Machine learning approach for intelligent vad wind estimation with polarimetric radars

Abstract

A method comprises: obtaining velocity azimuth display (VAD) rings from at least one radar scan; applying a hydrometeor classification algorithm (HCA) to the VAD rings to detect weather echoes, biological echoes, and other echoes; and applying a bird-insect ridge classifier (BIRC) to the biological echoes to generate birds-only VADs and insects-only VADs. An apparatus comprises: one or more memories configured to store instructions; and one or more processors coupled to the one or more memories and configured to execute the instructions to cause the apparatus to: obtain VAD rings from at least one radar scan; apply an HCA to the VAD rings to detect weather echoes, biological echoes, and other echoes; and apply a BIRC to the biological echoes to generate birds-only VADs and insects-only VADs.

Description

Atty. Docket No.4313-05001 (2024-001) Machine Learning Approach for Intelligent VAD Wind Estimation with Polarimetric Radars CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This claims priority to U.S. Prov. Patent App. No.63 / 578,588 filed on August 24, 2023, which is incorporated by reference. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with government support under Grant Number NA110OAR4320072 awarded by the National Oceanic and Atmospheric Administration. The government has certain rights in the invention. BACKGROUND

[0003] VWP is one of the most widely used meteorological radar products, provided at each of the 160+ stations comprising the NEXRAD network. VWP estimates the horizontal wind velocity at a given height using VAD, where a sinusoid is fitted to the ring of Doppler velocities or mean radial velocities obtained from different azimuth angles at that height. A ring is a circular shape defined by a slice of an inverted cone at each height of a beam of a rotating antenna at a given elevation. VWP estimates assume that the underlying wind is uniform and traced by radar echoes. However, different types of echoes have different wind relative motion. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] For a more complete understanding of this disclosure, reference is now made to the following brief description, taken in connection with the accompanying drawings and detailed description, wherein like reference numerals represent like parts.

[0005] FIG. 1A is a graph of daily averages of bird, insect, and weather echoes during nocturnal migration season.

[0006] FIG.1B is a graph of daily averages of bird, insect, and weather echoes during diurnal migration season.

[0007] FIG.2A is a graph of BIRC predictions for a nocturnal bird migration case.Atty. Docket No.4313-05001 (2024-001)

[0008] FIG. 2B is a graph of the number of bird and insect gates for the nocturnal bird migration case.

[0009] FIG.2C is a graph of an insects-to-birds ratio for the nocturnal bird migration case.

[0010] FIG.2D is a graph of BIRC predictions for an afternoon insect swarm.

[0011] FIG. 2E is a graph of the number of bird and insect gates for the an afternoon insect swarm.

[0012] FIG.2F is a graph of an insects-to-birds ratio for the afternoon insect swarm.

[0013] FIG. 3 is a graph of insects-birds ratios averaged by height and time of day for the nocturnal migration season.

[0014] FIG.4A is a graph of radial velocity for a nocturnal bird migration case.

[0015] FIG. 4B is a graph of wind components estimated from VAD for the nocturnal bird migration case.

[0016] FIG.4C is a graph of wind biases for the nocturnal bird migration case.

[0017] FIG.4D is a graph of radial velocity for a daytime insect swarm.

[0018] FIG. 4E is a graph of wind components estimated from VAD for the daytime insect swarm.

[0019] FIG.4F is a graph of wind biases for the daytime insect swarm.

[0020] FIG.5 is a graph of VAD wind biases averaged by height and time of day.

[0021] FIG.6A is a graph of wind biases averaged by height and the insects-birds ratio for the nocturnal migration season.

[0022] FIG. 6B is scatterplots for wind bias against the insects-birds ratio for the nocturnal migration season.

[0023] FIG. 7 is a graph comparing biases from bird-dominated and insect-dominated rings for the KOHX nocturnal migration season.

[0024] FIG.8A is a graph of radial velocities from an insect-dominated VAD.

[0025] FIG.8B is a graph of radial velocities from a mixed VAD.

[0026] FIG.8C is a graph of radial velocities from an bird-dominated VAD.

[0027] FIG.9 is a graph is a bird-insect bias difference averaged by height and insect-to-bird ratio for the nocturnal migration season.

[0028] FIG. 10A is a graph of height by insects-birds ratio averages of improvement for the KOHX nocturnal migration season.Atty. Docket No.4313-05001 (2024-001)

[0029] FIG. 10B is a graph of height by insects-birds ratio averages of the fraction of VADs lost for the KOHX nocturnal migration season.

[0030] FIG. 11A is a graph demonstrating an improvement of VAD using only insect echoes instead of all biological echoes with τ = 0.4.

[0031] FIG.11B is a graph demonstrating the improvement of VAD with τ = 0.3.

[0032] FIG.11C is a graph demonstrating the improvement of VAD with τ = 0.2.

[0033] FIG.11D is a graph demonstrating the improvement of VAD with τ = 0.1.

[0034] FIG.11E is a graph demonstrating a fraction of VADs lost with τ = 0.4.

[0035] FIG.11F is a graph demonstrating a fraction of VADs lost with τ = 0.3.

[0036] FIG.11G is a graph demonstrating a fraction of VADs lost with τ = 0.2.

[0037] FIG.11H is a graph demonstrating a fraction of VADs lost with τ = 0.1.

[0038] FIG. 12A is a graph of height by insects-birds ratio averages of wind biases for the KHGX diurnal migration season.

[0039] FIG.12B is a graph of height by insects-birds ratio averages of improvements of VAD for the KHGX diurnal migration season.

[0040] FIG.13 is a flow diagram of IVAD.

[0041] FIG.14A is a graph of HCA predictions for nocturnal birds from the 0.5° elevation.

[0042] FIG.14B is a graph of ρHVfor nocturnal birds.

[0043] FIG.14C is a graph of HCA predictions after correction.

[0044] FIG.14D is a graph of HCA predictions for afternoon insects from the 2.5° elevation.

[0045] FIG.14E is a graph of ρHVfor afternoon insects.

[0046] FIG.14F is a graph of HCA predictions after correction.

[0047] FIG.15 is a flowchart of a method of IVAD wind estimation.

[0048] FIG.16 is a schematic diagram of an apparatus. DETAILED DESCRIPTION

[0049] Clear-air conditions, where boundary layer radar echoes are predominantly from birds and arthropods (mostly insects), are discussed herein. Airborne insects are generally good wind tracers, producing reliable VAD winds. Returns from flying birds, on the other hand, have been found to contaminate Doppler velocities, resulting in biased VAD measurements. For instance,Atty. Docket No.4313-05001 (2024-001) NCEP suggested nocturnal bird migrants as the most common source of nighttime VAD wind contamination.

[0050] Given the disparate impacts of birds and insects, it would be beneficial to account for taxa in the VAD process. For example, meteorologists can obtain less contaminated wind products from insect echoes. VAD on bird flocks can be used to map their migratory pathways and develop warning systems to better mitigate collisions with aircraft, wind farms, offshore platforms, and other man-made structures. And aeroecological studies of each animal can be enhanced with more precise information.

[0051] Despite these benefits, the operational VWP does not account for taxa because it imposes two challenging constraints on distinguishing biological echoes. First, since VAD is estimated from a ring of gates, bird-insect classification would be needed at the gate level. As such, automated techniques for identifying bird contamination in the lowest elevation scan based on reflectivity and Doppler velocity and for extracting height profiles of bird density, speed, and direction at the C-band would both have the drawback of being inapplicable to insects or singular gates. A gate, which may also be referred to as a range gate or a resolution volume, is a selectable interval of range within which returning radar signals are measured. For instance, an observed space of 1 km is divided into 250 m gates. Secondly, classification should depend on level II data, from which VAD Doppler velocities are obtained. Consequently, while polarimetric spectral analysis has been exploited for distinguishing birds from insects in multimodal spectra, this approach is limited by the required level I time series data, which is not typically available from the operational WSR-88D.

