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A persistent shortwave radiative bias of Southern Ocean (SO) clouds in climate models is strongly associated with incorrect cloud phase representation, which impacts precipitation. Measurements characterizing precipitation in low‐level mixed‐phase clouds, which frequently form over the SO, are rare, and our understanding of precipitation efficacy within these clouds remains limited. The simulated surface precipitation bias has an indirect effect on determining global climate sensitivity and a direct impact on the hydrological cycle. This study investigates the representation of low clouds, cloud variability, and precipitation statistics over the SO in real‐case Icosahedral Nonhydrostatic (ICON) simulations at the kilometer scale. The simulations are contrasted with 48 hr of continuous shipborne observations of open and closed‐cell stratocumuli, south of Tasmania. Our simulations show the significance of heavily rimed particle formation, their in‐cloud growth, and subcloud melting to capture the observed cloud‐precipitation vertical structure. In addition, supercooled drizzle formation impacts the vertical structure and precipitation statistics. ICON captures the observed intermittency of precipitation even at a standard vertical resolution of 200 m in the boundary layer but only captures the observed sparse distribution of intense precipitation (>1 mm hr−1) when the maximum vertical resolution is reduced to 100 m. However, the simulations of the 2‐day accumulated precipitation and the radiative effect are largely insensitive to the vertical resolution. The cloud reflectivity of the broken cloud deck is underestimated due to negative biases in cloud optical depth. Plain Language Summary: Stratocumulus (Sc) clouds cover a large portion of the Southern Ocean (SO), where they substantially cool the ocean surface. Our understanding of the complex physics of these clouds, which include both liquid and ice remains incomplete. Accordingly, the representation of these clouds in global climate and weather models remains biased. In particular, their timing, frequency of occurrence, cloud phase and distribution, cloud cover, and precipitation characteristics are still associated with uncertainties. This results in biases in the energy balance over the SO and global equilibrium climate sensitivity. We use measurements from the Clouds, Aerosols, Precipitation, Radiation, and atmospherIc Composition Over the southeRn oceaN (CAPRICORN) voyage south of Tasmania, to evaluate the representation of broken cloud fields, the dominant ice processes, and the precipitation characteristics in high‐resolution numerical simulations. Our results suggest that, in addition to capturing the observed discrete cloud events, graupel‐like particle formation, its growth in the cloud layer, and subsequent melting in the subcloud layer are critical processes in accurately representing the SO broken low cloud fields and precipitation characteristics during CAPRICORN. Additionally, compared to observations, the simulated clouds are too reflective. Key Points: In‐cloud heavy riming, subcloud melting, and supercooled processes are vital to capture observed SO low‐cloud‐precipitation structureA finer vertical grid spacing of 100 m or less is needed to capture the frequency of observed intense surface precipitation (>1 mm hr−1) eventsWhile the simulated precipitation statistics agree with observations, the clouds are associated with a negative radiative bias [ABSTRACT FROM AUTHOR]

The cloud properties and governing processes in Southern Ocean marine boundary layer clouds have emerged as a central issue in understanding the Earth's climate sensitivity. While our understanding of Southern Ocean cloud feedbacks have evolved in the most recent climate model intercomparison, the background properties of simulated summertime clouds in the Southern Ocean are not consistent with measurements due to known biases in simulating cloud condensation nuclei concentrations. This paper presents several case studies collected during the Capricorn 2 and Marcus campaigns held aboard Australian research vessels in the Austral Summer of 2018. Combining the surface‐observed cases with MODIS data along forward and backward air mass trajectories, we demonstrate the evolution of cloud properties with time. These cases are consistent with multi‐year statistics showing that long trajectories of air masses over the Antarctic ice sheet are critical to creating high droplet number clouds in the high latitude summer Southern Ocean. We speculate that secondary aerosol production via the oxidation of biogenically derived aerosol precursor gasses over the high actinic flux region of the high latitude ice sheets is fundamental to maintaining relatively high droplet numbers in Southern Ocean clouds during Summer. Plain Language Summary: The amount of warming the Earth will experience because of increasing carbon dioxide levels in the atmosphere is sensitive to the properties of clouds that occur over the Southern Ocean. The atmosphere over the circumpolar Southern Ocean is poorly understood and presents significant challenges to climate models. Here we document the properties of the ubiquitous Southern Ocean low‐level clouds that exert a strong influence on the albedo of this region. We find that high cloud droplet number concentrations are associated with air masses that have taken paths over the high‐altitude ice sheets. The chemistry of the aerosol on which the cloud droplets form suggests that aerosols that have recently condensed from gasses emitted by phytoplankton in the highly productive waters near Antarctica are an important component of the cloud properties that must be correctly simulated in models. Key Points: High cloud droplet number concentrations are associated with air masses that have recently passed over continental AntarcticaCloud droplet number concentrations decrease with time as clouds evolve in over‐water trajectories due to scavenging by precipitationKatabatic flows bring high concentrations of cloud condensation nuclei to the marine boundary layer where they influence cloud properties [ABSTRACT FROM AUTHOR]

