Weather Radar Network Research Articles

Use of radar data for characterizing extreme precipitation at fine scales and short durations

Extreme precipitation is one of the most devastating forms of atmospheric phenomenon, causing severe damage worldwide, and is likely to intensify in strength and occurrence in a warming climate. This contribution gives an overview of the potential and challenges associated with using weather radar data to investigate extreme precipitation. We illustrate this by presenting radar data sets for Germany, the U.S. and the UK that resolve small-scale heavy rainfall events of just a few km2 with return periods of 5 years or more. Current challenges such as relatively short radar records and radar-based QPE uncertainty are discussed. An example from a precipitation climatology derived from the German weather radar network with spatial resolution of 1 km reveals the necessity of radars for observing short-term (1–6 h) extreme precipitation. Only 17.3% of hourly heavy precipitation events that occurred in Germany from 2001 to 2018 were captured by the rain gauge station network, while 81.8% of daily events were observed. This is underlined by a similar study using data from the UK radar network for 2014. Only 36.6% (52%) of heavy hourly (daily) rain events detected by the radar network were also captured by precipitation gauging stations. Implications for the monitoring of hydrologic extremes are demonstrated over the U.S. with a continental-scale radar-based reanalysis. Hydrologic extremes are documented over ∼1000 times more locations than stream gauges, including in the majority of ungauged basins. This underlines the importance of high-resolution weather radar observations for resolving small-scale rainfall events, and the necessity of radar-based climatological data sets for understanding the small-scale and high-temporal resolution characteristics of extreme precipitation.

Assimilation of Radial Winds Over India Using a Community GSI Analysis System

With the modernization of the India Meteorological Department, the observational network of the country, both in situ and remote sensing, is enhanced. The Doppler Weather Radar (DWR) network is improved, and the National Centre for Medium Range Weather Forecasting is acquiring radar data from 20 stations all over the country. The maximum utilization of DWR observations in the numerical models remains a challenging task. This study represents the first assessment of assimilation of radial wind observations from all 20 DWR stations, utilizing the resources of weather research and a forecasting model with a community grid-point statistical interpolation system. DWR observations are an important data source for mesoscale and microscale weather analysis and forecasting because of their high temporal and spatial resolution. However, the representation of DWR radial wind and reflectivity in a desired format seems to be crucial in the modeling approach. A series of experiments are conducted to evaluate the sensitivity of the analysis to the velocity azimuth display quality control (VADQC) and without VADQC (VARQC) to understand the effect of QC on analysis. The statistical analysis of assimilation of DWR radial wind suggests that a gate distance of 250 m or its multiple is imperative for the setup of the DWR. Additionally, the density of the super-observation is amplified in the VARQC approach. The analysis procedure is implemented for the recent severe cyclone Phethai (December 2018) over the Bay of Bengal, and a few preliminary results are discussed.

A Deep Learning Network for Cloud-to-Ground Lightning Nowcasting with Multisource Data

AbstractPrecise and timely lightning nowcasting is still a great challenge for meteorologists. In this study, a new semantic segmentation deep learning network for cloud-to-ground (CG) lightning nowcasting, named LightningNet, has been developed. This network is based on multisource observation data, including data from a geostationary meteorological satellite, Doppler weather radar network, and CG lightning location system. LightningNet, with an encoder–decoder architecture, consists of 20 three-dimensional convolutional layers, pooling and upsampling layers, normalization layers, and a softmax classifier. The central–eastern and southern China was selected as the study area, with considerations given to the topography and spatial coverage of the weather radar and lightning observation networks. Brightness temperatures (TB) of six infrared bands from theHimawari-8satellite, composite reflectivity mosaic, and CG lightning densities were used as the predictors because of their close relationships with lightning activity. The multisource data were first interpolated into a uniform spatial/temporal resolution of 0.05° × 0.05°/10 min, and then training and test datasets were constructed, respectively. LightningNet was trained to extract the features of lightning initiation, development, and dissipation. The evaluation results demonstrated that LightningNet was able to achieve good performance of 0–1-h lightning nowcasts using the multisource data. The probability of detection, the false alarm ratio, the area under relative operating characteristic curve, and the threat score (TS) of LightningNet with all three types of data reached 0.633, 0.386, 0.931, and 0.453, respectively. Because geostationary meteorological satellite and radar both possess the capability of capturing lightning initiation (LI) features, LightningNet also showed good performance in LI nowcasting. When all three types of data were used, more than 50% LI was predicted accurately and the TS exceeded 0.36. LightningNet’s nowcast performance using triple-source data was clearly superior to that using only single-source or dual-source data, and these findings indicate that LightningNet has good capability of combining multisource data effectively to produce more reliable lightning nowcasts.

