Next Generation Weather Radar Data Research Articles

BATS: Bat‐aggregated time series—A python‐based toolkit for landscape‐level monitoring of free‐tailed bats via weather radar

Abstract The US operates a system of 160 S‐band Doppler weather radars known as NEXRAD (NEXt generation weather RADar) that continuously monitors the airspace around the majority of the United States and outlying territories. These radars detect and track birds, insects, and bats. Free‐tailed bats (Genus Tadarida) provide considerable ecosystem services through their voracious insect consumption; but their movements and ecosystem service provision have historically been difficult to track/study in space and time. We introduce ‘BATS’, a Python toolkit that streamlines the process of downloading, classifying, and aggregating time series of free‐tailed bats across large landscapes. BATS retrieves data from NOAA's weather radar data repositories and classifies the processed radar data using a pre‐trained ML trained to detect and classify radar echoes associated with free‐tailed bats. We trained various machine learning approaches at classifying pixels containing free‐tailed bats and compared the effectiveness across approaches. With an AUC of 0.963, the neural network approach is highly effective in identifying free‐tailed bats in NEXRAD data over our study sites in California and Texas. Furthermore, BATS is capable of quickly distilling 6 months of radar data from a single tower (3.5 Tb) into a single 15 Mb‐sized map of bat occurrence, contingent on available computing resources. BATS will help scientists and stakeholders identify areas of high bat occupancy at the landscape level over long periods of time. This ability has the potential to increase our understanding of the economic and agricultural value of these species.

Volume scan modeling simulator of a 1D phased array weather radar

Phased array weather radar (PAWR) for hazardous weather phenomena observation has attracted considerable attention. In this paper, a PAWR simulator for volume scan mode modeling is designed and the volume scan model has been used to evaluate the one-dimensional (1D) PAWR beam pattern characteristics and the effect of scanning mode. Firstly, the three-dimensional weather field has been generated by interpolating spectral moment parameters from next-generation weather radar data (NEXRAD), including reflectivity factor, radial velocity and spectral width. Secondly, based on the parameters of radar system and volume scanning modeling, the relationship between phased array beam characteristics and volume scanning mode is established, which can fully simulate the scanning process of phased array radar at different elevation cuts. Finally, according to the concept of 'scattering center', the time series of each range bin during volume scanning is reconstructed. The performance of the PAWR simulator has been evaluated under typical weather scenario, such as tornadoes and the results indicated the effectiveness and superiority over traditional Doppler weather radar. Simulation examples are provided to illustrate the flexibility and practicability of the PAWR simulator, and the factors affecting its PAWR detection performance are evaluated. The research results represent that reducing the number of linear array elements and increasing the array angle will further exacerbate the degradation of data quality.

Unlocking the Potential of NEXRAD Data through NOAA’s Big Data Partnership

Abstract The National Oceanic and Atmospheric Administration’s (NOAA) Big Data Partnership (BDP) was established in April 2015 through cooperative research agreements between NOAA and selected commercial and academic partners. The BDP is investigating how the value inherent in NOAA’s data may be leveraged to broaden their utilization through modern cloud infrastructures and advanced “big data” techniques. NOAA’s Next Generation Weather Radar (NEXRAD) data were identified as an ideal candidate for such collaborative efforts. NEXRAD Level II data are valuable yet challenging to utilize in their entirety, and recent advances in weather radar science can be applied to both the archived and real-time data streams. NOAA’s National Centers for Environmental Information (NCEI) transferred the complete NEXRAD Level II historical archive, originating in 1991, through North Carolina State University’s Cooperative Institute for Climate and Satellites (CICS-NC) to interested BDP collaborators. Amazon Web Services (AWS) has received and made freely available the complete archived Level II data through its AWS platform. AWS then partnered with Unidata/University Corporation for Atmospheric Research (UCAR) to establish a real-time NEXRAD feed, thereby providing on-demand dissemination of both archived and current data seamlessly through the same access mechanism by October 2015. To organize, verify, and utilize the NEXRAD data on its platform, AWS further partnered with the Climate Corporation. This collective effort among federal government, private industry, and academia has already realized a number of new and novel applications that employ NOAA’s NEXRAD data, at no net cost to the U.S. taxpayer. The volume of accessed NEXRAD data, including this new AWS platform service, has increased by 130%, while the amount of data delivered by NOAA/NCEI has decreased by 50%.