[0052] In the NEXRAD, the HCA is used to separate biological echoes from ground clutter, and weather. Building upon HCA, a BIRC was also developed for discriminating these biological echoes into birds and insects using dual-polarization variables at individual range gates. However, BIRC has not been incorporated into VAD.

[0053] Disclosed herein are embodiments for a machine learning approach for intelligent VAD wind estimation with polarimetric radars, such as but not limited to S-band polarimetric radars . The embodiments improve clear-air VAD wind estimation by incorporating taxonomic information. The embodiments incorporate BIRC into VAD to form IVAD for identifying and ameliorating bird contamination. First, the HCA detects biological echoes. Second, BIRC bifurcates these echoes into birds and insects. Third, three new products are generated, includingAtty. Docket No.4313-05001 (2024-001) the insects-birds ratio, bird-only VAD, and insect-only VAD. These products are analyzed for one- month periods of nocturnal and diurnal bird migration. Wind bias is used as the evaluation metric and defined as the deviation of the predicted VAD from the true wind, as approximated from the RAP model. Results show an inverse relationship between biases and the insects-birds ratio, such that increasing the bird population or decreasing the insect population was accompanied by larger biases. Furthermore, contaminated VADs showed improvements when insects-only signals were used instead of all biological echoes. The disclosed embodiments can be incorporated into the VWP. First, the insect-birds ratio can be used to identify whether a given height is bird dominated, mixed, or insect dominated. For the mixed case, improved wind estimates can be obtained from insect-only VAD. Otherwise, bird-only VADs can be obtained from bird dominated heights, while insect-only VADs are obtained from insect dominated heights. The former can be used to track birds, while the latter tracks insects and the wind.

[0054] Before further describing various embodiments of the apparatus, component parts, and methods of the present disclosure in more detail by way of exemplary description, examples, and results, it is to be understood that the embodiments of the present disclosure are not limited in application to the details of apparatus, component parts, and methods as set forth in the following description. The embodiments of the apparatus, component parts, and methods of the present disclosure are capable of being practiced or carried out in various ways not explicitly described herein. As such, the language used herein is intended to be given the broadest possible scope and meaning; and the embodiments are meant to be exemplary, not exhaustive. Also, it is to be understood that the phraseology and terminology employed herein is for the purpose of description and should not be regarded as limiting unless otherwise indicated as so. Moreover, in the following detailed description, numerous specific details are set forth in order to provide a more thorough understanding of the disclosure. However, it will be apparent to a person having ordinary skill in the art that the embodiments of the present disclosure may be practiced without these specific details. In other instances, features which are well known to persons of ordinary skill in the art have not been described in detail to avoid unnecessary complication of the description. While the apparatus, component parts, and methods of the present disclosure have been described in terms of particular embodiments, it will be apparent to those of skill in the art that variations may be applied to the apparatus, component parts, and / or methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit, and scope of theAtty. Docket No.4313-05001 (2024-001) inventive concepts as described herein. All such similar substitutes and modifications apparent to those having ordinary skill in the art are deemed to be within the spirit and scope of the inventive concepts as disclosed herein.

[0055] All patents, published patent applications, and non-patent publications referenced or mentioned in any portion of the present specification are indicative of the level of skill of those skilled in the art to which the present disclosure pertains, and are hereby expressly incorporated by reference in their entirety to the same extent as if the contents of each individual patent or publication was specifically and individually incorporated herein.

[0056] Unless otherwise defined herein, scientific and technical terms used in connection with the present disclosure shall have the meanings that are commonly understood by those having ordinary skill in the art. Further, unless otherwise required by context, singular terms shall include pluralities and plural terms shall include the singular.

[0057] As utilized in accordance with the methods and compositions of the present disclosure, the following terms and phrases, unless otherwise indicated, shall be understood to have the following meanings: The use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims and / or the specification may mean “one,” but it is also consistent with the meaning of “one or more,” “at least one,” and “one or more than one.” The use of the term “or” in the claims is used to mean “and / or” unless explicitly indicated to refer to alternatives only or when the alternatives are mutually exclusive, although the disclosure supports a definition that refers to only alternatives and “and / or.” The use of the term “at least one” will be understood to include one as well as any quantity more than one, including but not limited to, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 100, or any integer inclusive therein. The phrase “at least one” may extend up to 100 or 1000 or more, depending on the term to which it is attached; in addition, the quantities of 100 / 1000 are not to be considered limiting, as higher limits may also produce satisfactory results. In addition, the use of the term “at least one of X, Y and Z” will be understood to include X alone, Y alone, and Z alone, as well as any combination of X, Y and Z.

[0058] As used in this specification and claims, the words “comprising” (and any form of comprising, such as “comprise” and “comprises”), “having” (and any form of having, such as “have” and “has”), “including” (and any form of including, such as “includes” and “include”) or “containing” (and any form of containing, such as “contains” and “contain”) are inclusive or open- ended and do not exclude additional, unrecited elements or method steps.Atty. Docket No.4313-05001 (2024-001)

[0059] The term “or combinations thereof” as used herein refers to all permutations and combinations of the listed items preceding the term. For example, “A, B, C, or combinations thereof” is intended to include at least one of: A, B, C, AB, AC, BC, or ABC, and if order is important in a particular context, also BA, CA, CB, CBA, BCA, ACB, BAC, or CAB. Continuing with this example, expressly included are combinations that contain repeats of one or more item or term, such as BB, AAA, AAB, BBC, AAABCCCC, CBBAAA, CABABB, and so forth. The skilled artisan will understand that typically there is no limit on the number of items or terms in any combination, unless otherwise apparent from the context.

[0060] Throughout this application, the terms “about” or “approximately” are used to indicate that a value includes the inherent variation of error for the apparatus, composition, or the methods or the variation that exists among the objects, or study subjects. As used herein the qualifiers “about” or “approximately” are intended to include not only the exact value, amount, degree, orientation, or other qualified characteristic or value, but are intended to include some slight variations due to measuring error, manufacturing tolerances, stress exerted on various parts or components, observer error, wear and tear, and combinations thereof, for example. The terms “about” or “approximately”, where used herein when referring to a measurable value such as an amount, percentage, temporal duration, and the like, is meant to encompass, for example, variations of ± 20% or ± 10%, or ± 5%, or ± 1%, or ± 0.1% from the specified value, as such variations are appropriate to perform the disclosed methods and as understood by persons having ordinary skill in the art. As used herein, the term “substantially” means that the subsequently described event or circumstance completely occurs or that the subsequently described event or circumstance occurs to a great extent or degree. For example, the term “substantially” means that the subsequently described event or circumstance occurs at least 90% of the time, or at least 95% of the time, or at least 98% of the time.

[0061] As used herein any reference to "one embodiment" or "an embodiment" means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment.

[0062] As used herein, all numerical values or ranges include fractions of the values and integers within such ranges and fractions of the integers within such ranges unless the contextAtty. Docket No.4313-05001 (2024-001) clearly indicates otherwise. Thus, to illustrate, reference to a numerical range, such as 1-10 includes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, as well as 1.1, 1.2, 1.3, 1.4, 1.5, etc., and so forth. Reference to a range of 1-50 therefore includes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, etc., up to and including 50, as well as 1.1, 1.2, 1.3, 1.4, 1.5, etc., 2.1, 2.2, 2.3, 2.4, 2.5, etc., and so forth. Reference to a series of ranges includes ranges which combine the values of the boundaries of different ranges within the series. Thus, to illustrate reference to a series of ranges, for example, a range of 1-1,000 includes, for example, 1-10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-75, 75-100, 100-150, 150-200, 200-250, 250-300, 300-400, 400-500, 500-750, 750-1,000, and includes ranges of 1-20, 10-50, 50-100, 100-500, and 500-1,000. The range 100 units to 2000 units therefore refers to and includes all values or ranges of values of the units, and fractions of the values of the units and integers within said range, including for example, but not limited to 100 units to 1000 units, 100 units to 500 units, 200 units to 1000 units, 300 units to 1500 units, 400 units to 2000 units, 500 units to 2000 units, 500 units to 1000 units, 250 units to 1750 units, 250 units to 1200 units, 750 units to 2000 units, 150 units to 1500 units, 100 units to 1250 units, and 800 units to 1200 units. Any two values within the range of about 100 units to about 2000 units therefore can be used to set the lower and upper boundaries of a range in accordance with the embodiments of the present disclosure. More particularly, a range of 10-12 units includes, for example, 10, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9, 11.0, 11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9, and 12.0, and all values or ranges of values of the units, and fractions of the values of the units and integers within said range, and ranges which combine the values of the boundaries of different ranges within the series, e.g., 10.1 to 11.5.

