Showing papers on "Weather radar published in 2022"

Convective Precipitation Nowcasting Using U-Net Model

Abstract: Convective precipitation nowcasting remains challenging due to the fast change in convective weather. Radar images are the most important data source in nowcasting research area. This study proposes a radar data-based U-Net model for precipitation nowcasting. The nowcasting problem is first transformed into an image-to-image translation problem in deep learning under the U-Net architecture, which is based on convolutional neural networks (CNNs). The input of the model is five consecutive radar images; the output is the predicted radar reflectivity image. The model consists of three operations: upsampling, downsampling, and skip connection. Three methods, U-Net, TREC, and TrajGRU, are used for comparison in the experiments. The experimental results show that both deep learning methods outperform the TREC method, and the CNN-based U-Net can achieve almost the same performance as TrajGRU which is a recurrent neural network (RNN)-based model. With the advantages that U-Net is simple, efficient, easy to understand, and customize, this result shows the great potential of CNN-based models in addressing time-series applications.

Weather Radar in Complex Orography

Abstract: Applications of weather radar data to complex orography are manifold, as are the problems. The difficulties start with the choice of suitable locations for the radar sites and their construction, which often involves long transport routes and harsh weather conditions. The next challenge is the 24/7 operation and maintenance of the remote, unmanned mountain stations, with high demands on the availability and stability of the hardware. The data processing and product generation also require solutions that have been specifically designed and optimised in a mountainous region. The reflection and shielding of the beam by the mountains, in particular, pose great challenges. This review article discusses the main problems and sources of error and presents solutions for the application of weather radar technology in complex orography. The review is focused on operational radars and practical applications, such as nowcasting and the automatic warning of thunderstorms, heavy rainfall, hail, flash floods and debris flows. The presented material is based, to a great extent, on experience collected by the authors in the Swiss Alps. The results show that, in spite of the major difficulties that emerge in mountainous regions, weather radar data have an important value for many practical quantitative applications.

RainPredRNN: A New Approach for Precipitation Nowcasting with Weather Radar Echo Images Based on Deep Learning

Abstract: Precipitation nowcasting is one of the main tasks of weather forecasting that aims to predict rainfall events accurately, even in low-rainfall regions. It has been observed that few studies have been devoted to predicting future radar echo images in a reasonable time using the deep learning approach. In this paper, we propose a novel approach, RainPredRNN, which is the combination of the UNet segmentation model and the PredRNN_v2 deep learning model for precipitation nowcasting with weather radar echo images. By leveraging the abilities of the contracting-expansive path of the UNet model, the number of calculated operations of the RainPredRNN model is significantly reduced. This result consequently offers the benefit of reducing the processing time of the overall model while maintaining reasonable errors in the predicted images. In order to validate the proposed model, we performed experiments on real reflectivity fields collected from the Phadin weather radar station, located at Dien Bien province in Vietnam. Some credible quality metrics, such as the mean absolute error (MAE), the structural similarity index measure (SSIM), and the critical success index (CSI), were used for analyzing the performance of the model. It has been certified that the proposed model has produced improved performance, about 0.43, 0.95, and 0.94 of MAE, SSIM, and CSI, respectively, with only 30% of training time compared to the other methods.

Rainfall estimation by weather radar

Abstract: Knowledge of the spatial and temporal variability of rainfall over a wide range of scales is required in a variety of disciplines. Examples range from hydrology, soil erosion, and cloud and precipitation physics to the design and operation of water management, telecommunication, and atmospheric remote sensing systems. As a result of the gradual development of radar technology over the past 70 years, ground-based weather radar represents now an established tool for quantitative rainfall measurement at a space-time resolution of typically 1 km 2 and 5 min, over a wide range of spatial and temporal scales. This chapter provides an outline of the principles of precipitation estimation by means of weather radar, with coverage of the main techniques for weather radar observation and of the methods used to generate rainfall products starting from weather radar observables.

