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The evaluation and quantification of Southern Ocean cloud–radiation interactions simulated by climate models are essential in understanding the sources and magnitude of the radiative bias that persists in climate models for this region. To date, most evaluation methods focus on specific synoptic or cloud-type conditions that do not consider the entirety of the Southern Ocean's cloud regimes at once. Furthermore, it is difficult to directly quantify the complex and non-linear role that different cloud properties have on modulating cloud radiative effect. In this study, we present a new method of model evaluation, using machine learning that can at once identify complexities within a system and individual contributions. To do this, we use an XGBoost (eXtreme Gradient Boosting) model to predict the radiative bias within a nudged version of the Australian Community Climate and Earth System Simulator – Atmosphere-only model, using cloud property biases as predictive features. We find that the XGBoost model can explain up to 55 % of the radiative bias from these cloud properties alone. We then apply SHAP (SHapley Additive exPlanations) feature importance analysis to quantify the role each cloud property bias plays in predicting the radiative bias. We find that biases in the liquid water path are the largest contributor to the cloud radiative bias over the Southern Ocean, though important regional and cloud-type dependencies exist. We then test the usefulness of this method in evaluating model perturbations and find that it can clearly identify complex responses, including cloud property and cloud-type compensating errors. [ABSTRACT FROM AUTHOR]

Large hail events are typically infrequent, with significant time gaps between occurrences at specific locations. However, when these events do happen, they can cause rapid and substantial economic losses within a matter of minutes. Therefore, it is crucial to have the ability to accurately observe and understand hail phenomena to improve the mitigation of this impact. While in situ observations are accurate, they are limited in number for an individual storm. Weather radars, on the other hand, provide a larger observation footprint, but current radar-derived hail size estimates exhibit low accuracy due to horizontal advection of hailstones as they fall, the variability of hail size distributions (HSDs), complex scattering and attenuation, and mixed hydrometeor types. In this paper, we propose a new radar-derived hail product developed using a large dataset of hail damage insurance claims and radar observations. We use these datasets coupled with environmental information to calculate a hail damage estimate (HDE) using a deep neural network approach aiming to quantify hail impact, with a critical success index of 0.88 and a coefficient of determination against observed damage of 0.79. Furthermore, we compared HDE to a popular hail size product (MESH), allowing us to identify meteorological conditions that are associated with biases on MESH. Environments with relatively low specific humidity, high CAPE and CIN, low wind speeds aloft, and southerly winds at the ground are associated with a negative MESH bias, potentially due to differences in HSD, hail hardness, or mixed hydrometeors. In contrast, environments with low CAPE, high CIN, and relatively high specific humidity aloft are associated with a positive MESH bias. [ABSTRACT FROM AUTHOR]

Weather radars are increasingly being used to study the interaction between wildfires and the atmosphere, owing to the enhanced spatio-temporal resolution of radar data compared to conventional measurements, such as satellite imagery and in situ sensing. An important requirement for the continued proliferation of radar data for this application is the automatic identification of fire-generated particle returns (pyrometeors) from a scene containing a diverse range of echo sources, including clear air, ground and sea clutter, and precipitation. The classification of such particles is a challenging problem for common image segmentation approaches (e.g. fuzzy logic or unsupervised machine learning) due to the strong overlap in radar variable distributions between each echo type. Here, we propose the following two-step method to address these challenges: (1) the introduction of secondary, texture-based fields, calculated using statistical properties of gray-level co-occurrence matrices (GLCMs), and (2) a Gaussian mixture model (GMM), used to classify echo sources by combining radar variables with texture-based fields from (1). Importantly, we retain all information from the original measurements by performing calculations in the radar's native spherical coordinate system and introduce a range-varying-window methodology for our GLCM calculations to avoid range-dependent biases. We show that our method can accurately classify pyrometeors' plumes, clear air, sea clutter, and precipitation using radar data from recent wildfire events in Australia and find that the contrast of the radar correlation coefficient is the most skilful variable for the classification. The technique we propose enables the automated detection of pyrometeors' plumes from operational weather radar networks, which may be used by fire agencies for emergency management purposes or by scientists for case study analyses or historical-event identification. [ABSTRACT FROM AUTHOR]

Over the remote Southern Ocean (SO), cloud feedbacks contribute substantially to Earth system model (ESM) radiative biases. The evolution of low Southern Ocean clouds (cloud-top heights < ∼ 3 km) is strongly modulated by precipitation and/or evaporation, which act as the primary sink of cloud condensate. Constraining precipitation processes in ESMs requires robust observations suitable for process-level evaluations. A year-long subset (April 2016–March 2017) of ground-based profiling instrumentation deployed during the Macquarie Island Cloud and Radiation Experiment (MICRE) field campaign (54.5 ∘ S, 158.9 ∘ E) combines a 95 GHz (W-band) Doppler cloud radar, two lidar ceilometers, and balloon-borne soundings to quantify the occurrence frequency of precipitation from the liquid-phase cloud base. Liquid-based clouds at Macquarie Island precipitate ∼ 70 % of the time, with deeper and colder clouds precipitating more frequently and at a higher intensity compared to thinner and warmer clouds. Supercooled cloud layers precipitate more readily than layers with cloud-top temperatures > 0 ∘ C, regardless of the geometric thickness of the layer, and also evaporate more frequently. We further demonstrate an approach to employ these observational constraints for evaluation of a 9-year GISS-ModelE3 ESM simulation. Model output is processed through the Earth Model Column Collaboratory (EMC 2) radar and lidar instrument simulator with the same instrument specifications as those deployed during MICRE, therefore accounting for instrument sensitivities and ensuring a coherent comparison. Relative to MICRE observations, the ESM produces a smaller cloud occurrence frequency, smaller precipitation occurrence frequency, and greater sub-cloud evaporation. The lower precipitation occurrence frequency by the ESM relative to MICRE contrasts with numerous studies that suggest a ubiquitous bias by ESMs to precipitate too frequently over the SO when compared with satellite-based observations, likely owing to sensitivity limitations of spaceborne instrumentation and different sampling methodologies for ground- versus space-based observations. Despite these deficiencies, the ESM reproduces the observed tendency for deeper and colder clouds to precipitate more frequently and at a higher intensity. The ESM also reproduces specific cloud regimes, including near-surface clouds that account for ∼ 25 % of liquid-based clouds during MICRE and optically thin, non-precipitating clouds that account for ∼ 27 % of clouds with bases higher than 250 m. We suggest that the demonstrated framework, which merges observations with appropriately constrained model output, is a valuable approach to evaluate processes responsible for cloud radiative feedbacks in ESMs. [ABSTRACT FROM AUTHOR]

Large hail events are typically infrequent, with significant time gaps between occurrences at specific locations. However, when these events do happen, they can cause rapid and substantial economic losses within a matter of minutes. Therefore, it is crucial to have the ability to accurately observe and understand hail phenomena to improve the mitigation of this impact. While in-situ observations are accurate, they are limited in number for an individual storm. Weather radars, on the other hand, provide a larger observation footprint, but current radar-derived hail size estimates exhibit low accuracy due to horizontal advection of hailstones as they fall, the variability of hail size distributions (HSD), complex scattering and attenuation, and mixed hydrometeor types. In this paper, we propose a new radar-derived hail product that is developed using a large dataset of hail damage insurance claims and radar observations. We use these datasets coupled with environmental information to calculate a Hail Damage Estimate (HDE) using a deep neural network approach aiming to quantify hail impact, with a critical success index of 0.88 and a coefficient of determination against observed damage of 0.78. Furthermore, we compared HDE to a popular hail size product (MESH), allowing us to identify meteorological conditions that are associated with biases on MESH. Environments with relatively low specific humidity, high CAPE and CIN, low wind speeds aloft and southerly winds at ground are associated with a negative MESH bias, potentially due to differences in HSD or mixed hydrometeors. In contrast, environments with low CAPE, high CIN, and relatively high specific humidity aloft are associated with a positive MESH bias. [ABSTRACT FROM AUTHOR]

