No abstract

No abstract

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 interannual 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 pre-north-south oriented ridges, forced lifting of moist air enhances precipitation on the

Abstract-Monitoring and preserving air quality has become one of the most essential activities in many industrial and urban areas today. The quality of air is adversely affected due to various forms of pollution caused by transportation, electricity, fuel uses etc. The deposition of harmful gases is creating a serious threat for the quality of life in smart cities. With increasing air pollution, we need to implement efficient air quality monitoring models which collect information about the concentration of air pollutants and provide assessment of air pollution in each area. Hence, air quality evaluation and prediction has become an important research area. The quality of air is affected by multi-dimensional factors including location, time, and uncertain variables. Recently, many researchers began to use the big data analytics approach due to advancements in big data applications and availability of environmental sensing networks and sensor data. The aim of this research paper is to investigate various big-data and machine learning based techniques for air quality forecasting. This paper reviews the published research results relating to air quality evaluation using methods of artificial intelligence, decision trees, deep learning etc. Furthermore, it throws light on some of the challenges and future research needs.Index Terms-Air quality evaluation, big data analytics, data-driven air quality evaluation, and air quality prediction.

Abstract. 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.