scholarly journals The Extra-Area Effect of Orographic Cloud Seeding: Observational Evidence of Precipitation Enhancement Downwind of the Target Mountain

2016 ◽  
Vol 55 (6) ◽  
pp. 1409-1424 ◽  
Author(s):  
Xiaoqin Jing ◽  
Bart Geerts ◽  
Bruce Boe
Keyword(s):  
Vertical Mixing ◽  
Radar Data ◽  
Control Area ◽  
Radar Reflectivity ◽  
Target Range ◽  
Target Area ◽  
Mountain Ranges ◽  
Ice Nuclei ◽  
Orographic Cloud ◽  
Glaciogenic Seeding

AbstractThis study uses scanning X-band Doppler on Wheels (DOW) radar data to examine whether ground-based glaciogenic seeding influences orographic precipitation, inadvertently, over the foothills of a mountain ~50 km downwind of the target mountain. The data were collected during seven storms during the 2012 AgI Seeding Cloud Impact Investigation (ASCII-12) campaign in Wyoming. The DOW was located on the Sierra Madre (the target range), with excellent low-level coverage toward the Medicine Bow (the downwind range). To examine the seeding impact, two study areas are designated, both over the foothills of the downwind range: one is directly downwind of the remote silver iodide (AgI) generators (target area), and the other is offset sideways (control area). Comparisons are made between radar reflectivity measurements from a treated period and those from an untreated period. The total treated (untreated) period over seven storms is 14.3 h (21.2 h). Independent measurements of ice nuclei concentrations indicate that ground-released AgI nuclei can disperse across two mountain ranges over a distance of ~80 km. Analyses of DOW transects, DOW echo-height maps, and Doppler velocities from an airborne profiling radar suggest three different mechanisms for the vertical mixing of AgI nuclei: in all cases boundary layer mixing is active, and in some cases convection, or a lee hydraulic jump, or both are present. In all cases the radar reflectivity is higher during seeding in the target region when compared with the trend over the same period in the control region. Note that the results are not definitive proof of a downwind seeding impact since natural variability of precipitation is large and the sample size examined is small.

Dual-Polarization Radar Data Analysis of the Impact of Ground-Based Glaciogenic Seeding on Winter Orographic Clouds. Part I: Mostly Stratiform Clouds

2015 ◽  
Vol 54 (9) ◽  
pp. 1944-1969 ◽  
Author(s):  
Xiaoqin Jing ◽  
Bart Geerts ◽  
Katja Friedrich ◽  
Binod Pokharel
Keyword(s):  
Radar Data ◽  
Control Area ◽  
Target Area ◽  
Dual Polarization ◽  
Stratiform Clouds ◽  
Orographic Clouds ◽  
Close Range ◽  
Glaciogenic Seeding ◽  
Differential Reflectivity ◽  
The Impact

AbstractThe impact of ground-based glaciogenic seeding on wintertime orographic, mostly stratiform clouds is analyzed by means of data from an X-band dual-polarization radar, the Doppler-on-Wheels (DOW) radar, positioned on a mountain pass. This study focuses on six intensive observation periods (IOPs) during the 2012 AgI Seeding Cloud Impact Investigation (ASCII) project in Wyoming. In all six storms, the bulk upstream Froude number below mountaintop exceeded 1 (suggesting unblocked flow), the clouds were relatively shallow (with bases below freezing), some liquid water was present, and orographic flow conditions were mostly steady. To examine the silver iodide (AgI) seeding effect, three study areas are defined (a control area, a target area upwind of the crest, and a lee target area), and comparisons are made between measurements from a treated period and those from an untreated period. Changes in reflectivity and differential reflectivity observed by the DOW at low levels during seeding are consistent with enhanced snow growth, by vapor diffusion and/or aggregation, for a case study and for the composite analysis of all six IOPs, especially at close range upwind of the mountain crest. These low-level changes may have been affected by natural changes aloft, however, as evident from differences in the evolution of the echo-top height in the control and target areas. Even though precipitation in the target region is strongly correlated with that in the control region, the authors cannot definitively attribute the change to seeding because there is a lack of knowledge about natural variability, nor can the outcome be generalized, because the sample size is small.

