SPATIOTEMPORAL RECOVERY OF HIMAWARI-8 HOURLY AEROSOL OPTICAL DEPTH PRODUCTS VIA THE NESTED BAYESIAN MAXIMUM ENTROPY METHOD

This work addresses a critical observational gap in the Southern Ocean—one of the most important regions for carbon uptake—by integrating comprehensive Argo float observations with historical ship-based measurements. Our findings demonstrate the feasibility of using machine learning models to integrate observations, and support in-depth analyses of carbon transport and storage mechanisms. This can foster broader utilization of Argo floats data in ocean carbon research.

Accurate wind profile estimation, especially for the lowest few hundred meters of the atmosphere, is of great significance for the weather, climate, and renewable energy sector. We propose a novel method that combines the power-law method with the random forest algorithm to extend wind profiles beyond the surface layer. Compared with the traditional algorithm, this method has better stability and spatial applicability and can be used to obtain the wind profiles on different land cover types.

To better understand the Asian aerosol environment, we studied distributions and trends of aerosol with different sizes and types. Over the past 2 decades, dust, sulfate, and sea salt aerosol decreased by 5.51 %, 3.07 %, and 9.80 %, whereas organic carbon and black carbon aerosol increased by 17.09 % and 6.23 %, respectively. The increase in carbonaceous aerosols was a feature of Asia. An exception is found in East Asia, where the carbonaceous aerosols reduced, owing largely to China's efforts.

Wind energy is one of the most essential clean and renewable forms of energy in today’s world. However, the traditional power law method generally estimates the hub-height wind speed by assuming a constant exponent between surface and hub-height wind speeds. This inevitably leads to significant uncertainties in estimating the wind speed profile. To minimize the uncertainties, we here use a machine learning algorithm known as random forest to estimate the wind speed at hub height.

CH₄ works as the second-most important greenhouse gas, its reported emission inventories being far less than CO₂. In this study, we developed a self-adjusted model to estimate the CH₄ emission rate from strong point sources by the UAV-based AirCore system. This model would reduce the uncertainty in CH₄ emission rate quantification accrued by errors in measurements of wind and concentration. Actual measurements on Pniówek coal demonstrate the high accuracy and stability of our developed model.