The research team, led by Weilong Deng and Jianping Guo of the Chinese Academy of Meteorological Sciences and Lanzhou University, focused on the eastern China plains, a densely populated belt spanning the North China Plain and the middle-and-lower Yangtze Plain. Warm-sector heavy rainfall, as this phenomenon is known, was first documented in South China during the pre-summer rainy season, but it has since been implicated in disasters across the country, including the localized Shijiazhuang rainstorm of July 2017 and the extreme Beijing rainstorm of late July 2025. Because organized frontal lifting is absent, these storms tend to be highly localized, develop abruptly, move slowly, and dump disproportionate amounts of water on small areas, producing urban waterlogging, flash floods, and river flooding that operational forecast models consistently struggle to anticipate.

To study the phenomenon rigorously, the team assembled an unusually rich observational arsenal covering the warm seasons of 2021 through 2025. Minute-resolution records from 493 surface rain gauges were combined with ERA5 reanalysis fields and a mesonet of radar wind profilers, instruments that measure wind speed and direction continuously at vertical resolutions of 120 meters and temporal resolutions of six minutes. The researchers built an objective identification framework that integrates automated front detection based on the thermal front parameter applied to pseudo-equivalent potential temperature at 850 hectopascals, a warm-sector mask requiring warm advection at both 925 and 1000 hectopascals with southerly flow, and a station-specific threshold set at the 99th percentile of hourly rainfall. Events had to occur between 200 and 400 kilometers from the detected front, and any rainfall influenced by a tropical cyclone was excluded to keep the sample clean.

The scheme identified 477 warm-sector heavy rainfall events alongside 6002 other heavy rainfall events across the study region. The contrast between the two populations was striking. Warm-sector events were spatially scattered and weakly organized: 196 of the 493 stations recorded none at all, while 297 recorded at least one, with no clear relationship to terrain elevation within the deliberately low-relief domain. Other heavy rainfall, by contrast, was ubiquitous and well organized, increasing from north to south, with every station recording at least five events. Temporally, warm-sector rainfall concentrated in midsummer, peaking in July and August, and showed a strong nocturnal preference, with a dominant onset mode between 20:00 and 03:00 Beijing Time and only a weaker afternoon mode around 13:00 to 16:00. Other heavy rainfall peaked sharply in the late afternoon, a signature of thermally driven convection.

The hazard posed by these storms becomes clear in the event statistics. Warm-sector heavy rainfall accounted for only about 5 to 10 percent of annual heavy rainfall occurrences, yet its events were shorter, with a median duration of 5.7 hours compared with 6.8 hours for other heavy rainfall, while delivering comparable or greater water. Median accumulations reached 70.3 millimeters for warm-sector events against 56.2 millimeters for the others, and the upper tail was markedly heavier, with a 90th percentile of 156.1 millimeters versus 120.2 millimeters. Among the 297 stations that recorded at least one such event, the mean contribution of warm-sector rainfall to total heavy-rainfall accumulation was 13.8 percent, with a maximum of 48.1 percent at a single station. In other words, at some sites, nearly half of all the heavy rain that fell over five warm seasons arrived in brief, front-free bursts.

Composite reanalysis fields revealed the thermodynamic stage on which these storms play out. At the hour of onset, event stations sat within a moist tongue at 850 hectopascals, with specific humidity reaching 14.8 grams per kilogram, near the northern leading edge of warm, humid air characterized by pseudo-equivalent potential temperatures of roughly 350 to 352 kelvin, immediately south of the tightly packed gradient marking the frontal zone. Critically, low-level moisture flux convergence between 1000 and 700 hectopascals intensified progressively in the hours before onset, deepening from a modest signal six hours out to a pronounced convergence core of less than minus five times ten to the minus four kilograms per square meter per second sitting essentially on the station at onset. This progressive moistening and convergence preconditions the column for deep convection, but under weak synoptic forcing it is not by itself sufficient to trigger the storms.

That is where the radar wind profilers came in. Aligning profiler observations with the precisely timed onset of rainfall at 37 co-located stations, the team composited 62 warm-sector heavy rainfall events over the 180 minutes preceding onset, and compared them with 271 warm-sector events that never reached heavy-rainfall intensity. The composites showed a marked kinematic evolution in the lowest levels of the atmosphere. Wind kinetic energy below three kilometers intensified during the final hour before onset. Low-level vertical wind shear, concentrated in shallow layers below about 1.5 kilometers and reaching values up to about fourteen times ten to the minus three per second, strengthened as onset approached. The winds veered clockwise with height, a signature of warm-air advection consistent with the moist southerly flow feeding the storms.

