Results for “field campaign radar” — Page 8

NASA NTRS2009

Winter QPF Sensitivities to Snow Parameterizations and Comparisons to NASA CloudSat Observations

Steady increases in computing power have allowed for numerical weather prediction models to be initialized and run at high spatial resolution, permitting a transition from larger scale parameterizations of the effects of clouds and precipitation to the simulation of specific microphysical processes and hydrometeor size distributions. Although still relatively coarse in comparison to true cloud resolving models, these high resolution forecasts (on the order of 4 km or less) have demonstrated value in the prediction of severe storm mode and evolution and are being explored for use in winter weather events . Several single-moment bulk water microphysics schemes are available within the latest release of the Weather Research and Forecast (WRF) model suite, including the NASA Goddard Cumulus Ensemble, which incorporate some assumptions in the size distribution of a small number of hydrometeor classes in order to predict their evolution, advection and precipitation within the forecast domain. Although many of these schemes produce similar forecasts of events on the synoptic scale, there are often significant details regarding precipitation and cloud cover, as well as the distribution of water mass among the constituent hydrometeor classes. Unfortunately, validating data for cloud resolving model simulations are sparse. Field campaigns require in-cloud measurements of hydrometeors from aircraft in coordination with extensive and coincident ground based measurements. Radar remote sensing is utilized to detect the spatial coverage and structure of precipitation. Here, two radar systems characterize the structure of winter precipitation for comparison to equivalent features within a forecast model: a 3 GHz, Weather Surveillance Radar-1988 Doppler (WSR-88D) based in Omaha, Nebraska, and the 94 GHz NASA CloudSat Cloud Profiling Radar, a spaceborne instrument and member of the afternoon or "A-Train" of polar orbiting satellites tasked with cataloguing global cloud characteristics. Each system provides a unique perspective. The WSR-88D operates in a surveillance mode, sampling cloud volumes of Rayleigh scatterers where reflectivity is proportional to the sixth moment of the size distribution of equivalent spheres. The CloudSat radar provides enhanced sensitivity to smaller cloud ice crystals aloft, as well as consistent vertical profiles along each orbit. However, CloudSat reflectivity signatures are complicated somewhat by resonant Mie scattering effects and significant attenuation in the presence of cloud or rain water. Here, both radar systems are applied to a case of light to moderate snowfall within the warm frontal zone of a cold season, synoptic scale storm. Radars allow for an evaluation of the accuracy of a single-moment scheme in replicating precipitation structures, based on the bulk statistical properties of precipitation as suggested by reflectivity signatures.

Molthan, Andrew↗

NASA NTRS2019

The RELAMPAGO Lightning Mapping Array: Preliminary Scientific Results and Application to GLM Calibration and Validation

During November 2018 through April 2019, an 11-station NASA lightning mapping array (LMA) was installed in the Cordoba region of Argentina, in support of GOES-16 Geostationary Lightning Mapper (GLM) calibration and validation, as well as the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. This region of Argentina is well known for frequent, intense thunderstorms and severe weather. The LMA was monitored remotely via the Internet throughout its deployment, but due to bandwidth limitations no real-time data were available. Custom GOES-16 imagery provided by NASA SPoRT assisted with monitoring of thunderstorm cases. Occasional site visits were done to obtain data disks, perform routine maintenance, and troubleshoot problems. During the deployment the network captured lightning in a variety of storm modes, including ordinary and severe multicells, supercells, and mesoscale convective systems. Many examples of normal-polarity thunderstorms, as well as a few examples of anomalously charged thunderstorms, were observed. Long (100+ km) horizontally stratified lightning flashes, as well as lightning in overshooting tops, also were frequently observed. Supporting research radar observations were available through January 2019, with operational radar coverage available after that time. Some cases featured supporting ABI meso scanning. This presentation will report on the LMA deployment in context with the RELAMPAGO field campaign, show results from some representative case studies, and will provide initial comparisons to GLM observations.