The WSR-88D Inanimate Hydrometeor Class

AbstractThe dual-polarization upgrade to the WSR-88D network of weather radars included the addition of a hydrometeor classification algorithm (HCA) to the Open Radar Product Generator (ORPG). The HCA product categorizes each Level-III radar range–azimuth cell into one of 10 classifications or marks it as “unknown.” However, not all target types fall under the 10 classification options. For this reason, multiple studies have examined adding new classes to the operational HCA. In this study, a new “inanimate” class is developed for additional hydrometeor classification in the ORPG and is described in detail. This class encompasses nonweather phenomena such as chaff (the primary motivation of this work), sea clutter, and some types of combustion debris and radio frequency interference. The design process is detailed, including human truthing, data selection, and optimization with a genetic algorithm. Multiple case examples are presented and analyzed, both qualitatively and quantitatively. Quantification is put into perspective with previous studies for a better understanding of the algorithm’s performance and impact. A discussion of applications, including subclassing, chaff detection, and implementation as an “aviation” classification algorithm in the ORPG, is presented.

Enhancing Precipitation Estimates Through the Fusion of Weather Radar, Satellite Retrievals, and Surface Parameters

Accurate and timely monitoring of precipitation remains a challenge, particularly in hyper-arid regions such as the United Arab Emirates (UAE). The aim of this study is to improve the accuracy of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission’s latest product release (IMERG V06B) locally over the UAE. Two distinct approaches, namely, geographically weighted regression (GWR), and artificial neural networks (ANNs) are tested. Daily soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission (9 km), terrain elevations from the Advanced Spaceborne Thermal Emission and Reflection digital elevation model (ASTER DEM, 30 m) and precipitation estimates (0.5 km) from a weather radar network are incorporated as explanatory variables in the proposed GWR and ANN model frameworks. First, the performances of the daily GPM and weather radar estimates are assessed using a network of 65 rain gauges from 1 January 2015 to 31 December 2018. Next, the GWR and ANN models are developed with 52 gauges used for training and 13 gauges reserved for model testing and seasonal inter-comparisons. GPM estimates record higher Pearson correlation coefficients (PCC) at rain gauges with increasing elevation (z) and higher rainfall amounts (PCC = 0.29 z0.12), while weather radar estimates perform better for lower elevations and light rain conditions (PCC = 0.81 z−0.18). Taylor diagrams indicate that both the GWR- and the ANN-adjusted precipitation products outperform the original GPM and radar estimates, with the poorest correction obtained by GWR during the summer period. The incorporation of soil moisture resulted in improved corrections by the ANN model compared to the GWR, with relative increases in Nash–Sutcliffe efficiency (NSE) coefficients of 56% (and 25%) for GPM estimates, and 34% (and 53%) for radar estimates during summer (and winter) periods. The ANN-derived precipitation estimates can be used to force hydrological models over ungauged areas across the UAE. The methodology is expandable to other arid and hyper-arid regions requiring improved precipitation monitoring.

Developing a multi-attribute decision aid model for selection of a weather radar supplier

Selecting a supplier for highly technical and expensive equipment as a weather radar is a very demanding process and a critical management task for procurement team. For such problems, the use of multiple criteria analysis is welcome. This paper introduces a decision aid model for weather radar supplier selection using a multi-attribute approach for public purchase procedures. As a model application, we developed the framework on a real public procurement to expand the weather radar network in Brazil, where two attributes were in conflict (cost and technical). The proposed model was demonstrated to be very useful in understanding the trade-off between cost and technical attributes, based on the stakeholders’ preferences. The model also supported the decision-maker in the negotiation process to receive better commercial or technical proposals from the buyers’ perspective. Based on these findings, it is advocated that for highly specialised equipment, a single attribute method centred on the lowest-cost bid is no longer supportive and robust enough in contemporary supplier selection. Modelling a multi-attribute decision process could also be a guide to improve interaction with expert and management teams in future weather radar selection evaluations.

