Weather Radar Measurements Research Articles

Can stormwater runoff measurements be used for weather radar rainfall adjustment?

ABSTRACT Predicting the response to rainfall in urban hydrological applications requires accurate precipitation estimates with a high spatiotemporal resolution to reflect the natural variability of rainfall. However, installing rain gauges under nearly ideal measurement conditions is often difficult in urban areas, if not impossible. This paper demonstrates the potential of deriving rainfall measurements in urban areas and bias-adjusting weather radar rainfall measurements using stormwater runoff measurements. As a supplement to point rainfall measurements from rain gauges, the developed bias adjustment approach uses catchment runoff-rainfall estimates derived from water level measurements of a stormwater detention pond. The study shows that the bias-adjusted radar product correlates highly with rain gauge measurements in the catchment. Moreover, the presented approach enables rainfall measurements within a catchment independent of rain gauges located in the catchment, making the technique highly applicable for increasing the density of ground observations and thus improving weather radar precipitation estimates over urban areas. The method also derives the catchment-specific runoff coefficient independently of expensive flow measurements in the catchment, making the method very scalable. This paper highlights the potential of using easily achievable catchment runoff-rainfall measurements to increase the density of available ground observations and thereby improve weather radar precipitation estimates.

An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar

The quality of weather radar affects the reliability and effectiveness of monitoring severe convective weather. Therefore, rigorous calibration and validation are the foundation for the quantitative application of weather radar. Among the available methods, radial velocity validation is of great significance for reducing the false alarm rate in the identification of tornadoes and thunderstorms. Based on the traditional method that utilizes internal and external instrument radar velocity measurements, we propose a weather radar radial velocity validation method that uses RTK UAV to simulate external targets. In addition, according to the characteristics of the UAV application scenarios, we introduce the evaluation parameter of optimal absolute accuracy to supplement the original parametric system. The experimental results show that the evaluation parameter of optimal absolute accuracy can effectively reduce the interference caused by the systematic deviation of the UAV due to the internal and external environment, which can affect the validation results. When the UAV velocity is not greater than 10 m/s, the optimal absolute accuracy of the radial velocity validation is less than 0.05 m/s, which is essentially consistent with the external instruments’ measurement results. This method can be effectively applied to the procedural handling of weather radar radial velocity validation. It is significant for ensuring the accuracy and quality of weather radar radial velocity measurements and improving the effectiveness of radar velocity data applications.

Cloud-Resolving Modeling and Evaluation of Microphysical Schemes for Flash Flood–Producing Convection over the Black Sea

Abstract In the present study, the convective event over the Black Sea area in September 2018 is analyzed using the Weather Research and Forecasting (WRF) Model configured with a fully convective-resolving setup. We test the WRF sensitivity to the choice of sea surface temperature (SST) dataset and microphysics scheme. The simulation is verified using weather radar measurements and ground observations. Both the choice of the microphysical scheme and SST dataset have a significant impact on the dynamic properties of the maritime convective system and associated rainfall. The best results are achieved with the WDM6 microphysical scheme and a more detailed and slightly warmer (compared to the default OSTIA SST) G1SST dataset. The optimally configured WRF simulations add value to coarser driving operational analysis, with more accurate amount and pattern of rainfall and the earlier arrival of the convective system, which is in better agreement with radar and weather station measurements. The vertical structure of the reflectivity profiles in the WDM6 scheme that simulates 15%–20% larger rainwater loading compared to other schemes agrees best with the observed data. Other schemes reproduce excessive reflectivity above the freezing level. Enhanced rainfall estimates and faster convective system propagation in the G1SST WDM6 simulations are linked to stronger cold pools caused by enhanced evaporation due to the higher rainwater content and droplet number concentrations. Stronger cold pools result in the 15%–20% enhancement of latent and sensible heat fluxes, reflecting the strong sensitivity of ocean–atmosphere heat and moisture exchange to the choice of microphysics scheme and SST dataset.

