Jingwei Xu

Long-term high-resolution spatial gridded altimeter-derived significant wave height (SWH) data with daily temporal resolution are fundamental to revealing the detailed processes through which weather systems influence the ocean. However, elucidation of those processes is hampered by the sparse coverage and narrow width of the swath of altimeter-derived SWHs. The core problem is how best to extract the spatial structure and then fill missing values around the daily swaths. Although recent developments in deep learning methods have improved the extraction of spatial features, progress regarding the reconstruction of gridded altimeter daily SWHs remains limited. This study reconstructed daily 0.25° × 0.25° gridded SWHs from 1993 to 2024 using a partial convolutional U-Net model with attention and residual blocks. Comparison between the daily reconstructed SWHs and ERA5 SWHs revealed that the reconstructed SWHs improved the accuracy for SWHs of >2.5 m, which are usually underestimated in the ERA5 data. The greatest differences were in China’s offshore waters, especially in the region of the Taiwan Strait and in waters influenced by the Huanghai Warm Current. This study highlights the importance of altimeter swath-derived SWHs in reconstructed gridded SWH datasets, particularly in complex straits and under high sea state conditions.

Fusarium head blight (FHB) represents a critical threat to wheat production globally, not only reducing yields but also contaminating crops with harmful mycotoxins. This study aimed to elucidate new spatiotemporal patterns of FHB incidence and to develop a comprehensive meteorological risk index to enhance scientific prevention and control of the disease. Through the analysis of annual and decadal variations from 1965 to 2023, the study assessed FHB trends across four agricultural regions (I, II, III, and IV) in Jiangsu Province, located in the middle and lower reaches of the Yangtze River—a hotspot for FHB in China. Key findings include the following: Since 1965, Regions I and III consistently exhibited higher FHB incidence rates compared with Regions II and IV. Post-2000, there was a notable increase in years with high incidence rates, with Region III overtaking Region I as the region with the highest incidence. Since 2010, occurrences of FHB reaching the most severe grade (Grade 5) surpassed those in previous decades across all regions. The study also revealed a stronger correlation between meteorological factors (cumulative precipitation, number of days with rainfall ≥0.1 mm, total rainy days with ≥2 and ≥3 consecutive days of rain, total rainy days with both average daily air temperature ≥15°C and daily rainfall ≥0.1 mm, days with average daily relative humidity ≥85%, cumulative sunshine hours, and cumulative cloudy days) and the FHB incidence rates during the heading–flowering–grain filling period in Regions I, II, and III, compared with the heading–flowering period alone. This indicates that optimal temperature and high humidity during the grain-filling stage significantly contribute to the final FHB incidence rates. Despite the less apparent correlation between temperature changes and disease rates, the significant warming trend observed since 2000 has likely fostered conditions conducive to the proliferation of FHB. The comprehensive meteorological risk index, constructed to incorporate key meteorological factors during the heading–flowering–filling period, showed a strong correlation with actual disease incidences. The index demonstrated fitting accuracy rates of 84.7% for Region I, 72.9% for Region II, 83.1% for Region III, and 90.9% for Region IV, underscoring its effectiveness in predicting FHB occurrences. This tool offers both convenience and practicality, providing valuable insights for strategically managing FHB risks based on local weather conditions.

Climate data derived from long-term, multisource altimeter significant wave height (SWH) measurements are more valuable than those obtained from a single altimeter source. Such data facilitate exploration of long-term air–sea momentum transfer and more comprehensive investigation of weather system dynamics processes over the ocean. Despite the deployment of the first satellite in the Chinese Haiyang-2 (HY-2) series more than 12 years ago, validation and integration of SWH data from China’s offshore waters, derived using Chinese altimeters, have been limited. This study constructed a high-resolution, long-term, multisource gridded SWH climate dataset using along-track data from the HY-2 series, CFOSAT, Jason-2, Jason-3, and Cryosat-2 altimeters. Validation against observations from 31 buoys covering China’s offshore waters indicated that the SWH variances from HY-2A, HY-2B, HY-2C, CFOSAT, and Jason-3 altimeters correlated well with observations, with a temporal correlation coefficient of approximately 0.95 (except HY-2A, correlation: 0.89). These SWH measurements generally showed a robust linear relationship with the buoy data. Additionally, cross-calibration between Jason-3 and the HY-2A, HY-2B, HY-2C, and CFOSAT altimeters also demonstrated a typically linear relationship for SWH > 6.0 m. Using this relationship, the SWH data were linearly corrected and integrated into a 10 d mean, long-term, multisource altimeter gridded SWH dataset. Compared with in situ observations, the merged 10 d mean SWHs are more accurate and closely match the observations, with temporal correlation coefficients improving from 0.87 to 0.90 and bias decreasing from 0.28 to 0.03 m. The merged gridded SWHs effectively represent the local spatial distribution of SWH. This study revealed the importance of observational data in the process of merging and recalibrating long-term multisource altimeter SWH datasets, particularly before their application in specific ocean regions.

