WSR-88D radar data

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Assessing the Impact of Non-Conventional Observations on  High-Resolution Analyses and Forecasts

Assessing the Impact of Non-Conventional Observations on High-Resolution Analyses and Forecasts

Sensors are calibrated prior to deployment and replaced at regular intervals, while observations are subject to automated and manual quality assurance techniques. Additional information on the Oklahoma Mesonet can be found in McPherson et al. (2007). Upper-air observations are obtained from multiple sources, including the Meteorological Data Collection and Reporting System (MDCRS), which provides observations of flight-level temperature, dew point, and wind for assimilation in forecast models. NWS radiosonde data are not available for the time period considered for this study, generally being available at 0000 and 1200 UTC, only. Data from eight radars in the WSR-88D network fall within the domain used for the 11 April 2016 case study, with the most notable being the KFWS radar in Fort Worth, Texas. Additional WSR-88D radars assimilated include Dyess Air Force Base, TX (KDYX), Frederick, OK (KFDR), Ft. Hood, TX (KGRK), Ft. Polk, LA (KPOE), Shreveport, LA (KSHV), Fort Smith, AR (KSRX), and Oklahoma City, OK (KTLX). Lastly, visible and infrared data from the Geostationary Operational Environmental Satellite (GOES) are incorporated in the complex cloud analysis, which is described in detail in Section 3.2.2.
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Of Fire and Smoke Plumes, Polarimetric Characteristics

Of Fire and Smoke Plumes, Polarimetric Characteristics

Abstract: Weather surveillance radars routinely detect smoke of various origin. Of particular significance to the meteorological community are wildfires in forests and/or prairies. For example, one responsibility of the National Weather Service in the USA is to forecast fire outlooks as well as to monitor wild fire evolution. Polarimetric variables have enabled relatively easy recognitions of smoke plumes in data fields of weather radars. Presented here are the fields of these variables from smoke plumes caused by grass fire, brush fire, and forest fire. Histograms of polarimetric data from plumes contrast these three cases. Most of the data are from the polarimetric Weather Surveillance Radar 1988 Doppler (WSR-88D aka Nexrad, 10 cm wavelength) hence the wavelength does not influence these comparisons. Nevertheless, in one case simultaneous observations of a plume by the operational Terminal Doppler Weather Radar (TDWR, 5 cm wavelength) and a WSR-88D is used to infer backscattering characteristic and hence sizes of dominant contributors to the returns. In addition, comparisons with observations by other investigators of plumes from urban area but at a 5 cm wavelength are made. To interpret some measurements Computational Electromagnetics (CEM) tools are applied.
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A Short Term Tornado Prediction Model Using Satellite Imagery

A Short Term Tornado Prediction Model Using Satellite Imagery

P [9] discusses and differentiates the methods that were used to predict tornadoes in the late 70's and 90's. Mitchell, E. De Wayne et al. [10] along with NSSL developed and tested a tornado detection algorithm that has been designed to recognize the locally intense vortices connected with tornadoes using the WSR-88d base velocity data. Colquhoun, J. R. [11] proposed a decision tree approach to forecast tornadoes which uses only meteorological parameters that are essential requirements for this phenomenon to develop. Hamill, Thomas M and Andrew T. Church [12] proposed a model that specifies the conditional probability that a major tornado will occur given that a thunderstorm occurs and given that certain RUC-2 CAPE and helicity values are forecasted. Trapp, Robert J. et al [13] collected a large dataset that were obtained using WSR- 88D mesocyclone detection algorithm to estimate the percentage of tornadic mesocyclones. Vasiloff, Steven V. [14] gave an alternate way where federal aviation administration’s terminal doppler weather radar can be used instead of the weather surveillance radar-1988 doppler (WSR-88D) for tornado detection in order to yield better results. Donaldson Ralph J and Paul R. Desrochers [15] showed that an improvement in the reliability and timeliness of tornado forecasting can be achieved through quantitative measurement by doppler radar of selected mesocyclone features such as excess rotational kinetic energy. Roger Edwards et al. [16] brought into light two loopholes in the present forecasting mechanisms they are 1) occasionally inaccurate synoptic-scale guidance by operational numerical weather prediction models, especially with regard to several critical parameters 2) model and observational difficulties regarding convective initiation and evolution. A. E. Mercer et al. [17] studied a sample of 50 tornado outbreaks and 50 primarily non tornadic outbreaks were simulated using the WRF to determine if they are able to distinguish outbreak type using synoptic-scale input.
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Estimating Precipitation from WSR-88D Observations and Rain
Gauge Data: Potential for Drought Monitoring

