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Geostationary Operational Environmental Satellites (GOES) Radar Estimation via Machine Learning to Inform NWP (GREMLIN) is a machine learning model that outputs composite reflectivity using GOES-R Series Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) input data. GREMLIN is useful for observing severe weather and initializing convection for short-term forecasts, especially over regions without ground-based radars. This study expands the evaluation of GREMLIN's accuracy against the Multi-Radar Multi-Sensor (MRMS) System to the entire contiguous United States (CONUS) for the entire annual cycle. Regional and temporal variation of validation metrics are examined over CONUS by season, day of year, and time of day. Since GREMLIN was trained with data in spring and summer, root-mean-square difference (RMSD) and bias are lowest in the order of summer, spring, fall, and winter. In summer, diurnal patterns of RMSD follow those of precipitation occurrence. Winter has the highest RMSD because of cold surfaces mistaken as precipitating clouds, but some of these errors can be removed by applying the ABI clear-sky mask product and correcting biases using a lookup table. In GREMLIN, strong echoes are closely related to the existence of lightning and corresponding low brightness temperatures, which result in different error distributions over different regions of CONUS. This leads to negative biases in cold seasons over Washington State, lower 30-dBZ critical success index caused by high misses over the Northeast, and higher false alarms over Florida that are due to higher frequency of lightning. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) can transport birds and insects near their center of circulation. In this study, we examined the maximum altitude, area and density of the radar‐derived bioscatter signature across a set of 42 TC centers of circulation sampled from 2011 to 2020. All TC events contained at least one time when a bioscatter signature was present. More intense hurricanes with closed eyes typically had taller and denser bioscatter signatures, and sometimes larger areas dominated by bioscatter. This indicated a larger number of organisms within the circulation of more intense hurricanes, supporting the speculation that those storms were most likely to trap birds that do not want to risk flying through their eyewall thunderstorms. Larger and denser bioscatter signatures, indicating a larger number of birds, tend to occur when fall migration brings a large bird population to the Gulf and East Coasts where most storms were sampled. TC formation location was not related to bioscatter characteristics, but storms sampled in the Gulf of Mexico and Florida tended to have larger and denser bioscatter signatures. [ABSTRACT FROM AUTHOR]

Raindrop size distribution (DSD) observed using a disdrometer can be represented by a constrained-gamma (C-G) DSD model based on the empirical relationship between shape (µ) and slope (Λ). The C-G DSD model can be used to retrieve DSDs and rain microphysical parameters from dual-polarization radar measurements of reflectivity (ZH) and differential reflectivity (ZDR). This study presents a new µ-Λ relationship to characterize rain microphysics in South Korea using a two-dimensional video disdrometer (2DVD) and Yong-in S-band dual-polarization radar. To minimize sampling errors from the 2DVD and radar measurements, measured size distributions are truncated by particle size and velocity-based filtering and compared with rain gauge measurement. The calibration biases of radar ZH and ZDR were calculated using the self-consistency constraint and vertical pointing measurements. The derived µ-Λ relationship was verified using the mass-weighted mean diameter (Dm) and standard deviation of the size distribution (σm), calculated from the 2DVD, for comparison with existing µ-Λ relationships for Florida and Oklahoma. The Dm-σm relationship derived from the 2DVD corresponded well with the µ-Λ relationship. The µ-Λ relationship derived for the Korean Peninsula was similar to Florida, and both generally had larger µ values than Oklahoma for the same Λ. The derived µ-Λ relationship was applied to retrieve DSD parameters from polarimetric radar data, and the retrieved DSDs and derived physical parameters were evaluated and compared with the 2DVD measurements. The polarization radar-based C-G DSD model characterized rain microphysics more accurately than the exponential DSD model. The C-G DSD model based on the newly derived µ-Λ relationship performed the best at retrieving rain microphysical parameters. [ABSTRACT FROM AUTHOR]

