The set‐up and evaluation of fine‐scale data assimilation for the urban climate of Amsterdam

Abstract

Ongoing urbanization highlights the need for a better understanding and high resolution modelling of the urban climate. In this study, we combine rural observations by WMO surface stations, weather radar data and urban crowd-sourced observations with very fine-scale modelling efforts for Amsterdam, The Netherlands. As a model, we use the Weather Research and Forecasting (WRF) mesoscale model with 3D variational data assimilation at a 100-m resolution in the innermost model domain. In order to enable the assimilation of observations within the urban canopy, we develop a scheme to reduce urban temperature biases by adjusting urban fabric temperatures. The scheme is tested against independent urban observations for the summer month of July 2014 and specifically for a hot period and an extreme precipitation event. We find data assimilation reduces biases in temperature and wind speed. Within the city, the most significant improvement is the reduction of negative temperature biases during clear nights, which implies a better prediction of the Urban Heat Island (UHI). Concerning precipitation, the fractional skill score improves incrementally when additional observations are assimilated, and the largest impact is seen from the assimilation of weather radar observations.

Parameterizing building effects on airflows within the urban canopy layer for high‐resolution models using a nudging approach

“…The weighting function has a value between 0 and 1. Often nudging is only performed at the model boundaries; however, the data (e.g., measurements) might also be distributed unevenly in space (Koopmans et al ., 2023). can be a spatial function, usually with a maximum amplitude of unity where the distance between the forcing data and nudging grid point is smallest, decreasing to zero at other grid points (Brill et al ., 1991).…”

Evaluation of the 3DVAR Operational Implementation of the Colombian Air Force for Aircraft Operations: A Case Study

“…Various DA techniques have been used within the framework of the WRF model. These include three-dimensional variational data assimilation (3DVAR) [23,24], four-dimensional variational data assimilation (4DVAR) [25][26][27], ensemble methods [28,29], and the ensemble transform Kalman filter-3DVAR (ETKF-3DVAR) system [30]. The operational implementation of the WRF in SIMFAC utilizes the 3DVAR method and assimilates METAR (Aviation Routine Weather Report), radio soundings, SYNOP, RADAR, and GOES-16.…”