Analysis and numerical simulation of a supercell tornado at the Hong Kong adjacent waters

Abstract: A tornado/waterspout associated with a supercell thunderstorm was observed in the adjacent waters of southern China on the evening of 18 April 2019. The case was documented using surface weather observations and Doppler radar data. A weather station near the tornado/waterspout, recorded wind gusts corresponding to hurricane intensity for a few seconds, never observed before in the region. The Doppler velocity associated with the tornado/waterspout was analysed and the vertical profile of the velocity could be very useful for wind engineering applications in the region. Dual‐polarization observations also were analysed and exhibited some similar signatures documented in supercell tornadoes in other parts of the world. Predictability of the tornado/waterspout was studied using a numerical weather prediction model. It showed that the location and timing of the waterspout/tornado could be roughly reproduced, and the model might be used to give earlier alerting to the vessels operating in the south China coastal waters.

Calibration of D3R Weather Radar Using UAV-Hosted Target

Abstract: The accuracy of weather radar-measured products, such as reflectivity, plays a crucial factor in obtaining good quality, derived remote sensing products, such as hydrometeor classification. A slight mismatch of a few decibels in reflectivity may cause the hydrometeor classification to deviate from the actual truth. In order to obtain accurate remote-sensing measurements, calibration of weather radars should be carried out at regular intervals of time. The dual-frequency, dual-polarization, Doppler radar (D3R) is a well-established tool for measuring light rain and snow. There are various methods which are used for the calibration of weather radars, such as suspending a metallic sphere from a weather balloon or using corner reflectors on top of a tower or structure. In this work, we have shown the potential of using a UAV to suspend a calibration sphere, which is then used for calibrating the D3R radar. In this work, the advantages along with the practical aspects to be considered for calibration using UAV are discussed in detail. From the calibration results, it was observed that an offset of 2.2 dB was present in the Ku H-Pol reflectivity, Ku V-Pol was well calibrated and offsets within 2 dB were observed in the Ka H-Pol and V-Pol reflectivities.

Retrievals of ice microphysical properties using dual-wavelength polarimetric radar observations during stratiform precipitation events

Abstract: Abstract. Ice growth processes within clouds affect the type and amount of precipitation. Hence, the importance of an accurate representation of ice microphysics in numerical weather and numerical climate models has been confirmed by several studies. To better constrain ice processes in models, we need to study ice cloud regions before and during monitored precipitation events. For this purpose, two radar instruments facing each other were used to collect complementary measurements. The C-band POLDIRAD weather radar from the German Aerospace Center (DLR) in Oberpfaffenhofen and the Ka-band MIRA-35 cloud radar from the Ludwig Maximilians University of Munich (LMU) were used to monitor stratiform precipitation in the vertical cross-sectional area between the two instruments. The logarithmic difference of radar reflectivities at two different wavelengths (54.5 and 8.5 mm), known as the dual-wavelength ratio, was exploited to provide information about the size of the detected ice hydrometeors, taking advantage of the different scattering behavior in the Rayleigh and Mie regime. Along with the dual-wavelength ratio, differential radar reflectivity measurements from POLDIRAD provided information about the apparent shape of the detected ice hydrometeors. Scattering simulations using the T-matrix method were performed for oblate and horizontally aligned prolate ice spheroids of varying shape and size using a realistic particle size distribution and a well-established mass–size relationship. The combination of dual-wavelength ratio, radar reflectivity, and differential radar reflectivity measurements as well as scattering simulations was used for the development of a novel retrieval for ice cloud microphysics. The development of the retrieval scheme also comprised a method to estimate the hydrometeor attenuation in both radar bands. To demonstrate this approach, a feasibility study was conducted on three stratiform snow events which were monitored over Munich in January 2019. The ice retrieval can provide ice particle shape, size, and mass information which is in line with differential radar reflectivity, dual-wavelength ratio, and radar reflectivity observations, respectively, when the ice spheroids are assumed to be oblates and to follow the mass–size relation of aggregates. When combining two spatially separated radars to retrieve ice microphysics, the beam width mismatch can locally lead to significant uncertainties. However, the calibration uncertainty is found to cause the largest bias for the averaged retrieved size and mass. Moreover, the shape assumption is found to be equally important to the calibration uncertainty for the retrieved size, while it is less important than the calibration uncertainty for the retrieval of ice mass. A further finding is the importance of the differential radar reflectivity for the particle size retrieval directly above the MIRA-35 cloud radar. Especially for that observation geometry, the simultaneous slantwise observation from the polarimetric weather radar POLDIRAD can reduce ambiguities in retrieval of the ice particle size by constraining the ice particle shape.