The most distinctive precursor involved the low-level jet, a band of fast wind in the lowest few kilometers of the atmosphere. A low-level jet appeared at least once during the analysis window in 46.8 percent of the heavy-rainfall events, compared with 39.5 percent of the non-heavy events. More telling was the timing: in the heavy-rainfall composites, jet occurrence frequency rose from roughly 5 to 11 percent at earlier lead times to a peak of 21.0 percent at 36 minutes before onset, whereas the non-heavy composites remained comparatively steady at 9 to 13 percent. The event-mean increase in jet occurrence between the early and late windows was 7.0 percentage points, though the authors caution that the bootstrap confidence interval of minus 0.25 to 14.18 percentage points includes zero, meaning the tendency carries appreciable uncertainty. Enhanced ascent also emerged, with the strongest upward air movements near 1 to 2 kilometers intensifying during roughly the final 30 minutes before onset, and the upper tail of ascent speeds growing more pronounced than in the non-heavy comparison sample.

The authors are careful about what these composites can and cannot establish. The distributions of jet properties and ascent speeds overlap substantially between the heavy and non-heavy samples, so the presence of a low-level jet or strong low-level shear alone does not guarantee a downpour. The analysis does not locate jet termini, resolve the horizontal convergence they might generate, or test the nocturnal inertial oscillation mechanism that has been invoked to explain nighttime warm-sector rainfall in South China. Nor can single-point profilers rule out contamination of vertical velocity measurements by falling hydrometeors aloft before rain reaches the gauge. What the study does deliver is a resolved, five-year, station-based picture of the pre-onset environment, and a set of candidate kinematic indicators that are continuously observable by operational profiler networks.

The implications for forecasting are significant. Quantitative precipitation forecast skill for warm-sector heavy rainfall remains low in both global and regional models, precisely because convective initiation in this regime is governed by mesoscale and boundary-layer processes that conventional observations and models resolve poorly. The new results suggest that monitoring the late-hour rise in low-level jet occurrence and the intensification of low-level ascent could provide short-range warning signals, complementing the thermodynamic information available from moisture and instability fields. Related work has already shown that profiler mesonets can resolve lower-tropospheric convergence preceding heavy rainfall, and observing system simulation experiments over the Beijing-Tianjin-Hebei region indicate that assimilating profiler data can improve mesoscale wind analyses and short-term rainfall forecasts. The next steps, the authors argue, are detailed case studies combining profiler records with weather-radar reflectivity and dual-polarization fields to test whether back-building or training storm organization underlies the large accumulations, convection-permitting simulations to verify the jet-convergence-ascent pathway, and event-level validation to establish the specificity, useful lead time, and false-alarm characteristics of the newly identified precursor signals.

Subject of Research: Warm-sector heavy rainfall identification and precursor low-level dynamic signals over the eastern China plains

Article Title: Warm-sector heavy rainfall over the eastern China plains: identification, characteristics, and precursor dynamic signals

Article References: Deng, W., Guo, J., Li, N., Zhang, Z., Wang, Y., & Yang, Y. (2026). Warm-sector heavy rainfall over the eastern China plains: identification, characteristics, and precursor dynamic signals. Atmospheric Chemistry and Physics, 26(19), 14259-14275. https://doi.org/10.5194/acp-26-14259-2026

Image Credits: AI Generated

DOI: 10.5194/acp-26-14259-2026

Keywords: warm-sector heavy rainfall, eastern China plains, convective initiation, low-level jet, radar wind profiler, moisture flux convergence, nowcasting, vertical wind shear, nocturnal rainfall, rain gauges, ERA5 reanalysis, forecasting

The study, published in the journal Wind Energy Science, was led by Nikolas Angelou of the Technical University of Denmark and Camille Dubreuil-Boisclair of Equinor. Their instrument of choice was not a towering meteorological mast, which is prohibitively expensive to install in deep water, but a nacelle-mounted Doppler wind lidar bolted directly onto the turbine itself. The device, a Vaisala Wind Iris, fires four laser beams upwind at azimuth angles of plus and minus 15 degrees and tilt angles of plus and minus 5 degrees, sampling radial wind speeds at ten distances ranging from 50 to 400 meters ahead of the rotor. Because the optical head follows the yaw of the nacelle, the lidar keeps its beams pointed into the wind almost continuously, maximizing data availability in a way that fixed masts and floating buoys cannot easily match.

