164 research outputs found

    An inverse method to retrieve 3D radar reflectivity composites

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    Dense radar networks offer the possibility of getting better Quantitative Precipitation Estimates (QPE) than those obtained with individual radars, as they allow increasing the coverage and improving quality of rainfall estimates in overlapping areas. Well-known sources of error such as attenuation by intense rainfall or errors associated with range can be mitigated through radar composites. Many compositing techniques are devoted to operational uses and do not exploit all the information that the network is providing. In this work an inverse method to obtain high-resolution radar reflectivity composites is presented. The method uses a model of radar sampling of the atmosphere that accounts for path attenuation and radar measurement geometry. Two significantly different rainfall situations are used to show detailed results of the proposed inverse method in comparison to other existing methodologies. A quantitative evaluation is carried out in a 12 h-event using two independent sources of information: a radar not involved in the composition process and a raingauge network. The proposed inverse method shows better performance in retrieving high reflectivity values and reproducing variability at convective scales than existing methods.Peer ReviewedPostprint (author's final draft

    Radar multi-sensor (RAMS) quantitative precipitation estimation (QPE)

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    Includes bibliographical references.2015 Summer.Quantitative precipitation estimation (QPE) continues to be one of the principal objectives for weather researchers and forecasters. The ability of radar to measure over broad spatial areas in short temporal successions encourages its application in the pursuit of accurate rainfall estimation, where radar reflectivity-rainfall (Z-R) relations have been traditionally used to derive quantitative precipitation estimation. The purpose of this research is to present the development of a regional dual polarization QPE process known as the RAdar Multi-Sensor QPE (RAMS QPE). This scheme applies the dual polarization radar rain rate estimation algorithms developed at Colorado State University into an adaptable QPE system. The methodologies used to combine individual radar scans, and then merge them into a mosaic are described. The implementation and evaluation is performed over a domain that occurs over a complex terrain environment, such that local radar coverage is compromised by blockage. This area of interest is concentrated around the Pigeon River Basin near Asheville, NC. In this mountainous locale, beam blockage, beam overshooting, orographic enhancement, and the unique climactic conditions complicate the development of reliable QPE's from radar. The QPE precipitation fields evaluated in this analysis will stem from the dual polarization radar data obtained from the local NWS WSR-88DP radars as well as the NASA NPOL research radar

    The Effects of Spatial Interpolation on a Novel, Dual-Doppler 3D Wind Retrieval Technique

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    Three-dimensional wind retrievals from ground-based Doppler radars have played an important role in meteorological research and nowcasting over the past four decades. However, in recent years, the proliferation of open-source software and increased demands from applications such as convective parameterizations in numerical weather prediction models has led to a renewed interest in these analyses. In this study, we analyze how a major, yet often-overlooked, error source effects the quality of retrieved 3D wind fields. Namely, we investigate the effects of spatial interpolation, and show how the common practice of pre-gridding radial velocity data can degrade the accuracy of the results. Alternatively, we show that assimilating radar data directly at their observation locations improves the retrieval of important dynamic features such as the rear flank downdraft and mesocyclone within a simulated supercell, while also reducing errors in vertical vorticity, horizontal divergence, and all three velocity components.Comment: Revised version submitted to JTECH. Includes new section with a real data cas

    Rainfall Nowcasting by Blending of Radar Data and Numerical Weather Prediction

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    In order to improve conventional rainfall nowcasting, radar extrapolation and high-resolution numerical weather prediction (NWP) were blended to get a 6-h quantitative precipitation forecast (QPF) over the Yangtze River Delta region of China. Modifications and calibrations were done to both the extrapolation and NWP in order to get an integrated result from the two, which mainly included the extension for the extrapolation time and region, intensity and position calibration for the NWP, weighted blending of extrapolation and NWP based on scale and time, and a final real-time Z-R relation conversion. Forecast experiments were done, and results show that the blending technique could effectively extend forecast time compared with conventional radar extrapolation, meanwhile applying a positive calibration to the NWP. The overall CSI score of 0–6 h reflectivity forecast was better than either single forecast

