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Jeffrey Czajkowski and James Done

Abstract

An understanding of the potential drivers of local-scale hurricane losses is developed through a case study analysis. Two recent category-3 U.S. landfalling hurricanes (Ivan in 2004 and Dennis in 2005) are analyzed that, although similar in terms of maximum wind speed at their proximate coastal landfall locations, caused vastly different loss amounts. In contrast to existing studies that assess loss mostly at the relatively aggregate level, detailed local factors related to hazard, exposure, and vulnerability are identified. State-level raw wind insured loss data split by personal, commercial, and auto business lines are downscaled to the census tract level using the wind field. At this scale, losses are found to extend far inland and across business lines. Storm size is found to play an important role in explaining the different loss amounts by controlling not only the size of the impacted area but also the duration of damaging winds and the likelihood of large changes in wind direction. An empirical analysis of census tract losses provides further evidence for the importance of wind duration and wind directional change in addition to wind speed. The importance of exposure values however is more sensitive to assumptions in how loss data are downscaled. Appropriate consideration of these local drivers of hurricane loss may improve historical loss assessments and may also act upscale to impact future projections of hurricane losses under climate and socioeconomic change.

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Erin Towler, Debasish PaiMazumder, and James Done

Abstract

Decadal prediction is a relatively new branch of climate science that bridges the gap between seasonal climate forecasts and multidecadal-to-century projections of climate change. This paper develops a three-step framework toward the potential application of decadal temperature predictions using the Community Climate System Model, version 4 (CCSM4). In step 1, the predictions are evaluated and it is found that the temperature hindcasts show skill over some regions of the United States and Canada. In step 2, the predictions are manipulated using two methods: a deterministic-anomaly approach (like climate change projections) and a probabilistic tercile-based approach (like seasonal forecasts). In step 3, the predictions are translated by adding a delta (for the anomaly manipulation) and conducting a weighted resample (for the probabilistic manipulation), as well as using a new hybrid method. Using the 2010 initialized hindcast, the framework is demonstrated for predicting 2011–15 over two case-study watersheds [Ottawa (Canada) and Colorado]. For the Colorado watershed, there was a noticeable shift toward higher temperatures, and the delta, weighted resample, and hybrid translations all were better at capturing the observed temperatures than was an approach that used climatological values. For the Ottawa watershed, the observed temperatures over the period of prediction were only subtly different than the climatological values; therefore, the difference between the translation methods was less noticeable. The advantages and disadvantages of the manipulation and translation approaches are discussed, as well as how their use will depend on the user context. The authors emphasize that skill evaluations should be tailored to particular applications and identify additional steps that are needed before the decadal temperature predictions can be readily incorporated into applications.

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Dereka Carroll-Smith, Robert J. Trapp, and James M. Done

Abstract

The overarching purpose of this study is to investigate the impacts of anthropogenic climate change both on the rainfall and tornadoes associated with tropical cyclones (TCs) making landfall in the U.S. Atlantic basin. The “pseudo–global warming” (PGW) approach is applied to Hurricane Ivan (2004), a historically prolific tropical cyclone tornado (TCT)-producing storm. Hurricane Ivan is simulated under its current climate forcings using the Weather Research and Forecasting Model. This control simulation (CTRL) is then compared with PGW simulations in which the current forcings are modified by climate-change differences obtained from the Community Climate System Model, version 4 (NCAR); Model for Interdisciplinary Research on Climate, version 5 (MIROC); and Geophysical Fluid Dynamics Laboratory Climate Model, version 3 (GFDL). Changes in TC intensity, TC rainfall, and TCT production, identified for the PGW-modified Ivan, are documented and analyzed. Relative to CTRL, all three PGW simulations show an increase in TC intensity and generate substantially more accumulated rainfall over the course of Ivan’s progression over land. However, only one of the TCs under PGW (MIROC) produced more TCTs than CTRL. Evidence is provided that, in addition to favorable environmental conditions, TCT production is related to the TC track length and to the strength of the interaction between the TC and an environmental midlevel trough. Enhanced TCT generation at landfall for MIROC and GFDL is attributed to increased values of convective available potential energy, low-level shear, and storm-relative environmental helicity.