This paper considers theoretical and observed uncertainties in the estimates of ZDR and ρHV(0) using data from an operational S-band radar and a mobile X-band radar. Cases of widespread uniform precipitation including bright-band, clear air, and ash echoes from forest fires are all considered in order to obtain a wide range of ρHV(0) values as this along with the radar frequency and spectrum width determines the uncertainties. The theoretical uncertainties in these parameters provide a good estimate of the lower bound of the standard deviations of the observed values where these have been estimated using the adjacent data to the target pixel. The implications for the accuracy of precipitation estimation, particle identification, and estimates of drop-size distributions are discussed. Significance Statement: High-quality quantitative precipitation and particle size/classification retrievals using weather radar are strongly dependent on the accuracy of ZDR and ρHV(0). This paper examines the theoretical limits to the measurement accuracy and verifies these limits with radar data at 10- and 3-cm wavelengths. [ABSTRACT FROM AUTHOR]

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]

In Australia, hailstorms present considerable public safety and economic risks, where they are considered the most damaging natural hazard in terms of annual insured losses. Despite these impacts, the current climatological distribution of hailfall across the continent is still comparatively poorly understood. This study aims to supplement previous national hail climatologies, such as those based on environmental proxies or satellite radiometer data, with more direct radar-based hail observations. The heterogeneous and incomplete nature of the Australian radar network complicates this task and prompts the introduction of some novel methodological elements. We introduce an empirical correction technique to account for hail reflectivity biases at C band, derived by comparing overlapping C- and S-band observations. Furthermore, we demonstrate how object-based hail swath analysis may be used to produce resolution-invariant hail frequencies, and describe an interpolation method used to create a spatially continuous hail climatology. The maximum estimated size of hail (MESH) parameter is then applied to a mixture of over 50 operational radars in the Australian radar archive, resulting in the first nationwide, radar-based hail climatology. The spatiotemporal distribution of hailstorms is examined, including their physical characteristics, seasonal and diurnal frequency, and regional variations of such properties across the continent. [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]

Clouds over the Southern Ocean can contain super-cooled liquid water, which means the water remains liquid below 0°C. This is because the water in these clouds has had little contact with pollution or dust, which can help water freeze. Super-cooled liquid water clouds reflect more sunlight back into space than ice clouds, which can affect the temperature of the ocean. The Southern Ocean is a unique environment with fewer ice nucleating particles, making it difficult to study. Scientists are conducting research expeditions to gather more data and improve climate models. [Extracted from the article]

