Publications
A deep learning-based velocity dealiasing algorithm derived from the WSR-88D open radar product generator
July 1, 2023
Journal Article
Published in:
Artificial Intelligence for the Earth Systems, Vol. 2, Issue 3, July 2023.
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.
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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Adapting deep learning models to new meteorological contexts using transfer learning
December 15, 2021
Conference Paper
Published in:
2021 IEEE International Conference on Big Data (Big Data), 2021, pp. 4169-4177, doi: 10.1109/BigData52589.2021.9671451.
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.
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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Polarimetric observations of chaff using the WSR-88D network
May 1, 2018
Journal Article
Published in:
J. Appl. Meteor. Climatol., Vol. 57, No. 5, 1 May 2018, pp. 1063-1081.
Summary
Chaff is a radar countermeasure typically used by military branches in training exercises around the United States. Chaff within view of the S-band WSR-88D radars can appear prominently on radar users displays. Knowledge of chaff characteristics is useful for radar users to discriminate between chaff and weather echoes and for automated algorithms to do the same. The WSR-88D network provides dual-polarimetric capabilities across the United States, leading to the collection of a large database of chaff cases. The database is analyzed to determine the characteristics of chaff in terms of the reflectivity factor and polarimetric variables on large scales. Particular focus is given to the dynamics of differential reflectivity (ZDR) in chaff and its dependence on height. Contrary to radar data observations of chaff for a single event, this study is able to reveal a repeatable and new pattern of radar chaff observations. A discussion regarding the observed characteristics is presented, and hypotheses for the observed ZDR dynamics are put forth.
Summary
Chaff is a radar countermeasure typically used by military branches in training exercises around the United States. Chaff within view of the S-band WSR-88D radars can appear prominently on radar users displays. Knowledge of chaff characteristics is useful for radar users to discriminate between chaff and weather echoes and for...
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Development of a new inanimate class for the WSR-88D hydrometeor classification algorithm
August 31, 2017
Conference Paper
Published in:
38th Conf. on Radar Meteorology, 27 August-1 September 2017.
Summary
The current implementation of the Hydrometeor Classification Algorithm (HCA) on the WSR-88D network contains two non-hydrometeor-based classes: ground clutter/anomalous propagation and biologicals. A number of commonly observed non-hydrometeor-based phenomena do not fall into either of these two HCA categories, but often are misclassified as ground clutter, biologicals, unknown, or worse yet, weather hydrometeors. Some of these phenomena include chaff, sea clutter, combustion debris and smoke, and radio frequency interference. In order to address this discrepancy, a new class (nominally named "inanimate") is being developed that encompasses many of these targets. Using this class, a distinction between non-biological and biological non-hydrometeor targets can be made and potentially separated into sub-classes for more direct identification. A discussion regarding the fuzzy logic membership functions, optimization of membership weights, and class restrictions is presented, with a focus on observations of highly stochastic differential phase estimates in all of the aforementioned targets. Recent attempts to separate the results into sub-classes using a support vector machine are presented, and examples of each target type are detailed. Details concerning eventual implementation into the WSR-88D radar product generator are addressed.
Summary
The current implementation of the Hydrometeor Classification Algorithm (HCA) on the WSR-88D network contains two non-hydrometeor-based classes: ground clutter/anomalous propagation and biologicals. A number of commonly observed non-hydrometeor-based phenomena do not fall into either of these two HCA categories, but often are misclassified as ground clutter, biologicals, unknown, or worse...
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WSR-88D chaff detection and characterization using an optimized hydrometeor classification algorithm
January 23, 2017
Conference Paper
Published in:
18th Conf. on Aviation, Range, and Aerospace Meteorology, 23-26 January 2017.
