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V. Lakshmanan

Abstract

Weather detection algorithms often rely on a simple rule base that is based on several features. Fuzzy logic can be used in the rule base, and the membership functions of the fuzzy sets can be tuned using a search or optimization algorithm that is based on the principles of natural selection.

The bounded weak echo region (BWER) detection algorithm was developed using a genetic algorithm to tune fuzzy sets. The run-time algorithm uses the tuning information produced by the genetic algorithm to differentiate between BWERs and non-BWERs and to assign confidence estimates to its detections. The genetic algorithm that was used to tune the fuzzy rule base of the BWER algorithm is described.

The paradigm of using a genetic algorithm to tune a fuzzy rule is a very general and useful one. It can be used to improve the performance of other weather detection algorithms. The paradigm makes it easy to change the behavior of a run-time algorithm according to locale and/or end users. The paradigm when applied to the BWER algorithm made it possible to tune the algorithm for use by forecasters as well as by a neural network.

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V. Lakshmanan

Abstract

Wolfson et al. introduced a storm tracking algorithm, called the Growth and Decay Storm Tracker, in which the large-scale features were extracted from radar data fields by using an elliptical filter. The elliptical filter as introduced was computationally too expensive to be performed in real time.

In this paper, it is shown that although the elliptical filter is nonlinear, it can be decomposed into two parts, one of which is, under some simplifying assumptions, linear and shift-invariant. The linear component can be accelerated using fast algorithms available to compute the digital Fourier transform (DFT). Furthermore, it is shown that the nonlinear part can be written as an update equation, thus reducing the amount of computer memory required. With these improvements to the basic large-scale filtering technique, this paper reports that the large-scale filtering can be done 1–2 orders of magnitude faster.

The improvement makes it possible to use the large-scale filtering technique in situations where the computational time and memory requirements have been prohibitive.

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Caren Marzban and V. Lakshmanan

Abstract

Gandin and Murphy (GM) have shown that if a skill score is linear in the scoring matrix, and if the scoring matrix is symmetric, then in the two-event case there exists a unique, “equitable” skill score, namely, the True Skill Score (or Kuipers’s performance index). As such, this measure is treated as preferable to other measures because of its equitability. However, in most practical situations the scoring matrix is not symmetric due to the unequal costs associated with false alarms and misses. As a result, GM’s considerations must be reexamined without the assumption of a symmetric scoring matrix. In this note, it will be proven that if the scoring matrix is nonsymmetric, then there does not exist a unique performance measure, linear in the scoring matrix, that would satisfy any constraints of equitability. In short, there does not exist a unique, equitable skill score for two-category events that have unequal costs associated with a miss and a false alarm.

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Nikhil R. Pal, Achintya K. Mandal, Srimanta Pal, Jyotirmay Das, and V. Lakshmanan

Abstract

A method for the detection of a bounded weak-echo region (BWER) within a storm structure that can help in the prediction of severe weather phenomena is presented. A fuzzy rule–based approach that takes care of the various uncertainties associated with a radar image containing a BWER has been adopted. The proposed technique automatically finds some interpretable (fuzzy) rules for classification of radar data related to BWER. The radar images are preprocessed to find subregions (or segments) that are suspected candidates for BWERs. Each such segment is classified into one of three possible cases: strong BWER, marginal BWER, or no BWER. In this regard, spatial properties of the data are being explored. The method has been tested on a large volume of data that are different from the training set, and the performance is found to be very satisfactory. It is also demonstrated that an interpretation of the linguistic rules extracted by the system described herein can provide important characteristics about the underlying process.

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Kimberly L. Elmore, Z. L. Flamig, V. Lakshmanan, B. T. Kaney, V. Farmer, Heather D. Reeves, and Lans P. Rothfusz

The Weather Service Radar-1988 Doppler (WSR-88D) network within the United States has recently been upgraded to include dual-polarization capability. Among the expectations that have resulted from the upgrade is the ability to discriminate between different precipitation types in winter precipitation events. To know how well any such algorithm performs and whether new algorithms are an improvement, observations of winter precipitation type are needed. Unfortunately, the automated observing systems cannot discriminate between some of the more important types. Thus, human observers are needed. Yet, to deploy dedicated human observers is impractical because the knowledge needed to identify the various precipitation types is common among the public. To most efficiently gather such observations would require the public to be engaged as citizen scientists using a very simple, convenient, nonintrusive method. To achieve this, a simple “app” called mobile Precipitation Identification Near the Ground (mPING) was developed to run on “smart” phones or, more generically, web-enabled devices with GPS location capabilities. Using mPING, anyone with a smartphone can pass observations to researchers at no additional cost to their phone service or to the research project. Deployed in mid-December 2012, mPING has proven to be not only very popular, but also capable of providing consistent, accurate observational data.

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