Review of Yilan Experiment of Severe Rainfall in 2020
(YESR2020)/TAHOPE2020-Winter andbriefing of the machine learning based data calibration system of"Storm tracker" mini-radiosonde
Shi-Hao Su1, Yu-Hung Chang2, Ching-Hwang Liu1, Wei-Ting Chen2*, (the rest in alphabeticorder) Wei-Yu Chang3, Jen-Ping Chen2, Wei-Nai Chen5, Kao-Sheng Chung3, Jou-Ping Hou5,
Hung-Chi Kuo2, Yu-Chi Lee6, Pay-Liam Lin3, Po-Hsiung Lin2, Min-Hui Lo2, Sheng-Hsiang
Wang3, Chien-Ming Wu2, Ming-Jen Yang2
1

Department of Atmospheric Sciences, Chinese Culture University, Taipei, Taiwan
Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
3
Department of Atmospheric Sciences, National Central University, Taoyuan, Taiwan
4
Research Center of Environmental Change, Academia Sinica, Taipei, Taiwan
5Department of Environmental Information and Engineering, Chung Cheng Institute ofTechnology, National Defense University, Taoyuan, Taiwan
6
Central Weather Bureau, Taipei, Taiwan
2ABSTRACT
We introduce a recent field experiment investigating the multi-scale terrain-circulationprecipitation interactions. When the synoptic-scale northeasterly wind prevails under the active
East Asian winter monsoon, stratocumulus cloud decks with severe rainfall exceeding 100
mm·d-1 frequently occur in the northeastern plain area and adjacent mountains in Yilan, Taiwan.
Yilan Experiment of Severe Rainfall (YESR2020)/TAHOPE 2020-winter was a field campaign
from 20 November 2020 to 24 November 2020 to survey the physical processes leading tosevere wintertime rainfall. High temporal and spatial resolution sounding observations canidentify the three-dimensional structure of the wind field and the atmospheric environment,
which is empowered by the novel “Storm Tracker (ST)” mini-radiosonde. During YESR2020,
the continuously collected meteorological data of two northeasterly episodes captured thevariability of local-scale wind patterns and the features of the severe rainfall induced bystratocumulus. A preliminary analysis indicated that a local-scale convergence line could appear
over the plain area of Yilan under the northeasterly environmental condition. Moreover, thesevere rainfall of the two northeasterly episodes spotlighted shallow cumulus under stratus withpure warm rain processes. The results of YESR2020 inspire the arrangement of future fieldobservations to explore detailed mechanisms of heavily-precipitating stratocumulus overcomplex topography.
In YESR2020, we also established a two-step machine-learning (ML) based radiosonde
calibration model for the " Storm Tracker (ST)" mini-radiosonde data correction. Due to therelatively low cost of a single launching, it has advantages for high-density/high-frequency
boundary layer observations. The general linear model (GLM) and the gradient boostingmachine model (GBM) showed excellent performance in sounding data correction. The
statistical characteristics are consistent with the Vaisala RS41 observations as the reference
dataset. Overall, after the calibration process, the ST sounding has a similar performance to thereference sounding and has reached the level required for environmental sampling and scientificresearch.
REFERENCES
Su, S.H., Chang, Y.H., Liu, C.H., Chen*, W.T., ... &Yang, M.Y., 2022: Observing Severe
Precipitation near Complex Topography during the Yilan Experiment of Severe Rainfall in 2020
(YESR2020), submitted to Quarterly Journal of the Royal Meteorological Society, underrevising
Su, S.H., Chang, C.W., Yo, T.S., Yu, H.J., Lin, P.H., &Kuo*, H.C., 2022: An artificialintelligence-based approach of the mini-radiosonde- "Storm Tracker" data calibration. Drafting
Keywords: heavily precipitating stratocumulus; terrain-circulation-precipitation interactions; Yilan
Experiment of Severe Rainfall; Storm Tracker mini-radiosonde; machine-learning (ML) based radiosonde
calibration model