General aviation pilots who encounter hazardous weather face a heightened risk of fatal accidents compared to those in other sectors of aviation. To help avoid unplanned weather encounters, accurate information on cloud type and sky conditions can enhance situational awareness and hazard recognition. Among available weather information resources, ground-based webcam networks are growing for aviation meteorology and other interests such as wildfire monitoring. These webcams can provide near-real-time visual weather information to pilots, especially in regions of complex terrain where traditional weather observation methods may lack adequate spatial and temporal coverage. To harness the benefits from webcams while reducing the need for manual interpretation, transfer learning is applied using off-the-shelf convolutional neural networks on a newly constructed meteorological dataset. This dataset, consisting of more than 15 500 rigorously labeled images from public webcam networks, is categorized into nine cloud types and weather conditions relevant to general aviation. Leveraging a five-model ensemble approach with the Inception-v3 architecture, a validation accuracy of 97.1% is achieved. Dataset classes are grouped into those that are typically considered nonhazardous or hazardous to general aviation operations, and a hazard-based classification accuracy of 99.5% is attained with the ensemble model.

General aviation (GA) pilots use different types of weather information when assessing weather conditions during pre-flight weather briefing tasks. Of concern is low altitude weather dynamics and variability between surface weather reporting stations where conditions are known. The study presented here investigated pilot decision making regarding interpolation of weather conditions to multiple potential target destinations, between known weather reporting stations, subject to the effects of differing terrain and microclimate weather patterns. Participants included 24 GA pilots, ranging from 156 to 40,000 h experience, who estimated flight rule conditions at three target locations each (including an actual accident site) in multiple geographic regions and observability of weather reporting stations. Pilots rarely provided correct estimations of flight rule conditions at target locations; accuracy did not always improve with increasing observability of other nearby reporting stations. Despite these errors, pilots were confident in their estimates of flight rule conditions at target locations.

We provide an updated analysis of the gamma ray signature of a terrestrial gamma ray flash (TGF) detected by the Fermi Gamma ray Burst Monitor first reported by Pu et al. (2020, https://doi.org/10.1029/2020GL089427 ). A TGF produced 3 ms prior to a negative cloud‐to‐ground return stroke was close to simultaneous with an isolated low‐frequency radio pulse during the leader’s propagation, with a polarity indicating downward moving negative charge. In previous observations, this “slow” low‐frequency signal has been strongly correlated with upward‐directed (opposite polarity) TGF events (Pu et al., 2019, https://doi.org/10.1029/2019GL082743 ; Cummer et al., 2011, https://doi.org/10.1029/2011GL048099 ), leading the authors to conclude that the Fermi gamma ray observation is actually the result of a reverse positron beam generating upward‐directed gamma rays. We investigate the feasibility of this scenario and determine a lower limit on the luminosity of the downward TGF from the perspective of gamma ray timing uncertainties, TGF Monte Carlo simulations, and meteorological analysis of a model storm cell and its possible charge structure altitudes. We determined that the most likely source altitude of the TGF reverse beam was 7.5 km ± 2.6 km, just below an estimated negative charge center at 8 km. At that altitude, the Monte Carlo simulations indicate a lower luminosity limit of 2 × 10 18 photons above 1 MeV for the main downward beam of the TGF, making the reverse beam detectable by the Fermi Gamma ray Burst Monitor.

Pilot Reports (PIREPs) are an important source of information that aids, other pilots, air traffic control, and operational aviation meteorologists in terms of forecasting and updating weather advisories such as SIGMETs. Pilots rely upon PIREPs so they can avoid hazardous weather and fly their aircraft in the safest manner possible. However, many PIREPs are not successfully submitted or transmitted to the many end users which impedes their ability to be used to keep the NAS safe. The National Transportation Safety Board (NTSB) made several recommendations for increasing the effectiveness and distribution of PIREPs, including receiving PIREPs from pilots directly and automatically (NTSB, 2017). We recruited eighty-four native-speaking participants to read a short, average, and long PIREP scripts in order to test the performance of various speech recognition systems (SRSs). The spoken PIREPs were transcribed by SRSs and compared to the original PIREP scripts. The words that were deleted, substituted, and inserted were identified and used to calculate the word error rate (WER) and word information loss (WIL). The WERs and WILs were separately analyzed with a repeated-measures marginal model to compare the accuracy between each of the SRSs. Also, the interaction between each SRS and gender was analyzed. The results demonstrated that Google, LilySpeech, and Transcribe had the same and superior performance when transcribing the average-length PIREPs than Braina and Dragon. All SRSs had equal performance at transcribing the short-length PIREPs. Dragon, Google, LilySpeech, and Transcribe had the same performance and superior when transcribing the long-length PIREPs than Braina. Additionally, we found that the short, average, and long-length transcriptions for all 5 commercial off-the-shelf (COTS) SRSs provided readable information for flight service stations (FSS) to enter valuable weather information into the PIREP system.

