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Kathrin Wapler, Todd P. Lane, Peter T. May, Christian Jakob, Michael J. Manton, and Steven T. Siems

Abstract

Nested cloud-system-resolving model simulations of tropical convective clouds observed during the recent Tropical Warm Pool-International Cloud Experiment (TWP-ICE) are conducted using the Weather Research and Forecasting (WRF) model. The WRF model is configured with a highest-resolving domain that uses 1.3-km grid spacing and is centered over Darwin, Australia. The performance of the model in simulating two different convective regimes observed during TWP-ICE is considered. The first regime is characteristic of the active monsoon, which features widespread cloud cover that is similar to maritime convection. The second regime is a monsoon break, which contains intense localized systems that are representative of diurnally forced continental convection. Many aspects of the model performance are considered, including their sensitivity to physical parameterizations and initialization time, and the spatial statistics of rainfall accumulations and the rain-rate distribution. While the simulations highlight many challenges and difficulties in correctly modeling the convection in the two regimes, they show that provided the mesoscale environment is adequately reproduced by the model, the statistics of the simulated rainfall agrees reasonably well with the observations.

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Thomas Chubb, Michael J. Manton, Steven T. Siems, Andrew D. Peace, and Shane P. Bilish

Abstract

Wind-induced losses, or undercatch, can have a substantial impact on precipitation gauge observations, especially in alpine environments that receive a substantial amount of frozen precipitation and may be exposed to high winds. A network of NOAH II all-weather gauges installed in the Snowy Mountains since 2006 provides an opportunity to evaluate the magnitude of undercatch in an Australian alpine environment. Data from two intercomparison sites were used with NOAH II gauges with different configurations of wind fences installed: unfenced, WMO standard double fence intercomparison reference (full DFIR) fences, and an experimental half-sized double fence (half DFIR). It was found that average ambient temperature over 6-h periods was sufficient to classify the precipitation phase as snow, mixed precipitation, or rain in a statistically robust way. Empirical catch ratio relationships (i.e., the quotient of observations from two gauges), based on wind speed, ambient temperature, and measured precipitation amount, were established for snow and mixed precipitation. An adjustment scheme to correct the unfenced NOAH II gauge data using the catch ratio relationships was cross validated with independent data from two additional sites, as well as from the intercomparison sites themselves. The adjustment scheme was applied to the observed precipitation amounts at the other sites with unfenced NOAH II gauges. In the worst-case scenario, it was found that the observed precipitation amount would need to be increased by 52% to match what would have been recorded had adequate shielding been installed. However, gauges that were naturally well protected, and those below about 1400 m, required very little adjustment. Spatial analysis showed that the average seasonal undercatch was between 6% and 15% for gauges above 1000 m MSL.

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Elizabeth E. Ebert, Michael J. Manton, Philip A. Arkin, Richard J. Allam, Gary E. Holpin, and Arnold Gruber

Three algorithm intercomparison experiments have recently been conducted as part of the Global Precipitation Climatology Project with the goal of (a) assessing the skill of current satellite rainfall algorithms, (b) understanding the differences between them, and (c) moving toward improved algorithms. The results of these experiments are summarized and intercompared in this paper.

It was found that the skill of satellite rainfall algorithms depends on the regime being analyzed, with algorithms producing very good results in the tropical western Pacific and over Japan and its surrounding waters during summer, but relatively poor rainfall estimates over western Europe during late winter. Monthly rainfall was estimated most accurately by algorithms using geostationary infrared data, but algorithms using polar data [Advanced Very High Resolution Radiometer and Special Sensor Microwave/Imager (SSM/I)] were also able to produce good monthly rainfall estimates when data from two satellites were available. In most cases, SSM/I algorithms showed significantly greater skill than IR-based algorithms in estimating instantaneous rain rates.

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Justin R. Peter, Michael J. Manton, Rodney J. Potts, Peter T. May, Scott M. Collis, and Louise Wilson

Abstract

The aim of this study is to examine the statistics of convective storms and their concomitant changes with thermodynamic variability. The thermodynamic variability is analyzed by performing a cluster analysis on variables derived from radiosonde releases at Brisbane Airport in Australia. Three objectively defined regimes are found: a dry, stable regime with mainly westerly surface winds, a moist northerly regime, and a moist trade wind regime. S-band radar data are analyzed and storms are identified using objective tracking software [Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN)]. Storm statistics are then investigated, stratified by the regime subperiods. Convective storms are found to form and maintain along elevated topography. Probability distributions of convective storm size and rain rate are found to follow lognormal distributions with differing mean and variance among the regimes. There was some evidence of trimodal storm-top heights, located at the trade inversion (1.5–2 km), freezing level (3.6–4 km), and near 6 km, but it was dependent on the presence of the trade inversion. On average, storm volume and height are smallest in the trade regime and rain rate is largest in the westerly regime. However, westerly regime storms occur less frequently and have shorter lifetimes, which were attributed to the enhanced stability and decreased humidity profiles. Furthermore, time series of diurnal rain rate exhibited early morning and midafternoon maxima for the northerly and trade regimes but were absent for the westerly regime. The observations indicate that westerly regime storms are primarily driven by large-scale forcing, whereas northerly and trade wind regime storms are more responsive to surface characteristics.