An Empirical Model for Assessing the Severe Weather Potential of Developing Convection

Abstract The formation and maintenance of thunderstorms that produce large hail, strong winds, and tornadoes are often difficult to forecast due to their rapid evolution and complex interactions with environmental features that are challenging to observe. Given inherent uncertainties in storm development, it is intuitive to predict severe storms in a probabilistic manner. This paper presents such an approach to forecasting severe thunderstorms and their associated hazards, fusing together data from several sources as input into a statistical model. Mesoscale numerical weather prediction (NWP) models have been developed in part to forecast environments favorable to severe storm development. Geostationary satellites, such as the Geostationary Operational Environmental Satellite (GOES) series, maintain a frequently updating view of growing cumulus clouds over the contiguous United States to provide temporal trends in developing convection to forecasters. The Next Generation Weather Radar (NEXRAD) network delivers repeated scans of hydrometeors inside storms, monitoring the intensification of hydrometeor size and extent, as well as hydrometeor motion. Forecasters utilize NWP models, and GOES and NEXRAD data, at different stages of the forecast of severe storms, and the model described in this paper exploits data from each in an attempt to predict severe hazards in a more accurate and timely manner while providing uncertainty information to the forecaster. A preliminary evaluation of the model demonstrates good skill in the forecast of storms, and also displays the potential to increase lead time on severe hazards, as measured relative to the issuance times of National Weather Service (NWS) severe thunderstorm and tornado warnings and occurrence times of severe events in local storm reports.

Quantitative evaluation of NEXRAD data and its application to the distributed hydrologic model BPCC

The next-generation weather radar (NEXRAD) can generally capture the spatial variability of rainfall fields, but fails to provide accurate depth measurements. A systematic strategy to evaluate the accuracy of radar data in depth measurement and its performance in hydrologic model is outlined. Statistical evaluation coefficients are calculated by comparing NEXRAD data with individual raingauges as well as subbasin-averaged interpolations, and point- and surface-average factors are introduced to revise radar data successively. Hydrologic simulations are then performed with a distributed hydrologic model, called basin pollution calculation center (BPCC) with both raingauge observations and revised NEXRAD estimates inputs. The BPCC model is applied to Clear Creek Watershed, IA, USA, on an hourly scale, and the calibration and validation parameters are semi-automatically optimized to improve manual calibration shortcomings. Results show that hydrographs generated from both gauge and NEXRAD are in good agreement with observed flow hydrographs. Coefficient statistics reveal that NEXRAD contributes to model performance, indicating that NEXRAD data has the potential to be used as an alternative source of precipitation data and improve the accuracy of hydrologic simulations.

Deriving Spatially Distributed Precipitation Data Using the Artificial Neural Network and Multilinear Regression Models

Precipitation is the primary driver for hydrologic modeling. Because hydrologic models often require long-term, spatially dis- tributed precipitation data sets for calibration and validation, a novel approach was developed to generate spatially distributed precipitation data using an artificial neural network (ANN) for the periods when Next-Generation Weather Radar (NEXRAD) data are either unavailable or the quality of the NEXRAD data is not good. The multilinear regression (MLR) technique was also evaluated for completeness. The study's focus was the Saugahatchee Creek watershed in southeast Alabama. In the study area, thewet seasons are dominated by frontal precipitations, whereas the dry seasons primarily contain patchy, convective thunderstorms. The basic approach was to train and validate the ANN and MLR models using recent NEXRAD and rain gauge precipitations, and then use the trained model with the rain gauge precipitation data to generate past, spatially distributed precipitation estimates at the NEXRAD grid locations. For the testing period, the ANN-simulated wet season precipitations in all the NEXRAD grids had a Nash-Sutcliffe efficiency greater than or equal to 0.72 and a mass balance error less than or equal to 14%. The same model performance parameters were 0.65 and 17%, respectively, for the dry season. The MLR model did not perform as well as the ANN model. For the MLR model, the wet season mass balance error ranged from 13-48%, whereas the dry season mass balance error ranged from 0.1-36% on the testing data set. An uncalibrated soil and water assessment tool model was used to assess the improvements in stream flow simulations with the ANN-simulated spatially distributed precipitation data. The stream flow simulations using ANN-generated, spatially distributed precipitations were closer to the observed stream flows relative to stream flows generated using the rain gauge precipitations. Overall, the results suggest that the method developed in this study can be used to generate past, spatially distributed precipitations at NEXRAD grid locations. DOI: 10.1061/(ASCE)HE.1943-5584.0000617. © 2013 American Society of Civil Engineers. CE Database subject headings: Precipitation; Neural networks; Hydraulic models; Hydrologic data. Author keywords: Precipitation; Next-Generation Weather Radar (NEXRAD); Artificial neural network; Multilinear regression; Hydrologic modeling; Soil and watershed assessment tool (SWAT).

