Aviation Forecasting Research Articles

Generic Deep-Learning-Based Time Series Models for Aviation Accident Analysis and Forecasting

Generic Deep-Learning-Based Time Series Models for Aviation Accident Analysis and Forecasting

A Deep Learning–Based Velocity Dealiasing Algorithm Derived from the WSR-88D Open Radar Product Generator

Abstract Radial velocity estimates provided by Doppler weather radar are critical measurements used by operational forecasters for the detection and monitoring of life-impacting storms. The sampling methods used to produce these measurements are inherently susceptible to aliasing, which produces ambiguous velocity values in regions with high winds and needs to be corrected using a velocity dealiasing algorithm (VDA). In the United States, the Weather Surveillance Radar-1988 Doppler (WSR-88D) Open Radar Product Generator (ORPG) is a processing environment that provides a world-class VDA; however, this algorithm is complex and can be difficult to port to other radar systems outside the WSR-88D network. In this work, a deep neural network (DNN) is used to emulate the two-dimensional WSR-88D ORPG dealiasing algorithm. It is shown that a DNN, specifically a customized U-Net, is highly effective for building VDAs that are accurate, fast, and portable to multiple radar types. To train the DNN model, a large dataset is generated containing aligned samples of folded and dealiased velocity pairs. This dataset contains samples collected from WSR-88D Level-II and Level-III archives and uses the ORPG dealiasing algorithm output as a source of truth. Using this dataset, a U-Net is trained to produce the number of folds at each point of a velocity image. Several performance metrics are presented using WSR-88D data. The algorithm is also applied to other non-WSR-88D radar systems to demonstrate portability to other hardware/software interfaces. A discussion of the broad applicability of this method is presented, including how other Level-III algorithms may benefit from this approach. Significance Statement Accurate and timely estimates of wind within storms are critically important for a number of applications, including severe storm nowcasting, maritime operational planning, aviation forecasting, and public safety coordination. Velocity aliasing is a common artifact that requires data quality control. While velocity dealiasing algorithms (VDAs) have been developed for decades, they remain a computationally complex and challenging problem. This paper presents an application of deep neural networks (DNNs) to increase the computational efficiency and portability of VDAs. A DNN is trained to emulate an operational algorithm, and performance is quantified over a large dataset. This work gives a convincing example of the benefits that deep learning can provide for radar algorithms, and future work highlighting these opportunities is discussed.

ТУМАН ТА ПОВ’ЯЗАНІ З НИМ МЕТЕОРОЛОГІЧНІ УМОВИ В РАЙОНІ АЕРОДРОМУ КИЇВ/АНТОНОВ-2

The State Enterprise “Antonov” Airport in the town of Hostomel occupies a strategic position due to its proximity to Kyiv, being less than 30 kilometers away. Moreover, it is capable of receiving and servicing large and specialized aircraft, making it a vital asset for aviation activities. One of the most important factors that significantly impact the aviation industry is meteorological conditions. This article examines fog and weather conditions that led to its formation in the vicinity of the Kyiv/Antonov-2 airport over a period of more than a decade from 2010 to 2020. Through careful analysis, not only the average and maximum frequencies of fog occurrence on a monthly, seasonal, and annual basis are considered, but also the characteristics of air temperature, wind, and visibility during fog episodes. The data source is information meticulously collected from the weather logs of AV-6 at the Kyiv/Antonov-2 airport during the specified period. The conclusions drawn are based on well-established methods of climatological analysis of meteorological data. The research has shown that fog is a meteorological phenomenon that is not frequently observed during the summer months in this region. However, as autumn sets in, their cumulative frequency steadily increases, reaching approximately 15% of the total number of fog occurrences. Interestingly, fogs are mostly observed at positive temperatures, although occasional exceptions occur when fog forms even in significantly cold conditions. Furthermore, it has been demonstrated that fogs at the Kyiv/Antonov-2 airport can occur independently of wind direction. It should be noted that fogs are most commonly observed with visibility ranging from 100 to 500 meters. The results of this study provide valuable information about the climate characteristics of the airport, which can be extremely useful for various stakeholders. For meteorologists and forecasters, these findings can serve as a valuable tool to improve the accuracy of fog-related forecasts at the airport, thereby promoting safer and more efficient aviation operations. The conclusions drawn from this research not only deepen the overall understanding of local weather conditions, but also hold practical significance for those involved in aviation and weather forecasting. Furthering such studies contributes to ensuring safer skies and more efficient airports.