Lang, Timothy J.↗

NASA NTRSDate not supplied

A New Statistical Estimate of the Radar Coverage of the Low Earth Orbit Debris Environment

For over three decades, the NASA Orbital Debris Program Office (ODPO) has used the Goldstone Orbital Debris Radar, Haystack Ultrawideband Satellite Imaging Radar (HUSIR), and Haystack Auxiliary (HAX) radar assets to collect data on the low Earth orbit (LEO) debris environment. Each radar, with its unique beamwidth, altitude and inclination coverage, and limiting size threshold, operates in a beam park mode to statistically sample the orbital debris population in LEO. Provided that these assets are shared with other users, the orbital debris data collection is not continuous; rather, intermittent data collects are acquired and sent to the NASA ODPO. To understand the sampling process conducted by each radar over time and any related observational biases, a Statistical Confirmation of Radar Uniformity or Bias (SCRUB) code has been implemented to model the coverage of these assets for informing future operations, as well as usage of the data collected from these ground-based sensors. For this analysis, Right Ascension of the Ascending Node (RAAN) is used as a metric to measure statistical coverage. A complete survey of the LEO environment is understood to be measurements that sufficiently sample all values of RAAN for each altitude-inclination pair visible from the radar asset. Regions of incompleteness or statistical bias can help inform future observation campaigns. The SCRUB tool evaluates the coverage of the LEO environment, not by examining individual objects that may pass through a sensor’s field of view (FOV), but by determining which orbit planes pass through the FOV. Once a pointing geometry for a radar site, observation time, and range extent are configured by the user, SCRUB computes the inclinations and altitudes that are visible by the sensor. For each inclination-altitude pair, there is a distinct pair of possible RAAN values, corresponding to the ascending and descending orbit passing through that point in the sensor cone. Repeating this process for all points in the beam, and for multiple time periods during an observation window, creates a matrix of all inclination-altitude-RAAN combinations that are visible during a sensor run. This process can then be repeated for all observations within a year (for an annual survey), propagating all the RAAN values to a common epoch, typically the start of a calendar year, and combining the observations to assemble a full estimate of the RAAN coverage of the LEO environment. This RAAN coverage can then be analyzed for uniformity of sampling, within a certain inclination-altitude pair, or between larger regions of the space environment. This paper provides a general overview of the radar assets utilized by ODPO, typical analysis data products assuming circular orbits, and a discussion of the algorithms that feed between modeling and measurement operations. Following the description of the algorithm, estimates of coverage using HUSIR radar data collected from multiple years are developed and compared.

Chris L Ostrom↗

DOE OSTI2021

Utilizing a Storm-Generating Hotspot to Study Convective Cloud Transitions: The CACTI Experiment

The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign was designed to improve understanding of orographic cloud life cycles in relation to surrounding atmospheric thermodynamic, flow, and aerosol conditions. The deployment to the Sierras de Córdoba range in north-central Argentina was chosen because of very frequent cumulus congestus, deep convection initiation, and mesoscale convective organization uniquely observable from a fixed site. The C-band Scanning Atmospheric Radiation Measurement (ARM) Precipitation Radar was deployed for the first time with over 50 ARM Mobile Facility atmospheric state, surface, aerosol, radiation, cloud, and precipitation instruments between October 2018 and April 2019. An intensive observing period (IOP) coincident with the RELAMPAGO field campaign was held between 1 November and 15 December during which 22 flights were performed by the ARM Gulfstream-1 aircraft. A multitude of atmospheric processes and cloud conditions were observed over the 7-month campaign, including numerous orographic cumulus and stratocumulus events; new particle formation and growth producing high aerosol concentrations; drizzle formation in fog and shallow liquid clouds; very low aerosol conditions following wet deposition in heavy rainfall; initiation of ice in congestus clouds across a range of temperatures; extreme deep convection reaching 21-km altitudes; and organization of intense, hail-containing supercells and mesoscale convective systems. These comprehensive datasets include many of the first ever collected in this region and provide new opportunities to study orographic cloud evolution and interactions with meteorological conditions, aerosols, surface conditions, and radiation in mountainous terrain.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