[0063] The present disclosure will now be discussed in terms of several specific, non-limiting, examples and embodiments. The examples described below, which include particular embodiments, will serve to illustrate the practice of the present disclosure, it being understood that the particulars shown are by way of example and for purposes of illustrative discussion of particular embodiments and are presented in the cause of providing what is believed to be a useful and readily understood description of procedures as well as of the principles and conceptual aspects of the present disclosure.

[0064] The following abbreviations apply: ASIC: application-specific integrated circuit BIRC: bird-insect ridge classifierAtty. Docket No.4313-05001 (2024-001) CPU: central processing unit DSP: digital signal processor EO: electrical-to-optical FPGA: field-programmable gate array HCA: hydrometeor classification algorithm IVAD: intelligent VAD km: kilometer(s) m: meter(s) ms: millisecond(s) NCEI: National Centers for Environmental Information NCEP: National Centers for Environmental Prediction NEXRAD: Next Generation Radar NWS: National Weather Service OE: optical-to-electrical RAM: random-access memory RAP: rapid refresh RF: radio frequency ROM: read-only memory RPG: radar product generator RX: receiver unit SRAM: static RAM TCAM: ternary content-addressable memory TPR: true positive rate TX: transmitter unit UTC: Coordinated Universal Time VAD: velocity azimuth display VWP: VAD wind profile WSR-88D: Weather Surveillance Radar - 1988 Doppler WX: weather ^: degree(s).Atty. Docket No.4313-05001 (2024-001)

[0065] IVAD’s development process is described in six main parts. The first lays out necessary materials and methods. Specifically, section I discusses the data preparation for two bird migration seasons. Section II details the IVAD algorithm based on BIRC products. Section III presents the problem of varying VAD biases relative to the desired wind. Section IV addresses this problem by exploring the effect of bird contamination on VAD and exploring subsequent improvements from focusing on insects. In order to ensure generality, similar analyses are repeated for a different site / season in.

[0066] I. Data Preparation

[0067] The disclosed IVAD will be evaluated with respect to ground truth wind measurements. Data for the former are obtained from NEXRAD while RAP model predictions are assumed to approximate the ground truth. This section details the preparation process for both data sets.

[0068] A. NEXRAD Data

[0069] Since insects are ubiquitous among clear-air radar echoes, data acquisition is focused on two seasons of bird migration. These include nocturnal migration during nighttime and diurnal migration during daytime. For nocturnal migration, 7,380 volume scans were collected from the KOHX Nashville, Tennessee radar during May 2018. For diurnal migration, 6,467 scans were collected from KHGX in Houston, Texas during May 2022. Table I summarizes both data sets. Table I. Summary of WSR-88D data for bird migration seasons WSR-88D Location Period Migration type Number of scans

[0070] For each scan, level II and III variables were downloaded from the NCEI NEXRAD data archive. Level II variables are obtained by processing raw time series signals. The level II variables include three base moments: reflectivity factor Z, Doppler velocity Vr, and spectrum width σv. The level II variables further include three dual polarization variables: differential reflectivity ZDR, cross-correlation coefficient ρHV, and differential phase φDP. Level III variables, on the other hand, are produced by the RPG for the detection of atmospheric features, operational forecasting, and data research analysis. Data from both levels serve different parts of IVAD. Specifically, HCA predictions are retrieved from level III, dual-polarization variables serve as inputs to BIRC, and Vris used to estimate VAD.Atty. Docket No.4313-05001 (2024-001)

[0071] The distribution of echoes were verified for both migration seasons. In each scan, HCA and BIRC were used to detect the frequency of weather, bird, and insect echoes, and their relative proportions, all obtained by normalization to sum to 1. FIGS. 1A and 1B show the averages of these proportions by the time of day over the month. Large-dashed lines correspond to birds, small- dashed lines to insects, and solid lines to weather. Moreover, nighttime is generally defined as 1– 11 UTC, while the remaining period is daytime. Thus, in FIGS. 1A and 1B, the black dashed vertical lines indicate sunset around 1 UTC and sunrise around 11 UTC. Daily interchanging domination of birds during nights and insect during daytimes can be observed during the nocturnal season as shown in FIG.1A, while bird domination is also present during daytime for the diurnal season as shown in FIG. 1B. This matches the expected bird migration behavior. The relative proportion of weather is also low at less than or equal to 35% for both seasons.

[0072] B. RAP as Ground Truth

[0073] Outside NEXRAD, other wind profiles can be obtained from measurements like radiosondes and RAP. The radiosonde by NWS measures wind speed and direction as a function of pressure level.

[0074] Sounding data can be downloaded from the University of Wyoming’s upper air data portal. RAP, on the other hand, is a continental scale assimilation and forecast system operational at NCEP. RAP provides a vast range of meteorological products, including the u and v horizontal wind components at different pressure levels. Alongside VAD, these profiles enable robust wind sensing across the continent, complementing each other with different estimation modalities at different times and locations.

[0075] RAP and sounding are compared in order to select the best source of ground truth wind. Given that VAD is estimated every 5–10 minutes by each WSR-88D, the associated ground truth should be collected at matching times from sources close to these radar stations. While soundings have the advantage of directly measuring the wind, their sparse data collection often mismatches VADs. Temporally, balloons are launched only twice every day, at 00:00 UTC and 12:00 UTC. Spatially, sounding stations are not always close enough to radar sites. For example, as shown in Table II, the KOHX radar ideally has the Nashville sounding station 0.74 km away, whereas KHGX’s closest station is 193.79 km away in Lake Charles, Louisiana. Table II. Distances between KOHX and KHGX and their closest RAP and sounding stations KOHX KHGXAtty. Docket No.4313-05001 (2024-001) Closest Rapid Refresh point (km) 2.68 6.71 Closest sounding station (km) 0.74 193.79

[0076] Overaundings within a 50 km range, while 53% have soundings within 100 km. RAP, on the other hand, is better synchronized with NEXRAD, providing 1-hour updates at 50 vertical levels and a 13 km horizontal grid spacing. Thus, the closest RAP points to KOHX and KHGX are at respective distances of 2.68 km and 6.71 km. Because of the better space-time coverage, RAP is used for evaluating VAD wind profiles.

[0077] II. IVAD

[0078] IVAD builds upon the existing VAD by integrating biological taxa to generate three new products including: the insects-birds ratio, birds-only VAD, and insects-only VAD. This section expands on IVAD and the associated pipeline for identifying taxa.