The Ability of the ICE-T Microphysics Scheme in HARMONIE-AROME to Predict Aircraft Icing

AbstractIn-cloud icing is a major hazard for aviation traffic and forecasting of these events is an important task for weather agencies worldwide. A common tool utilized by aviation forecasters is an icing intensity index based on supercooled liquid water from numerical weather prediction models. We seek to validate the modified microphysics scheme, ICE-T, in the HARMONIE-AROME numerical weather prediction model with respect to aircraft icing. Icing intensities and supercooled liquid water derived from two 3-month winter season simulations with the original microphysics code, CTRL, and ICE-T are compared with pilot reports of icing and satellite retrieved values of liquid and ice water content fromCloudSat–CALIPSOand liquid water path from AMSR-2. The results show increased supercooled liquid water and higher icing indices in ICE-T. Several different thresholds and sizes of neighborhood areas for icing forecasts were tested out, and ICE-T captures more of the reported icing events for all thresholds and nearly all neighborhood areas. With a higher frequency of forecasted icing, a higher false alarm ratio cannot be ruled out, but is not possible to quantify due to the lack of no-icing observations. The increased liquid water content in ICE-T shows a better match with the retrieved satellite observations, yet the values are still greatly underestimated at lower levels. Future studies should investigate this issue further, as liquid water content also has implications for downstream processes such as the cloud radiative effect, latent heat release, and precipitation.

Analysis of Meteorological Service Effect of a Low-cloud and Low-visibility Weather Process at Shanghai’s two Airports

The emergence of low-cloud and low-visibility can affect air activities, and bring huge economic losses to airlines. Based on the aviation meteorological observation, forecast and airport alert of Pudong and Hongqiao airports, this paper analyses the meteorological service effect of a low-cloud and low-visibility weather process in Shanghai from January 27th to 28th, 2016. The results show that: (1) The absolute error between forecast and actual situation of Pudong and Hongqiao airport presents an obvious normal distribution and the forecast accuracy of Pudong is higher than that of Hongqiao. (2) For the airport with cloud ceiling of 30m∼60m and prevailing visibility of 600m∼800m, the alert of Pudong airport is more accurate and advanced, but for the airport with cloud height of less than 30m and prevailing visibility of 400m∼600m, the airport alert is quite different from the actual situation, and lacks lead time. (3) The alert of Hongqiao airport is accurate for the airport with visibility less than 800m from 03:30 to 10:30 on 28th, and the lead time is relatively high. However, it lacks the lead time for the airport with prevailing visibility greater than 1000m at 11:00 on 28th.

Objective detection of gravity waves in Himawari‑8 imagery in support of aviation forecasting

Objective detection of gravity waves in Himawari‑8 imagery in support of aviation forecasting

Addressing the Cold Air Aloft Aviation Challenge with Satellite Sounding Observations

At high latitudes in winter, the atmosphere at flight levels used by passenger and cargo aircraft can reach temperatures cold enough to restrict the flow of jet fuel from the fuel tanks to the engine, due either to water freezing in the fuel or the fuel itself freezing. Currently, aviation forecasters rely on a combination of aircraft reports, pilot reports, a sparse network of radiosondes, and global model fieldsfor identifying and characterizing Cold Air Aloft (CAA) events. More atmospheric data are needed to improve forecasts of CAA placement and timing, and satellite observations can help fill the gap. In particular, products derived from the NOAA-Unique Combined Atmospheric Processing System (NUCAPS) can be utilized by National Weather Service (NWS) forecasters to assist in the production of aviation hazard products. NUCAPS combines measurements from infrared and microwave sounding instruments on polar-orbiting satellites to retrieve atmospheric profiles of temperature and moisture in the high latitudes. NWS forecasters have real-time access to NUCAPS soundings via the Advanced Weather Interactive Processing System-II (AWIPS-II). The Joint Polar Satellite System Sounding Applications Initiative created Gridded NUCAPS in order to view soundings as isobaric surfaces or vertical cross sections in AWIPS-II. The Cooperative Institute for Research in the Atmosphere (CIRA) developed a web-based product for displaying satellite-derived CAA information. This paper describes how the AWIPS-II and CIRA displays of satellite sounding observations augment aviation forecasting activities in Alaska using two specific CAA cases from the 2016–2017 and 2017–2018 winter seasons.

