Ingredients-based Methodology and Fuzzy Logic Combined Short-Duration Heavy Rainfall Short-Range Forecasting:An Improved Scheme

Short-duration heavy rainfall(SHR),as delineated by the National Meteorological Center of the China Me-teorological Administration,is characterized by hourly rainfall amounts no less than 20.0 mm.SHR is one of the most common convective weather phenomena that can cause severe damage.Short-range forecasting of SHR is an important part of operational severe weather prediction.In the present study,an improved objective SHR forecasting scheme was developed by adopting the ingredients-based methodology and using the fuzzy logic approach.The 1.0°×1.0°National Centers for Environmental Prediction(NCEP)final analysis data and the ordinary rainfall(0.1-19.9 mm h-1)and SHR observational data from 411 stations were used in the improved scheme.The best lifted index,the total precipitable water,the 925 hPa specific humidity(Q 925),and the 925 hPa divergence(DIV 925)were selected as predictors based on objective analysis.Continuously distributed membership functions of predictors were obtained based on relative frequency analysis.The weights of predictors were also objectively determined.Experiments with a typhoon SHR case and a spring SHR case show that the main possible areas could be captured by the improved scheme.Verification of SHR forecasts within 96 hours with NCEP global forecasts 1.0°×1.0°data initiated at 08:00 Beijing Time during the warm seasons in 2015 show the results were improved from both deterministic and probabilistic perspectives.This study provides an objectively feasible choice for short-range guidance forecasts of SHR.The scheme can be applied to other convective phenomena.

A New Multidimensional Time Series Forecasting Method Based on the EOF Iteration Scheme

In this paper a new .mnultidimensional time series forecasting scheme based on the empirical orthogonal function (EOF) stepwise iteration process is introduced. The scheme is tested in a series of forecast experiments of Nino3 SST anomalies and Tahiti-Darwin SO index. The results show that the scheme is feasible and ENSO predictable.

Dynamic-statistics combined forecast scheme based on the abrupt decadal change component of summer precipitation in East Asia

Based on the 1983~2011 CMAP data,the precipitation anomaly in East Asia and its nearby sea regions（hereafter called East Asia for short） demonstrates the ＂＋-＋＂ pattern before 1999 and the ＂-＋-＂ pattern afterwards; this decadal change is contained principally in the corresponding EOF3 component.However,the NCC_CGCM forecast results are quite different,which reveal the ＂＋-＋-＂ pattern before 1999 and the ＂-＋-＋＂ pattern afterwards.Meanwhile,the probability of improving NCC_CGCM＇s forecast accuracy based on these key SST areas is discussed,and the dynamic-statistics combined forecast scheme is constructed for increasing the information of decadal change contained in the summer precipitation in East Asia.The independent sample forecast results indicate that this forecasting scheme can effectively modify the NCC_CGCM＇s decadal change information contained in the summer precipitation in East Asia（especially in the area of 30°N–55°N）.The ACC is 0.25 and ACR is 61% for the forecasting result based on the V SST area,and the mean ACC is 0.03 and ACR is 51% for the seven key areas,which are better than NCC_CGCM＇s system error correction results（ACC is -0.01 and ACR is 49%）.Besides,the modified forecast results also provide the information that the precipitation anomaly in East Asia mainly shows the ＂＋-＋＂ pattern before 1999 and the ＂-＋-＂ pattern afterwards.

THE IMPACT OF A DIAGNOSTIC CLOUD SCHEME ON FORECASTS WITH A GLOBAL ATMOSPHERE MODEL

Experiments were conducted to test the impact of a cloud diagnosis scheme in place of prescribed zonal average cloud on medium and long range integrations with the Australian Bureau of Meteorology Research Centre(BMRC)global atmosphere model.The cloud scheme was shown to improve the temperature bias in the lower troposphere but there was deterioration in the upper troposphere,especially in the tropics,asso- ciated with underestimation of high cloud amount. Thirty day mean fields in a January integration showed greater amplitude in the Northern Hemisphere planetary waves and a deeper Antarctic circumpolar trough than the control experiment or a simulation with no cloud.The results for the diagnosed cloud case agree more closely with corresponding observed fields. There was also some reduction in the zonal average zonal wind component reflecting the additional zonal asymmetry introduced by the diagnostic cloud scheme.Similar trends were also noted in medium and long range forecasts for January and July conditions,although the impact on predictive skill was slight and in some cases detrimental. Areas for improving the diagnostic cloud scheme are noted.