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A novel way to process polarimetric radar data collected via plan position indicator (PPI) scans and display those data in a time–height format is introduced. The columnar vertical profile (CVP) methodology uses radar data collected via multiple elevation scans, limited to data within a set region in range and azimuth relative to the radar, to create vertical profiles of polarimetric radar data representative of that limited region in space. This technique is compared to others existing in the literature, and various applications are discussed. Polarimetric ice microphysical retrievals are performed on CVPs created within the stratiform rain region of two mesoscale convective systems sampled during two field campaigns, where CVPs follow the track of research aircraft. Aircraft in situ data are collocated to microphysical retrieval data, and the accuracy of these retrievals is tested against other retrieval techniques in the literature.

In this study, offshore wind conditions in coastal areas of Japan measured by single scanning Doppler lidar is investigated. The effects of measurement range as well as rainfall and snowfall on data availability of the Doppler scanning lidar are examined. Data filtering criteria are then proposed and verified by data thinning to meet both accuracy and post-processed data availability requirements for offshore wind measurements at multiple altitudes. Finally, one-year offshore wind measurement with three different elevation angles is conducted using a single Doppler scanning lidar to investigate wind conditions in the coastal region of Northern Japan. It is found that the accuracy of 15-second average wind speed measurements by PPI (Plan Position Indicator) scan depends on the sector size. An accurate 10-minute mean wind is measured when the sector size is larger than 39 degrees and proportion of valid data acquisition is more than 10% of the time duration. The vertical and horizontal distributions of offshore wind speed in different wind directions are also analyzed and the effects of onshore topography on offshore wind conditions are clarified.

A variational retrieval of rain microphysics from polarimetric radar data (PRD) has been developed through the use of S-band parameterized polarimetric observation operators. Polarimetric observations allow for the optimal retrieval of cloud and precipitation microphysics for weather quantification and data assimilation for convective-scale numerical weather prediction (NWP) by linking PRD to physical parameters. Rain polarimetric observation operators for reflectivity Z H , differential reflectivity Z DR , and specific differential phase K DP were derived for S-band PRD using T-matrix scattering amplitudes. These observation operators link the PRD to the physical parameters of water content W and mass-/volume-weighted diameter D m for rain, which can be used to calculate other microphysical information. The S-band observation operators were tested using a 1D variational retrieval that uses the (nonlinear) Gauss–Newton method to iteratively minimize the cost function to find an optimal estimate of D m and W separately for each azimuth of radar data, which can be applied to a plan position indicator (PPI) radar scan (i.e., a single elevation). Experiments on two-dimensional video disdrometer (2DVD) data demonstrated the advantages of including Φ DP observations and using the nonlinear solution rather than the (linear) optimal interpolation (OI) solution. PRD collected by the Norman, Oklahoma (KOUN) WSR-88D on 15 June 2011 were used to successfully test the retrieval method on radar data. The successful variational retrieval from the 2DVD and the radar data demonstrate the utility of the proposed method.

The monitoring and tracking of urban air pollution is a challenging environmental issue. The approach of synchronous 3-D detection of wind and pollution using a solo coherent Doppler wind lidar (CDWL) is developed and demonstrated. The 3-D distribution of pollutant is depicted by the backscatter coefficient based on signal intensity of CDWL. Then, a high-resolution wind field is derived to track the local air pollution source with its diffusion and to analyze transboundary air pollution episodes. The approach is experimentally implemented in a chemical industry park. Smoke plumes caused by point source pollutions are captured well using plan position indicator (PPI) scanning with low elevation. A typical source of pollution is located, combining the trajectory of the smoke plume and the horizontal wind vector. In addition, transboundary air pollution caused by the transport of dust storms is detected in a vertical profile scanning pattern, which is consistent with the results of national monitoring stations and backward trajectory models. Our present work provides a significant 3-D detection approach to air pollution monitoring with its sources, paths, and heights by using a solo-CDWL system.

As a still novel wind measurement technology, a dual-Doppler X-band wind radar system has been a substantial element of the full-scale onshore campaign AWAKEN (The American WAKE experimeNt). In order to select suitable further applications in the future and, particularly, the most optimal use cases in the wind industry for this technology, a line-of-sight wind speed verification campaign using a co-located scanning lidar as reference was set up as part of the AWAKEN campaign. The wind radar scanned in azimuth sector or plan position indicator (PPI) mode, with multiple elevations (volumetric PPI scan), while the scanning lidar remained fixed in a specific position during the verification campaign. Considering the individual spatial and temporal resolutions of the two systems, the closest points from the radar scanned volumes were compared with measurements from the scanning lidar after threshold-based and statistical data quality control. For a linear regression with 30-minute resolution data collected at 2 km range, a coefficient of determination of R 2 = 0.99 was found. Radar mean values, binned according to reference wind speed, do lie in part within the reference uncertainty but not consistently for the investigated range of line-of-sight wind speeds. Part of the reference uncertainty is hereby also associated with the procedure of comparison but kept as low as possible by optimizing the verification setup and procedure.

