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Danahé Paquin-Ricard, Colin Jones, and Paul A. Vaillancourt

Abstract

The total downwelling shortwave (SWD) and longwave (LWD) radiation and its components are assessed for the limited-area version of the Global Environmental Multiscale Model (GEM-LAM) against Atmospheric Radiation Measurements (ARM) at two sites: the southern Great Plains (SGP) and the North Slope of Alaska (NSA) for the 1998–2005 period. The model and observed SWD and LWD are evaluated as a function of the cloud fraction (CF), that is, for overcast and clear-sky conditions separately, to isolate and analyze different interactions between radiation and 1) atmospheric aerosols and water vapor and 2) cloud liquid water. Through analysis of the mean diurnal cycle and normalized frequency distributions of surface radiation fluxes, the primary radiation error in GEM-LAM is seen to be an excess in SWD in the middle of the day. The SWD bias results from a combination of underestimated CF and clouds, when present, possessing a too-high solar transmissivity, which is particularly the case for optically thin clouds. Concurrent with the SWD bias, a near-surface warm bias develops in GEM-LAM, particularly at the SGP site in the summer. The ultimate cause of this warm bias is difficult to uniquely determine because of the range of complex interactions between the surface, atmospheric, and radiation processes that are involved. Possible feedback loops influencing this warm bias are discussed. The near-surface warm bias is the primary cause of an excess clear-sky LWD. This excess is partially balanced with respect to the all-sky LWD by an underestimated CF, which causes a negative bias in simulated all-sky emissivity. It is shown that there is a strong interaction between all the components influencing the simulated surface radiation fluxes with frequent error compensation, emphasizing the need to evaluate the individual radiation components at high time frequency.

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Edward A. Brandes, Robert P. Davies-Jones, and Brenda C. Johnson

Abstract

The structure and steadiness of radar-observed supercell thunderstorms are examined in terms of their particular distribution of vorticity. The data confirm that the vorticity vector in supercells points in the direction of the storm-relative velocity vector and that supercell updrafts contain large positive helicity (V·ω). The alignment of vorticity and velocity vectors dictates that low pressure associates not only with vorticity but also with helicity. Accelerating pressure gradients and helicity, both thought important for suppressing small-scale features within supercells, may combine with shear-induced vertical pressure gradient forces to organize and maintain the large-scale persistent background updrafts that characterize supercells.

Rear downdrafts possess weak positive or negative helicity. Thus, the decline of storm circulation may be hastened by turbulent dissipation when the downdraft air eventually mixes into supercell updrafts.

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C. A. Doswell III, R. Davies-Jones, and D. L. Keller

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No abstract available

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Julia H. Keller, Sarah C. Jones, and Patrick A. Harr

Abstract

The extratropical transition (ET) of Hurricane Hanna (2008) and Typhoon Choi-Wan (2009) caused a variety of forecast scenarios in the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS). The dominant development scenarios are extracted for two ensemble forecasts initialized prior to the ET of those tropical storms, using an EOF and fuzzy clustering analysis. The role of the transitioning tropical cyclone and its impact on the midlatitude flow in the distinct forecast scenarios is examined by conducting an analysis of the eddy kinetic energy budget in the framework of downstream baroclinic development. This budget highlights sources and sinks of eddy kinetic energy emanating from the transitioning tropical cyclone or adjacent upstream midlatitude flow features. By comparing the budget for several forecast scenarios for the ET of each of the two tropical cyclones, the role of the transitioning storms on the development in downstream regions is investigated. Distinct features during the interaction between the tropical cyclone and the midlatitude flow turned out to be important. In the case of Hurricane Hanna, the duration of baroclinic conversion from eddy available potential into eddy kinetic energy was important for the amplification of the midlatitude wave pattern and the subsequent reintensification of Hanna as an extratropical cyclone. In the case of Typhoon Choi-Wan, the phasing between the storm and the midlatitude flow was one of the most critical factors for the future development.

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Robert Davies-Jones, Charles A. Doswell III, and Harold E. Brooks

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No Abstract Available

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Matthew S. Jones, Mark A. Saunders, and Trevor H. Guymer

Abstract

The Along Track Scanning Radiometer (ATSR) was launched in July 1991 on the European Space Agency's first remote sensing satellite ERS-1. ATSR has the potential to measure sea surface temperature (SST) to a precision of 0.3 K, which is more than double the accuracy of any previously flown infrared radiometer. A key factor limiting ATSR's performance is remnant cloud contamination. Examination of the 0.5° spatially averaged ATSR SST data (version 500) from the South Atlantic for the whole of 1992 and 1993 shows the presence of regional cloud contamination in the night SST measurements. The authors establish a figure of 5.7% as a lower limit for this nighttime cloud contamination. The contamination leads to differences between day and night mean SSTs and to poor comparisons with in situ thermosalinograph SST data. A new cloud filtering process designed for postprocessing of the data is proposed to remove the contamination. The algorithm presented here relies on assumptions that the day data are less cloud contaminated than the night data and that a large proportion of the SST variability can he explained by an annual and semiannual model. Testing the filtering algorithm shows that differences between the day and night SST signals are substantially reduced and that comparisons with the thermosalinograph SST data improve by a factor of 3 in rms scatter and by 0.3 K in the mean difference.

