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An integrated mobile field system for forecasting ground and in-flight aviation icing hazards would be a vital asset for winter operations. Improved icing forecasts are critical to managing aviation icing hazards.
Supercooled Large Droplet (SLD) icing conditions were implicated in at least one recent aircraft crash, and have been associated with other aircraft incidents. Inflight encounters with SLD can result in ice accreting on unprotected areas of the wing where it can not be removed. Because this ice can adversely affect flight characteristics of some aircraft, there has been concern about flight safety in these conditions. The FAA held a conference on in-flight icing in 1996 where the state of knowledge concerning SLD was explored. One outcome of these meetings was an identified need to acquire SLD flight research data, particularly in the Great Lakes Region. The flight research data was needed by the FAA to develop a better understanding of the meteorological characteristics associated with SLD and facilitate an assessment of existing aircraft icing certification regulations with respect to SLD. In response to this need, NASA, the Federal Aviation Administration (FAA), and the National Center for Atmospheric Research (NCAR) conducted a cooperative icing flight research program to acquire SLD flight research data. The NASA Glenn Research Center's Twin Otter icing research aircraft was flown throughout the Great Lakes region during the winters of 1996-97 and 1997-98 to acquire SLD icing and meteorological data. The NASA Twin Otter was instrumented to measure cloud microphysical properties (particle size, LWC (Liquid Water Content), temperature, etc.), capture images of wing and tail ice accretion, and then record the resultant effect on aircraft performance due to the ice accretion. A satellite telephone link enabled the researchers onboard the Twin Otter to communicate with NCAR meteorologists. who provided real-time guidance into SLD icing conditions. NCAR meteorologists also provided preflight SLD weather forecasts that were used to plan the research flights, and served as on-board researchers. This document contains an evaluation of the tools and techniques NCAR forecasters used to predict the location of SLD icing conditions during the winter of 1997-1998. The objectives of this report are to: (1) assess the tools used to forecast in-flight icing. (2) assess the success/failure rate of the forecasts, and (3) discuss suggested changes to forecast techniques.
The NASA Unified-Weather Research and Forecasting model (NU-WRF) will be included for testing and evaluation in the forecast demonstration project (FDP) of the International Collaborative Experiment −PyeongChang 2018 Olympic and Paralympic (ICE-POP) Winter Games. An international array of radar and supporting ground based observations together with various forecast and now-cast models will be operational during ICE-POP. In conjunction with personnel from NASA's Goddard Space Flight Center, the NASA Short-term Prediction Research and Transition (SPoRT) Center is developing benchmark simulations for a real-time NU-WRF configuration to run during the FDP. ICE-POP observational datasets will be used to validate model simulations and investigate improved model physics and performance for prediction of snow events during the research phase (RDP) of the project The NU-WRF model simulations will also support NASA Global Precipitation Measurement (GPM) Mission ground-validation physical and direct validation activities in relation to verifying, testing and improving satellite-based snowfall retrieval algorithms over complex terrain.
Led by the KMA as a component of the WMO's World Weather Research Program (WWRP) Research and Development and Forecast Demonstration Projects (RDP/FDP). Taken place during the Winter Olympics (February-March) of 2018. Goals of ICE-POP: To improve understanding on severe weathers (snowfalls, visibility, rapid wind changes and gusts,) over complex terrain; To improve the predictability of nowcasting and very-short range forecasting with a few kilometer horizontal resolution - Development of NWP-based nowcasting, multi-scale data assimilation and time-lagged ensemble for VSRF, and radar reflectivity and visibility data assimilation; To improve verification for high resolution model considering complex terrain.
As a component of the National Aeronautics and Space Administration (NASA) Weather Focus Area and Global Precipitation Measurement (GPM) Ground Validation participation in the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field research and forecast demonstration programs, hourly ocean surface meteorology properties were retrieved from the GPM microwave observations for January – March 2018. In this study, the retrieved ocean surface meteorological products – 2-m temperature, 2-m specific humidity, and 10-m wind speed were assimilated into a regional numerical weather prediction (NWP) framework to explore the application of these observations for two heavy snowfall events during the ICE-POP 2018: 27-28 February, and 7-8 March 2018. The Weather Research and Forecasting (WRF) model and the community Gridpoint Statistical Interpolation (GSI) were used to conduct high resolution simulations and data assimilation experiments. The results indicate that the data assimilation has a large influence on surface thermodynamic and wind fields in the model initial condition for both events. With cycled data assimilation, significantly positive influence of the retrieved surface observation was found for the March case with improved quantitative precipitation forecast and reduced error in temperature forecast. A slightly smaller yet positive impact was also found in the forecast of the February case.
