Id Cluckie

Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar

This paper presents the results of a study designed to classify weather radar clutter echoes obtained from ground-based dual-polarization weather radar systems. The clutter signals are due to ground clutter, sea clutter, and anomalous propagation echoes, which represent sources of error in quantitative radar rainfall estimation. Fuzzy and Bayes classifiers are evaluated as an alternative approach to traditional polarimetric-based methods. Both systems were trained and validated by using C-band dual- polarization radar measurements, and a novel technique is proposed to calculate the texture function to mitigate against the edge effects at the boundaries of precipitation regions. A methodology is presented to extract the membership functions and conditional probability density functions to train the classifiers. The critical success index indicates that the Bayes classifier has, on average, a slightly better performance than the fuzzy classifiers. However, when optimal weighting was applied, the fuzzy classifier gave one of the best performances. The classifiers are sufficiently robust to be used when only single-polarization radar measurements are available.

River Flow Modelling Using Fuzzy Decision Trees

A modern real time flood forecasting system requires itsmathematical model(s) to handle highly complex rainfall runoffprocesses. Uncertainty in real time flood forecasting willinvolve a variety of components such as measurement noise fromtelemetry systems, inadequacy of the models, insufficiency ofcatchment conditions, etc. Probabilistic forecasting is becomingmore and more important in this field. This article describes a novel attempt to use a Fuzzy Logic approach for river flow modelling based on fuzzy decision trees. These trees are learntfrom data using the MA-ID3 algorithm. This is an extension of Quinlans ID3 and is based on mass assignments. MA-ID3 allows for the incorporation of fuzzy attribute and class values intodecision trees aiding generalisation and providing a framework for representing linguistic rules. The article showed that with only five fuzzy labels, the FDT model performed reasonably welland a comparison with a Neural Network model (Back Propagation)was carried out. Furthermore, the FDT model indicated that therainfall values of four or five days before the prediction time are regarded as more informative to the prediction than the morerecent ones. Although its performance is not as good as the neural network model in the test case, its glass box nature couldprovide some useful insight about the hydrological processes.

Bright-band detection from radar vertical reflectivity profiles

The use of quantitative scanning weather radar for precipitation measurements is a vital element of modern hydrology and limits the development of all distributed models of catchment behaviour. The presence of the so‐called bright band (or melting layer) contaminates the quantitative precipitation estimates and has delayed the widespread take‐up of radar‐based precipitation estimates in operational models. The study of the Vertical Reflectivity Profile (VRP) of precipitation is important in order to develop algorithms to correct scanning weather radar measurements for the variation of the VRP at long ranges. Therefore, this paper presents an algorithm to detect the extent of the bright band using high‐resolution VRPs. The boundaries of the bright band are identified by a new algorithm which utilizes a rotational coordinate system for identifying the upper and lower parts of the bright band. This overcomes some of the difficulties experienced when using the gradient of the reflectivity in conventional bright‐band detection algorithms. The reflectivities above, within, and below the bright band are then used to construct idealized VRPs to correct scanning weather radar measurements.

Liuxihe Model and Its Modeling to River Basin Flood

Past research shows that physically based distributed hydrological model has the advantage of better representing the basin characteristics and the hydrologic processes to potentially simulate/predict river basin flood. But how to physically derive model parameters directly from terrain data and to acquire channel cross-sectional size are still difficult jobs in physically based distributed hydrological modeling that prevented its operational application in river basin flood forecasting. To deal with these challenges, this paper first presents a physically based, distributed hydrological model for river basin flood forecasting/simulation, called the Liuxihe model. Then a method for estimating channel cross-sectional size was proposed that utilizes a readily accessible public data set acquired by remote sensing techniques, which could be employed by other physically based, distributed hydrological models also. Finally, a method for deriving model parameters was proposed that adjusts model parameters with i...

Classification of Weather Radar Images using Linguistic Decision Trees with Conditional Labelling

This paper focuses on the application of LID3 (linguistic decision tree induction algorithm) to the classification of weather radar images. In radar analysis a phenomenon known as bright band occurs. This essentially is an amplification in reflectivity due to melted snow and leads to overestimation of precipitation. It is therefore beneficial to detect this bright band region and apply the appropriate corrections. This paper uses LID3 in order to identify the bright band region pixel by pixel in real time. This is not possible with the current differencing methods currently used for bright band detection. LID3 also allows us to infer a set of linguistic rules to further our understanding of the relationship between radar measurements and the classification of bright band. A new idea called conditional labeling is proposed, which attempts to ensure a more efficiently partitioned space, omitting relatively sparse branches caused by attribute dependencies.