NeXtNow: A Convolutional Deep Learning Model for the Prediction of Weather Radar Data for Nowcasting Purposes

Abstract: With the recent increase in the occurrence of severe weather phenomena, the development of accurate weather nowcasting is of paramount importance. Among the computational methods that are used to predict the evolution of weather, deep learning techniques offer a particularly appealing solution due to their capability for learning patterns from large amounts of data and their fast inference times. In this paper, we propose a convolutional network for weather forecasting that is based on radar product prediction. Our model (NeXtNow) adapts the ResNeXt architecture that has been proposed in the computer vision literature to solve the spatiotemporal prediction problem. NeXtNow consists of an encoder–decoder convolutional architecture, which maps radar measurements from the past onto radar measurements that are recorded in the future. The ResNeXt architecture was chosen as the basis for our network due to its flexibility, which allows for the design of models that can be customized for specific tasks by stacking multiple blocks of the same type. We validated our approach using radar data that were collected from the Romanian National Meteorological Administration (NMA) and the Norwegian Meteorological Institute (MET) and we empirically showed that the inclusion of multiple past radar measurements led to more accurate predictions further in the future. We also showed that NeXtNow could outperform XNow, which is a convolutional architecture that has previously been proposed for short-term radar data prediction and has a performance that is comparable to those of other similar approaches in the nowcasting literature. Compared to XNow, NeXtNow provided improvements to the critical success index that ranged from 1% to 17% and improvements to the root mean square error that ranged from 5% to 6%.

Dual-Polarization Radar Fingerprints of Precipitation Physics: A Review

Abstract: This article reviews how precipitation microphysics processes are observed in dual-polarization radar observations. These so-called “fingerprints” of precipitation processes are observed as vertical gradients in radar observables. Fingerprints of rain processes are first reviewed, followed by processes involving snow and ice. Then, emerging research is introduced, which includes more quantitative analysis of these dual-polarization radar fingerprints to obtain microphysics model parameters and microphysical process rates. New results based on a detailed rain shaft bin microphysical model are presented, and we conclude with an outlook of potentially fruitful future research directions.

The Use of Weather Radar Data: Possibilities, Challenges and Advanced Applications

Abstract: The climate in recent decades has aroused interest in the scientific community, prompting us to analyse the mechanisms that regulate it, to understand the climate change responsible for an increase in extreme phenomena. Consequently, the increase in hydrogeological instability in the Italian territory has led to an in-depth study of atmospheric parameters to understand the variations of the atmospheric system. One tool capable of detecting such variations is the weather radar. The weather radar data available in the area provided by the National Radar Network of the Department of Civil Protection allow the evaluation of variations on a national scale for hydro-meteorological-climatic monitoring as well as the disasters that have occurred. Using open-source programming software, the servers can be queried and data retrieved from a source to perform processing for specific purposes through data extraction techniques.

Development of the Real‐Time 30‐s‐Update Big Data Assimilation System for Convective Rainfall Prediction With a Phased Array Weather Radar: Description and Preliminary Evaluation

Abstract: We present the first ever real-time numerical weather prediction system with 30-s update cycles at a 500-m grid spacing for the prediction of convective precipitation in the subsequent 30 min using a new-generation multi-parameter phased array weather radar. The system comprises a regional atmospheric model known as the SCALE and the local ensemble transform Kalman filter (LETKF). To accelerate the SCALE-LETKF system, data transfer between the two aforementioned components is performed using a memory copy instead of a file I/O. A complete real-time workflow including domain nesting and observational data transfer is constructed. A real-time test in July and August 2020 showed that the system is fast enough for a real-time application of 30-s forecast-analysis cycles and 30-min prediction. The development includes a new thinning method considering the spatially correlated observation errors in the dense radar data. This new thinning method is effective in two past case studies in the summer of 2019.

