Alain Protat

Mixed-phase clouds over the Southern Ocean profoundly influence Earth's radiative balance, yet climate models persistently exhibit a positive surface shortwave radiation bias driven by over-glaciation in mixed-phase clouds. Addressing this bias requires accurate representation of ice-nucleating particles (INPs). This study presents the first online implementation of aerosol-aware marine INP parametrizations derived interactively from sea spray and marine organic aerosols within the high-resolution Unified Model. Using CAPRICORN-2 shipborne observations, we demonstrate that the default INP scheme overestimates INP concentrations by up to four orders of magnitude, causing a severely underestimated liquid water path. In contrast, empirical Antarctic and deterministic marine INP schemes reproduce the low INP concentrations typical of the pristine Southern Ocean, improving cloud and radiative properties. Crucially, microphysical and radiative responses to these INP reductions are strongly regime-dependent. In deep, pre-frontal mixed-phase clouds, suppressing INP concentrations effectively inhibits cloud glaciation, significantly enhancing supercooled liquid water and largely mitigating the surface shortwave radiation bias. However, in shallow, post-frontal stratocumulus clouds, altering INP parametrization yields negligible improvements. Radiosonde evaluations reveal this insensitivity is driven by several model deficits, including systematically smoothed boundary layer inversions leading to excessive dry air entrainment, exacerbated by underestimated cyclonic moisture transport. Consequently, these shallow clouds are thermodynamically starved of water vapor, rendering microphysical INP adjustments ineffective. Ultimately, fully resolving Southern Ocean cloud-radiation biases requires synergistic advancements in representing aerosols, boundary layer physics and large-scale meteorological forcings.

This study investigates hydrometeor fall speeds using a dual-frequency profiling radar operating during the 2005–2006 monsoon season near Darwin, Australia. Our focus is on tropical deep convection where the observations provide a new perspective on hydrometeor fall speeds within and near intense drafts having mixed-phase media. The techniques we employ avoid undue assumptions on the air motion or media distributions, offering a convenient path to estimate bulk radar reflectivity( Z )-weighted hydrometeor fall speed ( V _t ). While these mixed-phase media estimates are not specific to size or density, they may be replicated by models and are practical for radar-based retrievals that necessitate V _t assumptions. Tests performed under rain and snow conditions show comparable performance to disdrometer and other references. The standard deviation of residuals for rain and snow relationships are ≅1 and ≅0.4 m s ⁻¹ , respectively. In convective core regions aloft, Darwin observations align with existing graupel V _t - Z treatments, however mixed-phase media typically falls faster (>1–2 m s ⁻¹ ) for Z V _t - Z behavior in strong cores. However, Break conditions – those more favorable to intense daytime tropical convection – potentially indicate the presence of additional lofted liquid or melting media mixed in volumes at convective core peripheries Z V _t - Z pairs, with select samples having V _t faster than rain for a given Z that argues for partially-melted graupel coupled with size-sorting.

Clouds generated by intense wildfires can evolve from pyrocumulus (pyroCu) to pyrocumulonimbus (pyroCb), injecting smoke into the upper troposphere and occasionally the stratosphere. Automated satellite-based detection of pyroCb is therefore critical for monitoring extreme fire–atmosphere interactions, yet such approaches inherently rely on cloud-top properties rather than the underlying convective dynamics. Here, we evaluate the pyroCb detection algorithm of Peterson et al. (2017), originally developed for GOES observations over North America, in the Australian context using Himawari-8 multispectral imagery during the 2019–2020 Black Summer bushfires. Satellite-derived classifications are assessed against independent observations from operational weather radar and total lightning networks within a multi-sensor framework implemented in the PyroScope platform. An object-based tracking approach is used to compare the temporal evolution of radar- and satellite-derived pyroconvective features. Satellite observations reliably capture the onset of deep pyroconvective development, showing strong agreement with radar-derived echo-top heights and lightning activity during the early phase. However, a systematic divergence emerges as events evolve, with satellite-derived objects increasingly dominated by horizontally advected anvil structures rather than the actively convecting core. This behaviour is reflected in increasing centroid displacement, decreasing spatial overlap, and an apparent overestimation of plume extent and persistence in satellite retrievals. To investigate this limitation, we introduce an additional spectral test to separate detached or decaying anvils (class 4) from intense pyroCb (class 5), and explicitly evaluate the impact of this classification on object-based metrics. Restricting the analysis to class 5 reduces the spatial extent and persistence of satellite-derived objects, but does not fully eliminate the divergence with radar observations at later stages, highlighting the inherent limitations of an anvil-based detection paradigm. These results demonstrate that satellite-based pyroCb detection should be interpreted as a cloud-top representation of pyroconvection rather than a direct measure of the actively convecting core. Multi-sensor integration is therefore essential to distinguish between initiation, mature, and decay phases of pyroconvection. The object-based tracking framework developed here provides a foundation for future studies of pyroCb lifecycle, including the quantification of growth, decay, and potential precursors to pyroCb formation.

