Radar Icing Algorithm: Algorithm Description and Comparison with Aircraft Observations

2021 ◽  
Author(s):  
David J. Serke ◽  
Scott M. Ellis ◽  
Sarah A. Tessendorf ◽  
David E. Albo ◽  
John C. Hubbert ◽  
...  
Keyword(s):  
Solid Phase ◽  
Weather Prediction ◽  
Relative Size ◽  
Particle Diameter ◽  
Area Under The Curve ◽  
Mixed Phase ◽  
Small Drop ◽  
Large Drop ◽  
Statistical Measures ◽  
Research Aircraft

AbstractDetection 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.

scholarly journals Keys to Differentiating between Small- and Large-Drop Icing Conditions in Continental Clouds

2019 ◽  
Vol 58 (9) ◽  
pp. 1931-1953
Author(s):  
Ben C. Bernstein ◽  
Roy M. Rasmussen ◽  
Frank McDonough ◽  
Cory Wolff
Keyword(s):  
Boundary Layer ◽  
Drop Size ◽  
Liquid Water Content ◽  
Great Lakes Region ◽  
Small Drop ◽  
Size Discrimination ◽  
Large Drop ◽  
Flight Data ◽  
Local Boundary ◽  
Research Aircraft

AbstractUsing observations from research aircraft flights over the Great Lakes region, synoptic and mesoscale environments that appear to drive a relationship between liquid water content, drop concentration, and drop size are investigated. In particular, conditions that fell within “small drop” and “large drop” regimes are related to cloud and stability profiles, providing insight regarding whether the clouds are tied to the local boundary layer. These findings are supported by analysis of flight data from other parts of North America and used to provide context for several icing incidents and accidents where large-drop icing was noted as a contributing factor. The relationships described for drop size discrimination in continental environments provide clues that can be applied for both human- and model-generated icing forecasts, as well as automated icing algorithms.

Consolidation Features of Aluminum-Alumina Compositions by Powder Metallurgy Methods

2016 ◽  
Vol 254 ◽  
pp. 110-115
Author(s):  
Mihai Ovidiu Cojocaru ◽  
Mihaela Raluca Condruz ◽  
Florică Tudose
Keyword(s):  
Solid Phase ◽  
Particle Diameter ◽  
Aluminum Matrix ◽  
Aluminum Matrix Composites ◽  
Average Particle ◽  
Matrix Composites ◽  
Aluminum Particles ◽  
Transmission Electron ◽  
Tapped Density ◽  
Powder Metallurgy Methods

In this paper was followed the processing flow of aluminum-alumina compositions (10÷20% alumina) in powder state, aiming to obtain aluminum matrix composites reinforced with alumina particles, starting from selecting and mixing the grading fraction of both components reaching up to sintering; it was analyzed the way in which reflects the variation of grading fraction ratio (expressed through average particle diameter in the analyzed fractions limits) on the level of technological interest features: apparent density, tapped density, flowability, presability and on densification after sintering (in various environments). By transmission electron microscopy was observed that aluminum particles showed on the surface a nanoscale oxide film, so the sintering occurs between congeneric areas – by solid phase sintering mechanisms [1, 2, 3]. The analysis of thermophysical properties revealed a decrease of thermal diffusivity at an increase of alumina, simultaneous with the decrease of the densification level.

scholarly journals Calibration of the passive cavity aerosol spectrometer probe for airborne determination of the size distribution

2013 ◽  
Vol 6 (3) ◽  
pp. 4123-4152 ◽  
Author(s):  
Y. Cai ◽  
J. R. Snider ◽  
P. Wechsler
Keyword(s):  
Particle Size ◽  
Size Distribution ◽  
Scattered Light ◽  
Particle Diameter ◽  
Polystyrene Latex ◽  
Calibration Methods ◽  
Research Aircraft ◽  
Amplifier Gain ◽  
Gain Stage