MOSAiC Radar b1 Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed many instruments on board a German ice breaker, the Research Vessel (RV) Polarstern, for one year from October 2019 to October 2020. The purpose of the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign was to study the decline in the sea-ice pack around the North Pole, and what factors may be at play. After the campaign ended, efforts were undertaken to provide a calibrated radar data set for future studies. MOSAiC presented new challenges to this process, as existing methodologies often were not applicable for the frozen environment with little to no ground clutter, and concurrent engineering updates or calibrations could not be accomplished once the ship set off. Like the previous Cloud, Aerosol, and Complex Terrain Interactions (CACTI) and Cold-air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) documentation (Hardin et al. 2020, Matthews et al. 2023), the data correction and calibration process is known in ARM as creating a “b1” datastream. This means that the radar datastreams have been well characterized to the best possible quality. This report will detail the status of the raw “a1” level data sets during the MOSAiC campaign, the corrections that were applied to create the “b1” data files, and the details of the applied methods.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Ka-Band Scanning ARM Cloud Radar (a1)

The Ka-Band Scanning ARM Cloud Radar (KASACR) records cloud properties. ARM's scanning cloud radars are dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (1.7 kW peak power) or an X-band (20 kW peak power). Beamwidths for Ka-bands paired with W-bands are roughly matched at 0.3 degrees. The X-band beamwidth is approximately 1 degree. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the KASACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. Measurements collected with the KASACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, and linear depolarization ratio. KASACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Ka-Band Scanning ARM Cloud Radar (a1)

The Ka-Band Scanning ARM Cloud Radar (KASACR) records cloud properties. ARM's scanning cloud radars are dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (1.7 kW peak power) or an X-band (20 kW peak power). Beamwidths for Ka-bands paired with W-bands are roughly matched at 0.3 degrees. The X-band beamwidth is approximately 1 degree. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the KASACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. Measurements collected with the KASACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, and linear depolarization ratio. KASACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

kasacrcfrspcxpol (a1)

The Ka-Band Scanning ARM Cloud Radar (KASACR) records cloud properties. ARM's scanning cloud radars are dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (1.7 kW peak power) or an X-band (20 kW peak power). Beamwidths for Ka-bands paired with W-bands are roughly matched at 0.3 degrees. The X-band beamwidth is approximately 1 degree. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the KASACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. Measurements collected with the KASACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, and linear depolarization ratio. KASACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

kasacrcfrspccross (a1)

The Ka-Band Scanning ARM Cloud Radar (KASACR) records cloud properties. ARM's scanning cloud radars are dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (1.7 kW peak power) or an X-band (20 kW peak power). Beamwidths for Ka-bands paired with W-bands are roughly matched at 0.3 degrees. The X-band beamwidth is approximately 1 degree. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the KASACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. Measurements collected with the KASACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, and linear depolarization ratio. KASACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

kasacrcfrspccopol (a1)

The Ka-Band Scanning ARM Cloud Radar (KASACR) records cloud properties. ARM's scanning cloud radars are dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (1.7 kW peak power) or an X-band (20 kW peak power). Beamwidths for Ka-bands paired with W-bands are roughly matched at 0.3 degrees. The X-band beamwidth is approximately 1 degree. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the KASACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. Measurements collected with the KASACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, and linear depolarization ratio. KASACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

kasacr2cfrspccwrhicopol (a1)

The Ka-Band Scanning ARM Cloud Radar (KASACR) records cloud properties. ARM's scanning cloud radars are dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (1.7 kW peak power) or an X-band (20 kW peak power). Beamwidths for Ka-bands paired with W-bands are roughly matched at 0.3 degrees. The X-band beamwidth is approximately 1 degree. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the KASACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. Measurements collected with the KASACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, and linear depolarization ratio. KASACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Ka-Band Scanning ARM Cloud Radar (KASACR), 2nd Generation CF-Radial Spectral Data, Vertically-Pointing Scan, Cross Mode (a1-level)

The Ka-Band Scanning ARM Cloud Radar (KASACR) records cloud properties. ARM's scanning cloud radars are dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (1.7 kW peak power) or an X-band (20 kW peak power). Beamwidths for Ka-bands paired with W-bands are roughly matched at 0.3 degrees. The X-band beamwidth is approximately 1 degree. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the KASACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. Measurements collected with the KASACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, and linear depolarization ratio. KASACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

kasacr2cfrspccwrhixpol (a1)

