Search Results (3)

During the 2020/2021 winter season, three nationwide cold waves took place from 28 to 31 December 2020, as well as from 5 to 8 January and 14 to 17 January 2021. These cold waves resulted in extreme cold weather in northern and eastern China. In this study, the common features of these cold waves were analyzed, and the key factors contributing to cold waves were illustrated, and the performance of the CMA-GEPS numerical model was evaluated in predicting the cooling effect of the cold waves, and its predictability source was discussed. The results indicated that the cold waves were caused by synergistic effects in the mid- to high-latitude atmospheric circulation of both the upper and lower atmosphere, including polar vortex splitting, enhancement of blocking high, and increased meridional circulation anomaly in the Siberian high area. During the time of cold waves, the mid- to high-latitude atmospheric circulation was undergoing low-frequency adjustment, with the Arctic oscillation continuously weakening, while the blocking high and Siberian high gradually increased to historically high-intensity states. The outbreaks of the three cold waves occurred at the peak and declining points of the blocking high and Siberian high, respectively, acting as short- to medium-term forecast factors. The CMA-GEPS model demonstrated high forecasting ability for the cooling of the cold waves due to its ability to accurately predict the evolution of the Siberian high and blocking high prior to and after the cold wave with a long lead time. Predictability analysis suggested the strong variability of key factors (such as the Siberian high and blocking) in cold wave events may benefit the model’s prediction of cold wave events. These findings contribute to the understanding of the physical mechanisms behind cold waves and the potential for improved forecasting of extreme cold weather events. Full article

River flow forecasting is an essential tool to manage floods in the current era, especially for flash flooding scenarios in urban areas. This study focuses the flash flooding scenario in the Nullah Lai basin, which comprises the twin cities Islamabad and Rawalpindi. Steep slopes in the Margalla hills and Islamabad create high numbers of flash floods in the lower reaches of Rawalpindi, which are densely populated. When high-intensity rainfall occurs in the steep slopes of Margalla and Islamabad, high-volume floods with high velocity pour down, which instantaneously reaches the less-sloped Rawalpindi regions, which causes the raising of the water level in the stream, and flooding occurs. The section of the Nullah Lai Rawalpindi starting from the Qatarian bridge to the Gawalmandi bridge has always faced flash flooding over time. In the period of few hours, the water level reaches several fts in the nullah, which is why it is not possible to alert the people living on the banks in a timely manner, a problem that illuminates the need for a forecasting system at Nullah Lai. In the current research, the China Metrological Agency forecast center (CMA)’s ensemble forecast data have been utilized to achieve forecasts in the Nullah Lai. For this purpose, two initial objectives were set to achieve which basic needs are required process the data available in grib format from data centers. A digital model of the Nullah Lai was made using hydrology tools available in ArcGIS 10.3. A digital equation was obtained from gene expression modeling (GEP), which was later used to generate the ensemble stage forecast against the ensemble rainfall forecast. The results obtained show that the flash flooding phenomenon in Nullah Lai can, with some uncertainty, be predicted well in time. Using 3-days-ahead forecast data from CMA, the same floods were predicted 3 days before the event. This research also provides the procedure to use the ensemble forecast data in developing an automated model to generate the ensemble stage forecast for coming events. This study will help the administrative authorities better manage the upcoming floods and save lives and capital costs lost in the flash flooding phenomena which continuously happen in the basin of the Nullah Lai. Full article

The 2-m temperature is one of the important meteorological elements, and improving the accuracy of medium- and long-term forecasts of the 2-m temperature is important. The similarity forecasting method is widely used as a calibration technique in the statistical postprocessing of numerical weather prediction (NWP). In this study, the analogue ensemble averaging method is used to correct the deterministic forecast of the 2-m temperature with a forecast lead time from 180 h to 348 h using the CMA-GEPS model. The bias, mean absolute error (MAE), and root mean square error (RMSE) are used as the evaluation metrics. In comparison with NWP, the systematic error of the model for 2-m temperature is effectively reduced during each forecast period when using the analogue ensemble averaging method. In addition, the differences in forecast errors between regions are reduced, and the accuracy of 2-m temperature forecasts over complex terrain, especially in Southwest China, Northwest China, and North China, is improved using this method. In the future, there is certainly potential to apply the analogue ensemble averaging method to the bias correction of medium- and long-term forecasts of more meteorological elements. Full article