Aviation meteorological surface observations are a used to explore enhancement of advisory information for pilots as part of continued efforts by an FAA Center of Excellence on reducing gaps and risks associated with general aviation and/or low-altitude operations. The Endsley model of situational awareness is progressively layered from perception to comprehension to projection. In our work, the focus is on the analysis of relevant cues within surface meteorological weather observations as a basis but adding in additional geospatial and environmental factors such as the altitude of the weather stations and the climatic zones they reside in. Analysis of these data include statistical and machine learning tools at varied spatial and temporal scales with the purpose of defining the underlying representativeness of the observations to develop tools and/or advisory information for pilots that aid their efforts in integrating and projecting such information in the near term without significantly adding to pilot workload. Weather information is used from Automated Surface and Weather Observing System (ASOS/AWOS) stations within California as a test region with complex geographic and climatic variations with a particular focus on flight rules categories which stem from ceilings and visibility observations. The ASOS/AWOS data were supplemented by data from regional mesonets for some of the analysis effort. Weather information representativeness is shown to have impactful temporal patterns at time scales ranging from diurnal to interannual as well as for a range of spatial scales and are shown to be influenced by local and regional geographic and climatic factors. A framework for integrating this complex array of information into prototype advisory information, both pre-flight and en-route, is proposed.

We provide an updated analysis of the gamma-ray signature of a terrestrial gamma ray flash (TGF) detected by the Fermi Gamma-ray Burst Monitor first reported by Pu et al. 2020. Gamma-ray photons were produced 3ms prior to a negative cloud-to-ground return stroke and were close to simultaneous with an isolated low frequency radio pulse during the leaders propagation, with a polarity indicating downward moving negative charge. This ‘slow’ low frequency signal occurring prior to the main discharge has previously been strongly correlated with upward directed TGF events (Pu et al. 2019, Cummer et al. 2011) leading the authors to conclude that the Fermi detected counts just prior to the return stroke are the result of a reverse positron beam generating upward directed gamma rays. We investigate the feasibility of this scenario and constrain the limits on the origin altitude from the perspective of the gamma-ray signature timing uncertainties, TGF Monte Carlo simulations, estimates of intrinsic brightness as a function of altitude, and meteorological analysis of the storm and its possible charge structure and altitude.

This paper describes multiple research activities supported through an FAA Center of Excellence, focused on reducing gaps and risks associated with general aviation pilot decision making and use of aviation weather information. Multiple sources of information uncertainty and variability affect pilot decision making, including temporal (including delays in information availability), geographical / terrain, and sensor coverage considerations comparing towered airport, unstaffed airfields, and non-airfield locations. These issues of information availability / uncertainty / variability affect general aviation fixed-wing and rotorcraft operations, and different types of rotorcraft mission operations, in different ways. Operational use of such information for pilots in the cockpit is quite distinct from deeper meteorological research or forecasting questions or information use. This paper discusses some of these questions, and provides examples of recent areas of investigation, from a perspective (and recognizing constraints) of weather information presented to pilots to support weather-related aviation environment awareness and decision making.