Estimating Precipitation from WSR-88D Observations and Rain Gauge Data: Potential for Drought Monitoring

The WGRFC has been experimenting with a new precipitation estimation technique called Q2, which is the second technique derived by research meteorologists at the National Severe Storms Laboratory (NSSL). The National Mosaic and Multisensor QPE (NMQ) project is a joint initiative between the NSSL and other entities (such as the Federal Aviation Administration [FAA] and the University of Oklahoma). The National Mosaic and Q2 system is an experimental system designed to improve QPE and eventually very short-term Quantitative Precipitation Forecasts (QPF). For detailed information on the system, readers are referred to the NMQ web site at http://nmq.ou.edu. The NMQ ingests data from 128 WSR-88D stations every 5 min, quality controls the radar data, and derives a vertical profile of reflectivity from each radar. Analyses are done on eight tiles of radar data that are stitched together to form a continental U.S. (CONUS) three-dimensional (3-D) grid. Hybrid scan reflectivity and other products (such as a composite reflectivity map and precipitation flag prod- uct) are then derived to produce the experimental Q2 products. The products (such as QPE accumulations for the current hour or several hours of up to 72 h) are then translated over to the 4 km HRAP grid. The Q2 products hold several advantages over traditional radar-based estimates, with two primary advantages including an AP removal technique and rainfall estimates beyond the nominal 230 km range of the DPA files that are used in regions where radar umbrellas do not overlap. Because of these advantages, WGRFC HAS forecasters have the option of implementing Q2 as our final best estimate field.
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Adaptive Pattern Recognition for Tornado Detection

Adaptive Pattern Recognition for Tornado Detection

Current tornado detection algorithms rely on the difference of mean Doppler velocity between adjacent radar volumes in azimuth. Tornado spectra observed by the NSSL research WSR-88D (KOUN) located at Norman, Oklahoma on May 10, 2003 are analyzed. An F2-F3 tornado was reported approximately during the interval of time 9:30 - 10:00 pm (central time) starting south of Edmond, Oklahoma (more details of the tornado can be found from NOAA National Climate Data Center NCDC, http://www.ncdc.noaa.gov/oa/ncdc.html). Level I time series data were collected during the entire period of tornado. A well-defined hook signature and strong azimuthal
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NOWCASTING CONVECTIVE INITIATION AND THUNDERSTORM CHARACTERISTICS THROUGH THE USE OF REAL-TIME GEOSTATIONARY SATELLITE INFORMATION

NOWCASTING CONVECTIVE INITIATION AND THUNDERSTORM CHARACTERISTICS THROUGH THE USE OF REAL-TIME GEOSTATIONARY SATELLITE INFORMATION

IR multi-spectral band differencing techniques are used here to identify cumulus in a pre-CI state. An analysis comparing IR band differences to WSR-88D radar base reflectivity was performed for several case events (not presented in this study; the dependent data set) to identify the band difference values present before immature cumulus clouds evolved into rain-producing convective storms. From this analysis, IR band difference relationships for cumulus in a pre-CI state were formed. These values are subsequently used as CI interest fields within the nowcast algorithm, and are tested on the case described in the “Results” section (the independent data set). These band differencing techniques, and the critical values chosen for CI evaluation, are described below. In the discussion to follow, each IR-based interest field is related to the physical processes of cloud growth and precipitation formation in cumuliform clouds.
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RADAR-DISDROMETER COMPARISON TO REVEAL ATTENUATION EFFECTS ON CASA RADAR DATA. and ABSTRACT

RADAR-DISDROMETER COMPARISON TO REVEAL ATTENUATION EFFECTS ON CASA RADAR DATA. and ABSTRACT

X-band radar provides a high resolution image at the cost of significant attenuation. This is due to the X-EDQG¶V Vhort wavelength. In this study, a two- dimensional video disdrometer (2DVD) was deployed to the center of a triangle formed by three CASA dual-polarization X-band radars. The CASA radars provide measurements of reflectivity, differential reflectivity, specific differential phase, and co- polar cross-correlation coefficient of precipitation. Using drop size distributions obtained from the 2DVD, the radar variables are calculated and treated as the ground truth. The radar and disdrometer measurements are compared to reveal discrepancies. Biases and errors are calculated, and possible causes are investigated. These results can be used to further minimize the attenuation obstacle in X-band radar.
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Wavefront Reconstruction of Elevation Circular Synthetic Aperture Radar Imagery Using a Cylindrical Green's Function

