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Radio frequency interference censoring scheme for Canadian Weather Radar

July 12, 2023
  |
Project Report
Author:
John Y. Cho
Published in:
MIT Lincoln Laboratory Report ATC-454
Topic:
radar
R&D area:
R&D group:

Summary

An automated scheme is developed for the upgraded S-band polarimetric Canadian weather radars to detect and censor radio frequency interference from wireless communication devices. The suite of algorithms employed in this scheme effectively identifies and edits out interference-contaminated reflectivity data, while preserving data dominated by weather signals. This scheme was implemented in the NextGen Weather Processor test reference system for continuous real-time testing, and is expected to be incorporated into the new Canadian Aviation Weather Systems.
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Summary

An automated scheme is developed for the upgraded S-band polarimetric Canadian weather radars to detect and censor radio frequency interference from wireless communication devices. The suite of algorithms employed in this scheme effectively identifies and edits out interference-contaminated reflectivity data, while preserving data dominated by weather signals. This scheme was...

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Radio frequency interference censoring scheme for Canadian Weather Radar

A deep learning-based velocity dealiasing algorithm derived from the WSR-88D open radar product generator

July 1, 2023
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Journal Article
Author:
Mark S. Veillette
…
Published in:
Artificial Intelligence for the Earth Systems, Vol. 2, Issue 3, July 2023.
Topic:
radar
R&D area:
R&D group:

Summary

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.
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Summary

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...

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A deep learning-based velocity dealiasing algorithm derived from the WSR-88D open radar product generator

Visibility estimation through image analytics

June 20, 2023
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Project Report
Author:
Michael P. Matthews
Published in:
MIT Lincoln Laboratory Report ATC-453
Topic:
algorithms
R&D area:
R&D group:

Summary

MIT Lincoln Laboratory (MIT LL) has developed an algorithm, known as the Visibility Estimation through Image Analytics Algorithm (VEIA), that ingests camera imagery collected by the FAA Weather Cameras Program Office (WeatherCams) and estimates the meteorological visibility in statute miles. The algorithm uses the presence of edges in the imagery and the strength of those edges to provide an estimation of the meteorological visibility within the scene. The algorithm also combines the estimates from multiple camera images into one estimate for a site or location using information about the agreement between camera estimates and the position of the Sun relative to each camera's view. The final output for a site is a prevailing visibility estimate in statute miles that can be easily compared to existing automated surface observation systems (ASOS) and/or human-observed visibility. This report includes thorough discussion of the VEIA background, development methodology, and transition process to the WeatherCams office operational platform (Sections 2–4). A detailed software description with flow diagrams is also provided in Section 5. Section 6 provides a brief overview of future research and development related to the VEIA algorithm.
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Summary

MIT Lincoln Laboratory (MIT LL) has developed an algorithm, known as the Visibility Estimation through Image Analytics Algorithm (VEIA), that ingests camera imagery collected by the FAA Weather Cameras Program Office (WeatherCams) and estimates the meteorological visibility in statute miles. The algorithm uses the presence of edges in the imagery...

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Visibility estimation through image analytics

Extended polarimetric observations of chaff using the WSR-88D weather radar network

June 20, 2023
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Journal Article
Author:
James M. Kurdzo
…
Published in:
IEEE Transactions on Radar Systems, vol. 1, pp. 181-192, 2023.
Topic:
aviation weather
R&D area:
R&D group:

Summary

Military chaff is a metallic, fibrous radar countermeasure that is released by aircraft and rockets for diversion and masking of targets. It is often released across the United States for training purposes, and, due to its resonant cut lengths, is often observed on the S-band Weather Surveillance Radar–1988 Doppler (WSR-88D) network. Efforts to identify and characterize chaff and other non-meteorological targets algorithmically require a statistical understanding of the targets. Previous studies of chaff characteristics have provided important information that has proven to be useful for algorithmic development. However, recent changes to the WSR-88D processing suite have allowed for a vastly extended range of differential reflectivity, a prime topic of previous studies on chaff using weather radar. Motivated by these changes, a new dataset of 2.8 million range gates of chaff from 267 cases across the United States is analyzed. With a better spatiotemporal representation of cases compared to previous studies, new analyses of height dependence, as well as changes in statistics by volume coverage pattern are examined, along with an investigation of the new "full" range of differential reflectivity. A discussion of how these findings are being used in WSR-88D algorithm development is presented, specifically with a focus on machine learning and separation of different target types.
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Summary

Military chaff is a metallic, fibrous radar countermeasure that is released by aircraft and rockets for diversion and masking of targets. It is often released across the United States for training purposes, and, due to its resonant cut lengths, is often observed on the S-band Weather Surveillance Radar–1988 Doppler (WSR-88D)...

