Abstract. The East Asia Regional Reanalysis (EARR) system is developed based on the advanced hybrid gain data assimilation method (AdvHG) using the Weather Research and Forecasting (WRF) model and conventional observations. Based on EARR, the high-resolution regional reanalysis and reforecast fields are produced with 12 km horizontal resolution over East Asia for 2010–2019. The newly proposed AdvHG is based on the hybrid gain approach, weighting two different analyses for an optimal analysis. The AdvHG differs from the hybrid gain in that (1) E3DVAR is used instead of EnKF, (2) 6 h forecast of ERA5 is used to be more consistent with WRF, and (3) the preexisting, state-of-the-art reanalysis is used. Thus, the AdvHG can be regarded as an efficient approach for generating regional reanalysis datasets thanks to cost savings as well as the use of the state-of-the-art reanalysis. The upper-air variables of EARR are verified with those of ERA5 for January and July 2017 and the 10-year period 2010–2019. For upper-air variables, ERA5 outperforms EARR over 2 years, whereas EARR outperforms (shows comparable performance to) ERA-I and E3DVAR for January 2017 (July 2017). EARR represents precipitation better than ERA5 for January and July 2017. Therefore, although the uncertainties of upper-air variables of EARR need to be considered when analyzing them, the precipitation of EARR is more accurate than that of ERA5 for both seasons. The EARR data presented here can be downloaded from https://doi.org/10.7910/DVN/7P8MZT (Yang and Kim, 2021b) for data on pressure levels and https://doi.org/10.7910/DVN/Q07VRC (Yang and Kim, 2021c) for precipitation.

Abstract. The East Asia Regional Reanalysis (EARR) system is developed based on the advanced hybrid gain data assimilation method (AdvHG) using Weather Research and Forecasting (WRF) model and conventional observations. Based on EARR, the high-resolution regional reanalysis and reforecast fields are produced with 12 km horizontal resolution over East Asia for 2010–2019. The newly proposed AdvHG is based on the hybrid gain approach, weighting two different analysis for an optimal analysis. The AdvHG is different from the hybrid gain in that 1) E3DVAR is used instead of EnKF, 2) 6 h forecast of ERA5 is used to be more consistent with WRF, and 3) the pre-existing, state-of-the-art reanalysis is used. Thus, the AdvHG can be regarded as an efficient approach to generate regional reanalysis dataset due to cost savings as well as the use of the state-of-the-art reanalysis. The upper air variables of EARR are verified with those of ERA5 for January and July 2017 and the two-year period of 2017–2018. For upper air variables, ERA5 outperforms EARR over two years, whereas EARR outperforms (shows comparable performance to) ERA-I and E3DVAR for January in 2017 (July in 2017). EARR better represents precipitation than ERA5 for January and July in 2017. Therefore, though the uncertainties of upper air variables of EARR need to be considered when analyzing them, the precipitation of EARR is more accurate than that of ERA5 for both two seasons. The EARR data presented here can be downloaded from https://doi.org/10.7910/DVN/7P8MZT for data on pressure levels and https://doi.org/10.7910/DVN/Q07VRC for precipitation.

In this study, the effect of boundary condition configurations in the regional Weather Research and Forecasting (WRF) model on the adjoint-based forecast sensitivity observation impact (FSOI) for 24 h forecast error reduction was evaluated. The FSOI has been used to diagnose the impact of observations on the forecast performance in several global and regional models. Different from the global model, in the regional model, the lateral boundaries affect forecasts and FSOI results. Several experiments with different lateral boundary conditions were conducted. The experimental period was from 1 to 14 June 2015. With or without data assimilation, the larger the buffer size in lateral boundary conditions, the smaller the forecast error. The nonlinear and linear forecast error reduction (i.e., observation impact) decreased as the buffer size increased, implying larger impact of lateral boundaries and smaller observation impact on the forecast error. In all experiments, in terms of observation types (variables), upper-air radiosonde observations (brightness temperature) exhibited the greatest observation impact. The ranking of observation impacts was consistent for observation types and variables among experiments with a constraint in the response function at the upper boundary. The fractions of beneficial observations were approximately 60%, and did not considerably vary depending on the boundary conditions specified when calculating the FSOI in the regional modeling framework.

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