[0079] A. Overview of IVAD

[0080] Assume a wind field with constant horizontal velocity ^^^^^^within a domain prescribed by height h and time t from a given WSR-88D. In Cartesian coordinates, ^^^^^^can be expressed as ^^^^^^^ℎ, ^^ = ^^^^ (1) where u is the component along the east-west axis, while v is the north-south component. In polar coordinates, ^^^^^^^ℎ, ^^= ^∠^ (2) where A and δ are the wind’s speed and direction, respectively calculated as shown in Equations (3) and (4) below. ^ = ^^^^^^^^ =√^^+ ^^(3)(4)

[0081] ^^^^^^^ℎ, ^^is often unknown in practice, however, it contributes to the Doppler velocities obtained from range gates in the region. Consequently, VAD analyses on the latter can recover the underlying wind. Here, a constant velocity model is defined as ^"#^ℎ, $^ , ^^= ^%^ℎ, ^^cos^ )$^ + ^%^ℎ, ^^^(5)Atty. Docket No.4313-05001 (2024-001) where ϕiis the azimuth of the ith gate out of n in the domain. This model is fitted to the measured radial velocities Vr(h,ϕ,t), by minimizing their least squares error below: arg min∑^^ ^ 1^" ^ℎ, $ , ^^− ^^ℎ, $ , ^^ ^(6) 2346# ^ # ^8with respect to the common velocity of echoes, expressed as ^^9^^ℎ, ^^= ^%^ℎ, ^^∠^%^ℎ, ^^(7)

[0082] ^^9^is assumed to is onlyaccurate to the degree that the different groups of composing echoes are wind borne.

[0083] IVAD is specifically designed to track biological echoes. It therefore adapts Equation (7) to three conditions recommended as prerequisites for the similar task of extracting migrant flight orientation via :;1azimuth analysis. The first condition is the well filling of the domain with organisms. As such, data for each IVAD was aggregated from the four lowest elevation sweeps, 0.5 ^, 1.5 ^, 2.5 ^, and 3.5 ^, into 40 m height bins defined from 0 to 1,500 m. For each bin, a minimum sample size of 720 gates and coverage of 75% were enforced. Coverage is defined as the percentage of range gates containing radar echoes. The second requirement is horizontally homogeneous migrant flight orientation. This is often disrupted by the presence of meteorological echoes. Therefore, IVAD was limited to scans containing ≤ 5 % weather. A more detailed discussion on how weather echoes were identified is below. The third condition of homogeneous organismal composition is important because of the different wind dependence of different organisms. This is addressed by accounting for organism type in IVAD by (i) estimating the homogeneity of range rings via the ratio of insects to birds Ri, and (ii) generating separate VADs for birds and insects. In the latter case, Equation (7) becomes ^^9^^ℎ, ^, <^= ^%=^ℎ, ^^∠^%=^ℎ, ^^ (8) (9) where C is the taxa obtainedbirds. A more detailed discussion of C is provided below.

[0084] B. Identifying Taxa of Biological Echoes

[0085] Altogether, three categories of echoes are identified by synergizing two classifiers. First, HCA identifies which gates are associated with biological echoes and, secondly, BIRCAtty. Docket No.4313-05001 (2024-001) bifurcates these echoes into birds and insects, from which insects-to-birds ratios are obtained for each range ring.

[0086] 1. Detecting Biological Echoes using HCA

[0087] HCA is a fuzzy logic classifier for radar echoes. Reflectivity, differential reflectivity, a cross-correlation coefficient, a specific differential phase, a texture of reflectivity, and a texture of differential phase are inputs to the HCA and are used to classify radar echoes as one of the following outputs: ground clutter / anomalous propagation, biological echoes, or weather echoes. These weather echoes include dry aggregated snow, wet snow, crystals, graupel, big drops, light rain, moderate rain, heavy rain, and a mixture of rain and hail. In addition to HCA’s inputs, Doppler velocity is also used to separate ground clutter / anomalous propagation from hail. While the HCA is generally reliable, owing to its rigorous use and testing, one observed issue for detecting biological echoes is their occasional misclassification as weather. Thus, “weather” predictions are changed to biological echoes when the following two conditions are met: (i) their cross-correlation coefficient ρHV is ≤ 0.88, because weather echoes usually have ρHV ≫ 0.85, while biological echoes have ρHV≤ 0.83, and (ii) the gate’s height is below 1,500 m, where most organisms are expected to be found. Analyses of this procedure shows high recovery of misclassified bird migrants with ρHV ≤ 0.8 and low recovery of misclassified insects at higher elevations where ρHV approaches 1. Over all cases, however, the availability of biological echoes is increased.

[0088] Following correction, scans are controlled for clear-air conditions based on the percentage of gates containing meteorological echoes. Specifically, those with > 5% weather are excluded. Biological echoes from the remaining clear-air scans are passed to the next stage.

[0089] 2. Classifying Biological Echoes into Birds and Insects

[0090] BIRC classifies biological echoes using ZDR, φDP, and ρHV as inputs. On the output side, a score for bird presence f(w,b) is calculated based on the formula below: PQRwhere weights C =ECFGHThe predicted scores are converted into probabilities pi using a sigmoid function as shown in Equation 11: V^^ = (11)Atty. Docket No.4313-05001 (2024-001) The final taxa C is obtained based on a 0.5 threshold on p. For a gate located at height h, azimuth ϕi, and time t, this can be expressed as follows: <^ℎ, $^, t^ = ]1, V^ ≥ 0.50, V^ < 0.5b (12) where, as stated earlier, {C = 0} gate-level classification, the taxonomic range birds ratio: c ℎde^f,g^^^, ^^=de^f,g^Wdh^f,g^(13) where Nb(h,t) and Ni(h,t) are thecalculated using Equations (14) & (15): i\^ℎ, ^^=∑^^ <^ℎ, $^, ^^ (14) i^^ℎ, ^^=∑^^ 1 − <^ℎ, $^, ^^ (15)

[0091] FIGS.2A–2F exemplify the classification process on two scans from KOHX. The first scan was collected during nocturnal bird migration around midnight, 05:04 UTC, on May 8, 2018. In FIG.2A, BIRC’s predictions for the 0.5° elevation show a bird majority of ≈ 75%. Aggregating by range rings in FIG.2B further elucidates bird domination across all heights, especially between 200–1,200 m. As a result, the insects-birds ratios are consistently low about a median of 22% in FIG. 2C. The second case contains an insect swarm collected at 15:47 UTC the next day. BIRC predominantly predicts insects as ≈ 86% of echoes from the 0.5° scan in FIG.2D and consistently dominating all heights, especially between 200–800 m as shown in FIG. 2E, resulting in a high median insect-to-bird ratio of 90% in FIG.2F.

[0092] The insects-to-birds ratios were also analyzed for the whole KOHX nocturnal migration season. Their height by time of day averages are shown in FIG. 3. Local sunset and sunrise are respectively denoted by black dashed lines around 1 and 11 UTC. Matching expectations, nighttime has a low Ri~ 28% indicative of bird domination. Around sunrise, Rijumps to ~ 77% and persists over the course of daytime. This is consistent with a switch to insect domination at the beginning of dawn.

[0093] III. Wind Bias for Evaluating VAD

[0094] Wind bias is defined as the discordance between the estimated VAD and the underlying wind. For aerial organisms, this approximates their airspeed. Explained another way, VAD measures ground speed, the velocity of organisms relative to the stationary earth surface. GroundAtty. Docket No.4313-05001 (2024-001) speed includes contributions from independent flight and the wind. Thus, the flight component can be isolated by removing the wind contribution, resulting in airspeed. Specifically, wind bias is calculated as j^ℎ, ^, k^=^^^9^^ℎ, ^, k^− ^^^^^^^ℎ, ^^^(16) where ^^^^^^is obtained

[0095] VAD wind estimation presupposes low biases, however, many cases significantly deviate from the wind. This problem is exemplified in FIGS. 4A–4F, in which the biases of the scans in FIGS.2A–2F are analyzed. VADs here are obtained from all biological echoes. The first case contains nocturnal bird migration. Their radial velocities in FIG.4A show a general heading toward the radar’s northwest, though some echoes in the eastern fringe also move centrifugally in various directions. VAD is estimated from these velocities and compared to RAP in FIG. 4B, revealing substantial differences, especially between the v components at high altitudes. As a result, VAD wind biases are large, ranging from ~ 3 to 10 ms-1and increasing with height as shown in FIG. 4C. The second case contains an afternoon insect swarm. Observations of radial velocities in FIG.4D show mass alignment with a common heading towards the radar’s northeast. This alignment is due to echoes tracing the underlying wind, leading to strong concordance between VAD and RAP measurements in FIG.4E. Consequently, biases are low with values < 2.1 ms-1as shown in FIG.4F.