An Adaptive Rainfall Estimation Algorithm for Dual-Polarization Radar

Abstract: Dual-polarization radar provides information about precipitation microphysics through drop size distribution and hydrometeor classification, and, therefore, can produce improvement in quantitative precipitation estimation. Rainfall relations combination is an optimization algorithm; however, optimally selecting the rainfall relation is challenging in dual-polarization rainfall estimation. In this study, an adaptive rainfall algorithm is developed using a logistic regression model to guide the choice of the optimal radar rainfall relation. The logistic model is established according to the matched dual-polarization radar data and rain gauge data. Only liquid particles are considered for the rainfall estimation determined by the hydrometeor classification of dual-polarization radar, and the polarimetric rainfall relations are obtained with a neural network algorithm based on the disdrometer data. The proposed algorithm is validated with C-band dual-polarization radar data, and the results show that the adaptive algorithm outperforms the single rainfall relation and conventional combination algorithm.

Tracking Wildfires With Weather Radars

Abstract: There is a need for nowcasting tools to provide timely and accurate updates on the location and rate of spread (ROS) of large wildfires, especially those impacting communities in the wildland urban interface. In this study, we demonstrate how fixed-site weather radars can be used to fill this gap. Specifically, we develop and test a radar-based fire-perimeter tracking tool that leverages the tendency for local maxima in the radar reflectivity to be collocated with active fire perimeters. Reflectivity maxima are located using search radials from points inside a fire polygon, and perimeters are updated at intervals of ∼10 min. The algorithm is tested using publicly available Next Generation Weather Radar radar data for two large and destructive wildfires, the Camp and Bear Fires, both occurring in northern California, USA. The radar-based fire perimeters are compared with available, albeit limited, satellite and airborne infrared observations, showing good agreement with conventional fire-tracking tools. The radar data also provide insights into fire ROS, revealing the importance of long-range spotting in generating ROS that exceeds conventional estimates. One limitation of this study is that high-resolution fire perimeter validation data are sparsely available, precluding detailed error quantification for the radar estimates drawn from samples spanning a range of environmental conditions and radar configurations. Nevertheless, the radar tracking approach provides the basis for improved situational awareness during high-impact fires.

OpenMRG: Open data from Microwave links, Radar, and Gauges for rainfall quantification in Gothenburg, Sweden

Abstract: Abstract. Accurate rainfall monitoring is critical for sustainable societies and yet challenging in many ways. Opportunistic monitoring using commercial microwave links (CMLs) in telecommunication networks is emerging as a powerful complement to conventional gauges and weather radar. However, CML data are often inaccessible or incomplete, which limits research and application. Here, we aim to reduce this barrier by openly sharing data at 10 s resolution with true coordinates from a pilot study involving 364 bi-directional CMLs in Gothenburg, Sweden. To enable further comparative analyses, we also share high-resolution data from 11 precipitation gauges and the Swedish operational weather radar composite in the area. This article presents an overview of the data, including the collection approach, descriptive statistics, and a case study of a high-intensity event. The results show that the data collection was very successful, providing near-complete time series for the CMLs (99.99 %), gauges (100 %), and radar (99.6 %) in the study period (June–August 2015). The bandwidth consumed during CML data collection was small, and hence, the telecommunication traffic was not significantly affected by the collection. The gauge records indicate that total rainfall was approximately 260 mm in the study period, with rainfall occurring in 6 % of each 15 min interval. One of the most intense events was observed on 28 July 2015, during which the Torslanda gauge recorded a peak of 1.1 mm min−1. The variability in the CML data generally followed the gauge dynamics very well. Here we illustrate this for 28 July, where a nearby CML recorded a drop in received signal level of about 27 dB at the time of the peak. The radar data showed a good distribution of reflectivities for mostly stratiform precipitation but also contained some values above 40 dBZ, which is commonly seen as an approximate threshold for convective precipitation. Clutter was also found and was mostly prevalent around low reflectivities of −15 dBZ. The data are accessible at https://doi.org/10.5281/zenodo.7107689 (Andersson et al., 2022). We believe this Open sharing of high-resolution data from Microwave links, Radar, and Gauges (OpenMRG) will facilitate research on microwave-based environmental monitoring using CMLs and support the development of multi-sensor merging algorithms.

