The North Dakota Citation Research Aircraft Measurement Platform

Delene, David J.; Hibert, Kurt; Poellot, Michael R.; Brackin, Neil

doi:10.4271/2019-01-1990

Cited by 5 publications

(4 citation statements)

References 47 publications

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“…The formulation of the NN requires a large dataset of the three aforementioned input features (Z e , DFR Ku-Ka , and T), which in turn requires the use of an estimate of N(D i ). In situ data collected on the University of North Dakota's Citation Aircraft (Delene et al 2019) during the Midlatitude Continental Convective Clouds Experiment (MC3E; Jensen et al 2016), GCPEX (Skofronick-Jackson et al 2015) and the Olympic Mountains Experiment (OLYMPEX; Houze et al 2017) are used here. The N(D i ) is derived from measurements by two optical array probes (OAP) that capture silhouetted images of cloud and precipitation particles.…”

Section: Nasa Gv Observationsmentioning

confidence: 99%

A Dual-Frequency Radar Retrieval of Two Parameters of the Snowfall Particle Size Distribution Using a Neural Network

Chase

Nesbitt

McFarquhar

2021

Journal of Applied Meteorology and Climatology

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With the launch of the Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM-DPR) in 2014, renewed interest in retrievals of snowfall in the atmospheric column has occurred. The current operational GPM-DPR retrieval largely underestimates surface snowfall accumulation. Here, a neural network (NN) trained on data synthetically derived from state-of-the-art ice particle scattering models and measured in-situ particle size distributions (PSDs) is used to retrieve two parameters of the PSD: liquid equivalent mass weighted mean diameter (Dml) and the liquid equivalent normalized intercept parameter (Nwl). Evaluations against a test dataset showed statistically significant improved ice water content (IWC) retrievals compared to a standard power-law approach and an estimate of the current GPM-DPR algorithm. Furthermore, estimated median percent errors (MPE) on the test dataset were –0.7%, +2.6% and +1% for Dml, Nwl and IWC, respectively. An evaluation on three case-studies with co-located radar observations and in-situ microphysical data show that the NN retrieval has MPE of –13%, +120% and +10% for Dml, Nwl and IWC, respectively. The NN retrieval applied directly to GPM-DPR data provides improved snowfall retrievals compared to the default algorithm, removing the default algorithm’s ray-to-ray instabilities, and recreating the high-resolution radar retrieval results to within 15% MPE. Future work should aim to improve the retrieval by including PSD data collected in more diverse conditions and rimed particles. Furthermore, different desired outputs such as the PSD shape parameter and snowfall rate could be included in future iterations.

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Section: Nasa Gv Observationsmentioning

confidence: 99%

A Dual-Frequency Radar Retrieval of Two Parameters of the Snowfall Particle Size Distribution Using a Neural Network

Chase

Nesbitt

McFarquhar

2021

Journal of Applied Meteorology and Climatology

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“…During OLYMPEX, the University of North Dakota Cessna Citation II (N55DS) [37] flew 20 missions, collecting in-cloud measurements at temperatures between −60 • C and 12 • C. Optical Array Probes (OAPs) that obtain particle images from the occultation of a laser beam incident upon an array of fast response photodiodes included a 2D Stereo Probe (2D-S), nominally sizing particles from 10 to 1280 µm, and a Version 3 High Volume Precipitation Spectrometer (HVPS-3), sizing particles from 150 to 19,200 µm. Data from the OAPs were processed with the University of Illinois/Oklahoma Optical Array Probe Processing Software (UIOOPS) using particle inter-arrival times to remove shattered artifacts from the 2D-S data using algorithms introduced by [38,39].…”