Modern meteorological observations for aviation purposes

The purpose of this article. Meteorological support for aviation is a component of the safety systems. With the development of aviation technology, the requirements for it are growing. Today, aviation remains the most demanding user of meteorological information. The main parameters that affect the aircraft’s aerodynamic characteristics include air temperature, wind direction, atmospheric pressure, the height of the lower cloud boundary, the amount of precipitation and the frequency of dangerous meteorological phenomena. The aim of the study is a spatio-temporal analysis of meteorological parameters at the air base Chuguiv, Kharkiv region. Main material. Time series of daily average, average monthly air temperature, relative humidity, cloudiness, atmospheric pressure, atmospheric precipitation, wind direction and frequency of dangerous meteorological phenomena for the period 2008-2017 were processed as initial data. Changes in meteorological values during the year, season and month were studied. Favorable weather conditions for flights have been determined. Recommendations are developed regarding the performance of flights under various weather conditions. To date, many different aircraft flight models have been developed in non-standard weather situations. In T.V.Safonova’s work the forecast of the indicators of airports functioning and flights on routes in difficult meteorological conditions is considered. Consequently, aviation must have timely meteorological information increasing the safety, regularity and economy of flights for effective implementation of practical tasks. Conclusions. Analysis of the literature has showed that complex meteorological conditions create critical situations on routes and require further detailed study. To maintain a high level of meteorological service for aviation that meets international standards, it is necessary to conduct scientific and applied research in the field of aviation climatology and improve existing methods of aviation forecasting.

Forecaster Evaluations of High Temporal Satellite Imagery for the GOES-R Era at the NWS Operations Proving Ground

AbstractBetween February and April of 2015, the National Weather Service (NWS) Operations Proving Ground (OPG) facilitated an evaluation of the usefulness of 1-min satellite imagery for NWS operations in the Geostationary Operational Environmental Satellite-R (GOES-R) series era. The overarching goal of the evaluation was to provide quantitative and qualitative guidance to NWS management, including the regional NWS Scientific Services division chiefs, on how satellite imagery with a refresh rate of 1 min impacts NWS forecaster decision-making. During the simulations, forecasters evaluated 1- and 5-min satellite imagery while completing tasks ranging from aviation forecasting and wildfire decision support services to monitoring where convective initiation would occur and integrating the imagery into the convective warning decision-making process. Feedback was gathered to assess if the satellite imagery had influence on forecaster decision-making, if the satellite imagery provided them with more confidence in making those decisions, if forecasters could assimilate the data into operational practices, and if there were adverse impacts on forecaster workload. Forecasters overwhelmingly were of the opinion that 1-min satellite imagery improved their ability and increased their confidence to make effective forecast and warning decisions. The majority of participants expressed that they were able to internally assimilate the imagery with ease. However, feedback gathered when forecasters were asked how useful and easy the imagery was to use in convective warning operations was mixed. Some forecasters expressed difficulty integrating both satellite imagery and radar data while issuing convective warnings. Others felt that with ample training and experience the imagery would be invaluable in warning operations.

Investigation of the Effect of Weather Conditions on Solar Energy in Rivers State University of Science and Technology, Port Harcourt, Nigeria

This study presents the effect of temperature, relative humidity, rainfall and evaporation on solar energy in Port Harcourt City, Nigeria. The data was recorded every 5 minutes daily and monthly, covering 2008 – 2014, in The Rivers State University of Science and Technology, Port Harcourt Meteorological Logging Unit. The MATLAB software used in the analysis of the data reveal a direct relationship between solar radiation and temperature and evaporation, and an inverse relationship with rainfall and relative humidity. The mean atmospheric temperature is 26.02oC, while solar energy varies between 47.07 mJm-2 month-1 and 113.99 mJm-2month-1 with a mean of 87.87 mJm-2month-1. During the rainy season (April – September), the mean monthly solar energy is 86.69mJm-2month-1, while in the dry season (October – March), the mean is 89.05mJm-2month-1. The amount of evaporation per temperature ranges from 2.92 mm/oC to 4.47 mm/oC with a mean annual of 3.57 mm/oC. In the rainy season, this value varies from 2.79 mm/oC to 3.79 mm/oC. In the dry season months, the variation is from 3.12 mm/oC to 4.47 mm/oC. The rainfall-to-evaporation ratio of 5.71 in rainy month demonstrates that about the amount of rainfall is recorded for the amount of water evaporated. This correlates with the fact that humidity is high. The results of this work could be used in the design and construction of solar energy technologies to provide energy. Moreover, the results could also provide input for climate risk information, local and national planning and decision-making for sustainable development in the areas of agriculture, aviation, health, and weather forecasting.