The meteorological characteristics associated with thunderstorm-top turbulence and tropical cyclone (TC) gigantic jets (GJs) are investigated. Using reanalysis data and observations, the large-scale environment and storm-top structure of three GJ-producing TCs are compared to three non-GJ oceanic thunderstorms observed via low-light camera. Evidence of gravity wave (GW) breaking is manifest in the IR satellite images with cold ring and enhanced-V signatures prevalent in TCs Hilda and Harvey and embedded warm spots in the Dorian and null storms. Statistics from an additional six less prodigious GJ environments are also included as a baseline. Distinguishing features of the TC GJ environment include higher tropopause, colder brightness temperatures, more stable lower stratosphere/distinct tropopause, and reduced tropopause penetration. These factors support enhanced GW breaking near the cloud top (overshoot). The advantage of a higher tropopause is that both electrical conductivity and GW breaking increase with altitude and thus act in tandem to promote charge dilution by increasing the rate at which the screening layer forms as well as enhancing the storm-top mixing. The roles of the upper-level ambient flow and shear are less certain. Environments with significant upper-tropospheric shear may compensate for a lower tropopause by reducing the height of the critical layer which would also promote more intense GW breaking and turbulence near the cloud top.

Understanding barriers to submitting pilot weather reports (PIREPs) has been the focus of recent attention in the general aviation community. The goal is to help increase the submission frequency of these reports, which are valuable for aviation operations and situational awareness. Additionally, the perception of the quality of these reports by pilots can impact the level of trust users have in the data. This study aims to evaluate aspects of the reporting frequency and quality of PIREPs particularly from the general aviation perspective. PIREPs were subjected to a range of logical, qualitative, and quantitative tests. Commercial applications are shown to improve the data quantity transmitted in the reports, particularly the non-mandatory sections such as sky and weather conditions, as well as to help alleviate some of the transcription errors. Reported times of the PIREPs indicate impacts from rounding that may limit the utility of the data in some instances. Analysis of individual geophysical measurements show varying quality with potential gaps noted in the icing type assessment and a bias towards higher turbulence intensity reporting, though air temperature compares well to independent data.

We report on three classes of terrestrial gamma ray flashes (TGFs) from the (RHESSI) satellite. The first class drives the detectors into paralysis, being observed usually through a few counts on the rising edge and the later tail of Comptonized photons. These events-and any bright TGF-reveal their true luminosity more clearly via their Compton tail than via the main peak, since the former is unaffected by the unknown beaming pattern of the unscattered radiation, and Comptonization mostly isotropizes the flux. This technique could be applied to TGFs from any mission. The second class is more than usually bright and long in duration. When the magnetic field at the conjugate point is stronger than at the nearby footpoint, we find that 4 out of 11 such events show a significant signal at the time expected for a relativistic electron beam to make a round trip to the opposite footpoint and back. We conclude that a large fraction of TGFs lasting more than a few hundred microseconds may include counts due to the upward moving secondary particle beam ejected from the atmosphere. Finally, using a new search algorithm to find short TGFs in RHESSI, we see that these tend to occur more often over the oceans than land, relative to longer-duration events. In the feedback model of TGF production, this suggests a higher thunderstorm potential, since more feedback per avalanche implies fewer "generations" of avalanches needed to complete the TGF discharge.

Wave measurements, such as height and period, are important in understanding estuarine and coastal processes as well as air-sea interactions, which are important in the validation of hydrodynamic models and improvements in the parameterizations needed for atmospheric models. This paper presents and evaluates a relatively inexpensive and portable video-based method of measuring the nearshore wave properties of a coastal estuary. The proposed system uses a GoPro camera along with a set of scripts to perform camera calibration and remove lens distortions. Significant wave heights and peak frequencies are then extracted from the video without the need for the ground control points (objects of known physical location in relation to the camera) as used in previous studies. By not relying on ground control points, the system becomes portable and easy to deploy, allowing the collection of wave data from various locations on the estuary. The estimated significant wave heights and peak frequencies were validated with a sonic wave gauge located in the camera's field of view. Percent error and RMSE were calculated and indicate the proposed method is robust and the errors (estimated at less than 6%) are consistent with other video-based methods that use multiple cameras and ground control points.

