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Climate Responsive Architecture Arvind Krishan Pdf Download

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We are presenting this tutorial to help practitioners make use of remote sensing data, aid policy makers to develop cost effective multi-temporal solutions, and provide them with the latest developments in machine learning. The methods and case studies that will be shown will be applicable to several policy and economic challenges.

Abstract: Extreme precipitation and storm events are expected to increase with a changing climate. Global climate models indicate that precipitations will increase in frequency and intensity at tropical latitudes. Such amplifications could increase water losses during meteorological events resulting in floods, droughts and landslides. Understanding such linkages between the land and the ocean, between the atmosphere and the ocean and between the land and the ocean is therefore of importance for developing deployable solutions that are resilient for extreme events. In this work, we present the use of satellite and radar data to estimate precipitation and drive a land surface model. The end-to-end analysis illustrates how to drive a deployable water instrument that is capable of predicting the climate-driven land-sea interactions up to 2 weeks in advance. The model system that is used here integrates terrain models with hydrodynamic ocean general circulation and climate models, both of which are then coupled to radiation-surface models. The biogeochemical processes governing the land-ocean interactions are driven by gridded global carbon cycle models coupled to land surface land-sea interactions (both static and dynamic), keeping track of topographic details on a global scale.

Abstract: Remote sensing is a critical source of information for estimating large volumes of atmospheric humidity, surface winds, cloud cover, and rainfall from space. However, collecting radiation data for the estimation of these variables from space is quite hard because obtaining information from space remains an expensive task. Therefore, researchers develop and use machine learning methods to train these methods. For that purpose, satellite data can be used as an important input for estimating real-time weather conditions. d2c66b5586


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