Real-Time Mesoscale Forecast Support During the CLAMS Field Campaign

This paper reports the use of a specialized, mesoscale, numerical weather prediction （NWP） system and a satellite imaging and prediction system that were set up to support the CLAMS （Chesapeake Lighthouse and Aircraft Measurements for Satellites） field campaign during the summer of 2001. The primary objective of CLAMS was to validate satellite-based retrievals of aerosol properties and vertical profiles of the radiative flux, temperature and water vapor. Six research aircraft were deployed to make detailed coincident measurements of the atmosphere and ocean surface with the research satellites that orbited overhead. The mesoscale weather modeling system runs in real-time to provide high spatial and temporal resolution for forecasts that are delivered via the World Wide Web along with a variety of satellite imagery and satellite location predictions. This system is a multi-purpose modeling system capable of both data analysis/assimilation and multi-scale NWP ranging from cloud-scale to larger than regional scale. This is a three-dimensional, non-hydrostatic compressible model in a terrain-following coordinate. The model employs advanced numerical techniques and contains detailed interactive physical processes. The utility of the forecasting system is illustrated throughout the discussion on the impact of the surface-wind forecast on BRDF （Bidirectional Reflectance Distribution Function） and the description of the cloud/moisture forecast versus the aircraft measurement.

Intelligent Forecasting of Sintered Ore’s Chemical Components Based on SVM

Using object mathematical model of traditional control theory can not solve the forecasting problem of the chemical components of sintered ore.In order to control complicated chemical components in the manufacturing process of sintered ore,some key techniques for intelligent forecasting of the chemical components of sintered ore are studied in this paper.A new intelligent forecasting system based on SVM is proposed and realized.The results show that the accuracy of predictive value of every component is more than 90%.The application of our system in related companies is for more than one year and has shown satisfactory results.

Development of a method for comprehensive water quality forecasting and its application in Miyun reservoir of Beijing,China

Water quality forecasting is an essential part of water resource management. Spatiotemporal variations of water quality and their inherent constraints make it very complex. This study explored a data-based method for short-term water quality forecasting. Prediction of water quality indicators including dissolved oxygen, chemical oxygen demand by KMnQ and ammonia nitrogen using support vector machine was taken as inputs of the particle swarm algorithm based optimal wavelet neural network to forecast the whole status index of water quality. Gubeikou monitoring section of Miyun reservoir in Beijing, China was taken as the study case to examine effectiveness of this approach. The experiment results also revealed that the proposed model has advantages of stability and time reduction in comparison with other data-driven models including traditional BP neural network model, wavelet neural network model and Gradient Boosting Decision Tree model. It can be used as an effective approach to perform short-term comprehensive water quality prediction.