[0096] Wind biases were also analyzed statistically for the KOHX nocturnal migration season. Their height by time of day averages in FIG. 5 reveal a daily cycle where large biases ≥ 5 ms-1often occurred during nighttime, especially at heights > 200 m, while daytime has low bias levels. This cycle underscores the problem of VADs regularly deviating from the desired wind velocity.

[0097] IV. Analysis of IVAD

[0098] Birds have been reported to contaminate VAD winds, while insects are generally good tracers. Thus, IVAD should attribute large biases to birds and low biases to insects. In terms of the final products, high insects-birds ratios should correspond to low biases, and after bifurcation by taxa, birds-only VADs should have large biases while insects-only VADs should produce low bias wind estimates. These hypotheses are investigated below.

[0099] A. Identifying the Source of VAD Wind Biases Using Ri

[0100] Large biases for the nocturnal bird migration scan in FIG.4C correspond to low insect- bird ratios in FIG. 2C, whereas low biases for the insects-dominated scan in FIG. 4F correspondAtty. Docket No.4313-05001 (2024-001) to high ratios in FIG. 2F. This pattern is persistent across the nocturnal migration season. Comparing FIG.5 to FIG.3, heavily-biased nighttime VADs are bird dominated with low Ri, while low-bias daytime VADs are insect dominated with high Ri.

[0101] Analyzing the response of VAD wind biases to Rifurther illuminates inverse relationship. For instance, FIG.6A presents bias averages by height and Ri. Going from left to right at any given height, biases generally decrease as Ri increases. A similar scatter plot analysis is presented in FIG. 6B. Here, points correspond to singular VADs, shades denote height, and the solid line depicts the mean biases. From left to right, the mean and spread of biases can be observed to decrease as Ri increases.

[0102] Several conclusions are drawn from these results. First, birds are found to be the primary biological source of clear-air VAD biases, consistent with previous findings. Second, BIRC can be applied for directly measuring bird contamination via the insects-birds ratio. A standout feature of this approach is the use of only dual-polarization variables, making it applicable to any WSR-88D across NEXRAD, regardless of time and location. Finally, the results provide further support for the quality of BIRC’s predictions. Previous research has reported birds as having larger biases than insects, with a threshold of 5–7 ms-1separating both groups. To validate these findings, VADs are first partitioned into 3 groups, including bird-dominated, mixed, and insect-dominated rings, defined by Ri≤ 40%, 40% < Ri≤ 60%, and Ri> 60%, respectively. Biases from bird-dominated rings were then compared to insect-dominated rings. The results are shown in FIG.7. Medians for both regions were found to be 5.9 ms-1and 1.9 ms-1, respectively. Overall, a threshold of ~ 4 ms-1was found to separate both groups, similar to the aforementioned findings. The slight difference could be due to different VAD algorithms.

[0103] B. Improving Contaminated VAD Wind Estimates by Focusing on Insects-Only

[0104] Above, the wind biases of clear-air VADs were found to depend on their taxonomic distribution. For example, bird-dominated rings typically had larger biases of ~ 5.9 ms-1. However, the remaining gates could contain sufficient insect echoes from which improved wind estimates can be obtained. Conversely, VAD focused on birds can provide useful information for ornithological studies. Wind improvements and its necessary preconditions are discussed below.

[0105] 1. Identifying Conditions for VAD Improvement

[0106] A bird-contaminated VAD can be improved when its constituent insects’ velocities are different from those of birds, such that bifurcation by taxa unveils two distinct sinusoids. In thisAtty. Docket No.4313-05001 (2024-001) case, IVAD according to Equation (8) should generate a bird-only VAD with wind bias of ~ 4 ms-1larger than the insect-only VAD. Consequently, a bird-insect bias difference ∆e(h,t) is defined to assess the viability of improvement. Mathematically, ∆j^ℎ, ^^= j\^#^n^ℎ, ^^− j^^n^ℎ, ^^ (17) where ebirds and eins are the ≈ birdsand insects have equal biases, no on On the other hand, ∆e > 0 means birds have larger biases, and as such, improvement is possible. Insects having larger biases is highly unlikely.

[0107] Bird-insect bias differences are illustrated using the three cases presented in Table III. Table III. Example cases for bird-insect bias differences Case WSR-88D Date Time (UTC) Height (m) Radial velocities per azimuth for these cases are also respectively shown in FIGS. 8A–8C. Each point is colored by taxa, with black for insects and gray for birds. The first case contains one insect- dominated sinusoid (Ri ≈ 71.3%) shown in FIG. 8A. VAD was performed on these insects, and their biases were calculated using Equation (16). As expected, insects have low biases of ~ 1.2 ms-1. The few embedded bird echoes, on the other hand, are largely misclassifications and unrepresentative of another migration event. They are estimated with similar biases to insects. As a result, ∆e ~ 0.3 ms-1, indicating that further improvement is very limited. The second case is a mixed ring with Riof ~ 39.7% shown in FIG.8B. Simultaneous bird and insect migration can be observed in the form of two distinct sinusoids. Moreover, the bird VAD bias is ∆e ~ 2.6 ms-1greater than those from insects. Therefore, the insect-focused VAD would more accurately capture the wind. FIG. 8C shows a bird-dominated sinusoid (Ri ≈ 17.0%) from the final case. As expected, these birds have large biases of ~ 7.0 ms-1. The few insect echoes are largely misclassifications, hence their identical biases to those from birds. As a result, ∆e ~ 0.1 ms-1, indicating that further improvement is unlikely. Across these three cases, it can be seen that maximal ∆e for the mixed ring tapers off to 0 ms-1for homogeneous rings.Atty. Docket No.4313-05001 (2024-001)

[0108] This pattern is further affirmed by ∆e analysis across the KOHX nocturnal migration season. The results in FIG.9 show bias differences averaged by height and Ri. Maximal values can be observed in VADs where 30% < Ri < 60% and 600 < height < 1,400 m, consistent with improvable VADs comprising sufficient corrupting birds alongside sufficient wind-tracing insects. In the limiting case of homogeneity, insects render improvement unnecessary, while birds make improvement unfeasible. Therefore, ∆e generally approaches 0 ms-1as Ri approaches 0% and 100%.

[0109] 2. VAD Improvement

[0110] The operational VAD does not distinguish biological echoes by taxa. Moreover, focusing on insects has been shown to reduce wind biases. Thus, a cost-benefit analysis is conducted to compare the proposed insect-only approach to the existing all-biological one. Two metrics are considered. The first is improvement I, defined as the reduction in wind biases: o^ℎ, ^^= j\^p^ℎ, ^^− j^^n^ℎ, ^^ (18) where ebio(h,t) is the bias tofewer samples and potentially fewer VADs. Hence, the fraction of lost VAD estimates, hereafter bynamed availability, is also defined as follows: q^ℎ, ^^==her^f,g^^=est^f,g^=her^f,g^(19) where Cbio(h,t) is the number ofgenerated using only insect echoes.

[0111] Both metrics were analyzed for the nocturnal migration season. FIG.10A particularly shows averaged improvements by height and Ri. As expected, the largest values occur in VADs with maximal ∆e, where 600 < height < 1,400 m and 30% < Ri < 60%. I’s lower magnitudes results from bias comparison between insect-only and biological VADs, as opposed to birds for ∆e. Similar averages for F are shown in FIG. 10B. Coinciding with an insect’s absence, lower VAD availability can be observed for heights > 800 m, especially when bird dominated. For instance, significant 40 − 60% losses are incurred for an Ri of ~ 18% and a height of ~ 1,200 m.

