High False Alarm Ratio Research Articles

Severe hail identification model based on saliency characteristics

There are always high false alarm ratios when warning against the severe hail with the severe hail index (SHI) which is supplied by digital weather radar system. To solve this problem, the extraction algorithm with several novel features, such as "overhang", is designed and realized, and these features can describe the severe hail conceptual model from different aspects. Then we take short-time heavy rainfall cells which are easy to be confused with severe hail cells as counter examples to perform statistic analysis for these features and the SHI. Test results show that they have more significant difference between two kinds of samples and hence each of them can reflect one aspect characteristic of severe hail cells. Then a severe hail recognition model that is the Support Vector Machine with radial primary kernel function is learned. Finally, the normalized distance between the sample to be recognized and the optimal separating hyper-plane is determined as a new SHI for warning against the severe hail. Experimental results show that the method proposed in this paper makes severe hail hit ratio higher than the SHI being used and the false alarm ratio is reduced substantially.

Evaluation of TRMM Multi-satellite Precipitation Analysis (TMPA) performance in the Central Andes region and its dependency on spatial and temporal resolution

Abstract. Climate time series are of major importance for base line studies for climate change impact and adaptation projects. However, for instance, in mountain regions and in developing countries there exist significant gaps in ground based climate records in space and time. Specifically, in the Peruvian Andes spatially and temporally coherent precipitation information is a prerequisite for ongoing climate change adaptation projects in the fields of water resources, disasters and food security. The present work aims at evaluating the ability of Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) to estimate precipitation rates at daily 0.25° × 0.25° scale in the Central Andes and the dependency of the estimate performance on changing spatial and temporal resolution. Comparison of the TMPA product with gauge measurements in the regions of Cuzco, Peru and La Paz, Bolivia were carried out and analysed statistically. Large biases are identified in both investigation areas in the estimation of daily precipitation amounts. The occurrence of strong precipitation events was well assessed, but their intensities were underestimated. TMPA estimates for La Paz show high false alarm ratio. The dependency of the TMPA estimate quality with changing resolution was analysed by comparisons of 1-, 7-, 15- and 30-day sums for Cuzco, Peru. The correlation of TMPA estimates with ground data increases strongly and almost linearly with temporal aggregation. The spatial aggregation to 0.5°, 0.75° and 1° grid box averaged precipitation and its comparison to gauge data of the same areas revealed no significant change in correlation coefficients and estimate performance. In order to profit from the TMPA combination product on a daily basis, a procedure to blend it with daily precipitation gauge measurements is proposed. Different sources of errors and uncertainties introduced by the sensors, sensor-specific algorithm aspects and the TMPA processing scheme are discussed. This study reveals the possibilities and restrictions of the use of TMPA estimates in the Central Andes and should assist other researchers in the choice of the best resolution-accuracy relationship according to requirements of their applications.

Detection algorithm of targets for infrared search system based on area infrared focal plane array under complicated background

Infrared Search and Track (IRST) system is a wide field of view surveillance system, meant for autonomous search, detection, acquisition, and cue of potential targets. The first and second generation IRSTs utilized detectors with multiple elements followed by discrete preamplifiers for signal read-out. They have many performance limitations. With the advent of infrared focal plane array (IRFPA) sensors, the present trend is to build IRSTs based on line FPA sensors to achieve higher sensitivity and resolution. This article describes a new IRST system, different from other systems with area infrared focal plane array (IRFPA), using area infrared focal plane array (AFPA). This system has the simple and reliable structure. First this paper described the surveillance system based on AFPA. And the Point Scattering Function (PSF) of degradation model images blurred by AFPAs’ rotations and the imaging feature of the targets is presented. And the target detection algorithm is described and analyzed, the maximum filter and center filter is advanced. Finally the experiment results have proved that the IRST system using AFPA has high detection ratio and low false alarm ratio.

Evaluation of TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis)’s predictive skill for hurricane-triggered landslides: a case study in Macon County, North Carolina