Clouds generated by intense wildfires can evolve from pyrocumulus (pyroCu) to pyrocumulonimbus (pyroCb), injecting smoke into the upper troposphere and occasionally the stratosphere. Automated satellite-based detection of pyroCb is therefore critical for monitoring extreme fire–atmosphere interactions, yet such approaches inherently rely on cloud-top properties rather than the underlying convective dynamics. Here, we evaluate the pyroCb detection algorithm of Peterson et al. (2017), originally developed for GOES observations over North America, in the Australian context using Himawari-8 multispectral imagery during the 2019–2020 Black Summer bushfires. Satellite-derived classifications are assessed against independent observations from operational weather radar and total lightning networks within a multi-sensor framework implemented in the PyroScope platform. An object-based tracking approach is used to compare the temporal evolution of radar- and satellite-derived pyroconvective features. Satellite observations reliably capture the onset of deep pyroconvective development, showing strong agreement with radar-derived echo-top heights and lightning activity during the early phase. However, a systematic divergence emerges as events evolve, with satellite-derived objects increasingly dominated by horizontally advected anvil structures rather than the actively convecting core. This behaviour is reflected in increasing centroid displacement, decreasing spatial overlap, and an apparent overestimation of plume extent and persistence in satellite retrievals. To investigate this limitation, we introduce an additional spectral test to separate detached or decaying anvils (class 4) from intense pyroCb (class 5), and explicitly evaluate the impact of this classification on object-based metrics. Restricting the analysis to class 5 reduces the spatial extent and persistence of satellite-derived objects, but does not fully eliminate the divergence with radar observations at later stages, highlighting the inherent limitations of an anvil-based detection paradigm. These results demonstrate that satellite-based pyroCb detection should be interpreted as a cloud-top representation of pyroconvection rather than a direct measure of the actively convecting core. Multi-sensor integration is therefore essential to distinguish between initiation, mature, and decay phases of pyroconvection. The object-based tracking framework developed here provides a foundation for future studies of pyroCb lifecycle, including the quantification of growth, decay, and potential precursors to pyroCb formation.

Climate models have substantial biases in downwelling shortwave radiation over the Southern Ocean (SO) and Antarctica, primarily due to their inability to accurately represent the frequent occurrence of low‐level mixed‐phase clouds. Building on the evaluation of the Unified Model (UM) Regional Atmosphere and Land version 3 (RAL3), this study investigates the sensitivity of RAL3 to changing cloud and aerosol properties to improve the simulation of clouds and radiation over coastal Antarctica. We evaluate the impact of several approaches to reducing these biases. The main findings suggest that using a monthly aerosol climatology and an aerosol aware cloud‐droplet activation parameterization to diagnose droplet number concentrations substantially underestimates aerosol concentrations at the Antarctic coast, leading to large biases in radiation fluxes compared to surface observations. Increasing the prescribed fixed cloud droplet number concentration or incorporating more accurate orographic data has a limited effect on improving cloud and radiation biases. Additionally, using an ice nucleating particle (INP) parameterization tailored to the pristine Antarctica environment significantly increases the liquid water path and reduces temperature and radiation biases relative to the control simulation. These results underscore the necessity of employing region‐specific INP parameterizations to accurately simulate mixed‐phase clouds over the SO and Antarctica.