Submillimeter-wave radiometers provide vital information on ice clouds and precipitation. These data are key for improving weather forecasting, particularly for disruptive winter storms, and for better understanding climate. Our airborne radiometer will provide data that will allow us to better use data from upcoming space missions with these types of sensors.
This project investigated how clouds and atmosphere-sea ice interactions shape the central Arctic surface energy budget using observations from the MOSAiC expedition and coordinated model experiments. Analyses focused on three themes. First, a new method applied to thermistor string data revealed that snow thermal conductivity is systematically higher than assumed in most models, strongly influencing conductive heat flux and sea ice growth, especially under clear skies. Second, a full annual cycle of surface energy budget observations, combined with detailed cloud microphysical retrievals, demonstrated how cloud regimes regulate radiative, turbulent, and conductive fluxes, driving seasonal contrasts in sea ice energy balance: in winter, radiative forcing elicits strong turbulent and conductive responses, while in summer excess energy is primarily partitioned into surface melt. Third, these observations were used to evaluate and improve regional and global forecast models. While some models captured key Arctic cloud-radiation states, most exhibited persistent deficiencies in representing liquid-containing clouds, snow-on-sea-ice processes, and flux responses to radiative forcing. Together, these results provide unprecedented benchmarks for understanding coupled Arctic processes, improve parameterizations of snow, sea ice, and cloud interactions, and highlight critical pathways by which atmospheric variability drives sea ice change in a rapidly evolving Arctic system. Lastly, the project was a resounding success, resulting in 38 peer-reviewed publications and dozens of presentations, while also supporting an early career scientist.
The Arctic surface energy budget (SEB) couples the atmosphere with the sea ice, making it useful for both studying surface processes as well as evaluating models. Improved understanding of atmosphere-ice interactions is required to improve models, requiring year-round observations to address seasonally dependent biases. This work uses novel observations from the MOSAiC expedition to quantify the responses of surface fluxes to radiative forcing over sea ice throughout a complete annual cycle. We identify two primary regimes of flux response: an ice growth regime in winter and an ice melt regime in summer. In the growth regime, changes in radiative forcing impact upwelling longwave, sensible heat, and subsurface heat fluxes, whereas in the melt regime changes in radiative forcing primarily alter the amount of melt and subsurface transmission because the surface temperature is fixed. These observed responses of surface fluxes to radiative forcing are used to evaluate seven weather forecast models during the ice growth regime. In most models, the responses of surface fluxes to radiative forcing do not match observations. Many models also have biased downwelling longwave. One model (the Coupled Arctic Forecast System; CAFS) adequately captures both the mean radiative forcing and the flux responses in winter. CAFS is further evaluated against observations spanning the full MOSAiC year, demonstrating sufficient agreement to provide a more generalized understanding of these SEB process relationships across the Arctic.
The Hebrew University Cloud Model (HUCM) bin scheme and the Thompson bulk scheme in the Weather Research and Forecasting (WRF) model are compared to assess biases often found in simulated brightness temperature and radar reflectivity. Compared to our preceding study that evaluated several bulk schemes in the WRF model, the current study obtains a reduction of the bias from excessive microwave scattering by precipitation ice for both HUCM bin and the Thompson bulk microphysics schemes for a topographic winter precipitation event associated with an atmospheric river. The Thompson particle size distributions (PSDs) and snow particle density assumption are implemented into the Goddard Satellite Data Simulator Unit (G‐SDSU) and have produced improvements. Despite the greater sophistication of the bin scheme in representing cloud and precipitation processes, the simulation with the Thompson bulk scheme is generally in better agreement with observations for this winter event. The explicitly resolved hydrometeor PSDs in HUCM enable analysis of mass spectra variations in response to changes in microphysics assumptions. Two HUCM sensitivity runs tested the enhancement of snow particle breakup and the influence of ice nuclei (IN) concentration. Higher IN concentration resulted in increased snow mass and broadened the spectrum toward small‐size particles. Modified snow mass spectra and resultant changes in graupel contributed to modifications in scattering and reflectivity simulations. The article demonstrates the bin scheme's capability to provide a new means to improve our understanding of uncertainties in mesoscale weather models and radiative transfer models.
