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.
23
datasets available to search
ShareScore release 0.9.0
Dataset results
23 results for “Radar reflectivity”
Radar measurements for the article "Dynamic differential reflectivity calibration using vertical profiles in rain and snow"
<p><strong>Dataset documentation</strong></p> <p>The archives in hdf5 format provided at this link contain the datasets used in the manuscript <em>Dynamic differential reflectivity calibration using vertical profiles in rain and snow</em>, submitted to <em>Remote Sensing</em> (MDPI) by Alfonso Ferrone and Alexis Berne in 2020.</p> <p> </p> <p><strong>File content</strong></p> <p>Each file is structured as a table, with each column referring to a specific variable and each row containing a different realization (in space or time). The set of available variables is campaign dependent, and the possibilities are:</p> <ul> <li> <p><strong>idx</strong>, and integer index that starts at 1 for the first scan of the dataset and increases by 1 for every successive scan;</p> </li> <li> <p><strong>t</strong>, the timestamp of the scan, in seconds since seconds since Jan 01, 1970;</p> </li> <li> <p><strong>r</strong> or <strong>rg</strong> (depending on the file), the distance from the radar in meters;</p> </li> <li> <p><strong>az</strong>, the azimuth angle in degrees;</p> </li> <li> <p><strong>el</strong>, the elevation angle in degrees;</p> </li> <li> <p><strong>zdr</strong>, the uncalibrated differential reflectivity, in dB;</p> </li> <li> <p><strong>zh</strong>, the horizontal reflectivity, in dBZ;</p> </li> <li> <p><strong>rhovh</strong> or <strong>rho</strong>, the co-polar correlation coefficient, unitless;</p> </li> <li> <p><strong>snr_h</strong> or <strong>snr</strong>, the signal to noise ratio for the horizontal channel in dB;</p> </li> <li> <p><strong>snrv</strong>, the signal to noise ratio for the vertical channel, in dB,</p> </li> <li> <p><strong>ngates</strong>, the number of unique range gates.</p> </li> </ul> <p>For the comparison of the data collected by MXPol and DX50 during the PAYERNE campaign, two auxiliary variables were added to the archives:</p> <ul> <li> <p><strong>x</strong> the horizontal distance from the current radar, computed on a line passing through the location of two radars;</p> </li> <li> <p><strong>z</strong> the vertical distance from the current radar.</p> </li> </ul> <p> </p> <p><strong>Usage</strong></p> <p>The dataset are provided in the the Hierarchical Data Format version 5 (HDF5), an open source file format, supported by several programming language.</p> <p>They archives were created using the <em>vaex</em> library for Python 3:</p> <p>https://github.com/vaexio/vaex</p> <p>The function <em>vaex.open</em> from the same library can be used for accessing the archives and converting them to <em>vaex.DataFrame</em>.</p> <p> </p> <p><strong>Campaign-specific information</strong></p> <p>Some of the parameters associated to the variables included in the archives may change depending on the campaign. The following subsection provide a summary of these information.</p> <p> </p> <p><strong>dataframe_HYMEX_2013_from_20130907-040344_to_20131105-175944.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the HYMEX campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 44.61° N</p> </li> <li> <p>Longitude: 4.55° E</p> </li> <li> <p>Altitude: 604 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 204.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: Z-PHI method</p> </li> <li> <p>Note on reflectivity calibration: The original manufacturer calibration constant was 7.56 dBZ. The value used here derives from comparison with disdrometers during the HYMEX campaign.</p> </li> </ul> <p> </p> <p><strong>dataframe_PAYERNE_2014_from_20140321-160016_to_20140519-085808.hdf5</strong></p> <p>Contains PPI scans at 90° elevation performed by MXPol during the PAYERNE campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.81° N</p> </li> <li> <p>Longitude: 6.94° E</p> </li> <li> <p>Altitude: 496 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 204.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_DAVOS_2014_from_20140704-090224_to_20141231-105720.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the DAVOS campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.82° N</p> </li> <li> <p>Longitude: 9.82° E</p> </li> <li> <p>Altitude: 2220 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 204.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_APRES3_from_20151207-123944_to_20160129-125856.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the APRES3 campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 66.66 S</p> </li> <li> <p>Longitude: 140.00 E</p> </li> <li> <p>Altitude: 40 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 354.