The turbine under scrutiny, designated HS4, is one of five Siemens Gamesa SWT-6.0-154 machines at Hywind Scotland, the first commercial floating wind farm, operating since 2017 off the east coast of Scotland. Each unit has a hub height of 98.6 meters and a rotor diameter of 154 meters, mounted on a ballasted spar buoy. Floating turbines move in six degrees of freedom, three rotational and three translational, and at Hywind Scotland the dominant motion is pitch: the nacelle tilts backward by up to 7 degrees at wind speeds between cut-in and rated power, settling to about 2 degrees above rated. That tilt matters enormously for the lidar, because it warps the measurement geometry. When the turbine pitches to 5 degrees, two of the four beams become nearly horizontal while the other two sweep across the upper part of the rotor, and a motion-correction procedure using a motion reference unit on the nacelle is required to untangle the true wind from the swinging platform.

To make sense of the raw laser returns, the researchers built a mathematical model that expresses the four line-of-sight velocities as a function of the horizontal wind components at hub height, their vertical gradients, and an induction factor describing how the turbine’s own presence slows the air approaching the rotor. The model assumes that shear and veer, the change of wind speed and direction with height, remain constant across the upper rotor, and that the inflow is horizontally homogeneous. For the vast majority of cases, 88.5 percent of the dataset, this simple parameterization worked well, reproducing the observed radial speeds with root mean square errors below 0.2 meters per second. The lidar data even captured the turbine’s induction zone clearly: at around one rotor diameter upwind, radial speeds dropped noticeably, corresponding to induction factors of 0.38 to 0.41, consistent with the velocity deficit a turbine generates when operating below rated power.

But in 11 percent of the 10-minute periods, the model failed spectacularly. The culprit was the low-level jet, a river of fast air that forms in the lowest few hundred meters of the atmosphere, often under stable stratification when the sea surface is cool relative to the air above it. Within these jets, wind speed can peak at a certain altitude and then fall off above, producing wind speed inversions that no linear shear assumption can capture. Some profiles even displayed two inflection points, a maximum and a minimum, between 90 and 180 meters, forming a distinct core in the wind field. The researchers found that a local maximum in the vertical profile could be detected in 10 percent of all cases, with inversion heights typically clustered around 130 meters, squarely inside the rotor-swept area, and wind speed differences across the inversion usually between 0.25 and 0.5 meters per second, though in a tenth of cases exceeding 2 meters per second.

The climatology that emerged from six months of usable data, drawn from January 2019, autumn 2019, and summer 2020, is striking. Negative shear accounted for 22 percent of the examined periods, concentrated in the months from June to September, a figure consistent with earlier rawinsonde studies over the North Sea. These events were not tied to a single wind direction sector; in June and August they appeared across the entire 90 to 270 degree range analyzed, and they occurred at all times of day. Most negative-shear and inversion episodes lasted about an hour, but eight periods persisted for 700 to 900 minutes, meaning the turbine operated for more than half a day under atmospheric conditions that standard power performance methods simply do not anticipate. A detailed 12-hour case study on 26 June 2020, with hub-height winds swinging from 6 to 14 meters per second, showed deviations from nominal power reaching up to 50 percent when referenced against the power curve of an equivalent fixed-bottom turbine.

To quantify the effect systematically, the team performed a formal power curve verification using three different estimates of the reference wind speed: a wind field reconstruction from the farthest range gates at 2.6 rotor diameters, a reconstruction using all available range gates, and the rotor-equivalent wind speed recommended in the IEC 61400-12-1 standard. The results were unambiguous. Below rated wind speed, power production differed by 5 to 10 percent between periods with negative shear and periods with positive shear, depending on which wind speed definition was used. Under negative shear, output fell by up to 18 percent relative to the reference power curve, and the scatter in the measured power, expressed as the standard deviation of mean power in each wind speed bin, was nearly twice as large as in positive-shear conditions. Part of the explanation lies in veer: negative shear was usually accompanied by negative veer, a clockwise-with-height rotation of the wind that further reduces the energy available to the rotating blades.