Mosaicking Weather Radar Retrievals from an Operational Heterogeneous Network at C and X Band for Precipitation Monitoring in Italian Central Apennines

Abstract: Meteorological radar networks are suited to remotely provide atmospheric precipitation retrieval over a wide geographic area for severe weather monitoring and near-real-time nowcasting. However, blockage due to buildings, hills, and mountains can hamper the potential of an operational weather radar system. The Abruzzo region in central Italy’s Apennines, whose hydro-geological risks are further enhanced by its complex orography, is monitored by a heterogeneous system of three microwave radars at the C and X bands with different features. This work shows a systematic intercomparison of operational radar mosaicking methods, based on bi-dimensional rainfall products and dealing with both C and X bands as well as single- and dual-polarization systems. The considered mosaicking methods can take into account spatial radar-gauge adjustment as well as different spatial combination approaches. A data set of 16 precipitation events during the years 2018–2020 in the central Apennines is collected (with a total number of 32,750 samples) to show the potentials and limitations of the considered operational mosaicking approaches, using a geospatially-interpolated dense network of regional rain gauges as a benchmark. Results show that the radar-network pattern mosaicking, based on the anisotropic radar-gauge adjustment and spatial averaging of composite data, is better than the conventional maximum-value merging approach. The overall analysis confirms that heterogeneous weather radar mosaicking can overcome the issues of single-frequency fixed radars in mountainous areas, guaranteeing a better spatial coverage and a more uniform rainfall estimation accuracy over the area of interest.

Observing System Simulation Experiments of a Rich Phased Array Weather Radar Network Covering Kyushu for the July 2020 Heavy Rainfall Event

Abstract: This study investigates a potential impact of a rich phased array weather radar (PAWR) network covering Kyushu, Japan on numerical weather prediction (NWP) of the historic heavy rainfall event which caused a catastrophic disaster in southern Kumamoto on 4 July 2020. Perfect-model, identical-twin observing system simulation experiments (OSSEs) with 17 PAWRs are performed by the local ensemble transform Kalman filter (LETKF) with a regional NWP model known as the Scalable Computing for Advanced Library and Environment-Regional Model (SCALE-RM) at 1-km resolution. The nature run is generated by running the SCALE-RM initialized by the Japan Meteorological Agency (JMA) mesoscale model (MSM) analysis at 1800 JST 3 July 2020, showing sustained heavy rainfalls in southern Kumamoto on 4 July. Every 30-second synthetic reflectivity and radial winds are generated from the nature run at every model grid point below 20-km elevation within 60-km ranges from the 17 PAWRs. Two different control runs are generated, both failing to predict the heavy rainfalls in southern Kumamoto. In both cases, assimilating the PAWR data improves the heavy rainfall prediction mainly up to 1-hour lead time. The improvement decays gradually and is lost in about 3-hour lead time likely because the large-scale Baiu front dominates. Observing system simulation experiments of a rich phased array weather radar network for the heavy rainfall event. 2017), which consists of the LETKF and a regional NWP model called SCALE-RM standing for the Scalable Com puting for Advanced Library and Environment-Regional Model (Nishizawa et al. 2015). The ensemble-mean 5-day rainfall total forecasts by SCALE-LETKF agreed well with the integration of 1-km-mesh JMA precipitation analysis, whereas the heavy rainfall prediction over Kumamoto and Kagoshima prefectures was underestimated. It is still an open question what would be the benefit of capturing rapidly developing convective phenomena to the predictability of a synoptic-scale linear rain band in the July 2020 event. To address this issue, much more rapidly updated and higher-resolution data assimilation experiments should be performed. For this purpose, the phased array weather radar (PAWR; Yoshikawa et al. 2013; Ushio et al. 2015) is capable to capture rapid development of convective clouds every 30 seconds at roughly 100-m three-dimensional spatial resolution without gap. Miyoshi et al. (2016a, 2016b) developed a revolutionary NWP system with a 30-second-update, 100-m-mesh LETKF using a PAWR at Osaka University. Maejima et al. (2017, 2020) showed the impacts of a 30-second update PAWR data assimilation on a prediction for an isolated convective system. These studies used a single PAWR with an observing range of a 60-km radius and focused on convective-scale data assimilation in the limited area. However, no studies have investigated potential benefits of having a network of PAWRs covering a large area for prediction of an extensive convective activity such as the July 2020 rainfall event associated with the Baiu front. This study aims to investigate a potential impact of a rich

Five years calibrated observations from the University of Bonn X-band weather radar (BoXPol)

