Next Generation Weather Radar Research Articles

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters Using a Deep Neural Network

Abstract Geostationary satellite imagers provide historical and near-real-time observations of cloud-top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to Next Generation Weather Radar (NEXRAD) estimated maximum expected size of hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are made where satellites observe cold clouds, indicative of convection, located in favorable storm environments. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record. Statistical distributions of convective parameters from satellite and reanalysis show separation between nonsevere and severe hailstorm classes for predictors that include overshooting cloud-top temperature and area characteristics, vertical wind shear, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a likelihood estimate with a critical success index of 0.511 and Heidke skill score of 0.407, which is exceptional among analogous hail studies. Furthermore, applications of the DNN to case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-yr Geostationary Operational Environmental Satellite (GOES) image database from GOES-12/13 to derive a hail frequency and severity climatology, which denotes the central Great Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied.

Lifetimes of Overshooting Convective Events Using High-Frequency Gridded Radar Composites

Abstract Deep convection that penetrates the tropopause, referred to here as overshooting convection, is capable of lifting tropospheric air well into the stratosphere. In addition to water, these overshoots also transport various chemical species, affecting chemistry and radiation in the stratosphere. It is not currently known, however, how much transport is a result of this mechanism. To better understand overshooting convection, this study aims to characterize the durations of overshooting events. To achieve this, radar data from the Next Generation Weather Radar (NEXRAD) network is composited onto a three-dimensional grid at 5-min intervals. Overshoots are identified by comparing echo-top heights with tropopause estimates derived from ERA5 reanalysis data. These overshoots are linked in space from one analysis time to the next to form tracks. This process is performed for 12 four-day sample windows in the months May–August of 2017–19. Track characteristics such as duration, overshoot area, tropopause-relative altitude, and column-maximum reflectivity are investigated. Positive correlations are found between track duration and other track characteristics. Integrated track volume is found as a product of the overshoot area, depth, and duration, and provides a measure of the potential stratospheric impact of each track. Short-lived tracks are observed to contribute the most total integrated volume when considering track duration, while tracks that overshoot by 2–3 km show the largest contribution when considering overshoot depth. A diurnal cycle is observed, with peak track initiation around 1600–1700 local time. Track-mean duration peaks a few hours earlier, while track-mean area and tropopause-relative height peak a few hours later.

Improving the utility of weather radar for the spatial frequency analysis of extreme precipitation

Spatially seamless quantitative precipitation estimates (QPEs) from weather radars have the potential to address key limitations of intensity–duration–frequency (IDF) relations derived from sparse rain gage measurements. However, this potential has not been yet fully explored. Here, a methodological framework is designed for the spatial frequency analysis of extreme precipitation (P) with radar QPEs that leads to realistic quantile patterns while reducing the sampling uncertainty. The framework was applied with 19 years of QPEs from 1-h, 4-km Stage IV reanalysis from the Next Generation Weather Radar (NEXRAD) network and robustly tested against (1) a network of 204 high-resolution rain gages in central Arizona with one of the largest densities and spatial coverages in the world, and (2) extreme P quantiles from NOAA Atlas 14. It was first showed that (1) the generalized extreme value (GEV) is a suitable distribution to model the series of annual P maxima of gage records and radar QPEs across multiple durations from 1 h to 24 h, and (2) correcting the bias of the GEV shape parameter estimates due to the short sample size is a critical step. Spatial estimates of extreme P quantiles were then obtained through a hierarchical approach based on the index-flood method and the spatial smoothening (interpolation) of the GEV parameters estimated from radar QPEs (gage records). For each parameter, the most effective interpolation method was identified that limits the uncertainty caused by the short sample size and captures the local variability of extreme P. The extreme P quantiles generated from radar QPEs exhibited similar or, in some cases, higher accuracy than those generated by interpolating sparse gage information and exhibit more realistic patterns. While derived in central Arizona, the insights of this work are useful to incorporate radar QPEs into operational IDF curves in any region of the world monitored by weather radars.

When can local bird detection radars best complement broad‐scale early‐warning forecasts of risk potential for bird–aircraft strikes as part of an integrated approach to strike mitigation?