    Multiple Radar Data Merging In Hydro-NEXRAD

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    The Hydro-NEXRAD merging algorithms include two options: (1) data-based merging; and (2) product-based merging. Data-based merging algorithm takes volume scan reflectivity data from all radars involved through preprocessing algorithm that performs volume data quality control, interpolates data to synchronize temporal scale between individual radars, and finally combines data onto a common geographic grid. Reflectivity values for a given location are assigned by a weighting function with respect to the distance from the radar. This single reflectivity field is then converted to rainfall amounts using a user-requested standard approach. In product-based merging algorithm reflectivity data from multiple radars are all converted to rainfall using the same, user-specified algorithm. These products are then combined into the final one using a weighting function that expresses the uncertainty of estimated rainfall amounts. © 2008 ASCE

    The potential use of operational radar network data to evaluate the representation of convective storms in NWP models

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    Operational forecasting centres increasingly rely on convection-permitting NWP simulations to assist in their forecasting of convective events. The evaluation of upgrades in the underlying NWP modeling system normally happens through routine verification using traditional metrics on two-dimensional fields, such as gridded rainfall data. Object- and process-based evaluation can identify specific physical mechanisms for model improvement, but such evaluation procedures normally require targeted and expensive field campaigns. Here, we explore the potential use of the UK operational radar network observations and its derived 3D composite product for evaluating the representation of convective storms in the Met Office Unified Model. A comparison of the 1 km x 1 km x 0.5 km 3D radar composites against observations made with the research-grade radar at Chilbolton in the southern UK indicates that the 3D radar composite data can reliably be used to evaluate the morphology of convective storms. The 3D radar composite data are subsequently used to evaluate the development of convective storms in the Met Office Unified Model. Such analysis was heretofore unavailable due to a lack of high-frequency three-dimensional radar data. The operational nature of the UK radar data makes these 3D composites a valuable resource for future studies of the initiation, growth, development, and organisation of convective storms over the UK

    A variational approach to retrieve 3D radar reflectivity composites

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    This study proposes an alternative methodology to obtain high-resolution radar reflectivity composites based on a variational approach considering different error sources in an explicit manner. The methodology retrieves the 3-dimensional precipitation field most compatible with the observations from the different radars of the network. With this aim, the methodology uses a model that simulates the radar sampling of the atmosphere. The model settings are different for each radar and include features such as the radar location, hardware parameters (beam width, pulse length…) and the scan strategy. The methodology follows the concept of an inverse method based on the minimization of a cost function that penalizes discrepancies between the simulated and actual observations for each radar. The simulation model is able to reproduce the effect of beam broadening with the distance and attenuation by intense precipitation. The methodology has been applied on two radars close to Barcelona (Spain).Peer ReviewedPostprint (published version

    Estimation of monsoon rainfall by single polarization weather radar

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    Weather radar can offer synoptic measurement at a higher temporal and spatial resolution to extract the rain information. Rainfall can be inverted from the radar reflectivity using the power-law relation to ground rain gauge measurement. The relationship known as Z-R model has been established in many variants but the uncertainty from the sampling bias and the Z-R variability of single-polarization radar observation on monsoon rain becomes subject to research. This study reports a novel research framework to systematically estimate the monsoon rainfall using new Z-R model on the single-polarization weather radar in Kelantan. The sampling bias was quantified by the pixel matching procedure while the non-linear Levenberg Marquardt (LM) regression and the Artificial Neural Network (ANN) regression at different rain intensity and radar range were introduced to minimise the Spatio-temporal variability of the new Z-R model. This study uses 10-minute reflectivity data recorded in Kota Bahru radar station and hourly rain record at the nearby 58 gauge stations in 2013 to 2015. The three-dimensional nearest neighbour interpolation proves that the sampling bias can be quantified. The LM shows an improvement of about 12% if the spatial adjustment was applied in the regression. Unlike LM, the ANN is more robust and independent to the spatial adjustment thus it could provide more accurate and reliable monsoon rain information in heterogenous rainy condition. The ANN model provides accuracy of ± 0.4 mm/hr, ± 1.0 mm/hr and ± 8.2 mm/hr for low, medium and high rain intensity respectively with correlation coefficient > 0.7 (p 0.5 and accuracy improvement about 8 %, 10% and 5% for abovementioned rain intensity respectively. Radar derived rainfall maps present the rain distribution was more concentrated in all downstream but only covered 1/3 of the upstream in Kelantan rivers. Further research is needed before the technique could be applied to any single-polarization system in Southeast Asia to achieve better accuracy of rain information extraction
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