Abstract: Polarimetric weather radars offer a wealth of new information compared to conventional technology, not only to enhance quantitative precipitation estimation, warnings, and short-term forecasts, but also to improve our understanding of precipitation generating processes and their representation in numerical weather prediction models. To support such research opportunities, this paper describes an open-access dataset between 2014-2019 collected by the polarimetric Doppler X-band weather radar in Bonn (BoXPol), western Germany. To complement this dataset, the technical radar characteristics, scanning strategy and the best-practice for radar data processing are detailed. In addition, an investigation of radar calibration is presented. Reflectivity measurements from the Dual-frequency Precipitation Radar operating on the core satellite of the Global Precipitation Mission are compared to those of BoXPol to provide absolute calibration offsets with the dataset. The Relative Calibration Adjustment technique is applied to identify stable calibration periods. The absolute calibration of differential reflectivity is determined using the vertical scan and provided with the BoxPol dataset.

A convolutional recurrent neural network for strong convective rainfall nowcasting using weather radar data in Southeastern Brazil

Abstract: Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences. These events have a high spatio-temporal variability, being difficult to predict by standard meteorological numerical models. This work proposes the M5Images method for performing the very short-term prediction (nowcasting) of heavy convective rainfall using weather radar data by means of a convolutional recurrent neural network. The recurrent part of it is a Long Short-Term Memory (LSTM) neural network. Prediction tests were performed for the city and surroundings of Campinas, located in the Southeastern Brazil. The convolutional recurrent neural network was trained using time series of rainfall rate images derived from weather radar data for a selected set of heavy rainfall events. The attained pre-diction performance was better than that given by the persistence forecasting method for different prediction times. • The deep learning algorithm developed considering only severe rainfall events. • The neural network used in this research is a 3D convolutional LSTM. • An adjustment method is developed here and used as correction factor to improve the forecasts. • The region of study is located in Brazil (Campinas city, State of São Paulo), which is characterized by many extreme rainfall events.

Prediction of Arrival Flight Operation Strategies under Convective Weather Based on Trajectory Clustering

Abstract: An airport’s terminal area is the bottleneck of the air transport system. Convective weather can seriously affect the normal flight status of arrival and departure flights. At present, pilots take different flight operation strategies to avoid convective weather based on onboard radar, visual information, adverse weather experience, etc. This paper studies trajectory clustering based on the OPTICS algorithm to obtain the arrival of typical flight routes in the terminal area. Based on weather information of the planned typical flight route and flight plan information, Random Forest (RF), K-nearest Neighbor KNN (KNN), and Support Vector Machines (SVM) algorithms were used for training and establishing the Arrival Flight Operation Strategy Prediction Model (AFOSPM). In this paper, case studies of historical arrival flights in the Guangzhou (ZGGG) and Wuhan (ZHHH) terminal area were carried out. The results show that trajectory clustering results based on the OPTICS algorithm can more accurately reflect the regular flight routes of arrival flights in a terminal area. Compared to KNN and SVM, the prediction accuracy of AFOSPM based on RF is better, reaching more than 88%. On this basis, six features—including 90% VIL, weather coverage, weather duration, planned route, max VIL, and planned Arrival Gate (AF)—were used as the input features for AFOSPM, which can effectively predict various arrival flight operation strategies. For the most frequently used arrival flight operation strategies under convective weather conditions—radar guidance, AF changing, and diversion strategy—the prediction accuracy of the ZGGG and ZHHH terminal areas can exceed 95%, 85%, and 80%, respectively.

Weather Radar Nowcasting for Extreme Precipitation Prediction Based on the Temporal and Spatial Generative Adversarial Network

Abstract: Since strong convective weather is closely related to heavy precipitation, the nowcasting of convective weather, especially the nowcasting based on weather radar data, plays an essential role in meteorological operations for disaster prevention and mitigation. The traditional optical flow method and cross-correlation method have a low forecast accuracy and a short forecast leading time, while deep learning methods show remarkable advantages in nowcasting. However, most of the current forecasting methods based on deep learning suffer from the drawback that the forecast results become increasingly blurred as the forecast time increases. In this study, a weather radar nowcasting method based on the Temporal and Spatial Generative Adversarial Network (TSGAN) is proposed, which can obtain accurate forecast results, especially in terms of spatial details, by extracting spatial-temporal features, combining attention mechanisms and using a dual-scale generator and a multi-scale discriminator. The case studies on the forecast results of strong convective weather demonstrate that the GAN method performs well in terms of forecast accuracy and spatial detail representation compared with traditional optical flow methods and popular deep learning methods. Therefore, the GAN method proposed in this study can provide strong decision support for forecasting heavy precipitation processes. At present, the proposed method has been successfully applied to the actual weather forecasting business system.