Worldwide, wildlife–aircraft strikes cost more than US$1.2 billion in aircraft damage and downtime and jeopardize the safety of aircrews, passengers, and animals. Radar has long been used to monitor flying animal movements and can be a useful tool for strike mitigation. In the USA, the Avian Hazard Advisory System (AHAS) is an early‐warning system that integrates data from next‐generation weather radar (NEXRAD) weather surveillance radars (WSRs) with historic bird occurrence data to quantify avian activity and forecast the relative bird risk within a ~9.3‐km radius of military and civilian airfields. Bird detection radars (BDRs) with both horizontal‐surveillance and vertical‐scanning components are also available for monitoring local avian activity at airports, but we have little information regarding the congruence of broad‐scale warnings and local avian activity where WSRs and BDRs overlap. We quantified trends in biological activity recorded at hourly intervals by a BDR at an airfield in Texas, USA, and in the most frequently assigned AHAS risk forecasts for that site during the same intervals. We then examined the strength of association between these datasets by season and time of day to determine when information from BDRs might best complement forecasts from the broad‐scale AHAS system. We found a strong overall association between the datasets but weak or moderate agreement during daylight periods, when most strikes occur. NEXRAD WSRs see only limited bird activity near the Earth's surface, where the majority of damaging strikes take place and, not surprisingly, AHAS warnings during our study were best predicted by the BDR at higher altitudes. Our results suggest BDRs might best complement early‐warning systems, like AHAS, as part of integrated strike mitigation plans at airfields with large numbers of hazardous birds flying at low altitudes during daylight hours, especially in late afternoon.

Latent heating profiles from GOES-16 and its impacts on precipitation forecasts

Abstract. Latent heating (LH) is an important factor in both weather forecasting and climate analysis, being the essential factor affecting both the intensity and structure of convective systems. Yet, inferring LH rates from our current observing systems is challenging at best. For climate studies, LH has been retrieved from the precipitation radar on the Tropical Rainfall Measuring Mission (TRMM) using model simulations in a lookup table (LUT) that relates instantaneous radar data to corresponding heating profiles. These radars, first on TRMM and then the Global Precipitation Measurement Mission (GPM), provide a continuous record of LH. However, the temporal resolution is too coarse to have significant impacts on forecast models. In operational forecast models such as High-Resolution Rapid Refresh (HRRR), convection is initiated from LH derived from ground-based radars. Despite the high spatial and temporal resolution of ground-based radars, their data are only available over well-observed land areas. This study develops a method to derive LH from the Geostationary Operational Environmental Satellite-16 (GOES-16) in near-real time. Even though the visible and infrared channels on the Advanced Baseline Imager (ABI) provide mostly cloud top information, rapid changes in cloud top visible and infrared properties, when formulated as an LUT similar to those used by the TRMM and GPM radars, can successfully be used to derive LH profiles for convective regions based on model simulations with a convective classification scheme and channel 14 (11.2 µm) brightness temperatures. Convective regions detected by GOES-16 are assigned LH profiles from a predefined LUT, and they are compared with LH used by the HRRR model and one of the dual-frequency precipitation radar (DPR) products, the Goddard convective–stratiform heating (CSH). LH obtained from GOES-16 shows similar magnitude to LH derived from the Next Generation Weather Radar (NEXRAD) and CSH, and the vertical distribution of LH is also very similar with CSH. A three-month analysis of total LH from convective clouds from GOES-16 and NEXRAD shows good correlation between the two products. Finally, LH profiles from GOES-16 and NEXRAD are applied to WRF simulations for convective initiation, and their results are compared to investigate their impacts on precipitation forecasts. Results show that LH from GOES-16 has similar impacts to NEXRAD in terms of improving the forecast. While only a proof of concept, this study demonstrates the potential of using LH derived from GOES-16 for convective initialization.

High Spatial-Temporal PM2.5 Modeling Utilizing Next Generation Weather Radar (NEXRAD) as a Supplementary Weather Source