Data support activities for the Marine Weather Service Program are outlined. Forecasts, cover anomolous water levels, including sea and swell, surface and breakers, and storm surge. Advisories are also provided for sea ice on the Great Lake and Cook inlet in winter, and in the Bering, Chukchi, and Beaufort Seas in summer. Attempts were made to deal with ocean currents in the Gulf Stream, areas of upwelling, and thermal structure at least down through the mixed layer.
During the winter of 1996-1997, a flight research program was conducted at the NASA-Lewis Research Center to study the characteristics of Supercooled Large Droplets (SLD) within the Great Lakes region. This flight program was a joint effort between the National Aeronautics and Space Administration (NASA), the National Center for Atmospheric Research (NCAR), and the Federal Aviation Administration (FAA). Based on weather forecasts and real-time in-flight guidance provided by NCAR, the NASA-Lewis Icing Research Aircraft was flown to locations where conditions were believed to be conducive to the formation of Supercooled Large Droplets aloft. Onboard instrumentation was then used to record meteorological, ice accretion, and aero-performance characteristics encountered during the flight. A total of 29 icing research flights were conducted, during which "conventional" small droplet icing, SLD, and mixed phase conditions were encountered aloft. This paper will describe how flight operations were conducted, provide an operational summary of the flights, present selected experimental results from one typical research flight, and conclude with practical "lessons learned" from this first year of operation.
During the Arctic winter, the conductive heat flux through the sea ice and snow balances the radiative and turbulent heat fluxes at the surface. Snow on sea ice is a thermal insulator that reduces the magnitude of the conductive flux. The thermal conductivity of snow, that is, how readily energy is conducted, is known to vary significantly in time and space from observations, but most forecast and climate models use a constant value. This work begins with a demonstration of the importance of snow thermal conductivity in a regional coupled forecast model. Varying snow thermal conductivity impacts the magnitudes of all surface fluxes, not just conduction, and their responses to atmospheric forcing. Given the importance of snow thermal conductivity in models, we use observations from sea ice mass balance buoys installed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition to derive the profiles of thermal conductivity, density, and conductive flux. From 13 sites, median snow thermal conductivity ranges from 0.33 W m −1 K −1 to 0.47 W m −1 K −1 with a median from all data of 0.39 W m −1 K −1 from October to February. In terms of surface energy budget closure, estimated conductive fluxes are generally smaller than the net atmospheric flux by as much as 20 W m −2 , but the average residual during winter is −6 W m −2 , which is within the uncertainties. The spatial variability of conductive heat flux is highest during clear and cold time periods. Higher surface temperature, which often occurs during cloudy conditions, and thicker snowpacks reduce temporal and spatial variability. These relationships are compared between observations and the coupled forecast model, emphasizing both the importance and challenge of describing thermodynamic parameters of snow cover for modeling the Arctic as a coupled system.
Infrequent lightning events, particularly in stratiform precipitation, present a unique decision-support challenge to National Weather Service (NWS) forecasters and core partners. Anticipating thundersnow events, which are rare compared to warm season lightning, are especially difficult to anticipate due to slanted updrafts within the comma-head region of a mid-latitude cyclone. Researchers and operational forecasters have observed depolarization streaks in differential reflectivity (ZDR) as a result of ice crystal layers prior to thundersnow initiation. These depolarization streaks form as a result of ice crystals aligning themselves with enhancements in the electric field in stratiform precipitation and are associated with ZDR values near zero. During the 7 March 2018 winter storm, a teacher in New Jersey was struck by lightning while dismissing students and depolarization streaks were observed in weather radar observations. Therefore, it is advantageous to examine depolarization streaks in ZDR to determine whether it can be used to anticipate lightning potential in winter-time events. Multiple events will be analyzed using the National Lightning Detection Network (NLDN) and the Geostationary Lightning Mapper (GLM) to determine if thundersnow flashes coincide with ZDR depolarization streaks in weather radars. This study also expands on collaboration between the NWS Huntsville Forecast Office and NASA Short-term Prediction Research and Transition (SPoRT) program to determine how depolarization streaks can be used operationally to anticipate lightning within stratiform regions. Preliminary work has indicated noticeable potential in correlating depolarization streak as a precursor to thundersnow flashes in a variety of geographical regions.