Remote sensing of precipitation is critical for regional, continental, and global water and climate research. This study develops a deep learning mechanism to link between point‐wise rain gauge measurements, ground‐based, and spaceborne radar reflectivity observations. Two neural network models are designed to construct a hybrid rainfall system, where the ground radar is used to bridge the scale gaps between rain gauge and satellite. The first model is trained for ground radar using rain gauge data as target labels, whereas the second model is for spaceborne Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) using ground radar estimates as training labels. Data from 1 year of observations in Florida during 2009 are utilized to illustrate the application of this hybrid rainfall system. Validation using independent data in 2009, as well as 2‐year comparison against the standard PR products, demonstrates the promising performance and generality of this innovative rainfall algorithm. Plain Language Summary: The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) was the first spaceborne active sensor for observing precipitation over the tropics and subtropics. During its 17 years (1997–2014) in orbit and beyond, PR has been an important tool to characterize tropical precipitation microphysics and quantify rainfall rate over the globe. Ground validation is a critical component in the development of TRMM products. However, the ground‐based sensors have different characteristics from PR in terms of resolution, viewing angle, and uncertainties in the sensing environments, which are not taken into account in the operational parametric rainfall relations applied to PR measurements. This study develops a nonparametric machine learning technique for PR rainfall estimation. In the regions where substantial gauge and ground radar data are available, this approach can produce better rainfall estimates compared to the standard PR algorithm. In areas such as ocean and remote regions where no gauge or radar available, the proposed rainfall algorithm is easy to implement, and it can still produce reasonable estimates. With more and more gauges and radars being deployed and many of them become operational, this algorithm can be trained at different locations represented by different atmosphere properties to further improve the performance and generality. Key Points: Conventional parametric relationships between radar reflectivity Z and rain rate R are not sufficient to capture precipitation variabilitiesA hybrid deep neural network system is designed for improved space radar rainfall estimation [ABSTRACT FROM AUTHOR]

Thunderstorms in central Florida frequently halt outdoor activities, requiring that one wait some prescribed time after an assumed last flash before safely resuming activities. The goal of this research is to develop a high-skill probabilistic method that can be used in high pressure real-world operations to terminate lightning warnings more quickly while maintaining safety. Probabilistic guidance tools are created for isolated warm season storms in central Florida using dual-polarized radar data at 1-min intervals. The parameters examined are maximum reflectivity and graupel presence at the 0°, −5°, −10°, −15°, and −20°C levels as well as composite reflectivity. Random samples of the radar data are used to train a generalized linear model (GLM) to make a probabilistic prediction whether a given flash is the storm's last flash. The most statistically significant predictors for lightning cessation are found to be the storm's maximum reflectivity in the composite and the 0°C levels, along with graupel presence or absence at the −5°, −10°, −15°, and −20°C levels. Statistical verification is used to analyze the performance of the two GLMs at different probability thresholds (95.0%, 97.5%, and 99.0%). When applying the cessation guidance as though storms are occurring in real time, results showed ~99% of the storms produced no additional lightning after the GLM suggested cessation had already occurred. Although these results are encouraging, the procedure must be tested on much larger datasets having different convective modes and different areal coverages to prove its value compared to operational forecasters. [ABSTRACT FROM AUTHOR]

Wind warnings are the second-most-frequent advisory issued by the U.S. Air Force's 45th Weather Squadron (45WS) at Cape Canaveral, Florida. Given the challenges associated with nowcasting convection in Florida during the warm season, improvements in 45WS warnings for convective wind events are desired. This study aims to explore the physical bases of dual-polarization radar signatures within wet downbursts around Cape Canaveral and identify signatures that may assist the 45WS during real-time convective wind nowcasting. Data from the 45WS's C-band dual-polarization radar were subjectively analyzed within an environmental context, with quantitative wind measurements recorded by weather tower sensors for 32 threshold-level downbursts with near-surface winds ≥ 35 kt (1 kt ≈ 0.51 m s−1) and 32 null downbursts. Five radar signatures were identified in threshold-level downburst-producing storms: peak height of 1-dB differential reflectivity ZDR column, peak height of precipitation ice signature, peak reflectivity, height below 0°C level where ZDR increases to 3 dB within a descending reflectivity core (DRC), and vertical ZDR gradient within DRC. Examining these signatures directly in updraft–downdraft cycles that produced threshold-level winds yielded mean lead times of 20.0–28.2 min for cumulus and mature stage signatures and 12.8–14.9 min for dissipating stage signatures, with higher signature test values generally yielding higher skill scores. A conceptual test of utilizing signatures within earlier cells in multicell storms to indirectly predict the potential for intense downbursts in later cells was performed, which offered increased lead times and skill scores for an Eulerian forecast region downstream from the storm initiation location. [ABSTRACT FROM AUTHOR]