PM2.5, a type of fine particulate with a diameter equal to or less than 2.5 micrometers, has been identified as a major source of air pollution, and is associated with many health issues. Research on utilizing various data sources, such as remote sensing and in situ sensors, for PM2.5 concentrations modeling remains a hot topic. In this study, the Next Generation Weather Radar (NEXRAD) is used as a supplementary weather data source, along with European Centre for Medium-Range Weather Forecasts (ECMWF), solar angles, and Geostationary Operational Environmental Satellite (GOES16) Aerosol Optical Depth (AOD) to model high spatial-temporal PM2.5 concentrations. PM2.5 concentrations as well as in situ weather condition variables are collected from the 31 sensors that are deployed in the Dallas Metropolitan area. Four machine learning models with different predictor variables are developed based on an ensemble approach. Since in situ weather observations are not widely available, ECMWF is used as an alternative data source for weather conditions in studies. Hence, the four established models are compared in three groups. Both models in this first group use weather variables collected from deployed sensors, but one uses NEXRAD and the other does not. In the second group, the two models use weather variables retrieved from ECMWF, one using NEXRAD and one without. In the third group, one model uses weather variables from ECMWF, and the other uses in situ weather variables, both without NEXRAD. The first two environmental groups investigate how NEXRAD can enhance model performances with weather variables collected from in situ observations and ECMWF, respectively. The third group explores how effective using ECMWF as an alternative source of weather conditions. Based on the results, the incorporation of NEXRAD achieves an R2 score of 0.86 and 0.83 for groups 1 and 2, respectively, for an improvement of 2.8% and 9.6% over those models without NEXRAD. For group three, the use of ECMWF as an alternative source of in situ weather observations results in a 0.13 R2 drop. For PM2.5 estimation, weather variables including precipitation, temperature, pressure, and surface pressure from ECMWF and deployed sensors, as well as NEXRAD velocity, are shown to be significant factors.

A precipitation forecasting model using machine learning on big data in clouds environment

Numerical weather prediction (NWP) has long been a difficult task for meteorologists. Atmospheric dynamics is extremely complicated to model, and chaos theory teaches us that the mathematical equations used to predict the weather are sensitive to initial conditions; that is, slightly perturbed initial conditions could yield very different forecasts. Over the years, meteorologists have developed a number of different mathematical models for atmospheric dynamics, each making slightly different assumptions and simplifications, and hence each yielding different forecasts. It has been noted that each model has its strengths and weaknesses forecasting in different situations, and hence to improve performance, scientists now use an ensemble forecast consisting of different models and running those models with different initial conditions. This ensemble method uses statistical post-processing; usually linear regression. Recently, machine learning techniques have started to be applied to NWP. Studies of neural networks, logistic regression, and genetic algorithms have shown improvements over standard linear regression for precipitation prediction. Gagne et al proposed using multiple machine learning techniques to improve precipitation forecasting. They used Breiman’s random forest technique, which had previously been applied to other areas of meteorology. Performance was verified using Next Generation Weather Radar (NEXRAD) data. Instead of using an ensemble forecast, it discusses the usage of techniques pertaining to machine learning to improve the precipitation forecast.
 This paper is to present an approach for mapping of precipitation data. The project attempts to arrive at a machine learning method which is optimal and data driven for predicting precipitation levels that aids farmers thereby aiming to provide benefits to the agricultural domain.

Effects of delayed weather radar images on pilots’ spatial awareness

Data-linked Next Generation Weather Radar (NEXRAD) images can be delayed up to 20 min in the cockpit. Pilots' underappreciating or ignoring the time delay may be the major cause of two fatal accidents. No studies have connected spatial awareness with accidents. This study evaluated how delayed radar information affects the spatial awareness of pilots at three levels of analysis. Thirty-one student pilots and flight instructors completed three sequential estimation tasks (i.e., the current location of storms, the current relative distance to storms, and the future relative distance to storms). Fifty-four weather scenarios were developed for three factors (storm speeds, delays, displays) and presented to pilots. The results indicated that delays and the storm speed significantly affected the three levels of spatial awareness. Participants' estimation accuracy was the lowest under long delay and fast speed in the current location estimation, under medium delay and speed in the current distance estimation, and under short delay and slow speed in the future distance estimation. Spatial awareness could be high under the long delay and fast speed conditions if pilots had no time limits. Thus, pilots can process 20-min delayed radar information. However, there were no differences in estimation accuracy between the static and animation displays in any of the conditions. Well-designed features on displays, such as scale or distance measuring tools, can aid pilots’ spatial estimation and support all levels of spatial awareness.

Thunderstorms Producing Sferic-Geolocated Gamma-Ray Flashes Detected by TETRA-II.

