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NASA and the FAA are funding the development of ground-based remote sensing systems specifically designed to detect and quantify the icing environment aloft. The goal of the NASA activity is to develop a relatively low cost stand-alone system that can provide practical icing information to the flight community. The goal of the FAA activity is to develop more advanced systems that can identify supercooled large drop (SLD) as well as general icing conditions and be integrated into the existing weather information infrastructure. Both activities utilize combinations of sensing technologies including radar, radiometry, and lidar, along with Internet-available external information such as numerical weather model output where it is found to be useful. In all cases the measured data of environment parameters will need to be converted into a measure of icing hazard before it will be of value to the flying community.

A theoretically based algorithm to derive super-cooled liquid water (SLW) cloud macrophysical and microphysical properties is applied to operational satellite data and compared to pilot reports (PIREPS – from commercial and private aircraft) of icing and to in-situ measurements collected from a NASA icing research aircraft. The method has been shown to correctly identify the existence of SLW provided there are no higher-level ice crystal clouds (i.e. cirrus) above the SLW deck. The satellite-derived SLW cloud properties, particularly the cloud temperature, optical thickness or water path and water droplet size, show good qualitative correspondence with aircraft observations and icing intensity reports. Preliminary efforts to quantify the relationship between the satellite retrievals, PIREPS and aircraft measurements are reported here. The goal is to determine the extent to which the satellite-derived cloud parameters can be used to improve icing diagnoses and forecasts.

The objective of this work is to better understand freezing fog/drizzle conditions using observations collected during the Fog Remote Sensing and Modeling project (FRAM-S) that took place at St. John's International Airport, St. John's, NL, Canada. This location was ~1 km away from the Atlantic Ocean coast. During the project, the following measurements at one minute resolution were collected: precipitation rate (PR) and amount, fog/drizzle microphysics, 3D wind speed (Uh) and turbulence (Uh'), visibility (Vis), IR and SW radiative fluxes, temperature (T) and relative humidity (RH), and aerosol observations. The reflectivity and microphysical parameters obtained from the Metek Inc. MRR (Microwave Rain Radar) were also used in the analysis. The measurements were then used to obtain freezing fog/drizzle microphysical characteristics and their relation to visibility.

In the continental United States east of the Rocky Mountains cold fronts are quite common in wintertime due to the many cyclones moving through this region, and icing conditions in the vicinity of cold fronts are a major contributor to the overall occurrence of icing in the atmosphere. The conditions examined in this study will be those behind the cold front. Icing there is often found in stratocumulus clouds that form due to destabilization of the boundary layer through cold air advection and an inversion formed by subsidence aloft which caps their growth. Moist adiabatic lapse rates, small drop sizes, high drop concentrations, and moderate to high liquid water contents depending on the cloud depth often characterize these clouds.

This study presents an evaluation of in-flight icing severity forecasts produced for the eastern United States for the winter 09/10 using the German ADWICE icing forecasting system. An instance of the underlying COSMO-EU 7km model was run over the eastern CONUS to produce four months worth of NWP data for the ADWICE algorithm. The generated icing fields were then verified using pilot reports (PIREPS) as “truth” data. In order to be able to characterize ADWICE performance over this non-native domain against a known quantity for this part of the world, a comparative verification was performed with the American FIP icing product over 1.5 months of data, using a unified set of observation PIREPS and forecast times. Subsequently, ADWICE forecasts were verified over the whole time period and analyzed with respect to seasonal, regional or altitude variations.

The Current Icing Product (CIP) provides an hourly diagnosis of the severity of icing occurring based on multiple data sources. Pilot reports (PIREPs) and surface observations (METARs), as well as satellite, numerical weather prediction (NWP) model, radar, and lightning data are all utilized within the algorithm. The accurate identification of cloud base is a large factor in the algorithm's determination of icing severity. Current methods employ the METAR observation of ceiling to identify the cloud base over a specified area within the CIP domain. The temperature from the Rapid Update Cycle (RUC) NWP model at the height of the observed METAR ceiling can be utilized as a proxy for the amount of condensate in the cloud. The likelihood of a large amount of condensate in the identified cloud increases with increasing cloud base temperature. As the amount of liquid water diagnosed by CIP severity increases, so does the estimated icing severity.

The Current Icing Product (CIP; Bernstein et al. 2005) and Forecast Icing Product (FIP; Wolff et al. 2009) were originally developed by the United States’ National Center for Atmospheric Research (NCAR) under sponsorship of the Federal Aviation Administration (FAA) in the mid 2000’s and provide operational icing guidance to users through the NOAA Aviation Weather Center (AWC). The current operational version of FIP uses the Rapid Refresh (RAP; Benjamin et al. 2016) numerical weather prediction (NWP) model to provide hourly forecasts of Icing Probability, Icing Severity, and Supercooled Large Drop (SLD) Potential. Forecasts are provided out to 18 hours over the Contiguous United States (CONUS) at 15 flight levels between 1,000 ft and FL290, inclusive, and at a 13-km horizontal resolution.

Modern vehicles use automated driving assistance systems (ADAS) products to automate certain aspects of driving, which improves operational safety. In the U.S. in 2020, 38,824 fatalities occurred due to automotive accidents, and typically about 25% of these are associated with inclement weather. ADAS features have been shown to reduce potential collisions by up to 21%, thus reducing overall accidents. But ADAS typically utilize camera sensors that rely on lane visibility and the absence of obstructions in order to function, rendering them ineffective in inclement weather. To address this research gap, we propose a new technique to estimate snow coverage so that existing and new ADAS features can be used during inclement weather. In this study, we use a single camera sensor and historical weather data to estimate snow coverage on the road. Camera data was collected over 6 miles of arterial roadways in Kalamazoo, MI.

The Flight Operations Risk Assessment System (FORAS) is envisioned as a risk management tool that will enable operators at the safety, flight operations, and dispatch level to monitor and reduce the risks associated with individual flights, as well as the entire flight operation. FORAS will focus on flight operation processes and the initial work will provide a quantitative assessment of risk of controlled flight into terrain and risk of turbulence-related injury. The risk model is based on a large set of possible risk factors roughly classified under the categories of environment (including weather), operator, service provider, flight path, aircraft, cabin, and air handling. We present here a description of progress to date on FORAS, as well as plans for its future development.

Advancements in numerical weather prediction (NWP) models continue to enhance the quality of in-flight icing forecasts and diagnoses. When diagnosing current in-flight icing conditions, observational datasets are combined with NWP model output to form a more accurate representation of those conditions. Surface observations are heavily relied upon to identify cloud coverage and cloud base height above observing stations. One of the major challenges of using these point-based or otherwise limited observations of cloud properties is extending the influence of the observation to nearby points on the model grid. An alternate solution to the current method for incorporating these point-based observations into the in-flight icing diagnoses was developed. The basis for the new method is rooted in a concept borrowed from signal and image processing known as dithering.

Evaluating real-world hazards associated with perception subsystems is critical in enhancing the performance of autonomous vehicles. The reliability of autonomous vehicles perception subsystems are paramount for safe and efficient operation. While current studies employ different metrics to evaluate perception subsystem failures in autonomous vehicles, there still exists a gap in the development and emphasis on engineering requirements. To address this gap, this study proposes the establishment of engineering requirements that specifically target real-world hazards and resilience factors important to AV operation, using High-Definition Maps, Global Navigation Satellite System, and weather sensors. The findings include the need for engineering requirements to establish clear criteria for a high-definition maps functionality in the presence of erroneous perception subsystem inputs which enhances the overall safety and reliability of the autonomous vehicles.