Although the societal impact of a weather event increases with the rarity of the event, our current ability to assess extreme events and their impacts is limited by not only rarity but also by current model fidelity and a lack of understanding and capacity to model the underlying physical processes. This challenge is driving fresh approaches to assess highimpact weather and climate. Recent lessons learned in modeling high-impact weather and climate are presented using the case of tropical cyclones as an illustrative example. Through examples using the Nested Regional Climate Model to dynamically downscale large-scale climate data the need to treat bias in the driving data is illustrated. Domain size, location, and resolution are also shown to be critical and should be adequate to: include relevant regional climate physical processes; resolve key impact parameters; and accurately simulate the response to changes in external forcing. The notion of sufficient model resolution is introduced together with the added value in combining dynamical and statistical assessments to fill out the parent distribution of high-impact parameters.

This study examines how urbanization affects the precipitation climatology in Tokyo, Japan. A unique aspect of this study is that an ensemble, regional climatological simulation approach is used with sensitivity experiments to reduce uncertainty arising from nonlinearity in the precipitation simulations. Another aspect is that the robustness of the precipitation response is tested with ''stress response'' simulations with increasing urban forcing. The results show that urbanization causes a robust increase in the amount of precipitation in the Tokyo metropolitan area and a reduction in the inland areas. These anomalies are statistically significant at the 95% and 99% levels in some parts. There is no measureable change in the surrounding rural and ocean areas. These precipitation responses are attributed to an increase of surface sensible heat flux in Tokyo, which destabilizes the atmosphere and induces an anomalous surface low pressure pattern and the convergence of grid-scale horizontal moisture flux. The anomalous convergence of grid-scale horizontal moisture flux is a consequence of urbanization modifying the sea breeze.

This study evaluates the performance of a regional climate model in simulating two types of synoptic tropical weather disturbances: convectively-coupled Kelvin and easterly waves. Interest in these two wave modes stems from their potential predictability out to several weeks in advance, as well as a strong observed linkage between easterly waves and tropical cyclogenesis. The model is a recent version of the weather research and forecast (WRF) system with 36-km horizontal grid spacing and convection parameterized using a scheme that accounts for key convective triggering and inhibition processes. The domain spans the entire tropical belt between 45°S and 45°N with periodic boundary conditions in the east-west direction, and conditions at the meridional/lower boundaries specified based on observations. The simulation covers 6 years from 2000 to 2005, which is long enough to establish a statistical depiction of the waves through space-time spectral filtering of rainfall data, together with simple lagged-linear regression. Results show that both the horizontal phase speeds and three-dimensional structures of the waves are qualitatively well captured by the model in comparison to observations. However, significant biases in wave activity are seen, with generally overactive easterly waves and underactive Kelvin waves. Evidence is presented to suggest that these biases in wave activity (which are also correlated with biases in time-mean rainfall, as well as biases in the model's tropical cyclone climatology) stem in part from convection in the model coupling too strongly to rotational circulation anomalies. Nevertheless, the model is seen to do a reasonable job at capturing the genesis of tropical cyclones from easterly waves, with evidence for both wave accumulation and critical layer processes being importantly involved.

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