scholarly journals Forecaster Performance and Workload: Does Radar Update Time Matter?

2017 ◽  
Vol 32 (1) ◽  
pp. 253-274 ◽  
Author(s):  
Katie A. Wilson ◽  
Pamela L. Heinselman ◽  
Charles M. Kuster ◽  
Darrel M. Kingfield ◽  
Ziho Kang
Keyword(s):  
Lead Time ◽  
Phased Array ◽  
Probability Of Detection ◽  
Size Analysis ◽  
Cognitive Workload ◽  
Phased Array Radar ◽  
Subjective Assessments ◽  
Full Speed ◽  
Tornado Warning ◽  
The Impact

Abstract Impacts of radar update time on forecasters’ warning decision processes were analyzed in the 2015 Phased Array Radar Innovative Sensing Experiment. Thirty National Weather Service forecasters worked nine archived phased-array radar (PAR) cases in simulated real time. These cases presented nonsevere, severe hail and/or wind, and tornadic events. Forecasters worked each type of event with approximately 5-min (quarter speed), 2-min (half speed), and 1-min (full speed) PAR updates. Warning performance was analyzed with respect to lead time and verification. Combining all cases, forecasters’ median warning lead times when using full-, half-, and quarter-speed PAR updates were 17, 14.5, and 13.6 min, respectively. The use of faster PAR updates also resulted in higher probability of detection and lower false alarm ratio scores. Radar update speed did not impact warning duration or size. Analysis of forecaster performance on a case-by-case basis showed that the impact of PAR update speed varied depending on the situation. This impact was most noticeable during the tornadic cases, where radar update speed positively impacted tornado warning lead time during two supercell events, but not for a short-lived tornado occurring within a bowing line segment. Forecasters’ improved ability to correctly discriminate the severe weather threat during a nontornadic supercell event with faster PAR updates was also demonstrated. Forecasters provided subjective assessments of their cognitive workload in all nine cases. On average, forecasters were not cognitively overloaded, but some participants did experience higher levels of cognitive workload at times. A qualitative explanation of these particular instances is provided.

scholarly journals Tornado Warning Decisions Using Phased-Array Radar Data

2015 ◽  
Vol 30 (1) ◽  
pp. 57-78 ◽  
Author(s):  
Pamela Heinselman ◽  
Daphne LaDue ◽  
Darrel M. Kingfield ◽  
Robert Hoffman
Keyword(s):  
Lead Time ◽  
Phased Array ◽  
Cognitive Task ◽  
Radar Data ◽  
Probability Of Detection ◽  
Probability Of False Alarm ◽  
Temporal Sampling ◽  
Phased Array Radar ◽  
Tornado Warnings ◽  
Tornado Warning

Abstract The 2012 Phased Array Radar Innovative Sensing Experiment identified how rapidly scanned full-volumetric data captured known mesoscale processes and impacted tornado-warning lead time. Twelve forecasters from nine National Weather Service forecast offices used this rapid-scan phased-array radar (PAR) data to issue tornado warnings on two low-end tornadic and two nontornadic supercell cases. Verification of the tornadic cases revealed that forecasters’ use of PAR data provided a median tornado-warning lead time (TLT) of 20 min. This 20-min TLT exceeded by 6.5 and 9 min, respectively, participants’ forecast office and regions’ median spring season, low-end TLTs (2008–13). Furthermore, polygon-based probability of detection ranged from 0.75 to 1.0 and probability of false alarm for all four cases ranged from 0.0 to 0.5. Similar performance was observed regardless of prior warning experience. Use of a cognitive task analysis method called the recent case walk-through showed that this performance was due to forecasters’ use of rapid volumetric updates. Warning decisions were based upon the intensity, persistence, and important changes in features aloft that are precursors to tornadogenesis. Precursors that triggered forecasters’ decisions to warn occurred within one or two typical Weather Surveillance Radar-1988 Doppler (WSR-88D) scans, indicating PAR’s temporal sampling better matches the time scale at which these precursors evolve.

scholarly journals Impacts of Phased-Array Radar Data on Forecaster Performance during Severe Hail and Wind Events

2015 ◽  
Vol 30 (2) ◽  
pp. 389-404 ◽  
Author(s):  
Katie A. Bowden ◽  
Pamela L. Heinselman ◽  
Darrel M. Kingfield ◽  
Rick P. Thomas
Keyword(s):  
Decision Process ◽  
Phased Array ◽  
Statistical Significance ◽  
Radar Data ◽  
Probability Of Detection ◽  
Control Group ◽  
P Value ◽  
Phased Array Radar ◽  
First Case ◽  
Experimental Group