A Novel Hybrid Approach for the Identification of Clear Air and Rain Echoes in the 205 MHz ST Radar Spectrum

Abstract: Stratosphere–troposphere (ST) wind profiler radar (WPR) installed at Cochin, India, is a clear air VHF Doppler radar system designed for continuous wind velocity measurements under all-weather conditions. Clear air WPRs detect backscattered signals due to Bragg scattering from small irregularities in the refractive index caused by turbulence. The 205-MHz frequency band is able to detect both clear air and rain echos, simultaneously. Due to the large spectral width and merging of clear and rain echoes during rainy periods, the conventional signal processing techniques become inept at distinguishing the two echoes. An innovative hybrid approach is applied in this study to mitigate the ambiguity in identifying clear air and rain echoes separately. A combination of an exponentially modified Gaussian model and a pure Gaussian model is used to fit the raw spectrum in the hybrid approach adaptively. The data adaptive fitting procedure adopted can identify two distinct peaks during the rainy period, and the fitting process reduces to a single peak during non-rainy periods. Furthermore, the number of peaks is confirmed by locating the number of zero-crossings (NZCs) of the power gradient of the fit data. This unique approach is applied in the 205-MHz ST radar framework for the first time to solve the issue of multiple peaking in the ST radar spectrum.

Evaluation of convective cloud microphysics in numerical weather prediction models with dual-wavelength polarimetric radar observations: methods and examples

Abstract: Abstract. The representation of cloud microphysical processes contributes substantially to the uncertainty of numerical weather simulations. In part, this is owed to some fundamental knowledge gaps in the underlying processes due to the difficulty of observing them directly. On the path to closing these gaps, we present a setup for the systematic characterization of differences between numerical weather model and radar observations for convective weather situations. Radar observations are introduced which provide targeted dual-wavelength and polarimetric measurements of convective clouds with the potential to provide more detailed information about hydrometeor shapes and sizes. A convection-permitting regional weather model setup is established using five different microphysics schemes (double-moment, spectral bin (“Fast Spectral Bin Microphysics”, FSBM), and particle property prediction (P3)). Observations are compared to hindcasts which are created with a polarimetric radar forward simulator for all measurement days. A cell-tracking algorithm applied to radar and model data facilitates comparison on a cell object basis. Statistical comparisons of radar observations and numerical weather model runs are presented on a data set of 30 convection days. In general, simulations show too few weak and small-scale convective cells. Contoured frequency by altitude diagrams of radar signatures reveal deviations between the schemes and observations in ice and liquid phase. Apart from the P3 scheme, high reflectivities in the ice phase are simulated too frequently. Dual-wavelength signatures demonstrate issues of most schemes to correctly represent ice particle size distributions, producing too large or too dense graupel particles. Comparison of polarimetric radar signatures reveals issues of all schemes except the FSBM to correctly represent rain particle size distributions.

Improvement in algorithms for quality control of weather radar data (RADVOL-QC system)

Abstract: Abstract. Data from weather radars are commonly used in meteorology and hydrology, but they are burdened with serious disturbances, especially due to the appearance of numerous non-meteorological echoes. For this reason, these data are subject to advanced quality control algorithms. The paper presents a significant improvement of the RADVOL-QC system made necessary by the appearance of an increasing number of various disturbances. New algorithms are mainly addressed to the occurrence of clutter caused by wind turbines (DP.TURBINE algorithm) and other terrain obstacles (DP.NMET algorithm) as well as various forms of echoes caused by the interaction of a radar beam with RLAN signals (set of SPIKE algorithms). The individual algorithms are based on the employment of polarimetric data as well as on the geometric analysis of echo patterns. In the paper the algorithms are described along with examples of their performance and an assessment of their effectiveness, and finally examples of the performance of the whole system are discussed.

