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Lance M. Leslie
,
Bruce W. Buckley
, and
Mark Leplastrier

Abstract

The preparation of accurate operational weather forecasts and the timely issuance of severe marine weather and ocean warnings and advisories for major oceanic weather systems impacting both coastal areas and the open ocean are major forecasting problems facing the Australian Bureau of Meteorology’s Regional Forecast Centre (RFC) and its collocated Tropical Cyclone Warning Centre (TCWC) in Perth, Western Australia. The region of responsibility for the Perth RFC is vast, covering a large portion of the southeast Indian and Southern Oceans, both of which are extremely data sparse, especially for near-surface marine wind data. Given that these coastline and open-ocean areas are subject to some of the world’s most intense tropical cyclones, rapidly intensifying midlatitude cyclones, and powerful cold fronts, there is now a heavy reliance upon NASA Quick Scatterometer (QuikSCAT) data for both routine and severe weather warning forecasts.

The focus of this note is on the role of QuikSCAT data in the Perth RFC for the accurate and early detection of maritime severe weather systems, both tropical and extratropical. First, the role of QuikSCAT data is described, and then three cases are presented in which the QuikSCAT data were pivotal in providing forecast guidance. The cases are a severe tropical cyclone in its development phase off the northwest coast of Australia, a strong southeast Indian Ocean cold front, and an explosively developing midlatitude Southern Ocean cyclone. In each case, the Perth RFC would have been unable to provide early and high-quality operational forecast and warning guidance without the timely availability of the QuikSCAT surface wind data.

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Alexandre O. Fierro
,
Lance M. Leslie
,
Edward R. Mansell
, and
Jerry M. Straka

Abstract

A cloud scale model with a 12-class bulk microphysics scheme, including 10 ice phases and a 3D lightning parameterization, was used to investigate the electrical properties of a well-documented tropical squall line from the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE). Consistent with observations, the simulated maximum updraft speeds across the squall line seldom exceeded 10 m s−1, which was expected given the relatively shallow 30-dBZ echo tops that rarely extended above the top of the mixed-phase layer (−20°C isotherm). Enhanced warm rain processes caused most of the liquid water to precipitate near the gust front at lower levels (below 4 km AGL), which accounted for the small amounts of graupel and cloud water content present in the mixed-phase region and, consequently, for generally weak charging and electrification.

Most of the charge present in the squall line was generated within a few storm cells just behind the leading edge of the gust front that had sufficiently strong updraft speeds near the melting level to produce moderate values of graupel mixing ratio (>0.5 g kg−1). In contrast, the trailing stratiform region at the back of the line, which was mainly composed of ice crystals and snow particles, contained only weak net charge densities (<0.03 nC m−3). The spatial collocation of regions characterized by charge densities exceeding 0.01 nC m−3 and noninductive (NI) charging rates greater than 0.1 pC m−3 s−1 in this stratiform region suggests that NI charging is a plausible source for the majority of this charge, which was confined to discrete regions having small amounts of graupel (approximately 0.1–0.3 g kg−1) and cloud water content (CWC; ∼0.1 g m−3).

The simulated weak updraft speeds and shallow echo tops resulted in a system exhibiting little overall total lightning activity. Although the 5-min average intracloud (IC) flash rate rarely exceeded 10 flashes per minute and only 3 negative cloud-to-ground (−CG) lightning flashes were produced during the entire 4 h and 30 min of simulation, this still was more electrical activity than observed. This tendency for the model to generate more lightning flashes than observed remained when the inductive charging mechanism was turned off, which reduced the total amount of simulated flashes by about 43%. The three CG flashes and the great majority of the IC flashes occurred within the strongest cells located in the mature zone, which exhibited a normal tripole charge structure.

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Diandong Ren
,
Rong Fu
,
Robert E. Dickinson
,
Lance M. Leslie
, and
Xingbao Wang
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Hamish A. Ramsay
,
Lance M. Leslie
,
Peter J. Lamb
,
Michael B. Richman
, and
Mark Leplastrier

Abstract

This study investigates the role of large-scale environmental factors, notably sea surface temperature (SST), low-level relative vorticity, and deep-tropospheric vertical wind shear, in the interannual variability of November–April tropical cyclone (TC) activity in the Australian region. Extensive correlation analyses were carried out between TC frequency and intensity and the aforementioned large-scale parameters, using TC data for 1970–2006 from the official Australian TC dataset. Large correlations were found between the seasonal number of TCs and SST in the Niño-3.4 and Niño-4 regions. These correlations were greatest (−0.73) during August–October, immediately preceding the Australian TC season. The correlations remain almost unchanged for the July–September period and therefore can be viewed as potential seasonal predictors of the forthcoming TC season. In contrast, only weak correlations (<+0.37) were found with the local SST in the region north of Australia where many TCs originate; these were reduced almost to zero when the ENSO component of the SST was removed by partial correlation analysis. The annual frequency of TCs was found to be strongly correlated with 850-hPa relative vorticity and vertical shear of the zonal wind over the main genesis areas of the Australian region. Furthermore, correlations between the Niño SST and these two atmospheric parameters exhibited a strong link between the Australian region and the Niño-3.4 SST. A principal component analysis of the SST dataset revealed two main modes of Pacific Ocean SST variability that match very closely with the basinwide patterns of correlations between SST and TC frequencies. Finally, it is shown that the correlations can be increased markedly (e.g., from −0.73 to −0.80 for the August–October period) by a weighted combination of SST time series from weakly correlated regions.