The evaluation and quantification of Southern Ocean cloud-radiation interactions simulated by climate models is essential in understanding the sources and magnitude of the radiative bias that persists in climate models for this region. To date, most evaluation methods focus on specific synoptic or cloud type conditions and are unable to quantitatively define the impact of cloud properties on the radiative bias whilst considering the system as a whole. In this study, we present a new method of model evaluation, using machine learning, that can at once identify complexities within a system and individual contributions. To do this, we use an XGBoost model to predict the radiative bias within a nudged version of the Australian Community Climate and Earth System Simulator -- Atmosphere-only Model, using cloud property biases as predictive features. We find that the XGBoost model can explain up to 55% of the radiative bias from these cloud properties alone. We then apply SHapley Additive exPlanations feature importance analysis to quantify the role each cloud property bias plays in predicting the radiative bias. We find that biases in liquid water path is the largest contributor to the cloud radiative bias over the Southern Ocean, though important regional and cloud-type dependencies exist. We then test the usefulness of this method in evaluating model perturbations and find that it can clearly identify complex responses, including cloud property and cloud-type compensating errors. [ABSTRACT FROM AUTHOR]

The evaluation and quantification of Southern Ocean cloud-radiation interactions simulated by climate models is essential in understanding the sources and magnitude of the radiative bias that persists in climate models for this region. To date, most evaluation methods focus on specific synoptic or cloud type conditions and are unable to quantitatively define the impact of cloud properties on the radiative bias whilst considering the system as a whole. In this study, we present a new method of model evaluation, using machine learning, that can at once identify complexities within a system and individual contributions. To do this, we use an XGBoost model to predict the radiative bias within a nudged version of the Australian Community Climate and Earth System Simulator – Atmosphere-only Model, using cloud property biases as predictive features. We find that the XGBoost model can explain up to 55 % of the radiative bias from these cloud properties alone. We then apply SHapley Additive exPlanations feature importance analysis to quantify the role each cloud property bias plays in predicting the radiative bias. We find that biases in liquid water path is the largest contributor to the cloud radiative bias over the Southern Ocean, though important regional and cloud-type dependencies exist. We then test the usefulness of this method in evaluating model perturbations and find that it can clearly identify complex responses, including cloud property and cloud-type compensating errors. [ABSTRACT FROM AUTHOR]

The remoteness and extreme conditions of the Southern Ocean and Antarctic region have meant that observations in this region are rare, and typically restricted to summertime during research or resupply voyages. Observations of aerosols outside of the summer season are typically limited to long-term stations, such as Kennaook / Cape Grim (KCG; 40.7 ∘ S, 144.7 ∘ E), which is situated in the northern latitudes of the Southern Ocean, and Antarctic research stations, such as the Japanese operated Syowa (SYO; 69.0 ∘ S, 39.6 ∘ E). Measurements in the midlatitudes of the Southern Ocean are important, particularly in light of recent observations that highlighted the latitudinal gradient that exists across the region in summertime. Here we present 2 years (March 2016–March 2018) of observations from Macquarie Island (MQI; 54.5 ∘ S, 159.0 ∘ E) of aerosol (condensation nuclei larger than 10 nm, CN 10) and cloud condensation nuclei (CCN at various supersaturations) concentrations. This important multi-year data set is characterised, and its features are compared with the long-term data sets from KCG and SYO together with those from recent, regionally relevant voyages. CN 10 concentrations were the highest at KCG by a factor of ∼50% across all non-winter seasons compared to the other two stations, which were similar (summer medians of 530, 426 and 468 cm-3 at KCG, MQI and SYO, respectively). In wintertime, seasonal minima at KCG and MQI were similar (142 and 152 cm-3 , respectively), with SYO being distinctly lower (87 cm-3), likely the result of the reduction in sea spray aerosol generation due to the sea ice ocean cover around the site. CN 10 seasonal maxima were observed at the stations at different times of year, with KCG and MQI exhibiting January maxima and SYO having a distinct February high. Comparison of CCN 0.5 data between KCG and MQI showed similar overall trends with summertime maxima and wintertime minima; however, KCG exhibited slightly (∼10%) higher concentrations in summer (medians of 158 and 145 cm-3 , respectively), whereas KCG showed ∼40% lower concentrations than MQI in winter (medians of 57 and 92 cm-3 , respectively). Spatial and temporal trends in the data were analysed further by contrasting data to coincident observations that occurred aboard several voyages of the RSV Aurora Australis and the RV Investigator. Results from this study are important for validating and improving our models and highlight the heterogeneity of this pristine region and the need for further long-term observations that capture the seasonal cycles. [ABSTRACT FROM AUTHOR]

Over the remote Southern Ocean, cloud feedbacks contribute substantially to Earth system model (ESM) radiative biases. The evolution of low Southern Ocean clouds (cloud top heights < ~ 3 km) is strongly modulated by precipitation and/or evaporation, which act as the primary sink of cloud condensate. Constraining precipitation processes in ESMs requires robust observations suitable for process-level evaluations. A year-long subset (April 2016 - March 2017) of ground-based profiling instrumentation deployed during the Macquarie Island Cloud and Radiation Experiment (MICRE) field campaign (54.5° S, 158.9° E) combines a 95 GHz (W-band) Doppler cloud radar, two lidar ceilometers, and balloon-borne soundings to quantify the occurrence frequency of precipitation from liquid-phase cloud base. Liquid-based clouds at Macquarie Island precipitate ~ 70 % of the time, with deeper and colder clouds precipitating more frequently and at a higher intensity compared to thinner and warmer clouds. Supercooled cloud layers precipitate more readily than layers with cloud top temperatures > 0 °C, regardless of the geometric thickness of the layer, and also evaporate more frequently. We further demonstrate an approach to employ these observational constraints for evaluation of a 9-year GISS-ModelE3 ESM simulation. Model output is processed through the Earth Model Column Collaboratory (EMC²) radar and lidar instrument simulator with the same instrument specifications as those deployed during MICRE, therefore accounting for instrument sensitivities and ensuring a coherent comparison. Relative to MICRE observations, the ESM produces a smaller cloud occurrence frequency, smaller precipitation occurrence frequency, and greater sub-cloud evaporation. The lower precipitation occurrence frequency by the ESM relative to MICRE contrasts with numerous studies that suggest a ubiquitous bias by ESMs to precipitate too frequently over the SO when compared with satellite-based observations, likely owing to sensitivity limitations of space-borne instrumentation and different sampling methodologies for ground- versus space-based observations. Despite these deficiencies, the ESM reproduces the observed tendency for deeper and colder clouds to precipitate more frequently and at a higher intensity. The ESM also reproduces specific cloud regimes, including near-surface clouds that account for ~ 25 % of liquid-based clouds during MICRE and optically thin, non-precipitating clouds that account for ~ 27 % of clouds with bases higher than 250 m. We suggest that the demonstrated framework, which merges observations with appropriately constrained model output, is a valuable approach to evaluate processes responsible for cloud radiative feedbacks in ESMs. [ABSTRACT FROM AUTHOR]

Over the remote Southern Ocean, cloud feedbacks contribute substantially to Earth system model (ESM) radiative biases. The evolution of low Southern Ocean clouds (cloud top heights < ~ 3 km) is strongly modulated by precipitation and/or evaporation, which act as the primary sink of cloud condensate. Constraining precipitation processes in ESMs requires robust observations suitable for process-level evaluations. A year-long subset (April 2016 – March 2017) of ground-based profiling instrumentation deployed during the Macquarie Island Cloud and Radiation Experiment (MICRE) field campaign (54.5° S, 158.9° E) combines a 95 GHz (W-band) Doppler cloud radar, two lidar ceilometers, and balloon-borne soundings to quantify the occurrence frequency of precipitation from liquid-phase cloud base. Liquid-based clouds at Macquarie Island precipitate ~ 70 % of the time, with deeper and colder clouds precipitating more frequently and at a higher intensity compared to thinner and warmer clouds. Supercooled cloud layers precipitate more readily than layers with cloud top temperatures > 0 °C, regardless of the geometric thickness of the layer, and also evaporate more frequently. We further demonstrate an approach to employ these observational constraints for evaluation of a 9-year GISS-ModelE3 ESM simulation. Model output is processed through the Earth Model Column Collaboratory (EMC2) radar and lidar instrument simulator with the same instrument specifications as those deployed during MICRE, therefore accounting for instrument sensitivities and ensuring a coherent comparison. Relative to MICRE observations, the ESM produces a smaller cloud occurrence frequency, smaller precipitation occurrence frequency, and greater sub-cloud evaporation. The lower precipitation occurrence frequency by the ESM relative to MICRE contrasts with numerous studies that suggest a ubiquitous bias by ESMs to precipitate too frequently over the SO when compared with satellite-based observations, likely owing to sensitivity limitations of space-borne instrumentation and different sampling methodologies for ground- versus space-based observations. Despite these deficiencies, the ESM reproduces the observed tendency for deeper and colder clouds to precipitate more frequently and at a higher intensity. The ESM also reproduces specific cloud regimes, including near-surface clouds that account for ~ 25 % of liquid-based clouds during MICRE and optically thin, non-precipitating clouds that account for ~ 27 % of clouds with bases higher than 250 m. We suggest that the demonstrated framework, which merges observations with appropriately constrained model output, is a valuable approach to evaluate processes responsible for cloud radiative feedbacks in ESMs. [ABSTRACT FROM AUTHOR]

