Forecast of surface layer meteorological parameters at Cerro Paranal with a mesoscale atmospherical model

TL;DR

This study demonstrates that the mesoscale atmospheric model Meso-Nh can reliably forecast surface meteorological parameters like wind and temperature at Cerro Paranal, supporting astronomical applications with validated statistical performance over 129 nights.

Contribution

The paper confirms the robustness of Meso-Nh for surface parameter forecasting at Cerro Paranal using a large dataset and introduces contingency table analysis for operational assessment.

Findings

Model shows high accuracy in wind speed and temperature forecasts.

Performance validated over 129 nights with statistical metrics.

Contingency table analysis provides additional operational insights.

Abstract

This article aims at proving the feasibility of the forecast of all the most relevant classical atmospherical parameters for astronomical applications (wind speed and direction, temperature) above the ESO ground-base site of Cerro Paranal with a mesoscale atmospherical model called Meso-Nh. In a precedent paper we have preliminarily treated the model performances obtained in reconstructing some key atmospherical parameters in the surface layer 0-30~m studying the bias and the RMSE on a statistical sample of 20 nights. Results were very encouraging and it appeared therefore mandatory to confirm such a good result on a much richer statistical sample. In this paper, the study was extended to a total sample of 129 nights between 2007 and 2011 distributed in different parts of the solar year. This large sample made our analysis more robust and definitive in terms of the model performances and permitted us to confirm the excellent performances of the model. Besides, we present an independent analysis of the model performances using the method of the contingency tables. Such a method permitted us to provide complementary key informations with respect to the bias and the RMSE particularly useful for an operational implementation of a forecast system.