Impact of the Assimilation of Surface Observations on Limited-Area Forecasts Over Complex Terrain

Author(s)

Giorgio Doglioni, Stefano Serafin, Martin Weissmann, Gianluca Ferrari, Dino Zardi

Abstract

The article presents results from a computationally low-cost regional numerical weather prediction chain based on the Weather Research and Forecasting (WRF) model and its data assimilation (DA) suite WRFDA. Experiments with 24-h forecasts were performed twice daily (at 00 and 12 UTC) over a domain encompassing the European Alps and their surroundings with a 3.5 km grid spacing. The assimilation of surface observations with the 3D-Var algorithm improves near-surface temperature and humidity forecasts compared to control runs without assimilation. The forecast skill for near-surface variables is evaluated using independent surface observations. In the first six forecast hours, it is generally better in the assimilation experiments than in the control ones, with a mean error reduction of 0.26 K for temperature and 0.13 g kg⁻¹ for specific humidity in the 00 UTC runs, and of 0.12 K for temperature and 0.18 g kg⁻¹ for specific humidity in the 12 UTC runs. The assimilation reduces the standard deviation of the errors by a factor between 7% and 10% both for temperature and specific humidity. Verification with radiosonde measurements shows that assimilating surface observations increases the mean error in temperature and humidity forecasts within the planetary boundary layer (PBL), relative to the control. We show that the vertical structure of the adjustments to the model state resulting from DA (the analysis increments) is such that model biases are reduced near the surface but amplified higher up in the PBL. Finally, the assimilation of surface observations has a different impact on surface temperature forecasts in mountainous regions compared to adjacent plains. The error reduction is substantially higher in the plains than in the mountains, which likely depends on the inappropriate spreading of information along terrain-following model levels by the static covariances in 3D-Var. The relative accuracy of surface temperature forecasts in these two regions has a diurnal variability, with larger mean errors in the mountains during the day and in the plains at night.

Organisation(s)

Department of Meteorology and Geophysics

External organisation(s)

Università degli Studi di Trento, Hypermeteo srl

Journal

Meteorological Applications

Volume

32

ISSN

1350-4827

DOI

https://doi.org/10.1002/met.70107

Publication date

09-2025

Peer reviewed

Yes

Austrian Fields of Science 2012

105206 Meteorology

Keywords

ASJC Scopus subject areas

Atmospheric Science

Portal url

https://ucrisportal.univie.ac.at/en/publications/7994ed84-e124-4970-8cf9-621bf1c55d6b