Here we report the first observations of gigantic jets (GJs) by the Geostationary Lightning Mapper (GLM) on board the Geostationary Operational Environmental Satellite‐R series. Fourteen GJs produced by Tropical Storm Harvey on 19 August 2017 were observed by both GLM and a ground‐based low‐light‐level camera system. The majority of the GJs produced distinguishable signatures in the GLM data, which include long continuous emissions, large peak flash optical energies, and small lateral propagation distances in comparison with other flashes observed by GLM. For two GJs with the best ground‐based images, each have a single pixel that contains the largest optical energy throughout the duration of the GJ and also coincides with the azimuth of the GJ from the video images. The optical energy of the pixel increases as the GJ propagates upward, reaches its peak when the GJ connects to the ionosphere, and then fades away.

High resolution hydrodynamic models are computationally expensive to run – especially if ensemble forecasts are desired. This can be problematic within coastal estuaries which are not well resolved by today's operational meteorological forecast models. As an alternative, this paper evaluates the wind forcing for three setup parameterizations (based on the Zuiderzee, modified Zuiderzee, and long wave equations) using a combination of observed setup from in-situ water level gauges and local wind observations. In addition, three methods are explored for developing hourly time series of wind forcing from 5-min observations: top of the hour, hourly mean, and wind run approach. The wind forcings, which are weighted by the length of two lagoon-oriented axes, are used to drive the setup parameterizations. The observed setup is used to tune each of the parameterizations via a least squares approach. The observation spread, linear model residuals, coefficient of determination (R2), and root mean squared error (RMSE) indicate that the wind run out performs the other two methods. In terms of the three parameterizations, the modified Zuiderzee had consistently higher R2 values, lower RMSE, and narrower 95% confidence intervals than the two other methods. This optimized parameterization is currently being used operationally to generate ensemble setup forecasts for the Indian River Lagoon, a restricted estuary on Florida's east-central coast. These simple ensemble forecasts are designed to guide the National Weather Service (NWS) in identifying potentially significant setup events that warrant high resolution hydrodynamic simulations.

A computationally efficient method is developed that performs gridded postprocessing of ensemble 10-m wind vector forecasts. An expansive set of idealized WRF Model simulations are generated to provide physically consistent, high-resolution winds over a coastal domain characterized by an intricate land/water mask. The ensemble model output statistics (EMOS) technique is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. In a yearlong study, the method is applied to 24-h wind forecasts from the Global Ensemble Forecast System (GEFS) at 28 east-central Florida stations. Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicates that the postprocessed forecasts are calibrated. A downscaling case study illustrates the method as applied to a quiescent easterly flow event. Strengths and weaknesses of the approach are presented and discussed.

Gigantic jets are atmospheric electrical discharges that propagate from the top of thunderclouds to the lower ionosphere. They begin as lightning leaders inside the thundercloud, and the thundercloud charge structure primarily determines if the leader is able to escape upward and form a gigantic jet. No observationally verified studies have been reported on the thundercloud charge structures of the parent storms of gigantic jets. Here we present meteorological observations and lightning simulation results to identify a probable thundercloud charge structure of those storms. The charge structure features a narrow upper charge region that forms near the end of an intense convective pulse. The convective pulse produces strong storm top divergence and turbulence, as indicated by large values of storm top radial velocity differentials and spectrum width. The simulations show the charge structure produces leader trees closely matching observations. This charge structure may occur at brief intervals during a thunderstorm's evolution due to the brief nature of convective pulses, which may explain the rarity of gigantic jets compared to other forms of atmospheric electrical discharges.

This paper presents a method to bias correct and downscale wind speed over water bodies that are unresolved by numerical weather prediction (NWP) models and analyses. The dependency of wind speeds over water bodies to fetch length is investigated as a predictor of model wind speed error. Because model bias is found to be related to the forecast wind direction, a statistical method that uses the forecast fetch to remove wind speed bias is developed and tested. The method estimates wind speed bias using recent forecast errors from similar stations (i.e., those with comparable fetch lengths). As a result, the bias correction is not tied to local observations but instead to locations with similar land-water characteristics. Thus, it can also be used to downscale wind fields over inland and coastal water bodies. The fetch method is compared to four reference bias correction methods using one year's worth of wind speed output from three NWP analyses in Florida. The fetch method yields a bias error near zero and results in a reduction of the mean absolute error that is comparable to the reference methods. The fetch method is then used to bias correct and downscale a coarse analysis to 500-m grid spacing over a coastal estuary in central Florida.