Weather Radar Network Benefit Model for Flash Flood Casualty Reduction

ABSTRACTA monetized flash flood casualty reduction benefit model is constructed for application to meteorological radar networks. Geospatial regression analyses show that better radar coverage of the causative rainfall improves flash flood warning performance. Enhanced flash flood warning performance is shown to decrease casualty rates. Consequently, these two effects in combination allow a model to be formed that links radar coverage to flash flood casualty rates. When this model is applied to the present-day contiguous U.S. weather radar network, results yield a flash flood–based benefit of $316 million (M) yr−1. The remaining benefit pools are more modest ($13 M yr−1 for coverage improvement and $69 M yr−1 maximum for all areas of radar quantitative precipitation estimation improvements), indicative of the existing weather radar network’s effectiveness in supporting the flash flood warning decision process.

Deep-learning-based extraction of the animal migration patterns from weather radar images

Continental coverage and year-round operation of the weather radar networks provide an unprecedented opportunity for studying large-scale airborne migration. The broad and local-scale airborne information collected by these infrastructures can answer many ecological questions. However, extracting and interpreting the biological information from such massive weather radar data remains an intractable problem. Recently, many big-data problems have been solved using the deep learning technology. In this study, the biological information in the weather radar data is identified using the advanced deep learning method. The proposed method consists of two main parts, i.e., a rendering and casting procedure and an image segmentation procedure based on a convolutional neural network. The biological data are automatically extracted by rendering and mapping, image segmentation, and result masking. By analyzing the typical radar data from single and multiple stations, we partly reveal the intensity and speed of the migration pattern. We present the first feasibility study of the extraction of local and large-scale biological phenomena from the Chinese weather radar network data.

Monitoring the differential reflectivity and receiver calibration of the German polarimetric weather radar network

Abstract. It is a challenge to calibrate differential reflectivity ZDR to within 0.1–0.2 dB uncertainty for dual-polarization weather radars that operate 24∕7 throughout the year. During operations, a temperature sensitivity of ZDR larger than 0.2 dB over a temperature range of 10 ∘C has been noted. In order to understand the source of the observed ZDR temperature sensitivity, over 2000 dedicated solar box scans, two-dimensional scans of 5∘ azimuth by 8∘ elevation that encompass the solar disk, were made in 2018 from which horizontal (H) and vertical (V) pseudo antenna patterns are calculated. This assessment is carried out using data from the Hohenpeißenberg research radar which is identical to the 17 operational radar systems of the German Meteorological Service (Deutscher Wetterdienst, DWD). ZDR antenna patterns are calculated from the H and V patterns which reveal that the ZDR bias is temperature dependent, changing about 0.2 dB over a 12 ∘C temperature range. One-point-calibration results, where a test signal is injected into the antenna cross-guide coupler outside the receiver box or into the low-noise amplifiers (LNAs), reveal only a very weak differential temperature sensitivity (<0.02 dB) of the receiver electronics. Thus, the observed temperature sensitivity is attributed to the antenna assembly. This is in agreement with the NCAR (National Center for Atmospheric Research) S-Pol (S-band polarimetric radar) system, where the primary ZDR temperature sensitivity is also related to the antenna assembly (Hubbert, 2017). Solar power measurements from a Canadian calibration observatory are used to compute the antenna gain and to validate the results with the operational DWD monitoring results. The derived gain values agree very well with the gain estimate of the antenna manufacturer. The antenna gain shows a quasi-linear dependence on temperature with different slopes for the H and V channels. There is a 0.6 dB decrease in gain for a 10 ∘C temperature increase, which directly relates to a bias in the radar reflectivity factor Z which has not been not accounted for previously. The operational methods used to monitor and calibrate ZDR for the polarimetric DWD C-band weather radar network are discussed. The prime sources for calibrating and monitoring ZDR are birdbath scans, which are executed every 5 min, and the analysis of solar spikes that occur during operational scanning. Using an automated ZDR calibration procedure on a diurnal timescale, we are able to keep ZDR bias within the target uncertainty of ±0.1 dB. This is demonstrated for data from the DWD radar network comprising over 87 years of cumulative dual-polarization radar operations.

