAW.nc: raw data of Doppler spectra for rainfall events. Null values are denoted as -99900.</li> <li>ProcessedData/${year}${month}/MRR_ACE_${year}${month}${day}_PRO.nc: 10-second averages (highest resolution) for specific rain events, using the standard products from Metek (MRR physical basics, 2009).</li> <li>AveData/${year}${month}/MRR_ACE_${year}${month}${day}_AVE.nc: one-minute averages, using the standard products from Metek (MRR physical basics, 2009).</li> <li>Processed_IMProToo/${year}${month}/mrr_improtoo_0.101_ACE_${year}${month}${day}.nc: processed data for snow events using IMProToo (Maahn & Kollias 2012) in one-minute averages.</li> <li>RR_timeline/${year}${month}/RR_MRR_ACE_${year}${month}${day}.csv: 10-minute running mean with one-minute resolution of rain rate for [100-200]m and [200-300]m range gate. Null values are denoted as ‘nan’.</li> <li>RR_timeline/${year}${month}/RR_MRR_ACE_${year}${month}${day}.png: daily plots of rain rates.</li> <li>PrecipitationEvents.txt: text file classifying the precipitation events according to hydrometeors (rain, snow or hail).</li> <li>Graphs_Metek/${year}${month}/moments_MRR_ACE_${year}${month}${day}_AVEnc.png: daily plots of Radar reflectivity and Doppler velocity for rain events.</li> <li>Graphs_IMProToo/${year}${month}/moments_mrr_improtoo_0101_ACE_${year}${month}${day}.png: daily plots of Radar reflectivity and Doppler velocity for snow events.</li> <li>RawSpectra_data_file_header.txt, metadata, text format</li> <li>Metek_ProcessedData_data_file_header.txt, metadata, text format</li> <li>IMProToo_ProcessedData_data_file_header.txt, metadata, text format</li> <li>AveData_data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>change_log.txt, metadata, text format</li> </ul> <p><strong>Change log</strong></p> <ul> <li>v1.2 - Amended abstract text. Changed descriptions of files in dataset contents. Added dataset license to README. Updated README and change_log files.</li> <li>v1.1 - Added missing PrecipitationEvents.txt file. Added change_log.txt file.</li> <li>v1.0 - Initial version of dataset.</li> </ul> <p><strong>Dataset license</strong></p> <p>This vertical Doppler spectra profile dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Single scattering properties of ice hydrometeors between 0.2 and 99 um (Bi and Yang, 2017)
<p>This dataset contains the single-scattering properties of ice particles from Yang et al. (2013), including the refinements by Bi and Yang (2017), converted to the XML format of the Atmospheric Radiative Transfer Simulator (ARTS). The data correspond to various shapes and particle surface roughness, covering wavelengths between 0.2 and 99 um. The accompanying README gives a bit more of information. </p> <p>For questions on this version of the data, please contact patrick.eriksson@chalmers.se</p> <p>References:</p> <p>Bi, L., and P. Yang, 2017: Improved ice particle optical property simulations in the ultraviolet to far-infrared regime, J. Quant. Spectrosc. Radiat. Transfer, 189, 228-237.</p> <p>Yang, P., L. Bi, B.A. Baum, K.-N. Liou, G.W. Kattawar, M.I. Mishchenko, and B. Cole, 2013: Spectrally consistent scattering, absorption, and polarization properties of atmospheric ice crystals at wavelengths from 0.2 µm to 100µm, J. Atmos. Sci., 70, 330-347.<br><br></p>
META-DATA for IEA Wind Task 46 report: Atmospheric drivers of wind turbine blade leading edge erosion: Hydrometeors
<p>The objectives of the work summarized in the report that accompanies this dataset are to:</p> <ul> <li>Describe crucial meteorological parameters for wind turbine blade leading edge erosion</li> <li>Describe technologies appropriate to measurement of hydroclimates and specifically hydrometeor size distributions and phase</li> <li>Identify available data sets that are available to describe hydrometeor size distributions and phase and generate meta-data for data sets available for use in mapping wind turbine blade leading edge erosion potential. This dataset summarizes those meta-data. </li> <li>Identify priority geographic areas for geospatial mapping of wind turbine blade leading edge erosion potential <p> </p> </li> </ul>
Dataset for Supersaturation in the Wake of a Precipitating Hydrometeor and its Impact on Aerosol Activation
<p>******************Dataset Details******************</p> <p>1. Article Title: Supersaturation in the Wake of a Precipitating Hydrometeor and its Impact on Aerosol Activation<br> 2. Journal: Geophysical Research Letters, 47(22), e2020GL091179, <a href="https://doi.org/10.1029/2020GL091179">https://doi.org/10.1029/2020GL091179</a>, 2020<br> 3. Name and contact information:<br> Eberhard Bodenschatz,<br> Laboratory for Fluid Physics, Pattern Formation and Biocomplexity,<br> Max Planck Institute for Dynamics and Self-Organization,<br> Am Faßberg 17,<br> 37077 Göttingen, Germany<br> 4. Email: eberhard.bodenschatz@ds.mpg.de</p> <p>5. Funding: This research was funded by the Marie - Sk lodowska Curie Actions (MSCA) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 675675), and an extension to programme COMPLETE by Department of Applied Science and Technology, Politecnico di Torino.</p> <p>6. Abstract: The activation of aerosols impacts the life cycle of a cloud. A detailed understanding is necessary for reliable climate prediction. Recent laboratory experiments demonstrate that aerosols can be activated in the wake of precipitating hydrometeors. However, many quantitative aspects of this wake‐induced activation of aerosols remain unclear. Here, we report a detailed numerical investigation of the activation potential of wake‐induced supersaturation. By Lagrangian tracking of aerosols, we show that a significant fraction of aerosols are activated in the supersaturated wake. These “lucky aerosols” are entrained in the wake's vortices and reside in the supersaturated environment sufficiently long to be activated. Our analyses show that this wake‐induced activation of aerosols can contribute to the life cycle of the clouds.