Radar evidence of orographic enhancement due to the seeder feeder mechanism

of New Zealand’s Southern Alps. This enhancement is generally around a factor of four for individual storms and may be as high as a factor of eight, with much of the increase occurring in approximately 30 km between the coastal plains and windward slopes of the Alps (Henderson 1993). A physical mechanism for the observed enhancement is studied in Purdy & Austin (2003), and involves advection of cloud droplets due to synoptic scale lifting into the area of orographic lifting. The Southern Alps run almost the full 800 km length of SouthIslandandregularlyreachover2000minaltitude. As annual accumulations on the windward western slopes of the Southern Alps often exceed 10 m (Griffiths & McSaveny 1983) there is a real need for quality rainfall forecasts, which require an understanding of the contributions from such orographic processes as the seeder feeder mechanism. Most of the rainfall in this region is associated with cold fronts embedded in the prevailing moist westerlyflow (e.g. Ryan 1984; Sturman & Tapper 1996) and the example presented here fits this generalised pattern.

Fuzzy Bayesian Modeling of Sea-Level Along the East Coast of Britain

A fuzzy Bayesian algorithm is introduced, allowing for the incorporation of both uncertainty and fuzziness into data derived models. This is applied to predicting the sea-level near the Thames Estuary at Sheerness, from tidal gauge measurements down the east coast, astronomical tidal prediction, and meteorological data. We show that this approach can result in accurate, low-dimensional models with low computational costs and relatively fast execution times.

Using Weather Radars to Measure Rainfall in Urban Catchments

and on the eastern seaboard of the United States, this is achieved through combined urban drainage systems. In these cities, surface runoff from storms is channelled into pipe networks that are also used for disposing of industrial and domestic effluent. Hence the term “combined system.” The capacity of a pipe network has traditionally been designed by using empirical formulae such as the Rational Method used in the United Kingdom. However, because of underdesign, the addition of new connections to the network, and changes in land-use patterns, even moderate rainfalls tax the drainage systems of many cities. To prevent the surface flooding with polluted runoff that could occur in such cases, many systems include combined sewer overflows (CSOs) throughout their networks. CSOs are usually fixed weirs within the pipes that spill to adjacent watercourses when the depth of effluent exceeds predetermined limits. Sewer overflows can be significant sources of urban aquatic pollution when the capacity of the drainage system is inadequate to meet the combined flows of sewage and surface runoff. In the United Kingdom, this passive approach to urban drainage changed in the 1980s as a result of legislation that began placing limits

Using an Adaptive Neuro-fuzzy Inference System in the Development of a Real-Time Expert System for Flood Forecasting

This chapter describes the development of a prototype flood forecasting system provided in a real-time expert system shell called COGSYS KBS. Current efforts on the development of flood forecasting approaches have highlighted the need for fuzzy-based learning strategies to be used in extracting rules that are then encapsulated in an expert system. These strategies aim to identify heuristic relationships that exist between forecast points along the river. Each upstream forecast point automatically produces extra knowledge for target downstream forecast points. Importantly, these strategies are based on the adaptive network-based fuzzy inference system (ANFIS) technique, which is used to extract and incorporate the knowledge of each forecast point and generate a set of fuzzy “if–then” rules to be exploited in building a knowledge base. In this study, different strategies based on ANFIS were utilised. The ANFIS structure was used to analyse relationships between past and present knowledge of the upstream forecast points and the downstream forecast points, which were the target forecast points at which to forecast 6-hour-ahead water levels. During the latter stages of development of the prototype expert system, the extracted rules were encapsulated in COGSYS KBS. COGSYS KBS is a real-time expert system with facilities designed for real-time reasoning in an industrial context and also deals with uncertainty. The expert system development process showed promising results even though updating the knowledge base with reliable new knowledge is required to improve the expert system performance in real time.

Real-time flood forecasting using updateable linguistic decision trees.

This paper focuses on the application of LID3 (linguistic decision tree induction algorithm) to real-time flood forecasting. Specifically the prediction of the river level at locations along the River Severn, Britainpsilas largest river. Modelling river dynamics implies modelling a system that changes over time. It is therefore inappropriate to use a static model to model river levels, that are driven by an underlying dynamic system. Hence, an updateable version of LID3 is proposed. There are two main features of ULID3 (updateable LID3). The first being error-based updating, which weights new instances depending on the treepsilas current ability to describe each new example. The ability to update probability distributions at each node enables the tree to adapt and capture the new dynamic concept more effectively. The second feature is the ability to extend both the input and output domains, given new examples. This is necessary when the data available for updating, exceeds the current domain set by the training data. An algorithm is presented to update the new probability distributions throughout the tree, without the need for storing the complete set of examples at each node.