The structure of the coastal internal boundary layer (IBL) during a landfalling hurricane has important ramifications on operational forecasting, structural design, and poststorm damage assessment. Despite these important issues, the mean IBL structure at the coastline during landfall is poorly understood. Knowledge of the vertical kinematic structure within tropical cyclones over water has improved greatly through aircraft reconnaissance missions and the advent of GPS dropsondes and stepped frequency microwave radiometers. Unfortunately, reconnaissance and research aircraft are limited to overwater missions, resulting in a poor understanding of vertical kinematic structure near the coastal interface, where changes in IBL structure are expected due to changes in surface roughness. Composite single- and dual-Doppler radar observations col-lected by the Shared Mobile Atmospheric Research and Teaching Radars during the landfall of Hurricane Frances (2004) are presented. Data analyses from the Cape Canaveral, Florida, region reveal a pronounced IBL throughout the data collection period. As a result, significant variability in the analyzed wind speed and direction are found across and near the coastal interface. IBL height is found to be suppressed when compared to an accepted empirical growth model, while multiple abrupt roughness transitions associated with the Cape Canaveral region contribute to a complex mean IBL structure. [ABSTRACT FROM AUTHOR]

The South Florida Water Management District (SFWMD) relies on a network of nearly 300 rain gauges in order to provide rainfall data for use in operations, modeling, water supply planning, and environmental projects. However, the prevalence of convective and tropical disturbances in South Florida during the wet season presents a challenge in that the current rain gauge network may not fully capture rain events that demonstrate high spatial variability. Next Generation Radar (NEXRAD) technology offers the advantage of providing a spatial account of rainfall, although the quality of radar-rainfall measurements remains largely unknown. The comparison of rainfall estimates from a gauge-adjusted, NEXRAD-based product developed by the OneRain Company with precipitation measurements from SFWMD rain gauges was performed for the Upper and Lower Kissimmee River Basins over a four-year period from 2002 to 2005. Overall, NEXRAD was found to underestimate rainfall with respect to the rain gauges for the study period, demonstrating a radar to gauge ratio of 0.95. Further investigation of bias revealed the tendency for NEXRAD to overestimate small rainfall amounts and underestimate large rainfall amounts relative to the gauge network. The nature of bias present in the data led to the development of a radar-rain gauge relationship to predict radar precipitation estimates as a function of rain gauge measurements. The intent of this paper is to demonstrate the importance of identifying systematic offsets which may be present in radar-rainfall data before application in hydrologic analysis. [ABSTRACT FROM AUTHOR]