Metrological analysis of a gravimetric calibration system for tipping‐bucket rain gauges

ABSTRACTThe monitoring of hydro‐meteorological variables for operational and research purposes requires accurate measurements. The reliability of such measurements varies depending on the need to meet different requirements in several sectors, including aviation, agriculture, civil protection, entertainment and weather forecast. In the case of rain gauges, many factors and variables affect the measurement of liquid and solid precipitation in the field. Calibration is a primary tool for quality control and requires suitable infrastructure to perform the tests, and is done by using properly evaluated testing methods and procedures. The current study presents the analysis of a typical calibration system for tipping‐bucket rain gauges, using the gravimetric method, in accordance with the recommendations and requirements of both meteorology and metrology. As a result, the uncertainty contribution of each component of the system and an assessment of the resulting overall uncertainty budget are obtained.

Scale normalization for IFR-frequency effects in aviation forecast performance statistics

The National Weather Service uses probability of detection and false alarm ratio to assess forecast performance. Statistical evidence indicates that a quantitative relationship exists between these forecast performance metrics and the frequency with which a forecasted condition occurs. Current national aviation performance goals do not account for this relationship, which reduces their utility. There is meaningful evidence that indicates the influence of low ceiling/visibility frequency on national aviation forecast performance metric averages can be neutralized through scale normalization.

Micro-climatic study and trend analysis of fog characteristics at IGI airport New Delhi using hourly data (1981-2005)

Indira Gandhi International (IGI) airport, New Delhi where near about 675 flights on an averagedepart and arrive daily, is highly susceptible to dense fog occurrences during the winter season. In the present paper, anattempt has been made for development of an intensity based fog climatological information system for December andJanuary based on hourly visibility data of 25-years (1981-2005) recorded at IGI airport. Variations and trends if any werealso analyzed along with their extreme years and dates of occurrences. Data since 1964 were also used to find climaticjumps in the trend which includes various higher visibilities of no fog conditions. Besides various vital fog climatologicalinformation generated through the present study for use in aviation, the most important finding is the alarming increasingtrend of the dense fog (< 200m) occurrences in both the months up to as high as 10-20 times from 1960s in contrast tounusual drastic reduction of higher visibility hours to as low as one thirtieth to one fiftieth of hours which were observedin 1960s. Thus, finally making IGI airport, a unique airport in the world which hardly experiences good visibilityconditions (>5000m) in both the months. By considering the unexpected huge annual growth of 30% in both air trafficand passengers that India including IGI has presently been experiencing against the global average of 6%, such visibilitytrend also confirms that present flight disruptions and passengers sufferings in winter will be aggravated more severely indays to come unless CAT-III ILS implemented fully. Finally, we have computed further number of consecutive hours,spell periodicity, most favorable climatological timing of fog onset and fog dispersal based on various intensities for usein aviation and fog forecasting.

A Comparative Verification of Localized Aviation Model Output Statistics Program (LAMP) and Numerical Weather Prediction (NWP) Model Forecasts of Ceiling Height and Visibility

Abstract In an effort to support aviation forecasting, the National Weather Service’s Meteorological Development Laboratory (MDL) has recently redeveloped the Localized Aviation Model Output Statistics (MOS) Program (LAMP) system. LAMP is designed to run hourly in NWS operations and produce short-range aviation forecast guidance at 1-h projections out to 25 h. This paper compares and contrasts LAMP ceiling height and visibility forecasts with forecasts produced by the 20-km Rapid Update Cycle model (RUC20), the Weather Research and Forecasting Nonhydrostatic Mesoscale Model (WRF-NMM), and the Short-Range Ensemble Forecast system (SREF). RUC20 and WRF-NMM forecasts of continuous ceiling height and visibility were interpolated to stations and converted into categorical forecasts. These interpolated forecasts were also categorized into instrument flight rule (IFR) or lower conditions and verified against LAMP forecasts at stations in the contiguous United States. LAMP and SREF probabilistic forecasts of ceiling height and visibility from LAMP and the SREF system were also verified. This study demonstrates that for the 0000 and 1200 UTC cycles over the contiguous United States, LAMP station-based categorical forecasts of ceiling height, visibility, and IFR conditions or lower are more accurate than the RUC20 and WRF-NMM ceiling height and visibility forecasts interpolated to stations. Moreover, for the 0900 and 2100 UTC forecast cycles and verification periods studied here, LAMP ceiling height and visibility probabilities exhibit better reliability and skill than the SREF system.

