Accurate tropical cyclone (TC) intensity estimation is crucial for prediction and disaster prevention. Currently, significant progress has been achieved for the application of convolutional neural networks (CNNs) in TC intensity estimation. However, many studies have overlooked the fact that the local convolution used by CNNs does not consider the global spatial relationships between pixels. Hence, they can only capture limited spatial contextual information. In addition, the special rotation invariance and symmetry structure of TC cannot be fully expressed by convolutional kernels alone. Therefore, this study proposes a new deep learning-based model for TC intensity estimation, which uses a combination of rotation equivariant convolution and Transformer to address the rotation invariance and symmetry structure of TC. Combining the two can allow capturing both local and global spatial contextual information, thereby achieving more accurate intensity estimation. Furthermore, we fused multi-platform satellite remote sensing data into the model to provide more information about the TC structure. At the same time, we integrate the physical environmental field information into the model, which can help capture the impact of these external factors on TC intensity and further improve the estimation accuracy. Finally, we use TCs from 2003 to 2015 to train our model and use 2016 and 2017 data as independent validation sets to verify our model. The overall root mean square error (RMSE) is 8.19 kt. For a subset of 482 samples (from the East Pacific and Atlantic) observed by aircraft reconnaissance, the root mean square error is 7.88 kt.

Wind speed and direction are important parameters in meteorological observation. A solid wind sensor is needed with a small quadcopter for boundary layer meteorological observation. In this paper, the principle of a cylindrical two-dimensional wind sensor is reported and the data from wind tunnel experiments are analyzed. A model is proposed to describe the distribution of the pressure difference across a diameter of a cylinder, and the wind sensor is fabricated with MEMS (Micro-Electro-Mechanical System) differential pressure sensors. The wind sensor cylinder has a small size with a diameter of 30 mm and a height of 80 mm. In wind tunnel tests in the range of 1 to 40 m/s, the relative speed measuring errors and the direction measuring errors of the prototype are no more than ± (0.2 + 0.03 V) m/s (V is standard wind speed) and 5°, respectively. An inclination angle model is proposed to correct the influence of tilt angle on the quadcopter platform, the wind sensor can maintain the original wind speed and direction measurement accuracy within the 30° inclination range after compensation.

Wind speed and direction are important parameters in meteorological observation. Solid wind sensor has needed with small Quadcopter for boundary layer meteorological observation. In this paper the principle of a cylindrical two-dimensional wind sensor is reported by analyzing the data from wind tunnel experiments. A model is proposed to describe the distribution of the pressure difference across a diameter of a cylinder and the wind sensor is fabricated with MEMS differential pressure sensors. In wind tunnel tests in range of 1~40 m/s, the relative speed measuring errors and the direction measuring errors of the prototype are no more than ±(0.2 + 0.03 V) m/s and 5° respectively.

Land evaporation (LET) is an important variable in climate change, water cycle and water resources management. Mastering the projected changes in LET is significant for crop water requirements and the energy cycle. The global climate model (GCM) is a vital tool for future climate change research. However, the GCMs have low spatial resolution and relatively high errors. We use a deep learning (DL)-based model to deal with this problem. The DL approach can downscale the model data and merge simultaneously. We applied the DL approach to a suit of models from the Coupled Model Intercomparison Project 6th edition (CMIP6) LET data. From the result of all the evaluation metrics, the DL merged data greatly improved in both spatial and time dimensions. The mean RMSE is 5.85 mm and the correlation is 0.95 between the DL merged data and reference data (historical reliable evaporation data). The future LET evidently increases in four scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5), and the upward intensity rises from the low to high emission scenarios. The highest increasing regions are in the Tibet Plateau and the south of China and the trend is larger than 10 mm/decade in the high scenarios. From the seasonal point of view, the increasing trend in spring and summer is far larger than for autumn and winter. The Tibet Plateau and the northeast of China have the largest upward trend in the spring of SSP5–8.5, higher than 1.6 mm/decade.

Gravity-matching navigation is one of the important means for the covert navigation of underwater vehicles. The production and application of gravity reference maps as core key technologies have a very significant impact on the accuracy of underwater navigation. Firstly, this paper adopts the factor analysis method to obtain the comprehensive results of nine characteristic parameters such as the standard deviation and roughness of the gravity reference map by setting a range sliding window. Secondly, the TERCOM algorithm is introduced to conduct simulation verification calculations within the sliding window. After comparing and verifying with the comprehensive results of the factor analysis characteristic parameters, the limitations of statistical methods in the evaluation of the adaptability of gravity reference maps are analyzed. Thirdly, intelligent processing methods such as the learning vector quantization neural network algorithm and the extreme learning machine are proposed. The characteristic parameters of some sliding window gravity reference maps and the simulation verification results of the TERCOM algorithm are used as training samples to predict the adaptability evaluation effect of underwater gravity navigation for other sliding windows. The results show that the prediction results are generally in good agreement with the simulation verification results of the TERCOM algorithm. Compared with the learning vector quantization neural network algorithm, the extreme learning machine algorithm exhibits superior performance in terms of classification accuracy and computational efficiency.