Characterization of deep convective cells during the Indian summer monsoon using C-band polarimetric Doppler Weather Radar observations over Thumba (8.5o N, 77o E)

The vertical structure and dynamics of deep convective clouds plays an important role during the Indian summer monsoon period. The characteristics of deep convective cells (DCC) and their microphysics remain unclear, especially during the Indian summer monsoon (ISM). Further, the intensity of convection is an important element, which plays a key role in the progression of the monsoon. The present study focuses on characterizing DCC over a coastal location Thumba (8.50 N, 770 E) during the Indian summer monsoon periods of 2017, 2018 and 2019 using C-band polarimetric Doppler Weather Radar (DWR) measurements in terms of their intensity, vertical extent, top heights and their microphysical properties. A method is devised to identify the DCC from the DWR observed reflectivity cross-sections. The results showed that the occurrence of reflectivities in the range of 25–35 dBZ dominates below 6 km and the reflectivities in the range of 10–20 dBZ dominates above 6 km. The diurnal evolution of 40 dBZ depth radar echoes in DCC were analysed for June–September months, which showed peaks at preferential timings. The mean diurnal evolution of DCC also been investigated and it is found that these systems peak at three local time intervals viz., 00–05, 13–16 and 19–23 h during ISM period. Further, a separate analysis has been carried out for land and oceanic region for 40 dBZ depth radar echoes in DCC. The analysis show that the occurrence of 40 dBZ depth radar echoes peak at early-morning and late-night over the ocean and afternoon and late night over the land regions. The present results provide a quantitative assessment of DCC including their diurnal evolution during the Indian summer monsoon season over Thumba using DWR observations for the first time.

Processing of weather radar raw IQ-data towards the identification and correction of wind turbine interference – Project RIWER: Removing the Influence of Wind Park Echoes in Weather Radar Measurements

Abstract. In recent years, the fast construction, expansion and repowering of wind parks have been a major source of concern for the weather radar community and meteorological services. Among others because wind turbines are extremely tall, reflective, and moving objects, which make them a source of interference that is hard to distinguish from meteorological echoes and therefore difficult to filter and even more difficult to correct. Polarimetric C-Band Doppler weather radar measurements enable us to analyse and understand the impact of wind turbine interference on meteorological weather radar echoes and to build up knowledge. The main idea is to analyse the raw IQ-data in order to quantify the behaviour of wind turbine interference with meteorological scattering. As a first step in this direction, this paper will focus on the derivation and analysis of radar moments such as Reflectivity (Z), Differential Reflectivity (ZDR), Differential Propagation Phase (PHIDP), Mean Doppler Velocity (V), and Correlation Coefficient (RHOHV). We will consider two cases: (i) events with precipitation, and (ii) events without precipitation, in order to understand and model the impact of wind turbine interference (WTI). For this purpose, weather radar measurements from Deutscher Wetterdienst (DWD), recorded under the aegis of the project RIWER (Removing the Influence of Wind Park Echoes in Weather Radar Measurements), are presented, analysed and discussed in detail.

Convective Storm VIL and Lightning Nowcasting Using Satellite and Weather Radar Measurements Based on Multi-Task Learning Models

Convective Storm VIL and Lightning Nowcasting Using Satellite and Weather Radar Measurements Based on Multi-Task Learning Models

Measurement and characterization of infrasound waves from the March 25, 2023 thunderstorm at the near equatorial

<abstract> <p>Thunderstorm activity on March 25, 2023 provided a unique opportunity to study the mechanism of lightning events on changes in air pressure. In particular, this event made it possible to study changes in air pressure during thunderstorms using various instruments. This paper presented comprehensive results of infrasound, satellite data, weather radar and weather measurements at the ground during the storm. Observations of lightning events were confirmed using observational data from the International Space Station's Lightning Imaging Sensor (ISS LIS). This work estimated three spectral percentile values on infrasonic sensor data, time series interpolation of standard meteorology profiles, weather radar reflectivity and total radiant energy of lightning from ISS LIS observations during the day and night periods. As a result, during the investigation, it was seen that the recorded infrasound signal in the 0.6–0.8 Hertz (Hz) range was contaminated by background environmental noise, but in the 1–3 Hz band range it was consistent with the appearance of storms that produce high energy blows. Infrasound detection and electromagnetic lightning detection show good correlation up to a distance of 100 km from the infrasonic station. During a thunderstorm, the ISS LIS flight directly above the observation site detected more than 2,000 lightning events. In addition, the application of lightning detection from several independent instruments can provide a complete picture of the observed event.</p> </abstract>

