Abstract
In spite of efforts made by the scientific community during the last decades on the weather forecast improving, prediction of precipitation systems and fogs is still considered to be a difficult challenge. The main reason for the difficulties in prediction of these phenomena is the complexity of their formation, such as orography dependence, spatio-temporal inhomogeneity of land use and large scale synoptic conditions. Remote sensing and in-situ data assimilation have been applied to a number of studies in recent years, demonstrating significant improvements of the model results.
The objective of this study is to evaluate the performance of Weather Research and Forecasting (WRF) model, and assess the improvement in the short-term precipitation forecast, using high-resolution data assimilation of satellite and in-situ measurements. The study case is specific weather phenomenon for the Eastern parts of Balkan Peninsula - passing winter Mediterranean cyclone causing excessive amounts of rainfall in Bulgaria. A three-dimensional variational (3D-Var) data assimilation system is used in this study. The model results obtained using or not data assimilation procedure, are compared to demonstrate the impact of this method on the start time of precipitation, rainfall spacial distribution and amount.
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10 December 2020
In the originally published version of chapter 30 an acknowledgement was missing. This has been corrected.
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Acknowledgements
This work has been supported by Research Fund at the Bulgarian Ministry of Education and Science, grant number DN4/7 (Study of the PBL structure and dynamics over complex terrain and urban area). We acknowledge also the provided access to the e-infrastructure of the NCDSC - part of the Bulgarian National Roadmap on RIs, with the financial support by the Grant No D01-221/03.12.2018.
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Vladimirov, E., Dimitrova, R., Danchovski, V. (2020). Impact of Data Assimilation on Short-Term Precipitation Forecasts Using WRF-ARW Model. In: Lirkov, I., Margenov, S. (eds) Large-Scale Scientific Computing. LSSC 2019. Lecture Notes in Computer Science(), vol 11958. Springer, Cham. https://doi.org/10.1007/978-3-030-41032-2_30
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DOI: https://doi.org/10.1007/978-3-030-41032-2_30
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