Abstract. This work describes calibration methods for the particle sizing and particle concentration systems of the passive cavity aerosol spectrometer probe (PCASP). Laboratory calibrations conducted over six years, in support of the deployment of a PCASP on a cloud physics research aircraft, are analyzed. Instead of using the many calibration sizes recommended by the PCASP manufacturer, a relationship between particle diameter and scattered light intensity is established using three sizes of mobility-selected polystyrene latex particles, one for each amplifier gain stage. In addition, studies of two factors influencing the PCASP's determination of the particle size distribution – amplifier baseline and particle shape – are conducted. It is shown that the PCASP-derived size distribution is sensitive to adjustments of the sizing system's baseline voltage, and that for aggregate spheres, a PCASP-derived particle size and a sphere-equivalent particle size agree within uncertainty dictated by the PCASP's sizing resolution. Robust determination of aerosol concentration, and size distribution, also require calibration of the PCASP's aerosol flowrate sensor. Sensor calibrations, calibration drift, and the sensor's non-linear response are documented.

scholarly journals Basic Diagnosis and Prediction of Persistent Contrail Occurrence Using High-Resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part I: Effects of Random Error

2009 ◽  
Vol 48 (9) ◽  
pp. 1780-1789 ◽  
Author(s):  
David P. Duda ◽  
Patrick Minnis
Keyword(s):  
Vertical Velocity ◽  
Random Error ◽  
Weather Prediction ◽  
Logistic Models ◽  
Critical Threshold ◽  
Critical Probability ◽  
Random Measurement Error ◽  
Numerical Weather ◽  
Statistical Measures ◽  
Regional Prediction

Abstract Straightforward application of the Schmidt–Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper-tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy: the percent correct (PC) and the Hanssen–Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher (i.e., the forecasts are more skillful) when the climatological frequency of contrail occurrence is used as the critical threshold, whereas the PC scores are higher (i.e., the forecasts are more accurate) when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85% for the prediction of both contrail occurrence and nonoccurrence, although, in practice, larger errors would be anticipated.

scholarly journals Numerical Guidance Methods for Decision Support in Aviation Meteorological Forecasting

2005 ◽  
Vol 20 (1) ◽  
pp. 82-100 ◽  
Author(s):  
A. J. M. Jacobs ◽  
N. Maat
Keyword(s):  
Weather Prediction ◽  
Forecast Accuracy ◽  
Transformation Method ◽  
Skill Score ◽  
Cloud Base ◽  
Model Data ◽  
Operational Environment ◽  
Automatic Production ◽  
Statistical Measures ◽  
Nwp Model

Abstract Numerical guidance methods for decision making support of aviation meteorological forecasters are presented. The methods have been developed to enhance the usefulness of numerical weather prediction (NWP) model data and local and upstream observations in the production of terminal aerodrome forecasts (TAFs) and trend-type forecasts (TRENDs) for airports. In this paper two newly developed methods are described and it is shown how they are used to derive numerical guidance products for aviation. The first is a combination of statistical and physical postprocessing of NWP model data and in situ observations. This method is used to derive forecasts for all aviation-related meteorological parameters at the airport. The second is a high-resolution wind transformation method, a technique used to derive local wind at airports from grid-box-averaged NWP model winds. For operational use of the numerical guidance products encoding software is provided for automatic production of an alphanumeric TAF and TREND code. A graphical user interface with an integrated code editor enables the forecaster to modify the suggested automatic codes. For aviation, the most important parameters in the numerical guidance are visibility and cloud-base height. Both have been subjected to a statistical verification analysis, together with their automatically produced codes. The results in terms of skill score are compared to the skill of the forecasters’ TAF and TREND code. The statistical measures suggest that the guidance has the best skill at lead times of +4 h and more. For the short term, mainly trend-type forecasts, the persistence forecast based on recent observations is difficult to beat. Verification has also shown that the wind transformation method, which has been applied to generate 10-m winds at Amsterdam Airport Schiphol, reduces the mean error in the (grid box averaged) NWP model wind significantly. Among the potential benefits of these numerical guidance methods is increasing forecast accuracy. As a result, the related numerical guidance products and encoding software have been integrated in the operational environment for the production of TAFs and TRENDs.

scholarly journals Sensitivity of Cloud-Resolving Simulations of Warm-Season Convection to Cloud Microphysics Parameterizations