Windshear is a microscale meteorological phenomenon that can be dangerous to aircraft during the take-off and landing phases. Accurate windshear detection plays a significant role in air traffic control. In this paper, we aim to investigate a machine learning method for windshear detection based on previously collected wind velocity data and windshear records. Generally, the occurrence of windshear events are reported by pilots. However, due to the discontinuity of flight schedules, there are presumably many unreported windshear events when there is no flight, making it difficult to ensure that all the unreported events are all non-windshear events. Hence, one of the key issues for machine-learning-based windshear detection is determining how to correctly distinguish windshear cases from the unreported events. To address this issue, we propose to use a positive and unlabeled learning method in this paper to identify windshear events from unreported cases based on wind velocity data collected by Doppler light detection and ranging (LiDAR) plan position indicator (PPI) scans. An optimal-transport-based optimization model is proposed to distinguish whether a windshear event appears in a sample constructed by several LiDAR PPI scans. Then, a binary classifier is trained to determine whether a sample represents windshear. Numerical experiments based on the observational wind velocity data collected at the Hong Kong International Airport show that the proposed scheme can properly recognize potential windshear cases (windshear cases without pilot reports) and greatly improve windshear detection and prediction accuracy.

Lidar-based wind sensing technology, originally used in the wind energy sector, is now being utilized in wind engineering to monitor wind action for designing fjord-crossing infrastructure like long-span bridges. Accurate estimation of design wind loads is crucial for the design process of such bridges. This paper examines wind data from two pairs of long-range lidars positioned along one side of the Sulafjorden, Norway, in a measurement campaign initiated by the Norwegian Public Roads Administration (NPRA). Two different scanning modes, the Plan Position Indicator (PPI) mode and the staring mode, with the fixed line-of-sight (LOS) orientation, are used to determine the line-of-sight wind speeds, the wind direction, and the related along wind speed. An image processing approach is used to compute along wind velocity information from all range gates based on the LOS wind speeds from two near-parallel lidar beams. The estimated along wind velocity is validated through wind data calculated at the intersection point of the two lidars, which provides velocity data with two horizontal components. The image-based reconstruction is found to produce reasonable wind speed estimates with a goodness-of-fit coefficient of R 2 = 0.903. The mean wind direction estimate is smoothened but still comparable to the actual wind direction. The image processing approach shows promising potential to provide wind speed and direction information for all range gates for the lidar setup used, which can supplement traditional single-point wind velocity characterization by dual lidars.

Accurate wind profile measurements are important for applications ranging from aviation to numerical weather prediction. The spatial pattern of winds can be obtained with ground-based remote sensing instruments, such as weather radars and Doppler lidars. As the return signal in weather radars is mostly due to hydrometeors or insects, and in Doppler lidars due to aerosols, the instruments provide wind measurements in different weather conditions. However, the effect of various weather conditions on the measurement capabilities of these instruments has not been previously extensively quantified. Here we present results from a 7-month measurement campaign that took place in Vantaa, Finland, where a co-located Vaisala WRS400 X-band weather radar and WindCube 400S Doppler lidar were employed continuously to perform wind measurements. Both instruments measured plan position indicator (PPI) scans at 2.0∘ elevation from the horizontal. Direct comparison of radial Doppler velocities from both instruments showed good agreement with R2=0.96. We then examined the effect of horizontal visibility, cloud base height, and precipitation intensity on the measurement availability of each instrument. The Doppler lidar displayed good availability in clear air situations and the X-band radar in precipitation. Both instruments exhibited high availability in clear air conditions in summer when insects were present. The complementary performance in the measurement availability of the two instruments means that their combination substantially increases the spatial coverage of wind observations across a wide range of weather conditions.

Lightning is an important cause of casualties, and of the interruption of power supply and distribution facilities. Monitoring lightning locations is essential in disaster prevention and mitigation. Although there are many ways to obtain lightning information, there are still substantial problems in intelligent lightning monitoring. Deep learning combined with weather radar data and land attribute data can lay the foundation for future monitoring of lightning locations. Therefore, based on the residual network, the Lightning Monitoring Residual Network (LM-ResNet) is proposed in this paper to monitor lightning location. Furthermore, comparisons with GoogLeNet and DenseNet were also conducted to evaluate the proposed model. The results show that the LM-ResNet model has significant potential in monitoring lightning locations. In this study, we converted the lightning monitoring problem into a binary classification problem and then obtained weather radar product data (including the plan position indicator (PPI), composite reflectance (CR), echo top (ET), vertical integral liquid water (VIL), and average radial velocity (V)) and land attribute data (including aspect, slope, land use, and NDVI) to establish a lightning feature dataset. During model training, the focal loss function was adopted as a loss function to address the constructed imbalanced lightning feature dataset. Moreover, we conducted stepwise sensitivity analysis and single factor sensitivity analysis. The results of stepwise sensitivity analysis show that the best performance can be achieved using all the data, followed by the combination of PPI, CR, ET, and VIL. The single factor sensitivity analysis results show that the ET radar product data are very important for the monitoring of lightning locations, and the NDVI land attribute data also make significant contributions.

Convolutional neural networks (CNNs) have been utilized extensively to improve the resolution of weather radar. Most existing CNN-based super-resolution algorithms using PPI (Plan position indicator, which provides a maplike presentation in polar coordinates of range and angle) images plotted by radar data lead to the loss of some valid information by using image processing methods for super-resolution reconstruction. To solve this problem, a weather radar that echoes the super-resolution reconstruction algorithm—based on residual attention back-projection network (RABPN)—is proposed to improve the the radar base data resolution. RABPN consists of multiple Residual Attention Groups (RAGs) connected with long skip connections to form a deep network; each RAG is composed of some residual attention blocks (RABs) connected with short skip connections. The residual attention block mined the mutual relationship between low-resolution radar echoes and high-resolution radar echoes by adding a channel attention mechanism to the deep back-projection network (DBPN). Experimental results demonstrate that RABPN outperforms the algorithms compared in this paper in visual evaluation aspects and quantitative analysis, allowing a more refined radar echo structure, especially in terms of echo details and edge structure features.