Calibration of radar differential reflectivity using quasi-vertical profiles

Abstract. Accurate precipitation estimation with weather radars is essential for hydrological and meteorological applications. The differential reflectivity (ZDR) is a crucial weather radar measurement that helps to improve quantitative precipitation estimates using polarimetric weather radars. However, a system bias between the horizontal and vertical channels generated by the radar produces an offset in ZDR. Existing methods to calibrate ZDR measurements rely on the intrinsic values of the ZDR of natural targets (e.g. drizzle or dry snow) collected at high elevation angles (e.g. higher than 40∘ or even at 90∘), in which ZDR values close to 0 dB are expected. However, not all weather radar systems can scan at such high elevation angles or point the antenna vertically to collect precipitation measurements passing overhead. Therefore, there is a need to develop new methods to calibrate ZDR measurements using lower-elevation scans. In this work, we present and analyse a novel method for correcting and monitoring the ZDR offset using quasi-vertical profiles computed from scans collected at 9∘ elevations. The method is applied to radar data collected through 1 year of precipitation events by two operational C-band polarimetric weather radars in the UK. The proposed method shows a relative error of 0.1 dB when evaluated against the traditional approach based on ZDR measurements collected at 90∘ elevations. Additionally, the method is independently assessed using disdrometers located near the radar sites. The results showed a reasonable agreement between disdrometer-derived and radar-calibrated ZDR measurements.

Lagrangian Integro-Difference Equation Model for Precipitation Nowcasting

Abstract Delivering reliable nowcasts (short-range forecasts) of severe rainfall and the resulting flash floods is important in densely populated urban areas. The conventional method is advection-based extrapolation of radar echoes. However, during rapidly evolving convective rainfall this so-called Lagrangian persistence (LP) approach is limited to deterministic and very short-range nowcasts. To address these limitations in the 1-h time range, a novel extension of LP, called Lagrangian Integro-Difference equation model with Autoregression (LINDA), is proposed. The model consists of five components: 1) identification of rain cells, 2) advection, 3) autoregressive process describing growth and decay of the cells, 4) convolution describing loss of predictability at small scales, and 5) stochastic perturbations to simulate forecast uncertainty. Advection is separated from the other components that are applied in the Lagrangian coordinates. The reliability of LINDA is evaluated using the NEXRAD WSR-88D radar that covers the Dallas–Fort Worth metropolitan area, as well as the NEXRAD mosaic covering the continental United States. This is done with two different configurations: LINDA-D for deterministic and LINDA-P for probabilistic nowcasts. The validation dataset consists of 11 rainfall events during 2018–20. For predicting moderate to heavy rainfall (5–20 mm h−1), LINDA outperforms the previously proposed LP-based approaches. The most significant improvement is seen for the ETS and POD statistics with the 5 mm h−1 threshold. For 30-min nowcasts, they show 15% and 16% increases, respectively, to the second-best method and 48% and 34% increases compared to LP. For the 5 mm h−1 threshold, the increase in the relative operating characteristic (ROC) skill score of 30-min nowcasts from the second-best method is 10%. Significance Statement Delivering reliable forecasts of severe rainfall for the next few hours has a major societal importance. This is particularly true for densely populated urban areas, where flash floods can cause property damage and loss of lives. Such forecasts are conventionally produced by direct extrapolation of weather radar measurements. However, for intense localized rainfall this approach has low prediction ability beyond 30 min. To extend this limit, we propose a novel method that combines machine vision with a statistical model for growth and decay of rainfall. The method is designed for predicting highly localized rain cells and bands. In addition, a stochastic extension for producing probabilistic forecasts is developed. Using several verification metrics, we demonstrate that for predicting moderate to heavy rainfall (5–20 mm h−1), the proposed method has significantly improved forecast skill compared to the reference methods. The evaluation is done by using the NEXRAD WSR-88D that covers the Dallas–Fort Worth urban metroplex with a population of over 7 million. Demonstration of the applicability of LINDA in a larger domain is done by using the NEXRAD radar network that covers the continental United States.