Identification of the Hook on Investment Casting Shell Line Based on Machine Vision

2012 ◽  
Vol 220-223 ◽  
pp. 1356-1361
Author(s):  
Xi Jie Tian ◽  
Jing Yu ◽  
Chang Chun Li
Keyword(s):  
Machine Vision ◽  
Investment Casting ◽  
Hough Transform ◽  
Image Matching ◽  
Feature Matching ◽  
Image Acquisition ◽  
Target Range ◽  
Target Area ◽  
Matching Method ◽  
Vertical Projection

In this paper, the idea identify the hook on investment casting shell line based on machine vision has been proposed. According to the characteristic of the hook, we do the image acquisition and preprocessing, we adopt Hough transform to narrow the target range, and find the target area based on the method combining the level projection and vertical projection, use feature matching method SIFT to do the image matching. Finally, we get the space information of the target area of the hook.

Fuzzy Logic Classification of S-Band Polarimetric Radar Echoes to Identify Three-Body Scattering and Improve Data Quality

2014 ◽  
Vol 53 (8) ◽  
pp. 2017-2033 ◽  
Author(s):  
Vivek N. Mahale ◽  
Guifu Zhang ◽  
Ming Xue
Keyword(s):  
Fuzzy Logic ◽  
Standard Deviation ◽  
Radar Data ◽  
Classification Algorithm ◽  
Radar Reflectivity ◽  
Membership Functions ◽  
Polarimetric Radar ◽  
Radar Echo ◽  
Three Body ◽  
Reflectivity Factor

AbstractThe three-body scatter signature (TBSS) is a radar artifact that appears downrange from a high-radar-reflectivity core in a thunderstorm as a result of the presence of hailstones. It is useful to identify the TBSS artifact for quality control of radar data used in numerical weather prediction and quantitative precipitation estimation. Therefore, it is advantageous to develop a method to automatically identify TBSS in radar data for the above applications and to help identify hailstones within thunderstorms. In this study, a fuzzy logic classification algorithm for TBSS identification is developed. Polarimetric radar data collected by the experimental S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), are used to develop trapezoidal membership functions for the TBSS class of radar echo within a hydrometeor classification algorithm (HCA). Nearly 3000 radar gates are removed from 50 TBSSs to develop the membership functions from the data statistics. Five variables are investigated for the discrimination of the radar echo: 1) horizontal radar reflectivity factor ZH, 2) differential reflectivity ZDR, 3) copolar cross-correlation coefficient ρhv, 4) along-beam standard deviation of horizontal radar reflectivity factor SD(ZH), and 5) along-beam standard deviation of differential phase SD(ΦDP). These membership functions are added to an HCA to identify TBSSs. Testing is conducted on radar data collected by dual-polarization-upgraded operational WSR-88Ds from multiple severe-weather events, and results show that automatic identification of the TBSS through the enhanced HCA is feasible for operational use.

scholarly journals Mobile Radar Observations of the Evolving Debris Field Compared with a Damage Survey of the Shawnee, Oklahoma, Tornado of 19 May 2013

2020 ◽  
Vol 148 (5) ◽  
pp. 1779-1803 ◽  
Author(s):  
Roger M. Wakimoto ◽  
Zachary Wienhoff ◽  
Howard B. Bluestein ◽  
David J. Bodine ◽  
James M. Kurdzo
Keyword(s):  
Cross Correlation ◽  
Vertical Structure ◽  
Leading Edge ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Vertical Profiles ◽  
Damage Survey ◽  
X Band ◽  
First Time ◽  
Rapid Scanning