Summary
Chaff presents multiple issues for aviation, air traffic controllers, and the FAA, including false weather identification and areas where flight paths may need to be altered. Chaff is a radar countermeasure commonly released from aircraft across the United States and is comprised of individual metallic strands designed to reflect certain wavelengths. Chaff returns tend to look similar to weather echoes in the reflectivity factor and radial velocity fields, and can appear as clutter, stratiform precipitation, or deep convection to the radar operator or radar algorithms. When polarimetric fields are taken into account, however, discrimination between weather and non-weather echoes has relatively high potential for success. In this work, the operational Hydrometeor Classification Algorithm (HCA) on the WSR-88D is modified to include a chaff class that can be used as input to a Chaff Detection Algorithm (CDA). This new class is designed using human-truthed chaff datasets for the collection and quantification of variable distributions, and the collected chaff cases are leveraged in the tuning of algorithm weights through the use of a metaheuristic optimization. A final CDA uses various image processing techniques to deliver a filtered output. A discussion regarding WSR-88D observations of chaff on a broad scale is provided, with particular attention given to observations of negative differential reflectivity during different stages of chaff fallout. Numerous cases are presented for analysis and characterization, both as an HCA class and as output from the filtered CDA.
Summary
Chaff presents multiple issues for aviation, air traffic controllers, and the FAA, including false weather identification and areas where flight paths may need to be altered. Chaff is a radar countermeasure commonly released from aircraft across the United States and is comprised of individual metallic strands designed to reflect certain...
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The Offshore Precipitation Capability
September 16, 2016
Project Report
Published in:
MIT Lincoln Laboratory Report ATC-430
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Summary
In this work, machine learning and image processing methods are used to estimate radar-like precipitation intensity and echo top heights beyond the range of weather radar. The technology, called the Offshore Precipitation Capability (OPC), combines global lightning data with existing radar mosaics, five Geostationary Operational Environmental Satellite (GOES) channels, and several fields from the Rapid Refresh (RAP) 13 km numerical weather prediction model to create precipitation and echo top fields similar to those provided by existing Federal Aviation Administration (FAA) weather systems. Preprocessing and feature extraction methods are described to construct inputs for model training. A variety of machine learning algorithms are investigated to identify which provides the most accuracy. Output from the machine learning model is blended with existing radar mosaics to create weather radar-like analyses that extend into offshore regions. The resulting fields are validated using land radars and satellite precipitation measurements provided by the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement Mission (GPM) core observatory satellite. This capability is initially being developed for the Miami Oceanic airspace with the goal of providing improved situational awareness for offshore air traffic control.
Summary
In this work, machine learning and image processing methods are used to estimate radar-like precipitation intensity and echo top heights beyond the range of weather radar. The technology, called the Offshore Precipitation Capability (OPC), combines global lightning data with existing radar mosaics, five Geostationary Operational Environmental Satellite (GOES) channels, and...
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Airspace flow rate forecast algorithms, validation, and implementation
December 15, 2015
Project Report
Published in:
MIT Lincoln Laboratory Report ATC-428
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Summary
This report summarizes work performed by MIT Lincoln Laboratory during the period 1 February 2015 - 30 November 2015 focused on developing and improving algorithms to estimate the impact of convective weather on air traffic flows. The core motivation for the work is the need to improve strategic traffic flow management decision-making in the National Airspace System. The algorithms developed as part of this work translate multiple weather forecast products into a discrete airspace impact metric called permeability.
Summary
This report summarizes work performed by MIT Lincoln Laboratory during the period 1 February 2015 - 30 November 2015 focused on developing and improving algorithms to estimate the impact of convective weather on air traffic flows. The core motivation for the work is the need to improve strategic traffic flow...
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Forecast confidence measures for deterministic storm-scale aviation forecasts
February 5, 2014
Conference Paper
Published in:
4th Aviation, Range, and Aerospace Meteorology Special Symp., 2-6 February 2014.