Interpreting estimated observation error statistics of weather radar measurements using the ICON-LAM-KENDA system

Abstract. Assimilation of weather radar measurements including radar reflectivity and radial wind data has been operational at the Deutscher Wetterdienst, with a diagonal observation error (OE) covariance matrix. For an implementation of a full OE covariance matrix, the statistics of the OE have to be a priori estimated, for which the Desroziers method has been often used. However, the resulted statistics consists of contributions from different error sources and are difficult to interpret. In this work, we use an approach that is based on samples for truncation error in radar observation space to approximate the representation error due to unresolved scales and processes (RE) and compare its statistics with the OE statistics estimated by the Desroziers method. It is found that the statistics of the RE help the understanding of several important features in the variances and correlation length scales of the OE for both reflectivity and radial wind data and the other error sources from the microphysical scheme, radar observation operator and the superobbing technique may also contribute, for instance, to differences among different elevations and observation types. The statistics presented here can serve as a guideline for selecting which observations are assimilated and for assignment of the OE covariance matrix that can be diagonal or full and correlated.

A method to estimate the probability of strong winds occurrence using weather radar data

AbstractWeather radars are capable of detecting and displaying storm‐related turbulence as well as precipitation rate with fine spatial resolution and reliable quality. Especially, weather radars can enable the detection of atmospheric conditions in the vicinity of cities and thereby help to notify the strong winds. This article investigates the application of weather radar measurements for predicting strong wind and presents a new method for very short‐term storm prediction. In the proposed method, hidden Markov model (HMM) is used to classify atmospheric conditions to “potentially stormy” and “non‐stormy” states using the available radar data, and semi‐Markov theory is used to estimate the probability of storm occurrence with time. In fact, the probability of transition between “potentially stormy,” “non‐stormy,” and “stormy” states is modeled by a semi‐Markov model, to find the unconditional probability of storm occurrence with time. The model is implemented with the use of the data of Tehran C‐band weather radar and anemometer of Tehran international airport. Verification results show that the precision (forecast accuracy) is around 0.19 and the recall is around 0.67 in the presented classification method.

Analysis of the Z-R relationship using X-Band weather radar measurements in the area of Athens

Analysis of the Z-R relationship using X-Band weather radar measurements in the area of Athens

Evaluation of KDP Estimation Algorithm Performance in Rain Using a Known-Truth Framework

AbstractAccurate estimation of specific differential phase (KDP) is necessary for rain rate estimation, attenuation correction, and hydrometeor classification algorithms. There are numerous published methods to process polarimetric radar observations of propagation differential phase shift (ΦDP) and estimate KDP, but the corresponding KDP estimate uncertainty is unquantified. This study provides guidance on how commonly used KDP estimation algorithms perform in various environments. We create numerous synthetic (“true”) KDP profiles, integrate over them to obtain “smoothed” ΦDP, and then add noise typical of S-band operational weather radar measurements. Each algorithm is applied to our noisy ΦDP profiles and compared to the true KDP profile such that the errors and uncertainty are quantified. The synthetic KDP profiles are Gaussian in shape, which allows systematic variations in their magnitude and width to determine how each algorithm performs in smooth, slowly changing KDP profiles, as well as steep profiles. Results demonstrate that algorithm performance is dependent on the ΦDP field received. These results are further supported by an error analysis of each algorithm for two more complicated synthetic KDP profiles. Some KDP algorithms allow users to change various tuning parameters; a subset of these tuning parameters is tested to provide guidance on how changing these parameters impacts algorithm performance. We then provide evidence that our known-truth framework provides insight into algorithm performance in observed data through two case studies.

Combining citizen science and weather radar data to study large‐scale bird movements

The study of large‐scale animal mass movements requires suitable large‐scale sampling methods. Weather radar (WR) has been known to register biological targets since the 1960s. Arranged in large networks, radar is suitable to study regional to continent‐wide dynamics of aerofauna and to respond to increasing human–wildlife conflicts in the air. Tools for the spatiotemporal validation of faunistic interpretations of WR measurements in situ are only sparsely available. Citizen science (CS) bird observation repositories established in the past 20 years have accumulated millions of entries of species‐specific information well beyond their time of existence across vast areas. Together with other CS data sources, these databases can relieve the taxonomic shortcomings of WR and thus extend and refine the use of WR data. CS and WR data combined can efficiently provide large amounts of species‐specific data in three dimensions in a short time at low or no cost. Species‐specific data are particularly relevant to tackle loss of biodiversity, one of the greatest challenges in today's world. In this forum paper, we present features and qualities of ornithological CS and WR data, and their potential to provide unprecedented insights into regional to continent‐wide aerial movements of birds. We aim to discuss specific fields of applications where maximum information yield is to be expected, which is otherwise inaccessible, and in which way combined approaches would support biological research and derived data products and services for stakeholders, e.g. in aviation and the general public as beneficiaries.

