278 research outputs found
Refractivity and refractivity gradient estimation from radar phase data: a least squares based approach
Tropospheric refractivity, related to temperature, pressure, and humidity, is an interesting parameter for weather analysis, prediction, and study of climate trends. It has been shown to be useful for the detection and forecast of convective events. It has already been demonstrated that tropospheric refractivity can be estimated from radar phase measurements. In this article, a nonlinear least squares based approach for the estimation of the tropospheric refractivity that simultaneously provides the estimates of the refractivity vertical gradient is presented. A significant improvement of the presented technique is that it allows estimation of the refractivity over any terrain orography, flat, or hilly. Furthermore, the method developed can be implemented on klystron as well as on magnetron-based radars. Results for both radar types, at S- and C-bands, located over flat and hilly terrain show the potential of the method.European Climate, Infrastructure and Environment Executive Agency | Ref. Life16 Env/ES/000559Xunta de Galicia | Ref. GRC2019/02
Quantifying errors due to frequency changes and target location uncertainty for radar refractivity retrievals
Radar refractivity retrievals can capture near-surface humidity changes, but noisy phase changes of the ground clutter returns limit the accuracy for both klystron- and magnetron-based systems. Observations with a C-band (5.6 cm) magnetron weather radar indicate that the correction for phase changes introduced by local oscillator frequency changes leads to refractivity errors no larger than 0.25 N units: equivalent to a relative humidity change of only 0.25% at 20°C. Requested stable local oscillator (STALO) frequency changes were accurate to 0.002 ppm based on laboratory measurements. More serious are the random phase change errors introduced when targets are not at the range-gate center and there are changes in the transmitter frequency (ÎfTx) or the refractivity (ÎN). Observations at C band with a 2-ÎŒs pulse show an additional 66° of phase change noise for a ÎfTx of 190 kHz (34 ppm); this allows the effect due to ÎN to be predicted. Even at S band with klystron transmitters, significant phase change noise should occur when a large ÎN develops relative to the reference period [e.g., ~55° when ÎN = 60 for the Next Generation Weather Radar (NEXRAD) radars]. At shorter wavelengths (e.g., C and X band) and with magnetron transmitters in particular, refractivity retrievals relative to an earlier reference period are even more difficult, and operational retrievals may be restricted to changes over shorter (e.g., hourly) periods of time. Target location errors can be reduced by using a shorter pulse or identified by a new technique making alternate measurements at two closely spaced frequencies, which could even be achieved with a dualâpulse repetition frequency (PRF) operation of a magnetron transmitter
High temporal resolution refractivity retrieval from radar phase measurements
Knowledge of the spatial and temporal variability of near-surface water vapor is of great importance to successfully model reliable radio communications systems and forecast atmospheric phenomena such as convective initiation and boundary layer processes. However, most current methods to measure atmospheric moisture variations hardly provide the temporal and spatial resolutions required for detection of such atmospheric processes. Recently, considering the high correlation between refractivity variations and water vapor pressure variations at warm temperatures, and the good temporal and spatial resolution that weather radars provide, the measurement of the refractivity with radar became of interest. Firstly, it was proposed to estimate refractivity variations from radar phase measurements of ground-based stationary targets returns. For that, it was considered that the backscattering from ground targets is stationary and the vertical gradient of the refractivity could be neglected. Initial experiments showed good results over flat terrain when the radar and target heights are similar. However, the need to consider the non-zero vertical gradient of the refractivity over hilly terrain is clear. Up to date, the methods proposed consider previous estimation of the refractivity gradient in order to correct the measured phases before the refractivity estimation. In this paper, joint estimation of the refractivity variations at the radar height and the refractivity vertical gradient variations using scan-to-scan phase measurement variations is proposed. To reduce the noisiness of the estimates, a least squares method is used. Importantly, to apply this algorithm, it is not necessary to modify the radar scanning mode. For the purpose of this study, radar data obtained during the Refractivity Experiment for H2O Research and Collaborative Operational Technology Transfer (REFRACTT_2006), held in northeastern Colorado (USA), are used. The refractivity estimates obtained show a good performance of the algorithm proposed compared to the refractivity derived from two automatic weather stations located close to the radar, demonstrating the possibility of radar based refractivity estimation in hilly terrain and non-homogeneous atmosphere with high spatial resolution.Ministerio de EconomĂa y Competitividad | Ref. TEC2014-55735-C3-3-RXunta de Galicia | Ref. GRC2015/01
Improvements in forecasting intense rainfall: results from the FRANC (forecasting rainfall exploiting new data assimilation techniques and novel observations of convection) project
