Jinxiao Li

The accuracy of numerical weather prediction largely depends on the quality of the initial conditions. Global Navigation Satellite System radio occultation (GNSS-RO) observations, with their high vertical resolution, play an important role in reducing initial condition errors. In this study, multiple simulations with different initialization times were conducted during the development of Typhoon BEBINCA using the WRF-GSI assimilation system to evaluate the impact of YunYao GNSS-RO observations on improving extreme weather simulation performance and to investigate the sensitivity of refractivity assimilation to different cloud microphysics parameterization schemes. The results show that assimilating YunYao GNSS-RO data significantly improves the consistency between the model initial fields and observations and enhances the analysis quality in the middle and upper troposphere. Compared with ERA5 reanalysis data, the assimilation experiments better reproduce the spatial and temporal evolution of key atmospheric variables, and the improvements persist from 36 h to 120 h forecast lead time. Statistical results from multiple initializations show that the maximum RMSE reductions exceed 0.2 K for temperature, 0.1 m s⁻¹ for wind speed, and geopotential height shows consistent improvements throughout the entire atmosphere. In addition, the assimilation experiments improve the simulation of Typhoon BEBINCA’s track and intensity. Statistical results from multiple initializations indicate that the 84 h track error is reduced by approximately 30 km on average, and the minimum central pressure bias is also reduced. Sensitivity experiments further show that the WSM6 microphysics scheme performs better in track forecasting, while the Thompson scheme is more suitable for intensity forecasting. Overall, YunYao GNSS-RO assimilation effectively improves typhoon forecast accuracy and demonstrates strong potential for operational applications.

The 2020 Meiyu event over the middle and lower reaches of the Yangtze River (MLYR) broke historical records and caused catastrophic flooding across the basin. Based on daily NCEP/NCAR reanalysis and CN05.1 precipitation datasets for 1979–2020, this study investigated the intraseasonal oscillation characteristics of this extreme rainfall event and revealed its underlying dynamic mechanisms. Results showed that the 7–14-day oscillation was the dominant intraseasonal signal of the 2020 Meiyu rainfall over the MLYR, accounting for 41.9% of the total rainfall variance with a spectral peak at approximately 13 days. This oscillation was closely linked to an eastward-propagating Rossby wave train (RWT) stretching from the east coast of North America to East Asia, with the east coast of North America and the North Atlantic serving as key wave sources. Intense synoptic-scale transient eddy activities over northeastern North America and the northwestern Atlantic modulated the low-frequency circulation anomalies that excited this RWT. Energy analysis further revealed that baroclinic energy conversion dominated the growth and maintenance of the RWT, while barotropic energy conversion played a secondary role. Eastward propagation of the RWT modulated the spatiotemporally coupled upper- and lower-tropospheric circulation anomalies over East Asia, which in turn induced anomalous convergence/divergence and vertical motion over the MLYR, ultimately leading to the 7–14-day oscillation of the extreme Meiyu rainfall. The findings of this study suggest that circulation anomalies over the east coast of North America might serve as potential remote precursor signals for subseasonal prediction of extreme Meiyu events over the MLYR.

Asian topography plays a significant role in regional and global weather and climate change. Based on the dataset of climate system model named CAS FGOALS-f3 participated in Global monsoons Model Inter-comparison (GMMIP), the MIP endorsement of Coupled Model Intercomparison Project Phase 6 (CMIP6), the role of Asian topography to the formation and movement of tropical cyclones (TCs) are discussed in this study. This study provides the first comparative analysis of the dynamic and thermal effects of Asian topography on the regional and global activity of TCs. The results indicate that the Asian topography promotes the generation and development of TCs, especially in the Northwest Pacific (WNP). The contribution of the Asian topography to the number of TCs reached about 50% in WNP. It is worth noting that there are still positive biases of TC track density in the experiment named “AMIP-NS”, which means the thermal effect of Asian topography is also essential for TC formation and development in WNP, which has not received much attention before. Besides, the possible reasons for the modulation of TC activity are given from two aspects: (1) The existence of Asian topography has changed the large-scale factors related to TC activities such as warm core, sea-level pressure, genesis potential index (GPI), which are beneficial to the generation and movement of TC. (2) Asian topography promotes the spread of Madden–Julian oscillation (MJO), which is also beneficial to the generation and movement of TC. It is worthwhile to investigate further the mechanisms by which Asian topography affects the activity of TCs.