Cloud and aerosol lidars measuring backscatter and depolarization ratio are the most suitable lidars to detect cloud phase (liquid, ice, or mixed phase). However, such instruments are not widely deployed as part of operational networks. In this study, we propose a new algorithm to detect supercooled liquid water containing clouds (SLCC) based on ceilometers measuring only co-polarization backscatter. We utilize observations collected at Davis, Antarctica, where low-level, mixed-phase clouds, including supercooled liquid water (SLW) droplets and ice crystals, remain poorly understood due to the paucity of ground-based observations. A 3-month set of observations were collected during the austral summer of November 2018 to February 2019, with a variety of instruments including a depolarization lidar and a W-band cloud radar which were used to build a two-dimensional cloud phase mask distinguishing SLW and mixed-phase clouds. This cloud phase mask is used as the reference to develop a new algorithm based on the observations of a single polarization ceilometer operating in the vicinity for the same period. Deterministic and data-driven retrieval approaches were evaluated: an extreme gradient boosting (XGBoost) framework ingesting backscatter average characteristics was the most effective method at reproducing the classification obtained with the combined radar–lidar approach with an accuracy as high as 0.91. This study provides a new SLCC retrieval approach based on ceilometer data and highlights the considerable benefits of these instruments to provide intelligence on cloud phase in polar regions that usually suffer from a paucity of observations. Finally, the two algorithms were applied to a full year of ceilometer observations to retrieve cloud phase and frequency of occurrences of SLCC: SLCC was present 29 ± 6 % of the time for T19 and 24 ± 5 % of the time for G22-Davis over that annual cycle. [ABSTRACT FROM AUTHOR]

The Southern Ocean radiative bias continues to impact climate and weather models, including the Australian Community Climate and Earth System Simulator (ACCESS). The radiative bias, characterised by too much shortwave radiation reaching the surface, is attributed to the incorrect simulation of cloud properties, including frequency and phase. To identify cloud regimes important to the Southern Ocean, we use k -means cloud histogram clustering, applied to a satellite product and then fitted to nudged simulations of the latest-generation ACCESS atmosphere model. We identify instances when the model correctly or incorrectly simulates the same cloud type as the satellite product for any point in time or space. We then evaluate the cloud and radiation biases in these instances. We find that when the ACCESS model correctly simulates the cloud type, cloud property and radiation biases of equivalent, or in some cases greater, magnitude remain compared to when cloud types are incorrectly simulated. Furthermore, we find that even when radiative biases appear small on average, cloud property biases, such as liquid or ice water paths or cloud fractions, remain large. Our results suggest that simply getting the right cloud type (or the cloud macrophysics) is not enough to reduce the Southern Ocean radiative bias. Furthermore, in instances where the radiative bias is small, it may be so for the wrong reasons. Considerable effort is still required to improve cloud microphysics, with a particular focus on cloud phase. [ABSTRACT FROM AUTHOR]

The remoteness and extreme conditions of the Southern Ocean and Antarctic region have meant that observations in this region are rare, and typically restricted to summertime during research or resupply voyages. Observations of aerosols outside of the summer season are typically limited to long-term stations, such as Kennaook/Cape Grim (KCG, 40.7° S, 144.7° E) which is situated in the northern latitudes of the Southern Ocean, and Antarctic research stations, such as the Japanese operated Syowa (SYO, 69.0° S, 39.6° E). Measurements in the mid-latitudes of the Southern Ocean are important, particularly in light of recent observations that highlighted the latitudinal gradient that exists across the region in summertime. Here we present two years (March 2016–March 2018) of observations from Macquarie Island (MQI, 54.5° S, 159.0° E) of aerosol (condensation nuclei larger than 10 nm, CN10) and cloud condensation nuclei (CCN at various supersaturations) concentrations. This important multi-year data set is characterised, and its features are compared with the long-term data sets from KCG and SYO together with those from recent, regionally relevant voyages. CN10 concentrations were the highest at KCG by a factor of ∼50 % across all non-winter seasons compared to the other two stations which were similar (summer medians of 530 cm-3, 426 cm-3 and 468 cm-3 at KCG, MQI and SYO, respectively). In wintertime, seasonal minima at KCG and MQI were similar (142 cm-3 and 152 cm-3, respectively), with SYO being distinctly lower (87 cm-3), likely the result of the reduction in sea spray aerosol generation due to the sea-ice ocean cover around the site. CN10 seasonal maxima were observed at the stations at different times of year, with KCG and MQI exhibiting January maxima and SYO having a distinct February high. Comparison of CCN0.5 data between KCG and MQI showed similar overall trends with summertime maxima and wintertime minima, however KCG exhibited slightly (∼10 %) higher concentrations in summer (medians of 158 cm-3 and 145 cm-3, respectively), whereas KCG showed ∼40 % lower concentrations than MQI in winter (medians of 57 cm-3 and 92 cm-3, respectively). Spatial and temporal trends in the data were analysed further by contrasting data to coincident observations that occurred aboard several voyages of the RSV Aurora Australis and the RV Investigator. Results from this study are important for validating and improving our models, highlight the heterogeneity of this pristine region, and the need for further long-term observations that capture the seasonal cycles. [ABSTRACT FROM AUTHOR]

This study uses ship-based weather radar observations collected from research vessel Investigator to evaluate the Australian weather radar network calibration monitoring technique that uses spaceborne radar observations from the NASA Global Precipitation Mission (GPM). Quantitative operational applications such as rainfall and hail nowcasting require a calibration accuracy of ± 1 dB for radars of the Australian network covering capital cities. Seven ground-based radars along the western coast of Australia and the ship-based OceanPOL radar are first calibrated independently using GPM radar overpasses over a 3-month period. The calibration difference between the OceanPOL radar (used as a moving reference for the second step of the study) and each of the seven operational radars is then estimated using collocated, gridded, radar observations to quantify the accuracy of the GPM technique. For all seven radars the calibration difference with the ship radar lies within ± 0.5 dB, therefore fulfilling the 1 dB requirement. This result validates the concept of using the GPM spaceborne radar observations to calibrate national weather radar networks (provided that the spaceborne radar maintains a high calibration accuracy). The analysis of the day-to-day and hourly variability of calibration differences between the OceanPOL and Darwin (Berrimah) radars also demonstrates that quantitative comparisons of gridded radar observations can accurately track daily and hourly calibration differences between pairs of operational radars with overlapping coverage (daily and hourly standard deviations of ∼ 0.3 and ∼ 1 dB, respectively). [ABSTRACT FROM AUTHOR]

Cloud and aerosol lidars measuring backscatter and depolarization ratio are most suitable instruments to detect cloud phase (liquid, ice, or mixed phase). However, such instruments are not widely deployed as part of operational networks. In this study, we propose a new algorithm to detect supercooled liquid water clouds based solely on ceilometers measuring only co-polarisation backscatter. We utilise observations collected at Davis, Antarctica, where low-level, mixed phase clouds, including supercooled liquid water (SLW) droplets and ice crystals remain poorly understood, due to the paucity of ground-based observations. A 3-month set of observations were collected during the austral summer of November 2018-February 2019, with a variety of instruments including a depolarization lidar and a W-Band cloud radar which were used to build a 2-dimensional cloud phase mask distinguishing SLW and mixed phase clouds. This cloud phase mask is used as the reference to develop a new algorithm based on the observations of a single polarisation ceilometer operating in the vicinity for the same period. Deterministic and data-driven retrieval approaches were evaluated: an extreme gradient boosting (XGBoost) framework ingesting backscatter average characteristics was the most effective method at reproducing the classification obtained with the combined radar-lidar approach with an accuracy as high as 0.91. This study provides a new SLW retrieval approach based solely on ceilometer data and highlights the considerable benefits of these instruments to provide intelligence on cloud phase in polar regions that usually suffer from a paucity of observations. [ABSTRACT FROM AUTHOR]