The Ka-Band Scanning ARM Cloud Radar (KASACR) records cloud properties. ARM's scanning cloud radars are dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (1.7 kW peak power) or an X-band (20 kW peak power). Beamwidths for Ka-bands paired with W-bands are roughly matched at 0.3 degrees. The X-band beamwidth is approximately 1 degree. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the KASACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. Measurements collected with the KASACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, and linear depolarization ratio. KASACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Ka-Band Scanning ARM Cloud Radar (KASACR), 2nd Generation CF-Radial Spectral Data, Vertically-Pointing Scan, Cross-Polarization Mode (a1-level)

The Ka-Band Scanning ARM Cloud Radar (KASACR) records cloud properties. ARM's scanning cloud radars are dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (1.7 kW peak power) or an X-band (20 kW peak power). Beamwidths for Ka-bands paired with W-bands are roughly matched at 0.3 degrees. The X-band beamwidth is approximately 1 degree. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the KASACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. Measurements collected with the KASACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, and linear depolarization ratio. KASACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

kasacr2cfrspccwrhicross (a1)

The Ka-Band Scanning ARM Cloud Radar (KASACR) records cloud properties. ARM's scanning cloud radars are dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (1.7 kW peak power) or an X-band (20 kW peak power). Beamwidths for Ka-bands paired with W-bands are roughly matched at 0.3 degrees. The X-band beamwidth is approximately 1 degree. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the KASACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. Measurements collected with the KASACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, and linear depolarization ratio. KASACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2019

The Impact of Stable Layers on Downdrafts in Tropical Convection

While the idea that downdraft vertical velocity is influenced by thermodynamic stability may seem obvious, there remains a gap in the scientific literature surrounding the interaction between downdrafts and stable layers. A number of questions remain unanswered. Can a stable layer stop the downward progression of a downdraft or merely slow its velocity? Related to this, can a stable layer stop the injection of low entropy air into the boundary layer? Do downdrafts modify the environment in a way that removes pre-existing stable layers (limiting the influence of the stable layer)? Is it possible for evaporative cooling to generate and maintain a stable layer that is sufficiently strong to stop the downward progression of downdrafts?The present study aims to answer these questions using a combination of high-altitude airborne radar and dropsonde observations, both collected within four tropical cyclones (Gaston, Hermine, Karl, and Matthew) during the 2016 NOAA SHOUT field campaign, as well as idealized simulations of tropical deep convection. This presentation will focus on the potential for a stable layer to halt the downward progression of a downdraft (and the associated low entropy air) with particular attention given to conditions and processes that might enhance or mitigate the influence of the stable layer.

Helms, Charles N.↗

NASA NTRS2022

Snow Microphysical Retrieval from the NASA D3R Radar During ICE-POP 2018

A method is developed to use both polarimetric and dual-frequency radar measurements to retrieve microphysical properties of falling snow. It is applied to the Ku- and Ka-band measurements of the NASA dual-polarization, dual-frequency Doppler radar (D3R) obtained during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018) field campaign and incorporates the Atmospheric Radiative Transfer Simulator (ARTS) microwave singles cattering property database for oriented particles. The retrieval uses optimal estimation to solve for several parameters that describe the particle size distribution (PSD), relative contribution of pristine, aggregate, and rimed ice species, and the orientation distribution along an entire radial simultaneously. Examination of Jacobian matrices and averaging kernels shows that the dual-wavelength ratio (DWR) measurements provide information regarding the characteristic particle size, and to a lesser extent, the rime fraction and shape parameter of the size distribution, whereas the polarimetric measurements provide information regarding the mass fraction of pristine particles and their characteristic size and orientation distribution. Thus, by combining the dual-frequency and polarimetric measurements, some ambiguities can be resolved that should allow a better determination of the PSD and bulk microphysical properties (e.g., snowfall rate) than can be retrieved from single-frequency polarimetric measurements or dual-frequency, single-polarization measurements. The D3R ICE-POP retrievals were validated using Precipitation Imaging Package (PIP) and Pluvio weighing gauge measurements taken nearby at the May Hills ground site. The PIP measures the snow PSD directly, and its measurements can be used to derived the snowfall rate (volumetric and water equivalent), mean volume-weighted particle size, and effective density, as well as particle aspect ratio and orientation. Four retrieval experiments were performed to evaluate the utility of different measurement combinations: Kuonly, DWR-only, Ku-pol, and All-obs. In terms of correlation, the volumetric snowfall rate (r D 0:95) and snow water equivalent rate (r D 0:92) were best retrieved by the Ku-pol method, while the DWR-only method had the lowest magnitude bias for these parameters (-31% and -8 %, respectively). The methods that incorporated DWR also had the best correlation to particle size (r D 0:74 and r D 0:71 for DWR-only and All-obs, respectively), although none of the methods retrieved density particularly well (r D 0:43 for Allobs). The ability of the measurements to retrieve mean aspect ratio was also inconclusive, although the polarimetric methods (Ku-pol and All-obs) had reduced biases and mean absolute error (MAE) relative to the Ku-only and DWR-only methods. The significant biases in particle size and snowfall rate appeared to be related to biases in the measured DWR, emphasizing the need for accurate DWR measurements and frequent calibration in future D3R deployments.