Doppler radars measure Doppler velocity within the [−VN, VN] range, where VN is the Nyquist velocity. Doppler velocities outside this range are "folded" within this interval. All Doppler "unfolding" techniques use the folded velocities themselves. In this work, we investigate the potential of using velocities derived from optical flow techniques applied to the radar reflectivity field for that purpose. The analysis of wind speed errors using six months of multi-Doppler wind retrievals showed that 99.9% of all points are characterized by errors smaller than 26 m s−1 below 5-km height, corresponding to a failure rate of less than 0.1% if optical flow winds were used to unfold Doppler velocities for VN = 26 m s−1. These errors largely increase above 5-km height, indicating that vertical continuity tests should be included to reduce failure rates at higher elevations. Following these results, we have developed the Two-step Optical Flow Unfolding (TOFU) technique, with the specific objective to accurately unfold Doppler velocities with VN = 26 m s−1. The TOFU performance was assessed using challenging case studies, comparisons with an advanced Doppler unfolding technique using higher Nyquist velocities, and 6 months of high VN (47.2 m s−1) data artificially folded to 26 m s−1. TOFU failure rates were found to be very low. Three main situations contributed to these errors: high low-level wind shear, elevated cloud layers associated with high winds, and radar data artifacts. Our recommendation is to use these unfolded winds as the first step of advanced Doppler unfolding techniques. Significance Statement: The potential of using optical flow winds operationally to accurately unfold Doppler velocities is demonstrated in this work. The operational significance is that the Nyquist velocity can confidently be reduced to 26 m s−1, allowing for extended first trip radar maximum range and reduced contamination from dual pulse repetition frequency artifacts. [ABSTRACT FROM AUTHOR]

Three-dimensional wind retrievals from ground-based Doppler radars have played an important role in meteorological research and nowcasting over the past four decades. However, in recent years, the proliferation of open-source software and increased demands from applications such as convective parameterizations in numerical weather prediction models has led to a renewed interest in these analyses. In this study, we analyze how a major, yet often-overlooked, error source effects the quality of retrieved 3D wind fields. Namely, we investigate the effects of spatial interpolation, and show how the common practice of pregridding radial velocity data can degrade the accuracy of the results. Alternatively, we show that assimilating radar data directly at their observation locations improves the retrieval of important dynamic features such as the rear flank downdraft and mesocyclone within supercells, while also reducing errors in vertical vorticity, horizontal divergence, and all three velocity components. Significance Statement: We can attempt to estimate the wind speed and direction within a weather system when two weather radars measure it simultaneously. However, radars do not scan the whole atmosphere at once—instead, they measure along many cross sections, each at different heights. We show that a method commonly used to stitch the observations together degrades the accuracy of the winds. Additionally, we describe a way to feed the data directly into the analysis without stitching it together first, and show that this improves the wind retrievals considerably. We hope these improvements will help researchers better understand how various weather systems work, and help forecasters warn for dangerous weather such as tornadoes. [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]

Shallow cloud decks residing in or near the boundary layer cover a large fraction of the Southern Ocean (SO) and play a major role in determining the amount of shortwave radiation reflected back to space from this region. In this article, we examine the macrophysical characteristics and thermodynamic phase of low clouds (tops <3 km) and precipitation using ground‐based ceilometer, depolarization lidar and vertically‐pointing W‐band radar measurements collected during the Macquarie Island Cloud and Radiation Experiment (MICRE) from April 2016 to March 2017. During MICRE, low clouds occurred ∼65% of the time on average (slightly more often in austral winter than summer). About 2/3 of low clouds were cold‐topped (temperatures ≤0°C). These were thicker and had higher bases on average than warm‐topped clouds. 83%–88% of cold‐topped low clouds were liquid phase at cloud base (depending on the season). The majority of low clouds had precipitation in the vertical range 150–250 m below cloud base, a significant fraction of which did not reach the surface. Phase characterization is limited to the period between April 2016 and November 2016. Small‐particle (low‐radar‐reflectivity) precipitation (which dominates precipitation occurrence) was mostly liquid below‐cloud, while large‐particle precipitation (which dominates total accumulation) was predominantly mixed/ambiguous or ice phase. Approximately 40% of cold‐topped clouds had mixed/ambiguous or ice phase precipitation below (with predominantly liquid phase cloud droplets at cloud base). Below‐cloud precipitation with radar reflectivity factors below about −10 dBZ were predominantly liquid, while reflectivity factors above about 0 dBZ were predominantly ice. Plain Language Summary: The Southern Ocean is covered by low altitude cloud decks the majority of the time. Properties like cloud occurrence frequency, particle phase and precipitation habits determine how much solar radiation clouds reflect and how much infrared radiation they emit, which in turn affects the balance of the planet's incoming and outgoing radiation. In this paper, we examine low cloud properties observed from the ground at Macquarie Island, including how frequently they occur and at what temperatures. We study particle thermodynamic phase (liquid, ice or mixed) at cloud base and in precipitation below‐cloud. A majority of low clouds are predominantly composed of liquid phase droplets, although frozen precipitation is frequently found below cloud base. Low clouds form precipitation more often than not, much of which evaporates before reaching the ground. In below‐freezing low clouds, the majority of large raindrops & snowflakes that do reach the ground originate as frozen precipitation directly below cloud base. This indicates that ice formation is frequently active in clouds composed predominantly of liquid‐phase droplets. Lastly, we build upon an established radar‐lidar relationship that particles with radar reflectivity factors below −10 dBZ are generally liquid, whereas above 0 dBZ are most often ice phase. Key Points: Ground observations at Macquarie Island indicate low clouds occur ∼65% of the time, with about 2/3 having cloud top temperatures below 0°CCold‐topped low clouds have liquid‐phase bases ∼85% of the time & precipitate ∼3/4 of the time; below‐cloud precip. is 40% ice/mixed phaseBelow‐cloud radar reflectivity factors <−10 dBZ are predominately due to liquid phase precipitation, while >0 dBZ are predominantly ice [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]