Section: Olympex Measurementsmentioning

confidence: 99%

Dependence of Mass–Dimensional Relationships on Median Mass Diameter

et al. 2020

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Retrievals of ice cloud properties require accurate estimates of ice particle mass. Empirical mass–dimensional (m–D) relationships in the form m = a D b are widely used and usually universally applied across the complete range of particle sizes. For the first time, the dependence of a and b coefficients in m–D relationships on median mass diameter (Dmm) is studied. Using combined cloud microphysical data collected during the Olympic Mountains Experiment and coincident observations from Airborne Precipitation Radar Third Generation, Dmm-dependent (a, b) coefficients are derived and represented as surfaces of equally plausible solutions determined by some tolerance in the chi-squared difference χ 2 that minimizes the difference between observed and retrieved radar reflectivity. Robust dependences of a and b on Dmm are shown with both parameters significantly decreasing with Dmm, leading to smaller effective densities for larger Dmm ranges. A universally applied constant m–D relationship overestimates the mass of large aggregates when Dmm is between 3–6 mm and temperatures are between −15–0 °C. Multiple m–D relations should be applied for different Dmm ranges in retrievals and simulations to account for the variability of particle sizes that are responsible for the mass and thus for the variability of particle shapes and densities.

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“…The OID has been deployed on the North Dakota Citation Research Aircraft (Delene et al, 2019) during several field projects, including a 2015 field project (CAPE2015) to study Florida thunderstorms (Schmidt et al, 2019). The Citation Research Aircraft has conducted several research projects to collect cloud microphysical observations using various instrumentation configurations (Skofronick-Jackson et al, 2014;Jensen et al, 2015;Delene, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Ice water content (IWC) is not directly measured but rather calculated by subtracting LWC from TWC. Nevzorov probe data process-ing uses multiple linear regressions of the measured static pressure and indicated airspeed to obtain a per-flight calibration (Schwarzenboeck et al, 2009), and an automatic baseline correction ensures that the out-of-cloud water content is zero (Delene et al, 2019). The uncertainty of Nevzorov measurements increases with hydrometeor size, because large ice crystals may bounce out of the conical receiver before complete evaporation occurs, while large water droplets may not evaporate completely before bouncing from the conical receiver or shedding from the round wire.…”

Section: Introductionmentioning

confidence: 99%

Technique for comparison of backscatter coefficients derived from in situ cloud probe measurements with concurrent airborne lidar

Wagner

Delene

2022

Atmos. Meas. Tech.

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Abstract. Jet engine power loss due to ice particle accumulation is a recognized aviation hazard occurring in cloud conditions difficult to forecast or visually recognize. High-altitude cirrus clouds can have ice particle concentrations high enough to be dangerous; therefore, pilots must be informed when aircraft enter such environments. One approach to determining ice particle concentration is an onboard lidar system. Concurrent lidar measurements are compared to backscatter coefficients derived from particle size distributions obtained from wing-mounted, in situ probes during four case studies consisting of sixty-second flight segments at different temperatures: +7 and +4 ∘C for water droplet analysis, and −33 and −46 ∘C for ice particle analysis. Backscatter coefficients derived from external cloud probes (ECP) are correlated (0.91) with measurements by an airborne lidar system known as the Optical Ice Detector (OID). Differences between OID and ECP backscatter coefficients range from less than 1 to over 3 standard deviations in terms of uncertainties. The backscatter coefficients are mostly in agreement for liquid clouds and are in disagreement for the −33 and −46 ∘C cases, with ECP-derived backscatter coefficients lower than the OID for three out of the four cases. Measurements over four 60 s research flight segments show that measured total water content is correlated (0.74) with the OID backscatter coefficient, which indicates that the OID is a useful instrument for determining ice particle concentrations over a broad range of environments, including at ice water contents as low as 0.02 g m−3. Additionally, concurrent measurements from cloud imaging probes and the OID provide improved knowledge of cloud conditions, which may help in understanding cloud processes.

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The North Dakota Citation Research Aircraft Measurement Platform

Cited by 5 publications

References 47 publications

A Dual-Frequency Radar Retrieval of Two Parameters of the Snowfall Particle Size Distribution Using a Neural Network

A Dual-Frequency Radar Retrieval of Two Parameters of the Snowfall Particle Size Distribution Using a Neural Network

Dependence of Mass–Dimensional Relationships on Median Mass Diameter

Technique for comparison of backscatter coefficients derived from in situ cloud probe measurements with concurrent airborne lidar

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