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David J. Serke, Scott M. Ellis, Sarah A. Tessendorf, David E. Albo, John C. Hubbert, and Julie A. Haggerty

Abstract

Detection of in-flight icing hazard is a priority of the aviation safety community. The ‘Radar Icing Algorithm’ (RadIA) has been developed to indicate the presence, phase, and relative size of supercooled drops. This paper provides an evaluation of RadIA via comparison to in-situ microphysical measurements collected with a research aircraft during the 2017 'Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment' (SNOWIE) field campaign.

RadIA uses Level 2 dual-polarization radar moments from operational National Weather Service WSR-88D radar and a numerical weather prediction model temperature profile as inputs. Moment membership functions are defined based on the results of previous studies, and fuzzy logic is used to combine the output of these functions to create a 0 to 1 interest for detecting small-drop, large-drop and mixed phase icing.

Data from the 2D-S particle probe on board the University of Wyoming King Air aircraft were categorized as either liquid or solid phase water with a shape classification algorithm and binned by size. RadIA interest values from 17 cases were matched to statistical measures of the solid/liquid particle size distributions (such as maximum particle diameter) and values of LWC from research aircraft flights. Receiver Operating Characteristic Area Under the Curve (AUC) values for RadIA algorithms were 0.75 for large-drop, 0.73 for small-drop, and 0.83 for mixed-phase cases. RadIA is proven to be a valuable new capability for detecting the presence of in-flight icing hazards from ground-based precipitation radar.

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Anita Freundorfer, Karl Lapo, Johann Schneider, and Christoph K. Thomas

Abstract

In the atmospheric boundary layer, phenomena exist with challenging properties such as spatial heterogeneity, particularly during stable weak wind situations. Studying spatially heterogeneous features requires spatially distributed measurements on fine spatial and temporal scales. Fiber-Optic Distributed Sensing (FODS) can provide spatially distributed measurements, simultaneously offering a spatial resolution on the order of decimeters and a temporal resolution on the order of seconds. While FODS has already been deployed to study various variables, FODS wind direction sensing has only been demonstrated in idealized wind tunnel experiments. We present the first distributed observations of FODS wind directions from field data. The wind direction sensing is accomplished by using pairs of actively heated fiber optic cables with cone-shaped microstructures attached to them. Here we present three different methods of calculating wind directions from the FODS measurements, two based on using combined wind speed and direction information and one deriving wind direction independently from FODS wind speed. For each approach, the effective temporal and spatial resolution is quantified using spectral coherence. With each method of calculating wind directions, temporal resolutions on the order of tens of seconds can be achieved. The accuracy of FODS wind directions was evaluated against a sonic anemometer, showing deviations of less than 15° most of the time. The applicability of FODS for wind direction measurements in different environmental conditions is tested by analysing the dependence of FODS wind direction accuracy and observable scales on environmental factors. Finally, we demonstrate the potential of this technique by presenting a period that displays spatial and temporal structures in the wind direction.

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Jeana Mascio, Stephen S. Leroy, Robert P. d’Entremont, Thomas Connor, and E. Robert Kursinski

Abstract

Radio occultation (RO) measurements have little direct sensitivity to clouds, but recent studies have shown that they may have an indirect sensitivity to thin, high clouds that are difficult to detect using conventional passive space-based cloud sensors. We implement two RO-based cloud detection (ROCD) algorithms for atmospheric layers in the middle and upper troposphere. The first algorithm is based on the methodology of a previous study, which explored signatures caused by upper tropospheric clouds in RO profiles according to retrieved relative humidity, temperature lapse rate, and gradients in log-refractivity (ROCD-P), and the second is based on inferred relative humidity alone (ROCD-M). In both, atmospheric layers are independently predicted as cloudy or clear based on observational data, including high performance RO retrievals. In a demonstration, we use data from 10 days spanning seven months in 2020 of FORMOSAT-7/COSMIC-2. We use the forecasts of NOAA GFS to aid in the retrieval of relative humidity. The prediction is validated with a cloud truth dataset created from the imagery of the GOES-16 Advanced Baseline Imager (ABI) satellite and the GFS three-dimensional analysis of cloud state conditions. Given these two algorithms for the presence or absence of clouds, confusion matrices and receiver operating characteristic (ROC) curves are used to analyze how well these algorithms perform. The ROCD-M algorithm has a balanced accuracy, which defines the quality of the classification test that considers both the sensitivity and specificity, greater than 70% for all altitudes between 6 and 10.25 km.