The terrestrial gamma‐ray flash (TGF) and Energetic Thunderstorm Rooftop Array (TETRA‐II) detected 22 X‐ray/gamma‐ray flash events associated with lightning between October 2015 and March 2019 across three ground‐based detector locations in subtropical and tropical climates in Louisiana, Puerto Rico, and Panama. Each detector array consists of a set of bismuth germanate scintillators that record X‐ray and gamma‐ray bursts over the energy range 50 keV–6 MeV (million electron volts). TETRA‐II events have characteristics similar to both X‐ray bursts associated with lightning leaders and TGFs: sub‐millisecond duration, photons up to MeV energies, and association with nearby lightning (typically within 3 km). About 20 of the 22 events are geolocated to individual lightning strokes via spatiotemporally coincident sferics. An examination of radar reflectivity and derived products related to events located within the Next Generation Weather Radar (NEXRAD) monitoring region indicates that events occur within mature cells of severe and non‐severe multicellular and squall line thunderstorms, with core echo tops which are at or nearing peak altitude. Events occur in both high lightning frequency thunderstorm cells and low lightning frequency cells. Events associated with high frequency cells occur within 5 min of significant lightning jumps. Among NEXRAD‐monitored events, hail is present within 8 km and 5 min of all except a single low‐altitude cold weather thunderstorm. An association is seen with maximum thunderstorm development, lightning jumps, and hail cells, indicating that the TETRA‐II X‐ray/gamma‐ray events are associated with the peak storm electrification and development of electric fields necessary for the acceleration of electrons to high energies.

Validation of the High-Altitude Wind and Rain Airborne Profiler during the Tampa Bay Rain Experiment

This paper deals with the validation of rain rate and wind speed measurements from the High-Altitude Wind and Rain Airborne Profiler (HIWRAP), which occurred in September 2013 when the NASA Global Hawk unmanned aerial vehicle passed over an ocean rain squall line in the Gulf of Mexico near the North Florida coast. The three-dimensional atmospheric rain distribution and the associated ocean surface wind vector field were simultaneously measured by two independent remote sensing and two in situ systems, namely the ground-based National Weather Service Next-Generation Weather Radar (NEXRAD); the European Space Agency satellite Advanced Scatterometer (ASCAT), and two instrumented weather buoys. These independent measurements provided the necessary data to calibrate the HIWRAP radar using the measured ocean radar backscatter and to validate the HIWRAP rain and wind vector retrievals against NEXRAD, ASCAT and ocean buoys observations. In addition, this paper presents data processing procedures for the HIWRAP instrument, including the development of a geometric model to collocate time-morphed rain rates from the NEXRAD radar with HIWRAP atmospheric rain profiles. Results of the rain rate intercomparison are presented, and they demonstrate excellent agreement with the NEXRAD time-interpolated rain volume scans. In our analysis, we find that HIWRAP produces wind and rain rates that are consistent with the supporting ground and satellite estimates, thereby providing validation of the geolocation, the calibration, and the geophysical retrieval algorithms for the HIWRAP instrument.

Comparison of single-Doppler and multiple-Doppler wind retrievals in Hurricane Matthew (2016)

Abstract. Hurricane Matthew (2016) was observed by the ground-based polarimetric Next Generation Weather Radar (NEXRAD) in Miami (KAMX) and the National Oceanic and Atmospheric Administration WP-3D (NOAA P-3) airborne tail Doppler radar near the coast of the southeastern United States for several hours, providing a novel opportunity to evaluate and compare single- and multiple-Doppler wind retrieval techniques for tropical cyclone flows. The generalized velocity track display (GVTD) technique can retrieve a subset of the wind field from a single ground-based Doppler radar under the assumption of nearly axisymmetric rotational wind, but it has been shown to have errors from the aliasing of unresolved wind components. An improved technique that mitigates errors due to storm motion is derived in this study, although some spatial aliasing remains due to limited information content from the single-Doppler measurements. A spline-based variational wind retrieval technique called SAMURAI can retrieve the full three-dimensional wind field from airborne radar fore–aft pseudo-dual-Doppler scanning, but it has been shown to have errors due to temporal aliasing from the nonsimultaneous Doppler measurements. A comparison between the two techniques shows that the axisymmetric tangential winds are generally comparable between the two techniques, and the improved GVTD technique improves the accuracy of the retrieval. Fourier decomposition of asymmetric kinematic and convective structure shows more discrepancies due to spatial and temporal aliasing in the retrievals. The strengths and weaknesses of each technique for studying tropical cyclone structure are discussed and suggest that complementary information can be retrieved from both single- and dual-Doppler retrievals. Future improvements to the asymmetric flow assumptions in single-Doppler analysis and steady-state assumptions in pseudo-dual-Doppler analysis are required to reconcile differences in retrieved tropical cyclone structure.

