PREMIUM - Predictive Models for Extremes and High-impact Weather under Climate Change - Evaluation of the Multi-scale Impact of Soil - Atmosphere Feedbacks
Climate change has contributed to an increase in extreme weather events – including devastating heavy rains and dry spells. The increase in frequency and intensity of these events is of key importance to society due to their large impact through flooding and drought. Flood and extreme heat can cause a range of health impacts and risks. Despite recent advances in forecasting and climate monitoring, weather‐related disasters continue to kill or displace populations and damage property / infrastructure. Less severe weather events put an increasing strain on society, especially in countries with fragile economies.
The capability of predicting such dramatic events still is a great challenge. Significant progress has been made in terms of climate monitoring and weather and extreme event forecasting during recent years. However, many uncertainties remain, as illustrated by the inconsistent results given by current numerical weather and climate prediction models regarding the present and future distribution of e. g. precipitation.
Accurate prediction of the regional water cycle on weather and climate timescales has been found to be crucial to predicting hazardous weather. The lack of observations - insufficient time and spatial coverage - inadequate spatial resolution of models, misrepresentation, and poor understanding of physical processes and their interactions across multiple-space and time scales still hampers progress, for example, in the representation of Mesoscale Convective Systems (MCSs).
The objective of the junior research group PREMIUM is to constrain and overcome some of the previously mentioned deficiencies. Our goal is to improve the understanding and the representation of water cycle components and related feedbacks in numerical weather prediction (NWP) and regional climate models (RCMs) across multiple-space and time scales. Focus is placed on moisture effects on atmospheric convection and soil moisture-atmosphere interactions, the representation of which has been identified to be a key source of uncertainty in weather forecast and climate modeling. In a seamless weather-climate approach, a process-based analysis of mesoscale processes and scale interactions is used. Furthermore, the impact of realistic data initialization and assimilation is evaluated. State-of-the-art Earth Observation and in-situ/field experiment observational campaigns will be used to fill the existing observational gaps. The synergetic use of these measurements and the mesoscale atmospheric model COSMO, with parameterized and explicitly resolved convection and in the weather and climate mode, will help us better represent physical processes and their interactions.
Dr. Samiro Khodayar
Karlsruhe Institute of Technology
Tel.: +49 721 608 24225
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