Based on 35-year (1981–2015) ensemble (24 members) hindcasts of the IAP/LASG global seasonal prediction system named FGOALS-f2 V1.0 (FGOALS-f2), the tropical cyclone (TC) seasonal prediction skills over the South China Sea (SCS) during the TC peak season (July–November) are evaluated. Starting the prediction from June 20th, FGOALS-f2 can well capture the seasonal mean characteristics for both the genesis location and track of TCs over the SCS. For seasonal anomalous TC numbers, FGOALS-f2 underestimates the maximum and minimum of the TC number compared to the observation. The temporal correlation coefficients (TCCs) between FGOALS-f2 and the observation are 0.39 for the TC number and 0.51 for accumulated cyclone energy (ACE) over the SCS, respectively, which are both above the 95% significant level. Additionally, FGOALS-f2 has acceptable prediction skill for the seasonal mean number of TCs landing on three areas (coastal southeastern China, Indochina Peninsula, and Philippines) surrounding the SCS. The skillful prediction of SCS TCs could be ascribed to the well-predicted tropical anomaly of sea surface temperature (SSTA), TC and El Niño-Southern Oscillation (TC-ENSO) relations, and Genesis potential index (GPI).

The critical relative humidity (RH_c), which approximately measures the subgrid‐scale variability of moisture, is important to cloud parameterization. Based on the diagnostics from CloudSat/CALIPSO satellite data, we propose an improved RH_c formula that incorporates geographic dependence and allows for non‐monotonic variations in the vertical. With the parameterized RH_c, a cloud macrophysics scheme is constructed in which fractional cloudiness and subgrid‐scale condensation are synergistically solved, with the latter being calculated using two different approaches. Results show the new scheme largely alleviates the underestimation of high‐ and mid‐level clouds in the default model. The performance is also superior to the simulations applying a globally uniform RH_c as conventionally used in the literature. Varying RH_c and the techniques for computing subgrid‐scale condensation leads to a marked diversity in cloud feedback, which nearly replicates the range of uncertainty found in Coupled Model Intercomparison Project Phase 6 models. Using smaller RH_c leads to larger spread than using larger ones. And the spread caused by different techniques for calculating subgrid‐scale condensation is larger than that caused by the choice of RH_c. While many previous studies have emphasized the diversity of cloud feedback due to low clouds, varying RH_c and its implementation leads to diversity of cloud feedback being mainly due to optically thick clouds. These results highlight the importance of RH_c in inducing uncertainty of cloud feedback, as well as notes of caution to modelers when using RH_c to tune models.

Subseasonal-to-seasonal (S2S) prediction of winter wheat yields is crucial for farmers and decision-makers to reduce yield losses and ensure food security. Recently, numerous researchers have utilized machine learning (ML) methods to predict crop yield, using observational climate variables and satellite data. Meanwhile, some studies also illustrated the potential of state-of-the-art dynamical atmospheric prediction in crop yield forecasting. However, the potential of coupling both methods has not been fully explored. Herein, we aimed to establish a skilled ML–dynamical hybrid model for crop yield forecasting (MHCF v1.0), which hybridizes ML and a global dynamical atmospheric prediction system, and applied it to northern China at the S2S time scale. In this study, we adopted three mainstream machining learning algorithms (XGBoost, RF, and SVR) and the multiple linear regression (MLR) model, and three major datasets, including satellite data from MOD13C1, observational climate data from CRU, and S2S atmospheric prediction data from IAP CAS, used to predict winter wheat yield from 2005 to 2014, at the grid level. We found that, among the four models examined in this work, XGBoost reached the highest skill with the S2S prediction as inputs, scoring R² of 0.85 and RMSE of 0.78 t/ha 3–4 months, leading the winter wheat harvest. Moreover, the results demonstrated that crop yield forecasting with S2S dynamical predictions generally outperforms that with observational climate data. Our findings highlighted that the coupling of ML and S2S dynamical atmospheric prediction provided a useful tool for yield forecasting, which could guide agricultural practices, policy-making and agricultural insurance.

Subseasonal-to-seasonal (S2S) prediction of winter wheat yields is crucial for farmers and decision-makers to reduce yield losses and ensure food security. Recently, numerous researchers have utilized machine learning (ML) methods to predict crop yield using observational climate variables and satellite data. Meanwhile, some studies also illustrate the potential of state-of-the-art dynamical atmospheric prediction in crop yield forecasting. However, the potential of coupling both methods has not been fully exploited. Herein, we aim to establish a skilled ML–dynamical hybrid model for crop yield forecasting (MHCF v1.0), which hybridizes ML and a global dynamical atmospheric prediction system, and apply it to northern China at the S2S time scale. MHCF v1.0 demonstrates that crop yield forecasting with S2S dynamical predictions generally outperforms that with observational climate data. The coupling of ML and S2S dynamical atmospheric prediction provides a useful tool for yield forecasting, which could guide agricultural practices, policy-making and agricultural insurance.