</p> <p>******************Dataset Organization******************</p> <p>1. Datasets are organized in folders which correspond to Figures in the Letter or in the Supporting Information (e.g., Figure 1 or Figure S2).<br> 2. Details of the entries can be found in the header of each dataset.<br> 3. Details of the simulation setup can be found in the text of each Figure.</p> <p>******************Software Details******************</p> <p>1. Open-source LBM library Palabos (Latt et al., 2020). Link: https://palabos.unige.ch/<br> 2. Data visualization is achieved using open-source library ParaView version 5.7 and Gnuplot Version 5.2.</p>
Dataset for the paper "Ensemble optimization retrieval algorithm of hydrometeor profiles for the Ice Cloud Imager submillimeter-wave radiometer'
<p>1. "Retrieval_Database" file contains the pre-calculated retrieval database.</p> <p>2. "Algorithm_Input" file contains the input of the ensemble optimization retrieval algorithm.</p> <p>3. "TrueProfiles" file contains the true profiles corresponding to the input brightness temperatures. </p> <p>4. "Algorithm_Output" file contains the output of the ensemble optimization retrieval algorithm.</p>
Data on streamer initiation from charged hydrometeors
<p>This dataset contains data from a series of simulations of streamers Initiation from spherical and column hydrometeors in thundercloud fields. The corresponding simulation experiments are described in paper "Jinjin Hu, Yikai Ma, Xin Gao, and Ningyu Liu(2021) Study on Dominant Parameters Determining Streamer Initiation from Hydrometeors in Thundercloud Fields. Journal of Geophysical Research: Atmospheres, doi: 10.1029/2021JD034936". The simulation mainly investigated the three factors affecting streamers Initiation: the size of hydrometeors, the shape of hydrometeors (spherical and column), and the quantity of electric charge carried on the surface of hydrometeors (0pC, 180pC, 200pC, 300pC, 400pC). In addition, the simulation of streamers initiation at different initial electron densities is also studied. That is, this dataset includes two parts of simulated experimental data: one is the simulated data of spherical hydrometeors with different sizes and electric charges; the other is the simulated data of column hydrometeors with different sizes and electric charges. Based on the simulation results, the Meek criterion was used to analyze the lowest background electric field causing the streamers from spherical and column hydrometeors, and the condition of streamer initiation from spherical and column hydrometeors was compared. The main results of the simulation experiment are as follows:(1)A larger hydrometeor is more likely to initiate a streamer discharge at a lower ambient field for noncharged hydrometeors.(2)The charge on hydrometeor plays a prominent role when a charged hydrometeor has a smaller size, and it is easier to initiate a streamer in this case. (3) The charge on hydrometeor plays a dominant role in streamer initiation when hydrometeor is smaller, but hydrometeor shape becomes more important for larger hydrometeor.(4) Streamer discharge can be predicted by Meek number, but stable streamer can not be ensured.(5) Initial electron and ion pair density has little effects on the streamer initiation. Only the moments starting to form streamer discharge depend on the initial density.</p> <p><a name="_Hlk78241047"> </a></p>
Data on streamer initiation from charged hydrometeors
Open the record for dataset details and reuse information.
Xie_et_al raw data for ' Vertcal Inhomogeneity Effect of Frozen Hydrometeor Habits in All-sky Passive Microwave Simulations'
<p>Raw Data</p> <p>'Inhomogeneity Effect of Frozen Hydrometeor Habits in All-sky Passive Microwave Simulations'</p>
TRMM Microwave Imager Hydrometeor Profile L2 1.5 hours V7 (TRMM_2A12) at GES DISC
The new version of these data is in GPM-like format and can be found under the name GPM_2AGPROFTRMMTMI_CLIM. This dataset, 2A12, ”TMI Profiling”, generates surface rainfall and vertical hydrometeor profiles on a pixel by pixel basis from the TRMM Microwave Imager (TMI) brightness temperature data using the Goddard Profiling algorithm GPROF2010. Because the vertical information comes from a radiometer, it is not written out in independent vertical layers like the TRMM Precipitation Radar. Instead, the output is referenced to one of 100 typical structures for each hydrometeor or heating profile. These vertical structures are referenced as clusters in the output structure. Vertical hydrometeor profiles can be reconstructed to 28 layers by knowing the cluster number (i.e. shape) of the profile and a scale factor that is written for each pixel. This product contains hydrometeor profiles of cloud liquid water, precipitation water, cloud ice water, precipitation ice, rainfall type, and latent heating in 28 atmospheric layers. Changes in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001: Pre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 760 km; Horizontal Resolution: 4.4 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 878 km; Horizontal Resolution: 5.1 km
On the cluster scales of hydrometeors in mixed-phase stratiform clouds [dataset]
<p>Dataset used in "On the cluster scales of hydrometeors in mixed-phase stratiform clouds".</p>
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