The research draws on a uniquely capable instrument: a dual-frequency radar wind profiler operated near Darwin, Australia, during the 2005–2006 monsoon season. The system pairs a 50-MHz profiler with a 920-MHz profiler, and the combination exploits a subtle physical distinction. The 50-MHz radar detects not only Rayleigh scattering from hydrometeors but also Bragg scattering from turbulent fluctuations in the refractive index of clear air. Because those turbulent irregularities move with the air itself, the 50-MHz Doppler spectrum contains two peaks: one tracking the vertical air motion and another tracking the falling particles. The 920-MHz radar, sensitive only to the hydrometeor signal at these heights, is used to mask out the particle peak in the 50-MHz spectra, isolating the vertical air motion. The bulk reflectivity-weighted particle fall speed then emerges as a simple residual — the difference between the measured mean Doppler velocity and the retrieved air motion, adjusted to sea level pressure using the classic Foote and du Toit approximation.

This residual approach carries a crucial advantage over conventional techniques. Most radar retrievals of vertical air motion must first assume an empirical relationship between radar reflectivity factor Z and particle fall speed Vt, then subtract that assumed fall speed from the observed Doppler velocity. Any error in the assumed relationship propagates directly into the estimated updraft or downdraft, and such assumptions are widely recognized as the dominant source of error in profiler-based air motion retrievals. By retrieving air motion without any fall speed assumption and then solving for Vt as the leftover, the Darwin team sidesteps that circularity. The result is a bulk, power-weighted fall speed for each radar volume, biased toward the largest and densest particles that dominate the radar return — precisely the quantities that matter for interpreting scanning radar observations and for testing the fall speed prescriptions used in cloud models.

Before venturing into the uncharted territory of storm cores, the authors validated the technique against conditions with well-established reference behavior. Using roughly 16,000 one-minute samples of convective rain and 38,000 samples of stratiform rain below the melting layer, they fitted power-law relationships of the form Vt = aZ^b. The convective fit yielded a coefficient of 2.59 for a fixed exponent of 0.1, closely matching an independent reference derived from more than 4,000 drop size distributions recorded by a collocated two-dimensional video disdrometer, which gave a matched coefficient of 2.7. The stratiform fit produced a coefficient of 3.3, slightly higher than the disdrometer’s 3.1, but the discrepancy proved physically meaningful rather than erroneous: the team had deliberately restricted stratiform samples to columns with pronounced bright-band signatures, where aggregation and breakup below the melting layer favor fewer, larger drops for a given reflectivity. When all stratiform echoes were included, the coefficient settled to 3.0, essentially identical to the surface instrument. Residual standard deviations were about 1 meter per second for rain.

Snow presented a similar consistency check. Above the bright band, the profilers collected more than 44,000 samples between 4.5 and 6.5 kilometers altitude and over 16,000 samples between 6.5 and 8.5 kilometers. The retrieved fall speeds exceeded 1 meter per second for reflectivity above 20 dBZ and 1.4 meters per second at 30 dBZ, with residual standard deviations of roughly 0.3 to 0.4 meters per second — comparable to the error bars reported in earlier Darwin profiler work by Protat and Williams in 2011. The best-fitting relationships favored a lower exponent than some prior studies, and the team noted that shifts in the coefficients with altitude make physical sense: near the melting layer, aggregation produces large, slowly falling aggregates that inflate reflectivity relative to fall speed, while higher aloft, smaller and denser unaggregated ice falls faster for its reflectivity.

With confidence established, the study turned to its centerpiece: fall speeds within deep convective cores above the melting level, between 5.5 and 7.5 kilometers altitude, drawing on about 31,000 one-minute samples from 43 storm events. These volumes contain complicated mixtures of lofted rain, frozen drops, graupel, and small hail, and no direct observational reference existed for them before. The Darwin observations landed between two previously published graupel relationships — one derived for the Amazon, which implies faster-falling particles for a given reflectivity, and one for Oklahoma, which implies slower-falling particles. But the most striking result emerged at the lower end of the reflectivity range: for Z below 35 dBZ, the tropical particles fell faster than prior relationships predicted by more than 1 to 2 meters per second. Because these moderate-reflectivity samples typically come from the peripheries of convective cores rather than their most intense centers, the finding suggests that existing retrieval treatments systematically underestimate fall speeds in these flank regions.