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_VALAIS_2016_from_20161104-154312_to_20170306-195912.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the VALAIS campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.12 N</p> </li> <li> <p>Longitude: 7.10 E</p> </li> <li> <p>Altitude: 460 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.27°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 30 m</p> </li> <li> <p>Range to the first gate: 226.95 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_DX50_from_20140501-020000_to_20140524-015752.hdf5</strong></p> <p>Contains PPI scans at 90° elevation performed by DX50 during the PAYERNE campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.84° N</p> </li> <li> <p>Longitude: 6.92° E</p> </li> <li> <p>Altitude: 450 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.459 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.273°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 0.0 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.0</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_DX50_RHI_201405.hdf5</strong></p> <p>Contains RHI scans from DX50 the PAYERNE campaign.</p> <p>The remaining information equal to the ones listed for <em>dataframe_DX50_from_20140501-020000_to_20140524-015752.hdf5</em>.</p> <p> </p> <p><strong>dataframe_MXPol_RHI_201405.hdf5</strong></p> <p>Contains RHI scans from MXPol the PAYERNE campaign.</p> <p>The remaining information is equal to the ones listed for <em>dataframe_PAYERNE_2014_from_20140321-160016_to_20140519-085808.hdf5</em>.</p>
Radar Reflectivity at Whillans Ice Plain
<p>This is an extracted data product for radar bed reflectivity from Whillans Ice Plain, West Antarctica. The original data are hosted by the Center for Remote Sensing of Ice Sheets (CReSIS; see associated citation below). The files here can be recalculate and are meant to be used within a set of computational notebooks here:<br>https://doi.org/10.5281/zenodo.10859135</p> <p>There are two csv files included here, each structured as a Pandas dataframe. You can load them in Python like:<br><code>df = pd.read_csv('./Picked_Bed_Power.csv')</code></p> <p>The first file, 'Picked_Bed_Power.csv' is the raw, uncorrected power from the radar image at the bed pick provided by CReSIS. There are also other useful variables for georeferencing, flight attributes, etc.</p> <p>The second file, 'Processed_Reflectivity.csv' is processed from the first file. Processing includes: 1) a spreading correction; 2) an attenuation correction; and, 3) a power adjustment flight days based on compared power at crossover points. This file also has identifiers for regions including "grounded ice", "ungrounded ice", and "subglacial lakes".</p>
Supplementary data: What millimeter-wavelength radar reflectivity reveals about snowfall: An information-centric analysis
<p>This dataset includes supplementary data used in the analyses described in Wood, N. B., and T. S. L'Ecuyer, 2020: What millimeter-wavelength radar reflectivity reveals about snowfall: An information-centric analysis. Atmospheric Measurement Techniques, doi:10.5194/amt-2020-216.</p>
Potential impact of assimilating visible and infrared satellite observations compared to radar reflectivity for convective‐scale NWP
<p>Contains</p> <ul> <li>raw_data <ul> <li>nature run initial conditions for the cases "random" and "warm-bubble"</li> <li>ensemble initial condition sounding profiles</li> </ul> </li> <li>evaluation_metrics <ul> <li>csv files of FSS, RMSE, MAE, ensemble spread, mean difference<br> to reproduce figures of FSS, RMSE and spread in the paper and more.<br> See the README.txt</li> </ul> </li> <li>figures <ul> <li>Map_VIS06 ... Simulated satellite images of visible reflectance for ensemble members and truth</li> <li>Map_VIS06_probability ... Map of ensemble probability for visible reflectance > 0.6 and the nature in red contours</li> </ul> </li> </ul> <p> </p>
Data for "Multifractal comparison of reflectivity and polarimetric rainfall data from C- and X-band radars and respective hydrological responses of a complex catchment model"
<p>The data files arranged here correspond to the data used in the paper: “Multifractal comparison of reflectivity and polarimetric rainfall data from C- and X-band radars and respective hydrological responses of a complex catchment model”, submitted to <em>Water</em>.</p> <p>The data organized as follows:</p> <ul> <li>Data_type_20150912_time_steps.mat: the rainfall data for 3 different products of the X-band radar (FIR filter, a=200, b=1.6; FIR filter, a=150, b=1.3; simple filter, a=150, b=1.3) for the event of 12-13 September 2015, over an area of 64 km x 64 km.</li> <li>Data_type_Event_time_steps.mat: X-band radar data (FIR filter, a=150, b=1.3) for the events of 16 September 2015 and 5-6 October 2015, over an area of 64 km x 64 km.