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HiVE is a CMEMS funded collaboration between the atmospheric Numerical Weather Prediction (NWP) verification and the ocean community within the Met Office, aimed at demonstrating the use of spatial verification methods originally developed for the evaluation of high-resolution NWP forecasts, to CMEMS ocean model forecast products. Spatial verification methods provide more scale appropriate ways to better assess forecast characteristics and accuracy of km-scale forecasts, where the detail looks realistic but may not be in the right place at the right time. As a result, it can be the case that coarser resolution forecasts verify better (e.g. lower root-mean-square-error) than the higher resolution forecast. In this instance the smoothness of the coarser resolution forecast is rewarded, though the higher-resolution forecast may be better. The project utilised open source code library known as Model Evaluation Tools (MET) developed at the US National Center for Atmospheric Research (NCAR). This project saw, for the first time, the application of spatial verification methods to sub-10 km resolution ocean model forecasts. The project consisted of two parts. Part 1 is described in the companion poster to this one. Part 2 describes the skill of CMEMS products for forecasting events or features of interest such as algal blooms. The Method for Object-based Diagnostic Evaluation (MODE) and the time dimension version MODE Time Domain (MTD) were applied to daily mean chlorophyll forecasts for the European North West Shelf from the FOAM-ERSEM model on the AMM7 grid. The forecasts are produced from a “cold start”, i.e. no data assimilation of biological variables. Here the entire 2019 algal bloom season was analysed to understand: intensity and spatial (area) biases; location and timing errors. Forecasts were compared to the CMEMS daily cloud free (L4) multi-sensor chlorophyll-a product. It has been found that there are large differences between forecast and observed concentrations of chlorophyll. This has meant that a quantile mapping approach for removing the bias was necessary before analysing the spatial properties of the forecast. Despite this the model still produces areas of chlorophyll which are too large compared to the observed. The model often produces areas of enhanced chlorophyll in approximately the right locations but the forecast and observed areas are rarely collocated and/or overlapping. Finally, the temporal analysis shows that the model struggled to get the onset of the season (being close to a month too late), but once the model picked up the signal there was better correspondence between the observed and forecast chlorophyll peaks for the remainder of the season. There was very little variation in forecast performance with lead time, suggesting that chlorophyll is a very slowly varying quantity. Comparing an analysis which included the assimilation of observed chlorophyll shows that it is much closer to the observed L4 product than the non-biological assimilative analysis. It must be concluded that if the forecast were started from a DA analysis that included chlorophyll, it would lead to forecasts with less bias, and possibly a better detection of the onset of the bloom. |