The effects of horizontal resolution on the simulation of tropical cyclones were studied using the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System Finite-Volume version 3 (FGOALS-f3) climate system model from the High-Resolution Model Intercomparison Project (HighResMIP) for the Coupled Model Intercomparison Project phase 6 (CMIP6). Both the low-resolution (about 100 km resolution) FGOALS-f3 model (FGOALS-f3-L) and the high-resolution (about 25 km resolution) FGOALS-f3 (FGOALS-f3-H) models were used to achieve the standard Tier 1 experiment required by HighResMIP. FGOALS-f3-L and FGOALS-f3-H have the same model parameterizations with the exactly the same parameters. The only differences between the two models are the horizontal resolution and the time step. The performance of FGOALS-f3-H and FGOALS-f3-L in simulating tropical cyclones was evaluated using observations. FGOALS-f3-H (25 km resolution) simulated more realistic distributions of the formation, movement and intensity of the climatology of tropical cyclones than FGOALS-f3-L at 100 km resolution. Although the number of tropical cyclones increased by about 50 % at the higher resolution and better matched the observed values in the peak month, both FGOALS-f3-L and FGOALS-f3-H appear to replicate the timing of the seasonal cycle of tropical cyclones. The simulated average and interannual variabilities of the number of tropical cyclones and the accumulated cyclone energy were both significantly improved from FGOALS-f3-L to FGOALS-f3-H over most of the ocean basins. The characteristics of tropical cyclones (e.g., the average lifetime, the wind–pressure relationship and the horizontal structure) were more realistic in the simulation using the high-resolution model. The possible physical linkage between the performance of the tropical cyclone simulation and the horizontal resolution were revealed by further analyses. The improvement in the response between the El Niño–Southern Oscillation and the number of tropical cyclones and the accumulated cyclone energy in FGOALS-f3 contributed to the realistic simulation of tropical cyclones. The genesis potential index and the vorticity, relative humidity, maximum potential intensity and the wind shear terms were used to diagnose the effects of resolution. We discuss the current insufficiencies and future directions of improvement for the simulation of tropical cyclones and the potential applications of the FGOALS-f3-H model in the subseasonal to seasonal prediction of tropical cyclones.

The effects of horizontal resolution on the simulation of tropical cyclones were studied using the Chinese Academy of Sciences FGOALS-f3 climate system model from the High-Resolution Model Intercomparison Project (HighResMIP) for the Coupled Model Intercomparison Project Phase 6 (CMIP6). Both the low-resolution (approximately 100 km resolution) FGOALS-f3 model (FGOALS-f3-L) and the high-resolution (approximately 25 km resolution) FGOALS-f3 (FGOALS-f3-H) model were used to achieve the standard Tier1 experiment required by the HighResMIP. FGOALS-f3-L and FGOALS-f3-H have the same model parameterizations with exactly the same parameters. The only differences between the two models are the horizontal resolution and the time step. The performance of FGOALS-f3-H and FGOALS-f3-L in simulating tropical cyclones was evaluated with the observation firstly. FGOALS-f3-H (25 km resolution) simulated more realistic distributions of the formation, movement and intensity of the climatology of tropical cyclones than FGOALS-f3-L at 100 km resolution. The seasonal cycles of the number of tropical cyclones increased by about 50 % at the higher resolution and better matched the observed values in the peak month, especially in the eastern Pacific, northern Atlantic, southern Indian and southern Pacific oceans. The simulated variabilities of the number of tropical cyclones and the accumulated cyclone energy were both significantly improved from FGOALS-f3-L to FGOALS-f3-H over most of the ocean basins on the interannual timescale. The characteristics of the tropical cyclones (e.g., the average lifetime, the wind–pressure relationship and the horizontal structure) were more realistic in the simulation using the high-resolution model. The possible physical linkage between the performance of the tropical cyclone simulation and the horizontal resolution were revealed by further analyses. The improvement in the Madden–Julian oscillation from FGOALS-f3-H contributed to the realistic simulation of tropical cyclones. The genesis potential index and the vorticity, relative humidity, maximum potential intensity and the wind shear terms were used to diagnose the effects of the resolution. The current insufficiencies and future directions of improvement for the simulation of tropical cyclones and the potential applications of the FGOALS-f3-H model in the sub-seasonal to seasonal prediction of tropical cyclones are discussed.