</li> <li>Sub-catchment_name_Data_type_Event.txt: the rainfall series for each of 26 sub-catchments of the model, for 3 different types of rainfall data (C-band, X-band and rain gauges) for the events of 12-13 September 2015, 16 September 2015 and 5-6 October 2015.</li> <li>X-band_Pixels_Event.txt: the rainfall series for all 6 X-band radar pixels corresponding to the 6 rain gauges for the 3 studied events (12-13 September 2015, 16 September 2015, and 5-6 October 2015).</li> <li>X-Band_Optim 20150916_Measurement_point_name.txt: flow simulated at each of the 4 measurement points with X-band data for the 16 September 2015 event, with the implementation of the tool mimicking the regulation optimization.</li> <li>Data_type_Event_Measurement_point_name.txt: flow simulated at each of the 4 measurement points with 3 different types of rainfall data (C-band, X-band and rain gauges) for the 3 studied events (12-13 September 2015, 16 September 2015, and 5-6 October 2015), without the implementation of the tool mimicking the regulation optimization.</li> </ul> <p>The original C-band radar data remains property of Météo-France and was provided to the authors for this research study, without any possibility of data disclosure.</p> <p>The details on how the rainfall series were generated over each sub-catchment could be found in the paper.</p> <p>The authors greatly acknowledge partial financial supports of the Chair “Hydrology for resilient cities” endowed by Veolia, and of the Department of Science and Technology of the Brazilian Army. The authors are thankful to M Bernard Urban (Météo-France) for providing access to the C-band radar data and documentation in the framework of the INTERREG NWE RainGain project.</p>
Animation of 24h-metereologic radar data (maximum reflectivity and CFAD) for the Liebenbergvlei, Freestate, South Africa on December 30 2001
<p>The visualisation of metereologic radar data for the Liebenbergvlei, RSA, covers the time between December 30 2001 00:00:00 hours and 23:59:00 hours in five minute steps. The data war recorded by the X-band metereological radar station operated by the South African Weather Service in the Liebenbergvlei in the Freestate, South Africa. For each time step, the maximum reflectitivity is depicted on the left, while the contoured frequency by altitude (CFAD) diagram is shown on the right (derived from plan position indicator (PPI) volume data from 1 to 18km height above ground). The processing of the data based on MDV-files was done in GRASS GIS v5.0.</p>
Centimetre radar reflectance signal for material classification
Open the record for dataset details and reuse information.
Calibration Bias Evaluation and Correction of S-Band Ground-Based Radar Reflectivity Using Ku-Band Space-Borne Radar Observations Along the East Coast of India
Open the record for dataset details and reuse information.
Animation of Hohenpeissenberg radar reflectivity cross sections (C-band)
<p>This animation displays radar reflectivity cross sections (dBZ) obtained from range height indicator scans plotted over latitude and for altitudes between 0 km and 10 km in a two-minute interval during a one-hour flight above Southern Germany in the scope of the CIRRUS-HL campaign. Data were provided by the MOHp from the <em>Deutscher Wetterdienst</em> (DWD). The orange vertical line marks the LMU radar station that was overpassed during this flight as well. The gray line marks the HALO-aircraft flight path and the black line HALO’s position one min before the cross section is depicted.</p>
SWAN 3D Radar Reflectivity
<p><strong>The SWAN 3D mosaicked radar reflectivity:</strong></p> <p>Wavelength: S-band</p> <p>Time period: 2018/06/29 - 2018/06/30</p> <p>Coverage: 105 - 117 deg E, 27 - 35 deg N</p> <p>Horizontal information: 1200 x 800 grids with 1 km horizontal resolution </p> <p>Vertical levels: 21 levels with 500 m resolution from 0.5 to 8 km AGL and 1km resolution above 8 km AGL</p> <p>Format: netCDF4</p> <p> </p>
AIRS-AMSU variables-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRSM_CPR_MAT) at GES DISC
This is AIRS-CloudSat collocated subset, in NetCDF 4 format. These data contain collocated: AIRS/AMSU retrievals at AMSU footprints, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project.The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each "scene" (instrument look) using the cloud information,and develop a merged, multi-sensor climatology of atmospheric water vapor as afunction of altitude, stratified by the cloud classes. This is a large scienceanalysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, findspace/time "matchups" between pairs of instruments, and process years ofsatellite data to produce the climate data records.The short name for this collection is AIRSM_CPR_MATParameters contained in the data files include the following:Variable Name|Description|Units CH4_total_column|Retrieved total column CH4| (molecules/cm2) CloudFraction|CloudSat/CALIPSO