Detecting Birds and Insects in the Atmosphere Using Machine Learning on NEXRAD Radar Echoes

NEXRAD radars detect biological scatterers in the atmosphere, i.e., birds and insects, without distinguishing between them. A method is proposed to discriminate these bird and insect echoes. Multiple scans are collected for mass migration of birds (insects) and coherently averaged along their different aspects to improve the data quality. Additional features are also computed to capture the dependence of bird (insect) echoes on the observed aspect, range, and local regions of space. Next, ridge classifier and decision tree machine learning algorithms are trained on the collected data. For each method, classifiers are trained, first with the averaged dual pol inputs and then different combinations of the remaining features are added. The performance of both methods, are analyzed using metrics computed on a held-out test data set. Further case studies on roosting birds, bird migration, and insect migration cases, are conducted to investigate the performance of the classifiers when applied to new scenarios. Overall, the ridge classifier using only dual polarization variables was found to perform consistently well on both the test data and in the case studies. This classifier is recommended for operational use on the US Next-Generation Radars (NEXRAD) in conjunction with the existing Hydrometeor Classification Algorithm (HCA). The HCA would be used first to separate biological from non-biological echoes, then the ridge classifier could be applied to categorize biological echoes into birds and insects. To the best of our knowledge, this study is the first to train a machine learning classifier that can detect diverse patterns of bird and insect echoes, based on dual polarization variables at each range gate.

Effects of Probabilistic Risk Situation Awareness Tool (RSAT) on Aeronautical Weather-Hazard Decision Making

We argue that providing cumulative risk as an estimate of the uncertainty in dynamically changing risky environments can help decision-makers meet mission-critical goals. Specifically, we constructed a simplified aviation-like weather decision-making task incorporating Next-Generation Radar (NEXRAD) images of convective weather. NEXRAD radar images provide information about geographically referenced precipitation. NEXRAD radar images are used by both pilots and laypeople to support decision-making about the level of risk posed by future weather-hazard movements. Using NEXRAD, people and professionals have to infer the uncertainty in the meteorological information to understand current hazards and extrapolate future conditions. Recent advancements in meteorology modeling afford the possibility of providing uncertainty information concerning hazardous weather for the current flight. Although there are systematic biases that plague people’s use of uncertainty information, there is evidence that presenting forecast uncertainty can improve weather-related decision-making. The current study augments NEXRAD by providing flight-path risk, referred to as the Risk Situational Awareness Tool (RSAT). RSAT provides the probability that a route will come within 20 NMI radius (FAA recommended safety distance) of hazardous weather within the next 45 min of flight. The study evaluates four NEXRAD displays integrated with RSAT, providing varying levels of support. The “no” support condition has no RSAT (the NEXRAD only condition). The “baseline” support condition employs an RSAT whose accuracy is consistent with current capability in meteorological modeling. The “moderate” support condition applies an RSAT whose accuracy is likely at the top of what is achievable in meteorology in the near future. The “high” support condition provides a level of support that is likely unachievable in an aviation weather decision-making context without considerable technological innovation. The results indicate that the operators relied on the RSAT and improved their performance as a consequence. We discuss the implications of the findings for the safe introduction of probabilistic tools in future general aviation cockpits and other dynamic decision-making contexts. Moreover, we discuss how the results contribute to research in the fields of dynamic risk and uncertainty, risk situation awareness, cumulative risk, and risk communication.

A Simulation Framework to Support the Design and Evaluation of Adaptive Scanning for Phased-Array Weather Radars

We propose a simulation framework that can be used to design and evaluate the performance of adaptive scanning algorithms on different phased-array weather radar designs. The simulator is proposed as tool to 1) compare the performance of different adaptive scanning algorithms on the same weather event, 2) evaluate the performance of a given adaptive scanning algorithm on several weather events, and 3) evaluate the performance of a given adaptive scanning algorithm on a given weather event using different radar designs. We illustrate the capabilities of the proposed framework to design and evaluate the performance of adaptive algorithms aimed at reducing the update time using adaptive scanning. The example concept of operations is based on a fast low-fidelity surveillance scan and a high-fidelity adaptive scan. The flexibility of the proposed simulation framework is tested using two phased-array-radar designs and three complementary adaptive scanning algorithms: focused observations, beam clustering, and dwell tailoring. Based on a significant weather event observed by an operational NEXRAD radar, our experimental results consist of radar data that were simulated as if the same event had been observed by arbitrary combinations of radar systems and adaptive scanning configurations. Results show that simulated fields of radar data capture the main data-quality impacts from the use of adaptive scanning and can be used to obtain quantitative metrics and for qualitative comparison and evaluation by forecasters. That is, the proposed simulator could provide an effective interface with meteorologists and could support the development of concepts of operations that are based on adaptive scanning to meet the evolutionary observational needs of the U.S. National Weather Service.

