The purpose of this paper is to synthesize some common synoptic and mesoscale environments conducive to heavy orographic rainfall. Previous studies of U.S. and Alpine cases and new analyses of some Alpine and east Asian cases have shown the following common synoptic and mesoscale environments are conducive to heavy orographic rainfall: 1) a conditionally or potentially unstable airstream impinging on the mountains, 2) a very moist low-level jet (LLJ), 3) a steep mountain, and 4) a quasi-stationary synoptic system to slow the convective system over the threat area. A deep short-wave trough is found to approach the threat area in the U.S. and European cases, but is not found in the east Asian cases. On the other hand, a high convective available potential energy (CAPE) value is observed in east Asian cases, but is not consistently observed in the U.S. and European cases. The enhancement of low-level upward motion and the increase of instability below the trough by the approaching deep short-wave tr... Abstract The purpose of this paper is to synthesize some common synoptic and mesoscale environments conducive to heavy orographic rainfall. Previous studies of U.S. and Alpine cases and new analyses of some Alpine and east Asian cases have shown the following common synoptic and mesoscale environments are conducive to heavy orographic rainfall: 1) a conditionally or potentially unstable airstream impinging on the mountains, 2) a very moist low-level jet (LLJ), 3) a steep mountain, and 4) a quasi-stationary synoptic system to slow the convective system over the threat area. A deep short-wave trough is found to approach the threat area in the U.S. and European cases, but is not found in the east Asian cases. On the other hand, a high convective available potential energy (CAPE) value is observed in east Asian cases, but is not consistently observed in the U.S. and European cases. The enhancement of low-level upward motion and the increase of instability below the trough by the approaching deep short-wave tr...

In this study, a nonhydrostatic mesoscale model [Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] was adopted to simulate Supertyphoon Bilis (2000) and investigate the dynamics of orographic rain and track deflection accompanying the storm as it passes the Central Mountain Range (CMR) of Taiwan. Both the storm track and its associated orographic rainfall distribution are well predicted by the numerical model. The intensity of the storm is underpredicted, resulting in a discontinuous track, due to the lack of specifying a “bogus” vortex at the time of model initialization. Cyclonic curvature of the storm track over the island topography track as well as major circulation features are similar to previous studies of landfalling typhoons affecting Taiwan. The model overpredicts the total amount of accumulated rainfall. Generalization of the flux model proposed in a 2001 study by Lin and coauthors is used to help predict and understand the observed rainfall distribution by calculating bo... Abstract In this study, a nonhydrostatic mesoscale model [Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] was adopted to simulate Supertyphoon Bilis (2000) and investigate the dynamics of orographic rain and track deflection accompanying the storm as it passes the Central Mountain Range (CMR) of Taiwan. Both the storm track and its associated orographic rainfall distribution are well predicted by the numerical model. The intensity of the storm is underpredicted, resulting in a discontinuous track, due to the lack of specifying a “bogus” vortex at the time of model initialization. Cyclonic curvature of the storm track over the island topography track as well as major circulation features are similar to previous studies of landfalling typhoons affecting Taiwan. The model overpredicts the total amount of accumulated rainfall. Generalization of the flux model proposed in a 2001 study by Lin and coauthors is used to help predict and understand the observed rainfall distribution by calculating bo...

Over the years, rampant wildfires have plagued the state of California, creating economic and environmental loss. In 2018, wildfires cost nearly 800 million dollars in economic loss and claimed more than 100 lives in California. Over 1.6 million acres of land has burned and caused large sums of environmental damage. Although, recently, researchers have introduced machine learning models and algorithms in predicting the wildfire risks, these results focused on special perspectives and were restricted to a limited number of data parameters. In this paper, we have proposed two data-driven machine learning approaches based on random forest models to predict the wildfire risk at areas near Monticello and Winters, California. This study demonstrated how the models were developed and applied with comprehensive data parameters such as powerlines, terrain, and vegetation in different perspectives that improved the spatial and temporal accuracy in predicting the risk of wildfire including fire ignition. The combined model uses the spatial and the temporal parameters as a single combined dataset to train and predict the fire risk, whereas the ensemble model was fed separate parameters that were later stacked to work as a single model. Our experiment shows that the combined model produced better results compared to the ensemble of random forest models on separate spatial data in terms of accuracy. The models were validated with Receiver Operating Characteristic (ROC) curves, learning curves, and evaluation metrics such as: accuracy, confusion matrices, and classification report. The study results showed and achieved cutting-edge accuracy of 92% in predicting the wildfire risks, including ignition by utilizing the regional spatial and temporal data along with standard data parameters in Northern California.

Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. The latest numerical weather forecast model could provide 1–15-day quantitative precipitation forecasting products in grid format, and by coupling this product with a distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe model with the Weather Research and Forecasting quantitative precipitation forecast (WRF QPF) for large watershed flood forecasting in southern China. The QPF of WRF products has three lead times, including 24, 48 and 72 h, with the grid resolution being 20 km × 20 km. The Liuxihe model is set up with freely downloaded terrain property; the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with the WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post-process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also. This suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of the WRF QPF decreases, as does the flood forecasting capability. Flood forecasting products produced by coupling the Liuxihe model with the WRF QPF provide a good reference for large watershed flood warning due to its long lead time and rational results.

The extent to which orography may be a product of climate-erosion interactions is largely unknown. One grand challenge is to quantify the precipitation regimes of mountainous regions at the spatial and temporal scales relevant for investigating the interplay of erosion and tectonics in active orogens. In this paper, our objective is to synthesize recent research integrating numerical model simulations, satellite data, and surface observations in the Himalaya to elucidate the role of weather and climate in mountain evolution. We focus on the seasonal and interannual space-time variability of precipitation in the Great Himalayas by studying two preeminent storm regimes in detail—monsoon onsets and depressions in general, and wintertime Western Disturbances (cold season events). High-resolution simulations of heavy precipitation storms for two monsoon onset conditions (1999 and 2001) and one wintertime storm (2000) are used to illustrate the complex patterns of interaction between the mountains and the atmosphere, and to show how these affect the spatial distribution of precipitation. Along with observations from an existing ground-based network, these simulations provide unique insights into the space-time features of seasonal and inter-annual variability of precipitation. Our analysis indicates that the trajectory of monsoon storms during onset events exerts a strong control on the precipitation amounts and rainfall penetration into the rain shadow. Spatial variability of subsequent storm tracks in any given year helps explain the interannual variability of monsoon precipitation. Both observational data and our simulations define striking spatial variability in precipitation on upwind and downwind fianks of ridges that project into obliquely impinging storms. Specifically, as southeasterly monsoon winds encounter north-south oriented ridges, forced lifting of moist air enhances precipitation on the upwind fianks, whereas less precipitation occurs on downwind fianks. This variability is observed at spatial scales as short as ∼10 km—a distance equivalent to the spacing of major ridge crests. Because infrequent, singular storm events appear to control the mass input to glaciers, and may determine the frequency and spatial distribution of landslides, these findings provide physically based insight into decoupling high-frequency (seasonal to interannual time scales) from low-frequency (multidecadal to centennial and longer time scales) signals in the interpretation of climate and erosion records in the Himalayas. Furthermore, this research suggests that integrative studies aimed at unraveling the role of climate in landscape evolution must include consideration of storm frequency and intensity along with spatial variability at scales consistent with regional climate forcing.