On the association between lightning and precipitation microphysics

Lightning is one of the most important extreme weather conditions and is associated with heavy convective rain and thunderstorms. However, the present understanding of such systems as well their effect on precipitation characteristics are insufficient for proper modelling. In this paper, the rain microphysics is studied at Kolkata using an optical disdrometer in relation to the lightning activities. Lightning information is obtained from ground based World Wide Lightning Location Network and Lightning Imaging Sensor onboard International Space Station. The associated cloud features are studied with Doppler weather radar and MODIS satellite measurements. Results indicate significant change in rain microphysics between events with lightning and without lightning. The cloud microphysics indicates that the cloud top temperature is lower and cloud effective droplet size is smaller in case of lightning in comparison to non-lightning situation. On the other hand, large drops are abundant while small drops are less in numbers in rain with lightning than rain without lightning. The a value in Z-R relation is found to be significantly higher for rain with lightning than other type. The rain attenuation is also found to have significant variation under these two conditions. The implication of such varied rain microphysics on GPM Ku and Ka band radar are also reported.

A Bayesian Hydrometeor Classification Algorithm for C-Band Polarimetric Radar

A hydrometeor classification algorithm is developed by applying Bayes’ theorem to C-band polarimetric weather radar measurements. The Bayesian hydrometeor classification algorithm (BHCA) includes eight hydrometeor types: hail, rain, graupel, dry snow, wet snow, crystal, biological scatterers (BS) and ground clutter (GC). The conditional likelihood probability distribution functions (PDFs) for each hydrometeor type are constructed with training data from radar observations. The prior PDFs include not only temperature information but also background information about occurrence frequency of hydrometeor types at each altitude, which is incorporated by a hydrometeor classification algorithm for the first time. The BHCA is evaluated by comparing with the Marzano-Bayesian hydrometeor classification algorithm (MBHC) and NCAR fuzzy logic classifier (NFLC). Results show that wet snow is largely missed in MBHC, while crystals are not adequately identified by NFLC. This may be due to the inappropriate conditional likelihood PDFs or membership functions. The prior PDFs in the MBHC may cause unexpected hail due to unreasonable variation above 0 °C. In addition, the prior PDFs of graupel and dry snow in the MBHC appear below −52 °C, which is not realistic. The BHCA proposed in this study overcomes these shortcomings in the prior PDFs and produces an overall reasonable classification product over the Yangtze-Huaihe River Basin (YHRB), Eastern China.

Radar-derived convective storms' climatology for the Prut River basin: 2003–2017

Abstract. Weather radar measurements are used to study the climatology of convective storms and their characteristics in the transboundary Prut River basin. The Storm Cell Identification and Tracking (SCIT) algorithm was used to process the volumetric reflectivity measurements, in order to identify, characterize, and track the convective storm cells. The storm attribute table output of the algorithm was used to separate the convective from the stratiform storm cells, by applying a simple selection criterion based on the average vertically integrated liquid (VIL) values. The radar-derived characteristics of convective storms were used to document the spatial and temporal distributions and storm properties in terms of duration, distance travelled, movement direction, and intensity. The results show that 94.3 % of all convective storm cells were detected during May–August, with the peak in July. The peak time for convective storm cells' occurrence was in the afternoon and evening hours between 10:00 and 18:00 UTC. The median duration of a convective storm was 42 min, the median distance travelled was 23 km, and the median movement speed was 7.7 m s−1. The average movement of storms varied with months, but overall most convective storms move from the south-west and south–south-east. Also, the analysis shows that the longer-lasting convective storms were the most intense. The spatial distribution of the convective cells reveals yearly variation patterns and hotspots but also highlights the limitations of radar measurement at longer distances. Reanalysis data suggest that low values of sea level pressure over the Black Sea can act as a dynamical driver of convective storms in the analysed area.

