####################################################################################### Colorado State University C-band Hydrological Instrument for Volumetric Observation (CSU-CHIVO) Data During ESCAPE provided by Stony Brook University Authors (Lead) Mariko Oue Stony Brook University, Stony Brook, New York, USA (mariko.oue@stonybrook.edu) Pavlos Kollias Stony Brook University, Stony Brook, New York, USA Brookhaven National Laboratory, Upton, New York, USA (pavlos.kollias@stonybrook.edu) Bernat Puigdom‚nech Treserras McGill University, Montreal, Quebec, Canada (bernat.puigdomenech-treserras@mcgill.ca) Edward P. Luke Brookhaven National Laboratory, Upton, New York, USA (eluke@bnl.gov) V. Chandrasekar Colorado State University, Fort Collins, CO, USA (chandra@colostate.edu) Corresponding author: Mariko Oue School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY 11794-5000. Phone: 631-632-8691 Email: mariko.oue@stonybrook.edu https://you.stonybrook.edu/marikooue/ Research Assistant Professor, Stony Brook University ####################################################################################### 1. Data Set Description 1.1 Abstract One of the challenges to analyze the convective cell properties is quick evolution of the individual convective cells. While the operational radar data provide great dataset to analyze of evolution of radar observables of convective precipitation clouds statistically, the previous studies also suggested the quick evolution of cell lifecycle that the conventional radar volume scan strategies taking ~5-7 minutes might not capture the detailed evolutions. During the Experiment of Sea Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE) field campaign in Houston (Jensen et al.,2022), Colorado State University C-band Hydrological Instrument for Volumetric Observation (CSU-CHIVO) performed cell tracking scans from La Porte Airport, TX, using frequent updates of range-height indicator (RHI) scans from August 1st to September 25th, 2023. A new cell tracking framework, Multisensor Agile Adaptive Sampling (MAAS, Kollias et al. 2020; Lamer et al. 2023), guided the CHIVO's cell tracking. About every 40 seconds, CHIVO performed 3-4 RHI scans based on different criteria (e.g., maximum reflectivity, cell centroid, maximum gradient of Doppler velocity) targeting single convective cells. The CHIVO's cell tracking was synchronized with the cell tracking by the second-generation C-Band Scanning Atmospheric Radiation Measurement Precipitation Radar (CSAPR2), so that both radars targeted the same convective cells. The data files include processed radar variables including: noise-masked reflectivity and differential reflectivity corrected for rain attenuation and systematic biases, noise-masked dealiased radial velocity, specific differential phase, locations of target cells (latitude, longitude, radar range), and radar-echo classification. 1.2 Data version and data Version 1.0, published on September 12, 2023 1.3 Data Status Preliminary 1.4 Time period covered by the data August 1 to September 25, 2022. 1.5 Physical locations of the measurements CHIVO was deployed at La Porte Airport, TX (29.6701N, -95.0585E) 1.6 Data frequency RHI data were collected every approximately 10 seconds. They were bundled every cycle that consists of 3-4 RHI scans (approximately every 40 seconds). 1.7 Web address references Please see http://radarscience.weebly.com/maas1.html for details about the Multisensor Agile Adaptive Sampling (MAAS) flamework. 1.8 Dataset restrictions The data set should be restricted by password. The data set should be available for the ESCAPE PIs for one year after the ESCAPE field campaign completed and be available for beyond PIs after one year. 2. Instrument Description The Colorado State University C-band Hydrological Instrument for Volumetric Observation (CHIVO) is a C-band commercial grade dual-polarization Doppler radar capable of collecting measurements of radar reflectivity, mean Doppler velocity, spectrum width as well as differential phase shift and differential reflectivity. During ESCAPE, the CHIVO was configured for a maximum unambiguous range of 124 km and a range gate spacing of 150 m. In Houston during the ESCAPE campaign, the CHIVO radar was not allowed to transmit in the direction of the azimuth, as mandated by local airport operations. Table 1. General characteristics of CHIVO during the campaign -------------------------------------------------------------------------------- Parameter | Range -------------------------------------------------------------------------------- Operational frequency | 5.6 GHz Antenna beamwidth | 0.95 deg Pulse repetition frequency| Selectable, typically 1.2 kHz Pulse width | Selectable, typically 1 us Instrumented range | 124 km Range-gate spacing | 150 m --------------------------------------------------------------------------------- 3. Data Collection and Processing For each target cell, CHIVO collected at least one scan bundle composed of 3-4 RHIs based on different criteria guided by MAAS. The criteria include: 1. Aiming towards the cellfs region of maximum radar reflectivity, which was determined through analysis of the CSAPR2 sector plan position indicator (PPI) scans. 2. The cellfs region of maximum vertically-integrated liquid (VIL) determined through the analysis of NEXRAD observations 3. The cellfs region of maximum differential reflectivity (ZDR) determined through analysis of the CSAPR2 sector PPI 4. Azimuths 2 km to the right and left of the cellfs region of maximum radar reflectivity in the plane perpendicular to the radar beam. 5. The cellfs region of maximum radial gradient of the Doppler velocity determined through analysis of the CSAPR2 low-level PPI LROSE software (http://lrose.net/) was used to convert the CHIVO output into CfRadial format and perform attenuation corrections for reflectivity and differential reflectivity and radar echo classification. LROSE is open-source software to analyze radar data developed by Colorado State University (CSU) under a support from National Science Foundation (NSF). We also used the dealias_region_based function built in PyART (https://arm-doe.github.io/pyart-docs-travis/index.html ) to dealias mean Doppler velocity. The radar variables included in the data files are listed in Table 2. Table 2: Radar variables included in the data files. --------------------------------------------------------------------------------- Variable | Unit | Description --------------------------------------------------------------------------------- DBZ_TOT (reflectivity) |dBZ |Reflectivity Z_ATTEN_CORRECTED |dBZ |Reflectivity corrected for attenuation u (reflectivity_attenuation_corrected) | | using specific differential phase ZDR |dB |Differential reflectivity ZDRATTEN_CORRECTED |dB |Differential reflectivity corrected for attenuation (differential_reflectivity_attenuation_corrected)| | using specific differential phase RHOHV |- |Correlation coefficient between H and V returns PHIDP |degrees |Differential phase KDP |degrees/km|Specific differential phase SNR |dB |Signal to noise ratio for H VEL |m/s |Doppler velocity mean_doppler_velocity_dealiased |m/s |Dealiased Doppler velocity WIDTH |m/s |Doppler spectrum width --------------------------------------------------------------------------------- 4. Data Format The data files are NetCDF 4.0. The file name convention follows: houchivorhicelltracking.lv2.[yyyymmdd][hhnnss].nc The all data are RHI stored in a radar-coordinate CfRadial format having dimensions of range and time. The data files are stored in subdirectories separated by date. 5. References Kollias, P., Luke, E., Oue, M., and Lamer, K.: Agile adaptive radar sampling of fast-evolving atmospheric phenomena guided by satellite imagery and surface cameras. Geophysical Research Letters, 45, e2020GL088440. https://doi.org/10.1029/2020GL088440, 2020. Lamer, K., P. Kollias, E. P. Luke, B. P. Treserras, M. Oue, and B. Dolan: Multisensor Agile Adaptive Sampling (MAAS): a methodology to collect radar observations of convective cell life cycle. Journal of Atmospheric and Oceanic Technology. in review. 6. Appendix GCMD Science Keywords: Convective cloud systems (observed) Precipitating convective cloud systems Deep convective cloud systems Atmospheric wind Cloud dynamics