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Probability forecast is an objective product derived from ensemble forecasts and contains information of uncertainty, which has important reference value for operational decision-making services. In the traditional neighborhood ensemble probability method (NEP), the neighborhood radius is always a constant and does not conform to the scale spectrum involved in the practical weather events. Therefore, the Neighborhood Ensemble Probability based on Ensemble Agreement Scale (EAS_NEP) was introduced, and comprehensive evaluations were conducted for two Meiyu frontal heavy rainfall events in southern China, in order to verify the applicability of this method in such events and promote its popularization in practical operation. The ensemble forecasts obtained by combining the initial condition, lateral boundary and physical process of perturbations can better represent the practical prediction uncertainty. Based on this, the Grid-to-grid Probability (GP), NEP with different radii and EAS_NEP were compared for accuracy, forecast skill and other aspects of performance. The results showed that EAS_NEP can adjust the neighborhood radius adaptively according to the agreement between ensemble members and its radii tend to be lager for concentrated precipitation than dispersed precipitation. These dynamically adjusted radii not only avoid excessive smoothing and key information loss when the neighborhood radius is too large, but also eliminate the singular points caused by small radius and possess stepped spatial distribution with better continuity. In addition, quantitative evaluation results such as BS, FSS and ROC curves also confirm that EAS_NEP has improved forecasting skills compared to traditional methods, especially in the case of dispersed precipitation and high threshold situation. The results suggest that EAS_NEP has a good application prospect in the Meiyu frontal heavy rainfall events and can effectively improve the quality of probability forecast. [ABSTRACT FROM AUTHOR]

This paper focuses on developing an agricultural droughty monitoring method in north Jiangsu province based on the measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS). In order to build soil moisture estimation model, we collected gravimetric water content of soil at experimental sites in 2011, measured the soil moisture of the sites in 2012, and downloaded the 8-day MODIS reflectance and land surface temperature data from January to May in 2011 and 2012 in this study region. The gravimetric water content of soil included soil moisture at 10 cm depth and at 20 cm depth. The used MODIS data have some noise from atmospheric effects, and some data can not be acquired because of cloud. Therefore, a Savitzky-Golay (S-G) filter method was selected to remove NDVI and LST noise, and generate lost NDVI and LST. Then, the Temperature-Vegetation Dryness Index (TVDI) was calculated from the re-created NDVI and LST data. A correlation analysis between TVDI and soil moisture at 10 cm and 20 cm depth were conducted. The results showed that TVDI was more correlative with soil moisture at 10 cm depth compared to at 20 cm depth, and that soil moisture at 10 cm depth was highly correlative with soil moisture at 20 cm depth. Based on the TVDI and soil moisture data at 10 cm depth, an empirical model for soil moisture estimation was built and validated. In addition, an empirical model was also built to describe the relationship between soil moisture at 10 cm and 20 cm depth. Finally, the two models was utilized to estimate soil moisture at 20 cm depth in the area from MODIS data, and the estimated soil moisture was used to monitor field droughty status with a criterion about wheat field draughty evaluation. The results show that S-G filter method removes the MODIS data noise, and can be used to generate the lost data. The correlation analysis between soil moisture and TVDI shows that TVDI has higher correlation with soil moisture at 10 cm depth, and a linear model can be used to best-fit the relationship between TVDI and the soil moisture at 10 cm depth. The correlation analysis between soil moisture at 10 cm depth and at 20 cm depth shows that soil moisture at 20 cm depth has higher correlation with soil moisture at 10 cm depth, and a linear model can be used to best-fit the relationship between soil moisture at 10 cm depth and at 20 cm depth. The validation experiments show that the model obtains a high accuracy of soil moisture estimation with an r2 of 0.575 and a RMSE of 2.59 %. Using this model, soil moisture maps at 10 cm depth were obtained. The linear model describing the relationship between soil moisture at 10 cm and 20 cm depth was used to obtain soil moisture maps at 20 cm depth. Wheat field draught maps in north Jiangsu Province were obtained by the criterion about wheat field draughty evaluation. Validation experiments showed that the experiments showed the droughty monitoring method was promising in monitoring the droughty, which appeared in north Jiangsu province. [ABSTRACT FROM AUTHOR]