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Summary
Deterministic storm-scale weather forecasts, such as those generated from the FAA's 0-8 hour CoSPA system, are highly valuable to aviation traffic managers. They provide forecasted characteristics of storm structure, strength, orientation, and coverage that are very helpful for strategic planning purposes in the National Airspace System (NAS). However, these deterministic weather forecasts contain inherent uncertainty that varies with the general weather scenario at the forecast issue time, the predicted storm type, and the forecast time horizon. This uncertainty can cause large changes in the forecast from update to update, thereby eroding user confidence and ultimately reducing the forecast's effectiveness in the decision-making process. Deterministic forecasts generally lack objective measures of this uncertainty, making it very difficult for users of the forecast to know how much confidence to have in the forecast during their decision-making process. This presentation will describe a methodology to provide measures of confidence for deterministic storm-scale forecasts. The method inputs several characteristics of the current and historical weather forecasts, such as spatial scale, intensity, weather type, orientation, permeability, and run-to-run variability of the forecasts, into a statistical model to provide a measure of confidence in a forecasted quantity. In this work, the forecasted quantity is aircraft blockage associated with key high-impact Flow Constrained Areas (FCAs) in the NAS. The results from the method, which will also be presented, provide the user with a measure of forecast confidence in several blockage categories (none, low, medium, and high) associated with the FCAs. This measure of forecast confidence is geared toward helping en-route strategic planners in the NAS make better use of deterministic storm-scale weather forecasts for air traffic management.
Summary
Deterministic storm-scale weather forecasts, such as those generated from the FAA's 0-8 hour CoSPA system, are highly valuable to aviation traffic managers. They provide forecasted characteristics of storm structure, strength, orientation, and coverage that are very helpful for strategic planning purposes in the National Airspace System (NAS). However, these deterministic...
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Wind-shear detection performance analysis for MPAR risk reduction
September 16, 2013
Conference Paper
Published in:
36th Conf. on Radar Meteorology, 16 September 2013.
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Summary
Multifunction phased array radars (MPARs) of the future that may replace the current terminal wind-shear detection systems will need to meet the Federal Aviation Administration's (FAA) detection requirements. Detection performance issues related to on-airport siting of MPAR, its broader antenna beamwidth relative to the Terminal Doppler Weather Radar (TDWR), and the change in operational frequency from C band to S band are analyzed. Results from the 2012 MPAR Wind-Shear Experiment are presented, with microburst and gust-front detection statistics for the Oklahoma City TDWR and the National Weather Radar Testbed (NWRT) phased array radar, which are located 6 km apart. The NWRT has sensitivity and beamwidth similar to a conceptual terminal MPAR (TMPAR), which is a scaled-down version of a full-size MPAR. The micro-burst results show both the TDWR probability of detec-tion (POD) and the estimated NWRT POD exceeding the 90% requirement. For gust fronts, however, the overall estimated NWRT POD was more than 10% lower than the TDWR POD. NWRT data are also used to demonstrate that rapid-scan phased array radar has the potential to enhance microburst prediction capability.
Summary
Multifunction phased array radars (MPARs) of the future that may replace the current terminal wind-shear detection systems will need to meet the Federal Aviation Administration's (FAA) detection requirements. Detection performance issues related to on-airport siting of MPAR, its broader antenna beamwidth relative to the Terminal Doppler Weather Radar (TDWR), and...
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Wind-shear detection performance study for multifunction phased array radar (MPAR) risk reduction
June 12, 2013
Project Report
Published in:
MIT Lincoln Laboratory Report ATC-409
Summary
Multifunction phased array radars (MPARs) of the future that may replace the current terminal wind-shear detection systems will need to meet the Federal Aviation Administration's (FAA) detection requirements. Detection performance issues related to on-airport siting of MPAR, its broader antenna beamwidth relative to the TDWR, and the change in operational frequency from C band to S band are analyzed. Results from the 2012 MPAR Wind-Shear Experiment (WSE) are presented, with microburst and gust-front detection statistics for the Oklahoma City TDWR and the National Weather Radar Testbed (NWRT) phased array radar, which are located 6 km apart. The NWRT has sensitivity and beamwidth similar to a conceptual terminal MPAR (TMPAR), which is a scaled-down version of a full-size MPAR. The microburst results show both the TDWR probability of detection (POD) and the estimated NWRT POD exceeding the 90% requirement. For gust fronts, however, the overall estimated NWRT POD was more than 10% lower than the TDWR POD. NWRT data is also used to demonstrate that rapid-scan phased array radar has the potential to enhance microburst prediction capability.
Summary
Multifunction phased array radars (MPARs) of the future that may replace the current terminal wind-shear detection systems will need to meet the Federal Aviation Administration's (FAA) detection requirements. Detection performance issues related to on-airport siting of MPAR, its broader antenna beamwidth relative to the TDWR, and the change in operational...
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