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Patrick A. Harr, Doris Anwender, and Sarah C. Jones

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Measures of the variability among ensemble members from the National Centers for Environmental Prediction ensemble prediction system are examined with respect to forecasts of the extratropical transition (ET) of Typhoon Nabi over the western North Pacific during September 2005. In this study, variability among ensemble members is used as a proxy for predictability. The time–longitude distribution of standard deviations among 500-hPa height fields from the ensemble members is found to increase across the North Pacific following the completion of the extratropical transition. Furthermore, the increase in ensemble standard deviation is organized such that an increase is associated with the extratropical transition and another increase extends downstream from the ET event. The organization and amplitude of the standard deviations increase from 144 h until approximately 72–48 h prior to the completion of the extratropical transition, and then decrease as the forecast interval decreases.

An empirical orthogonal function analysis of potential temperature on the dynamic tropopause is applied to ensemble members to identify the spatial and temporal organization of centers of variability related to the extratropical transition. The principal components are then used in a fuzzy cluster analysis to examine the grouping of forecast sequences in the collection of ensemble members. The number of forecast groups decreases as the forecast interval to the completion of the ET decreases. However, there is a systematic progression of centers of variability downstream of the ET event. Once the variability associated with the ET begins to decrease, the variability downstream of the ET event also begins to decrease.

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Doris Anwender, Patrick A. Harr, and Sarah C. Jones

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The extratropical transition (ET) of tropical cyclones often has a negative impact on the predictability of the atmospheric situation both around the ET event and farther downstream. The predictability of five ET cases of different intensities in the North Atlantic and the western North Pacific is investigated using the ECMWF ensemble prediction system. The variability in the ensemble members is regarded as a measure of the predictability. Plumes of forecast uncertainty spread downstream of each ET event. Initialization times closer to the ET events yield higher predictability of the downstream flow independent of forecast lead time.

Principal component analysis and fuzzy clustering is used to assess the variability in the ensemble members and to identify groupings of the members that contribute in a similar way to the variability patterns. Applying the method to the potential temperature on the dynamic tropopause reveals a characteristic variability pattern in all five cases that is closely related to the synoptic patterns of the ET events. Clusters that contribute in a similar manner to the variability patterns exhibit similar ET developments in the different cases. A probability can be assigned to a given group based on the number of members in the group. The number of clusters decreases with shorter forecast intervals and the difference between the clusters becomes less marked. This indicates an increase of predictability.

The usefulness of ensemble prediction is highlighted in the weak ET cases in that a low probability is assigned to the erroneous deterministic forecasts.

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Charles A. Doswell III, Robert Davies-Jones, and David L. Keller

Abstract

The so-called True Skill Statistic (TSS) and the Heidke Skill Score (S), as used in the context of the contingency, table approach to forecast verification, are compared. It is shown that the TSS approaches the Probability of Detection (POD) whenever the forecasting is dominated by correct forecasts of non-occurrence, i.e., forecasting rare events like severe local storms. This means that the TSS is vulnerable to “hedging” in rare event forecasting. The S-statistic is shown to be superior to the TSS in this situation, accounting for correct forecasts of null events in a controlled fashion. It turns out that the TSS and S values are related in a subtle way, becoming identical when the expected values (due to chance in a k × k contingency table) remain unchanged when comparing the actual forecast table to that of a hypothetical perfect set of forecasts. Examples of the behavior of the TSS and S values in different situations are provided which support the recommendation that S be used in preference to TSS for rare event forecasting. A geometrical interpretation is also given for certain aspects of the 2 × 2 contingency table and this is generalized to the k × l case. Using this geometrical interpretation, it is shown to be possible to apply dichotomous verification techniques in polychotomous situations, thus allowing a direct comparison between dichotomous and polychotomous forecasting.

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Matthew S. Jones, Brian A. Colle, and Jeffrey S. Tongue

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A short-range ensemble forecast system was constructed over the northeast United States down to 12-km grid spacing using 18 members from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The ensemble consisted of 12 physics members with varying planetary boundary layer schemes and convective parameterizations as well as seven different initial conditions (ICs) [five National Centers for Environmental Prediction (NCEP) Eta-bred members at 2100 UTC and the 0000 UTC NCEP Global Forecast System (GFS) and Eta runs]. The full 18-member ensemble (ALL) was verified at the surface for the warm (May–September 2003) and cool (October 2003–March 2004) seasons. A randomly chosen subset of seven physics (PHS) members at each forecast hour was used to quantitatively compare with the seven IC members. During the warm season, the PHS ensemble predictions for surface temperature and wind speed had more skill than the IC ensemble and a control (shared PHS and IC member) run initialized 12 h later (CTL12). During the cool and warm seasons, a 14-day running-mean bias calibration applied to the ALL ensemble (ALLBC) added 10%–30% more skill for temperature, wind speed, and sea level pressure, with the ALLBC far outperforming the CTL12. For the 24-h precipitation, the PHS ensemble had comparable probabilistic skill to the IC ensemble during the warm season, while the IC subensemble was more skillful during the cool season. All ensemble members had large diurnal surface biases, with ensemble variance approximating ensemble uncertainty only for wind direction. Selection of ICs was also important, because during the cool season the NCEP-bred members introduced large errors into the IC ensemble for sea level pressure, while none of the subensembles (PHS, IC, or ALL) outperformed the GFS–MM5 for sea level pressure.

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