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Extended polarimetric observations of chaff using the WSR-88D weather radar network

Science applications of phased array radars

October 1, 2022
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Journal Article
Author:
Pavlos Kollias
…
Published in:
Bull. Amer. Meteorological Soc. (BAMS), Vol. 103, No. 10, 2022, pp. E2370-E2390.
Topic:
radar
R&D area:
R&D group:

Summary

Phased array radars (PARs) are a promising observing technology, at the cusp of being available to the broader meteorological community. PARs offer near-instantaneous sampling of the atmosphere with flexible beam forming, multifunctionality, and low operational and maintenance costs and without mechanical inertia limitations. These PAR features are transformative compared to those offered by our current reflector-based meteorological radars. The integration of PARs into meteorological research has the potential to revolutionize the way we observe the atmosphere. The rate of adoption of PARs in research will depend on many factors, including (i) the need to continue educating the scientific community on the full technical capabilities and trade-offs of PARs through an engaging dialogue with the science and engineering communities and (ii) the need to communicate the breadth of scientific bottlenecks that PARs can overcome in atmospheric measurements and the new research avenues that are now possible using PARs in concert with other measurement systems. The former is the subject of a companion article that focuses on PAR technology while the latter is the objective here.
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Summary

Phased array radars (PARs) are a promising observing technology, at the cusp of being available to the broader meteorological community. PARs offer near-instantaneous sampling of the atmosphere with flexible beam forming, multifunctionality, and low operational and maintenance costs and without mechanical inertia limitations. These PAR features are transformative compared to...

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Science applications of phased array radars

Multimodal physiological monitoring during virtual reality piloting tasks

August 25, 2022
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Journal Article
Author:
Hrishikesh Mohan Rao
…
Published in:
Multimodal Physiological Monitoring During Virtual Reality Piloting Tasks (version 1.0.0). PhysioNet. https://doi.org/10.13026/azwa-ge48.
Topic:
health
R&D area:
R&D group:

Summary

This dataset includes multimodal physiologic, flight performance, and user interaction data streams, collected as participants performed virtual flight tasks of varying difficulty. In virtual reality, individuals flew an "Instrument Landing System" (ILS) protocol, in which they had to land an aircraft mostly relying on the cockpit instrument readings. Participants were presented with four levels of difficulty, which were generated by varying wind speed, turbulence, and visibility. Each of the participants performed 12 runs, split into 3 blocks of four consecutive runs, one run at each difficulty, in a single experimental session. The sequence of difficulty levels was presented in a counterbalanced manner across blocks. Flight performance was quantified as a function of horizontal and vertical deviation from an ideal path towards the runway as well as deviation from the prescribed ideal speed of 115 knots. Multimodal physiological signals were aggregated and synchronized using Lab Streaming Layer. Descriptions of data quality are provided to assess each data stream. The starter code provides examples of loading and plotting the time synchronized data streams, extracting sample features from the eye tracking data, and building models to predict pilot performance from the physiology data streams.
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Summary

This dataset includes multimodal physiologic, flight performance, and user interaction data streams, collected as participants performed virtual flight tasks of varying difficulty. In virtual reality, individuals flew an "Instrument Landing System" (ILS) protocol, in which they had to land an aircraft mostly relying on the cockpit instrument readings. Participants were...

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Multimodal physiological monitoring during virtual reality piloting tasks

Fun as a strategic advantage: applying lessons in engagement from commercial games to military logistics training

July 1, 2022
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Journal Article
Author:
Jesslyn D. Alekseyev
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Published in:
Train., Educ., Learn. Sci., Vol. 59, 2022, p. 180-7.
Topic:
serious games
R&D area:
R&D group:

Summary

Digital games offer many elements to augment traditional classroom lectures and reading assignments. They enable players to explore concepts through repeat play in a low-risk environment, and allow players to integrate feedback given during gameplay and evaluate their own performance. Commercial games leverage a number of features to engage players and hold their attention. But do those engagement-improving methods have a place in instructional environments with a captive and motivated audience? Our experience building a logistics supply chain training game for the Marine Corps University suggests that yes; applying lessons in engagement from commercial games can both help improve player experience with the learning environment, and potentially improve learning outcomes.
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Summary

Digital games offer many elements to augment traditional classroom lectures and reading assignments. They enable players to explore concepts through repeat play in a low-risk environment, and allow players to integrate feedback given during gameplay and evaluate their own performance. Commercial games leverage a number of features to engage players...