Mixed-phase clouds over the Southern Ocean profoundly influence Earth's radiative balance, yet climate models persistently exhibit a positive surface shortwave radiation bias driven by over-glaciation in mixed-phase clouds. Addressing this bias requires accurate representation of ice-nucleating particles (INPs). This study presents the first online implementation of aerosol-aware marine INP parametrizations derived interactively from sea spray and marine organic aerosols within the high-resolution Unified Model. Using CAPRICORN-2 shipborne observations, we demonstrate that the default INP scheme overestimates INP concentrations by up to four orders of magnitude, causing a severely underestimated liquid water path. In contrast, empirical Antarctic and deterministic marine INP schemes reproduce the low INP concentrations typical of the pristine Southern Ocean, improving cloud and radiative properties. Crucially, microphysical and radiative responses to these INP reductions are strongly regime-dependent. In deep, pre-frontal mixed-phase clouds, suppressing INP concentrations effectively inhibits cloud glaciation, significantly enhancing supercooled liquid water and largely mitigating the surface shortwave radiation bias. However, in shallow, post-frontal stratocumulus clouds, altering INP parametrization yields negligible improvements. Radiosonde evaluations reveal this insensitivity is driven by several model deficits, including systematically smoothed boundary layer inversions leading to excessive dry air entrainment, exacerbated by underestimated cyclonic moisture transport. Consequently, these shallow clouds are thermodynamically starved of water vapor, rendering microphysical INP adjustments ineffective. Ultimately, fully resolving Southern Ocean cloud-radiation biases requires synergistic advancements in representing aerosols, boundary layer physics and large-scale meteorological forcings.

Mixed-phase clouds over the Southern Ocean profoundly influence Earth's radiative balance, yet climate models persistently exhibit a positive surface shortwave radiation bias driven by over-glaciation in mixed-phase clouds. Addressing this bias requires accurate representation of ice-nucleating particles (INPs). This study presents the first online implementation of aerosol-aware marine INP parametrizations derived interactively from sea spray and marine organic aerosols within the high-resolution Unified Model. Using CAPRICORN-2 shipborne observations, we demonstrate that the default INP scheme overestimates INP concentrations by up to four orders of magnitude, causing a severely underestimated liquid water path. In contrast, empirical Antarctic and deterministic marine INP schemes reproduce the low INP concentrations typical of the pristine Southern Ocean, improving cloud and radiative properties. Crucially, microphysical and radiative responses to these INP reductions are strongly regime-dependent. In deep, pre-frontal mixed-phase clouds, suppressing INP concentrations effectively inhibits cloud glaciation, significantly enhancing supercooled liquid water and largely mitigating the surface shortwave radiation bias. However, in shallow, post-frontal stratocumulus clouds, altering INP parametrization yields negligible improvements. Radiosonde evaluations reveal this insensitivity is driven by several model deficits, including systematically smoothed boundary layer inversions leading to excessive dry air entrainment, exacerbated by underestimated cyclonic moisture transport. Consequently, these shallow clouds are thermodynamically starved of water vapor, rendering microphysical INP adjustments ineffective. Ultimately, fully resolving Southern Ocean cloud-radiation biases requires synergistic advancements in representing aerosols, boundary layer physics and large-scale meteorological forcings.

Clouds generated by intense wildfires can evolve from pyrocumulus (pyroCu) to pyrocumulonimbus (pyroCb), injecting smoke into the upper troposphere and occasionally the stratosphere. Automated satellite-based detection of pyroCb is therefore critical for monitoring extreme fire–atmosphere interactions, yet such approaches inherently rely on cloud-top properties rather than the underlying convective dynamics. Here, we evaluate the pyroCb detection algorithm of Peterson et al. (2017), originally developed for GOES observations over North America, in the Australian context using Himawari-8 multispectral imagery during the 2019–2020 Black Summer bushfires. Satellite-derived classifications are assessed against independent observations from operational weather radar and total lightning networks within a multi-sensor framework implemented in the PyroScope platform. An object-based tracking approach is used to compare the temporal evolution of radar- and satellite-derived pyroconvective features. Satellite observations reliably capture the onset of deep pyroconvective development, showing strong agreement with radar-derived echo-top heights and lightning activity during the early phase. However, a systematic divergence emerges as events evolve, with satellite-derived objects increasingly dominated by horizontally advected anvil structures rather than the actively convecting core. This behaviour is reflected in increasing centroid displacement, decreasing spatial overlap, and an apparent overestimation of plume extent and persistence in satellite retrievals. To investigate this limitation, we introduce an additional spectral test to separate detached or decaying anvils (class 4) from intense pyroCb (class 5), and explicitly evaluate the impact of this classification on object-based metrics. Restricting the analysis to class 5 reduces the spatial extent and persistence of satellite-derived objects, but does not fully eliminate the divergence with radar observations at later stages, highlighting the inherent limitations of an anvil-based detection paradigm. These results demonstrate that satellite-based pyroCb detection should be interpreted as a cloud-top representation of pyroconvection rather than a direct measure of the actively convecting core. Multi-sensor integration is therefore essential to distinguish between initiation, mature, and decay phases of pyroconvection. The object-based tracking framework developed here provides a foundation for future studies of pyroCb lifecycle, including the quantification of growth, decay, and potential precursors to pyroCb formation.

