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Following previous studies of the rainfall forecast in Shenzhen owing to landfalling tropical cyclones (TCs), a nonparametric statistical scheme based on the classification of the landfalling TCs is applied to analyze and forecast the rainfall induced by landfalling TCs in the coastal area of Guangdong province, China. All the TCs landfalling with the distance less than 700 kilometers to the 8 coastal stations in Guangdong province during 1950-2013 are categorized according to their landfalling position and intensity. The daily rainfall records of all the 8 meteorological stations are obtained and analyzed. The maximum daily rainfall and the maximum 3 days'accumulated rainfall at the 8 coastal stations induced by each category of TCs during the TC landfall period (a couple of days before and after TC landfalling time) from 1950 to 2013 are computed by the percentile estimation and illustrated by boxplots. These boxplots can be used to estimate the rainfall induced by landfalling TC of the same category in the future. The statistical boxplot scheme is further coupled with the model outputs from the European Centre for Medium-Range Weather Forecasts (ECMWF) to predict the rainfall induced by landfalling TCs along the coastal area. The TCs landfalling in south China from 2014 to 2017 and the corresponding rainfall at the 8 stations area are used to evaluate the performance of these boxplots and coupled boxplots schemes. Results show that the statistical boxplots scheme and coupled boxplots scheme can perform better than ECMWF model in the operational rainfall forecast along the coastal area in south China. [ABSTRACT FROM AUTHOR]

Based on observed rainfall data, this study makes a composite analysis of rainfall asymmetry in tropical cyclones (TCs) after making landfall in Guangdong province (GD) during 1998-2015. There are 3.0 TCs per year on average making landfall in GD and west of GD (WGD) has the most landfall TCs. Most of TCs make landfall in June, July, August, and September at the intensities of TY, STS, and TS. On average, there is more rainfall in the southwest quadrant of TC in CGD (center of GD), WGD, and GD as a whole, and the maximum rainfall is located in the southwest near the TC center. The mean TC rainfall in the east of GD (EGD) leans to the eastern side of TC. The TC rainfall distributions in June, July, August, and September all lean to the southwest quadrant and the maximum rainfall is located in the southwest near the TC center. The same features are found in the mean rainfall of TD, TS, STS, TY, and STY. The maximum rainfall is mainly in the downshear of vertical wind shear. Vertical wind shear is probably the dominate factor that determines asymmetric rainfall distribution of TCs in GD. Storm motion has little connection with TC rainfall asymmetry in GD. [ABSTRACT FROM AUTHOR]

The relationship between the factor of temperature difference of the near-surface layer (T1 000 hPa-T2m) and sea fog is analyzed using the NCEP reanalysis with a horizontal resolution of 1°1° (2000 to 2011) and the station observations (2010 to 2011). The element is treated as the prediction variable factor in the GRAPES model and used to improve the regional prediction of sea fog on Guangdong coastland. (1) The relationship between this factor and the occurrence of sea fog is explicit: When the sea fog happens, the value of this factor is always large in some specific periods, and the negative value of this factor decreases significantly or turns positive, suggesting the enhancement of warm and moist advection of air flow near the surface, which favors the development of sea fog. (2) The transportation of warm and moist advection over Guangdong coastland is featured by some stages and the jumping among these states. It also gets stronger over time. Meanwhile, the northward propagation of warm and moist advection is quite consistent with the northward advancing of sea fog from south to north along the coastland of China. (3) The GRAPES model can well simulate and realize the factor of near-surface temperature difference. Besides, the accuracy of regional prediction of marine fog, the relevant threat score and Heidke skill are all improved when the factor is involved. [ABSTRACT FROM AUTHOR]

By analyzing the NCEP 1° × 1° reanalysis (2004-2008), a number of predictors (factors of variables) are established with the output from the GRAPES model and with reference to the sea fog data from observational stations (2004-2008) and field observations (2006-2008). Based on the criteria and conditions for sea fog appearance at the stations of Zhanjiang, Zhuhai and Shantou, a Model Output Statistics (MOS) scheme for distinguishing and forecasting 24-h sea fog is established and put into use for three representative coastal areas of Guangdong. As shown in an assessment of the forecasts for Zhanjiang and Shantou (March of 2008) and Zhuhai (April of 2008), the scheme was quite capable of forecasting sea fog on the coast of the province, with the accuracy ranging from 84% to 90%, the threat score from 0.40 to 0.50 and the Heidke skill from 0.52 to 0.56. [ABSTRACT FROM AUTHOR]