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Agustinus Ribal, Ali Tamizi, and Ian R. Young

Abstract

Four scatterometers, namely: METOP-A, METOP-B, ERS-2 and OCEANSAT-2 were re-calibrated against combined National Data Buoy Center (NDBC) data and aircraft Stepped Frequency Microwave Radiometer (SFMR) data from hurricanes. As a result, continuous calibration relations over the wind speed range 0 to 45 ms -1 were developed. The calibration process uses match-up criteria of 50 km and 30 min for the buoy data. However, due to the strong spatio-temporal wind speed gradients in hurricanes, a method which considers both scatterometer and SFMR data in a storm-centred translating frame of reference is adopted. The results show that although the scatterometer radar cross-section is degraded at high wind speeds, it is still possible to recover wind speed data using the re-calibration process. Data validation between the scatterometers shows that the calibration relations produce consistent results across all scatterometers and reduce the bias and root mean square error compared to previous calibrations. In addition, the results extend the useful range of scatterometer measurements to as high as 45 ms -1.

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Emily M. Riley Dellaripa, Aaron Funk, Courtney Schumacher, Hedanqiu Bai, and Thomas Spangehl

Abstract

Comparisons of precipitation between general circulation models (GCMs) and observations are often confounded by a mismatch between model output and instrument measurements, including variable type and temporal and spatial resolutions. To mitigate these differences, the radar-simulator Quickbeam within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates reflectivity from model variables at the subgrid scale. This work adapts Quickbeam to the dual-frequency precipitation radar (DPR) on board the Global Precipitation Measurement (GPM) satellite. The longer wavelength of the DPR is used to evaluate moderate to heavy precipitation in GCMs, which is missed when Quickbeam is used as a cloud radar simulator. Latitudinal and land–ocean comparisons are made between COSP output from the Community Atmospheric Model version 5 (CAM5) and DPR data. Additionally, this work improves the COSP subgrid algorithm by applying a more realistic, nondeterministic approach to assigning GCM gridbox convective cloud cover when convective cloud is not provided as a model output. Instead of assuming a static 5% convective cloud coverage, DPR convective precipitation coverage is used as a proxy for convective cloud coverage. For example, DPR observations show that convective rain typically only covers about 1% of a 2° grid box, but that the median convective rain area increases to over 10% in heavy rain cases. In our CAM5 tests, the updated subgrid algorithm improved the comparison between reflectivity distributions when the convective cloud cover is provided versus the default 5% convective cloud-cover assumption.

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Mark Curtis, Sandy Dance, Valentin Louf, and Alain Protat

Abstract

For mechanically scanning weather radars, precise pointing of the antenna is a key factor in ensuring accurate observation of the atmosphere at far range. Since operational radars typically scan the atmosphere using a series of 360° sweeps at fixed elevation angles, level scanning during azimuthal rotation is also important but often is not actively monitored after installation. One method of gauging pointing accuracy of a radar is to use solar interference that occurs as the antenna sweeps past the sun. By comparing the observed position of the sun with its known position, an estimate of pointing error in both elevation and azimuth can be obtained. A basic model for this error assumes that the radar sweep is perfectly level and that biases in elevation are therefore independent of azimuth. We extend this model to allow for the possibility that the plane of rotation may not be level. Consequently, the direction and severity of tilt may be diagnosed in addition to any constant error in elevation and azimuth pointing. The extended model was applied to a subset of radars from the Australian weather radar network, resulting in the discovery of several out-of-level radars. One radar, Captains Flat near Canberra, showed a severe tilt of 0.81° prompting inspection by a technician. This revealed that mounting studs on the pedestal of the radar tower were badly worn and loose. Correction of this issue resolved the tilt component of the diagnosed elevation error and prevented further mechanical damage to the instrument.