Clouds generated by intense wildfires can evolve from pyrocumulus (pyroCu) to pyrocumulonimbus (pyroCb), injecting smoke into the upper troposphere and occasionally the stratosphere. Automated satellite-based detection of pyroCb is therefore critical for monitoring extreme fire–atmosphere interactions, yet such approaches inherently rely on cloud-top properties rather than the underlying convective dynamics. Here, we evaluate the pyroCb detection algorithm of Peterson et al. (2017), originally developed for GOES observations over North America, in the Australian context using Himawari-8 multispectral imagery during the 2019–2020 Black Summer bushfires. Satellite-derived classifications are assessed against independent observations from operational weather radar and total lightning networks within a multi-sensor framework implemented in the PyroScope platform. An object-based tracking approach is used to compare the temporal evolution of radar- and satellite-derived pyroconvective features. Satellite observations reliably capture the onset of deep pyroconvective development, showing strong agreement with radar-derived echo-top heights and lightning activity during the early phase. However, a systematic divergence emerges as events evolve, with satellite-derived objects increasingly dominated by horizontally advected anvil structures rather than the actively convecting core. This behaviour is reflected in increasing centroid displacement, decreasing spatial overlap, and an apparent overestimation of plume extent and persistence in satellite retrievals. To investigate this limitation, we introduce an additional spectral test to separate detached or decaying anvils (class 4) from intense pyroCb (class 5), and explicitly evaluate the impact of this classification on object-based metrics. Restricting the analysis to class 5 reduces the spatial extent and persistence of satellite-derived objects, but does not fully eliminate the divergence with radar observations at later stages, highlighting the inherent limitations of an anvil-based detection paradigm. These results demonstrate that satellite-based pyroCb detection should be interpreted as a cloud-top representation of pyroconvection rather than a direct measure of the actively convecting core. Multi-sensor integration is therefore essential to distinguish between initiation, mature, and decay phases of pyroconvection. The object-based tracking framework developed here provides a foundation for future studies of pyroCb lifecycle, including the quantification of growth, decay, and potential precursors to pyroCb formation.

This study investigates hydrometeor fall speeds using a dual-frequency profiling radar operating during the 2005–2006 monsoon season near Darwin, Australia. Our focus is on tropical deep convection where the observations provide a new perspective on hydrometeor fall speeds within and near intense drafts having mixed-phase media. The techniques we employ avoid undue assumptions on the air motion or media distributions, offering a convenient path to estimate bulk radar reflectivity(Z)-weighted hydrometeor fall speed Vt. While these mixed-phase media estimates are not specific to size or density, they may be replicated by models and are practical for radar-based retrievals that necessitate Vt assumptions. Tests performed under rain and snow conditions show comparable performance to disdrometer and other references. The standard deviation of residuals for rain and snow relationships are ≅ 1 ms^-1 and ≅ 0.4 ms^-1, respectively. In convective core regions aloft, Darwin observations align with existing graupel Vt-Z treatments, however mixed-phase media typically falls faster (> 1–2 ms^-1) for Z < 35 dBZ than prior relationships. Breakdowns suggest that Active and Break monsoon conditions favor a similar Vt-Z behavior in strong cores. However, Break conditions – those more favorable to intense daytime tropical convection – potentially indicate the presence of additional lofted liquid or melting media mixed in volumes at convective core peripheries Z < 35 dBZ. Break events also show higher variability in Vt-Z pairs, with select samples having Vt faster than rain for a given Z that argues for partially-melted graupel coupled with size-sorting.