• Surface Canopy Water (SCW) affects radar backscatter, cross ratio and RVI. • SCW affects the relations between L-band observables and crop biophysical variables. • SCW has a confounding influence on VOD estimation. • Overpass time and daily patterns in SCW affect the retrieval of crop parameters. The presence of surface water on the canopy affects radar backscatter. However, its influence on the relationship between radar backscatter and crop biophysical parameters has not been investigated. The aim of this study was to quantify the influence of surface canopy water (SCW) on the relationship between L-band radar backscatter and biophysical variables of interest in agricultural monitoring. In this study, we investigated the effect of SCW on the relationship between co- and cross-polarized radar backscatter, cross ratios (VH/VV and HV/HH), and radar vegetation index (RVI) and dry biomass, vegetation water content (VWC), plant height and leaf area index (LAI). In addition, the effect of SCW on estimated vegetation optical depth (VOD) and its relationship with internal VWC was investigated. The analysis was based on data collected during a field experiment in Florida, USA in 2018. A corn field was scanned with a truck-mounted, fully polarimetric, L-band radar along with continuous monitoring of SCW (dew, interception) and soil moisture every 15 min for 58 days. In addition, pre-dawn destructive sampling was conducted to measure internal vegetation water content and dry biomass. Results showed that the presence of SCW can increase the radar backscatter up to 2 dB and this effect was lower for cross ratios (CRs) and RVI. The Spearman's rank correlations between radar observables and biophysical parameters were, on average, 0.2 higher for dry vegetation compared to wet vegetation. The estimated VOD from wet vegetation was generally higher than those from dry vegetation, which led to different fitting parameter (so-called b) values in the linear fit between VOD and VWC. The results presented here underscore the importance of considering the influence of SCW on the retrieval of biophysical variables of interest in agricultural monitoring. In particular, they highlight the importance of overpass time, and the impact that daily patterns in dew and interception can have on the retrieval of biophysical variables of interest. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) was used to monitor 12 pig burials in Florida, each of which contained a small pig cadaver. Six of the cadavers were buried in sand at a depth of 0.50–0.60 m, and the other six were buried in sand at a depth of 1.00–1.10 m to represent deep and shallow burials that are generally encountered in forensic scenarios. Four control excavations with no pig interment were also constructed as blank graves and monitored with GPR. The burials were monitored for durations of either 13 or 21 months, and were then excavated to correlate the decomposition state of the cadaver with the GPR imagery. Overall, this study demonstrated that it may be difficult to detect small cadavers buried in sand soon after they are skeletonized because the area surrounding the body, or the grave, may not provide a strong enough contrasting area to be detected by GPR when compared to that of the surrounding undisturbed soil. Also, depth of burial appears to influence grave detection because bodies that are buried at deeper depths may be detected for a longer period of time due to reduced decomposition rates. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration''s (NOAA''s) Air Resources Laboratory (ARL), using the NOAA Twin Otter aircraft, made meteorological and chemical measurements during 21 flights in May 2002 in and around the Tampa Bay, Florida area as part of the Bay Region Atmospheric Chemistry Experiment (BRACE). One or more vertical profiles were flown during each flight both over land and over the Gulf of Mexico. NOAA''s Environmental Technology Laboratory (ETL; now part of NOAA''s Earth System Research Laboratory (ESRL)) deployed three surface-based 915-MHz radar wind profilers equipped with radio acoustic sounding systems (RASS) at Ruskin, Sydney, and St. Petersburg. The National Weather Service Office in Tampa (NWS/TBW) released rawinsondes twice daily from the Ruskin site. The measurements of temperature, dew point, potential temperature, ozone, and condensation nuclei acquired during the aircraft profiles are analyzed, and in combination with the profiler and sounding data, are used to determine the structure of the boundary layer over the Tampa Bay region and the temporal and spatial changes that occurred in that structure during representative flights. [Copyright &y& Elsevier]

To assess the spatial structures and temporal evolutions of distinct physical processes on the West Florida Shelf, patterns of ocean current variability are extracted from a joint HF radar and ADCP dataset acquired from August to September 2003 using Self-Organizing Map (SOM) analyses. Three separate ocean–atmosphere frequency bands are considered: semidiurnal, diurnal, and subtidal. The currents in the semidiurnal band are relatively homogeneous in space, barotropic, clockwise polarized, and have a neap-spring modulation consistent with semidiurnal tides. The currents in the diurnal band are less homogeneous, more baroclinic, and clockwise polarized, consistent with a combination of diurnal tides and near-inertial oscillations. The currents in the subtidal frequency band are stronger and with more complex patterns consistent with wind and buoyancy forcing. The SOM is shown to be a useful technique for extracting ocean current patterns with dynamically distinctive spatial and temporal structures sampled by HF radar and supporting in situ measurements. [ABSTRACT FROM AUTHOR]