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Diandong Ren
,
Rong Fu
,
Lance M. Leslie
,
Jianli Chen
,
Clark R. Wilson
, and
David J. Karoly

Abstract

This study applies a multiphase, multiple-rheology, scalable, and extensible geofluid model to the Greenland Ice Sheet (GrIS). The model is driven by monthly atmospheric forcing from global climate model simulations. Novel features of the model, referred to as the scalable and extensible geofluid modeling system (SEGMENT-Ice), include using the full Navier–Stokes equations to account for nonlocal dynamic balance and its influence on ice flow, and a granular sliding layer between the bottom ice layer and the lithosphere layer to provide a mechanism for possible large-scale surges in a warmer future climate (granular basal layer is for certain specific regions, though). Monthly climate of SEGMENT-Ice allows an investigation of detailed features such as seasonal melt area extent (SME) over Greenland. The model reproduced reasonably well the annual maximum SME and total ice mass lost rate when compared observations from the Special Sensing Microwave Imager (SSM/I) and Gravity Recovery and Climate Experiment (GRACE) over the past few decades.

The SEGMENT-Ice simulations are driven by projections from two relatively high-resolution climate models, the NCAR Community Climate System Model, version 3 (CCSM3) and the Model for Interdisciplinary Research on Climate 3.2, high-resolution version [MIROC3.2(hires)], under a realistic twenty-first-century greenhouse gas emission scenario. They suggest that the surface flow would be enhanced over the entire GrIS owing to a reduction of ice viscosity as the temperature increases, despite the small change in the ice surface topography over the interior of Greenland. With increased surface flow speed, strain heating induces more rapid heating in the ice at levels deeper than due to diffusion alone. Basal sliding, especially for granular sediments, provides an efficient mechanism for fast-glacier acceleration and enhanced mass loss. This mechanism, absent from other models, provides a rapid dynamic response to climate change. Net mass loss estimates from the new model should reach ~220 km3 yr−1 by 2100, significantly higher than estimates by the Intergovernmental Panel on Climate Change (IPCC) Assessment Report 4 (AR4) of ~50–100 km3 yr−1. By 2100, the perennial frozen surface area decreases up to ~60%, to ~7 × 105 km2, indicating a massive expansion of the ablation zone. Ice mass change patterns, particularly along the periphery, are very similar between the two climate models.

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Ashton Robinson Cook
,
Lance M. Leslie
,
David B. Parsons
, and
Joseph T. Schaefer

Abstract

In recent years, the potential of seasonal outlooks for tornadoes has attracted the attention of researchers. Previous studies on this topic have focused mainly on the influence of global circulation patterns [e.g., El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation, or Pacific decadal oscillation] on spring tornadoes. However, these studies have yielded conflicting results of the roles of these climate drivers on tornado intensity and frequency. The present study seeks to establish linkages between ENSO and tornado outbreaks over the United States during winter and early spring. These linkages are established in two ways: 1) statistically, by relating raw counts of tornadoes in outbreaks (defined as six or more tornadoes in a 24-h period in the United States east of the Rocky Mountains), and their destructive potential, to sea surface temperature anomalies observed in the Niño-3.4 region, and 2) qualitatively, by relating ENSO to shifts in synoptic-scale atmospheric phenomena that contribute to tornado outbreaks. The latter approach is critical for interpreting the statistical relationships, thereby avoiding the deficiencies in a few of the previous studies that did not provide physical explanations relating ENSO to shifts in tornado activity. The results suggest that shifts in tornado occurrence are clearly related to ENSO. In particular, La Niña conditions consistently foster more frequent and intense tornado activity in comparison with El Niño, particularly at higher latitudes. Furthermore, it is found that tornado activity changes are tied not only to the location and intensity of the subtropical jet during individual outbreaks but also to the positions of surface cyclones, low-level jet streams, and instability axes.

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Zewdu T. Segele
,
Michael B. Richman
,
Lance M. Leslie
, and
Peter J. Lamb

Abstract

An ensemble-based multiple linear regression technique is developed to assess the predictability of regional and national June–September (JJAS) anomalies and local monthly rainfall totals for Ethiopia. The ensemble prediction approach captures potential predictive signals in regional circulations and global sea surface temperatures (SSTs) two to three months in advance of the monsoon season. Sets of 20 potential predictors are selected from visual assessments of correlation maps that relate rainfall with regional and global predictors. Individual predictors in each set are utilized to initialize specific forward stepwise regression models to develop ensembles of equal number of statistical model estimates, which allow quantifying prediction uncertainties related to individual predictors and models. Prediction skill improvement is achieved through error minimization afforded by the ensemble.

