The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the refo...The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory(Conv LSTM)Network. The reforecast experiments demonstrate that Conv LSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.展开更多
To deal with the universal problem of parasitical frequency spectrum in China New Generation Weather Radar transmitter, this paper establishes mathematical models for parasitical signals existing in radar transmitters...To deal with the universal problem of parasitical frequency spectrum in China New Generation Weather Radar transmitter, this paper establishes mathematical models for parasitical signals existing in radar transmitters and analyzes their effects on weather radar performance. Based on an engineering analysis of their possible sources, a step-by-step method to eliminate parasitical spec- trum is presented, which is applied to troubleshooting experimental weather radar. Eventually, parasitical spectrum is basically eliminated. As a result, improved spectrum purity and reduced phase noise is achieved. Moreover, accuracy for velocity estimate as well as ground clutter suppression ability of the radar system is enhanced.展开更多
基金supported by the National Key R&D Program of China (Grant No.2022YFE0106300)the National Natural Science Foundation of China (Grant Nos.41941009 and 42006191)+2 种基金the China Postdoctoral Science Foundation (Grant No.2023M741526)the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (Grant Nos.SML2022SP401 and SML2023SP207)the Program of Marine Economy Development Special Fund under Department of Natural Resources of Guangdong Province (Grant No.GDNRC [2022]18)。
文摘The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory(Conv LSTM)Network. The reforecast experiments demonstrate that Conv LSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.
基金supported by the National Natural Science Foundation of China (40975018)the Meteorological Observation Centre of China Meteorological Administration
文摘To deal with the universal problem of parasitical frequency spectrum in China New Generation Weather Radar transmitter, this paper establishes mathematical models for parasitical signals existing in radar transmitters and analyzes their effects on weather radar performance. Based on an engineering analysis of their possible sources, a step-by-step method to eliminate parasitical spec- trum is presented, which is applied to troubleshooting experimental weather radar. Eventually, parasitical spectrum is basically eliminated. As a result, improved spectrum purity and reduced phase noise is achieved. Moreover, accuracy for velocity estimate as well as ground clutter suppression ability of the radar system is enhanced.
