Hybrid data assimilation (DA) methods have received extensive attention in the field of numerical weather prediction. In this study, a hybrid gain data assimilation (HGDA) method that combined the gain matrices of ensemble and variational methods was first applied in the mesoscale version of the Global/Regional Assimilation and Prediction System (GRAPES_Meso). To evaluate the performance of the HGDA method in the GRAPES_Meso model, different DA schemes, including the three-dimensional variational (3DVAR), local ensemble transform Kalman filter (LETKF), and HGDA schemes, were compared across eight tropical cyclone (TC) cases, and FY-4A atmospheric motion vectors were assimilated. The results indicated that the HYBRID scheme outperformed the 3DVAR and LETKF schemes in TC position forecasting, and with ensemble forecasting techniques, the HYBRID scheme promoted the accuracy of the prediction TC intensity. The threat score (TS) values for the light and medium precipitation forecasts obtained in the HYBRID experiment were higher than those for the forecasts obtained in the 3DVAR and LETKF experiments, which may be attributed to the forecasting accuracy for the TC position. Regarding heavy and extreme rainfall, the HYBRID scheme achieved a more stable effect than those of the 3DVAR and LETKF schemes. The results demonstrated the superiority of the HGDA scheme in TC prediction with the GRAPES_Meso model.

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.

This study applied the three-cornered hat method to the error variance estimations of temperature profiles retrieved from the Geosynchronous Interferometric Infrared Sounder (GIIRS), the first hyperspectral infrared sounder onboard Fengyun-4A (FY-4A) geostationary satellite. An accurate observational error is a crucial input parameter for FY-4A temperature profile assimilation. Using multiple data sets from 2020, this study examined the error variances of FY-4A temperature profiles in clear-sky and cloudy-sky respectively. Because of the effects of clouds on GIIRS, the error variances in cloudy-sky are much larger than in clear-sky. Provided that error variances less than 4K² are significant for data assimilation, the FY-4A temperature profiles in clear-sky from 900 to 150 hPa are meaningful to be assimilated. Results reveal that the latitudinal dependency of error variances is strong at 100 hPa. Encouraged by the vertical distributions of the temperature profile differences between FY-4A and other data sets, the error variance of total potential energy is also examined in this study. Although the detailed structures of temperature profiles are removed, the vertical integration obtains a more minor percentage error. Further studies on data assimilation are worth discussing if total potential energy is more meaningful than the FY-4A temperature profiles.

High frequency (20 Hz) aircraft observations from the Government Flying Service of the Hong Kong Government, penetrating a tropical cyclone (TC) at low altitude over the South China Sea had an extremely inhomogeneous distribution. Today the remoting observations have been widely used, but our work demonstrates aircraft observations still play an important role in TCs forecasts.To investigate an effective scheme for assimilating inhomogeneous aircraft observations, a multigrid 3D variation (m3DVAR) system, with the assistance of a bogus vortex, was employed. Track and intensity forecasts were improved by assimilating aircraft observations and bogus data. The assimilation of pressure (horizontal wind) was also found mainly to contribute to the large magnitude (sophisticated distribution) of increments.These aircraft observations were also 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. 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 fact, the west Pacific had access to aircraft observations but these observations stopped in 1987. We hope we can call attentions of governors and scientists to reboot in situ observations on aircraft platform in the west Pacific by disseminating our results. This can be a significant benefit to improving the regional real-time forecasts and understanding the climate variabilities of TCs. We already had two publications related to the assimilation of aircraft observations (Gao et al., 2019; Gao et al., 2019). References:Gao, Y, Xiao, H, Chan, PW, Hon, Kai kwong, Wan, Q, Ding, W. Application of the multigrid 3D variation method to a combination of aircraft observations and bogus data for Typhoon Nida (2016). Meteorol Appl. 2019; 26: 312– 323. https://doi.org/10.1002/met.1764. 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. https://doi.org/10.3390/atmos10120754.

The ecological security of the Qinghai–Tibet Plateau (QTP) alpine transitional region plays a critical role in regional and global climate regulation. Conventional Remote Sensing Ecological Index (RSEI) assessments, however, rely on single-date imagery and often fail to account for cumulative climatic effects. To address this limitation, we refined the traditional RSEI through a multi-principal component weighting scheme that retains over 95% of the cumulative variance. Using imagery from five discrete periods between 2000 and 2020, we evaluated the spatiotemporal evolution of regional ecological quality in the alpine transitional region in Gannan. Results show an overall improvement: mean RSEI increased from 0.63 to 0.69, Good/Excellent areas expanded from 65.6% to 79.9%, and the interquartile range decreased from 0.19 to 0.14. Spatially, ~59% of the area improved, whereas ~41% degraded, primarily in western transition zones and urban peripheries. As an evaluation of internal index contributions, NDVI remained the dominant structural component. Climatic constraints shifted from temperature dominance between 2005 and 2010 to precipitation dominance by 2020, with all interactions exhibiting synergistic enhancement. Cumulative climatic variables during the growing season, with an optimal seven month accumulation window, substantially outperformed single month variables in explaining ecological variations. Specifically, mean q-values increased by 0.12 for temperature and 0.18 for precipitation, yielding a 15 to 25 percent relative improvement, which validates the necessity of incorporating cumulative effects. This framework provides a robust foundation for long-term monitoring and differentiated management of fragile alpine ecosystems.