Assimilation of Radial Velocity from Coastal NEXRAD into HWRF for Improved Forecasts of Landfalling Hurricanes

AbstractThe benefits of assimilating Next Generation Weather Radar (NEXRAD) radial velocity data for convective systems have been demonstrated in previous studies. However, impacts of assimilation of such high spatial and temporal resolution observations on hurricane forecasts have not been demonstrated with the National Centers for Environmental Prediction (NCEP) Hurricane Weather and Research Forecasting (HWRF) system. This study investigates impacts of NEXRAD radial velocity data on forecasts of the evolution of landfalling hurricanes with different configurations of data assimilation. The sensitivity of data assimilation results to influencing parameters within the data assimilation system, such as the maximum range of the radar data, superobservations, horizontal and vertical localization correlation length scale, and weight of background error covariances, are examined. Two hurricane cases, Florence and Michael, which occurred in the summer of 2018 are chosen to conduct a series of experiments. Results show that hurricane intensity, asymmetric structure of inland wind and precipitation, and quantitative precipitation forecasting are improved. Suggestions for implementation of operational configurations are provided.

Effect of Precipitation Sampling Error on Flash Flood Monitoring and Prediction: Anticipating Operational Rapid-Update Polarimetric Weather Radars

AbstractQuantitative precipitation estimates (QPE) at high spatiotemporal resolution are essential for flash flood forecasting, especially in urban environments and headwater areas. An accurate quantification of precipitation is directly related to the temporal and spatial sampling of the precipitation system. The advent of phased array radar (PAR) technology, a potential next-generation weather radar, can provide updates that are at least 4-5 times faster than the conventional WSR-88D scanning rate. In this study, data collected by the KOUN WSR-88D radar with ~1 minute temporal resolution is used as an approximation of data that a future PAR system could provide to force the Ensemble Framework for Flash Flood Forecasting (EF5) hydrologic model. To assess the effect of errors resulting from temporal and spatial sampling of precipitation on flash flood warnings, KOUN precipitation data (1-km/1-min) is used to generate precipitation products at other spatial/temporal resolutions commonly used in hydrologic models, such as those provided by conventional WSR-88D radar (1-km/5-min), spaced-based observations (10-km/30-min), and hourly rainfall products (1-km/60-min). The effect of precipitation sampling errors on flash flood warnings are then examined and quantified by using discharge simulated from KOUN (1-km/1-min) as truth to assess simulations conducted using other generated coarser spatial/temporal resolutions of other precipitation products. Our results show that: 1) observations with coarse spatial and temporal sampling can cause large errors in quantification of the amount, intensity, and distribution of precipitation, 2) time series of precipitation products show that QPE peak values decrease as the temporal resolution gets coarser, and 3) the effect of precipitation sampling error on flash flood forecasting is large in headwater areas and decrease quickly as drainage area increases.

A 23-Year Severe Hail Climatology Using GridRad MESH Observations.

Assessments of spatiotemporal severe hailfall characteristics using hail reports are plagued by serious limitations in report databases, including biases in reported sizes, occurrence time, and location. Multiple studies have used Next Generation Weather Radar (NEXRAD) network observations or environmental hail proxies from reanalyses. Previous work has specifically utilized the single-polarization radar parameter maximum expected size of hail (MESH). In addition to previous work being temporally limited, updates are needed to include recent improvements that have been made to MESH. This study aims to quantify severe hailfall characteristics during a 23-yr period, markedly longer than previous studies, using both radar observations and reanalysis data. First, the improved MESH configuration is applied to the full archive of gridded hourly radar observations known as GridRad (1995-2017). Next, environmental constraints from the Modern-Era Retrospective Analysis for Research and Applications, version 2, are applied to the MESH distributions to produce a corrected hailfall climatology that accounts for the reduced likelihood of hail reaching the ground. Spatial, diurnal, and seasonal patterns show that in contrast to the report climatology indicating one high-frequency hail maximum centered on the Great Plains, the MESH-only method characterizes two regions: the Great Plains and the Gulf Coast. The environmentally filtered MESH climatology reveals improved agreement between report characteristics (frequency, location, and timing) and the recently improved MESH calculation methods, and it reveals an overall increase in diagnosed hail days and westward broadening in the spatial maximum in the Great Plains than that seen in reports.