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Hsu-Feng Teng, James M. Done, Cheng-Shang Lee, Huang-Hsiung Hsu, and Ying-Hwa Kuo

Abstract

The development of tropical cloud clusters (TCCs) to tropical cyclones (TCs) is the process of TC formation. This study identifies five main environmental transitions for the development of TCCs to TCs in the western North Pacific by using a cluster analysis method. Of these, three transitions indicate TCCs that develop in monsoon environments and two in easterly environments. Their numbers, distributions, and interannual variability differ. On average, the development time, defined as the period from the TCC forming to it developing into a TC, for TCCs that develop in easterly environments is shorter than that in monsoon environments. For the development of TCC to TC in easterly environments, TCCs have fewer embedded mesoscale convective systems (MCSs), which are located closer to the TCC center. Moreover, there is a stronger inward short-term (less than 10 days) angular momentum flux (AMF) at middle levels (800–500 hPa) before TCC formation. Conversely, in monsoon environments, TCCs have more MCSs, which are located farther from the TCC center. A stronger inward short-term AMF at low levels (1000–850 hPa) is observed before TCC formation and develops upward during the development of TCC to TC. The characteristics of MCS and AMF are significantly correlated with the development time of TCC to TC. In summary, large-scale easterly and monsoon environments cause TCCs to have different MCS and AMF characteristics, leading to higher efficiency for TCCs developing into TCs in easterly environments compared to monsoon environments.

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Hsu-Feng Teng, Cheng-Shang Lee, Huang-Hsiung Hsu, James M. Done, and Greg J. Holland

Abstract

This study uses a nonhierarchical cluster analysis to identify the major environmental circulation patterns associated with tropical cloud cluster (TCC) formation in the western North Pacific. All TCCs that formed in July–October 1981–2009 are examined based on their 850-hPa wind field around TCC centers. Eight types of environmental circulation patterns are identified. Of these, four are related to monsoon systems (trough, confluence, north of trough, and south of trough), three are related to easterly systems (low-latitude zone, west of subtropical high, and southwest of subtropical high), and one is associated with low-latitude cross-equatorial flow. The genesis potential index (GPI) is analyzed to compare how favorable the environmental conditions are for tropical cyclone (TC) formation when TCCs form. Excluding three cluster types with the GPI lower than the climatology of all samples, TCCs formed in monsoon environments have larger sizes, lower brightness temperatures, longer lifetimes, and higher GPIs than those of TCCs formed in easterly environments. However, for TCCs formed in easterly environments, the average GPI for those TCCs that later develop into TCs (developing TCCs) is higher than that for other TCCs (nondeveloping TCCs). This difference is nonsignificant for TCCs formed in monsoon environments. Conversely, the average magnitudes of GPI are similar for developing TCCs, regardless of whether TCCs form in easterly or monsoon environments. In summary, the probability of a TCC to develop into a TC is more sensitive to the environmental conditions for TCCs formed in easterly environments than those formed in monsoon environments.

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Steven M. Cavallo, Ryan D. Torn, Chris Snyder, Christopher Davis, Wei Wang, and James Done

Abstract

Real-time analyses and forecasts using an ensemble Kalman filter (EnKF) and the Advanced Hurricane Weather Research and Forecasting Model (AHW) are evaluated from the 2009 North Atlantic hurricane season. This data assimilation system involved cycling observations that included conventional in situ data, tropical cyclone (TC) position, and minimum SLP and synoptic dropsondes each 6 h using a 96-member ensemble on a 36-km domain for three months. Similar to past studies, observation assimilation systematically reduces the TC position and minimum SLP errors, except for strong TCs, which are characterized by large biases due to grid resolution. At 48 different initialization times, an AHW forecast on 12-, 4-, and 1.33-km grids is produced with initial conditions drawn from a single analysis member. Whereas TC track analyses and forecasts exhibit a pronounced northward bias, intensity forecast errors are similar to (lower than) the NWS Hurricane Weather Research Model (HWRF) and GFDL forecasts for forecast lead times ≤60 h (>60 h), with the largest track errors associated with the weakest systems, such as Tropical Storm (TS) Erika. Several shortcomings of the data assimilation system are addressed through postseason sensitivity tests, including using the maximum 800-hPa circulation to identify the TC position during assimilation and turning off the quality control for the TC minimum SLP observation when the initial intensity is far too weak. In addition, the improved forecast of TS Erika relative to HWRF is shown to be related to having initial conditions that are more representative of a sheared TC and not using a cumulus parameterization.

