Yanyu Lu

Soil moisture simulations in semi-arid inland river basins remain highly uncertain due to complex land–atmosphere interactions and multiple parameterization schemes in land surface models. This study evaluated the ability of the Noah-Multiparameterization Land Surface Model (Noah-MP) to simulate soil moisture at meteorological sites representing the upstream, midstream and downstream regions of a semi-arid inland river basin with contrasting climates. A large physics-ensemble experiment (17,280 simulations per site) combining different parameterization schemes for 10 main physical processes was conducted. Natural selection, Tukey’s test and uncertainty contribution analysis were applied to identify sensitive processes and quantify their contributions to simulation uncertainty. Results indicate that Noah-MP captures soil moisture variability across the basin but with notable biases. Three physical processes—frozen soil permeability, supercooled liquid water in frozen soil and ground resistance to sublimation—were sensitive at all sites, whereas radiation transfer and surface albedo were consistently insensitive. At the upstream and midstream sites, supercooled liquid water contributed about half of the ensemble uncertainty, and at the downstream site ground resistance to sublimation contributed roughly 51%. These findings reveal which physical processes most strongly affect Noah-MP soil moisture simulations in semi-arid basins and provide guidance for improving parameterization schemes to reduce uncertainty.

Accurate detection of wind profiles within the troposphere is essential for atmospheric dynamics research and extreme weather forecasting. Coherent Doppler wind lidar (CDWL) is widely regarded as the most suitable technique for high spatial and temporal resolution wind profile detection. However, since coherent detection relies heavily on the concentration of aerosol particles, which cause Mie scattering, the received backscattering lidar signal exhibits significantly low intensity at high altitudes. As a result, conventional methods, such as spectral centroid estimator, often fail to produce credible and accurate wind retrieval results in these regions. To address this issue, we introduce a deep learning model for lidar wind profile retrieval, which integrates Transformer architecture with the Kolmogorov-Arnold network. Our model is trained solely on targets derived from the traditional wind retrieval algorithm and utilizes radiosonde measurements as the ground truth for test results evaluation. Experimental results demonstrate that our model not only extends the maximum vertical wind profile detection range but also produces more accurate results, exhibiting a level of precision that surpasses the labeled targets. This phenomenon, which we refer to as super-accuracy, is explored by investigating the potential underlying factors that contribute to this intriguing occurrence. In addition, we compare the performance of our method with other state-of-the-art (SOTA) models, highlighting its superior effectiveness and capability in high-resolution wind retrieval. In the future, our proposed model could potentially be extended to other remote sensing spectrum signal processing applications (e.g., microwave Doppler lidar), thereby establishing a benchmark for future research and driving the advancement of deep learning models in this domain.

Quantitative measurements of atmospheric total OH reactivity (kOH′) provide crucial insights into atmospheric photochemistry. However, widespread application of total OH reactivity measurements is challenging due to insufficient equipment and the complexity of existing instrumentation. In this work, we report the development of a portable laser-flash photolysis Faraday rotation spectroscopy (LP-FRS) instrument for real-time and in situ measurement of kOH′. To achieve efficient overlapping between the pump and probe laser and realize a long effective absorption path length, thus enabling high-sensitivity measurement, a specific Herriott-type pump–probe optical multi-pass cell was designed. The instrument's optical box dimensions were 130 cm × 40 cm × 35 cm. The obtained effective absorption path was ∼ 28.5 m in a base length of 77.2 cm. The kOH′ detection precisions of the LP-FRS instrument were 2.3 and 1.0 s ⁻¹ with averaging times of 60 and 300 s, respectively. The kOH′ measurement uncertainty was evaluated to be within 2 s ⁻¹ . Field measurement was performed, and the difference between the measured kOH′ and the model simulated from the measured reactive species was analysed. The developed portable LP-FRS instrument extends the measurement methods of atmospheric total OH reactivity and has certain advantages in terms of cost, operation, and transportation, which will play an increasingly important role in future atmospheric chemistry research.

As climate change intensifies, extreme drought events have become more frequent, and investigating the mechanisms of watershed drought has become highly significant for basin water resource management. This study utilizes the WRF-Hydro model in conjunction with standardized drought indices, including the standardized precipitation index (SPI), standardized soil moisture index (SSMI), and Standardized Streamflow Index (SSFI), to comprehensively investigate the spatiotemporal characteristics of drought in the Huai River Basin, China, from 2012 to 2018. The simulation performance of the WRF-Hydro model was evaluated by comparing model outputs with reanalysis data at the regional scale and site observational data at the site scale, respectively. Our results demonstrate that the model showed a correlation coefficient of 0.74, a bias of −0.29, and a root mean square error of 2.66% when compared with reanalysis data in the 0–10 cm soil layer. Against the six observational sites, the model achieved a maximum correlation coefficient of 0.81, a minimum bias of −0.54, and a minimum root mean square error of 3.12%. The simulation results at both regional and site scales demonstrate that the model achieves high accuracy in simulating soil moisture in this basin. The analysis of SPI, SSMI, and SSFI from 2012 to 2018 shows that the summer months rarely experience drought, and droughts predominantly occurred in December, January, and February in the Huai River Basin. Moreover, we found that the drought characteristics in this basin have significant seasonal and interannual variability and spatial heterogeneity. On the one hand, the middle and southern parts of the basin experience more frequent and severe agricultural droughts compared to the northern regions. On the other hand, we identified a time–lag relationship among meteorological, agricultural, and hydrological droughts, uncovering interactions and propagation mechanisms across different drought types in this basin. Finally, we concluded that the WRF-Hydro model can provide highly accurate soil moisture simulation results and can be used to assess the spatiotemporal variations in regional drought events and the propagation mechanisms between different types of droughts.

