The Sensitivity of the Icosahedral Non-Hydrostatic Numerical Weather Prediction Model over Greece in Reference to Observations as a Basis towards Model Tuning

Author:

Avgoustoglou Euripides1,Shtivelman Alon2,Khain Pavel2,Marsigli Chiara34,Levi Yoav2ORCID,Cerenzia Ines4

Affiliation:

1. Hellenic National Meteorological Service (HNMS), Helliniko, 16777 Athens, Greece

2. Israel Meteorological Service (IMS), Bet-Dagan 5025001, Israel

3. Deutscher Wetterdienst German Weather Service (DWD), 63067 Offenbach am Main, Germany

4. Agenzia Regionale per la Prevenzione, l’Ambiente e l’Energia Emilia Romagna (ARPAE), Largo Caduti del Lavoro, 4, 40122 Bologna, Italy

Abstract

The ICON (icosahedral non-hydrostatic) numerical weather prediction model (NWP)’s sensitivity is evaluated for the geographical area of Greece. As ICON model has recently been endorsed operationally by the Members of the COSMO (Consortium for Small-scale Modeling); this attempt is in line with the further understanding of the model features, especially in the considered domain, characterized by a complex orography as well as an almost equally partitioned land–sea surface area. An extraordinary number of 24 model parameters have been tested for the whole 2020 year in reference to 88 Greek meteorological stations, with regard to the standard synoptic meteorological variables of 2 m temperature, 2 m minimum and maximum temperatures, dew-point temperature, 10 m wind intensity and 12 h accumulated precipitation. For these variables, the model sensitivity is given in terms of the annual average of all stations for the fifth lead day of the model runs when the sensitivity is expected to reach its peak. It was found that there is a considerable impact regarding the minimum and maximum values for many of the examined parameters in reference to their default values, and consideration is given to a heuristic recommendation on the selection of the most sensitive parameters.

Publisher

MDPI AG

Subject

Atmospheric Science,Environmental Science (miscellaneous)

Reference36 articles.

1. Avgoustoglou, E., Voudouri, A., Carmona, I., Bucchignani, E., Levi, Y., and Bettems, J.-M. (2023, October 22). A Methodology Towards the Hierarchy of COSMO Parameter Calibration Tests Via the Domain Sensitivity Over the Mediterranean Area. Available online: https://www.cosmo-model.org/content/model/cosmo/techReports/docs/techReport42.pdf.

2. Determining the sensitive parameters of the Weather Research and Forecasting (WRF) Model for the simulation of tropical cyclones in the Bay of Bengal using global sensitivity analysis and machine learning;Baki;Geosci. Model Dev.,2022

3. Campos, T.B., Sapucci, L.F., Lima, W., and Silva Ferreira, D. (2018). Sensitivity of Numerical Weather Prediction to the Choice of Variable for Atmospheric Moisture Analysis into the Brazilian Global Model Data Assimilation System. Atmosphere, 9.

4. Effect of observation error variance adjustment on numerical weather prediction using forecast sensitivity to error covariance parameters;Kim;Tellus A,2018

5. Sensitivity of Convection-Permitting Regional Climate Simulations to Changes in Land Cover Input Data: Role of Land Surface Characteristics for Temperature and Climate Extremes;Merja;Front. Earth Sci. Sec. Atmos. Sci.,2021

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