Cloud Fraction| (None) CloudLayers| Number of hydrometeor layers| (count) clrolr|Clear-sky Outgoing Longwave Radiation|(Watts/m**2) CO_total_column|Retrieved total column CO| (molecules/cm2) CPR_Cloud_mask| CPR Cloud Mask |(None) Data_quality| Data Quality |(None) H2OMMRSat|Water vapor saturation mass mixing ratio|(gm/kg) H2OMMRStd|Water Vapor Mass Mixing Ratio |(gm/kg dry air) MODIS_Cloud_Fraction| MODIS 250m Cloud Fraction| (None) MODIS_scene_var |MODIS scene variability| (None) nSurfStd|1-based index of the first valid level|(None) O3VMRStd|Ozone Volume Mixing Ratio|(vmr) olr|All-sky Outgoing Longwave Radiation|(Watts/m**2) Radar_Reflectivity| Radar Reflectivity Factor| (dBZe) Sigma-Zero| Sigma-Zero| (dB*100) TAirMWOnlyStd|Atmospheric Temperature retrieved using only MW|(K) TCldTopStd|Cloud top temperature|(K) totH2OStd|Total precipitable water vapor| (kg/m**2) totO3Std|Total ozone burden| (Dobson) TSurfAir|Atmospheric Temperature at Surface|(K) TSurfStd|Surface skin temperature|(K)End of parameter information
Airborne Cloud Radar (ACR) Reflectivity, Wakasa Bay, Japan, Version 1
This data set includes 94 GHz co- and cross-polarized radar reflectivity. The Airborne Cloud Radar (ACR) sensor was mounted to a NASA P-3 aircraft flown over the Sea of Japan, the Western Pacific Ocean, and the Japanese Islands.
AIRS-CloudSat cloud mask and radar reflectivities collocation indexes V4.0 (AIRS_CPR_IND) at GES_DISC
Version 4.1 is the current version of the data set. Previous versions are no longer available and have been superseded by Version 4.1.This is AIRS-AMSU-CloudSat collocation indexes, in netCDF-4 format. These data map CloudSat profile indexes to the collocated AMSU field of views, and AIRS IR footprints, per AIRS 6-min granule time. Hence it can be considered as Level 1. These data are created within the frames of the MEaSUREs project.The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, & CloudSat), classify each "scene" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time "matchups" between pairs of instruments, and process years of satellite data to produce the climate data records.The short name for this collection is AIRS_CPR_IND
AIRS-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRS_CPR_MAT) at GES DISC
This is AIRS-CloudSat collocated subset, in NetCDF-4 format. These data contain collocated: AIRS Level 1b radiances spectra, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project. The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each "scene" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time "matchups" between pairs of instruments, and process years of satellite data to produce the climate data records.The short name for this collection is AIRS_CPR_MATParameters contained in the data files include the following:Variable Name|Description|Units CldFrcStdErr|Cloud Fraction|(None) CloudLayers| Number of hydrometeor layers| (count) CPR_Cloud_mask| CPR Cloud Mask| (None) DEM_elevation| Digital Elevation Map| (m) dust_flag|Dust Flag|(None) latAIRS|AIRS IR latitude|(deg) Latitude|CloudSat Latitude |(degrees) LayerBase| Height of Layer Base| (m) LayerTop| Height of layer top| (m) lonAIRS|AIRS IR longitude|(deg) Longitude|CloudSat Longitude| (degrees) MODIS_cloud_flag| MOD35_bit_2and3_cloud_flag| (None) Radar_Reflectivity| Radar Reflectivity Factor| (dBZe) radiances|Radiances|(milliWatts/m**2/cm**-1/steradian) Sigma-Zero| Sigma-Zero| (dB*100) spectral_clear_indicator|Spectral Clear Indicator|(None) Vertical_binsize|CloudSat vertical binsize| (m)End of parameter information
Data used in the publication: Study on The Error Structure of Radar Reflectivity Using The Symmetric Rainrate Predictor
<p>These data support the results presented in the manuscript titled "Study on The Error Structure of Radar Reflectivity Using The Symmetric Rainrate Predictor". They consist of Observation minus Background data and rainrate data that are used to build the symmetric error model of reflectivity. We uploaded two data sets that support both original and revised manuscripts.</p>
TRMM Ground Validation Calibrated QC Radar Reflectivity Full Volume Scan L1C 1 hour V7 (TRMM_1C51) at GES DISC
The purpose of the 1C51 algorithm is to remove non-meteorological radar echoes that adversely affect the quality of higher level products, such as clutter associated with insects, birds, chaff, wildfires, antenna towers, and anomalous propagation (AP). Eight adjustable parameters, three echo height thresholds and five radar reflectivity thresholds, are used to optimize the performance of the algorithm. Optimum performance is time consuming and requires an analyst to select different sets of parameters on a per volume scan basis, and on occasions performing several iterations while adjusting one or more of the parameters. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.