Multiple-Doppler radar and rawinsonde data are used to examine misocyclone characteristics along gust fronts observed during the Convection and Precipitation/Electrification (CaPE) project in Florida. The objective of this study is to investigate the observational representativeness of previous numerical simulations of misocyclones by employing a consistent analysis strategy to 11 gust fronts observed in the same region. The investigation focuses on the intensity range of misocyclones and their organization along gust fronts; the relationship between misocyclone intensity and horizontal wind shear, vertical wind shear, and static stability; and the relationship between misocyclones and convection initiation. The intensity of misocyclones, as indicated by the maximum values of vertical vorticity, varied from 2.8 × 10-3 to 13.9 × 10-3 s-1, although all but one case exhibited values less than 6.4 × 10-3 s-1. Organized misocyclone patterns were only found along small segments of gust fronts. Within those segments misocyclones were spaced between 3 and 7 km. Results show that the intensity of misocyclones was most closely related to the strength of horizontal wind shear across the gust front. The relationship between misocyclone intensity and vertical wind shear and static stability was not as clear. Although convection was initiated along the gust front in 7 of the 11 cases, those regions were not collocated with or in close proximity to misocyclones. [ABSTRACT FROM AUTHOR]

Airborne radar reflectivity measurements at frequencies of 9.6 and 94 GHz, with collocated, in situ particle size distribution and ice water content measurements from the Cirrus Regional Study of Tropical Anvils and Cirrus Layers (CRYSTAL) Florida Area Cirrus Experiment (FACE) in Florida in July 2002, offer one of the first opportunities to evaluate and improve algorithms for retrieving ice water content from single-wavelength spaceborne radar measurements. Both ice water content and radar reflectivity depend on the distribution of particle mass with size. It is demonstrated that single, power-law, mass dimensional relationships are unable to adequately account for the dominating contribution of small particles at lower reflectivities and large particles at higher reflectivities. To circumvent the need for multiple, or complex, mass dimensional relationships, analytic expressions that use particle ensemble mean ice particle densities that are derived from the coincident microphysical and radar observations are developed. These expressions, together with more than 5000 CRYSTAL FACE size distributions, are used to develop radar reflectivity–ice water content relationships for the two radar wavelengths that appear to provide improvements over earlier relationships, at least for convectively generated stratiform ice clouds. [ABSTRACT FROM AUTHOR]

This paper describes a method of absolutely calibrating and routinely monitoring the reflectivity calibration from a scanning weather radar using a vertically profiling radar that has been absolutely calibrated using a collocated surface disdrometer. The three instruments have different temporal and spatial resolutions, and the concept of upscaling is used to relate the small resolution volume disdrometer observations with the large resolution volume scanning radar observations. This study uses observations collected from a surface disdrometer, two profiling radars, and the National Weather Service (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88D) scanning weather radar during the Texas–Florida Underflight-phase B (TEFLUN-B) ground validation field campaign held in central Florida during August and September 1998. The statistics from the 2062 matched profiling and scanning radar observations during this 2-month period indicate that the WSR-88D radar had a reflectivity 0.7 dBZ higher than the disdrometer-calibrated profiler, the standard deviation was 2.4 dBZ, and the 95% confidence interval was 0.1 dBZ. This study implies that although there is large variability between individual matched observations, the precision of a series of observations is good, allowing meaningful comparisons useful for calibration and monitoring. [ABSTRACT FROM AUTHOR]