Abstract The ongoing Phased Array Radar Innovative Sensing Experiment (PARISE) investigates the impacts of higher-temporal-resolution radar data on the warning decision process of NWS forecasters. Twelve NWS forecasters participated in the 2013 PARISE and were assigned to either a control (5-min updates) or an experimental (1-min updates) group. Participants worked two case studies in simulated real time. The first case presented a marginally severe hail event, and the second case presented a severe hail and wind event. While working each event, participants made decisions regarding the detection, identification, and reidentification of severe weather. These three levels compose what has now been termed the compound warning decision process. Decisions were verified with respect to the three levels of the compound warning decision process and the experimental group obtained a lower mean false alarm ratio than the control group throughout both cases. The experimental group also obtained a higher mean probability of detection than the control group throughout the first case and at the detection level in the second case. Statistical significance (p value = 0.0252) was established for the difference in median lead times obtained by the experimental (21.5 min) and control (17.3 min) groups. A confidence-based assessment was used to categorize decisions into four types: doubtful, uninformed, misinformed, and mastery. Although mastery (i.e., confident and correct) decisions formed the largest category in both groups, the experimental group had a larger proportion of mastery decisions, possibly because of their enhanced ability to observe and track individual storm characteristics through the use of 1-min updates.

The Influence of Weather Watch Type on the Quality of Tornado Warnings and its Implications for Future Forecasting Systems

2021 ◽  
Author(s):  
Makenzie J. Krocak ◽  
Harold E. Brooks
Keyword(s):  
False Alarm ◽  
Lead Time ◽  
Severe Weather ◽  
Probability Of Detection ◽  
False Alarm Ratio ◽  
Tornado Warnings ◽  
General Improvement ◽  
The Difference ◽  
Tornado Warning

AbstractWhile many studies have looked at the quality of forecast products, few have attempted to understand the relationship between them. We begin to consider whether or not such an influence exists by analyzing storm-based tornado warning product metrics with respect to whether they occurred within a severe weather watch and, if so, what type of watch they occurred within.The probability of detection, false alarm ratio, and lead time all show a general improvement with increasing watch severity. In fact, the probability of detection increased more as a function of watch-type severity than the change in probability of detection during the time period of analysis. False alarm ratio decreased as watch type increased in severity, but with a much smaller magnitude than the difference in probability of detection. Lead time also improved with an increase in watch-type severity. Warnings outside of any watch had a mean lead time of 5.5 minutes, while those inside of a particularly dangerous situation tornado watch had a mean lead time of 15.1 minutes. These results indicate that the existence and type of severe weather watch may have an influence on the quality of tornado warnings. However, it is impossible to separate the influence of weather watches from possible differences in warning strategy or differences in environmental characteristics that make it more or less challenging to warn for tornadoes. Future studies should attempt to disentangle these numerous influences to assess how much influence intermediate products have on downstream products.

A New Algorithm for Phased Array Radar Search Function Improvement in Overload Situations

2011 ◽  
Vol 57 (1) ◽  
pp. 55-63
Author(s):  
Reza Mofrad ◽  
Ramazan Sadeghzadeh
Keyword(s):  
Phased Array ◽  
Simulation Software ◽  
Probability Of Detection ◽  
Beam Forming ◽  
Test Bed ◽  
Simulation Test ◽  
Search Function ◽  
Phased Array Radar ◽  
Maximum Range ◽  
And Performance

A New Algorithm for Phased Array Radar Search Function Improvement in Overload SituationsA new algorithm is proposed for phased array radar search function resource allocation. The proposed algorithm adaptively priorities radar search regions and in overload situations, based on available resources, radar characteristics, maximum range and search regions, optimally allocates radar resources in order to maximize probability of detection. The performance of new algorithm is evaluated by the multifunction phased array radar simulation test bed. This simulation test bed provides capability to design and evaluate the performance of different radar resource management, target tracking and beam forming algorithms. Some results are presented that show capabilities of this simulation software for multifunction radar algorithms design and performance evaluation.

scholarly journals Phased-Array Radar System Simulator (PASIM): Development and Simulation Result Assessment

2019 ◽  
Vol 11 (4) ◽  
pp. 422 ◽  
Author(s):  
Zhe Li ◽  
Sudantha Perera ◽  
Yan Zhang ◽  
Guifu Zhang ◽  
Richard Doviak
Keyword(s):  
Data Quality ◽  
Pulse Compression ◽  
Phased Array ◽  
Weather Radar ◽  
Radar System ◽  
Quality Analysis ◽  
Convective Precipitation ◽  
Error Statistics ◽  
Phased Array Radar ◽  
The Impact

