Neural network design
Hybrid approach to MK classification of stars neural networks and knowledge-based systems
AIKED'07 Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases - Volume 6
Fuzzy neural network models for multispectral image analysis
CSECS'06 Proceedings of the 5th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing
Comparison studies on classification for remote sensing image based on data mining method
WSEAS Transactions on Computers
Warm rainy clouds and droplet size distribution
WSEAS TRANSACTIONS on SYSTEMS
A satellite rainfall detection algorithm
IMCAS'09 Proceedings of the 8th WSEAS international conference on Instrumentation, measurement, circuits and systems
A projection algorithm for satellite rainfall detection
WSEAS TRANSACTIONS on SYSTEMS
| Hi-index | 0.00 |
Validation of the Hydro-Estimator (HE) and the Next Generation Radar (NEXRAD) during heavy storms over Puerto Rico (PR) is reported. The HE is a high resolution rainfall retrieval algorithm based on satellite and numerical weather prediction model data. The accuracy of the HE and the NEXRAD rainfall estimates can be measured by decomposing the rainfall process into sequences of discrete (rain / no rain) and continuous (rainfall rate) random variables. Validation results are based on five heavy storms that seriously impacted human life and the economy of PR during the period 2003 to 2005. The average discrete validation results indicate acceptable hit rate values for both the HE and NEXRAD (0.76 vs. 0.87) and reasonable discrete bias ratios (1.04 vs. 0.73) but a very low of probability of detection of rain for both the HE and NEXRAD (0.36 vs. 0.52). The HE shows an overestimation on average whereas the NEXRAD exhibits underestimation in the continuous validation results (continuous bias ratio of 1.14 vs 0.70 for NEXRAD), which contributes to moderate overall errors for the HE and NEXRAD in terms of root mean squared error (2.14 mm vs. 1.66 mm) and mean absolute error (0.96 mm vs. 0.77 mm). The HE algorithm was designed to operate over US continental areas and satisfactory results have been reported. However, over tropical regions it was determined that warm clouds can generate substantial rainfall amounts that are not detected by the HE algorithm. It is known that precipitation processes in clouds with warm tops are very sensitive to the microphysical structure of their tops. Specifically, precipitation processes are more efficient when water droplets or/and ice particles grow to larger sizes. It has been shown that the uses of the reflected portion of the near-infrared during the daytime indicates the presence large cloud-top particles and suggest rain in warm-top clouds. It has been used the effective radius of clouds particles to detect raining clouds. However, the available algorithms to estimate the effective radius are designed to operate over ice clouds. We are in the process of developing an algorithm to extract the microphysical structure for rainy warm top clouds, and the first step of this algorithm is to estimate the emittance of near infrared window, which is described in this paper.