Wavefront Reconstruction of Elevation Circular Synthetic Aperture Radar Imagery Using a Cylindrical Green's Function

Nevertheless, current E-CSAR wavefront reconstruction approaches still present limitations such as the execution times in the order of hours and altitude constraints [8, 11]. These considerations limit the widespread use of E- CSAR techniques in scenarios where the geometry of the scan region or target detection requirements suit the E- CSAR advantages, such as novel near field applications like breast microwave imaging [12], wood inspection [13], and low altitude SAR imaging scenarios [14]. In this paper, a novel E-CSAR wavefront reconstruction algorithm is proposed. Unlike current E-CSAR wavefront reconstruction approaches, the proposed method uses a novel formulation of Green’s function of the E-CSAR scan geometry that does not include a Hankel function and imposes no altitude restrictions on the inversion algorithm. The algorithm presented in this paper is an extension of the work presented by the authors in [15] for radar data sets acquired along cylindrical scan geometries. This paper is organized as follows. The E-CSAR signal model is explained in Section 2. In Section 3, the spectrum of Green’s function corresponding to the E-CSAR scan geometry is calculated. The proposed reconstruction method is described in Section 4. A theoreti- cal analysis of the point spread function of the E-CSAR imag- ing geometry, including aspects such as the spatial sampling constraints and resolution, is done in Section 4. In Section 5, the feasibility of the proposed method is assessed using experimental data sets. Lastly, some concluding remarks are mentioned in Section 6.
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Ultrafast Kelvin waves in the MLT airglow and wind, and their interaction with the atmospheric tides

Ultrafast Kelvin waves in the MLT airglow and wind, and their interaction with the atmospheric tides

The LS periodograms of Fig. 2 were obtained using the airglow data acquired during the whole night. We performed additional LS periodogram analysis without considering the entire airglow nightly measurements. Instead, we take the air- glow data only within specific time intervals during the night. This is because it is well established from ground-based and satellite-borne measurements and modeling studies that the atmospheric tides strongly affect the MLT equatorial airglow and exhibit pronounced nocturnal variation (e.g., Shepherd et al., 1995; Yee et al., 1997; Takahashi et al., 1998). In ad- dition, the tides can interact with other waves (e.g., Teitel- baum and Vial, 1991; Pancheva, 2001; England et al., 2012; Alves et al., 2013), leading to changes in their amplitudes and phases, which in turn could affect the airglow emissions. As there was a 1-day gap on 4 March in the airglow data, we take for this particular analysis the data from 5 to 14 March, which present excellent quality and continuity, and are still long enough to allow the study of the 3–4-day oscillations. First we analyzed the airglow time series built only with data obtained between 18:00 and 00:00 LT. In this case, the pe- riodicities in the 3–4-day band exhibit essentially the same features as those observed in the LS periodogram built with the whole-night data presented in Fig. 2; i.e., the OH and O 2
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Short term Precipitation Prediction Based on a Neural Network Method from Radar Observations

Short term Precipitation Prediction Based on a Neural Network Method from Radar Observations

Abstract. Based on the CINRAD_SA Doppler radar data and the rain gauge data of regional stations in Wuhan, the nowcasting of the radar echoes and rain intensity was conducted using artificial neural networks (ANN). First of all, the reflectivity values extracted from the raw data were interpolated to the three-dimensional rectangular lattice grid of 1km*1km in horizontal direction at the height of 1.5 and 3 km. The nearest 25 grid points above the stations were chosen as the input layer of the neural networks. The results show that the correlation coefficient R in the radar-rainfall estimation is more than 0.6, and the RMSE is less than 5 (0.1mm/6min) in most sites. The ANN extrapolation experiments indicate that the accuracy rate of 36mins forecast is higher than 50% at the threshold of 5 dBz. With the extension of forecast lead time, the accuracy rate decreases and drops to 45% for the 1 h forecasts.
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<p>MultiCenter Interrupted Time Series Analysis: Incorporating Within and Between-Center Heterogeneity</p>

<p>MultiCenter Interrupted Time Series Analysis: Incorporating Within and Between-Center Heterogeneity</p>