Coral reefs have been found to produce the sulfur compound dimethyl sulfide (DMS), a climatically relevant aerosol precursor predominantly associated with phytoplankton. Until recently, the role of coral-reef-derived DMS within the climate system had not been quantified. A study preceding the present work found that DMS produced by corals had negligible long-term climatic forcing at the global–regional scale. However, at sub-daily timescales more typically associated with aerosol and cloud formation, the influence of coral-reef-derived DMS on local aerosol radiative effects remains unquantified. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) has been used in this work to study the role of coral-reef-derived DMS at sub-daily timescales for the first time. WRF-Chem was run to coincide with an October 2016 field campaign over the Great Barrier Reef, Queensland, Australia, against which the model was evaluated. After updating and scaling the DMS surface water climatology, the model reproduced DMS and sulfur concentrations well. The inclusion of coral-reef-derived DMS resulted in no significant change in sulfate aerosol mass or total aerosol number. Subsequently, no direct or indirect aerosol effects were detected. The results suggest that the co-location of the Great Barrier Reef with significant anthropogenic aerosol sources along the Queensland coast prevents coral-reef-derived aerosol from having a modulating influence on local aerosol burdens in the current climate. [ABSTRACT FROM AUTHOR]

An algorithm based on triple-frequency (X, Ka, W) radar measurements that retrieves the size, water content and degree of riming of ice clouds is presented. This study exploits the potential of multi-frequency radar measurements to provide information on bulk snow density that should underpin better estimates of the snow characteristic size and content within the radar volume. The algorithm is based on Bayes' rule with riming parameterised by the "fill-in" model. The radar reflectivities are simulated with a range of scattering models corresponding to realistic snowflake shapes. The algorithm is tested on multi-frequency radar data collected during the ESA-funded Radar Snow Experiment For Future Precipitation Mission. During this campaign, in situ microphysical probes were mounted on the same aeroplane as the radars. This nearly perfectly co-located dataset of the remote and in situ measurements gives an opportunity to derive a combined multi-instrument estimate of snow microphysical properties that is used for a rigorous validation of the radar retrieval. Results suggest that the triple-frequency retrieval performs well in estimating ice water content (IWC) and mean mass-weighted diameters obtaining root-mean-square errors of 0.13 and 0.15, respectively, for log⁡10IWC and log⁡10Dm. The retrieval of the degree of riming is more challenging, and only the algorithm that uses Doppler information obtains results that are highly correlated with the in situ data. [ABSTRACT FROM AUTHOR]

This study uses weather radar observations collected from Research Vessel Investigator to evaluate the Australian weather radar network calibration monitoring technique that uses spaceborne radar observations from the NASA Global Precipitation Mission (GPM). Quantitative operational applications such as rainfall and hail nowcasting require a calibration accuracy of 1 dB for radars of the Australian network covering capital cities. Seven ground-based radars along the coast and the ship-based OceanPOL radar are first calibrated independently using GPM radar overpasses over a 3-month period. The calibration difference between the OceanPOL radar and each of the 7 operational radars is then estimated using collocated, gridded, radar observations to evaluate the accuracy of the GPM technique. For all seven radars the calibration difference with the ship radar lies within ± 0.5 dB, therefore fulfilling the 1 dB requirement. This result validates the concept of using the GPM spaceborne radar observations to calibrate national weather radar networks (provided that the spaceborne radar maintains a high calibration accuracy). The analysis of the day-to-day and hourly variability of calibration differences between the OceanPOL and Darwin (Berrimah) radars also demonstrates that quantitative comparisons of gridded radar observations can accurately track daily and hourly calibration differences between pairs of operational radars with overlapping coverage (daily and hourly standard deviations of ~ 0.3 dB and ~ 1 dB, respectively). [ABSTRACT FROM AUTHOR]

The Southern Ocean region is one of the most pristine in the world and serves as an important proxy for the pre-industrial atmosphere. Improving our understanding of the natural processes in this region is likely to result in the largest reductions in the uncertainty of climate and earth system models. While remoteness from anthropogenic and continental sources is responsible for its clean atmosphere, this also results in the dearth of atmospheric observations in the region. Here we present a statistical summary of the latitudinal gradient of aerosol (condensation nuclei larger than 10 nm, CN 10) and cloud condensation nuclei (CCN at various supersaturations) concentrations obtained from five voyages spanning the Southern Ocean between Australia and Antarctica from late spring to early autumn (October to March) of the 2017/18 austral seasons. Three main regions of influence were identified: the northern sector (40–45 ∘ S), where continental and anthropogenic sources coexisted with background marine aerosol populations; the mid-latitude sector (45–65 ∘ S), where the aerosol populations reflected a mixture of biogenic and sea-salt aerosol; and the southern sector (65–70 ∘ S), south of the atmospheric polar front, where sea-salt aerosol concentrations were greatly reduced and aerosol populations were primarily biologically derived sulfur species with a significant history in the Antarctic free troposphere. The northern sector showed the highest number concentrations with median (25th to 75th percentiles) CN 10 and CCN 0.5 concentrations of 681 (388–839) cm -3 and 322 (105–443) cm -3 , respectively. Concentrations in the mid-latitudes were typically around 350 cm -3 and 160 cm -3 for CN 10 and CCN 0.5 , respectively. In the southern sector, concentrations rose markedly, reaching 447 (298–446) cm -3 and 232 (186–271) cm -3 for CN 10 and CCN 0.5 , respectively. The aerosol composition in this sector was marked by a distinct drop in sea salt and increase in both sulfate fraction and absolute concentrations, resulting in a substantially higher CCN 0.5/ CN 10 activation ratio of 0.8 compared to around 0.4 for mid-latitudes. Long-term measurements at land-based research stations surrounding the Southern Ocean were found to be good representations at their respective latitudes; however this study highlighted the need for more long-term measurements in the region. CCN observations at Cape Grim (40∘39′ S) corresponded with CCN measurements from northern and mid-latitude sectors, while CN 10 observations only corresponded with observations from the northern sector. Measurements from a simultaneous 2-year campaign at Macquarie Island (54∘30′ S) were found to represent all aerosol species well. The southernmost latitudes differed significantly from both of these stations, and previous work suggests that Antarctic stations on the East Antarctic coastline do not represent the East Antarctic sea-ice latitudes well. Further measurements are needed to capture the long-term, seasonal and longitudinal variability in aerosol processes across the Southern Ocean. [ABSTRACT FROM AUTHOR]

The U.S. Department of Energy Atmospheric Radiation Measurement program Tropical Western Pacific site hosted a C-band polarization (CPOL) radar in Darwin, Australia. It provides 2 decades of tropical rainfall characteristics useful for validating global circulation models. Rainfall retrievals from radar assume characteristics about the droplet size distribution (DSD) that vary significantly. To minimize the uncertainty associated with DSD variability, new radar rainfall techniques use dual polarization and specific attenuation estimates. This study challenges the applicability of several specific attenuation and dual-polarization-based rainfall estimators in tropical settings using a 4-year archive of Darwin disdrometer datasets in conjunction with CPOL observations. This assessment is based on three metrics: statistical uncertainty estimates, principal component analysis (PCA), and comparisons of various retrievals from CPOL data. The PCA shows that the variability in R can be consistently attributed to reflectivity, but dependence on dual-polarization quantities was wavelength dependent for 110mmh-1. Rainfall estimates during these conditions primarily originate from deep convective clouds with median drop diameters greater than 1.5 mm. An uncertainty analysis and intercomparison with CPOL show that a Colorado State University blended technique for tropical oceans, with modified estimators developed from video disdrometer observations, is most appropriate for use in all cases, such as when 110mmh-1 (deeper convective rain). [ABSTRACT FROM AUTHOR]