A Unified Formulation of Polarimetric Weather Radar With Application to IQ Data Simulation

The meteorological parameters derived from polarimetric weather radar measurements are of interest for nowcasting and to feed numerical models for forecasting. The accuracy of the derived meteorological parameters is affected by the polarimetric measurement scheme considered as well as by the system hardware (e.g., the antenna radiation patterns or receive channels imbalances). However, determining the accuracy of the estimated meteorological parameters is difficult since no ground truth is available. This fact has motivated the development of weather radar data simulators that allow comparison of the assumed weather parameters used to feed the system and the finally estimated weather parameters. In this paper, the weather radar model is reviewed. A unified formulation applicable to all polarimetric measurement systems is developed. This formulation shows that antenna effects can be decoupled from scattering effects. Based on this formulation, a weather radar simulator is proposed. This radar simulator does not require the fine sampling in the elevation and azimuth directions required by previous simulators. Therefore, the computational load and execution times are significantly reduced. Finally, it is important to point out that the radar simulator proposed provides as much fidelity to the actual radar system as previous simulators.

Estimating rainfall using machine learning strategies based on weather radar data

SummaryThe precision of forecasting rainfall is vital owing to current world climate change. As deterministic weather forecasting models are usually time consuming, it becomes challenging to efficiently use this large volume of data in hand. Machine learning methods are already proven to be good replacement for traditional deterministic approaches in weather prediction. This paper presents an approach using recurrent neural networks (RNN) and long short term memory (LSTM) techniques to improve the rainfall forecast performance. This will be compared with the random forest classifier and XGBoost as well. The goal is to predict a set of hourly rainfall levels from sequences of weather radar measurements. Python libraries are utilized to forecast the time series data. The training set comprises of data from first 20 days of every month and the inference set data from the continuing days. This makes sure that both train and inference sets are more or less independent. The idea resides in implementing an end‐to‐end learning framework.

Use of polarimetric radar measurements to constrain simulated convective cell evolution: a pilot study with Lagrangian tracking

Abstract. To probe the potential value of a radar-driven field campaign to constrain simulation of isolated convection subject to a strong aerosol perturbation, convective cells observed by the operational KHGX weather radar in the vicinity of Houston, Texas, are examined individually and statistically. Cells observed in a single case study of onshore flow conditions during July 2013 are first examined and compared with cells in a regional model simulation. Observed and simulated cells are objectively identified and tracked from observed or calculated positive specific differential phase (KDP) above the melting level, which is related to the presence of supercooled liquid water. Several observed and simulated cells are subjectively selected for further examination. Below the melting level, we compare sequential cross sections of retrieved and simulated raindrop size distribution parameters. Above the melting level, we examine time series of KDP and radar differential reflectivity (ZDR) statistics from observations and calculated from simulated supercooled rain properties, alongside simulated vertical wind and supercooled rain mixing ratio statistics. Results indicate that the operational weather radar measurements offer multiple constraints on the properties of simulated convective cells, with substantial value added from derived KDP and retrieved rain properties. The value of collocated three-dimensional lightning mapping array measurements, which are relatively rare in the continental US, supports the choice of Houston as a suitable location for future field studies to improve the simulation and understanding of convective updraft physics. However, rapid evolution of cells between routine volume scans motivates consideration of adaptive scan strategies or radar imaging technologies to amend operational weather radar capabilities. A 3-year climatology of isolated cell tracks, prepared using a more efficient algorithm, yields additional relevant information. Isolated cells are found within the KHGX domain on roughly 40 % of days year-round, with greatest concentration in the northwest quadrant, but roughly 5-fold more cells occur during June through September. During this enhanced occurrence period, the cells initiate following a strong diurnal cycle that peaks in the early afternoon, typically follow a south-to-north flow, and dissipate within 1 h, consistent with the case study examples. Statistics indicate that ∼ 150 isolated cells initiate and dissipate within 70 km of the KHGX radar during the enhanced occurrence period annually, and roughly 10 times as many within 200 km, suitable for multi-instrument Lagrangian observation strategies. In addition to ancillary meteorological and aerosol measurements, robust vertical wind speed retrievals would add substantial value to a radar-driven field campaign.