Evaluation of Radar-Gauge Merging Techniques to Be Used in Operational Flood Forecasting in Urban Watersheds

Demand for radar Quantitative Precipitation Estimates (QPEs) as precipitation forcing to hydrological models in operational flood forecasting has increased in the recent past. It is practically impossible to get error-free QPEs due to the intrinsic limitations of weather radar as a precipitation measurement tool. Adjusting radar QPEs with gauge observations by combining their advantages while minimizing their weaknesses increases the accuracy and reliability of radar QPEs. This study deploys several techniques to merge two dual-polarized King City radar (WKR) C-band and two KBUF Next-Generation Radar (NEXRAD) S-band operational radar QPEs with rain gauge data for the Humber River (semi-urban) and Don River (urban) watersheds in Ontario, Canada. The relative performances are assessed against an independent gauge network by comparing hourly rainfall events. The Cumulative Distribution Function Matching (CDFM) method performed best, followed by Kriging with Radar-based Error correction (KRE). Although both WKR and NEXRAD radar QPEs improved significantly, NEXRAD Level III Digital Precipitation Array (DPA) provided the best results. All methods performed better for low- to medium-intensity precipitation but deteriorated with the increasing rainfall intensities. All methods outperformed radar only QPEs for all events, but the agreement is best in the summer.

Soil moisture–precipitation coupling: observations from the Oklahoma Mesonet and underlying physical mechanisms

Interactions between soil moisture and the atmosphere are driven by the partitioning of sensible and latent heating, through which soil moisture has been connected to atmospheric modifications that could potentially lead to the initiation of convective precipitation. The majority of previous studies linking the land surface to subsequent precipitation have used atmospheric reanalysis or model data sets. In this study, we link in situ observations of soil moisture from more than 100 stations in Oklahoma to subsequent unorganized afternoon convective precipitation. We use hourly next generation (NEXRAD) radar-derived precipitation to identify convective events, and then compare the location of precipitation initiation to underlying soil moisture anomalies in the morning. Overall we find a statistically significant preference for convective precipitation initiation over drier than normal soils, with over 70 % of events initiating over soil moisture below the long-term median. The significant preference for precipitation initiation over drier than normal soils is in contrast with previous studies using satellite-based precipitation to identify the region of maximum precipitation accumulation. We evaluated 19 convective events occurring near Lamont, Oklahoma, where soundings of the atmospheric profile at 06:00 and 12:00 LST are also available. For these events, soil moisture has strong negative correlations with the level of free convection (LFC), planetary boundary layer (PBL) height, and surface temperature changes between 06:00 and 12:00 LST. We also find strong positive correlations between morning soil moisture and morning-to-afternoon changes in convective available potential energy and convective inhibition. In general, the results of this study demonstrate that both positive and negative soil moisture feedbacks are important in this region of the USA.

Soil moisture–precipitation coupling: observations from the Oklahoma Mesonet and underlying physical mechanisms

Interactions between soil moisture and the atmosphere are driven by the partitioning of sensible and latent heating, through which, soil moisture has been connected to atmospheric modification that could potentially lead to initiation of convective precipitation. The majority of previous studies linking the land surface to subsequent precipitation have used atmospheric reanalysis or model datasets. In this study, we link in situ observations of soil moisture from more than 100 stations in Oklahoma to subsequent unorganized afternoon convective precipitation. We use hourly, high resolution NEXRAD radar-derived precipitation to identify convective events, and then compare the location of precipitation initiation to underlying soil moisture anomalies the morning prior. Overall we find a statistically significant preference for convective precipitation initiation over drier than normal soils, with over 70% of events initiating over soil moisture below the long-term median. The significant preference for precipitation initiation over drier than normal soils is in contrast with previous studies using satellite-based precipitation products to identify the region of maximum precipitation accumulation. We sub-sample 19 convective events occurring near Lamont, Oklahoma, where soundings of the atmospheric profile at 06:00 and 12:00 LST are also available. For these events, soil moisture is strongly, negatively correlated with the level of free convection, planetary boundary layer height, and surface temperature changes from 06:00 to 12:00 LST. We also find strong, positive correlations between morning soil moisture and morning-to-afternoon changes in convective available potential energy and convective inhibition. In general, the results of this study demonstrate that both positive and negative soil moisture feedbacks to the atmosphere are relevant in this region of the United States.