The U.S. Department of Energy Atmospheric Radiation Measurement program Tropical Western Pacific site hosted a C-band POLarization (CPOL) radar in Darwin, Australia. It provides two decades of tropical rainfall characteristics useful for validating global circulation models. Rainfall retrievals from radar assume characteristics about the droplet size distribution (DSD) that vary significantly. To minimize the uncertainty associated with DSD variability, new radar rainfall techniques use dual polarization and specific attenuation estimates. This study challenges the applicability of several specific attenuation and dual-polarization based rainfall estimators in tropical settings using a 4-year archive of Darwin disdrometer datasets in conjunction with CPOL observations. This assessment is based on three metrics: statistical uncertainty estimates, principal component analysis (PCA), and comparisons of various retrievals from CPOL data. The PCA shows that over 99 % of the variability in estimated rainfall rate R can be explained by radar reflectivity factor for rainfall rates 1 < R <10 mm hr-1. These rates primarily originate from stratiform clouds and weak convection (median drop diameters less than 1.5 mm). The dual-polarization specific differential phase increases in usefulness for rainfall estimators in times with R > 10 mm hr-1. Rainfall estimates during these conditions primarily originate from deep convective clouds with median drop diameters greater than 1.5 mm. Using specific attenuation for estimating R generally does not provide additional skill beyond other metrics for Darwin. An uncertainty analysis and intercomparison with CPOL show that a CSU-blended technique for tropical oceans, with modified estimators developed from VDIS observations, is most appropriate for use in all cases, such as when 1 < R < 10 mm hr-1 (stratiform rain), and when R > 10 mm hr-1 (deeper convective rain). [ABSTRACT FROM AUTHOR]

In this study, a shipborne 95 GHz Doppler cloud radar mounted on a stabilized platform is used to retrieve vertical profiles of three-dimensional (3D) winds by sequentially pointing the stabilized platform in different directions. A specific challenge is that the maximum angle off zenith is 8 ∘ , which implies that the projection of the horizontal wind components onto the radar beam directions is a small component of Doppler velocity in most cases. A variational 3D wind retrieval technique is then described, allowing for 1 min 3D wind profiles to be retrieved. Statistical comparisons with 3-hourly radiosonde launches from the ship indicate that horizontal wind profiles can be obtained from such cloud radar observations at small off-zenith angles with biases less than 0.2 m s -1 and standard deviations of differences with radiosonde winds less than 2.5 m s -1. [ABSTRACT FROM AUTHOR]

This study presents a statistical analysis of the properties of ice hydrometeors in tropical mesoscale convective systems observed during four different aircraft campaigns. Among the instruments on board the aircraft, we focus on the synergy of a 94 GHz cloud radar and two optical array probes (OAP; measuring hydrometeor sizes from 10 µm to about 1 cm). For two campaigns, an accurate simultaneous measurement of the ice water content is available, while for the two others, ice water content is retrieved from the synergy of the radar reflectivity measurements and hydrometeor size and morphological retrievals from OAP probes. The statistics of ice hydrometeor properties are calculated as a function of radar reflectivity factor measurement percentiles and temperature. Hence, mesoscale convective systems (MCS) microphysical properties (ice water content, visible extinction, mass–size relationship coefficients, total concentrations, and second and third moments of hydrometeor size distribution) are sorted in temperature (and thus altitude) zones, and each individual campaign is subsequently analyzed with respect to median microphysical properties of the merged dataset (merging all four campaign datasets). The study demonstrates that ice water content (IWC), visible extinction, total crystal concentration, and the second and third moments of hydrometeor size distributions are similar in all four types of MCS for IWC larger than 0.1 gm-3. Finally, two parameterizations are developed for deep convective systems. The first concerns the calculation of the visible extinction as a function of temperature and ice water content. The second concerns the calculation of hydrometeor size distributions as a function of ice water content and temperature that can be used in numerical weather prediction. [ABSTRACT FROM AUTHOR]

Knowledge of the full rainfall drop size distribution (DSD) is critical for characterising liquid water precipitation for applications such as rainfall retrievals using electromagnetic signals and atmospheric model parameterisation. Southern Hemisphere temperate latitudes have a lack of DSD observations and their integrated variables. Laser-based disdrometers rely on the attenuation of a beam by falling particles and are currently the most commonly used type of instrument to observe the DSD. However, there remain questions on the accuracy and variability in the DSDs measured by co-located instruments, whether identical models, different models or from different manufacturers. In this study, raw and processed DSD observations obtained from two of the most commonly deployed laser disdrometers, namely the Parsivel 1 from OTT and the Laser Precipitation Monitor (LPM) from Thies Clima, are analysed and compared. Four co-located instruments of each type were deployed over 3 years from 2014 to 2017 in the proximity of Melbourne, a region prone to coastal rainfall in south-eastern Australia. This dataset includes a total of approximately 1.5 million recorded minutes, including over 40 000 min of quality rainfall data common to all instruments, equivalent to a cumulative amount of rainfall ranging from 1093 to 1244 mm (depending on the instrument records) for a total of 318 rainfall events. Most of the events lasted between 20 and 40 min for rainfall amounts of 0.12 to 26.0 mm. The co-located LPM sensors show very similar observations, while the co-located Parsivel 1 systems show significantly different results. The LPM recorded 1 to 2 orders of magnitude more smaller droplets for drop diameters below 0.6 mm compared to the Parsivel 1 , with differences increasing at higher rainfall rates. The LPM integrated variables showed systematically lower values compared to the Parsivel 1. Radar reflectivity–rainfall rate (ZH – R) relationships and resulting potential errors are also presented. Specific ZH – R relations for drizzle and convective rainfall are also derived based on DSD collected for each instrument type. Variability of the DSD as observed by co-located instruments of the same manufacturer had little impact on the estimated ZH – R relationships for stratiform rainfall, but differs when considering convective rainfall relations or ZH – R relations fitted to all available data. Conversely, disdrometer-derived ZH – R relations as compared to the Marshall–Palmer relation ZH=200R1.6 led to a bias in rainfall rates for reflectivities of 50 dBZ of up to 21.6 mm h -1. This study provides an open-source high-resolution dataset of co-located DSD to further explore sampling effects at the micro scale, along with rainfall microstructure. [ABSTRACT FROM AUTHOR]

We present a Lagrangian convective transport scheme developed for global chemistry and transport models, which considers the variable residence time that an air parcel spends in convection. This is particularly important for accurately simulating the tropospheric chemistry of short-lived species, e.g., for determining the time available for heterogeneous chemical processes on the surface of cloud droplets. In current Lagrangian convective transport schemes air parcels are stochastically redistributed within a fixed time step according to estimated probabilities for convective entrainment as well as the altitude of detrainment. We introduce a new scheme that extends this approach by modeling the variable time that an air parcel spends in convection by estimating vertical updraft velocities. Vertical updraft velocities are obtained by combining convective mass fluxes from meteorological analysis data with a parameterization of convective area fraction profiles. We implement two different parameterizations: a parameterization using an observed constant convective area fraction profile and a parameterization that uses randomly drawn profiles to allow for variability. Our scheme is driven by convective mass fluxes and detrainment rates that originate from an external convective parameterization, which can be obtained from meteorological analysis data or from general circulation models. We study the effect of allowing for a variable time that an air parcel spends in convection by performing simulations in which our scheme is implemented into the trajectory module of the ATLAS chemistry and transport model and is driven by the ECMWF ERA-Interim reanalysis data. In particular, we show that the redistribution of air parcels in our scheme conserves the vertical mass distribution and that the scheme is able to reproduce the convective mass fluxes and detrainment rates of ERA-Interim. We further show that the estimated vertical updraft velocities of our scheme are able to reproduce wind profiler measurements performed in Darwin, Australia, for velocities larger than 0.6 ms-1. SO2 is used as an example to show that there is a significant effect on species mixing ratios when modeling the time spent in convective updrafts compared to a redistribution of air parcels in a fixed time step. Furthermore, we perform long-time global trajectory simulations of radon-222 and compare with aircraft measurements of radon activity. [ABSTRACT FROM AUTHOR]