[0112] 3. Filtering Insect Predictions by Confidence

[0113] Further improvements could require more accurate insect predictions. While BIRC boasts a TPR of 83.2–89.2%, this implies that 10.8%–16.8% of bird contaminants persist as misidentified insects. Eliminating these false detections should benefit I. Recall that BIRC predictsAtty. Docket No.4313-05001 (2024-001) a score for insect presence p, such that p = 0 indicates high confidence and p = 1 indicates low confidence. Thresholds on p are used to filter insects so that BIRC’s output becomes <^ℎ, $^, t^ = ]iuvj, V0, V^ > x^≤ xb (20)where {C = 0} are insects andIn the limit, τ = 0 discards all τ =

[0114] With the new setup, cost-benefit analyses were re-conducted for varying τ during the nocturnal migration season. The respective improvements for τ = 0.4, 0.3, 0.2, and 0.1 are shown in FIGS. 11A–11D. Within the 30% < Ri< 60% and 600 < height < 1,400 m region, median improvements can be observed to increase from 0.46 ms-1to 0.84 ms-1as τ decreases from 0.4 to 0.1. Availability, on the other hand, generally decreases with τ as shown in FIGS.11E–11H. This is especially salient in bird-dominated and high-altitude rings, where insects are less common. Overall, these results affirm the hypothesis of better improvements arising from more confident predictions. The cost of low availability can be addressed by refinements of bird-insect classification.

[0115] C. Testing IVAD on the KHGX Diurnal Migration Season

[0116] Other analyses of IVAD products focus on the nocturnal season, where birds migrate at night and insects dominate daytimes. Central deductions include (i) an inverse relationship between Riand wind biases, and (ii) maximal improvements for mixed VADs. The generality of these deductions are investigated for the diurnal migration season, where birds also migrate during the day. The results are shown in FIGS.12A–12B.

[0117] FIG. 12A specifically presents wind biases averaged by height and Ri. Affirming previous findings, biases can be observed to decrease as Riincreases, i.e., scanning from left to right at any given height. The omnipresence of birds is also evident as an accumulation of the diurnal season’s biases toward Ri≈ 0 (compared to FIG.6A). Similar analyses for improvements are shown in FIG. 12B. Maximal I can be observed in VADs where 600 < height < 1,400 m and 20% < Ri < 55%. Compared to the nocturnal season, this region is 5–10% shifted toward the left due to higher bird contamination, especially at higher altitudes. However, improvements are still maximized in mixed VADs.

[0118] V. IVAD Flow Diagram

[0119] Coupling VAD and BIRC enables the identification and reduction of bird contamination. The integration of both systems is shown in FIG. 13. For each VAD ring, HCAAtty. Docket No.4313-05001 (2024-001) detects gates as biological, weather, or others. The correction procedure is then applied to rectify misclassifications, after which BIRC is used to bifurcate biological echoes into birds and insects. These classifications are used to generate the three IVAD products, namely the insects-birds ratio Ri, birds-only VAD, and insects-only VAD.

[0120] VI. Correcting Misclassification of Biological Echoes From the HCA

[0121] FIGS.14A–14F demonstrate the correction procedure on two cases from KOHX. The first case contains nocturnal bird migration around midnight. This scan was collected from the 0.5° elevation at 05:08 UTC on May 1, 2018. HCA’s predictions in FIG. 14A indicate prevalent “weather” within 100 km of the radar site, which is unlikely given their ρHV ≤ 0.8 as shown in FIG. 14B. The correction procedure changes most of these echoes to biological, resulting in the updated predictions in FIG. 14C. The second case is a scan of a more complicated scenario of insect domination collected from the 2.5° elevation at 18:06 UTC on May 13, 2018. FIG. 14D shows that HCA classifies gates close to the radar as biological, while the farthest ring of echoes is designated “weather.” These rings are a peculiar feature of clear-air insect-dominated scans in the dataset and are likely caused by insects. Regardless of the rings’ source, their ρHV of ≈ 1 resembles weather as shown in FIG.14E. Thus, correction is ineffective for this case as shown in FIG.14F and can potentially lead to a lower availability of biological echoes.

[0122] VII. Conclusion

[0123] Insects-birds ratios have been shown to measure bird contamination or insect tracing levels by their inverse relationship to wind biases. They can be exapted as wind tracing scores for clear-air VADs such that Ri~ 0% indicates poor wind tracing, while Ri~ 100% indicates reliable wind tracing. Furthermore, thresholds on Ri can be used to demarcate three important regions of bird / insect activity: Ri < 40% can be bird dominated, 40% ≤ Ri < 60% can be mixed, and 60% ≤ Ri< 100% can be insect dominated. Usage of the remaining products can be adapted to these regions.

[0124] Bird-dominated regions have large median biases of ~ 5.5 ms-1and low improvements of ~ 0 ms-1indicative of aligned bird migration as shown in FIGS.6 and 10, respectively. As such, bird-only VADs can be used to retrieve the flock’s velocity. VAD on all biological echoes can alternatively estimate bird velocities because of the low impact of the few insects on the model. However, estimates would become less reliable as Ri increases. Insect-dominated regions have low biases of ~ 2.0 ms-1and low improvements of ~ 0 ms-1because of good wind tracing. Insect-onlyAtty. Docket No.4313-05001 (2024-001) VADs can therefore be used to obtain more accurate wind estimates. Once again, biological VADs can also provide good wind approximation, but become less reliable as Ri decreases. The final mixed region has intermediate biases of ~ 2.85 ms-1, which is closer to insects, and maximum improvements reaching up to 1.5 ms-1. Thus, focusing VAD on insect echoes can reduce wind biases. Several industries would benefit from having these products. Meteorologists can obtain more accurate winds from insect velocities while the aviation industry can leverage bird velocities to understand their migratory activity between airports. Moreover, aero-ecological studies of both taxa would be facilitated by the disclosed embodiments.

[0125] There are still some areas that can be improved with better echo classification. For example, HCA’s misclassification of insects as weather leads to lower insect availability in some cases. To prevent the inheritance of such errors, the taxonomic classifier can be disentangled from the HCA by including new classes for weather and other types of biological echoes. Furthermore, improvements in wind estimation were observed as less confident insect points were filtered prior to the VAD process. Consequently, better bird-insect classification would guarantee more reliable winds.

[0126] VIII. Embodiments

[0127] FIG.15 is a flowchart of a method 1500 of IVAD wind estimation. At step 1510, VAD rings are obtained from at least one radar scan. At step 1520, an HCA is applied to the VAD rings to detect weather echoes, biological echoes, and other echoes. “Other echoes,” as defined herein, refers to echoes from ground clutter or echoes due to anomalous propagation. At step 1530, a BIRC is applied to the biological echoes to generate birds-only VADs and insects-only VADs.

[0128] The method 1500 may implement additional embodiments. For instance, obtaining the VAD rings from the at least one radar scan comprises performing the at least one radar scan. The VAD rings are based on height, azimuths, and times.

[0129] The method further comprises: determining a cross-correlation coefficient of a first weather echo of the weather echoes; determining a gate height associated with the first weather echo; adding the first weather echo to the biological echoes when the cross-correlation coefficient and the gate height meet a condition; and retaining the first weather echo in the weather echoes when the cross-correlation coefficient and the gate height do not meet the condition. The condition is that the cross-correlation coefficient is less than or equal to about 0.83 and that the gate height is less than about 1,500 m.Atty. Docket No.4313-05001 (2024-001)

[0130] Applying the BIRC to the biological echoes to generate the birds-only VADs and the insects-only VADs comprises: using differential reflectivities, differential phases, and cross- correlation coefficients as inputs; and outputting bird presence scores based on the inputs, weights, and biases. Applying the BIRC to the biological echoes to generate the birds-only VADs and the insects-only VADs further comprises obtaining insects-birds ratios based on the bird presence scores. The biological echoes comprise first biological echoes and second biological echoes, the first biological echoes have first insects-birds ratios of the insects-birds ratios, the second biological echoes have second insects-birds ratios of the insects-birds ratios, and applying the BIRC to the biological echoes to generate the birds-only VADs and the insects-only VADs further comprises: including the first biological echoes in the birds-only VADs when first the first insects- birds ratios are below a first threshold; and including the second biological echoes in the insects- only VADs when first the second insects-birds ratios are above a second threshold. The first threshold is about 40%, and the second threshold is about 60%.

