Abstract
Abstract. Operational meteo-hydrological forecasting chains are affected by many sources of uncertainty. In coastal areas characterized by complex topography, with several medium-to-small size catchments, quantitative precipitation forecast becomes even more challenging due to the interaction of intense air–sea exchanges with coastal orography. For such areas, which are quite common in the Mediterranean Basin, improved representation of sea surface temperature (SST) space–time patterns can be particularly important. The paper focuses on the relative impact of different resolutions of SST representation on regional operational forecasting chains (up to river discharge estimates) over coastal Mediterranean catchments, with respect to two other fundamental options while setting up the system, i.e. the choice of the forcing general circulation model (GCM) and the possible use of a three-dimensional variational assimilation (3D-Var) scheme. Two different kinds of severe hydro-meteorological events that affected the Calabria region (southern Italy) in 2015 are analysed using the WRF-Hydro atmosphere–hydrology modelling system in its uncoupled version. Both of the events are modelled using the 0.25∘ resolution global forecasting system (GFS) and the 16 km resolution integrated forecasting system (IFS) initial and lateral atmospheric boundary conditions, which are from the European Centre for Medium-Range Weather Forecasts (ECMWF), applying the WRF mesoscale model for the dynamical downscaling. For the IFS-driven forecasts, the effects of the 3D-Var scheme are also analysed. Finally, native initial and lower boundary SST data are replaced with data from the Medspiration project by Institut Français de Recherche pour L'Exploitation de la Mer (IFREMER)/Centre European Remote Sensing d'Archivage et de Traitement (CERSAT), which have a 24 h time resolution and a 2.2 km spatial resolution. Precipitation estimates are compared with both ground-based and radar data, as well as discharge estimates with stream gauging stations' data. Overall, the experiments highlight that the added value of high-resolution SST representation can be hidden by other more relevant sources of uncertainty, especially the choice of the general circulation model providing the boundary conditions. Nevertheless, in most cases, high-resolution SST fields show a non-negligible impact on the simulation of the atmospheric boundary layer processes, modifying flow dynamics and/or the amount of precipitated water; thus, this emphasizes the fact that uncertainty in SST representation should be duly taken into account in operational forecasting in coastal areas.
Highlights
Operational river flood forecasting is a highly challenging activity for several reasons that go beyond strictly scientific aspects
Though originating from a high-resolution dataset, do not provide a clear improvement of skin sea surface temperature (SST) representation compared to the original general circulation models (GCMs) fields
The main features highlighted by the skin SST maps are the strong underestimation of native integrated forecasting system (IFS) fields close to the coastline and the overestimation, especially in the Tyrrhenian Sea, of the native global forecasting system (GFS) fields
Summary
Operational river flood forecasting is a highly challenging activity for several reasons that go beyond strictly scientific aspects. Refined modelling chains have been developed in recent years (e.g. UK Environmental Prediction research, Lewis et al, 2019a; Canadian Great Lakes, Gronewold et al, 2011; the US Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System COAMPS®, Hodur, 1997) Despite their complexity, these systems all have to deal with some inherent limitations of the meteorological and hydrological models. The main sources of errors in weather forecasts are connected to both inaccuracy in defining the initial state, due to the lack of available measures or observation/assimilation errors, and approximations of the models, whose structures are not capable of properly representing the phenomena of interest (Allen et al, 2002; Buizza, 2018) These problems are exacerbated by the chaotic nature of the atmosphere. As catchments are very seldom perfect natural systems, some effects of human disturbances can virtually not be modelled
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