Improving Urban Climate Modeling: Integrating Fine-Scale Data for Amsterdam

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We use the Weather Research and Forecasting (WRF) mesoscale model with 3D variational data assimilation at a 100-meter resolution in the innermost model domain. To incorporate urban observations into the model, we develop a scheme to reduce urban temperature biases by adjusting urban fabric temperatures.

We tested this scheme using independent urban observations for July 2014, focusing on a hot period and an extreme precipitation event. Our results show that data assimilation reduces biases in temperature and wind speed. Particularly, within the city, we significantly improved the prediction of the Urban Heat Island (UHI) effect by reducing negative temperature biases during clear nights.

Regarding precipitation, the fractional skill score improved with the assimilation of additional observations, with the most significant impact observed from assimilating weather radar data.