The Warning Decision Support System–Integrated Information

Lakshmanan, Valliappa; Smith, Travis; Stumpf, Gregory J.; Hondl, Kurt

doi:10.1175/waf1009.1

Cited by 232 publications

(147 citation statements)

References 15 publications

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“…AALTO was developed to incorporate practices from radar-based tracking algorithms such as TITAN (Dixon and Wiener, 1993), SCIT , the NSSL MDA , w2segmotion (Lakshmanan et al, 2007(Lakshmanan et al, , 2009, ThOR (Barjenbruch and Houston, 2006;Barjenbruch, 2008;Lahowetz et al, 2010), E-TITAN (Han Scharenbrouch et al, 2010;Root et al, 2011;Lakshmanan et al, 2013;Miller et al, 2013) and satellite-based tracking algorithms (Zahraei et al, 2012;Sieglaff et al, 2013). Each of these algorithms adopts different approaches to forecasting object motion, searching for new objects and identifying the best object to use in continuing a track.…”

Section: Algorithm Designmentioning

confidence: 99%

“…Although existing algorithms designed to detect thunderstorms, mesocyclones and tornado vortex signatures, such as thunderstorm identification, tracking, analysis and nowcasting (TITAN; Dixon and Wiener, 1993); storm cell identification and tracking (SCIT; Johnson et al, 1998); the National Severe Storms Laboratory (NSSL) Tornado vortex signature detection algorithm (TDA; Mitchell et al, 1998); the NSSL mesocyclone detection algorithm (MDA; Stumpf et al, 1998); w2segmotion (Lakshmanan et al, 2007(Lakshmanan et al, , 2009; Thunderstorm observation by radar (ThOR; Barjenbruch and Houston, 2006;Barjenbruch, 2008;Lahowetz et al, 2010) and enhanced-TITAN (E-TITAN; Han et al, 2009) include a tracking component, many recommendations to improve tracking in legacy algorithms have been proposed. For example, Johnson et al (1998) propose that SCIT should incorporate different estimates of initial storm motion throughout the domain and impose limits upon the ability of tracks to change direction.…”

Section: Introductionmentioning

confidence: 99%

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The advanced algorithm for tracking objects (AALTO)

Limpert

Houston

Lock

2015

Meteorological Applications

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ABSTRACT:The advanced algorithm for the tracking of objects (AALTO) constructs tracks from objects, such as thunderstorms or mesocyclones, detected by multiple weather radars at irregular time intervals. It is important to have high accuracy in tracking thunderstorms to generate skilful forecasts and high-quality climatologies and, fundamentally, to ensure that any derived product from tracks captures only that particular storm, and in its entirety. AALTO incorporates many of the best practices of existing tracking algorithms and techniques employed by meteorologists in constructing tracks. AALTO differs from existing algorithms designed to track meteorological phenomena that manifest in radar data in the following ways: (1) AALTO is designed to track objects from multiple radars, enabling analysis over a larger domain than if a single radar was used; (2) improved tracking is realized through improved initial motion estimates and directional thresholding and (3) AALTO looks both at the track history and at the subsequent possible positions along a track when constructing the best possible tracks, mimicking the approach that would be taken by a human meteorologist. Verification was done using metrics that were objectively determined to distinguish between good and degraded tracks; a description of the approach to determine the appropriate metrics is presented. An overview of the AALTO tracking procedure and an example case are presented in this study.

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Section: Algorithm Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The advanced algorithm for tracking objects (AALTO)

Limpert

Houston

Lock

2015

Meteorological Applications

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“…Files were processed using the Warning Decision Support System -Integrated Information (WDSS-II) program (Lakshmanan et al, 2007a) to assess reflectivity (Z) in addition to dual-polarized radar variables including differential reflectivity and specific differential phase shift. Three other variables were also generated based on a KDPbased smoothing field (Ryzhkov et al, 2003) for reflectivity, differential reflectivity, and specific differential phase: DSMZ, DZDR, and DKDP, respectively.…”

Section: Radar Data and Radar Rainfall Algorithmsmentioning

confidence: 99%

Dual-polarized quantitative precipitation estimation as a function of range

Simpson

Fox

2018

Hydrol. Earth Syst. Sci.