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurements (ARM) facility has been at the forefront of millimeter-wavelength radar development and operations since the late 1990s. The operational performance of the ARM cloud radar network is very high; however, the calibration of the historical record is not well established. Here, a well-characterized spaceborne 94 GHz cloud profiling radar (CloudSat) is used to characterize the calibration of the ARM cloud radars. The calibration extends from 2007 to 2017 and includes both fixed and mobile deployments. Collectively, over 43 years of ARM profiling cloud radar observations are compared to CloudSat and the calibration offsets are reported as a function of time using a sliding window of 6 months. The study also provides the calibration offsets for each operating mode of the ARM cloud radars. Overall, significant calibration offsets are found that exceed the uncertainty of the technique (1–2 dB). The findings of this study are critical to past, ongoing, and planned studies of cloud and precipitation and should assist the DOE ARM to build a legacy decadal ground-based cloud radar dataset for global climate model validation. [ABSTRACT FROM AUTHOR]

Knowledge of the full rainfall Drop Size Distribution (DSD) is critical for characterising liquid water precipitation for applications such as rainfall retrievals using electromagnetic signals and atmospheric model parameterisation. Southern Hemisphere temperate latitudes have a lack of DSD observations and their integrated variables. Laser-based disdrometers rely on the attenuation of a beam by falling particles and is currently the most commonly used type of instrument to observe the DSD. However, there remain questions on the accuracy and variability in the DSDs measured by co-located instruments wether identical models, different models or from different manufacturers. In this study, raw and processed DSD observations obtained from two of the most commonly deployed laser disdrometers, namely the Parsivel1 from OTT and the Laser Precipitation Monitor (LPM) from Thies Clima, are analysed and compared. Four co-located instruments of each type were deployed over 3 years from 2014 to 2017 in the proximity of Melbourne, a region prone to coastal rainfall in Southeast Australia. This dataset includes a total of approximately 1.5 million recorded minutes, including over 40,000 minutes of quality rainfall data common to all instruments, equivalent to a cumulative amount of rainfall ranging from 1093 to 1244 mm (depending on the instrument records) for a total of 318 rainfall events. Most of the events lasted between 20 and 40 min for rainfall amounts of 0.12 mm to 26.0 mm. The co-located LPM sensors show very similar observations while the co-located Parsivel1 systems show significantly different results. The LPM recorded one to two orders of magnitude more smaller droplets for drop diameters below 0.6 mm compared to the Parsivel1, with differences increasing at higher rainfall rates. The LPM integrated variables showed systematically lower values compared to the Parsivel1. Radar reflectivity-rainfall rate (ZH-R) relationships and resulting potential errors are also presented. Specific ZH-R relations for drizzle and convective rainfall are also derived based on DSD collected for each instrument type. Variability of the DSD as observed by co-located instruments of the same manufacturer had little impact on the estimated ZH-R relationships for stratiform rainfall, but differs when considering convective rainfall relations or ZH-R relations fitted to all available data. Conversely, disdrometer-derived ZH-R relations as compared to the Marshall-Palmer relation ZH =200R1.6 led to a bias in rainfall rates for reflectivities of 50 dBZ of up to 21.6 mm h−1. This study provides an open-source high-resolution dataset of co-located DSD to further explore sampling effects at micro-scale, along with rainfall microphysics. [ABSTRACT FROM AUTHOR]

This study presents a statistical analysis of the properties of ice hydrometeors in tropical mesoscale convective systems observed during four different aircraft campaigns. Among the instruments on board the aircraft, we focus on the synergy of a 94 GHz cloud radar and 2 optical array probes (OAP; measuring hydrometeor sizes from 10 µm to about 1 cm). For two campaigns, an accurate simultaneous measurement of the ice water content is available, while for the two others, ice water content is retrieved from the synergy of the radar reflectivity measurements and hydrometeor size and morphological retrievals from OAP probes. The statistics of ice hydrometeor properties is calculated as a function of radar reflectivity factor measurement percentiles and temperature. Hence, MCS microphysical properties (ice water content, visible extinction, mass-size relationship coefficients, total concentrations and second and third moment of hydrometeors size distribution) are sorted in temperature (thus altitude) zones, and subsequently each individual campaign is analysed with respect to median microphysical properties of the global dataset (merging all 4 campaign datasets). The study demonstrates that ice water content, visible extinction, total crystal concentration, and second and third moments of hydrometeors size distributions are similar in all 4 type of MCS for IWC larger than 0.1 g m−3. Finally, two parameterizations are developed for deep convective systems. The first one concerns the calculation of the visible extinction as a function of temperature and ice water content. The second one concerns the calculation of hydrometeor size distributions as a function of ice water content and temperature that can be used in numerical weather prediction. [ABSTRACT FROM AUTHOR]

The US Department of Energy (DOE) Atmospheric Radiation Measurements (ARM) program has been at the forefront of millimeter wavelength radar development and operations since the late 90’s. The operational performance of the ARM cloud radar network is very high; however, the calibration of the historical record is not well established. Here, we use a well-characterized spaceborne 94-GHz cloud profiling radar (CloudSat) to characterize the calibration of the different generations of the ARM cloud radars from 2007 to 2017 over a variety of climatological regimes and for fixed and mobile deployments. Over 43 years of ARM profiling cloud radar observations are compared to CloudSat and the calibration offsets are reported as a function of time using a sliding window of 6 months. The study also provides the calibration offsets for each operating mode of the ARM cloud radars. Overall, significant calibration offsets are found that exceed the uncertainty of the technique (1–2 dB). The findings of this study are critical to past, on-going and planned studies of cloud and precipitation and should assist the DOE ARM to build a legacy decadal ground-based cloud radar dataset for global climate model validation. [ABSTRACT FROM AUTHOR]

The validation of convective processes in global climate models (GCMs) could benefit from the use of large datasets that provide long-term climatologies of the spatial statistics of convection. To that regard, echo top heights (ETHs), convective areas, and frequencies of mesoscale convective systems (MCSs) from 17 years of data from a C-band polarization (CPOL) radar are analyzed in varying phases of the Madden–Julian Oscillation (MJO) and northern Australian monsoon in order to provide ample validation statistics for GCM validation. The ETHs calculated using velocity texture and reflectivity provide similar results, showing that the ETHs are insensitive to various techniques that can be used. Retrieved ETHs are correlated with those from cloud top heights retrieved by Multifunctional Transport Satellites (MTSATs), showing that the ETHs capture the relative variability in cloud top heights over seasonal scales. Bimodal distributions of ETH, likely attributable to the cumulus congestus clouds and mature stages of convection, are more commonly observed when the active phase of the MJO is over Australia due to greater mid-level moisture during the active phase of the MJO. The presence of a convectively stable layer at around 5 km altitude over Darwin inhibiting convection past this level can explain the position of the modes at around 2–4 km and 7–9 km. Larger cells were observed during break conditions compared to monsoon conditions, but only during the inactive phase of the MJO. The spatial distributions show that Hector, a deep convective system that occurs almost daily during the wet season over the Tiwi Islands, and sea-breeze convergence lines are likely more common in break conditions. Oceanic MCSs are more common during the night over Darwin. Convective areas were generally smaller and MCSs more frequent during active monsoon conditions. In general, the MJO is a greater control on the ETHs in the deep convective mode observed over Darwin, with higher distributions of ETH when the MJO is active over Darwin. [ABSTRACT FROM AUTHOR]

The validation of convective processes in general circulation models requires the use of large datasets that provide long term climatologies of the spatial statistics of convection. To that regard, echo top heights (ETHs) retrieved from 17 years of data from C-band POLarization (CPOL) Radar are analyzed in varying phases of the Madden-Julian Oscillation (MJO) and Northern Australian Monsoon in order to provide ample validation statistics for the Department of Energy's next generation Earth Energy Exascale Model. In this paper, ETHs are retrieved using a novel methodology that uses the texture of radial velocity. Comparisons of retrieved ETHs against satellite retrieved cloud top heights from the split window technique show that the estimated ETH are correlated with, and, on average, are within 3 km of satellite retrieved cloud top heights. Using this technique gives comparable ETHs compared to using a reflectivity threshold. Bimodal distributions of ETH, likely attributable to the cumulus congestus and mature stages of convection, are more commonly observed when the active phase of the MJO is away from Australia. The presence of a convectively stable layer at around 5 km altitude over Darwin inhibiting convection past this level can explain the position of the modes at around 5 to 6 km and 12 to 13 km respectively. The spatial distributions show that Hector, a deep convective system that occurs almost daily during the wet season over the Tiwi Islands, and seabreeze convergence lines are likely more common in break conditions. Oceanic mesoscale convective systems (MCSes) are likely more common during the night. Unimodal distributions of ETH are more common during monsoon conditions and during an active MJO over Darwin, consistent with the presence of widespread MCSes that are commonly associated with both the MJO and the Northern Australian Monsoon. In general, the MJO is a greater control of the ETHs observed over Darwin, with generally both lower and more unimodal distributions of ETH when the MJO is active over Darwin. [ABSTRACT FROM AUTHOR]

