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Impacts of Thinning Aircraft Observations on Data Assimilation and Its Prediction during Typhoon Nida (2016)

1
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
2
Guangzhou Institute of Tropical and Marine Meteorology, Guangzhou 510640, China
3
Hong Kong Observatory, Hong Kong, China
4
Meteorological Center, China Meteorological Administration, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(12), 754; https://doi.org/10.3390/atmos10120754
Received: 21 October 2019 / Revised: 23 November 2019 / Accepted: 26 November 2019 / Published: 28 November 2019
High frequency aircraft observations from the Government Flying Service of the Hong Kong Government, penetrating a tropical cyclone at low altitude over the South China Sea, were thinned by arithmetic means over different time intervals to identify structures of tropical cyclone at different scales. It is found that the thinning process can reduce serial correlation in observational errors and enhance the representation of aircraft observations. Assimilation experiments demonstrate that aircraft observations can improve the track and intensity forecasts of Typhoon Nida (2016). The changes in dynamic structures indicate that the imbalance generated from assimilating aircraft observations at the sub-grid scale can be alleviated by using longer time intervals of the arithmetic mean. Assimilating aircraft observations at the grid scale achieves optimal forecasts based on verifications against independent observations and investigations of environmental and ventilation flows. In addition, it is indicated that decreasing the quality control threshold and changing the observational error of aircraft observations in the data assimilation can reduce the representation errors.
Keywords: aircraft observation; data assimilation; forecasts of tropical cyclones aircraft observation; data assimilation; forecasts of tropical cyclones
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MDPI and ACS Style

Gao, Y.; Xiao, H.; Jiang, D.; Wan, Q.; Chan, P.W.; Hon, K.K.; Deng, G. Impacts of Thinning Aircraft Observations on Data Assimilation and Its Prediction during Typhoon Nida (2016). Atmosphere 2019, 10, 754.

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