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Junhong Wang, Jerald Brotzge, Jacob Shultis, and Nathan Bain

Abstract

The accurate detection and monitoring of freezing rain and icing conditions at the surface is a notoriously challenging but important problem. This work attempts to enhance icing detection and characterization utilizing data from the New York State Mesonet (NYSM). NYSM is the first operational network measuring winds at 10 m from two independent sensors: propeller and sonic anemometers. During and after freezing rain events, large wind speed differences are frequently reported between the two anemometers because the propeller develops a coating of ice, thus either stopping or slowing its rotation. Such errors of propeller data provide a signal for identifying icing conditions. An automated method for identifying “active freezing rain” (AFR) and a continuation of “frozen surface” (FS) conditions is developed. Hourly maps of AFR and FS sites are generated using four criteria: 1) a wind speed difference (sonic − propeller) of >1 m s−1 or 0 m s−1 propeller wind speed for at least a half hour, 2) a temperature threshold of −5° to 2°C for AFR and less than 2°C for FS, 3) insignificant hourly snow accumulation, and 4) with (without) significant hourly precipitation accumulation for AFR (FS). The AFR events detected by the automated method for last four winters (2017–21) show very good agreements in starting and ending times with that from the Automated Surface Observing System (ASOS) data. A case study of the ice storm during 14–16 April 2018 further demonstrates the validity of the methodology and highlights the benefit of NYSM profiler and camera data.

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A. V. Kochin, F. A. Zagumennov, and V. L. Fomenko

Abstract

Improving the quality of weather forecasts and the reliability of climate research requires increasing the reliability of measurements. This paper presents results for optical sensors attached to radiosondes. These sensors can measure cloud-top height (CTH) with high accuracy and determine the presence of precipitation particles in clouds and the height of the boundary between the tropospheric and stratospheric air masses. These research findings are especially important in the Arctic, where the reliability of cloud data is poor, especially during polar nights. With the help of a visible range optical sensor, during the daytime, it is possible to measure CTH with an accuracy of 50 m. Using data from an IR sensor it is possible to measure CTH both day and night. The paper also discusses the possibility of using optical sensors in an observational network. The results from this study could be useful for both weather forecasting and climate research.

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Hans van Haren, Roel Bakker, Yvo Witte, Martin Laan, and Johan van Heerwaarden

Abstract

The redistribution of matter in the deep sea depends on water-flow currents and turbulent exchange, for which breaking internal waves are an important source. As internal waves and turbulence are essentially three-dimensional (3D), their dynamical development should ideally be studied in a volume of seawater. However, this is seldom done in the ocean where 1D observations along a single vertical line are already difficult. We present the design, construction, and successful deployment of a half-cubic-hectometer (480 000 m3) 3D-T mooring array holding 2925 high-resolution temperature sensors to study weakly density-stratified waters of the 2500-m-deep western Mediterranean. The stand-alone array samples temperature at a rate of 0.5 Hz, with precision < 0.5 mK, noise level < 0.1 mK, and expected endurance of 3 years. The independent sensors are synchronized inductively every 4 h to a single standard clock. The array consists of 45 vertical lines 125 m long at 9.5 m horizontally from their nearest neighbor. Each line is held under tension of 1.3 kN by a buoyancy element that is released chemically 1 week after deployment. All fold-up lines are attached to a grid of cables that is tensioned in a 70-m-diameter ring of steel tubes. The array is built up in harbor waters, with air filling the steel tubes for floatation. The flat-form array is towed to the mooring site under favorable sea-state conditions. By opening valves in the steel tubes, the array is sunk and its free fall is controlled by a custom-made drag parachute reducing the average sinking speed to 1.3 m s−1 and providing smooth horizontal landing on the flat seafloor.

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Armand Neukermans, Gary Cooper, Jack Foster, Lee Galbraith, and Sudhanshu Jain

Abstract

Two methods for the laboratory-scale formation of aerosols of nanosized particles of precipitated calcium carbonate (PCC), for potential use in stratospheric aerosol injection as a solar radiation management technique, are described. The first uses the coarse fluidization of bulk PCC in a simple vessel, followed by dispersal using a commercially available two-fluid nozzle. The manufacturer’s measured particle mass distribution for the bulk material and sprayed aerosol particle mass distributions are compared, indicating that the sprayed particles are well separated despite a notoriously problematic agglomeration tendency. The method is suitable for scale-up. A second dispersal method, useful for small laboratory experiments, using liquid carbon dioxide as a dispersant as well as spray propellant, gave similar results. The mass mode diameters measured here (0.89–1.4 μm) differ from that stated by the manufacturer (0.7 μm), but the distributions are consistent in showing complete separation of the particles.

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