Research on strong earthquake type division and forecast method for subsequent strong earthquakes

The relationships between energy, amplitude and frequency of eanhquake are correlative with the property of the seismic source. And the grade of the correlativity can be used as an index to distinguish the types of strong earth quakes. Primarily the strong earthquake can be divided into three types of main-after earthquakes, double-main earthquakes and swarm of strong earthquake. There are similarity and a certain repeatability at the quantificational indexes of hypocenter property between the same type of strong earthquakes, which supply basis for the forecast of subsequent strong shocks. The reference indexes of after strong shock forecast which are valuable for the applica tions of the method of type-divided forecast come from the analysis about more than fifty strong shock wide-band (BPZ wave) recording data of CDSN from 1988 to 1997.

Parametric sensitivity analysis of precipitation and temperature based on multi-uncertainty quantification methods in the Weather Research and Forecasting model

Sensitivity analysis(SA) has been widely used to screen out a small number of sensitive parameters for model outputs from all adjustable parameters in weather and climate models, helping to improve model predictions by tuning the parameters. However, most parametric SA studies have focused on a single SA method and a single model output evaluation function, which makes the screened sensitive parameters less comprehensive. In addition, qualitative SA methods are often used because simulations using complex weather and climate models are time-consuming. Unlike previous SA studies, this research has systematically evaluated the sensitivity of parameters that affect precipitation and temperature simulations in the Weather Research and Forecasting(WRF) model using both qualitative and quantitative global SA methods. In the SA studies, multiple model output evaluation functions were used to conduct various SA experiments for precipitation and temperature. The results showed that five parameters(P3, P5, P7, P10, and P16) had the greatest effect on precipitation simulation results and that two parameters(P7 and P10) had the greatest effect for temperature. Using quantitative SA, the two-way interactive effect between P7 and P10 was also found to be important, especially for precipitation. The microphysics scheme had more sensitive parameters for precipitation, and P10(the multiplier for saturated soil water content) was the most sensitive parameter for both precipitation and temperature. From the ensemble simulations, preliminary results indicated that the precipitation and temperature simulation accuracies could be improved by tuning the respective sensitive parameter values, especially for simulations of moderate and heavy rain.