Abstract Aerosol layer vertical distributions strongly influence their direct and indirect effects on the Earth system. This study presents an aerosol feature detection method using high spectral resolution lidar (HSRL) measurements, adapted from an established aerosol identification technique using Raman lidar (RL) measurements. Compared to RL, HSRL shows higher sensitivity, detecting more layers at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site. Applying this method to the ARM Surface Atmosphere Integrated Field Laboratory (SAIL) campaign, we observed substantial seasonal variations in aerosol occurrence frequency profiles. Aerosol layers exhibit the highest occurrence frequencies below the planetary boundary layer height (PBLH) during summer and the lowest during winter. Elevated aerosol layers (above the PBLH) occur most frequently in spring, likely linked to long-range transport. The HSRL measurements reveal distinct aerosol particle characteristics within and above the PBL: particles become more spherical as they grow larger within the PBL, while this size-shape relationship is weaker above the PBL. Relationships between relative humidity and particle shape and size are more pronounced above the PBL, likely because aerosols within the PBL have undergone less aging and are therefore more likely to be externally mixed and exhibit a wider range of RH responses. Trajectory analysis shows that aerosol particles associated with different source regions exhibit distinct linear depolarization ratios and color ratios, suggesting potential source attribution. This study highlights the effectiveness of HSRL for characterizing aerosol vertical distributions and demonstrates the utility of aerosol feature masks combined with meteorological information, including back trajectories.
Abstract North and South Carolina (i.e., the Carolinas) are more drought prone than other parts of the country due to higher rates of evapotranspiration, warm temperatures, and reliance on precipitation from tropical cyclones and summer storms. In this study, we analyze droughts in the Carolinas on three time scales: short-term, flash, and long-term. Short-term and long-term droughts are defined by duration, whereas flash droughts are differentiated by their rapid onset. Between 1980 and 2022, short-term and flash droughts in the Carolinas occur most frequently in the Coastal Plain, while long-term droughts occur most frequently in the Piedmont and Mountain subregions. Short-term and long-term drought occurrence exhibit pronounced seasonality, whereas flash droughts exhibit little seasonality and instead show distinct geographical preference. We find that flash droughts are more likely to evolve into long-term droughts than short-term droughts. Notably, we find that long-term and flash droughts have decreased in frequency, while short-term droughts have increased in occurrence. This shift toward more frequent short-term droughts implies that droughts have decreased in duration, possibly due to an increase in high-intensity precipitation events. The Coastal Plain may be the most impacted by this transition, as it historically experiences the most short-term droughts and is experiencing the largest increase in their occurrence. More short-term droughts could have negative implications for both aquaculture and agriculture in this area.
Abstract Accurate prediction of summer precipitation is vital for disaster prevention in eastern China. However, skillful forecasts one year ahead remain a significant challenge. This study evaluates the predictability of summer precipitation over eastern China one year ahead in the Japanese SINTEX-F2 model. Evaluations indicate that the SINTEX-F2 model lacks skills in directly predicting the dominant modes and interannual variations of summer precipitation. Nevertheless, the model demonstrates skills in predicting some key drivers of summer precipitation over eastern China: sea surface temperature anomalies over the Maritime Continent and the tropical Atlantic, and an anomalous cyclone over southern Maritime Continent. We therefore establish a statistical relationship between these drivers and summer precipitation in eastern China in observations. By applying the SINTEX-F2 predicted drivers to the observed statistical relationship and subsequently using a cumulative distribution function matching correction, a hybrid dynamical-statistical prediction scheme is developed. This scheme overall improves forecast skills for precipitation interannual increment over eastern China, notably increasing the anomaly correlation coefficient from approximately 0.15 to 0.45 over Central-North China. It also effectively predicts the amplitude of the observed interannual variability. Additionally, benefiting from the improved prediction of interannual increment, the summer precipitation prediction skill also increases over South China, the northern part of North China, and the northwestern part of Northeast China. These findings highlight a scalable hybrid prediction scheme that improves the long-term predictability of eastern China summer precipitation. This methodology holds significant potential for enhancing early warning systems and informing climate adaptation planning.
