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Showing posts sorted by date for query WRF. Sort by relevance Show all posts

Wednesday, March 11, 2026

 

Where does northwest China's increasing moisture come from? New study points to local sources




Institute of Atmospheric Physics, Chinese Academy of Sciences

Humidification 

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Schematic illustration of the mechanisms responsible for the humidification in Northwest China.

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Credit: Haipeng Yu





For the millions living in Northwest China's arid expanse, where water scarcity has shaped life for centuries, a quiet question has emerged: why is it getting wetter?

Now, scientists who have spent their careers studying this region offer a surprising answer. The recent increase in precipitation is not arriving from afar. It is, instead, mostly rising up from the land itself.

Published in Advances in Atmospheric Sciences, the study was conducted by researchers from the Northwest Institute of Eco-Environment and Resources at the Chinese Academy of Sciences, Lanzhou University, and the Lanzhou Institute of Arid Meteorology of China Meteorological Administration. The team shares deep roots in the region they study.

Lead author Professor Haipeng Yu first arrived in Lanzhou as a student in 2005, drawn to study China's dryland climate. He never left. Senior authors Professors Jianping Huang and Qiang Zhang were both born in Northwest China and have devoted their entire careers to understanding its arid and semi-arid regional climate.

Their combined decades of observation have tracked a fundamental shift.

A Turning Point in the 1990s

Traditionally, precipitation in Northwest China was thought to depend heavily on moisture carried in from outside. The new research confirms that while external moisture still dominates the long-term average, the region's trend toward humidification since the late 20th century is being driven largely by local sources: enhanced evaporation from soil, plants, and water bodies—a process intensified by warming temperatures and ecological changes.

The research identifies the late 1990s as a critical turning point. Around that time, summer precipitation shifted from a long-term decline to a sustained increase. Spatially, the changes are uneven—western areas around the Tianshan and Altun Mountains have seen substantial increases, while some eastern parts have experienced drying trends.

Tracing the Source

Using a Dynamic Recycling Model, the team quantified the contributions of different moisture sources. Comparing 1961–1997 with 1998–2020, they found that annual precipitation increased by 10.62 mm (9.18%), while local evapotranspiration rose by 10.42 mm (9.12%). Crucially, nearly 78% of the increase in precipitation came from locally recycled moisture, with only about 22% from enhanced external transport.

This marks a fundamental shift in understanding. More than half of the region's average precipitation still comes from outside, but the increase since the late 1990s is overwhelmingly local in origin.

Land–Atmosphere Coupling

Warmer temperatures, increased meltwater from glaciers and snowpack, and vegetation recovery have all contributed to rising evapotranspiration, creating a feedback loop that fuels additional precipitation.

"For decades, the textbook answer was that Northwest China's rain comes from somewhere else," says Yu. "Our findings show that since the late 1990s, the dominant contribution to precipitation growth has shifted to local moisture recycling. After spending my entire adult life here, it's remarkable to see the data confirm that something fundamental is shifting."

Implications and Uncertainties

The study notes that large-scale oceanic variability, such as the Atlantic Multidecadal Oscillation, may add complexity to future projections. The authors also caution that the current trend may not be sustainable. As glaciers and snow reserves decline under continued warming, the meltwater that supports enhanced evapotranspiration could diminish, potentially slowing or reversing the humidification.

"This work provides quantitative evidence that local hydrological feedbacks have become the dominant mechanism behind recent precipitation increases," says Professor Zhang. "That has important implications for drought monitoring, prediction, and water-resource management."

"The warm–wet shift reflects an integrated response of the regional water cycle to warming, cryospheric changes, and ecosystem recovery.” Professor Huang adds: Having grown up here, I know these aren't abstract questions—they affect real communities, real farms, real lives."

Finer-scale simulations show promise for forecasting dangerous valley storms




Institute of Atmospheric Physics, Chinese Academy of Sciences
The Tethering Horse Post 

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The Tethering Horse Post on Laji Mountain stands as a dramatic sentinel over the complex terrain of Eastern Qinghai. This towering limestone pillar, rising abruptly from the ridge, exemplifies the kind of rugged topography that makes weather forecasting in the region so challenging.

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Credit: Qinghai Meteorological Observatory





As climate change intensifies the water cycle, communities in mountainous regions face growing threats from flash floods and landslides triggered by sudden, violent rainstorms. An international research team has shown that increasing the resolution of weather forecasting models to the kilometre scale could improve the ability to predict these events—not just in China's Qinghai Province, but in complex terrain worldwide.

The study, published in Advances in Atmospheric Sciences, focused on a catastrophic rainstorm that struck the Hongshui River valley in eastern Qinghai on 13 August 2022. The event caused widespread flooding, damaged crops, and affected nearly 6,000 households. Using the Weather Research and Forecasting (WRF) model, scientists compared simulations at different resolutions: 9 kilometres, 3 kilometres (matching China's operational forecasts), and 1 kilometre.

