Impacts of satellite data assimilation with different model vertical levels on QPFs downstream of the Tibetan Plateau
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ORIGINAL PAPER
Impacts of satellite data assimilation with different model vertical levels on QPFs downstream of the Tibetan Plateau Zhengkun Qin1 · Xiaolei Zou1 Received: 17 March 2020 / Accepted: 6 October 2020 © Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract Measurements from various sounding instruments onboard polar-orbiting meteorological satellites quantify contributions to the total radiation at various microwave or infrared frequencies from different levels of the atmosphere. Satellite data assimilation adjusts model profiles of temperature and water vapor by minimizing the differences between observations and model simulations to search for the maximum likelihood estimate of the atmospheric states. The accuracy and precision of satellite data assimilations depend on the model vertical resolution. Sensitivity studies are carried out to compare the data assimilation and forecast results over a domain centered on the Tibetan Plateau (TP) using three different model vertical resolutions: 43, 61, and 92 vertical levels from the surface to ~ 1 hPa. The NCEP Gridpoint Statistical Interpolation (GSI) analysis system and the Advanced Research Weather Research and Forecasting (ARW) model are used with a domain size of 600 × 500 grid boxes at a 15-km horizontal resolution. It is shown that the ARW/GSI system with the coarsest (highest) model vertical resolution outperforms the remaining two for the 24-h short-range (48-h medium-range) quantitative precipitation forecasts (QPFs) downstream of the TP. The satellite data assimilation at the highest model vertical resolution produced more significant positive impacts on the 36-h forecasts of a mid-tropospheric trough located to the northeast of the TP that lead to a localized precipitation event. Improvements in the QPFs with the 92-vertical-level configuration come mainly from the best match of rainfall distributions between observations and forecasts.
1 Introduction Satellite radiance measurements are now routinely assimilated in global medium-range forecast modeling systems, contributing significantly to the high forecast skill of global medium-range (~ 10 days) numerical weather prediction (NWP; Derber and Wu 1998; Simmons and Hollingsworth, 2002; McNally et al. 2006), as well as regional mesoscale predictions (~ 48 h) (Montmerle et al. 2007; Stengel et al. 2009; Gustafsson et al. 2011; Zou et al. 2011, 2013a, b, 2015, 2017; Qin and Zou 2016, 2018; Qin et al. 2013, 2017). However, satellite data assimilation over the Tibetan Plateau (TP) for downstream quantitative precipitation forecasts (QPFs) deserves further investigation. The atmospheric Responsible Editor: Sang-Woo Kim. * Xiaolei Zou [email protected] 1
Joint Center of Data Assimilation for Research and Application, Nanjing University of Information Science and Technology, Nanjing, China
transmittance and the gradient of satellite-measured brightness temperatures (TBs) are related to the atmospheric state variables (Eyre 1989; Garand et al. 2001). The wealth of more accurate remot
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