Improving Multimodel Weather Forecast of Monsoon Rain Over China Using FSU Superensemble

In this paper we present the current capabilities for numerical weather prediction of precipitation over China using a suite of ten multimodels and our superensemble based forecasts. Our suite of models includes the operational suite selected by NCARs TIGGE archives for the THORPEX Program. These are： ECMWF, UKMO, JMA, NCEP, CMA, CMC, BOM, MF, KMA and the CPTEC models. The superensemble strategy includes a training and a forecasts phase, for these the periods chosen for this study include the months February through September for the years 2007 and 2008. This paper addresses precipitation forecasts for the medium range i.e. Days 1 to 3 and extending out to Day 10 of forecasts using this suite of global models. For training and forecasts validations we have made use of an advanced TRMM satellite based rainfall product. We make use of standard metrics for forecast validations that include the RMS errors, spatial correlations and the equitable threat scores. The results of skill forecasts of precipitation clearly demonstrate that it is possible to obtain higher skills for precipitation forecasts for Days 1 through 3 of forecasts from the use of the multimodel superensemble as compared to the best model of this suite. Between Days 4 to 10 it is possible to have very high skills from the multimodel superensemble for the RMS error of precipitation. Those skills are shown for a global belt and especially over China. Phenomenologically this product was also found very useful for precipitation forecasts for the Onset of the South China Sea monsoon, the life cycle of the mei-yu rains and post typhoon landfall heavy rains and flood events. The higher skills of the multimodel superensemble make it a very useful product for such real time events.

Heavy metal contamination, physico-chemical and microbial evaluation of water samples collected from chromite mine environment of Sukinda, India

Water samples from chromite mine quarry of Sukinda and its adjacent areas were analyzed for their heavy metal contamination along with physico-chemical and microbial contents. The chromite mine water samples possessed high concentrations of heavy metals in the order of Cr〉Fe〉Zn〉Ni〉Co〉Mn while ground water did not show any heavy metal contamination except Fe. Physico-chemical parameters of mine water samples showed deviation from those of normal water. Mine water harboured low microbial populations of bacteria, fungi and actinomycetes in comparison with mine adjacent water samples. The correlation of data between metals with physico-chemical parameters showed both positive and negative responses while that of metal and microbial population exhibited negative correlation. Bacterial strains isolated from chromite mine water exhibited high tolerance towards chromium and other heavy metals as well as antibiotics which could be used as an indicator of heavy metal pollution.

利用小波变换法对地震数据进行频率—时间域分解

谱分析是重要的地震信号处理工具。将地震数据变换到频率域是众多处理算法和解释方法的基础。然而,如果地震信号的频率成分随时间发生变化,那么,进行简单的一维(傅里叶)频率变换是不够的。通过滑动时窗(时间较短)可以提高频一时(FT)空间的谱分解的质量,但该方法的缺点是FT分辨率有限。最近提出的以新的小波分析数学理论为基础的变换法能避免这种分辨率的限制,进行质量更高的谱分解。与短时窗傅氏变换相比,可更好地从概念上搞清比例尺—平移平面的连续小波变换。离散小波变换和匹配追逐算法为可以选择的两种小波变换法,对地震记录成图时可将其变换到F—T空间。合成数据和经标定的炸药震源地震数据的F—T分解表明,匹配追逐算法能很好地使谱局部化,并可清地晰地分辨出反射波、直达波、面波和人为噪声。以小波变换为基础的处理算法为改进处理算法和谱解释法提供了新的机会。