In this paper, a system-specific phased-array radar system simulator was developed, based on a time-domain modeling and simulation method, mainly for system performance evaluation of the future Spectrum-Efficient National Surveillance Radar (SENSR). The goal of the simulation study was to establish a complete data quality prediction method based on specific radar hardware and electronics designs. The distributed weather targets were modeled using a covariance matrix-based method. The data quality analysis was conducted using Next-Generation Radar (NEXRAD) Level-II data as a basis, in which the impact of various pulse compression waveforms and channel electronic instability on weather radar data quality was evaluated. Two typical weather scenarios were employed to assess the simulator’s performance, including a tornado case and a convective precipitation case. Also, modeling of some demonstration systems was evaluated, including a generic weather radar, a planar polarimetric phased-array radar, and a cylindrical polarimetric phased-array radar. Corresponding error statistics were provided to help multifunction phased-array radar (MPAR) designers perform trade-off studies.

scholarly journals Comparison of the Analyses and Forecasts of a Tornadic Supercell Storm from Assimilating Phased-Array Radar and WSR-88D Observations

2017 ◽  
Vol 32 (4) ◽  
pp. 1379-1401 ◽  
Author(s):  
Timothy A. Supinie ◽  
Nusrat Yussouf ◽  
Youngsun Jung ◽  
Ming Xue ◽  
Jing Cheng ◽  
...  
Keyword(s):  
Phased Array ◽  
Weather Forecasting ◽  
Temporal Frequency ◽  
Severe Weather ◽  
Ensemble Forecasts ◽  
Rapid Scan ◽  
Supercell Storm ◽  
Phased Array Radar ◽  
Skill Scores ◽  
The Impact

Abstract NOAA’s National Severe Storms Laboratory is actively developing phased-array radar (PAR) technology, a potential next-generation weather radar, to replace the current operational WSR-88D radars. One unique feature of PAR is its rapid scanning capability, which is at least 4–5 times faster than the scanning rate of WSR-88D. To explore the impact of such high-frequency PAR observations compared with traditional WSR-88D on severe weather forecasting, several storm-scale data assimilation and forecast experiments are conducted. Reflectivity and radial velocity observations from the 22 May 2011 Ada, Oklahoma, tornadic supercell storm are assimilated over a 45-min period using observations from the experimental PAR located in Norman, Oklahoma, and the operational WSR-88D radar at Oklahoma City, Oklahoma. The radar observations are assimilated into the ARPS model within a heterogeneous mesoscale environment and 1-h ensemble forecasts are generated from analyses every 15 min. With a 30-min assimilation period, the PAR experiment is able to analyze more realistic storm structures, resulting in higher skill scores and higher probabilities of low-level vorticity that align better with the locations of radar-derived rotation compared with the WSR-88D experiment. Assimilation of PAR observations for a longer 45-min time period generates similar forecasts compared to assimilating WSR-88D observations, indicating that the advantage of rapid-scan PAR is more noticeable over a shorter 30-min assimilation period. An additional experiment reveals that the improved accuracy from the PAR experiment over a shorter assimilation period is mainly due to its high-temporal-frequency sampling capability. These results highlight the benefit of PAR’s rapid-scan capability in storm-scale modeling that can potentially extend severe weather warning lead times.

The ROLE OF CHINA-PAK ECONOMIC CORRIDOR (CPEC) IN LOGISTIC SYSTEM OF PAKISTAN’S AND CHINA’S

2019 ◽  
Vol 6 (1) ◽  
pp. 48-50
Author(s):  
Ikram Uddin
Keyword(s):  
Persian Gulf ◽  
Lead Time ◽  
Fiber Optic ◽  
Delivery Time ◽  
Fiber Optic Cable ◽  
Total Cost ◽  
Economic Complexity ◽  
Shipping Cost ◽  
The Impact

This study will explain the impact of China-Pak Economic Corridor (CPEC) on logistic system of China and Pakistan. This project is estimated investment of US $90 billion, CPEC project is consists of various sub-projects including energy, road, railway and fiber optic cable but major portion will be spent on energy. This project will start from Kashgar port of china to Gwadar port of Pakistan. Transportation is sub-function of logistic that consists of 44% total cost of logistic system and 20% total cost of production of manufacturing and mainly shipping cost and transit/delivery time are critical for logistic system. According to OEC (The Observing Economic Complexity) currently, china is importing crude oil which 13.4% from Persian Gulf. CPEC will china for lead time that will be reduced from 45 days to 10 days and distance from 2500km to 1300km. This new route will help to china for less transit/deliver time and shipping cost in terms of logistic of china. Pakistan’s transportation will also improve through road, railway and fiber optic cabal projects from Karachi-Peshawar it will have speed 160km per hour and with help of pipeline between Gwadar to Nawabshah gas will be transported from Iran. According to (www.cpec.inf.com) Pakistan logistic industry will grow by US $30.77 billion in the end of 2020.

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