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Fun as a strategic advantage: applying lessons in engagement from commercial games to military logistics training

Impact of WSR-88D intra-volume low-level scans on sever weather warning performance

July 1, 2022
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Journal Article
Author:
John Y. Cho
…
Published in:
Weather Forecast., Vol. 37, No. 7, July 2022, p. 1169-98.
Topic:
weather
R&D area:
R&D group:

Summary

The statistical relationship between supplemental adaptive intra-volume low-level scan (SAILS) usage on the Weather Surveillance Radar-1988 Doppler and National Weather Service severe storm warning performance during 2014–20 is analyzed. Results show statistically significant improvement in severe thunderstorm (SVR), flash flood (FF), and tornado (TOR) warning performance associated with SAILS-on versus SAILS-off. Within the three possible SAILS modes of one (SAILSx1), two (SAILSx2), and three (SAILSx3) additional base scans per volume, for SVR, SAILSx2 and SAILSx3 are associated with better warning performance compared to SAILSx1; for FF and TOR, SAILSx3 is associated with better warning performance relative to SAILSx1 and SAILSx2. Two severe storm cases (one that spawned a tornado, one that did not) are presented where SAILS usage helped forecasters make the correct TOR warning decision, lending real-life credence to the statistical results. Furthermore, a statistical analysis of automated volume scan evaluation and termination effects, parsed by SAILS usage and mode, yield a statistically significant association between volume scan update rate and SVR warning lead time.
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Summary

The statistical relationship between supplemental adaptive intra-volume low-level scan (SAILS) usage on the Weather Surveillance Radar-1988 Doppler and National Weather Service severe storm warning performance during 2014–20 is analyzed. Results show statistically significant improvement in severe thunderstorm (SVR), flash flood (FF), and tornado (TOR) warning performance associated with SAILS-on versus...

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Impact of WSR-88D intra-volume low-level scans on sever weather warning performance

Radar coverage analysis for the Terminal Precipitation on the Glass Program

March 4, 2022
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Project Report
Author:
John Y. Cho
…
Published in:
MIT Lincoln Laboratory Report ATC-450
Topic:
radar
R&D area:
R&D group:

Summary

The Terminal Precipitation on the Glass (TPoG) program proposes to improve the STARS precipitation depiction by adding an alternative precipitation product based on a national weather-radar-based mosaic, i.e., the NextGen Weather System (aka NextGen Weather Processor [NWP] and Common Support Services Weather [CSS-Wx]). This report describes spatial and temporal domain analyses conducted over the 146 terminal radar approach control (TRACON) airspaces that are within scope of TPoG to identify and quantify future TPoG benefits, as well as potential operational issues.
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Summary

The Terminal Precipitation on the Glass (TPoG) program proposes to improve the STARS precipitation depiction by adding an alternative precipitation product based on a national weather-radar-based mosaic, i.e., the NextGen Weather System (aka NextGen Weather Processor [NWP] and Common Support Services Weather [CSS-Wx]). This report describes spatial and temporal domain...

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Radar coverage analysis for the Terminal Precipitation on the Glass Program

Adapting deep learning models to new meteorological contexts using transfer learning

December 15, 2021
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Conference Paper
Author:
Pooya R. Khorrami
…
Published in:
2021 IEEE International Conference on Big Data (Big Data), 2021, pp. 4169-4177, doi: 10.1109/BigData52589.2021.9671451.
Topic:
machine learning
R&D area:
R&D group:

Summary

Meteorological applications such as precipitation nowcasting, synthetic radar generation, statistical downscaling and others have benefited from deep learning (DL) approaches, however several challenges remain for widespread adaptation of these complex models in operational systems. One of these challenges is adequate generalizability; deep learning models trained from datasets collected in specific contexts should not be expected to perform as well when applied to different contexts required by large operational systems. One obvious mitigation for this is to collect massive amounts of training data that cover all expected meteorological contexts, however this is not only costly and difficult to manage, but is also not possible in many parts of the globe where certain sensing platforms are sparse. In this paper, we describe an application of transfer learning to perform domain transfer for deep learning models. We demonstrate a transfer learning algorithm called weight superposition to adapt a Convolutional Neural Network trained in a source context to a new target context. Weight superposition is a method for storing multiple models within a single set of parameters thus greatly simplifying model maintenance and training. This approach also addresses the issue of catastrophic forgetting where a model, once adapted to a new context, performs poorly in the original context. We apply weight superposition to the problem of synthetic weather radar generation and show that in scenarios where the target context has less data, a model adapted with weight superposition is better at maintaining performance when compared to simpler methods. Conversely, the simple adapted model performs better on the source context when the source and target contexts have comparable amounts of data.
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Summary

Meteorological applications such as precipitation nowcasting, synthetic radar generation, statistical downscaling and others have benefited from deep learning (DL) approaches, however several challenges remain for widespread adaptation of these complex models in operational systems. One of these challenges is adequate generalizability; deep learning models trained from datasets collected in specific...

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Adapting deep learning models to new meteorological contexts using transfer learning
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