Recent studies have found that ingestion of high mass concentrations of ice particles in regions of deep convective storms, with radar reflectivity considered safe for aircraft penetration, can adversely impact aircraft engine performance. Previous aviation industry studies have used the term high ice water content (HIWC) to define such conditions. Three airborne field campaigns were conducted in 2014 and 2015 to better understand how HIWC is distributed in deep convection, both as a function of altitude and proximity to convective updraft regions, and to facilitate development of new methods for detecting HIWC conditions, in addition to many other research and regulatory goals. This paper describes a prototype method for detecting HIWC conditions using geostationary (GEO) satellite imager data coupled with in situ total water content (TWC) observations collected during the flight campaigns. Three satellite-derived parameters were determined to be most useful for determining HIWC probability: (1) the horizontal proximity of the aircraft to the nearest overshooting convective updraft or textured anvil cloud, (2) tropopause-relative infrared brightness temperature, and (3) daytime-only cloud optical depth. Statistical fits between collocated TWC and GEO satellite parameters were used to determine the membership functions for the fuzzy logic derivation of HIWC probability. The products were demonstrated using data from several campaign flights and validated using a subset of the satellite-aircraft collocation database. The daytime HIWC probability was found to agree quite well with TWC time trends and identified extreme TWC events with high probability. Discrimination of HIWC was more challenging at night with IRonly information. The products show the greatest capability for discriminating TWC ≥0.5 gm-3. Product validation remains challenging due to vertical TWC uncertainties and the typically coarse spatio-temporal resolution of the GEO data. [ABSTRACT FROM AUTHOR]

Recent studies have found that flight through deep convective storms and ingestion of high mass concentrations of ice crystals, also known as high ice water content (HIWC), into aircraft engines can adversely impact aircraft engine performance. These aircraft engine icing events caused by HIWC have been documented during flight in weak reflectivity regions near convective updraft regions that do not appear threatening in onboard weather radar data. Three airborne field campaigns were conducted in 2014 and 2015 to better understand how HIWC is distributed in deep convection, both as a function of altitude and proximity to convective updraft regions, and to facilitate development of new methods for detecting HIWC conditions, in addition to many other research and regulatory goals. This paper describes a prototype method for detecting HIWC conditions using geostationary (GEO) satellite imager data coupled with in-situ total water content (TWC) observations collected during the flight campaigns. Three satellite-derived parameters were determined to be most useful for determining HIWC probability: 1) the horizontal proximity of the aircraft to the nearest overshooting convective updraft or textured anvil cloud, 2) tropopause-relative infrared brightness temperature, and 3) daytime-only cloud optical depth. Statistical fits between collocated TWC and GEO satellite parameters were used to determine the membership functions for the fuzzy logic derivation of HIWC probability. The products were demonstrated using data from several campaign flights and validated using a subset of the satellite-aircraft collocation database. The daytime HIWC probability was found to agree quite well with TWC time trends and identified extreme TWC events with high probability. Discrimination of HIWC was more challenging at night with IR-only information. The products show the greatest capability for discriminating TWC ≥ 0.5 g m-3. Product validation remains challenging due to vertical TWC uncertainties and the typically coarse spatio-temporal resolution of the GEO data. [ABSTRACT FROM AUTHOR]

The High Altitude Ice Crystals - High Ice Water Content (HAIC-HIWC) joint field campaign produced aircraft retrievals of total condensed water content (TWC), hydrometeor particle size distributions (PSDs), and vertical velocity (w) in high ice water content regions of mature and decaying tropical mesoscale convective systems (MCSs). The resulting dataset is used here to explore causes of the commonly documented high bias in radar reflectivity within cloud-resolving simulations of deep convection. This bias has been linked to overly strong simulated convective updrafts lofting excessive condensate mass but is also modulated by parameterizations of hydrometeor size distributions, single particle properties, species separation, and microphysical processes. Observations are compared with three Weather Research and Forecasting model simulations of an observed MCS using different microphysics parameterizations while controlling for w, TWC, and temperature. Two popular bulk microphysics schemes (Thompson and Morrison) and one bin microphysics scheme (fast spectral bin microphysics) are compared. For temperatures between -10 and -40 °C and TWC > 1 g m-3, all microphysics schemes produce median mass diameters (MMDs) that are generally larger than observed, and the precipitating ice species that controls this size bias varies by scheme, temperature, and w. Despite a much greater number of samples, all simulations fail to reproduce observed high-TWC conditions (> 2 g m-3) between -20 and -40 °C in which only a small fraction of condensate mass is found in relatively large particle sizes greater than 1 mm in diameter. Although more mass is distributed to large particle sizes relative to those observed across all schemes when controlling for temperature, w, and TWC, differences with observations are significantly variable between the schemes tested. As a result, this bias is hypothesized to partly result from errors in parameterized hydrometeor PSD and single particle properties, but because it is present in all schemes, it may also partly result from errors in parameterized microphysical processes present in all schemes. Because of these ubiquitous ice size biases, the frequently used microphysical parameterizations evaluated in this study inherently produce a high bias in convective reflectivity for a wide range of temperatures, vertical velocities, and TWCs. [ABSTRACT FROM AUTHOR]

This study presents the evaluation of a technique to estimate cloud condensed water content (CWC) in tropical convection from airborne cloud radar reflectivity factors at 94 GHz and in situ measurements of particle size distributions (PSDs) and aspect ratios of ice crystal populations. The approach is to calculate from each 5 s mean PSD and flight-level reflectivity the variability of all possible solutions of m(D) relationships fulfilling the condition that the simulated radar reflectivity factor (T-matrix method) matches the measured radar reflectivity factor. For the reflectivity simulations, ice crystals were approximated as oblate spheroids, without using a priori assumptions on the mass-size relationship of ice crystals. The CWC calculations demonstrate that individual CWC values are in the range ±32% of the retrieved average CWC value over all CWC solutions for the chosen 5 s time intervals. In addition, during the airborne field campaign performed out of Darwin in 2014, as part of the international High Altitude Ice Crystals/High Ice Water Content (HAIC/HIWC) projects, CWCs were measured independently with the new IKP-2 (isokinetic evaporator probe) instrument along with simultaneous particle imagery and radar reflectivity. Retrieved CWCs from the T-matrix radar reflectivity simulations are on average 16% higher than the direct CWCIKP measurements. The differences between the CWCIKP and averaged retrieved CWCs are found to be primarily a function of the total number concentration of ice crystals. Consequently, a correction term is applied (as a function of total number concentration) that significantly improves the retrieved CWC. After correction, the retrieved CWCs have a median relative error with respect to measured values of only -1 %. Uncertainties in the measurements of total concentration of hydrometeors are investigated in order to calculate their contribution to the relative error of calculated CWC with respect to measured CWCIKP. It is shown that an overestimation of the concentration by about +50% increases the relative errors of retrieved CWCs by only +29 %, while possible shattering, which impacts only the concentration of small hydrometeors, increases the relative error by about +4 %. Moreover, all cloud events with encountered graupel particles were studied and compared to events without observed graupel particles. Overall, graupel particles seem to have the largest impact on high crystal number-concentration conditions and show relative errors in retrieved CWCs that are higher than for events without graupel particles. [ABSTRACT FROM AUTHOR]