High-Resolution Spatial Distribution of Bird Movements Estimated from a Weather Radar Network

Weather radars provide detailed information on aerial movements of organisms. However, interpreting fine-scale radar imagery remains challenging because of changes in aerial sampling altitude with distance from the radar. Fine-scale radar imagery has primarily been used to assess mass exodus at sunset to study stopover habitat associations. Here, we present a method that enables a more intuitive integration of information across elevation scans projected in a two-dimensional spatial image of fine-scale radar reflectivity. We applied this method on nights of intense bird migration to demonstrate how the spatial distribution of migrants can be explored at finer spatial scales and across multiple radars during the higher flying en-route phase of migration. The resulting reflectivity maps enable explorative analysis of factors influencing their regional and fine-scale distribution. We illustrate the method’s application by generating time-series of composites of up to 20 radars, achieving a nearly complete spatial coverage of a large part of Northwest Europe. These visualizations are highly useful in interpreting regional-scale migration patterns and provide detailed information on bird movements in the landscape and aerial environment.

Development of a Low-Cost Multistatic Passive Weather Radar Network

Practical and accurate estimation of 3-D wind fields is an ongoing challenge in radar meteorology. Multistatic (single transmitter/multiple receivers) radar architectures offer a cost-effective solution for obtaining the multiple Doppler measurements necessary to achieve such estimates. Existing multistatic weather radars, while less costly than comparable monostatic networks, have been constrained by the need for specialized equipment and software to perform frequency and pulse timing synchronization with the transmitting radar. This article describes the implementation of a passive radar network that performs pulse timing and carrier frequency synchronization through measurements of the sidelobe radiation of the transmitting radar. This makes it possible for the receiver modules to be constructed with minimal size and cost, and it allows for their use in coordination with any in-band transmitter capable of recording time-stamped pointing angle information. A prototype network consisting of two passive receivers has been constructed in the Oklahoma City, OK, USA. Weather observations collected using the radiation from a WSR-88D have been used to validate the accuracy of velocity measurements obtained through this technique.

Blob Extraction Algorithm in Detection of Convective Cells for Data Fusion

Earth’s atmosphere is monitored by a multitude of sensors. It is the troposphere that is of crucial importance for human activity, as it is there that the weather phenomena take place. Weather observations are performed by surface sensors monitoring, inter alia, humidity, temperature and winds. In order to observe the developments taking place in the atmosphere, especially in the clouds, weather radars are commonly used. They monitor severe weather that is associated with storm clouds, cumulonimbuses, which create precipitation visible on radar screens. Therefore, radar images can be utilized to track storm clouds in a data fusion system. In this paper an algorithm is developed for the extraction of blobs (interesting areas in radar imagery) used within data fusion systems to track storm cells. The algorithm has been tested with the use of real data sourced from a weather radar network. 100% of convection cells were detected, with 90% of them being actual thunderstorms.

Consistency of spatio‐temporal patterns of avian migration across the Swiss lowlands

AbstractEach year, billions of birds migrate across the continents by day and night through airspaces increasingly altered by human activity, resulting in the deaths of millions of birds every year through collisions with man‐made structures. To reduce these negative impacts on wildlife, forecasts of high migration intensities are needed to apply mitigation actions. While existing weather radar networks offer a unique possibility to monitor and forecast bird migration at large spatial scales, forecasts at the fine spatial scale within a complex terrain, such as the mountainous Swiss landscape, require a small‐scale network of ornithological radars. Before attempting to build such a network, it is crucial to first investigate the consistency of the migratory flow across space and time. In this study, we simultaneously operated three ornithological radar systems across the Swiss lowlands to assess the spatio‐temporal consistency of diurnal and nocturnal bird movements during the spring and autumn migration season. The relative temporal course of migration intensities was generally consistent between sites during peak migration, in particular for nocturnal movements in autumn, but absolute intensities differed greatly between sites. Outside peak migration, bird movement patterns were much less consistent and, unexpectedly, some presumably non‐migratory bird activity achieved intensities close to peak migration intensities, but without spatial correlations. Only nocturnal migration intensity in autumn could be predicted with consistently high accuracy, but including parameters of atmospheric conditions in the model improved predictability of diurnal movements considerably. Predictions for spring were less reliable, probably because we missed an important part of the migration season. Our results show that reliable forecasts of bird movements within a complex terrain call for a network of year‐round bird monitoring systems, whereas accurate information of atmospheric conditions can help to limit the number of measurement points.