The results showed clear differences. Only the 1-kilometre simulation accurately captured the storm's intensity, timing, and location.

"The 1-kilometre grid spacing allowed the model to capture the subtle wind patterns within the valley that actually triggered the storm," said Yongling Su, lead author of the study and a forecaster at the Qinghai Meteorological Observatory. "As the sun heated the valley slopes during the day, predictable upslope winds developed. But in the evening, these collided with cooler air draining down the mountainsides, creating narrow lines of forced rising air that ignited the thunderstorm cells. At coarser resolutions, these critical details were simply smoothed out."

The research revealed that the thermodynamic conditions for storms—instability and moisture—were similar across all simulations. The critical difference lay in how well the models represented the low-level valley winds that provide the final trigger for convection.

"For forecasting extreme precipitation in complex mountainous terrain, increasing resolution from 3 kilometres to 1 kilometre can yield noticeable forecast improvements," said Robert Plant, Professor of Meteorology at the University of Reading and corresponding author of the study. "The 1-kilometre grid enables the model to better simulate the intricate flow structures within valleys that govern where and when the most dangerous storms develop. This is relevant not just for Qinghai but for mountain valleys in many parts of the world."

The findings have implications for operational forecasting. While running ultra-high-resolution models across entire continents remains computationally demanding, the researchers propose a more targeted approach.

"Our goal is practical," Su explained. "We want to provide forecasters in Qinghai and similar mountainous regions with more precise tools. By running higher-resolution 'on-demand' forecasts when and where dangerous storms are anticipated—essentially zooming in on high-risk areas within broader operational models—we may be able to issue heavy precipitation warnings earlier and more accurately."

The study also highlighted a limitation of traditional "convective parameterization" schemes—mathematical formulas that approximate storm development. In simulations where these schemes were active, weak rainfall began too early, followed by a delay in the main storm, effectively disrupting the model's timing.

"Using a convection parameterization scheme led to premature removal of early atmospheric instability," Plant noted. "This delayed the real storm and reduced its intensity in the simulation. When we let the model represent convection directly at high resolution, the timing and magnitude aligned more closely with observations."

While the study analysed one event in depth with supporting evidence from a second, the researchers suggest the underlying mechanisms are likely applicable more broadly.

"This is about understanding how valley circulations develop—how air moves up slopes during the day and drains down at night—and how these flows can contribute to storm triggers," Plant added. "Better representation of these wind patterns in models supports better predictions."

This approach, the researchers suggest, could strengthen disaster prevention efforts in mountainous regions globally, from the Andes to the Alps, the Himalayas to the Rockies.

 

Sunday, February 15, 2026

 

Drones equipped with cost-effective sensors can help to monitor air quality more effectively



Study in Indian megacity Delhi highlights the importance of vertical measurements




Leibniz Institute for Tropospheric Research (TROPOS)

drone 

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The drone with the payloads PM-LCS, AE-51 micro-Aethalometer, and meteorological sensors.

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Credit: Ajit Ahlawat, TU Delft / TROPOS





New Delhi/ Delft/ Leipzig. Cost-effective sensors on drones may be an effective tool for better investigating the lowest layers of the atmosphere. If ground-based air quality measurements were supplemented by such drone measurements, air quality models, strategies to combat air pollution could be improved. This is the conclusion of an international research team from a field study in the Indian metropolitan region of Delhi, which showed that particulate matter (PM) concentrations depend heavily on height above ground level. For example, at a height of 100 meters, PM2.5 concentrations were up to 60 percent higher than at ground level. The results suggest that current model simulations significantly underestimate PM2.5 concentrations during morning smog phases, the researchers write in the journal Nature npj Clean Air.

Researchers from India, the Netherlands, Germany, China, Greece, Great Britain, Thailand, Czechia, and Cyprus participated in the study in Delhi. It was coordinated by Asst. Prof. Ajit Ahlawat from the Leibniz Institute for Tropospheric Research (TROPOS), who now conducts research at TU Delft. With over 30 million people, the metropolitan area around India's capital New Delhi is one of the largest and most densely populated megacities in the world. Air pollution there is also among the highest in the world. Particularly in winter smog, particulate matter concentrations reach extremely hazardous levels.

 

Heavy smog often prevails in northern India, especially after the monsoon and in winter. For this reason, a series of ground-based measurements have recently been carried out to better understand the causes and mechanisms of air pollution. Most studies conducted in India are based either on satellite observations from space or on ground-based measurements. In contrast, there is hardly any data available from the lowest layers of the atmosphere. However, the vertical distribution of air pollutants and meteorological conditions up to an altitude of about one kilometre are of great importance because they have a decisive influence on how high the concentration of pollutants in the air can become.