Land surface response to precipitation events using MODIS and NEXRAD data

We have developed a wavelet‐based information theoretic approach to examine the interaction between precipitation (PPT) forcing events and the land surface response. Combining Next Generation Weather Radar (NEXRAD) PPT with Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation (NDVI) and surface temperature (T s) data over the Missouri Basin in the north‐central USA, we are able to address the spatial and temporal fluctuations surrounding the hydrometeorology of grassland ecosystems. Information theory metrics of entropy and mutual information content are combined with a wavelet multi‐resolution analysis to examine to what extent the observed PPT signal directly determines the spatial distribution of the land surface temperature and vegetation and how this relationship varies with spatial scale. Results indicate that (1) there is a reduction in the temporal variance of the wavelet coefficients as the signal is transferred from the PPT into the surface temperature and finally the vegetation signal, (2) there are significant correlations as a function of spatial resolution between PPT–NDVI and PPT–T s signals which generally increase with spatial resolution, while there is little correlation between the NDVI and T s signals as a function of resolution, and (3) the scale‐wise entropy and the mutual information content of the signals increase for all fields as the spatial resolution increases. This provides a methodology for determining the relative impact of regional climatology and local land–atmosphere interactions as a function of spatial scale.

Hydrologic Modeling of an Eastern Pennsylvania Watershed with NEXRAD and Rain Gauge Data

This paper applies the Soil Water Assessment Tool (SWAT) to model the hydrology in the Pocono Creek watershed located in Monroe County, Pa. The calibrated model will be used in a subsequent study to examine the impact of population growth and rapid urbanization in the watershed on the base flow and peak runoff. Of particular interest in this paper is the exploration of potential use of Next Generation Weather Radar (NEXRAD) technology as an alternative source of precipitation data to the conventional surface rain gauges. NEXRAD estimated areal average precipitations are shown to compare well with the gauge measured ones at two climate stations in the study area. Investigation of the spatially distributed NEXRAD precipitation estimates revealed that average annual precipitation can vary spatially as much as 12% in the Pocono Creek watershed. The SWAT model is calibrated and validated for monthly stream flow, base flow, and surface runoff. Hydrographs generated from both gauge and NEXRAD driven model simula...

STREAM FLOW ESTIMATION USING SPATIALLY DISTRIBUTED RAINFALL IN THE TRINITY RIVER BASIN, TEXAS

Rainfall is the driving force behind all hydrologic processes in a watershed, and therefore the driving force in hydrologic modeling. In the past, raingauge data has been used as the primary input for these models. However, raingauge networks are generally sparse and insufficient to capture the spatial variability across large watersheds. A relatively new alternative is high-resolution radar rainfall data from weather radar systems, such as the Next Generation Weather Radar (NEXRAD) of the National Weather Service (NWS). In this study, raingauge data were compared to NEXRAD data at each raingauge location to evaluate the accuracy and validity of rainfall data measured by radar. The main objective of this study was to evaluate the use of spatially distributed rainfall on stream flow estimation using radar rainfall inputs to a hydrologic model. SWAT, a distributed-parameter continuous-time hydrologic/water quality model, was used to estimate stream flow for a watershed in the Trinity River Basin of northeast Texas. Results obtained from simulations using NEXRAD rainfall inputs were compared to those obtained using traditional raingauge data as input to the same model. Estimation efficiency analysis was used to compare the storage volume for the Cedar Creek Reservoir with daily, ten-day, and monthly accumulated flow from SWAT simulations using raingauge and NEXRAD rainfall inputs. The efficiency for both models was similar; however, NEXRAD rainfall inputs seem to provide a better flow estimate. The accuracy of the model results suggest that NEXRAD is a good alternative to raingauge data, and can be extremely valuable in large watersheds without readily available raingauge data or sparse raingauge networks. In addition, NEXRAD can capture rainfall from localized events that may be missed by raingauge networks but that still contribute to overland runoff, thus contributing to stream flow.