We present an integrated framework that leverages multiple weather radar calibration and monitoring techniques to provide real-time diagnostics on reflectivity calibration, antenna pointing, and dual-polarization moments. This framework uses a volume-matching technique to track the absolute calibration of radar reflectivity with respect to the Global Precipitation Measurement (GPM) spaceborne radar, the relative calibration adjustment (RCA) technique to track relative changes in the radar calibration constant, the solar calibration technique to track daily change in solar power and antenna pointing error, and techniques that track properties of light-rain medium to monitor the differential reflectivity and dual-polarization moments. This framework allows for an evaluation of various calibration and monitoring techniques. For example, we found that a change in the RCA is highly correlated to a change in absolute calibration, with respect to GPM, if a change in antenna pointing can first be ruled out. It is currently monitoring 67+ radars from the Australian radar network. Because of the diverse and evolving nature of the Australian radar network, flexibility and modularity are at the core of the calibration framework. The framework can tailor its diagnostics to the specific characteristics of a radar (band, beamwidth, etc.). Because of its modularity, it can be expanded with new techniques to provide additional diagnostics (e.g., monitoring of radar sensitivity). The results are presented in an interactive dashboard at different level of details for a wide and diverse audience (radar engineers, researchers, forecasters, and management), and it is operational at the Australian Bureau of Meteorology. Significance Statement: Weather radars, like all instruments, require maintenance and upgrades. Rainfall measurements are highly variable and sensitive to change, and this can lead to inconsistencies within a radar network. Calibration is the process to counteract those inconsistencies. Any calibration requires a fixed standard to which the changed/upgraded radar can be compared. The SCAR calibration framework presented herein makes use of several standards to retrieve a full set of diagnostics about the radar data. We apply these techniques over the entire Australian weather radar network and demonstrate that, by using this integrated approach, absolute calibration can be achieved to within 1 dBZ of reflectivity, antenna pointing can be monitored within 0.1°, and the various measurements of the radars can be quality controlled. [ABSTRACT FROM AUTHOR]