NASA is developing the Flight Awareness Collaboration Tool (FACT) to support airline and airport operations during winter storms. The goal is to reduce flight delays and cancellations due to winter weather. FACT concentrates relevant information from the Internet and FAA databases on one screen for easy access. It provides collaboration tools for those managing the winter weather event including the airline operations center, airport authority (runway treatment), the Federal Aviation Administration air traffic control tower, and de-icing operators. Prediction tools are being added to improve FACT capabilities including one that anticipates changes in airport departure rates from weather forecasts. We have formed two user teams from affected airports to guide the design and evaluate the web-based prototype. Future work includes adding more automated capabilities and conducting a simulation to evaluate FACT in a realistic environment.
NASA is developing the Flight Awareness Collaboration Tool (FACT) to support airline and airport operations during winter storms. The goal is to reduce flight delays and cancellations due to winter weather. FACT concentrates relevant information from the Internet and FAA databases on one screen for easy access. It provides collaboration tools for those managing the winter weather event including the airline operations center, airport authority (runway treatment), the Federal Aviation Administration air traffic control tower, and de-icing operators. Prediction tools are being added to improve FACT capabilities including one that anticipates changes in airport departure rates from weather forecasts. Future work includes adding more automated capabilities and collaborating with industry.
NASA is developing the Flight Awareness Collaboration Tool (FACT) to support airline and airport operations during winter storms. The goal is to reduce flight delays and cancellations due to winter weather. FACT concentrates relevant information from the Internet and FAA databases on one screen for easy access. It provides collaboration tools for those managing the winter weather event including the airline operations center, airport authority (runway treatment), the Federal Aviation Administration air traffic control tower, and de-icing operators. Prediction tools are being added to improve FACT capabilities including one that anticipates changes in airport departure rates from weather forecasts. We have formed two user teams from affected airports to guide the design and evaluate the web-based prototype. Future work includes adding more automated capabilities and conducting a simulation to evaluate FACT in a realistic environment.
The meteorology of high southern latitudes during winter is simulated using a cloud-free version of The Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model version 4 (MM4) with a 100-km horizontal resolution. Comparisons between idealized simulations of Antarctic with MM4 and with the mesoscale model of Parish and Waight reveal that both models produce similarly realistic velocity fields in the boundary layer. The latter model tends to produce slightly faster drainage winds over East Antarctica. The intensity of the katabatic winds produced by MM4 is sensitive to parameterizations of boundary layer fluxes. Two simulations performed with MM4 using analyses from the European Center for Medium-Range Weather Forecasts (ECMWF) for June 1988 as initial and boundary conditions. A simulation of the period from 000 UTC 2 June to 0000 UTC 8 June produces realistic synoptic phenomena including ridge development over East Antarctica, frontogenesis over the Amundsen Sea, and a katabatic surge over the Ross Ice Shelf. The simulated time-averaged fields for June 1988, particularly that of a 500-hPa height, are in good agreement with time-averaged fields analyzed by the ECMWF. The results of the simulations provide detailed features of the Antarctic winter boundary layer along the steeply sloping terrain. Highest boundary layer wind speeds averaged over the month-long simulation are approximately 20 m/s. The lack of latent heating in the simulations apparently results in some bias in the results. In particular, the cloud-free version of MM4 underpredicts the intensity of lows in the sea level pressure field.
Experimental satellite-based icing products developed by the NASA Langley Research Center provide new tools to identify the locations of icing and its intensity. Since 1997, research forecasters at the National Center for Atmospheric Research (NCAR) have been helping to guide the NASA Glenn Research Center's Twin Otter aircraft into and out of clouds and precipitation for the purpose of characterizing in-flight icing conditions, including supercooled large drops, the accretions that result from such encounters and their effect on aircraft performance. Since the winter of 2003-04, the NASA Langley satellite products have been evaluated as part of this process, and are being considered as an input to NCAR s automated Current Icing Potential (CIP) products. This has already been accomplished for a relatively straightforward icing event, but many icing events have much more complex characteristics, providing additional challenges to all icing diagnosis tools. In this paper, four icing events with a variety of characteristics will be examined, with a focus on the NASA Langley satellite retrievals that were available in real time and their implications for icing nowcasting and potential applications in CIP.