The YOPP site Model Intercomparison Project (YOPPsiteMIP) phase 1: project overview and Arctic winter forecast evaluation

• Main Authors: Day, Jonathan, Svensson, Gunilla, Casati, Barbara, Uttal, Taniel, Khalsa, Siri-Jodha, Bazile, Eric, Akish, Elena, Azouz, Niramson, Ferrighi, Lara, Frank, Helmut, Gallagher, Michael, Godøy, Øystein, Hartten, Leslie, Huang, Laura X., Holt, Jareth, Stefano, Massimo, Suomi, Irene, Mariani, Zen, Morris, Sara, O'Connor, Ewan, Pirazzini, Roberta, Remes, Teresa, Fadeev, Rostislav, Solomon, Amy, Tjernström, Johanna, Tolstykh, Mikhail
• Format: Text
• Language: English
• Published: 2023
• Subjects: Antarctic; Arctic; Antarc*
• Online Access: https://doi.org/10.5194/egusphere-2023-1951; https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1951/

Although the quality of weather forecasts in the polar regions is improving, forecast skill there still lags the lower latitudes. So far there have been relatively few efforts to evaluate processes in Numerical Weather Prediction systems using in-situ and remote sensing datasets from meteorological observatories in the terrestrial Arctic and Antarctic, compared to the mid-latitudes. Progress has been limited both by the heterogeneous nature of observatory and forecast data but also by limited availability of the parameters needed to perform process-oriented evaluation in multi-model forecast archives. The YOPP site Model Inter-comparison Project (YOPPsiteMIP) is addressing this gap by producing Merged Observatory Data Files (MODFs) and Merged Model Data Files (MMDFs), bringing together observations and forecast data at polar meteorological observatories in a format designed to facilitate process-oriented evaluation. An evaluation of forecast performance was performed at seven Arctic sites, focussing on the first YOPP Special Observing Period in the Northern Hemisphere (SOP1), February and March 2018. It demonstrated that although the characteristics of forecast skill vary between the different sites and systems, an underestimation in boundary layer temperature variance across models, which goes hand in hand with an inability to capture cold extremes, is a common issue at several sites. Diagnostic analysis using surface fluxes suggests that this is at least partly related to insufficient thermal representation of the land-surface in the models, which all use a single layer snow model.