Coordinate Transformations for Using NEXRAD Data in GIS-Based Hydrologic Modeling

Gridded precipitation products created as part of the U.S. National Weather Service's Next Generation Weather Radar (NEXRAD) program are referenced to a national grid called the Hydrologic Rainfall Analysis Project (HRAP) grid, which is used to mosaic precipitation estimates from different radars into a single national precipitation map. The HRAP grid is defined in a polar stereographic map projection that is formed on a plane intersecting a spherical earth datum at 60° N. This paper describes a method for transforming HRAP grid cells into a coordinate system commonly used for mapping geographic information system data sets, quantifies mapping errors associated with using the HRAP coordinate system, and outlines an approach to reduce these mapping errors. The NEXRAD radar rainfall processing software assumes that the earth is spherical rather than using a more accurate ellipsoidal representation. This assumption causes east-west distances to be distorted relative to north-south distances. The magnitude of...

Fractional Coverage of Rainfall Over a Grid: Analyses of NEXRAD Data Over the Southern Plains

An important parameter in land surface hydrology is the fractional area of a model grid receiving rainfall when rain is observed. Using NEXRAD (Next Generation Weather Radar) hourly rainfall estimates for the southern Plains, we examined the temporal variability in this quantity and tested the important assumptions of the threshold method. The following conclusions are reached. First, seasonal/diurnal variations explain less than 18% of the variance at all spatial scales examined; an efficient method of computing fractional coverage must consider the large event‐scale variability. Second, stationarity in conditional distributions of spatial rainfall, a key assumption in the threshold method, is not warranted at the event scale for small grids or at the seasonal/diurnal scale for large grids. Third, biases are introduced by the high correlation between the parameter and the independent variable of the threshold method. Fourth, the spatial conditional mean rain rate is higher than its temporal counterpart for all cases examined; the ergodicity assumption overpredicts the fractional coverage. Two simple methods were proposed to solve the above problems. Results of an intercomparison showed that they can modestly improve the threshold method.

Meteorological Considerations Used in Planning the NEXRAD Network

A systematic and objective approach was used to optimize the siting of the individual radars forming the Next Generation Weather Radar (NEXRAD) network. Prime consideration was given to meteorological factors, in conjunction with the user agencies' needs and the population distribution. The latter was assessed by a novel technique using weather satellite photographs showing urban illumination at night. Priority coverage areas were identified for population centers based on the expected paths of storms and their travel speeds. Radar viewing of the priority coverage areas down to low altitudes is needed so that approaching storms can be detected and warnings issued as early as possible. Other siting criteria taken into account included consideration of terrain features and local obstructions, locations of airways and civilian and military airports, electromagnetic interference, and integration of NEXRAD data into the national weather system. The methodology for selecting the network is described. Environmen...

A physical basis for NEXRAD data update rates

The Next Generation Weather Radar (NEXRAD) is slated to play an important role in the integrated and automated air traffic control system being currently planned by the Federal Aviation Administration One major point of debate is the minimum rate at which weather data collected by NEXRAD must be updated so that no phenomena potentially hazardous to aviation go undetected This paper presents the methodology and results of a study to estimate the lifetimes of significant features in typical storm phenomena for situations other than takeoff or landing These results are of direct relevance in deciding NEXRAD system data update rates. Two methods are used for the lifetime study: photo interpretive and computer based correlation Several storms are studied using both methods It is found that the storms studied contain no feature that might have been missed by a 5-min scan cycle provided that concurrent reflectivity, radial velocity, and Doppler spectrum width data at several elevations are utilized in the detection of hazardous phenomena.