TRMM Precipitation Radar Power and Reflectivity L1C 1.5 hours V7 (TRMM_1C21) at GES DISC
The TRMM Precipitation Radar (PR), the first of its kind in space, is an electronically scanning radar, operating at 13.8 GHz that measures the 3-D rainfall distribution over both land and ocean, and defines the layer depth of the precipitation. The 1C21 calculates the effective radar reflectivity factor at 13.8 GHz without any propagation loss (due to rain or any other atmospheric gas) correction (Zm). Therefore, the Zm value can be calculated just by applying a radar equation for volume scatter with PR system parameters. The noise-equivalent Zm is about 21 dBZ. Through the subtraction of the system noise, the Zm value as small as 16 or 18 dBZ are still usable although the data quality is marginal. In 1C21, all echoes stored in 1B21 are converted to "dBZ" unit. This is not relevant for "non-rain" echo; however, this policy is adopted so that the 1B21 and 1C21 product format should be as close as possible except for the following points: - Radar quantity is Zm in dBZ unit instead of received power (dBm). - Data at echo-free range bins judged in 1B21 are replaced with a dummy value. 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: 215 km; Horizontal Resolution: 4.3 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km
TRMM Precipitation Radar Power and Reflectivity L1B 1.5 hours V7 (TRMM_1B21) at GES DISC
The TRMM Precipitation Radar (PR), the first of its kind in space, is an electronically scanning radar, operating at 13.8 GHz that measures the 3-D rainfall distribution over both land and ocean, and defines the layer depth of the precipitation. The 1B21 calculates the received power at the PR receiver input point from the Level-0 count value which is linearly proportional to the logarithm of the PR receiver output power. To convert the count value to the input power, extensive internal calibrations are applied, which are mainly based upon the system model, temperature dependence of model parameters and many temperature sensors attached at various locations of the PR. Periodically the input-output characteristics are measured using an internal calibration loop for the IF unit and later receiver stages. To make an absolute calibration, an Active Radar Calibrator (ARC) is placed at Kansai Branch of CRL and overall system gain of the PR is being measured every 2 months. Using the transfer function based on the above internal and external calibrations, the PR received power is obtained. Note that the value assumes that the signal follows the Rayleigh fading, so if the fading characteristics of a scatter is different, a small bias error may occur (within 1 or 2 dB). 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: 215 km; Horizontal Resolution: 4.3 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 247 km; Horizontal Resolution: 5.0 km
TRMM Ground Validation Radar Site 3D Reflectivity L2 1 hour V7 (TRMM_2A55) at GES DISC
'Radar Site 3-D Reflectivities', is composed of 3 different fields. The first field has an array of 3-D reflectivities in Cartesian coordinates with a 2 km horizontal resolution over an area of 300 km x 300 km for single radar sites, and 724 km x 568 km for Texas multiple radar site, 512 km x 704 km for Florida multiple radar site. It has a vertical resolution of 1.5km and a height range up to 19.5 km. The second field has an array of vertical profiles based on the first field, and the third field has an array of the Contoured Frequency by Altitude Diagram (CFAD) data based on the first and second field. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.
TRMM Ground Validation Radar Reflectivity L1B 1 hour V7 (TRMM_1B51) at GES DISC
The TRMM_1B51 product displays the existence of rain in the radar volume scan. 'Existence' is the fraction of the radar FOV which has measurable precipitation. The GV radar FOV is defined as a base scan (i.e., the lowest level sweep). Each product file has the Existence data of one site (not one radar) for one month. A key component of the TRMM project is the Ground Validation (GV) effort which consists of collecting data from ground-based radar, rain gauges and disdrometers. The data is quality-controlled, and then validation products are produced for comparison with TRMM satellite products. The four primary GV sites are: + Darwin, Australia; + Houston, Texas; + Kwajalein, Republic of the Marshall Islands; + Melbourne, Florida. A significant effort is also being supported at NASA Wallops Flight Facility (WFF) and vicinity to provide high quality, long-term measurements of rain rates (via a network of rain gauges collocated with National Weather Service gauges), as well as drop size distributions (DSD) using a variety of instruments, including impact-type Joss Waldvogel, laser-optical Parsivel, as well as two-dimensional video disdrometers. DSD measurements are also being collected at Melbourne and Kwajalein using Joss-Waldvogel disdrometers.
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.