Airborne measurements of vertical incidence radar reflectivity and radial velocity are analyzed for some 21 231 km of high-altitude flight tracks over tropical precipitation systems, in order to describe their characteristic vertical structure. The strength of the radar dataset lies in its superb vertical resolution, sufficient to detect unambiguously a bright band and the coincident Doppler velocity change, which identify the melting layer in stratiform precipitation. In this first of a two-part study, a technique based on the detection of this stratiform precipitation signature is developed to classify hydrometer profiles as convective, stratiform, or shallow. Even though the profiles are classified individually, stratiform and convective regions emerge, whose characteristics are described. The hydrometeor vertical velocity variability is smaller in stratiform profiles, which is consistent with the physical concept of a stratiform region. The purpose of the classification is to describe, in, the composite vertical structure of the various rain types in hurricanes, as well as in isolated to organized precipitating convection sampled in Florida and Brazil. [ABSTRACT FROM AUTHOR]

Presents the first documented large-amplitude cloud radar observations of Kelvin-Helmholtz instability in a Florida tropical anvil. Usability of radar in detecting shear instability in the atmosphere; Invalidity of the frequent assumption that an anvil is a well-mixed layer; Existence of several identifiable weaker shear layers in the anvil and in the Doppler spread.

Characterizes the coastal ocean environment at the South Florida Ocean Measurement Center, using observations from very high frequency (VHF) radar. Background on the concept of using high frequency and VHF radar pulses to probe surface ocean currents; Description of the experimental design using the VHF radar; Surface current observations.

The effective vertical resolution of the Kennedy Space Center 50-MHz Doppler radar wind profiler is deter-mined using vertical wavenumber spectra and temporal coherence. The resolution ranges from being Nyquist limited at 300 m to as coarse as 900 m. The average resolution is about 500 m. [ABSTRACT FROM AUTHOR]

Retrievals of specific attenuation at 5.5-cm wavelength from dual-radar observations of a summer convective storm in Florida are presented. The retrieved specific attenuation is positive except in regions near the radars where the observed reflectivity factors suffered from contamination by ground clutter. The specific attenuations ranged between 0.0 and 2.0 dB km[sup-1]; they are small at higher levels of the storm, and high reflectivity cores are generally associated with higher specific attenuations. A plot of the retrieved specific attenuation against reflectivity factor at 10-cm wavelength shows that a majority of the retrieved values agree with those calculated from empirical relationships between reflectivity factor and specific attenuation. A small fraction of the points having high reflectivity factors have smaller than empirically predicted attenuations; these are attributed to dry ice particles. A larger fraction of the points having low reflectivity factors, less than about 30 dBZ, have higher than empirically predicted attenuations; these are attributed to attenuation by cloud liquid water and mixed-phase hydrometeors. A scatterplot of the differential reflectivity factor at 10-cm wavelength against the ratio of the retrieved specific attenuation to the reflectivity factor at 10-cm wavelength agrees generally with a theoretically expected relationship between the two parameters for horizontally oriented deformed raindrops, giving credence to the retrievals. However, the points scatter rather widely around the theoretical curve and the scatter is attributed to 1) signal fluctuations; 2) physical factors, namely, cloud water, ice particles, and mixed-phase particles of various shapes; 3) instrumental factors, namely, ground clutter, finite pulse volume, mismatched radar antenna patterns, and sidelobes; and 4) numerical procedures used in the retrievals, namely, data interpolation, smoothing, and differentiation. [ABSTRACT FROM AUTHOR]

Distinguishing forested wetland from dry forest using aerial photographs is handicapped because photographs often do not reveal the presence of water below tree canopies. Radar images obtained by the Seasat satellite reveal forested wetland as highly reflective patterns on the coastal plain between Maryland and Florida. Seasat radar images may complement aerial photographs for compiling maps of wetland A test with experienced photo interpreters revealed that interpretation accuracy was significantly higher when using Seasat radar images than when using only conventional sources. [ABSTRACT FROM AUTHOR]

The article reports on the discussion on the use of software GR2Analyst for examining severe weather signatures through radar analysis during the meeting of the West Central Florida chapter of American Meteorological Society in the computer laboratory in the Geography Department at the University of South Florida in Tampa, Florida. The difference between Level II and Level III data used in radar analysis was explained. Meteorologist Andy Johnson presented the May 3, 1999 Moore, Oklahoma tornado using GR2Analyst.