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Abstract. Since the advent of dual-polarization radar technology, many studies have been conducted to determine the extent to which the differential reflectivity (ZDR) and specific differential phase shift (KDP) add benefits to estimating rain rates (R) compared to reflectivity (Z) alone. It has been previously noted that this new technology provides significant improvement to rain-rate estimation, primarily for ranges within 125 km of the radar. Beyond this range, it is unclear as to whether the National Weather Service (NWS) conventional R(Z)-convective algorithm is superior, as little research has investigated radar precipitation estimate performance at larger ranges. The current study investigates the performance of three radars -St. Louis (KLSX), Kansas City (KEAX), and Springfield (KSGF), MO -with 15 tipping bucket gauges serving as ground truth to the radars. With over 300 h of precipitation data being analyzed for the current study, it was found that, in general, performance degraded with range beyond, approximately, 150 km from each of the radars. Probability of detection (PoD) in addition to bias values decreased, while the false alarm rates increased as range increased. Bright-band contamination was observed to play a potential role as large increases in the absolute bias and overall error values near 120 km for the cool season and 150 km in the warm season. Furthermore, upwards of 60 % of the total error was due to precipitation being falsely estimated, while 20 % of the total error was due to missed precipitation. Correlation coefficient values increased by as much as 0.4 when these instances were removed from the analyses (i.e., hits only). Overall, due to the lowest normalized standard error (NSE) of less than 1.0, a National Severe Storms Laboratory (NSSL) R(Z,ZDR) equation was determined to be the most robust, while a R(ZDR,KDP) algorithm recorded NSE values as high as 5. The addition of dual-polarized technology was shown to estimate quantitative precipitation estimates (QPEs) better than the conventional equation. The analyses further our understanding of the strengths and limitations of the Next Generation Radar (NEXRAD) system overall and from a seasonal perspective.

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“…Raw radar data were analyzed using the Weather Decision Support System -Integrated 141 Information (WDSS-II) program (Lakshmanan et al, 2007a) to assess reflectivity (Z) in addition to dual-142 polarized radar variables including differential reflectivity (ZDR) and specific differential phase shift 143 (KDP). Many different quality control techniques (e.g., Lakshmanan et al, 2007bLakshmanan et al, , 2010Lakshmanan et al, , 2014 were 144 implemented to the weather radar data processing with WDSS-II.…”

Section: Most Studies In the Us Have Utilized The National Weather Sementioning

confidence: 99%

X-band dual-polarized radar quantitative precipitation estimate analyses in the Midwestern United States

Simpson

Fox

2017

Preprint

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Abstract. Over the past decade, polarized weather radars have been at the forefront of the search 19 for a replacement of estimating precipitation over the spatially, and temporally inferior tipping buckets. 20However, many radar-coverage gaps exist within the Continental US (CONUS), proposing a dilemma in 21 that radar rainfall estimate quality degrades with range. One possible solution is that of X-band weather 22 radars. However, the literature as to their long-term performance is lacking. Therefore, the overarching 23 objective of the current study was to analyze two year's worth of radar data from the X-band dual-24 polarimetric MZZU radar in central Missouri at four separate ranges from the radar, utilizing tipping-25 buckets as ground-truth precipitation data. The conventional R(Z)-Convective equation, in addition to 26 several other polarized algorithms, consisting of some combinations of reflectivity (Z), differential 27 reflectivity (ZDR), and the specific differential phase shift (KDP) were used to estimate rainfall. Results 28 indicated that the performance of the algorithms containing ZDR were superior in terms of the normalized 29 standard error (NSE), missed and false precipitation amounts, and the overall precipitation errors. | P a g eFurthermore, the R(Z,ZDR) and R(ZDR,KDP) algorithms were the only ones which reported NSE values 31 below 100%, whereas R(Z) and R(KDP) equations resulted in false precipitation amounts equal to or 32 greater than 65% of the total gauge recorded rainfall amounts. The results show promise in the utilization 33 of the smaller, more cost-effective X-band radars in terms of quantitative precipitation estimation at 34 ranges from 30 to 80 km from the radar. 35 36

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The Warning Decision Support System–Integrated Information

Cited by 232 publications

References 15 publications

The advanced algorithm for tracking objects (AALTO)

The advanced algorithm for tracking objects (AALTO)

Dual-polarized quantitative precipitation estimation as a function of range

X-band dual-polarized radar quantitative precipitation estimate analyses in the Midwestern United States

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