S Joseph Munchak↗

NASA NTRS2015

Construction of Polarimetric Radar-Based Reference Rain Maps for the Iowa Flood Studies Campaign

The Global Precipitation Measurement (GPM) Mission Iowa Flood Studies (IFloodS) campaign was conducted in central and northeastern Iowa during the months of April-June, 2013. Specific science objectives for IFloodS included quantification of uncertainties in satellite and ground-based estimates of precipitation, 4-D characterization of precipitation physical processes and associated parameters (e.g., size distributions, water contents, types, structure etc.), assessment of the impact of precipitation estimation uncertainty and physical processes on hydrologic predictive skill, and refinement of field observations and data analysis approaches as they pertain to future GPM integrated hydrologic validation and related field studies. In addition to field campaign archival of raw and processed satellite data (including precipitation products), key ground-based platforms such as the NASA NPOL S-band and D3R Ka/Ku-band dual-polarimetric radars, University of Iowa X-band dual-polarimetric radars, a large network of paired rain gauge platforms, and a large network of 2D Video and Parsivel disdrometers were deployed. In something of a canonical approach, the radar (NPOL in particular), gauge and disdrometer observational assets were deployed to create a consistent high-quality distributed (time and space sampling) radar-based ground "reference" rainfall dataset, with known uncertainties, that could be used for assessing the satellite-based precipitation products at a range of space/time scales. Subsequently, the impact of uncertainties in the satellite products could be evaluated relative to the ground-benchmark in coupled weather, land-surface and distributed hydrologic modeling frameworks as related to flood prediction. Relative to establishing the ground-based "benchmark", numerous avenues were pursued in the making and verification of IFloodS "reference" dual-polarimetric radar-based rain maps, and this study documents the process and results as they pertain specifically to efforts using the NPOL radar dataset. The initial portions of the "process" involved dual-polarimetric quality control procedures which employed standard phase and correlation-based approaches to removal of clutter and non-meteorological echo. Calculation of a scale-adaptive KDP was accomplished using the method of Wang and Chandrasekar (2009; J. Atmos. Oceanic Tech.). A dual-polarimetric blockage algorithm based on Lang et al. (2009; J. Atmos. Oceanic Tech.) was then implemented to correct radar reflectivity and differential reflectivity at low elevation angles. Next, hydrometeor identification algorithms were run to identify liquid and ice hydrometeors. After the quality control and data preparation steps were completed several different dual-polarimetric rain estimation algorithms were employed to estimate rainfall rates using rainfall scans collected approximately every two to three minutes throughout the campaign. These algorithms included a polarimetrically-tuned Z-R algorithm that adjusts for drop oscillations (via Bringi et al., 2004, J. Atmos. Oceanic Tech.), and several different hybrid polarimetric variable approaches, including one that made use of parameters tuned to IFloodS 2D Video Disdrometer measurements. Finally, a hybrid scan algorithm was designed to merge the rain rate estimates from multiple low level elevation angle scans (where blockages could not be appropriately corrected) in order to create individual low-level rain maps. Individual rain maps at each time step were subsequently accumulated over multiple time scales for comparison to gauge network data. The comparison results and overall error character depended strongly on rain event type, polarimetric estimator applied, and range from the radar. We will present the outcome of these comparisons and their impact on constructing composited "reference" rainfall maps at select time and space scales.

Radar↗