For retroactive validation (RV), the ensemble predictions reproduce well the observed all-Ethiopian JJAS rainfall variability two months in advance. The ensemble mean prediction outperforms climatology, with mean square error reduction (SSClim) of 62%. The skill of the prediction remains high for leave-one-out cross validation (LOOCV), with the observed–predicted correlation r (SSClim) being +0.81 (65%) for 1970–2002. For tercile predictions (below, near, and above normal), the ranked probability skill score is 0.45, indicating improvement compared to climatological forecasts. Similarly high prediction skill is found for local prediction of monthly rainfall total at Addis Ababa (r = +0.72) and Combolcha (r = +0.68), and for regional prediction of JJAS standardized rainfall anomalies for northeastern Ethiopia (r = +0.80). Compared to the previous generation of rainfall forecasts, the ensemble predictions developed in this paper show substantial value to benefit society.

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Diandong Ren
,
Lance M. Leslie
,
Rong Fu
,
Robert E. Dickinson
, and
Xiang Xin

Abstract

Predicting the location and timing of mudslides with adequate lead time is a scientifically challenging problem that is critical for mitigating landslide impacts. Here, a new dynamic modeling system is described for monitoring and predicting storm-triggered landslides and their ecosystem implications. The model ingests both conventional and remotely sensed topographic and geologic data, whereas outputs include diagnostics required for the assessment of the physical and societal impacts of landslides.

The system first was evaluated successfully in a series of experiments under idealized conditions. In the main study, under real conditions, the system was assessed over a mountainous region of China, the Yangjiashan Creeping (YC) slope. For this data-rich section of the Changjiang River, the model estimated creeping rates that had RMS errors of ∼0.5 mm yr−1 when compared with a dataset generated from borehole measurements. A prediction of the creeping curve for 2010 was made that suggested significant slope movement will occur in the next 5 years, without any change in the current precipitation morphology. However, sliding will become imminent if a storm occurs in that 5-yr period that produces over 150 mm of precipitation. A sensitivity experiment shows that the identified location fails first, triggering domino-effect slides that progress upslope. This system for predicting storm-triggered landslides is intended to improve upon present warning lead times to minimize the impacts of shallow, fast moving, and therefore hazardous landslides.

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Graeme D. Hubbert
,
Greg J. Holland
,
Lance M. Leslie
, and
Michael J. Manton

Abstract

The depth-averaged, numerical storm-surge model developed by Hubbert et al. (1990) has been configured to provide a stand-alone system to forecast tropical cyclone storm surges. The atmospheric surface pressure and surface winds are derived from the analytical-empirical model of Holland (1980) and require only cyclone positions, central pressures, and radii of maximum winds. The model has been adapted to run on personal computers in a few minutes so that multiple forecast scenarios can be tested in a forecast office in real time.

The storm surge model was tested in hindcast mode on four Australian tropical cyclones. For these case studies the model predicted the sea surface elevations and arrival times of surge peaks accurately, with typical elevation errors of 0.1 to 0.2 m and arrival time errors of no more than 1 h. Second order effects, such as coastally-trapped waves, were also well simulated. The model is now being used by the Australian Tropical Cyclone Warning Centres (TCWC's) for operational forecasting. It will also be released as part of a tropical cyclone workstation that has recently been recommended for use by WMO member nations.

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Stephanie M. Verbout
,
Harold E. Brooks
,
Lance M. Leslie
, and
David M. Schultz

Abstract

Over the last 50 yr, the number of tornadoes reported in the United States has doubled from about 600 per year in the 1950s to around 1200 in the 2000s. This doubling is likely not related to meteorological causes alone. To account for this increase a simple least squares linear regression was fitted to the annual number of tornado reports. A “big tornado day” is a single day when numerous tornadoes and/or many tornadoes exceeding a specified intensity threshold were reported anywhere in the country. By defining a big tornado day without considering the spatial distribution of the tornadoes, a big tornado day differs from previous definitions of outbreaks. To address the increase in the number of reports, the number of reports is compared to the expected number of reports in a year based on linear regression. In addition, the F1 and greater Fujita-scale record was used in determining a big tornado day because the F1 and greater series was more stationary over time as opposed to the F2 and greater series. Thresholds were applied to the data to determine the number and intensities of the tornadoes needed to be considered a big tornado day. Possible threshold values included fractions of the annual expected value associated with the linear regression and fixed numbers for the intensity criterion. Threshold values of 1.5% of the expected annual total number of tornadoes and/or at least 8 F1 and greater tornadoes identified about 18.1 big tornado days per year. Higher thresholds such as 2.5% and/or at least 15 F1 and greater tornadoes showed similar characteristics, yet identified approximately 6.2 big tornado days per year. Finally, probability distribution curves generated using kernel density estimation revealed that big tornado days were more likely to occur slightly earlier in the year and have a narrower distribution than any given tornado day.

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