Use of a Reflectivity Operator Based on Two-Moment Thompson Microphysics for Direct Assimilation of Radar Reflectivity in GSI-based Hybrid En3DVar

Abstract: The assimilation of reflectivity (Z) within 3DVar or hybrid ensemble-3DVar (En3DVar) requires the adjoint of the Z observation operator. With the 3DVar or En3DVar method, previous studies often use Z operators consistent with a single-moment microphysics scheme even when the forecast model uses a double-moment scheme. As such, only the mixing ratios of hydrometeors are directly updated by the data assimilation (DA) system, leading to inconsistency between the analyzed microphysics state variables and the microphysics scheme in the prediction model. In this study, we formulated a Z operator consistent with the double-moment Thompson microphysics used in the numerical integrations; in the operator the snow and graupel reflectivity components are simplified using functions fitted to T-matrix simulation results. This operator and its adjoint are implemented within the GSI hybrid En3DVar DA system to enable direct assimilation of Z with a consistent operator. The impacts of this new operator on convective storm analysis through DA cycles, and on the ensuing 3-h forecasts are first examined in detail for a tornado outbreak case of 16 May 2017 in Texas and Oklahoma, and then for five additional thunderstorm cases. Forecast reflectivity, hourly precipitation and updraft helicity tracks are subjectively evaluated, while neighborhood ETSs and performance diagrams are examined for reflectivity and/or precipitation. Compared to experiments using a Z operator consistent with a single-moment microphysics scheme, the Z operator consistent with double-moment Thompson microphysics used in the forecast model produces better forecasts of reflectivity, hourly precipitation and updraft helicity tracks with smaller biases, and the improvement is somewhat larger for a higher Z threshold.

A Network of X-Band Meteorological Radars to Support the Motorway System (Campania Region Meteorological Radar Network Project)

Abstract: The transport sector and road infrastructures are very sensitive to the issues connected to the atmospheric conditions. The latter constitute a source of relevant risk, especially for roads running in mountainous areas, where a wide spectrum of meteorological phenomena, such as rain showers, snow, hail, wind gusts and ice, threatens drivers’ safety. In such contexts, to face out critical situations it is essential to develop a monitoring system that is able to capillary surveil specific sectors or very small basins, providing real time information that may be crucial to preserve lives and assets. In this work, we present the results of the “Campania Region Meteorological Radar Network”, which is focused on the development of X-band radar-based meteorological products that can support highway traffic management and maintenance. The X-band measurements provided by two single-polarization systems, properly integrated with the observations supplied by disdrometers and conventional automatic weather stations, were involved in the following main tasks: (i) the development of a radar composite product; (ii) the devise of a probability of hail index; (iii) the real time discrimination of precipitation type (rain, mixed and snow); (iv) the development of a snowfall rate estimator. The performance of these products was assessed for two case studies, related to a relevant summer hailstorm (which occurred on 1 August 2020) and to a winter precipitation event (which occurred on 13 February 2021). In both cases, the X-band radar-based tools proved to be useful for the stakeholders involved in the management of highway traffic, providing a reliable characterization of precipitation events and of the fast-changing vertical structure of convective cells.

Radar Composite Reflectivity Reconstruction Based on FY-4A Using Deep Learning

Abstract: Weather radars are commonly used to track the development of convective storms due to their high resolution and accuracy. However, the coverage of existing weather radar is very limited, especially in mountainous and ocean areas. Geostationary meteorological satellites can provide near global coverage and near real-time observations, which can compensate for the lack of radar observations. In this paper, a deep learning method was used to estimate the radar composite reflectivity from observations of China’s new-generation geostationary meteorological satellite FY-4A and topographic data. The derived radar reflectivity products from satellite observations can be used over regions without radar coverage. In general, the deep learning model can reproduce the overall position, shape, and intensity of the radar echoes. In addition, evaluation of the reconstruction radar observations indicates that a modified model based on the attention mechanism (Attention U-Net model) has better performance than the traditional U-Net model in terms of all statistics such as the probability of detection (POD), critical success index (CSI), and root-mean-square error (RMSE), and the modified model has stronger capability on reconstructing details and strong echoes.