We use data from the Mobility of Vulnerable Elders in Ontario (MOVE-ON) project for our empirical evaluation. 23,24 The original MOVE-ON study involved implementation and evaluation of the impact of an evi- dence-based educational intervention, which aimed to promote early mobilization and prevent functional decline among older patients admitted to acute-care aca- demic hospitals across Ontario. An interrupted time series design was used to evaluate the impact of the interven- tion. The study was conducted across 14 hospitals con- sisting of 30 units that provide care to inpatients aged 65 and older. Data were collected over a period of 38 weeks, with a 10-week pre-implementation period followed by an 8-week implementation period, where the intervention was rolled out, and a 20-week post-implementation per- iod was also considered. The primary outcome of the study was the mobilization status of patients, who were assessed on twice-weekly visual audits, which occurred three times per day (in the morning, at lunch, and in the afternoon). Patients were considered mobilized if they were observed out of bed (mobility score > 2). 23,24 Further details of the study can be found elsewhere. 23,24 In this paper, we will consider the primary outcome and present the proposed method using these data as an illus- tration. We also compare our method with previous meth- ods empirically.
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Nonlinear estimation of short time precipitation using weather radar and surface observations

Nonlinear estimation of short time precipitation using weather radar and surface observations

The term radar was coined in 1940 as an acronym for RAdio Detection And Ranging. At that time, radar was used by the army, allowing military personnel to detect the enemy at suffi- ciently long distances to be able to react to the threat. They were huge devices (to transmit long radio frequency waves and receive echoes bouncing off targets) and the angular resolu- tions were poor. The technological development of magnetron allowed radar to use shorter wavelengths, microwaves. As a result, the size of radar device was largely shrinked and could be easily moved and installed on aircraft. Soon, large patches of echoes of unknown origin were observed by these magnetron-based radars, and it was soon realized that these echoes were caused by precipitation. Hence, the potential of radar to detect meteorological phenomena was discovered. After the war, radars were put to use in research to observe and understand thunderstorms and their life cycle, and research to understand cloud and pre- cipitation mechanisms, as well as research focusing on technical improvement of weather radars [Fabry, 2015]. Radars, specially designed for meteorological purpose, were deployed in the early 1950s. Since then, weather radar has undergone tremendous progress.
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Incorporating Satellite Precipitation Estimates into a Radar-Gage Multi-Sensor Precipitation Estimation Algorithm

Incorporating Satellite Precipitation Estimates into a Radar-Gage Multi-Sensor Precipitation Estimation Algorithm

The NWS MPE was conceived in the early 2000s to provide a full range of capabilities for radar QPE correction and multisensor blending[1,2,14]. The earlier versions of MPE ingested radar-only precipitation estimates, including the Digital Precipitation Array (DPA) generated by the WSR-88D Precipitation Processing System (PPS), rain gauge records, and externally defined radar climatology to create bias-corrected multi-radar and multi-sensor QPEs. The current version is able to ingest Multi-Radar Multi-Sensor (MRMS) [2] radar-only estimates, and incorporates basic quality-assurance procedures. The primary products include mean-field bias (MFB) correction to correct radar systematical biases, local bias (LB) correction to correct rainfall inhomogeneities and range- dependent biases, and multi-sensor estimation using the objective analysis techniques of Single Optimal Estimation (SOE) or Double Optimal Estimation (DOE)[15-18]. MFB determines a uniform gauge-radar ratio during each time step based on recursive estimation and exponential smoothing, the ratio was estimated by dividing the average rainfall measured at all of the valid rain gauges by the average radar rainfall estimates at the radar pixels over those gauges. LB calculation generates one bias value for each grid bin within a given coverage area; this bias may be spatially inhomogeneous since it is a function of range, azimuth, rainfall type, and other location-dependent factors. In MPE-SOE/DOE, weighting factors are calculated based on a covariance vector and matrix based on distances among gauges and radar values in the influencing radius for each bin. Heavy weight is placed on the gauge value for bins immediately surrounding the gauge, while the weight of the radar values increases as the distance from the gauge increases. Areas not covered by gauge or radar are filled in by the estimates from nearby gauge and radar values using the objective analysis technique to merge them by calculating their optimal weights and influence radius. SOE uses ordinary kriging to directly estimate rainfall amount; DOE uses indicator (which is a kriging analysis performed on a binary-transformed sample population[19]) and ordinary kriging to estimate probability of rainfall and rainfall amount under the condition that it is raining[17]. The operational version of the MPE system is configured to run in the Advanced Weather Interactive Prediction System (AWIPS) of the NWS.
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Understanding climate-driven processes for data-scarce mountain glaciers : insights from reanalysis data and global climate models, as well as in-situ and remote sensing sources

Understanding climate-driven processes for data-scarce mountain glaciers : insights from reanalysis data and global climate models, as well as in-situ and remote sensing sources