 

In recent years, significant advances have been made in both drone (uncrewed aerial vehicle/UAV) technology and cost-effective particulate matter sensors. Mass production and miniaturization offer new possibilities, which were tested by researchers in a field trial in March 2021 at the Indian Institute of Technology (IIT) Delhi and compared with standard measurements from stationary measuring devices. To this end, the research team equipped and modified a drone from the Indian start-up BotLab Dynamics with low-cost fine PM sensors: "A significant development was the construction of a custom-made vertical aerosol inlet, which was positioned about 30 centimetres above the drone's rotor blades. This enabled us to take measurements that were as accurate as possible, which is otherwise a major problem with drones, whose rotor blades cause significant air turbulence, “reports Prof. Ajit Ahlawat. “Another challenge was the high humidity, a meteorological factor that is not particularly rare in this region. Since air sampling and analysis are difficult under such conditions, a custom-designed silica gel dehumidifier was installed to ensure reliable results.” This enabled the researchers to investigate vertical fluctuations in air pollutant concentrations at different altitudes and at different times of day. The focus was on hazy and non-hazy morning hours in Delhi in order to find out more about the causes of smog.

 

Organic substances dominated during the day, while inorganic substances such as nitrate and chloride increased significantly at night. This trend indicates an increased contribution, which is likely due to the combustion of biomass and waste as well as industrial emissions during the evening and night hours. Nitrate and ammonium were strongest in the early morning, suggesting their condensation into the aerosol phase under humid and cold conditions. As the boundary layer height increased after sunrise, dilution effects led to a rapid decrease in chloride mass concentration. NOx levels peaked around 9:00 p.m. local time, caused by vehicle and industrial emissions trapped under a stable boundary layer. In contrast, fine particulate matter (PM2.5) rose steadily from around 80 micrograms per cubic meter at 6:00 p.m. local time to around 150 micrograms per cubic meter at 8:00 a.m. local time, underscoring the role of fresh primary emissions and secondary aerosol formation during smog formation. An example illustrates how much PM concentrations can vary depending on altitude: on March 18, the PM2.5 concentration rose by a remarkable 60 percent with increasing altitude, reaching around 160 micrograms per cubic meter at higher elevations compared to around 100 micrograms per cubic meter at ground level. The morning inversion had obviously caused the pollutants to accumulate particularly strongly in the lower boundary layer. Relative humidity was above 80% at night, which promotes the formation of secondary aerosols and the growth of particles through water absorption. This was also highlighted by the proxy indicator e.g. PM ratio used during the study. When the temperature rose above 30°C in the morning, the relative humidity fell below 40% and the haze dissipated.

 

The accumulation of pollutants and high humidity at night are the main reasons for the formation of ground-level smog layers in Delhi. The rapid dissipation of haze after sunrise is facilitated by the expansion of the boundary layer, reduced relative humidity, and increased photochemical oxidation. These findings underscore the need for emission control measures targeting nocturnal sources and humidity-driven secondary aerosol processes, as well as their understanding, particularly in vertical columns, in order to reduce smog in Delhi.

 

Another important finding of the study emerged from a comparison of the measurements with the WRF-Chem model, which is frequently used worldwide to predict air quality: the results indicate that current model simulations significantly underestimate PM2.5 concentrations during morning smog phases. ‘This may be due to the dry bias of the model, which limits its ability to simulate aerosol hygroscopic growth at high humidity values’ explains Prof. Mira Pöhlker from TROPOS and the University of Leipzig. 

These deviations are greatest when there is heavy haze. It also shows that high-resolution vertical measurements are important for validating air quality models in the lower boundary layer and for improving urban air quality predictions,’ explains Prof. Sagnik Dey from Indian Institute of Technology, Delhi.

 

The team believes that the study is an important step towards integrating cost-effective particulate matter sensors into existing air monitoring systems and closing observation gaps in the lower boundary layer. ‘By directly quantifying the interactions between relative humidity and particulate matter, as well as model deviations under real smog conditions, our results pave the way for next-generation air quality models that consider aerosol chemistry and dynamic boundary layer coupling,’ emphasises Ajit Ahlawat. These innovations are crucial not only for improving predictions and public health measures in megacities such as Delhi, but also for developing global strategies to mitigate air pollution in rapidly urbanising regions and its climate impacts. Tilo Arnhold

The drone carrying the payload hovering at high altitude.

Credit

Rohit K. Choudhary (University of Delhi)

The drone hovering at high altitude with a smoggy/hazy background in the backdrop.

Credit

Ajit Ahlawat, TU Delft / TROPOS