European climatology of fog and low stratus based on geostationary satellite observations

AbstractThe distribution of fog and low stratus (FLS) is of importance in nowcasting, aviation forecasting and climate applications. This paper presents satellite‐derived FLS maps as a basis for building a European FLS climatology with high spatial and temporal resolution. Averaged maps covering several winter seasons of Meteosat Second Generation (MSG) data are analysed and compared to a cloud climatology based on a 26‐year record of ground‐based visual cloud observations. The general patterns seen in both products are found to be in good agreement and plausible. Copyright © 2009 Royal Meteorological Society

An advanced model for predicting clear‐air visibility

AbstractThis article describes a new model to predict clear‐air visibility as a function of bearing with respect to the Sun. Its primary use is for aviation forecasting, and it is capable of providing estimates of visual range for any point along an aircraft flight path, using normal output from a numerical forecast model. It is also capable of forecasting line‐of‐sight visibility along airport approach paths. The model uses Mie theory to predict scattering from the direct solar beam into the line of sight, and combines this with a diffuse and Rayleigh component of the extinction coefficient to provide visual range estimates. The methodology is presented together with some examples of how visibility varies with solar bearing angle. © Crown copyright 2007. Reproduced with the permission of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd

Developments in aviation forecasting in the UK

AbstractThe main developments in aviation forecasting over the 50 years up to 1985 are summarised. The importance of subsequent increasing Regional and World Area Forecasting commitments at the UK Meteorological Office is stressed, along with a demonstration of the more recent advances in forecasting low‐level and middle/upper‐level significant weather, respectively. Meteorological hazards requiring warnings (e.g. SIGMETS) and current aviation forecast output are considered, along with a glance into the future, cautioning that the trend towards fully automated products may be hindered by slower improvements in some aspects of model technology such as derived‐field forecasts of fog and low cloud. Copyright © 1998 Royal Meteorological Society

Distributions of Surface-Layer Buoyancy Versus Lifting Condensation Level over a Heterogeneous Land Surface

Onset and coverage of small cumulus clouds depend on the relative abundance of surface-layer air parcels possessing favourable buoyancy and moisture—two variables that are coupled through the surface energy budget. This abundance is described using a joint frequency distribution (JFD) as a function of virtual potential temperature θv. and height of lifting condensation level zLCL. It is shown analytically that the shape and spread of this JFD depends on die ranges of Bowen ratios and solar forcings (albedoes, cloud shading, etc.) that exist within a domain of heterogeneous land use. To sample the character of such JFDs in the real atmosphere, a case study is presented using turbulence data gathered by aircraft flying in the surface layer of southwest France. This case study includes 4 days of clear skies during the Hydrologic Atmospheric Pilot Experiment (HAPEX) of 1986. The full flight track during HAPEX overflew a wide range of land use including evergreen forest, corn, vineyards, pastures, and irrigated fields over varied topography. The JFDs from these full tracks are found to he quite complex, being frequently multimodal with a convoluted perimeter. However. when a full track is broken into segments, each over a subdomain of quasi-homogeneous land use, the resulting segment JFDs are mono-modal with simpler topology. Such a characterization of IFDs provides guidance toward eventual subgrid cumulus parameterization in large-scale forecast models, with associated impacts in aviation forecasting, pollutant venting and chemical reactions, vertical dispersion and turbulence modulation, and radiation balance in climate-change models.

Weather forecasting on parallel architectures

Global forecasting at the National Meteorological Center is performed using a spherical harmonics spectral model. The model is used in global data assimilation, and in the aviation and medium range forecast production suites. In this presentation, the major numerical and physical aspects of the model are highlighted, and parallelization considerations are analyzed and applied to three parallel computer architectures. The first parallelization is on a parallel vector processor shared memory platform such as Cray YMP or C90. The other two are on distributed memory platforms: a data parallel implementation on Connection Machines and a single program multiple data (SPMD) adaptation to a Cray T3D machine.

Educational Opportunities in Aviation Education

Editor's Note: This speech was given at the fifth annual General Aviation Forecast Conference on March 17, 199.5, in Phoenix, Ariz. This speech presents a synopsis of the ability of the University Aviation Association (UAA) to provide aviation education for students preparing for entry-level positions in the aviation industry. The aviation education environment presently provides education experiences for more than 41,000 students interested in careers in general aviation and in commercial aviation. As change occurs in the aviation industry, specifically in general aviation, we must be mindful of the affect of this change on the educational system and its ability to respond to change. The need for partnerships and cooperative efforts between government, the aviation industry, and educational institutions of higher learning is highlighted. The capabilities of the aviation education system are discussed in light of the forecast needs of the aviation industry, with the goal of maintaining the present high quality of education and the present levels of safety in the aviation industry.