[0131] The method further comprises: using the birds-only VADs to determine bird migration between airports, and altering a path of an in-flight aircraft based on the bird migration; or using the birds-only VADs to determine bird movements in a vicinity of an airport, and altering a landing path of an in-flight aircraft or a take-off path of an on-ground aircraft based on the bird movements. Alternatively, the method further comprises using the insects-only VADs to alter a wind estimate or to determine insect migration patterns. Once altered, the wind estimate is more accurate and can be used to monitor gust fronts to predict the behavior of and extinguish wildfires, or to observe local changes in vertical wind shear, increases in speed shear, and the development or evolution of jet streams.

[0132] FIG. 16 is a schematic diagram of an apparatus 1600. The apparatus 1600 may implement the disclosed embodiments. The apparatus 1600 comprises ingress ports 1610 and an RX 1620 to receive data; a processor 1630, or logic unit, baseband unit, or CPU, to process the data; a TX 1640 and egress ports 1650 to transmit the data; and a memory 1660 to store the data. The apparatus 1600 may also comprise OE components, EO components, or RF components coupled to the ingress ports 1610, the RX 1620, the TX 1640, and the egress ports 1650 to provide ingress or egress of optical signals, electrical signals, or RF signals.

[0133] The processor 1630 is any combination of hardware, middleware, firmware, or software. The processor 1630 comprises any combination of one or more CPU chips, cores,Atty. Docket No.4313-05001 (2024-001) FPGAs, ASICs, or DSPs. The processor 1630 communicates with the ingress ports 1610, the RX 1620, the TX 1640, the egress ports 1650, and the memory 1660. The processor 1630 comprises an IVAD component 1670, which implements the disclosed embodiments. The inclusion of the IVAD component 1670 therefore provides a substantial improvement to the functionality of the apparatus 1600 and effects a transformation of the apparatus 1600 to a different state. Alternatively, the memory 1660 stores the IVAD component 1670 as instructions, and the processor 1630 executes those instructions.

[0134] The memory 1660 comprises any combination of disks, tape drives, or solid-state drives. The apparatus 1600 may use the memory 1660 as an overflow data storage device to store programs when the apparatus 1600 selects those programs for execution and to store instructions and data that the apparatus 1600 reads during execution of those programs. The memory 1660 may be volatile or non-volatile and may be any combination of ROM, RAM, TCAM, or SRAM.

[0014] A computer program product may comprise computer-executable instructions that are stored on a computer-readable medium and that, when executed by a processor, cause an apparatus to perform any of the embodiments. The non-transitory medium may be the memory 1660, the processor may be the processor 1630, and the apparatus may be the apparatus 1600.

[0135] A first aspect relates to a method comprising: obtaining VAD rings from at least one radar scan; applying an HCA to the VAD rings to detect weather echoes, biological echoes, and other echoes; and applying a BIRC to the biological echoes to generate birds-only VADs and insects-only VADs.

[0136] In a first implementation of the first aspect, obtaining the VAD rings from the at least one radar scan comprises performing the at least one radar scan.

[0137] In a second implementation or any preceding implementation of the first aspect, the VAD rings are based on height, azimuths, and times.

[0138] In a third implementation or any preceding implementation of the first aspect, the method further comprises: determining a cross-correlation coefficient of a first weather echo of the weather echoes; determining a gate height associated with the first weather echo; adding the first weather echo to the biological echoes when the cross-correlation coefficient and the gate height meet a condition; and retaining the first weather echo in the weather echoes when the cross- correlation coefficient and the gate height do not meet the condition.Atty. Docket No.4313-05001 (2024-001)

[0139] In a fourth implementation or any preceding implementation of the first aspect, the condition is that the cross-correlation coefficient is less than or equal to about 0.83 and that the gate height is less than about 1,500 m.

[0140] In a fifth implementation or any preceding implementation of the first aspect, applying the BIRC to the biological echoes to generate the birds-only VADs and the insects-only VADs comprises: using differential reflectivities, differential phases, and cross-correlation coefficients as inputs; and outputting bird presence scores based on the inputs, weights, and biases.

[0141] In a sixth implementation or any preceding implementation of the first aspect, applying the BIRC to the biological echoes to generate the birds-only VADs and the insects-only VADs further comprises obtaining insects-birds ratios based on the bird presence scores.

[0142] In a seventh implementation or any preceding implementation of the first aspect, the biological echoes comprise first biological echoes and second biological echoes, the first biological echoes have first insects-birds ratios of the insects-birds ratios, the second biological echoes have second insects-birds ratios of the insects-birds ratios, and applying the BIRC to the biological echoes to generate the birds-only VADs and the insects-only VADs further comprises: including the first biological echoes in the birds-only VADs when first the first insects-birds ratios are below a first threshold; and including the second biological echoes in the insects-only VADs when first the second insects-birds ratios are above a second threshold.

[0143] In an eighth implementation or any preceding implementation of the first aspect, the first threshold is about 40%, and the second threshold is about 60%.

[0144] In a ninth implementation or any preceding implementation of the first aspect, the method further comprises: using the birds-only VADs to determine a bird migration pattern between airports, and altering a path of an in-flight aircraft based on the bird migration pattern; or using the birds-only VADs to determine bird movements in a vicinity of an airport, and altering a landing path of an in-flight aircraft or a take-off path of an on-ground aircraft based on the bird movements.

[0145] In a tenth implementation or any preceding implementation of the first aspect, the method further comprises using the insects-only VADs to alter a wind estimate.

[0146] In a second implementation, an apparatus comprises: one or more memories configured to store instructions; and one or more processors coupled to the one or more memories and configured to execute the instructions to cause the apparatus to: obtain VAD rings from at leastAtty. Docket No.4313-05001 (2024-001) one radar scan; apply an HCA to the VAD rings to detect weather echoes, biological echoes, and other echoes; and apply a BIRC to the biological echoes to generate birds-only VADs and insects- only VADs.

[0147] In a first implementation of the second aspect, the one or more processors are further configured to execute the instructions to cause the apparatus to obtain the VAD rings from the at least one radar scan by performing the at least one radar scan.

[0148] In a second implementation or any preceding implementation of the second aspect, the VAD rings are based on height, azimuths, and times.

[0149] In a third implementation or any preceding implementation of the second aspect, the one or more processors are further configured to execute the instructions to cause the apparatus to: determine a cross-correlation coefficient of a first weather echo of the weather echoes; determine a gate height associated with the first weather echo; add the first weather echo to the biological echoes when the cross-correlation coefficient and the gate height meet a condition; and retain the first weather echo in the weather echoes when the cross-correlation coefficient and the gate height do not meet the condition.

[0150] In a fourth implementation or any preceding implementation of the second aspect, the condition is that the cross-correlation coefficient is less than or equal to about 0.83 and that the gate height is less than about 1,500 m.

[0151] In a fifth implementation or any preceding implementation of the second aspect, the one or more processors are further configured to execute the instructions to cause the apparatus to apply the BIRC to the biological echoes to generate the birds-only VADs and the insects-only VADs by: using differential reflectivities, differential phases, and cross-correlation coefficients as inputs; and outputting bird presence scores based on the inputs, weights, and biases.