Urban Runoff Simulation: How Do Land Use/Cover Change Patterning and Geospatial Data Quality Impact Model Outcome?

With the increase in global urbanization, satellite imagery and other types of geospatial data have been extensively used in urban landscape change research, which includes environmental modeling in order to assess the change impact on urban watersheds. For urban hydrological modeling, as a focus of this study, several related research questions are raised: (1) How sensitive are runoff simulation to land use and land cover change patterning? (2) How will input data quality impact the simulation outcome? (3) How effective is integrating and synthesizing various forms of geospatial data for runoff modeling? These issues were not fully or adequately addressed in previous related studies. With the aim of answering these questions as research objectives, we conducted a spatial land use and land cover (LULC) change analysis and an urban runoff simulation in the Blue River watershed in the Kansas City metropolitan area between 2003 and 2017. In this study, approaches were developed to incorporate the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model with remote sensing, geographic information systems (GIS), and radar rainfall data. The impact of data quality on the model simulation outcome was also analyzed. The results indicate that there are no significant differences between simulated runoff responses in the two study years (2003 and 2017) due to spatial and temporal heterogeneity of urbanization processes in the region. While the metropolitan area has been experiencing remarkable urban development in the past few decades, the gain in built-up land in the study watershed during the study period is insignificant. On the other hand, the gain in vegetated land caused by forestation activities is offset by a decrease in farmland and grassland. The results show that increasing spatial data resolution does not necessarily or noticeably improve the HEC-HMS model performance or outcomes. Under these conditions, using Next Generation Weather Radar (NEXRAD) rainfall data in the simulation provides a satisfactory fit in hydrographs’ shapes, peak discharge amounts and time after calibration efforts, while they may overestimate the amount of rainfall as compared with gauge data. This study shows that the developed approach of synthesizing satellite, GIS, and radar rainfall data in hydrological modeling is effective and useful for incorporating urban landscape and precipitation change data in dynamic flood risk assessment at a watershed level.

Monitoring the impacts of weather radar data quality control for quantitative application at the continental scale

AbstractAs part of a suite of quality control methods applied to Canadian and American weather radar data before their assimilation into a numerical weather prediction model, the combination of thresholded depolarization ratio and a speckle filter was applied to American data with the purpose of identifying and removing non‐precipitation echoes. This polarimetric quality control replaces a set of image‐analysis‐based methods used in a previous study and based on reflectivity information only. The old and new quality‐controlled results were objectively assessed using meteorological aerodrome report (METAR)‐based precipitation occurrence observations and a set of five common contingency table skill scores with all available Next Generation Weather Radar (NEXRAD) Level II data from the contiguous United States for August 2016. The new quality control yields consistently improved skill scores, indicating higher quality radar data for downstream application. The process whereby the radar data are quality controlled and assessed comprises a framework with the ability to monitor the impacts of quality control to radar data quality over time. In turn, this allows for the introduction of changes to data acquisition and processing with the ability to monitor the impacts on data quality: a scientific evidence‐based quality assurance process as part of change management.

Application of NEXRAD Radar-Based Quantitative Precipitation Estimations for Hydrologic Simulation Using ArcPy and HEC Software

Recent availability of various spatial data, especially for gridded rainfall amounts, provide a great opportunity in hydrological modeling of spatially distributed rainfall–runoff analysis. In order to support this advantage using gridded precipitation in hydrological application, (1) two main Python script programs for the following three steps of radar-based rainfall data processing were developed for Next Generation Weather Radar (NEXRAD) Stage III products: conversion of the XMRG format (binary to ASCII) files, geo-referencing (re-projection) with ASCII file in ArcGIS, and DSS file generation using HEC-GridUtil (existing program); (2) eight Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) models of ModClark and SCS Unit Hydrograph transform methods for rainfall–runoff flow simulations using both spatially distributed radar-based and basin-averaged lumped gauged rainfall were respectively developed; and (3) three storm event simulations including a model performance test, calibration, and validation were conducted. For the results, both models have relatively high statistical evaluation values (Nash–Sutcliffe efficiency—ENS 0.55–0.98 for ModClark and 0.65–0.93 for SCS UH), but it was found that the spatially distributed rainfall data-based model (ModClark) gives a better fit regarding observed streamflow for the two study basins (Cedar Creek and South Fork) in the USA, showing less requirements to calibrate the model with initial parameter values. Thus, the programs and methods developed in this research possibly reduce the difficulties of radar-based rainfall data processing (not only NEXRAD but also other gridded precipitation datasets—i.e., satellite-based data, etc.) and provide efficiency for HEC-HMS hydrologic process application in spatially distributed rainfall–runoff simulations.