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Hsu-Feng Teng, James M. Done, Cheng-Shang Lee, and Ying-Hwa Kuo

Abstract

This study investigates the probabilistic quantitative precipitation forecast (PQPF) performance of typhoons that affected Taiwan during 2011–16. In this period, a total of 19 typhoons with a land warning issued by the Central Weather Bureau (CWB) are analyzed. The PQPF is calculated using the ensemble precipitation forecast data from the Taiwan Cooperative Precipitation Ensemble Forecast Experiment (TAPEX), and the verification data, verification thresholds, and typhoon characteristics are obtained from the CWB. The overall PQPF performance of TAPEX has an acceptable reliability and discrimination ability, and the higher probability error is distributed at the mountainous area of Taiwan. The PQPF performance is significantly influenced by typhoon characteristics (e.g., typhoon tracks, sizes, and forward speeds). The PQPFs for westward-moving, large, or slow typhoons have higher reliability and discrimination ability, and lower-probability error than those for northward-moving, small, or fast typhoons, except for similar reliability between fast and slow typhoons. Because northward-moving or small typhoons have larger forecast track error, and their PQPF performance is sensitive to the accuracy of the forecast track, a higher probability error occurs than that for westward-moving or large typhoons. Furthermore, because there is no difference in track error between fast and slow typhoons, the larger track spread for slow typhoons increases the rainfall forecast spread and reduces the probability error. The orientation of Taiwan’s topography and the topographic effect also influence and increase the distribution and value of probability error for northward-moving, small, or fast typhoons. In summary, forecast track characteristics are influenced by typhoon characteristics and further affect the PQPF performance.

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Andreas F. Prein, Gregory J. Holland, Roy M. Rasmussen, James Done, Kyoko Ikeda, Martyn P. Clark, and Changhai H. Liu

Abstract

Summer and winter daily heavy precipitation events (events above the 97.5th percentile) are analyzed in regional climate simulations with 36-, 12-, and 4-km horizontal grid spacing over the headwaters of the Colorado River. Multiscale evaluations are useful to understand differences across horizontal scales and to evaluate the effects of upscaling finescale processes to coarser-scale features associated with precipitating systems.

Only the 4-km model is able to correctly simulate precipitation totals of heavy summertime events. For winter events, results from the 4- and 12-km grid models are similar and outperform the 36-km simulation. The main advantages of the 4-km simulation are the improved spatial mesoscale patterns of heavy precipitation (below ~100 km). However, the 4-km simulation also slightly improves larger-scale patterns of heavy precipitation.

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James Done, Aixue Hu, E. Christa Farmer, Jianjun Yin, Susan Bates, Amy B. Frappier, Dariaj. Halkides, K. Halimeda Kilbourne, Ryan Sriver, and Jonathan Woodruff
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Ethan D. Gutmann, Roy M. Rasmussen, Changhai Liu, Kyoko Ikeda, Cindy L. Bruyere, James M. Done, Luca Garrè, Peter Friis-Hansen, and Vidyunmala Veldore

Abstract

Tropical cyclones have enormous costs to society through both loss of life and damage to infrastructure. There is good reason to believe that such storms will change in the future as a result of changes in the global climate system and that such changes may have important socioeconomic implications. Here a high-resolution regional climate modeling experiment is presented using the Weather Research and Forecasting (WRF) Model to investigate possible changes in tropical cyclones. These simulations were performed for the period 2001–13 using the ERA-Interim product for the boundary conditions, thus enabling a direct comparison between modeled and observed cyclone characteristics. The WRF simulation reproduced 30 of the 32 named storms that entered the model domain during this period. The model simulates the tropical cyclone tracks, storm radii, and translation speeds well, but the maximum wind speeds simulated were less than observed and the minimum central pressures were too large. This experiment is then repeated after imposing a future climate signal by adding changes in temperature, humidity, pressure, and wind speeds derived from phase 5 of the Coupled Model Intercomparison Project (CMIP5). In the current climate, 22 tracks were well simulated with little changes in future track locations. These simulations produced tropical cyclones with faster maximum winds, slower storm translation speeds, lower central pressures, and higher precipitation rates. Importantly, while these signals were statistically significant averaged across all 22 storms studied, changes varied substantially between individual storms. This illustrates the importance of using a large ensemble of storms to understand mean changes.

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