Along with air temperatures, the freezing level height (FLH) has risen over the last decades. The mass balance of tropical glaciers in Peru is highly sensitive to a rise in the FLH, mainly due to a decrease in accumulation and increase of energy for ablation caused by reduced albedo. Knowledge of future changes in the FLH is thus crucial to estimating changes in glacier extents. Since in-situ data are scarce at altitudes where glaciers exist (above ca. 4800 m asl.), reliable FLH estimates must be derived from multiple data types. Here, we assessed the FLHs and their spatiotemporal variability, as well as the related snow/rain transition in the two largest glacier-covered regions in Peru by combining ERA-Interim and MERRA2 reanalysis, TRMM Precipitation Radar Bright Band, Micro Rain Radar data and meteorological ground station measurements. The mean FLH lies at 4900 and 5010 m asl., for the Cordillera Blanca and Vilcanota, respectively. During the wet season, the FLH in the Cordillera Vilcanota lies ca. 150 m higher compared to the Cordillera Blanca, which is in line with the higher glacier terminus elevations. CMIP5 climate model projections reveal that by the end of the 21st century, the FLH will rise by 230 m (±190 m) for RCP2.6 and 850 m (±390 m) for RCP8.5. Even under the most optimistic scenario, glaciers may continue shrinking considerably, assuming a close relation between FLH and glacier extents. Under the most pessimistic scenario, glaciers may only remain at the highest summits above approximately 5800 m asl.
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Stich, Dennis
  

(2013):


	Convection initiation: detection and nowcasting with multiple data sources.


Dissertation, LMU München: Fakultät für Physik

Stich, Dennis (2013): Convection initiation: detection and nowcasting with multiple data sources. Dissertation, LMU München: Fakultät für Physik

The performance of the Cb-TRAM CI stage is evaluated for 86 days in summer 2009. These are all days within the summer period from May 15 2009 until August 31 2009 where no bigger data gaps during daytime in the rapid scan satellite data or one of the additional data sources obscures the evaluation. The restriction that only time steps during daytime are evaluated is due to the need of availability of the HRV channel for the Cb-TRAM CI detection. The evaluation starts at least half an hour after the daytime detection with the HRV channel is available over the whole analyzed area, from that time Cb-TRAM is able to determine the needed timetrends in the HRV properly. Furthermore it ends for a time step where one more hour of daytime detection data is available, to be able to check for long living yellow cells, which will not be detected anymore after sunset. Also days without or with only few noteworthy convective developments above Europe are within the analyzed days which is an important dierence to many other verication approaches for CI tools. Days where over large areas rst cumulus development can be observed from the satellite view but the overall forcing is to weak to nally initiate usually show a larger amount of false alarms. The values presented within this Section are mean values over all time steps and days. The verication routine computes oPOD and oFAR for each of the used daytime time steps and calculates a mean value for the whole day. Afterwards the 86 daily mean values are used to build a mean value for the performance. Table 4.1 shows the results for the 15, 30, 45, and 60 minute nowcasts as well as for the accumulated evaluation (acc). "Accumulated" means overlap of nowcast and analysis in at least one of the analyzed time steps (15 to 60 minute nowcasts) as criterion for a hit with no focus on any special nowcast time step, as described earlier. The results for the cell ID based evaluation are shown as well. The rst two rows show the results allowing (long living) yellow cells along with stage 2 or 3 detections as hits too, the latter rows show the results if further development to stages 2 or 3 (dev) is required for a hit (oPOD dev and oFAR dev).
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VARIA REARVIEW RADAR RVR315. Owner s Manual

VARIA REARVIEW RADAR RVR315. Owner s Manual

You can remotely control your radar device and customize settings using your compatible Edge device. This section contains instructions for the Edge 1030 device. Other compatible Edge devices contain similar settings and controls (Other Compatible Devices, page 4).

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197602 pdf

197602 pdf

A background in some of these areas will be considered: Data Base Management; Radar & Communications System Programming; Radar, Weapons, Missile Simulation; Operating System Software Dev[r]

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197512 pdf

197512 pdf

A background in some of these areas will be considered: Data Base Management; Radar & Communications System Programming; Radar, Weapons, Missile Simulation; Operating System Software Dev[r]

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197510 pdf

197510 pdf

A background in some of these areas will be considered: Data Base Management; Radar & Communications System Programming; Radar, Weapons, Missile Simulation; Operating System Software Dev[r]

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197509 pdf

197509 pdf

A background in some of these areas will be considered: Data Base Management; Radar & Communications System Programming; Radar, Weapons, Missile Simulation; Operating System Software Dev[r]

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