Abstract A detailed damage survey is combined with high-resolution mobile, rapid-scanning X-band polarimetric radar data collected on the Shawnee, Oklahoma, tornado of 19 May 2013. The focus of this study is the radar data collected during a period when the tornado was producing damage rated EF3. Vertical profiles of mobile radar data, centered on the tornado, revealed that the radar reflectivity was approximately uniform with height and increased in magnitude as more debris was lofted. There was a large decrease in both the cross-correlation coefficient (ρhv) and differential radar reflectivity (ZDR) immediately after the tornado exited the damaged area rated EF3. Low ρhv and ZDR occurred near the surface where debris loading was the greatest. The 10th percentile of ρhv decreased markedly after large amounts of debris were lofted after the tornado leveled a number of structures. Subsequently, ρhv quickly recovered to higher values. This recovery suggests that the largest debris had been centrifuged or fallen out whereas light debris remained or continued to be lofted. Range–height profiles of the dual-Doppler analyses that were azimuthally averaged around the tornado revealed a zone of maximum radial convergence at a smaller radius relative to the leading edge of lofted debris. Low-level inflow into the tornado encountering a positive bias in the tornado-relative radial velocities could explain the existence of the zone. The vertical structure of the convergence zone was shown for the first time.

scholarly journals A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data

2019 ◽  
Vol 12 (9) ◽  
pp. 4031-4051 ◽  
Author(s):  
Shizhang Wang ◽  
Zhiquan Liu
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Radar Data ◽  
Poor Quality ◽  
Variational Data Assimilation ◽  
Radar Reflectivity ◽  
Background Error ◽  
Background Fields ◽  
Ice Phase

Abstract. A reflectivity forward operator and its associated tangent linear and adjoint operators (together named RadarVar) were developed for variational data assimilation (DA). RadarVar can analyze both rainwater and ice-phase species (snow and graupel) by directly assimilating radar reflectivity observations. The results of three-dimensional variational (3D-Var) DA experiments with a 3 km grid mesh setting of the Weather Research and Forecasting (WRF) model showed that RadarVar was effective at producing an analysis of reflectivity pattern and intensity similar to the observed data. Two to three outer loops with 50–100 iterations in each loop were needed to obtain a converged 3-D analysis of reflectivity, rainwater, snow, and graupel, including the melting layers with mixed-phase hydrometeors. It is shown that the deficiencies in the analysis using this operator, caused by the poor quality of the background fields and the use of the static background error covariance, can be partially resolved by using radar-retrieved hydrometeors in a preprocessing step and tuning the spatial correlation length scales of the background errors. The direct radar reflectivity assimilation using RadarVar also improved the short-term (2–5 h) precipitation forecasts compared to those of the experiment without DA.

Quantifying snowfall from orographic cloud seeding

2020 ◽  
Author(s):  
Katja Friedrich ◽  
Kyoko Ikeda ◽  
Sarah Tessendorf ◽  
Jeffrey French ◽  
Robert Rauber ◽  
...  
Keyword(s):  
Water Management ◽  
Silver Iodide ◽  
Management Strategy ◽  
Radar Reflectivity ◽  
Cloud Seeding ◽  
Straight Line ◽  
Orographic Clouds ◽  
Increasing Demand ◽  
Orographic Cloud ◽  
Water Management Strategy

<p>Cloud seeding has been used as one water management strategy to overcome the increasing demand for water despite decades of inconclusive results on the efficacy of cloud seeding. In this study snowfall accumulation from glaciogenic cloud seeding is quantified based on snow gauge and radar observations from three days in January 2017, when orographic clouds in the absent of natural precipitation were seeded with silver iodide (AgI) in the Payette basin of Idaho during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE). On each day, a seeding aircraft equipped with AgI flares flew back and forth on a straight-line flight track producing a zig-zag pattern representing two to eight lines of clouds visible through enhancements in radar reflectivity. As these seeding lines started to form precipitation, they passed over several snow gauges and through the radar observational domain. For the three cases presented here, precipitation gauges measured increases between 0.05-0.3 mm as precipitation generated by cloud seeding pass over the instruments. A variety of relationships between radar reflectivity factor and liquid equivalent snowfall rate were used to quantify snowfall within the radar observation domain. For the three cases, snowfall occurred within the radar observational domain between 25 -160 min producing a total amount of water generated by cloud seeding ranging from 123,220 to 339,540 m3 using the best-match Ze-S relationship. Uncertainties in radar reflectivity estimated snowfall are provided by considering not only the best-match Ze-S relationship but also an ensemble of Ze-S relationships based on the range of coefficients published from previous studies and then examining the percentile of snowfall estimates based on all of the Ze-S relationships within the ensemble. Considering the interquartile range and 5<sup>th</sup>/95<sup>th</sup> percentiles, uncertainties in total amount of water generated by cloud seeding can range between 20-45% compared to the best-math estimates. These results provide new insights towards understanding how cloud seeding impacts precipitation and its distribution across a region.</p>