Areas of high ice water content (HIWC) within cloud systems may cause power loss events and engine damage in jet aircraft due to ice crystal icing (ICI). The Algorithm for Prediction of HIWC Areas (ALPHA) was developed to identify these regions and enable provision of guidance to airlines. ALPHA combines numerical weather prediction model data, satellite data, and radar data (where available), and applies fuzzy logic to identify the likely presence of HIWC. In a collaboration between the U.S. National Center for Atmospheric Research, Australian Bureau of Meteorology, U.S. Federal Aviation Administration, and Australian airlines, a trial of ALPHA was conducted for an area across Indonesia, Papua New Guinea (PNG), and northern Australia, a region with frequent deep convection and a relatively high incidence of ICI events. ALPHA was adapted to ingest data from the Australian Community Climate and Earth System Simulator model and the Japanese Himawari-8 geostationary meteorological satellite. Radar data was not used. The HIWC product was made available to stakeholder groups for evaluation. Independent validation of the HIWC product was undertaken by comparing it with retrieved profiles of ice water content (IWC) from the cloud profiling radar on the NASA polar-orbiting CloudSat satellite. Conduct of the ALPHA trial and results from validation of the HIWC product provides confidence in the potential utility for flight planning, maintaining situational awareness, and flight monitoring. [ABSTRACT FROM AUTHOR]

We use dual-polarization C-band data collected in the Southern Ocean to examine the properties of snow observed during a voyage in the austral summer of 2018. Using existing forward modeling formalisms based on an assumption of Rayleigh scattering by soft spheroids, an optimal estimation algorithm is implemented to infer snow properties from horizontally polarized radar reflectivity, the differential radar reflectivity, and the specific differential phase. From the dual-polarization observables, we estimate ice water content qi, the mass-mean particle size Dm, and the exponent of the mass–dimensional relationship bm that, with several assumptions, allow for evaluation of snow bulk density, and snow number concentration. Upon evaluating the uncertainties associated with measurement and forward model errors, we determine that the algorithm can retrieve qi, Dm, and bm within single-pixel uncertainties conservatively estimated in the range 120%, 60%, and 40%, respectively. Applying the algorithm to open-cellular convection in the Southern Ocean, we find evidence for secondary ice formation processes within multicellular complexes. In stratiform precipitation systems we find snow properties and infer processes that are distinctly different from the shallow convective systems with evidence for riming and aggregation being common. We also find that embedded convection within the frontal system produces precipitation properties consistent with graupel. Examining 5 weeks of data, we show that snow in open-cellular cumulus has higher overall bulk density than snow in stratiform precipitation systems with implications for interpreting measurements from space-based active remote sensors. [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]

We evaluated the performance in Australia of proxies designed to identify atmospheric conditions prone to hail and severe storms. In a convection-resolving but short-duration simulation, proxies that use instability and wind shear thresholds overestimated the probability of hail occurring when compared to the estimated occurrence of surface graupel in the model, particularly in Australia's tropical north. We used reanalysis data and the Australian Bureau of Meteorology severe storm archive to examine atmospheric conditions at times and locations when hailstorms, other storms, and no storms were reported between January 1979 and March 2021. In instability–shear space, the best discriminator between hail and no-storm times was found to vary predictably with melting-level height, allowing a new proxy to better represent latitudinal trends in atmospheric conditions. We found extra conditions that can be applied to the new proxy to efficiently reduce the number of false alarms. The new proxy outperforms the tested existing proxies for detection of hail-prone conditions in Australia. Significance Statement: Hail proxies take a description of the atmosphere, such as its temperature, moisture content, and wind properties at various heights, and determine the likelihood of hail forming and hitting the ground. It is a difficult task prone to uncertainty, but in many locations there are no direct observations of hail, and in these places information from proxies is valuable. Existing proxies have a tendency to overestimate the probability of hail falling in the north of Australia. In this study we developed an updated proxy that uses information about the atmosphere's melting-level height to refine its hail predictions. The new proxy outperforms other tested proxies for hail in Australia. Accurate hail proxies are important for assessment of past and future changes to hail hazard and risk. [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]

