Abstract
The purpose of this study was to develop an optimal estimation model for rainfall rate retrievals using radar reflectivity, thereby gaining an effective grasp of rainfall information for disaster prevention uses. A process was designed for evaluating the optimal retrieval models using various dataset combinations with radar reflectivity and ground meteorological attributes. Various ground meteorological attributes (such as relative humidity, wind speed, precipitation, etc.) were obtained using the land-based weather stations affiliated with Taiwan’s Central Weather Bureau (CWB). This study used nine radar reflectivity provided by the Hualien weather surveillance radar station’s Volume Cover Pattern 21 system. The developed models are built using multiple machine learning algorithms, including linear regression (REG), support vector regression (SVR), and extreme gradient boosting (XGBoost), in addition to the Marshall–Palmer formula (MP). The study examined 14 typhoons that occurred from 2008 to 2017 at Chenggong station in southeast Taiwan, and Lanyu station in the outlying islands, and the top four major rainfall events were designated as test typhoons—Nanmadol (2011), Tembin (2012), Matmo (2014), and Nepartak (2016). The results indicated that for rainfall retrievals, radar reflectivity at a scanning (elevation) angle of 6.0° combined with ground meteorological attributes were the optimal input variables for the Chenggong station, whereas radar reflectivity at an elevation angle of 4.3° combined with ground meteorological attributes were optimal for the Lanyu station. In terms of model performance, XGBoost models had the lowest error index at Chenggong and Lanyu stations compared with MP, REG, and SVR models. XGBoost models at Lanyu station had the highest efficiency coefficient (0.903), and those at Chenggong station had the second highest (0.885). As a result, pairing the combination of optimal radar reflectivity and ground meteorological attributes, as verified by the evaluation process, with a high-efficiency algorithm (XGBoost) can effectively increase the accuracy of rainfall retrieval during typhoons.
Highlights
Typhoons are extreme weather systems that occur more frequently during summer and fall in Taiwan
Because of radars’ higher resolution and real-time data acquisition, many radar rainfall prediction systems employ the relationship between radar reflectivity and rainfall intensity, expressed as the relationship between radar reflectivity (Z) and rainfall rate (R); for instance, the Marshall–Palmer formula [9] (Z = 200 R1.6 where Z is in mm6/m3 and R is in mm/h) converts radar reflectivity into rainfall rate
The {Z} dataset contained radar reflectivity from all elevation angles, and the mathematical expression was {Z}={Zi}I=1,9, where i represents the radar elevation angles in the VCP21 system (i is from 1 to 9 representing the elevation angles at 0.5, 1.4, 2.4, 3.4, 4.3, 6.0, 9.9, 14.6, and 19.5◦, respectively). {G} contained meteorological attributes filtered through correlation analyses, expressed mathematically as {G}={Gk}k=1,6, where k represents the meteorological attributes. {R} was the rainfall dataset
Summary
Typhoons are extreme weather systems that occur more frequently during summer and fall in Taiwan. Few studies have compared errors in rainfall estimates from a single scan with estimates from scans at multiple elevations; for example, the terrain might block the radar reflectivity, making rainfall conditions behind the mountains unobservable. The aim of this study was to develop an optimal estimation model for rainfall retrievals during typhoons in Taiwan. This study used the radar reflectivity from various elevation angles as input variables for retrieval models and evaluated the optimal. In the rainfall retrieval models established in the study, additional inputs included ground meteorological attributes as well as radar reflectivity. The presented retrieval models used the data combination of radar reflectivity and ground meteorological attributes with a specific gauge instrument.
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