[0152] In a sixth implementation or any preceding implementation of the second aspect, the one or more processors are further configured to execute the instructions to cause the apparatus to apply the BIRC to the biological echoes to generate the birds-only VADs and the insects-only VADs further by obtaining insects-birds ratios based on the bird presence scores.

[0153] In a seventh implementation or any preceding implementation of the second aspect, the biological echoes comprise first biological echoes and second biological echoes, the first biological echoes have first insects-birds ratios of the insects-birds ratios, the second biological echoes have second insects-birds ratios of the insects-birds ratios, and the one or more processors are furtherAtty. Docket No.4313-05001 (2024-001) configured to execute the instructions to cause the apparatus to apply the BIRC to the biological echoes to generate the birds-only VADs and the insects-only VADs further by: including the first biological echoes in the birds-only VADs when first the first insects-birds ratios are below a first threshold; and including the second biological echoes in the insects-only VADs when first the second insects-birds ratios are above a second threshold.

[0154] In a third aspect, a computer program product comprises instructions that are stored on a computer-readable medium and that, when executed by one or more processors, cause an apparatus to: obtain VAD rings from at least one radar scan; apply an HCA to the VAD rings to detect weather echoes, biological echoes, and other echoes; and apply a BIRC to the biological echoes to generate birds-only VADs and insects-only VADs.

[0155] While several embodiments have been provided, the disclosed systems and methods might be embodied in many other specific forms without departing from the spirit or scope of the present disclosure. The present examples are to be considered as illustrative and not restrictive, and the intention is not to be limited to the details given herein. For example, the various elements or components may be combined or integrated in another system or certain features may be omitted, or not implemented. Likewise, where single components, apparatuses, or systems are described as performing functions, multiple such components, apparatuses, or systems may implement the functions.

[0156] In addition, techniques, systems, subsystems, and methods described and illustrated in the various embodiments as discrete or separate may be combined or integrated with other systems, components, techniques, or methods without departing from the scope of the present disclosure. Other items shown or discussed as coupled may be directly coupled or may be indirectly coupled or communicating through some interface, device, or intermediate component whether electrically, mechanically, or otherwise. Other examples of changes, substitutions, and alterations are ascertainable by one skilled in the art and may be made without departing from the spirit and scope disclosed herein.

Claims

Atty. Docket No.4313-05001 (2024-001) CLAIMS What is claimed is:

1. A method comprising: obtaining velocity azimuth display (VAD) rings from at least one radar scan; applying a hydrometeor classification algorithm (HCA) to the VAD rings to detect weather echoes, biological echoes, and other echoes; and applying a bird-insect ridge classifier (BIRC) to the biological echoes to generate birds- only VADs and insects-only VADs.

2. The method of claim 1, wherein obtaining the VAD rings from the at least one radar scan comprises performing the at least one radar scan.

3. The method of claim 1, wherein the VAD rings are based on height, azimuths, and times.

4. The method of claim 1, further comprising: determining a cross-correlation coefficient of a first weather echo of the weather echoes; determining a gate height associated with the first weather echo; adding the first weather echo to the biological echoes when the cross-correlation coefficient and the gate height meet a condition; and retaining the first weather echo in the weather echoes when the cross-correlation coefficient and the gate height do not meet the condition.

5. The method of claim 4, wherein the condition is that the cross-correlation coefficient is less than or equal to about 0.83 and that the gate height is less than about 1,500 meters (m).

6. The method of claim 1, wherein applying the BIRC to the biological echoes to generate the birds-only VADs and the insects-only VADs comprises: using differential reflectivities, differential phases, and cross-correlation coefficients as inputs; and outputting bird presence scores based on the inputs, weights, and biases.Atty. Docket No.4313-05001 (2024-001) 7. The method of claim 6, wherein applying the BIRC to the biological echoes to generate the birds-only VADs and the insects-only VADs further comprises obtaining insects-birds ratios based on the bird presence scores.

8. The method of claim 7, wherein the biological echoes comprise first biological echoes and second biological echoes, wherein the first biological echoes have first insects-birds ratios of the insects-birds ratios, wherein the second biological echoes have second insects-birds ratios of the insects-birds ratios, and wherein applying the BIRC to the biological echoes to generate the birds- only VADs and the insects-only VADs further comprises: including the first biological echoes in the birds-only VADs when first the first insects- birds ratios are below a first threshold; and including the second biological echoes in the insects-only VADs when first the second insects-birds ratios are above a second threshold.

9. The method of claim 8, wherein the first threshold is about 40%, and wherein the second threshold is about 60%.

10. The method of claim 1, further comprising: using the birds-only VADs to determine a bird migration pattern between airports, and altering a path of an in-flight aircraft based on the bird migration pattern; or using the birds-only VADs to determine bird movements in a vicinity of an airport, and altering a landing path of an in-flight aircraft or a take-off path of an on-ground aircraft based on the bird movements.

11. The method of claim 1, further comprising using the insects-only VADs to alter a wind estimate.

12. An apparatus comprising: one or more memories configured to store instructions; and one or more processors coupled to the one or more memories and configured to execute the instructions to cause the apparatus to:Atty. Docket No.4313-05001 (2024-001) obtain velocity azimuth display (VAD) rings from at least one radar scan; apply a hydrometeor classification algorithm (HCA) to the VAD rings to detect weather echoes, biological echoes, and other echoes; and apply a bird-insect ridge classifier (BIRC) to the biological echoes to generate birds-only VADs and insects-only VADs.

13. The apparatus of claim 12, wherein the one or more processors are further configured to execute the instructions to cause the apparatus to obtain the VAD rings from the at least one radar scan by performing the at least one radar scan.

14. The apparatus of claim 12, wherein the VAD rings are based on height, azimuths, and times.

15. The apparatus of claim 12, wherein the one or more processors are further configured to execute the instructions to cause the apparatus to: determine a cross-correlation coefficient of a first weather echo of the weather echoes; determine a gate height associated with the first weather echo; add the first weather echo to the biological echoes when the cross-correlation coefficient and the gate height meet a condition; and retain the first weather echo in the weather echoes when the cross-correlation coefficient and the gate height do not meet the condition.

16. The apparatus of claim 15, wherein the condition is that the cross-correlation coefficient is less than or equal to about 0.83 and that the gate height is less than about 1,500 meters (m).

17. The apparatus of claim 12, wherein the one or more processors are further configured to execute the instructions to cause the apparatus to apply the BIRC to the biological echoes to generate the birds-only VADs and the insects-only VADs by: using differential reflectivities, differential phases, and cross-correlation coefficients as inputs; and outputting bird presence scores based on the inputs, weights, and biases.Atty. Docket No.4313-05001 (2024-001) 18. The apparatus of claim 17, wherein the one or more processors are further configured to execute the instructions to cause the apparatus to apply the BIRC to the biological echoes to generate the birds-only VADs and the insects-only VADs further by obtaining insects-birds ratios based on the bird presence scores.

19. The apparatus of claim 18, wherein the biological echoes comprise first biological echoes and second biological echoes, wherein the first biological echoes have first insects-birds ratios of the insects-birds ratios, wherein the second biological echoes have second insects-birds ratios of the insects-birds ratios, and wherein the one or more processors are further configured to execute the instructions to cause the apparatus to apply the BIRC to the biological echoes to generate the birds-only VADs and the insects-only VADs further by: including the first biological echoes in the birds-only VADs when first the first insects- birds ratios are below a first threshold; and including the second biological echoes in the insects-only VADs when first the second insects-birds ratios are above a second threshold.

20. A computer program product comprising instructions that are stored on a computer- readable medium and that, when executed by one or more processors, cause an apparatus to: obtain velocity azimuth display (VAD) rings from at least one radar scan; apply a hydrometeor classification algorithm (HCA) to the VAD rings to detect weather echoes, biological echoes, and other echoes; and apply a bird-insect ridge classifier (BIRC) to the biological echoes to generate birds-only VADs and insects-only VADs.

Images