2007 ◽  
Vol 135 (8) ◽  
pp. 2854-2868 ◽  
Author(s):  
Changhai Liu ◽  
Mitchell W. Moncrieff
Keyword(s):  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warm Season ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Fine Grid ◽  
Upper Level ◽  
Heating Profiles ◽  
Microphysical Parameterization

Abstract This paper investigates the effects of cloud microphysics parameterizations on simulations of warm-season precipitation at convection-permitting grid spacing. The objective is to assess the sensitivity of summertime convection predictions to the bulk microphysics parameterizations (BMPs) at fine-grid spacings applicable to the next generation of operational numerical weather prediction models. Four microphysical parameterization schemes are compared: simple ice (Dudhia), four-class mixed phase (Reisner et al.), Goddard five-class mixed phase (Tao and Simpson), and five-class mixed phase with graupel (Reisner et al.). The experimentation involves a 7-day episode (3–9 July 2003) of U.S. midsummer convection under moderate large-scale forcing. Overall, the precipitation coherency manifested as eastward-moving organized convection in the lee of the Rockies is insensitive to the choice of the microphysics schemes, and the latent heating profiles are also largely comparable among the BMPs. The upper-level condensate and cloudiness, upper-level radiative cooling/heating, and rainfall spectrum are the most sensitive, whereas the domain-mean rainfall rate and areal coverage display moderate sensitivity. Overall, the three mixed-phase schemes outperform the simple ice scheme, but a general conclusion about the degree of sophistication in the microphysics treatment and the performance is not achievable.

Validating snow surface radiative transfer models between 89 and 243GHz using airborne observations over Arctic tundra

2020 ◽  
Author(s):  
Kirsty Wivell ◽  
Melody Sandells ◽  
Nick Rutter ◽  
Stuart Fox ◽  
Chawn Harlow ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Weather Prediction ◽  
Arctic Tundra ◽  
Scanning System ◽  
Polar Regions ◽  
Atmospheric Measurements ◽  
Microphysical Properties ◽  
Snow Properties ◽  
Research Aircraft ◽  
Microwave Radiances

<p>Satellite microwave radiances in atmospheric sounding bands, such as the 183GHz water vapour band, are an important source of data for Numerical Weather Prediction. However, these observations are frequently discarded in polar regions as they are also sensitive to the surface, and there is large uncertainty in the background surface emissivity which depends on the microphysical properties of the snowpack. We evaluate simulations of brightness temperature and emissivity from the Snow Microwave Radiative Transfer (SMRT) model for Arctic tundra snow at frequencies between 89 and 243GHz to assess the potential of being able to assimilate observations at key sounding frequencies, such as 183GHz. In-situ measurements of the surface snowpack were collected for 36 snow pits in Trail Valley Creek, near Inuvik, Canada during the March 2018 Measurements of Arctic Cloud, Snow, and Sea Ice nearby the Marginal Ice Zone (MACSSIMIZE) campaign, a collaboration between the Met Office, Northumbria University, Edinburgh University and the Universite de Sherbrooke. These snowpack measurements provide realistic microphysical snow properties as input to SMRT. We present the evaluation of SMRT simulations against surface-based radiometer observations and airborne observations taken with the Microwave Airborne Radiometer Scanning System (MARSS) and International Submillimetre Airborne Radiometer (ISMAR) on the Facility for Airborne Atmospheric Measurements (FAAM) BAe 146 research aircraft.</p>

scholarly journals Bulk Hook Echo Raindrop Sizes Retrieved Using Mobile, Polarimetric Doppler Radar Observations

2015 ◽  
Vol 54 (2) ◽  
pp. 423-450 ◽  
Author(s):  
Michael M. French ◽  
Donald W. Burgess ◽  
Edward R. Mansell ◽  
Louis J. Wicker
Keyword(s):  
Doppler Radar ◽  
High Spatial Resolution ◽  
Life Cycles ◽  
Environmental Data ◽  
Size Distributions ◽  
Small Drop ◽  
Large Drop ◽  
Radar Observations ◽  
X Band ◽  
Drop Size Distributions