Accurate detection of wind fields within the troposphere is essential for atmospheric dynamics research and plays a crucial role in extreme weather forecasting. Coherent Doppler wind lidar (CDWL) is widely regarded as the most suitable technique for high spatial and temporal resolution wind field detection. However, since coherent detection relies heavily on the concentration of aerosol particles, which cause Mie scattering, the received backscattering lidar signal exhibits significantly low intensity at high altitudes. As a result, conventional methods, such as spectral centroid estimation, often fail to produce credible and accurate wind retrieval results in these regions. To address this issue, we propose LWFNet, the first Lidar-based Wind Field (WF) retrieval neural Network, built upon Transformer and the Kolmogorov-Arnold network. Our model is trained solely on targets derived from the traditional wind retrieval algorithm and utilizes radiosonde measurements as the ground truth for test results evaluation. Experimental results demonstrate that LWFNet not only extends the maximum wind field detection range but also produces more accurate results, exhibiting a level of precision that surpasses the labeled targets. This phenomenon, which we refer to as super-accuracy, is explored by investigating the potential underlying factors that contribute to this intriguing occurrence. In addition, we compare the performance of LWFNet with other state-of-the-art (SOTA) models, highlighting its superior effectiveness and capability in high-resolution wind retrieval. LWFNet demonstrates remarkable performance in lidar-based wind field retrieval, setting a benchmark for future research and advancing the development of deep learning models in this domain.

Quantitative measurements of atmospheric total OH reactivity (kOH') provide crucial insights into atmospheric photochemistry. However, widespread application of total OH reactivity measurements is challenging due to insufficient equipment and the complexity of existing instrumentation. In this work, we report the development of a portable laser-flash photolysis Faraday rotation spectroscopy (LP-FRS) instrument for real-time and in-situ measurement of kOH'. To achieve efficient overlapping between the pump and probe laser and realize a long effective absorption path length, thus enabling high sensitivity measurement, a specific Herriott-type pump-probe optical multi-pass cell was designed with an overlapping factor of up to 75.4 %. The instrument’s optical box dimensions were 130 cm × 40 cm × 35 cm. The obtained efficient absorption path was ~ 28.5 m in a base length of 77.2 cm. The kOH' detection precisions of the LP-FRS instrument were 2.3 s-1 and 1.0 s-1 with averaging times of 60 s and 300 s, respectively. The kOH' measurement uncertainty was evaluated to be within 2 s-1. Field measurement was performed, and the difference between the measured kOH' and the model simulated from the measured reactive species was analysed. The developed portable LP-FRS instrument extends the measurement methods of atmospheric total OH reactivity, and has certain advantages in cost, operation, and transportation, which will play an increasingly important role in future atmospheric chemistry research.

Background: China’s terrestrial ecosystems play a pronounced role in the global carbon cycle. Here we combine spatially-explicit information on vegetation, soil, topography, climate and land use change with a process-based biogeochemistry model to quantify the responses of terrestrial carbon cycle in China during the 20th century. Results: At a century scale, China’s terrestrial ecosystems have acted as a carbon sink averaging at 96 Tg C yr^− 1, with large inter-annual and decadal variabilities. The regional sink has been enhanced due to the rising temperature and CO₂ concentration, with a slight increase trend in carbon sink strength along with the enhanced net primary production in the century. The areas characterized by C source are simulated to extend in the west and north of the Hu Huanyong line, while the eastern and southern regions increase their area and intensity of C sink, particularly in the late 20th century. Forest ecosystems dominate the C sink in China and are responsible for about 64% of the total sink. On the century scale, the increase in carbon sinks in China’s terrestrial ecosystems is mainly contributed by rising CO₂. Afforestation and reforestation promote an increase in terrestrial carbon uptake in China from 1950s. Although climate change has generally contributed to the increase of carbon sinks in terrestrial ecosystems in China, the positive effect of climate change has been diminishing in the last decades of the 20th century. Conclusion: This study focuses on the impacts of climate, CO₂ and land use change on the carbon cycle, and presents the potential trends of terrestrial ecosystem carbon balance in China at a century scale. While a slight increase in carbon sink strength benefits from the enhanced vegetation carbon uptake in China’s terrestrial ecosystems during the 20th century, the increase trend may diminish or even change to a decrease trend under future climate change.

China’s terrestrial ecosystems play a pronounced role in the global carbon cycle. Here we combine spatially-explicit information on vegetation, soil, topography, climate and land use change with a process-based biogeochemistry model to quantify the responses of terrestrial carbon cycle in China during the 20^th century. We find that that the regional soil thermal and moisture regimes have dramatically changed. Specifically, evapotranspiration increased due to rising temperature and soils were drying in the last two decades of the 20^th century. At a century scale, China’s terrestrial ecosystems have acted as a carbon sink averaging at 0.09 Pg C yr^-1, with large inter-annual and decadal variabilities. The regional sink has been enhanced due to the rising temperature and CO₂ concentration, with a slight increase trend in carbon sink strength along with the enhanced net primary production in the century. Meanwhile, the heterotrophic respiration increased in response to warming. The spatial and temporal variabilities of carbon balance in China are due to multiple controlling factors including temperature and precipitation and changing atmospheric CO₂ concentrations. Land-use changes including reforestation and afforestation during the late 20^th century partially contributed to the increase in carbon sink at the national scale.