The implications ripple well beyond one Scottish turbine. Power curve verification is the contractual backbone of the wind industry, the procedure by which manufacturers prove their machines deliver the energy they promise. The current IEC standards were written for turbines on flat or complex terrain and do not account for platform motion, nor for wind profiles that deviate from the textbook logarithmic law. Yet this study shows such deviations occur in a third of offshore cases during summer and autumn. The authors also note a practical limitation: because the lidar only probed the upper half of the rotor, the exact altitude of inversions in negative-shear cases could not be pinned down, which prevented a full rotor-equivalent wind speed calculation for those conditions. Temperature gradient measurements were likewise unavailable, so atmospheric stability could not be classified directly, leaving open the question of whether the observed jets were driven by stable stratification as models suggest.

The remedy, the researchers argue, is a measurement strategy that captures wind across the entire rotor-swept area, not just at hub height. Nacelle-mounted lidars with more beams or more range gates, combined with induction-zone models to correct for the turbine’s own influence on the approaching flow, could provide the full three-dimensional picture that floating wind demands. As floating offshore wind expands into deeper waters worldwide, and as rotors grow ever taller into the layer where low-level jets live, the message from the North Sea is clear: the wind a floating turbine actually harvests is far stranger, and far more structured, than the smooth curve on the datasheet, and only by measuring it honestly can the industry certify, and ultimately improve, the performance of its machines.

Subject of Research: Vertical wind shear and low-level jet effects on floating offshore wind turbine power production

Article Title: Offshore wind profile characteristics and their impact on floating wind turbine power production

Article References: Angelou, N., & Dubreuil-Boisclair, C. (2026). Offshore wind profile characteristics and their impact on floating wind turbine power production. Wind Energy Science, 11(9), 3745-3762. https://doi.org/10.5194/wes-11-3745-2026

Image Credits: AI Generated

DOI: 10.5194/wes-11-3745-2026

Keywords: floating offshore wind, wind shear, low-level jet, nacelle lidar, power curve verification, Hywind Scotland, North Sea, wind speed inversion, rotor-equivalent wind speed, atmospheric boundary layer, wind energy, IEC standards

The research tackles a long-standing puzzle in meteorology: extreme rainfall over complex terrain is born from the interaction between the synoptic-scale circulation, the sweeping patterns of high and low pressure that span thousands of kilometers, and the local convective processes that unfold over just a few kilometers. Scientists have long studied these two scales separately, but the link between a region’s characteristic weather types and the minute-by-minute mechanics of a specific flood-producing storm has remained murky. Li and Xu set out to close that gap by combining two powerful tools: objective weather typing, which sorts historical weather patterns into distinct families, and convection-permitting dynamical downscaling, which uses a high-resolution numerical model to simulate the fine-grained physics of a single event.

The statistical backbone of the study is a technique called obliquely rotated T-mode principal component analysis, applied to 324 six-hourly atmospheric circulation samples. These samples were extracted from 81 warm-season extreme precipitation events that occurred between 2013 and 2022 in the western part of northern Xinjiang. The analysis revealed four distinct weather types, labeled T1 through T4, that govern when the region gets hammered by heavy rain. Two of them dominate: type T1 accounted for 53.1 percent of the circulation samples and type T2 for 28.4 percent, meaning that more than eight in ten extreme rainfall episodes unfold under just two recurring large-scale configurations.

Geography matters just as much as circulation. When the researchers mapped where the 81 events actually dropped their heaviest rain, they found a striking concentration: 72.8 percent of the events had their precipitation centers in the Ili River Valley and the mountains that surround it. This valley, wedged between towering ranges of the Tianshan, acts as a funnel for moisture and a trigger for uplift, making it the undisputed hotspot of extreme precipitation in the region. The finding confirms what local forecasters have long suspected, but it also quantifies the risk in a way that can feed directly into hazard planning for communities and agriculture in the valley.