A new method that automatically determines the modality of an observed particle size distribution (PSD) and the representation of each mode as a gamma function was used to characterize data obtained during the High Altitude Ice Crystals and High Ice Water Content (HAIC-HIWC) project based out of Cayenne, French Guiana, in 2015. PSDs measured by a 2D stereo probe and a precipitation imaging probe for particles with maximum dimension (Dmax) > 55 μm were used to show how the gamma parameters varied with environmental conditions, including temperature (T) and convective properties such as cloud type, mesoscale convective system (MCS) age, distance away from the nearest convective peak, and underlying surface characteristics. Four kinds of modality PSDs were observed: unimodal PSDs and three types of multimodal PSDs (Bimodal1 with breakpoints 100 ± 20 μm between modes, Bimodal2 with breakpoints 1000 ± 300 μm, and Trimodal PSDs with two breakpoints). The T and ice water content (IWC) are the most important factors influencing the modality of PSDs, with the frequency of multimodal PSDs increasing with increasing T and IWC. An ellipsoid of equally plausible solutions in (No–λ–μ) phase space is defined for each mode of the observed PSDs for different environmental conditions. The percentage overlap between ellipsoids was used to quantify the differences between overlapping ellipsoids for varying conditions. The volumes of the ellipsoid decrease with increasing IWC for most cases, and (No–λ–μ) vary with environmental conditions related to distribution of IWC. HIWC regions are dominated by small irregular ice crystals and columns. The parameters (No–λ–μ) in each mode exhibit mutual dependence. [ABSTRACT FROM AUTHOR]

This study focuses on methods to estimate dry marine aerosol surface area (SA) from bulk optical measurements. Aerosol SA is used in many models' ice nucleating particle (INP) parameterizations, as well as influencing particle light scattering, hygroscopic growth, and reactivity, but direct observations are scarce in the Southern Ocean (SO). Two campaigns jointly conducted in austral summer 2018 provided co‐located measurements of aerosol SA from particle size distributions and lidar to evaluate SA estimation methods in this region. Mie theory calculations based on measured size distributions were used to test a proposed approximation for dry aerosol SA, which relies on estimating effective scattering efficiency (Q) as a function of Ångström exponent (å). For distributions with dry å < 1, Q = 2 was found to be a good approximation within ±50%, but for distributions with dry å > 1, an assumption of Q = 3 as in some prior studies underestimates dry aerosol SA by a factor of 2 or more. We propose a new relationship between dry å and Q, which can be used for −0.2 < å < 2, and suggest å = 0.8 as the cutoff between primary and secondary marine aerosol‐dominated distributions. Application of a published methodology to retrieve dry marine aerosol SA from lidar extinction profiles overestimated aerosol SA by a factor of 3–5 during these campaigns. Using Microtops aerosol optical thickness measurements, we derive alternative lidar conversion parameters from our observations, applicable to marine aerosol over the SO. Plain Language Summary: The Southern Ocean (SO) surrounding Antarctica is one of the few places where aerosol concentrations and composition are similar to pre‐industrial values. This makes data collected in this region important for improving and understanding climate model simulations. However, direct observations of aerosols are rare because of the remoteness, frequent storms, and high winds and waves common to the SO. In this study, we use some of these rare aerosol observations to test methods for estimating important aerosol quantities using other measurements that are easier to collect. The improvements presented here may increase the availability of key data for improving climate models by replacing rare measurements with ones that can be collected continuously and autonomously. Key Points: Methods to estimate dry marine aerosol surface area (SA) from bulk optical measurements were tested for the Southern Ocean regionA new relationship between effective scattering efficiency and dry Ångström exponent is proposed for nephelometer measurementsOverestimation of aerosol SA from previous methods is reduced by derivation of new lidar backscatter conversion parameters [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]

Observations made by weather radars play a central role in many aspects of meteorological research and forecasting. These applications commonly require that radar data be supplied on a Cartesian grid, necessitating a coordinate transformation and interpolation from the radar's native spherical geometry using a process known as gridding. In this study, we introduce a variational gridding method and, through a series of theoretical and real data experiments, show that it outperforms existing methods in terms of data resolution, noise filtering, spatial continuity, and more. Known problems with existing gridding methods (Cressman weighted average and nearest neighbor/linear interpolation) are also underscored, suggesting the potential for substantial improvements in many applications involving gridded radar data, including operational forecasting, hydrological retrievals, and three-dimensional wind retrievals. [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]