A Dynamic and Flexible State Model for Rainfall Nowcasting

2020 ◽  
Author(s):  
Marc Schleiss ◽  
Venkat Roy
Keyword(s):  
Temporal Evolution ◽  
Weather Prediction ◽  
Radar Reflectivity ◽  
State Model ◽  
Dynamic State ◽  
Model Parameters ◽  
Target Area ◽  
Stochastic Perturbations ◽  
Spatio Temporal ◽  
Linear State

<p>We present a dynamic state model estimation method for rainfall nowcasting in which we assume that the short term spatio-temporal evolution of rainfall can be approximated by a linear state model with stochastic perturbations.  We estimate the model parameters using radar reflectivity measurements for one-step as well as multiple-step ahead rainfall nowcasting. If the rainfall intensity at location <strong>x</strong> and time index t is given by u<sub>t</sub>(<strong>x</strong>), then the overall rainfall field intensity vector at any time t over N pixels (of the target area) can be represented by <strong>u</strong><sub>t</sub> = [u<sub>t</sub>(<strong>x</strong><sub>N</sub>),...u<sub>t</sub>(<strong>x</strong><sub>N</sub>)]<sup>T</sup><sub>.</sub> Following the aforementioned formalism, the spatio-temporal evolution of the rainfall field can be described by the following linear state model given by<br><strong>u</strong><sub>t</sub> = <strong>H</strong><sub>t</sub><strong>u</strong><sub>t-1</sub> + <strong>q</strong><sub>t</sub><br>where <strong>H</strong><sub>t</sub> is an unknown time-varying state-transition matrix of dimensions NxN and <strong>q</strong><sub>t</sub> is a stochastic process noise vector of length N. We present an iterative least squares based method to estimate <strong>H</strong><sub>t </sub>and explore simpler algebraic structures (e.g., scaled affine transformations) to reduce the numbers of unknown parameters during estimation. We evaluate the performances of the proposed model using simulations and radar reflectivity data from the Royal Netherlands Meteorological Institute (KNMI). We observe that the nowcasting performances strongly depend on the size of the target area (number of pixels N), the type of events as well as the parameterization of <strong>H</strong><sub>t</sub>. The key advantage of the proposed approach over classical nowcasting methods based on Lagrangian persistence is the possibility to incorporate prior information about future rainfall evolution from external sources of information such as satellites or numerical weather prediction models during the estimation of the parameters.</p>

Impact of Doppler radar reflectivity and velocity data assimilation on the quality of precipitation forecasting in Belarus in different seasons

2020 ◽  
Author(s):  
Palina Zaiko ◽  
Siarhei Barodka ◽  
Aliaksandr Krasouski
Keyword(s):  
Data Assimilation ◽  
Weather Radar ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Doppler Weather Radar ◽  
Velocity Data ◽  
Numerical Weather ◽  
Convective Processes ◽  
Different Seasons

<p>Heavy precipitation forecast remains one of the biggest problems in numerical weather prediction. Modern remote sensing systems allow tracking of rapidly developing convective processes and provide additional data for numerical weather models practically in real time. Assimilation of Doppler weather radar data also allows to specify the position and intensity of convective processes in atmospheric numerical models.</p><p>The primary objective of this study is to evaluate the impact of Doppler  radar reflectivity and velocity assimilation in the WRF-ARW mesoscale model for the territory of Belarus in different seasons of the year. Specifically, we focus on the short-range numerical forecasting of mesoscale convective systems passage over the territory of Belarus in 2017-2019 with assimilated radar data.</p><p>Proceeding with weather radar observations available for our cases, we first perform the necessary processing of the raw radar data to eliminate noise, reflections and other kinds of clutter. For identification of non-meteorological noise fuzzy echo classification was used. Then we use the WRF-DA (3D-Var) system to assimilate the processed radar observations from 3 Belarusian Doppler weather radar in the WRF model. Assimilating both radar reflectivity and radial velocity data in the model we aim to better represent not only the distribution of clouds and their moisture content, but also the detailed dynamical aspects of convective circulation. Finally, we analyze WRF modelling output obtained with assimilated radar data and compare it with available meteorological observations and with other model runs (including control runs with no data assimilation or with assimilation of conventional weather stations data only), paying special attention to the accuracy of precipitation forecast 12 hours in advance.</p>

scholarly journals Comparison of 3DVar and EnSRF Data Assimilation Using Radar Observations for the Analysis and Prediction of an MCS