Simulation of broad front bird migration across Western Europe

The spatial and temporal patterns of broad front bird migration are governed by the geospatial distributions of landmasses, mountain ranges, and weather conditions. These distributions interact with the birds' innate program during migration and are critical to successful migration. Hence, favourable environmental conditions for migration consequently lead to spatio-temporal concentrations, and the evolution of specific migratory flyways. Based on the aerodynamic properties of the ten most abundant nocturnal long-distance migrants, we developed a computational framework to simulate millions of individual trajectories across Europe using an agent-based simulation approach. We simulated a three-week period of autumn migration with a temporal resolution of 30 s. Departure conditions were derived from bird densities observed within the first two hours after sunset, which were extracted from the weather radar network. Individual itineraries were strongly influenced by the behavioural reactions to the environment such as the wind flow, coastlines and mountain ranges. Wind speed and direction are among the key factors that shape migration patterns such as reverse movements observed within some nights. Accumulation of migration was triggered by the combined effect of geographic barriers (coastlines, mountain ranges) and wind. The overall result of the simulation corresponds well with the large-scale pattern of bird migration intensities measured across the study area, for instance the high migration intensity observed between the Atlantic coast and the Pyrenees. Our model framework conjoins all important domains, such as the birds’ preconditions and behavioural scope, as well as the spatio-temporal dynamics of the environment. However, for the time being we cannot decide whether local discrepancies between model and data are due to environmental effects that are not yet captured by our simulation (i.e. effect of rain on migration intensity), or caused by differences in the sensitivity of the local weather radar systems. Due to the limited time period and lack of more accurate data for validation, our findings are preliminary. Further progress in data quality and hopefully better access of bird profiles from the continent-wide weather radar network will allow to improve model performance and implement a bird migration forecasting similar to weather forecasts. Apart from assessing conflicts between human activities and bird movements, this would greatly enhance our understanding of biomass flow on a large scale.

A Geostatistical Approach to Estimate High Resolution Nocturnal Bird Migration Densities from a Weather Radar Network

Quantifying nocturnal bird migration at high resolution is essential for (1) understanding the phenology of migration and its drivers, (2) identifying critical spatio-temporal protection zones for migratory birds, and (3) assessing the risk of collision with artificial structures. We propose a tailored geostatistical model to interpolate migration intensity monitored by a network of weather radars. The model is applied to data collected in autumn 2016 from 69 European weather radars. To validate the model, we performed a cross-validation and also compared our interpolation results with independent measurements of two bird radars. Our model estimated bird densities at high resolution (0.2° latitude–longitude, 15 min) and assessed the associated uncertainty. Within the area covered by the radar network, we estimated that around 120 million birds were simultaneously in flight (10–90 quantiles: 107–134). Local estimations can be easily visualized and retrieved from a dedicated interactive website. This proof-of-concept study demonstrates that a network of weather radar is able to quantify bird migration at high resolution and accuracy. The model presented has the ability to monitor population of migratory birds at scales ranging from regional to continental in space and daily to yearly in time. Near-real-time estimation should soon be possible with an update of the infrastructure and processing software.