The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the projectâs achievements. We highlight new radar techniques: correcting for attenuation of the radar return; correction for beams that are over 90% blocked by trees or towers close to the radar; and direct assimilation of radar reflectivity and refractivity. We discuss the treatment of uncertainty in data assimilation: new methods for estimation of observation uncertainties with novel applications to Doppler radar winds, Atmospheric Motion Vectors, and satellite radiances; a new algorithm for implementation of spatially-correlated observation error statistics in operational data assimilation; and innovative treatment of moist processes in the background error covariance model. We present results indicating a link between the spatial predictability of convection and convective regimes, with potential to allow improved forecast interpretation. The research was carried out as a partnership between University researchers and the Met Office (UK). We discuss the benefits of this approach and the impact of our research, which has helped to improve operational forecasts for convective rainfall event
On the Implementation of a regional X-bandweather radar network
In the last few years, the number of worldwide operational X-band weather radars has rapidly been growing, thanks to an established technology that offers reliability, high performance, and reduced efforts and costs for installation and maintenance, with respect to the more widespread C- and S-band systems. X-band radars are particularly suitable for nowcasting activities, as those operated by the LaMMA (Laboratory of Monitoring and Environmental Modelling for the sustainable development) Consortium in the framework of its institutional duties of operational meteorological surveillance. In fact, they have the capability to monitor precipitation, resolving very local scales, with good spatial and temporal details, although with a reduced scanning range. The Consortium has recently installed a small network of X-band weather radars that partially overlaps and completes the existing national radar network over the north Tyrrhenian area. This paper describes the implementation of this regional network, detailing the aspects related with the radar signal processing chain that provides the final reflectivity composite, starting from the acquisition of the signal power data. The network performances are then qualitatively assessed for three case studies characterised by different precipitation regimes and different seasons. Results are satisfactory especially during intense precipitations, particularly regarding what concerns their spatial and temporal characterisation
Using The New Dual-polarimetric Capability Of WSR-88D To Eliminate Anomalous Propagation And Wind Turbine Effects In Radar-rainfall
This study addresses the effect that the interaction between anomalous radar beam propagation (AP) and wind turbines that are located far from the radar has on radar-rainfall estimates. The interference of wind turbines in radar observations may lead to significant errors in rainfall estimates since wind turbines are often clustered to form wind farms. In this study, we propose a novel approach - based on the polarimetric capability recently added to the WSR-88D NEXRAD radars - that identifies and eliminates wind turbine clutter along with common ground clutter AP effects. Our primary objective is to devise a physically meaningful and fully automated dual-polarimetric method that effectively handles clutter features, which are hard to detect using single-channel reflectivity data alone. To address this issue, we explore the feasibility of using polarimetric variables such as differential reflectivity (ZDR), copolar correlation (RHO), and differential phase (PHIDP). Accordingly, we developed three new approaches using polarimetric variables, which are combined with the AP detection algorithm that uses a three-dimensional structure of reflectivity. We evaluate the new algorithms in terms of both eliminating non-meteorological radar returns and preserving returns from actual rain. The proposed algorithm, which uses RHO conditioned on horizontal reflectivity values while also accounting for the variation of ZDR or PHIDP, shows good performance for the presented cases
Application of Compressive Sensing to Weather Radars
The capability and importance of weather radar are proven for hazardous weathers detection, monitoring, and prediction in both research and operations. Continuous efforts have been made in improving radar performance in terms of spatial and temporal resolutions, data quality, new capabilities, etc. On the other hand, compressive sensing (CS) theory has been developed for solving underdetermined problems using l1-norm minimization. It has been shown that CS is capable of reconstructing the sparse images from a limited number of measurements. In this work, CS is specifically applied to two weather radar problems of (1) refractivity retrieval using a network of radars, and (2) retrieving reflectivity and velocity from an imaging radar.
In the first study, CS is proposed to improve the refractivity retrieval since the performance of a conventional constraint least squares method can be degraded significantly by the measurement noise and the limited number of high-quality ground returns. The application of CS to refractivity retrieval is formulated using a linear model and subsequently the feasibility is demonstrated and verified using simulations. In the second study, the problem of digital beamforming (DBF) is posed as an inverse problem and formulated using a linear model for both reflectivity and velocity estimation for CS. The application of CS is investigated using both simulation and real data. In simulations, the performance of CS is quantified and compared to the traditional Fourier beamforming and high resolution Capon beamforming for various conditions. The feasibility of CS to weather observations is further demonstrated using the data collected by the Atmospheric Imaging Radar (AIR), developed at the Advanced Radar Research Center (ARRC) of the University of Oklahoma, on 15 April 2012
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