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Shibo Gao ◽  
Jinzhong Min
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Mesoscale Convective System ◽  
Final Analysis ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Convective System ◽  
Wind Fields ◽  
Radar Observations

Using radar observations, the performances of the ensemble square root filter (EnSRF) and an indirect three-dimensional variational (3DVar) data assimilation method were compared for a mesoscale convective system (MCS) that occurred in the Front Range of the Rocky Mountains, Colorado (USA). The results showed that the root mean square innovations (RMSIs) of EnSRF were lower than 3DVar for radar reflectivity and radial velocity and that the spread of EnSRF was generally consistent with its RMSIs. EnSRF substantially improved the analysis of the MCS compared with an experiment without radar data assimilation, and it produced a slight but noticeable improvement over 3DVar in terms of both coverage and intensity. Forecast results initiated from the final analysis revealed that EnSRF generally produced the best prediction of the MCS, with improved quantitative reflectivity and precipitation forecast skills. EnSRF also demonstrated better performance than 3DVar in the prediction of neighborhood probability for reflectivity at thresholds of 20 and 35 dBZ, which better matched the observed radar reflectivity in terms of both shape and extension. Additionally, the humidity, temperature, and wind fields were also improved by EnSRF; the largest error reduction was found in the water vapor field near the surface and at upper levels.

scholarly journals Constructing Three-Dimensional Multiple-Radar Reflectivity Mosaics: Examples of Convective Storms and Stratiform Rain Echoes

2005 ◽  
Vol 22 (1) ◽  
pp. 30-42 ◽  
Author(s):  
Jian Zhang ◽  
Kenneth Howard ◽  
J. J. Gourley
Keyword(s):  
High Resolution ◽  
Coordinate System ◽  
Real Time ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Raw Data ◽  
Spherical Coordinate ◽  
Base Level ◽  
Analysis Scheme ◽  
Reflectivity Data

Abstract The advent of Internet-2 and effective data compression techniques facilitates the economic transmission of base-level radar data from the Weather Surveillance Radar-1988 Doppler (WSR-88D) network to users in real time. The native radar spherical coordinate system and large volume of data make the radar data processing a nontrivial task, especially when data from several radars are required to produce composite radar products. This paper investigates several approaches to remapping and combining multiple-radar reflectivity fields onto a unified 3D Cartesian grid with high spatial (≤1 km) and temporal (≤5 min) resolutions. The purpose of the study is to find an analysis approach that retains physical characteristics of the raw reflectivity data with minimum smoothing or introduction of analysis artifacts. Moreover, the approach needs to be highly efficient computationally for potential operational applications. The appropriate analysis can provide users with high-resolution reflectivity data that preserve the important features of the raw data, but in a manageable size with the advantage of a Cartesian coordinate system. Various interpolation schemes were evaluated and the results are presented here. It was found that a scheme combining a nearest-neighbor mapping on the range and azimuth plane and a linear interpolation in the elevation direction provides an efficient analysis scheme that retains high-resolution structure comparable to the raw data. A vertical interpolation is suited for analyses of convective-type echoes, while vertical and horizontal interpolations are needed for analyses of stratiform echoes, especially when large vertical reflectivity gradients exist. An automated brightband identification scheme is used to recognize stratiform echoes. When mosaicking multiple radars onto a common grid, a distance-weighted mean scheme can smooth possible discontinuities among radars due to calibration differences and can provide spatially consistent reflectivity mosaics. These schemes are computationally efficient due to their mathematical simplicity. Therefore, the 3D multiradar mosaic scheme can serve as a good candidate for providing high-spatial- and high-temporal-resolution base-level radar data in a Cartesian framework in real time.

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