This study has two objectives: (1) it characterizes contrails at very low temperatures and (2) it discusses convective cirrus in which the contrails occurred. (1) Longlived contrails and cirrus from overshooting convection are investigated above the tropical tropopause at low temperatures down to -88 °C from measurements with the Russian high-altitude research aircraft M-55 "Geophysica", as well as related observations during the SCOUT-O3 field experiment near Darwin, Australia, in 2005. A contrail was observed to persist below ice saturation at low temperatures and low turbulence in the stratosphere for nearly 1 h. The contrail occurred downwind of the decaying convective system "Hector" of 16 November 2005. The upper part of the contrail formed at 19 km altitude in the tropical lower stratosphere at ~60% relative humidity over ice at -82 °C. The ~1 h lifetime is explained by engine water emissions, slightly enhanced humidity from Hector, low temperature, low turbulence, and possibly nitric acid hydrate formation. The long persistence suggests large contrail coverage in case of a potential future increase of air traffic in the lower stratosphere. (2) Cirrus observed above the strongly convective Hector cloud on 30 November 2005 was previously interpreted as cirrus from overshooting convection. Here we show that parts of the cirrus were caused by contrails or are mixtures of convective and contrail cirrus. The in situ data together with data from an upward-looking lidar on the German research aircraft "Falcon", the CPOL radar near Darwin, and NOAAAVHRR satellites provide a sufficiently complete picture to distinguish between contrail and convective cirrus parts. Plume positions are estimated based on measured or analyzed wind and parameterized wake vortex descent. Most of the non-volatile aerosol measured over Hector is traceable to aircraft emissions. Exhaust emission indices are derived from a self-match experiment of the Geophysica in the polar stratosphere in 2010. The number of ice particles in the contrails is less than 1% of the number of non-volatile aerosol particles, possibly because of sublimation losses and undetected very small ice particles. The radar data show that the ice water content in convective overshoots is far higher than measured along the flight path. These findings add insight into overshooting convection and are of relevance with respect to hydration of the lower stratosphere. [ABSTRACT FROM AUTHOR]

Contrails of the Russian high-flying research aircraft M-55 "Geophysica" are investigated in measurements above the tropical tropopause during the SCOUT-O3 field-experiment near Darwin, Australia, in 2005. The aircraft reached 19 km altitude, far above the tropopause with -87°C temperature at 17 km. In-situ, lidar, and microwave-temperature profiler measurements on board the Geophysica are used. An upward-looking lidar on the German research aircraft "Falcon", the CPOL radar near Darwin, and NOAA-AVHRR satellites provide complementary data. Exhaust emission indices are derived from a self-match experiment of the Geophysica in the polar stratosphere in 2010. Plume positions are estimated based on measured or analyzed wind and parameterized wake vortex descent. One contrail is detectable in a photo, and characterized in-situ during contrail formation downwind of the overshooting convective system "Hector" of 16 November 2005. The upper part of the contrail formed in the tropical lower stratosphere at ~60 % relative humidity over ice at -82°C. The ~1-h lifetime is explained by engine water emissions, slightly enhanced humidity from Hector, low temperature, low turbulence, and possibly nitric-acid hydrate formation. The long persistence suggests large contrail coverage from future high-flying aircraft. Further Geophysica contrail parts are found in the measurements inside the strongly convective Hector clouds on 30 November 2005. Most of the non-volatile aerosol measured over Hector is traceable to aircraft emissions. Cirrus clouds observed by lidar above the anvil occur in coincidence with computed contrail positions. The upper part of the stratospheric anvil can be explained as contrail cirrus in this case. The radar indicates that the cirrus was measured in-situ mostly besides and above overshooting convection, and the maximum ice water content in the overshoots is far higher than measured along the flight path. The evidence suggests that parts of the ice clouds measured are contrails or mixtures of convective and contrail cirrus. The number of ice particles in the contrails is less than 1 % of the number of non-volatile aerosol particles, possibly because of sublimation losses and undetected very small ice particles. The findings are of relevance with respect to hydration of the lower stratosphere, overshooting convection, and future increases of air traffic in the lower stratosphere. [ABSTRACT FROM AUTHOR]

This study presents the evaluation of a technique to estimate cloud condensed water content (CWC) in tropical convection from airborne cloud radar reflectivity factors at 94 GHz and in-situ measurements of particle size distributions (PSDs) and aspect ratios of ice crystal populations. The approach is to calculate the variability of 5 second average PSD CWCs and all possible solutions of corresponding m(D) relationships fulfilling the condition that the simulated radar reflectivity factors (T-matrix method) matches the measured reflectivity. For the reflectivity simulations, ice crystals were approximated as oblate spheroids, without using a priori assumptions on the mass-size relationship of ice crystals. The CWC calculations demonstrate that measured CWC values are in the range ±32 % of the average CWC value: averaged over all CWC solutions for the chosen 5s time intervals. In addition, during the airborne field campaign performed out of Darwin in 2014, as part of the international High Altitude Ice Crystals (HAIC)--High Ice Water Content (HIWC) projects, CWCs were measured directly with the new IKP-2 (isokinetic evaporator probe) instrument along with simultaneous particle imagery and radar reflectivity. Averaged CWC retrieved from the radar reflectivity simulations are roughly 16 % higher than the IKP-2 CWC measurements. The differences between the IKP-2 and PSD derived CWCs from the entire set of realistic m(D) solutions for T-matrix retrievals is found to be a function of the total number concentration of ice crystals. Consequently, a correction term is applied (as a function of total number concentration) that significantly improves the retrieved CWC. After correction, the retrieved CWC have a median error relative to measured values of only -1 %. [ABSTRACT FROM AUTHOR]

Simulations of tropical convection from an operational numerical weather prediction model are evaluated with the focus on the model's ability to simulate the observed high ice water contents associated with the outflow of deep convection and to investigate the modelled processes that control the phase composition of tropical convective clouds. The 1 km horizontal grid length model that uses a singlemoment microphysics scheme simulates the intensification and decay of convective strength across the mesoscale convective system. However, deep convection is produced too early, the OLR (outgoing longwave radiation) is underestimated and the areas with reflectivities >30 dBZ are overestimated due to too much rain above the freezing level, stronger updraughts and larger particle sizes in the model. The inclusion of a heterogeneous rain-freezing parameterisation and the use of different ice size distributions show better agreement with the observed reflectivity distributions; however, this simulation still produces a broader profile with many high-reflectivity outliers demonstrating the greater occurrence of convective cells in the simulations. Examining the phase composition shows that the amount of liquid and ice in the modelled convective updraughts is controlled by the following: the size of the ice particles, with larger particles growing more efficiently through riming and producing larger IWC (ice water content); the efficiency of the warm rain process, with greater cloud water contents being available to support larger ice growth rates; and exclusion or limitation of graupel growth, with more mass contained in slower falling snow particles resulting in an increase of in-cloud residence times and more efficient removal of LWC (liquid water content). In this simulated case using a 1 km grid length model, horizontal mass divergence in the mixed-phase regions of convective updraughts is most sensitive to the turbulence formulation. Greater mixing of environmental air into cloudy updraughts in the region of -30 to 0 °C produces more mass divergence indicative of greater entrainment, which generates a larger stratiform rain area. Above these levels in the purely ice region of the simulated updraughts, the convective updraught buoyancy is controlled by the ice particle sizes, demonstrating the importance of the microphysical processes on the convective dynamics in this simulated case study using a single-moment microphysics scheme. The single-moment microphysics scheme in the model is unable to simulate the observed reduction of mean mass-weighted ice diameter as the ice water content increases. The inability of the model to represent the observed variability of the ice size distribution would be improved with the use of a double-moment microphysics scheme. [ABSTRACT FROM AUTHOR]

We present highlights of recent Australian-led clouds and aerosol research over the SouthernOcean and coastal East Antarctica as part of the broader SOCRATES field campaigns. Usinga combined Raman-elastic backscatter lidar deployed aboard a ship, we quantify theproperties of aerosols within the remote Southern Ocean marine boundary layer between43∘S and 66∘S. In clean, marine air, we determine a lidar ratio of S = (18 ± 2) sr, aerosoloptical depths within the well-mixed near-surface layer of τ = (0.12 ± 0.08) north of 55∘Sand τ = (0.07 ± 0.04) further south. Dried sea salt is observed in the lower part ofdehumidified decoupled layers across all latitudes. Merging cloud data from this lidar and aW-band cloud radar allows us to quantify various cloud properties across the SouthernOcean; we will also present results of the differences in cloud properties acrossthe oceanic polar front boundary. Lastly, we present the seasonal cycle of cloudproperties at Macquarie Island (54∘S) based on data from our lidar and the cloud radar.Initial highlights from our current Davis (69∘S) deployment will also be presented. [ABSTRACT FROM AUTHOR]