Most standalone atmospheric models do not perform well in simulations of coastal boundary layer jets (BLJs), important weather processes that can trigger heavy rain in coastal areas by supplying both moisture and dynamic lifting. We compared 33‐year simulations with a coupled atmosphere–ocean model and its standalone atmospheric component, the REgional atmosphere MOdel (REMO), forced by the prescribed sea surface temperature (SST). We validated our results using the Tropical Rainfall Measuring Mission SST and the ERA5 hourly reanalysis data set. We found that the coupled model gave a more realistic SST standard deviation than the REMO on BLJ days and corrected the overestimated air temperature over land during the day. The coupled atmosphere–ocean model showed a lower land–sea thermal contrast in the boundary layer. This increased the effects of inertial oscillation, which caused the ageostrophic flows to veer southwest, which is the direction of the maximum wind speed on BLJ days. This reproduced a more reasonable land–sea thermal contrast in the boundary layer as a result of strong air–sea mixing in coastal weather processes, which led to a more robust inertial oscillation and a larger SST standard deviation over the central South China Sea. These findings deepen our understanding of the influence of a fully mixed air–sea boundary on coastal weather processes. These results show that operational numerical weather prediction models can be improved by applying atmosphere–ocean coupling to advance their ability to forecast the weather (e.g., BLJ events) in coastal areas.

The offshore waters of China are a typical monsoon−affected area where the significant wave height (SWH) is strongly influenced by the different seasonal mean flow in winter and summer. However, limited in situ validations of the SWH have been performed on the China–France Oceanography Satellite (CFOSAT) in these waters. This study focused on validating CFOSAT nadir SWH data with SWH data from in situ buoy observations for China’s offshore waters and the Haiyang−2B (HY−2B) satellite, from July 2019 to December 2021. The validation against the buoy data showed that the relative absolute error has a seasonal cycle, varying in a narrow range near 35%. The RMSE of the CFOSAT nadir SWH was 0.29 m when compared against in situ observations, and CFOSAT was found to be more likely to overestimate the SWH under calm sea conditions. The sea−surface winds play a key role in calm sea conditions. The spatial distributions of the CFOSAT and HY−2B seasonal SWHs were similar, with a two−year mean SWH−field correlation coefficient of 0.98. Moreover, the coherence between the two satellites’ SWH variance increased with SWH magnitude. Our study indicates that, in such typical monsoon−influenced waters, attention should be given to the influence of sea conditions on the accuracy of CFOSAT SWH, particularly in studies that combine data from multiple, long−duration space−based sensors.

One of the main water vapor sources of eastern China is the Bay of Bengal, over which the circulation is influenced by the Tropical Indian Ocean Dipole (TIOD). The TIOD has a long-lasting effect on weather patterns, which in turn influence the rice yield and quality in eastern China, such as in Jiangsu Province. To identify the main mechanism involved, we perform a detailed investigation of the connections between the TIOD-like sea surface temperature (SST) and the climatic suitability for growing rice, and the subsequent rice yield anomalies, in Jiangsu Province. In particular, we compare the relationships, and identify the underlying mechanisms, of TIOD SST with suitable sunshine duration, temperature and precipitation during the period of rice culture in the province. Singular Value Decomposition (SVD) results show that the TIOD-like SST has a close correlation with the rice yield anomalies, with a temporal correlation coefficient of 0.43 for 53 years, passing the 99% significance level. Furthermore, in the negative TIOD-like SST years, the background circulation weakens the transport effect of the atmospheric river through which water vapor is transported from the Bay of Bengal to eastern China. This decreased amount of transported water vapor decreases the precipitation and total cloud cover in the province. In turn, this significantly increases the sunshine duration, which plays a key role in rice yield anomalies. The increased sunshine duration and higher temperatures lead to positive rice yield anomalies, and vice versa. Our findings highlight that climatic factors, such as TIOD-like SST, have a far-reaching influence on agricultural production (in this case, rice yield), and thus special attention should be paid to interdisciplinary research in the fields of climate and agriculture.