To probe the physical controls behind this behavior, the team separated events by monsoon regime using established radiosonde-based classifications. Active monsoon conditions, associated with the deepest moist westerly flow and the highest rainfall accumulations, contributed 11 events, while Break monsoon conditions — drier midlevels, higher convective available potential energy, and more intense daytime convection — contributed 23 events. Both regimes produced similar fall speed behavior in the strongest core regions with reflectivity above 35 dBZ, but Break events showed markedly greater variability and a propensity for faster fall speeds at lower reflectivities. Some Break samples even exceeded the fall speeds expected of rain at the same reflectivity, with values above 6 meters per second for Z below 35 dBZ. The authors interpret these outliers as evidence of partially melted graupel coupled with size sorting, in which stronger updrafts loft liquid and rimed ice above the melting level and winds sort the fastest particles toward core peripheries.

The regime comparison also carries a provocative implication for modelers. Prior arguments, rooted in midlatitude Oklahoma simulations, held that stronger updrafts favor larger but lower-density graupel that falls more slowly. The Darwin observations only partially support that picture. While the strongest cores did show relatively slow-falling, presumably lower-density graupel consistent across both regimes, the Break monsoon’s more vigorous storms clearly produced faster and more diverse mixed-phase media aloft than the weaker Active monsoon storms. Notably, the profilers rarely observed reflectivity above 45 dBZ at these altitudes, indicating that even the most intense tropical Break storms did not sustain large hail aloft the way continental storms can. The authors suggest that in humid tropical environments, graupel and frozen drops melt and shrink more readily, pushing bulk fall speeds toward rain-like values — a picture consistent with recent modeling by Vagasky and colleagues showing that warmer environments accelerate hail melting.

The practical consequences extend well beyond radar meteorology. Fall speeds govern how much condensate rains out versus detrains into anvil clouds, and recent Earth system model analyses have shown that convective fall speeds control shortwave radiation and convective precipitation in simulations, while stratiform fall speeds influence outgoing longwave radiation and global-scale thermodynamics. Many convective parameterizations hard-wire fall speed assumptions for graupel and mixed-phase media, often tuned to midlatitude conditions. If substituting the older Oklahoma-style curves for the Darwin observations, retrieved updraft intensities in the strongest tropical cores could shift by 1 to 5 meters per second — a substantial error in the quantities used to evaluate some of the most uncertain processes in climate projection. Encouragingly, the finding that core-region graupel fall speeds appear relatively invariant across a modest range of tropical thermodynamic environments hints that a single, simpler tropical relationship might suffice, potentially simplifying both retrievals and parameterizations. As longer profiler records accumulate and forward radar operators improve, the Darwin dataset stands as a rare observational anchor for one of the stormiest, least sampleable corners of Earth’s atmosphere.

Subject of Research: Hydrometeor fall speed retrieval in tropical deep convection using dual-frequency radar wind profilers

Article Title: Dual-frequency profiler study of hydrometeor fall speeds in tropical deep convection

Article References: Giangrande, S. E., Williams, C. R., & Protat, A. (2026). Dual-frequency profiler study of hydrometeor fall speeds in tropical deep convection. Atmospheric Measurement Techniques, 19(19), 6251-6265. https://doi.org/10.5194/amt-19-6251-2026

Image Credits: AI Generated

DOI: 10.5194/amt-19-6251-2026

Keywords: tropical convection, hydrometeor fall speed, dual-frequency radar, wind profiler, graupel, radar retrievals, Darwin Australia, monsoon, cloud microphysics, vertical air motion, deep convective cores, Atmospheric Measurement Techniques

The research team assembled seasonal hindcasts, retrospective forecasts of past winters, from eight state-of-the-art operational prediction systems. Hindcasting is the standard proving ground for seasonal prediction: models are initialized before known winters and their output is scored against observations, allowing scientists to quantify skill without the benefit of hindsight. The team evaluated how well each system reproduced the dominant mode of East Asian Winter Monsoon variability that is tied to the El Niño–Southern Oscillation, the planet’s most influential year-to-year climate fluctuation, which originates in the tropical Pacific through coupled interactions between sea surface temperatures and atmospheric convection.

The headline number from the assessment is a correlation skill of roughly 0.4 for the best-performing model. In seasonal forecasting, where the atmosphere’s own memory fades within about two weeks, any correlation above zero represents genuine predictive information, and values near 0.4 indicate that a meaningful but limited fraction of monsoon variability is captured. The spread among the eight systems was substantial, and that spread became the central puzzle of the study. If all models see the same tropical sea surface temperatures, why do some translate them into East Asian winter circulation far better than others?

The researchers’ diagnostics point to two distinct teleconnection pathways through which ENSO influences the monsoon, one in the lower troposphere and one in the upper troposphere. The lower-tropospheric pathway is the classical picture: ENSO-related heating anomalies modify the western Pacific subtropical circulation, the Philippine Sea anticyclone and the low-level winds that carry cold air southward. This pathway has long been the focus of model evaluation. The upper-tropospheric pathway, by contrast, operates roughly ten kilometers above the surface and has received far less attention in the seasonal prediction literature, despite the new evidence that it may be the decisive factor separating skillful models from unskillful ones.