AbstractPolarimetric radar observations obtained by the NOAA/National Severe Storms Laboratory mobile, X-band, dual-polarization radar (NOXP) are used to investigate “hook echo” precipitation properties in several tornadic and nontornadic supercells. Hook echo drop size distributions (DSDs) were estimated using NOXP data obtained from 2009 to 2012, including during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2). Differences between tornadic and nontornadic hook echo DSDs are explored, and comparisons are made with previous observations of estimated hook echo DSDs made from stationary S- and C-band Doppler radars. Tornadic hook echoes consistently contain radar gates that are characterized by small raindrops; nontornadic hook echoes are mixed between those that have some small-drop gates and those that have almost no small-drop gates. In addition, the spatial distribution of DSDs was estimated using the high-spatial-resolution data afforded by NOXP. A unique polarimetric signature, an area of relatively low values of differential radar reflectivity factor ZDR south and east of the tornado, is observed in many of the tornadic cases. Also, because most data were obtained using 2-min volumetric updates, the evolution of approximated hook echo precipitation properties was studied during parts of the life cycles of three tornadoes. In one case, there is a large decrease in the percentage of large-raindrop gates and an increase in the percentage of small-raindrop gates in the minutes leading up to tornado formation. The percentage of large-drop gates generally increases prior to and during tornado dissipation. Near-storm environmental data are used to put forth possible relationships between bulk hook echo DSDs and tornado production and life cycle.

scholarly journals Driftsondes: Providing In Situ Long-Duration Dropsonde Observations over Remote Regions

2013 ◽  
Vol 94 (11) ◽  
pp. 1661-1674 ◽  
Author(s):  
Stephen A. Cohn ◽  
Terry Hock ◽  
Philippe Cocquerez ◽  
Junhong Wang ◽  
Florence Rabier ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Field Experiments ◽  
Prediction Models ◽  
Weather Prediction ◽  
Lower Boundary ◽  
Remote Sensors ◽  
Long Duration ◽  
Level Data ◽  
Research Aircraft

Constellations of driftsonde systems— gondolas floating in the stratosphere and able to release dropsondes upon command— have so far been used in three major field experiments from 2006 through 2010. With them, high-quality, high-resolution, in situ atmospheric profiles were made over extended periods in regions that are otherwise very difficult to observe. The measurements have unique value for verifying and evaluating numerical weather prediction models and global data assimilation systems; they can be a valuable resource to validate data from remote sensing instruments, especially on satellites, but also airborne or ground-based remote sensors. These applications for models and remote sensors result in a powerful combination for improving data assimilation systems. Driftsondes also can support process studies in otherwise difficult locations—for example, to study factors that control the development or decay of a tropical disturbance, or to investigate the lower boundary layer over the interior Antarctic continent. The driftsonde system is now a mature and robust observing system that can be combined with flight-level data to conduct multidisciplinary research at heights well above that reached by current research aircraft. In this article we describe the development and capabilities of the driftsonde system, the exemplary science resulting from its use to date, and some future applications.

scholarly journals Evaluation of Precipitation from Numerical Weather Prediction Models and Satellites Using Values Retrieved from Radars

2007 ◽  
Vol 135 (11) ◽  
pp. 3750-3766 ◽  
Author(s):  
Slavko Vasić ◽  
Charles A. Lin ◽  
Isztar Zawadzki ◽  
Olivier Bousquet ◽  
Diane Chaumont
Keyword(s):  
United States ◽  
Prediction Models ◽  
Weather Prediction ◽  
Decomposition Methods ◽  
Grid Size ◽  
Eastern United States ◽  
Model Resolution ◽  
Forecast Period ◽  
Statistical Measures ◽  
Numerical Weather Prediction Models

Abstract Precipitation is evaluated from two weather prediction models and satellites, taking radar-retrieved values as a reference. The domain is over the central and eastern United States, with hourly accumulated precipitation over 21 days for the models and radar, and 13 days for satellite. Conventional statistical measures and scale decomposition methods are used. The models generally underestimate strong precipitation and show nearly constant modest skill over a 24-h forecast period. The scale decomposition results show that the effective model resolution for precipitation is many times the grid size. The model predictability extends beyond a few hours for only the largest scales.

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