Abstract Subgrade plain soil filling is a crucial process in road construction prone to particulate matter (PM) emissions. In this study, we conducted a key factor analysis and concentration prediction of PM emissions in a subgrade plain soil filling process based on interpretable machine learning algorithms. Real-time monitoring of particulate pollutant emissions, using a drone and distributed ground-fixed sensors, was conducted to collect data on a new national highway project using spatiotemporal Internet of Things technology. The interpretable machine learning models were developed using Bayesian optimization to predict PM 10 and PM 2.5 concentrations. The results revealed that extreme gradient boosting exhibited the highest predictive performance. Additionally, Shapley additive explanations, partial dependence plot, and individual conditional expectation provided enhanced interpretability. PM 2.5 background concentration and humidity mostly affected the prediction results. Higher PM 2.5 background concentration levels notably increased the predicted values of both PM 10 and PM 2.5 . Moreover, continuous PM emissions from construction activities can mask the natural reduction of PM 10 and PM 2.5 caused by high humidity. This study provides guidance for dust estimation and environmental protection during road construction. Significance Statement Particulate matter (PM) pollution is a major source of air pollution and health problems globally. PM exposure causes many premature deaths worldwide each year according to the World Health Organization. Subgrade plain soil filling is a crucial process in road construction prone to PM emissions. This study contributes to accurately monitor and predict PM by using spatiotemporal Internet of Things and interpretable machine learning, which helps to understand key influencing factors for better dust suppression during road construction. Further, it provides valuable data for environmental protection agencies to make informed decisions, thereby protecting ambient air quality and public health.
Abstract This work is part of the European Union Individual Change of Habits Needed for Green European Transition (EU-I-CHANGE) Living-Laboratories project in Jerusalem, Israel, including citizen science data along with cellular providers. Cellular network data enable high-spatial-resolution humidity monitoring, compared to low-spatial-resolution observations from surface stations. Contrary to stations, cellular microwave link (CML) is with high spatial resolution. Humidity is an important variable in atmospheric processes and closely linked to clouds and rain. Humidity above ground is highly influenced by surface characteristics and measuring the near-surface humidity, where most of the sinks and sources of humidity are, can be done via a novel approach of using CMLs, a large part of the cellular networks backhaul. The data used include absolute humidity (AH) measured at four links at and around the Israel Meteorological Service (IMS) stations. Plots show AH values for each link against IMS stations and Icosahedral Nonhydrostatic (ICON) model AH values. Correlations, mean, root-mean-square error (RMSE), and index of agreement values were calculated for the comparisons between the two AH values. Results indicate that CML-derived AH captures a substantial portion of the observed humidity variability and shows generally good agreement with both IMS observations and ICON analyses during summer 2021. For example, for Generali IMS station, correlation values reached 0.75 (CML–IMS) and 0.74 (CML–ICON), with an average RMSE of ∼ 4 g m −3 . Mean AH for CML, IMS, and ICON are 13.0, 11.1, and 11.2 g m −3 , correspondingly. This is the first study in a city with such a unique climate such as in Jerusalem, with Mediterranean climate in the west versus arid climate in the east. These findings suggest that CML observations may provide useful high-resolution humidity information in complex urban environments, although further validation across additional seasons and locations is required. Significance Statement This study demonstrates the use of cellular microwave links to measure absolute humidity in an urban setting with complex climate conditions, such as Jerusalem. By utilizing CML data, this research provides high-spatial-resolution humidity monitoring compared to traditional surface stations. The findings indicate that CML-derived AH closely correlates with both the Israeli Meteorological Service data and the ICON model, suggesting that CML can offer reliable humidity measurements in urban environments. These results have significant implications for enhancing weather monitoring and forecasting in regions with diverse topography and climate, where conventional methods may fall short. This novel approach could advance atmospheric studies and improve understanding of humidity dynamics.