A Geostatistical Approach to Estimate High Resolution Nocturnal Bird Migration Densities from a Weather Radar Network

A Geostatistical Approach to Estimate High Resolution Nocturnal Bird Migration Densities from a Weather Radar Network

Fusion of High-Resolution Reflectivity for a New Array Weather Radar

Array Weather Radar (AWR) is a novel type weather radar equipped with a distributed phased array technology. As a new instrument with new technology, the AWR offers very high spatiotemporal resolution that enables detection of the fine-scale flow field and reflectivity of severe convective storms. This new AWR provides coordinated observations of a target from three subarrays of transmitter-receiver antenna units. This paper introduces a resolution enhancement concept that the very high range resolution of one subarray can be used to compensate lower azimuth and elevation resolutions of the other subarrays of the AWR. The resolution enhancement effect is estimated using data point density. A data fusion method is then presented to obtain a unified high-resolution reflectivity from the networked and coordinated AWR subarray observations. First, based on the reflectivity data from the AWR subarray volume scans, numbers of the data-point filling in both the azimuth and elevation directions are calculated. Then, the fusion of three subarray reflectivity data is achieved through the vertical and horizontal filling and merging in a common coordinate system. The final product of the fused high-resolution reflectivity is verified using both subjective and objective evaluations. The verification experiments included radar echoes of two simulated weather scenarios, a small-scale heavy precipitation and a tornado, along with a real precipitation event. The real precipitation event was observed from the AWR system that is installed and operational at the Changsha Huanghua International Airport. The performance of the proposed high-resolution reflectivity fusion method yields a 35% smaller root mean square error and an 11% increase in the correlation coefficient to the maximum extent. The real event result shows that the final fused high-resolution reflectivity depicted a more detailed and complete echo structure compared to the China New Generation Weather Radar network observation.

Decline of the North American avifauna.

Species extinctions have defined the global biodiversity crisis, but extinction begins with loss in abundance of individuals that can result in compositional and functional changes of ecosystems. Using multiple and independent monitoring networks, we report population losses across much of the North American avifauna over 48 years, including once-common species and from most biomes. Integration of range-wide population trajectories and size estimates indicates a net loss approaching 3 billion birds, or 29% of 1970 abundance. A continent-wide weather radar network also reveals a similarly steep decline in biomass passage of migrating birds over a recent 10-year period. This loss of bird abundance signals an urgent need to address threats to avert future avifaunal collapse and associated loss of ecosystem integrity, function, and services.

Weather surveillance radar as an objective tool for monitoring bat phenology and biogeography

Bats provide a wealth of vital services to humans, such as pollinating plants and managing populations of insects that act as agricultural pests and disease vectors. Despite their direct importance, it is inherently difficult to monitor bats across large spatiotemporal scales, in part due to their nocturnal activity and long-distance, high-altitude flights. Networks of weather surveillance radars provide continuous measurements of the airspace at continental scales, revealing the abundance, distribution, behaviour, and phenology of bats aloft. Using a network of polarimetric Doppler weather radars in the USA, we demonstrate applications of this technology to monitor large bat colonies, highlight recent discoveries made using radar surveillance of bats, and discuss future prospects for extending these techniques as part of a standardised system of global bat monitoring infrastructure.

Observed Link of Extreme Hourly Precipitation Changes to Urbanization over Coastal South China

AbstractUnderstanding changes in subdaily rainfall extremes is critical to urban planners for building more sustainable and resilient cities. In this study, the hourly precipitation data in 1971–2016 from 61 rain gauges are combined with historical land-use change data to investigate changes in extreme hourly precipitation (EXHP) in the Pearl River delta (PRD) region of South China. Also, 120 extreme rainfall events (EXREs) during 2011–16 are analyzed using observations collected at densely distributed automatic weather stations and radar network. Statistically significant increase of hourly precipitation intensity leads to higher annual amounts of both total and extreme precipitation over the PRD urban cluster in the rapid urbanization period (about 1994–2016) than during the preurbanization era (1971 to about 1993), suggesting a possible link between the enhanced rainfall and the rapid urbanization. Those urbanization-related positive trends are closely related to more frequent occurrence of abrupt rainfall events with short duration (≤6 h) than the continuous or growing rainfall events with longer duration. The 120 EXREs in 2011–16 are categorized into six types according to the originating location and movement of the extreme-rain-producing storms. Despite the wide range of synoptic backgrounds and seasons, rainfall intensification by the strong urban heat island (UHI) effect is a clear signal in all the six types, especially over the inland urban cluster with prominent UHIs. The UHI thermal perturbation probably plays an important role in the convective initiation and intensification of the locally developed extreme-rain-producing storms during the daytime.