Jakarta, a megacity in Indonesia, experiences recurrent floods associated with heavy rainfall. Characteristics of subdaily rainfall and the local factors influencing rainfall around Jakarta have not been thoroughly investigated, primarily because of data limitations. In this study, we examine the frequency and intensity of hourly and daily rain rate, including spatial characteristics and variations across time scales. We use 6-min C-band Doppler radar and 1-min in situ data during 2009–12 to resolve spatial rain-rate characteristics at higher resolution than previous studies. A reflectivity–rain rate (Z–R) relationship is derived (Z = 102.7R1.75) and applied to estimate hourly rain rate. Our results show that rain rate around Jakarta is spatially inhomogeneous. In the rainy season [December–February (DJF)], rain rate exhibits statistical properties markedly different from other seasons, with much higher frequency of rain, but, on average, less intense rain rate. In all seasons, there is a persistent higher hourly and daily mean rain rate found over mountainous areas, indicating the importance of local orographic effects. In contrast, for hourly rain-rate extremes, peaks are observed mostly over the coastal land and lowland areas. For the diurnal cycle of mean rain rate, a distinct afternoon peak is found developing earlier in DJF and later in the dry season. This study has implications for other analyses of mesoscale rain-rate extremes in areas of complex topography and suggests that coarse-grain products may miss major features of the rain-rate variability identified in our study. Significance Statement: For many years, Jakarta and its surrounding regions have been repeatedly inundated by flooding triggered by short-duration heavy rainfall or rainfall accumulated over multiple days. Little is known about the distribution of local rainfall and how it differs between seasons. In this study, we used high-resolution C-band Doppler radar during 2009–12 to understand the characteristics of rainfall over this complex topography. The results demonstrate that the rainfall features vary spatially and seasonally. In the wet season, rainfall is more frequent but, on average, lighter relative to other seasons. In all seasons, the highest hourly and daily mean rain rate persistently occurs over the mountains, indicating the vital role of topography in generating rainfall in the region. [ABSTRACT FROM AUTHOR]

Research on the interaction between the Madden–Julian oscillation (MJO) and rainfall around Jakarta is limited, although the influence of the MJO on increased rainfall is acknowledged as one of the primary causes of flooding in the region. This paper investigates the local rainfall response around Jakarta to the MJO. We used C-band Doppler radar in October–April during 2009–12 to study rain-rate characteristics at much higher resolution than previous analyses. Results show that the MJO strongly modulates rain rates over the region; however, its effect varies depending on topography. During active phases, MJO induces a high rain rate over the ocean and coast, meanwhile during suppressed phases, it generates a high rain rate mainly over the mountains. In phase 2 of the MJO we find the strongest increase in mean and extreme rain rate, which is earlier in the MJO cycle than most studies reported, based on lower-resolution data. This higher rain rate is likely due to increases in convective and stratiform activities. The MJO promotes more stratiform rain once it resides over Indonesia. In phase 5, over the northwestern coast and western part of the radar domain, the MJO might bring forward the peak of the hourly rain rate that occurs in the early morning. This is likely due to a strong westerly flow arising from MJO superimposed westerly monsoonal flow, blocked by the mountains, inducing a strong wind propagating offshore resulting in convection near the coast in the morning. Our study demonstrates the benefits of using high-resolution radar for capturing local responses to the larger-scale forcing of the MJO in Indonesia. Significance Statement: Rainfall in Jakarta and its surroundings is highly variable and often heavy resulting in devastating floods. In this region, in the wet season, rainfall is influenced by large-scale climate variability including the Madden–Julian oscillation (MJO) characterized by eastward propagation of clouds near the equatorial regions on intraseasonal time scales. The MJO has been known to increase the probability of rainfall occurrence and its magnitude, but we show that the impact differs in varying topography. The frequency and intensity of rainfall increase over land areas including mountains even when MJO has not arrived in Indonesia. Meanwhile, once MJO moves through Indonesia, the frequency and magnitude of the rainfall increases over the northern coast and ocean as well as in the west of the radar domain. [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]