Validation of the Hurricane Imaging Radiometer Forward Radiative Transfer Model for a Convective Rain Event

The airborne Hurricane Imaging Radiometer (HIRAD) was developed to remotely sense hurricane surface wind speed (WS) and rain rate (RR) from a high-altitude aircraft. The approach was to obtain simultaneous brightness temperature measurements over a wide frequency range to independently retrieve the WS and RR. In the absence of rain, the WS retrieval has been robust; however, for moderate to high rain rates, the joint WS/RR retrieval has not been successful. The objective of this paper was to resolve this issue by developing an improved forward radiative transfer model (RTM) for the HIRAD cross-track viewing geometry, with separated upwelling and specularly reflected downwelling atmospheric paths. Furthermore, this paper presents empirical results from an unplanned opportunity that occurred when HIRAD measured brightness temperatures over an intense tropical squall line, which was simultaneously observed by a ground based NEXRAD (Next Generation Weather Radar) radar. The independently derived NEXRAD RR created the simultaneous 3D rain field “surface truth”, which was used as an input to the RTM to generate HIRAD modeled brightness temperatures. This paper presents favorable results of comparisons of theoretical and the simultaneous, collocated HIRAD brightness temperature measurements that validate the accuracy of this new HIRAD RTM.

A Radar Radial Velocity Dealiasing Algorithm for Radar Data Assimilation and its Evaluation with Observations from Multiple Radar Networks

Automated and accurate radar dealiasing algorithms are very important for their assimilation into operational numerical weather forecasting models. A radar radial velocity dealiasing algorithm aimed at radar data assimilation is introduced and assessed using from several S-band and C-band radar observations under the severe weather conditions of hurricanes, typhoons, and deep continental convection in this paper. This dealiasing algorithm, named automated dealiasing for data assimilation (ADDA), is a further development of the dealiasing algorithm named the China radar network (CINRAD) improved dealiasing algorithm (CIDA), originally developed for China’s CINRAD (China Next Generation Weather Radar) radar network. The improved scheme contains five modules employed to remove noisy data, select the suitable first radial, preserve the convective regions, execute multipass dealiasing in both azimuthal and radial directions and conduct the final local dealiasing with an error check. This new dealiasing algorithm was applied to two hurricane cases, two typhoon cases, and three intense-convection cases that were observed from the CINRAD of China, Taiwan‘s radar network, and NEXRAD (Next Generation Weather Radar) of the U.S. with a continuous period of more than 12 h for each case. The dealiasing results demonstrated that ADDA performed better than CIDA for all selected cases. This algorithm not only produced a high success rate for the S-band radar, but also a reasonable performance for the C-band radar.

Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data.

Millions of people have an allergic reaction to pollen. The impact of pollen allergies is on the rise due to increased pollen levels caused by global warming and the spread of highly invasive weeds. The production, release, and dispersal of pollen depend on the ambient weather conditions. The temperature, rainfall, humidity, cloud cover, and wind are known to affect the amount of pollen in the atmosphere. In the past, various regression techniques have been applied to estimate and forecast the daily pollen concentration in the atmosphere based on the weather conditions. In this research, machine learning methods were applied to the Next Generation Weather Radar (NEXRAD) data to estimate the daily Ambrosia pollen over a 300 km × 300 km region centered on a NEXRAD weather radar. The Neural Network and Random Forest machine learning methods have been employed to develop separate models to estimate Ambrosia pollen over the region. A feasible way of estimating the daily pollen concentration using only the NEXRAD radar data and machine learning methods would lay the foundation to forecast daily pollen at a fine spatial resolution nationally.