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Simon Caine, Todd P. Lane, Peter T. May, Christian Jakob, Steven T. Siems, Michael J. Manton, and James Pinto

Abstract

This study presents a method for comparing convection-permitting model simulations to radar observations using an innovative object-based approach. The method uses the automated cell-tracking algorithm, Thunderstorm Identification Tracking Analysis and Nowcasting (TITAN), to identify individual convective cells and determine their properties. Cell properties are identified in the same way for model and radar data, facilitating comparison of their statistical distributions. The method is applied to simulations of tropical convection during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) using the Weather Research and Forecasting Model, and compared to data from a ground-based radar. Simulations with different microphysics and model resolution are also conducted. Among other things, the comparisons between the model and the radar elucidate model errors in the depth and size of convective cells. On average, simulated convective cells reached higher altitudes than the observations. Also, when using a low reflectivity (25 dBZ) threshold to define convective cells, the model underestimates the size of the largest cells in the observed population. Some of these differences are alleviated with a change of microphysics scheme and higher model resolution, demonstrating the utility of this method for assessing model changes.

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Yi Huang, Steven T. Siems, Michael J. Manton, Daniel Rosenfeld, Roger Marchand, Greg M. McFarquhar, and Alain Protat

Abstract

This study employs four years of spatiotemporally collocated A-Train satellite observations to investigate cloud and precipitation characteristics in relation to the underlying properties of the Southern Ocean (SO). Results show that liquid-phase cloud properties strongly correlate with the sea surface temperature (SST). In summer, ubiquitous supercooled liquid water (SLW) is observed over SSTs less than about 4°C. Cloud-top temperature (CTT) and effective radius of liquid-phase clouds generally decrease for colder SSTs, whereas the opposite trend is observed for cloud-top height, cloud optical thickness, and liquid water path. The deduced cloud depth is larger over the colder oceans. Notable differences are observed between “precipitating” and “nonprecipitating” clouds and between different ocean sectors. Using a novel joint SST–CTT histogram, two distinct liquid-phase cloud types are identified, where the retrieved particle size appears to increase with decreasing CTT over warmer water (SSTs >~7°C), while the opposite is true over colder water. A comparison with the Northern Hemisphere (NH) storm-track regions suggests that the ubiquitous SLW with markedly smaller droplet size is a unique feature for the cold SO (occurring where SSTs <~4°C), while the presence of this cloud type is much less frequent over the NH counterparts, where the SSTs are rarely colder than about 4°C at any time of the year. This study also suggests that precipitation, which has a profound influence on cloud properties, remains poorly observed over the SO with the current spaceborne sensors. Large uncertainties in precipitation properties are associated with the ubiquitous boundary layer clouds within the lowest kilometer of the atmosphere.

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Robert A. Warren, Alain Protat, Steven T. Siems, Hamish A. Ramsay, Valentin Louf, Michael J. Manton, and Thomas A. Kane

Abstract

Calibration error represents a significant source of uncertainty in quantitative applications of ground-based radar (GR) reflectivity data. Correcting it requires knowledge of the true reflectivity at well-defined locations and times during a volume scan. Previous work has demonstrated that observations from certain spaceborne radar (SR) platforms may be suitable for this purpose. Specifically, the Ku-band precipitation radars on board the Tropical Rainfall Measuring Mission (TRMM) satellite and its successor, the Global Precipitation Measurement (GPM) mission Core Observatory satellite together provide nearly two decades of well-calibrated reflectivity measurements over low-latitude regions (±35°). However, when comparing SR and GR reflectivities, great care must be taken to account for differences in instrument sensitivity and frequency, and to ensure that the observations are spatially and temporally coincident. Here, a volume-matching method, developed as part of the ground validation network for GPM, is adapted and used to quantify historical calibration errors for three S-band radars in the vicinity of Sydney, Australia. Volume-matched GR–SR sample pairs are identified over a 7-yr period and carefully filtered to isolate reflectivity differences associated with GR calibration error. These are then used in combination with radar engineering work records to derive a piecewise-constant time series of calibration error for each site. The efficacy of this approach is verified through comparisons between GR reflectivities in regions of overlapping coverage, with improved agreement when the estimated errors are removed.

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Vidhi Bharti, Eric Schulz, Christopher W. Fairall, Byron W. Blomquist, Yi Huang, Alain Protat, Steven T. Siems, and Michael J. Manton

Abstract

Given the large uncertainties in surface heat fluxes over the Southern Ocean, an assessment of fluxes obtained by European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) product, the Australian Integrated Marine Observing System (IMOS) routine observations, and the Objectively Analyzed Air–Sea Heat Fluxes (OAFlux) project hybrid dataset is performed. The surface fluxes are calculated using the COARE 3.5 bulk algorithm with in situ data obtained from the NOAA Physical Sciences Division flux system during the Clouds, Aerosols, Precipitation, Radiation, and Atmospheric Composition over the Southern Ocean (CAPRICORN) experiment on board the R/V Investigator during a voyage (March–April 2016) in the Australian sector of the Southern Ocean (43°–53°S). ERA-Interim and OAFlux data are further compared with the Southern Ocean Flux Station (SOFS) air–sea flux moored surface float deployed for a year (March 2015–April 2016) at ~46.7°S, 142°E. The results indicate that ERA-Interim (3 hourly at 0.25°) and OAFlux (daily at 1°) estimate sensible heat flux H s accurately to within ±5 W m−2 and latent heat flux H l to within ±10 W m−2. ERA-Interim gives a positive bias in H s at low latitudes (<47°S) and in H l at high latitudes (>47°S), and OAFlux displays consistently positive bias in H l at all latitudes. No systematic bias with respect to wind or rain conditions was observed. Although some differences in the bulk flux algorithms are noted, these biases can be largely attributed to the uncertainties in the observations used to derive the flux products.

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