To understand what actually happens inside one of these storms, the team zoomed in on a spectacular case: the event of 31 July to 1 August 2016, which delivered 100.1 millimeters of rain in just 24 hours at the Kurdening station. That is roughly a year’s worth of precipitation for parts of this arid region, compressed into a single day. The researchers brought multiple lines of evidence to bear, including surface and upper-air observations, back-trajectory analysis using NOAA’s HYSPLIT model to trace where the moisture came from, and a 3-kilometer-resolution simulation with the Weather Research and Forecasting model, a configuration fine enough to explicitly resolve thunderstorms rather than approximate them with statistical shortcuts.

The simulation revealed a storm that unfolded in two distinct phases, each governed by different physics. Before the first phase, the atmosphere grew increasingly unstable: convective available potential energy, a measure of the fuel available to thunderstorms, climbed from 964 to 1733 joules per kilogram. As convection fired and consumed that reservoir of instability, the value collapsed to below 200 joules per kilogram, a classic signature of an atmosphere that has been wrung out by vigorous storms. At 14 UTC on 31 July, the model showed a negative moist potential vorticity center of minus 6 PVU extending to about 6 kilometers in altitude over the windward slope of the mountains, flagging a deep layer primed for instability.

The evolution of moist potential vorticity, a quantity that combines rotation and moisture gradients, told an even more subtle story. As the storm progressed, the diagnostic MPV1 component turned positive while the total moist potential vorticity stayed negative, driven by a dominant negative contribution from the MPV2 component. In the language of atmospheric dynamics, this sequence signals a transition from convective instability, the ordinary kind that fuels thunderstorms, to conditional symmetric instability, a slantwise form of instability that releases energy along tilted surfaces and can sustain banded precipitation. It is the kind of fingerprint that only emerges when a model can resolve the storm’s internal structure, and it explains why the rain persisted even after the initial burst of convection had spent its fuel.

The second phase of the storm was a study in cooperation between wind, water, and stone. An easterly low-level jet blowing at 8 meters per second steered fresh moisture toward the slopes, while the cold pool, the dome of rain-chilled air left behind by the first round of storms, pushed outward like a miniature cold front. Where this outflow collided with the incoming moist flow and with the mountains themselves, the air had nowhere to go but up. Frontogenesis, the sharpening of temperature and wind contrasts, and orographic lifting by the terrain worked together to reignite convection, producing the second wave of heavy rain. Cold pools acting as conveyor belts of moisture, a mechanism documented in regions from the Sahara to coastal China, here proved equally decisive in a semi-arid mountain setting.

What makes this study more than a case report is its architecture. By first classifying the region’s extreme rainfall into a small number of weather types and then dissecting a representative event at storm scale, the authors built a bridge between the climatological view, useful for understanding long-term risk, and the process view, useful for forecasting. The multiscale approach means that when forecasters see a T1 or T2 circulation pattern setting up over Central Asia, they now have a physically grounded expectation of where in the terrain the rain will concentrate and through which mechanisms, whether instability release, symmetric instability, low-level jets, cold pools, or orographic uplift, the storm will intensify.

The implications reach well beyond Xinjiang. Mountainous regions worldwide, from the Alps to the Andes to the Himalaya, face intensifying extreme rainfall as the atmosphere warms and holds more moisture, yet their sparse observation networks make these events hard to predict and even harder to study. The recipe demonstrated here, pairing objective circulation classification with convection-permitting downscaling and diagnostic tools like moist potential vorticity and trajectory analysis, offers a transferable template for other data-sparse mountain regions. For the residents of the Ili River Valley, whose fields and towns sit beneath slopes capable of squeezing a hundred millimeters of rain from a desert sky, the study transforms a seemingly capricious hazard into a process that can be anticipated, monitored, and planned for.

Subject of Research: Multiscale mechanisms of extreme precipitation over complex terrain in northern Xinjiang

Article Title: Multiscale diagnosis of extreme precipitation over complex terrain in the western part of northern Xinjiang by integrating weather typing and dynamical downscaling

Article References: Li, Z., & Xu, C. (2026). Multiscale diagnosis of extreme precipitation over complex terrain in the western part of northern Xinjiang by integrating weather typing and dynamical downscaling. Natural Hazards, 122(20), Article 656. https://doi.org/10.1007/s11069-026-08430-0

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08430-0

Keywords: extreme precipitation, Xinjiang, Ili River Valley, weather typing, dynamical downscaling, WRF model, moist potential vorticity, orographic lifting, cold pools, low-level jet, convection, Natural Hazards