The key to advancing the predictability of rainfall-triggered landslides is to use physically based slope-stability models that simulate the transient dynamical response of the subsurface moisture to spatiotemporal variability of rainfall in complex terrains. TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis) is a USGS landslide prediction model, coded in Fortran, that accounts for the influences of hydrology, topography, and soil physics on slope stability. In this study, we quantitatively evaluate the spatiotemporal predictability of a Matlab version of TRIGRS (MaTRIGRS) in the Blue Ridge Mountains of Macon County, North Carolina where Hurricanes Ivan triggered widespread landslides in the 2004 hurricane season. High resolution digital elevation model (DEM) data (6-m LiDAR), USGS STATSGO soil database, and NOAA/NWS combined radar and gauge precipitation are used as inputs to the model. A local landslide inventory database from North Carolina Geological Survey is used to evaluate the MaTRIGRS’ predictive skill for the landslide locations and timing, identifying predictions within a 120-m radius of observed landslides over the 30-h period of Hurricane Ivan’s passage in September 2004. Results show that within a radius of 24 m from the landslide location about 67% of the landslide, observations could be successfully predicted but with a high false alarm ratio (90%). If the radius of observation is extended to 120 m, 98% of the landslides are detected with an 18% false alarm ratio. This study shows that MaTRIGRS demonstrates acceptable spatiotemporal predictive skill for landslide occurrences within a 120-m radius in space and a hurricane-event-duration (h) in time, offering the potential to serve as a landslide warning system in areas where accurate rainfall forecasts and detailed field data are available. The validation can be further improved with additional landslide information including the exact time of failure for each landslide and the landslide’s extent and run out length.

Predictability of seasonal east coast winter storm surge impacts with application to New York's Long Island

AbstractThe characteristics of seasons with enhanced east coast winter storm (ECWS) and storm surge activity are identified from among a set of global atmospheric circulation indices and local land and sea surface temperature (SST) anomalies. Without regard for storm strength or surge potential, the most active ECWS seasons occur in association with El Niño events. There is also some indication that such seasons are preferred under the positive phase of the Pacific decadal oscillation (PDO). In terms of storm surge potential, forecasts of strong ECWS activity are more skillful than direct forecasts of the number of extreme surge events. In both cases, SSTs off the southeast US coast and in the Gulf of Mexico differentiate high seasonal activity from relatively inactive seasons. Warmer‐than‐normal SSTs in both regions during summer provide a measure of storm activity in the subsequent winter.The results provide a means of anticipating seasonal ECWS activity, and to some degree impacts, that is similar to widely used forecasts of tropical storm activity. From a predictive standpoint, forecasts of active strong storm seasons and low surge activity exhibit fairly high false alarm ratios. However, the false alarm rate for forecasts of low storm activity or high surge activity is less than 10%. Copyright © 2008 Royal Meteorological Society

Ground Fog Detection from Space Based on MODIS Daytime Data—A Feasibility Study

Abstract The distinction made by satellite data between ground fog and low stratus is still an open problem. A proper detection scheme would need to make a determination between low stratus thickness and top height. Based on this information, stratus base height can be computed and compared with terrain height at a specific picture element. In the current paper, a procedure for making the distinction between ground fog and low-level stratus is proposed based on Moderate Resolution Imaging Spectroradiometer (MODIS, flying on board the NASA Terra and Aqua satellites) daytime data for Germany. Stratus thickness is alternatively derived from either empirical relationships or a newly developed retrieval scheme (lookup table approach), which relies on multiband albedo and radiative transfer calculations. A trispectral visible–near-infrared (VIS–NIR) approach has been proven to give the best results for the calculation of geometrical thickness. The comparison of horizontal visibility data from synoptic observing (SYNOP) stations of the German Weather Service and the results of the ground fog detection schemes reveals that the lookup table approach shows the best performance for both a valley fog situation and an extended layer of low stratus with complex local visibility structures. Even if the results are very encouraging [probability of detection (POD) = 0.76], relatively high percentage errors and false alarm ratios still occur. Uncertainties in the retrieval scheme are mostly due to possible collocation errors and known problems caused by comparing point and pixel data (time lag between satellite overpass and ground observation, etc.). A careful inspection of the pixels that mainly contribute to the false alarm ratio reveals problems with thin cirrus layers and the fog-edge position of the SYNOP stations. Validation results can be improved by removing these suspicious pixels (e.g., percentage error decreases from 28% to 22%).

Modélisation d'un système de monitoring. Application de méthodologies

The Monitoring Systems used in Coronary Intensive Care Units still suffer from lacks such as high false alarm ratios. In order to give an answer to this fact, an architecture for a Monitoring System taking advantage from Intelligence concepts is now described, by means of real time systems design methods. The system is been tackled according to three views: The functional view specifies a global architecture, with the SA/RT methodology and leads to a decomposition of the system into Entite Logicielle Intelligente (ELI), deterministic and intelligent models. The dynamic view designs, with Petri Nets, the time behaviour of ELI. The structural view express integration of these ELI into an object environment, using UML notation. A patient simulator, and a monitoring system prototype, integrating various ELIs responsible for monitoring in Cardiology, have been developed, allowing to validate the foundation of its architecture.