The study names this upper-level feature the Southeast Asia–Japan pattern. In observations, tropical convective anomalies clustered around the Maritime Continent, the archipelago-rich region spanning Indonesia, Malaysia and surrounding seas, generate a divergence anomaly in the upper troposphere over the southeastern Tibetan Plateau. That divergence acts as a source of stationary Rossby wave activity, exciting a wave train that propagates northeastward toward Japan. The physics is rooted in the vorticity budget of the tropical upper troposphere: divergent outflow from deep convection generates rotational flow through the well-known mechanism described by Sardeshmukh and Hoskins, and the resulting wave energy travels along the westerly jet, guided by the propagation characteristics of the background flow.

When this wave train arrives over Japan, it establishes a barotropic circulation anomaly, meaning a coherent signal through a deep layer of the atmosphere, which then modulates the East Asian trough in the mid-troposphere and the meridional winds near the surface. The East Asian trough is a keystone of the winter circulation, anchoring the storm track and controlling the southward penetration of cold surges. By shifting and reshaping the trough, the upper-tropospheric wave train ultimately influences surface temperature, snowfall and cold-air outbreak frequency across East Asia. The pathway therefore links tropical convection to East Asian winter climate through a chain that runs from divergence over the Tibetan Plateau, through a northeastward-propagating wave, to midlatitude circulation and finally to surface weather.

The crucial finding is that the Southeast Asia–Japan pattern depends sensitively on the longitudinal position and amplitude of the ENSO-related tropical convective anomalies. Models that place their convective responses in the right location, with realistic amplitude, reproduce the divergence over the southeastern Tibetan Plateau and hence the observed wave train and its downstream impacts. Models in which the convective response is shifted too far eastward, or is too weak, displace the divergence source and consequently fail to reproduce the teleconnection. The wave train is not simply absent in these models; it is mislocated, and a mislocated wave train lands its circulation anomalies in the wrong place, degrading the forecast precisely where users need it most.

This diagnosis reframes the challenge of East Asian winter seasonal prediction. Traditional evaluation has emphasized the lower-tropospheric circulation, such as the subtropical high and low-level monsoon winds, as the primary conduit of ENSO influence. The new multi-model assessment demonstrates that an accurate representation of the upper-tropospheric teleconnection is at least as important, and possibly more so, for determining whether a prediction system can anticipate the monsoon’s ENSO-related mode. For model developers, the practical implication is that improving the simulation of tropical convection over the Maritime Continent, a notoriously difficult problem because convection is parameterized rather than explicitly resolved, should pay dividends far downstream over the midlatitude Pacific rim.

The study also carries a broader message about the limits and promise of seasonal forecasting. The modest skill ceiling, even among eight leading operational systems, underscores the inherent difficulty of predicting the East Asian Winter Monsoon, whose variability is shaped not only by ENSO but also by the Arctic Oscillation, Siberian snow cover, Arctic sea ice and internal atmospheric dynamics that are largely unpredictable at seasonal range. Yet by identifying the specific process, the Maritime Continent convection feeding the Southeast Asia–Japan wave train, where model errors concentrate, the research converts a vague skill deficit into a concrete target. The work was supported by the Japan Society for the Promotion of Science and the Environmental Restoration and Conservation Agency, and its diagnostics draw on established reanalysis products including JRA-3Q and precipitation data from the Global Precipitation Climatology Project. For forecasters bracing for the next severe cold surge, the path to better warnings may run ten kilometers overhead, through the divergent outflow of tropical thunderstorms and the Rossby waves they send racing toward Japan.

Subject of Research: Seasonal prediction of the East Asian Winter Monsoon and the upper- and lower-tropospheric pathways of ENSO influence

Article Title: Multi-model assessment of East Asian Winter Monsoon predictions: upper- and lower-tropospheric pathways of ENSO impacts

Article References: Sakamoto, R., Takaya, Y., Hirahara, S., Naoe, H., & Ueda, H. (2026). Multi-model assessment of East Asian Winter Monsoon predictions: upper- and lower-tropospheric pathways of ENSO impacts. Climate Dynamics, 64(10), Article 437. https://doi.org/10.1007/s00382-026-08367-x

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08367-x

Keywords: East Asian Winter Monsoon, ENSO, seasonal prediction, Rossby wave train, Maritime Continent, tropical convection, upper troposphere, Southeast Asia–Japan pattern, East Asian trough, teleconnections, Climate Dynamics, multi-model assessment