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Preprints
https://doi.org/10.5194/os-2020-100
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-2020-100
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  02 Nov 2020

02 Nov 2020

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This preprint is currently under review for the journal OS.

Using feature-based verification methods to explore the spatial and temporal characteristics of forecasts of the 2019 Chlorophyll-a bloom season over the European North-West Shelf

Marion Mittermaier1, Rachel North1, Jan Maksymczuk2, Christine Pequignet2, and David Ford2 Marion Mittermaier et al.
  • 1Verification, Impacts and Post-Processing, Weather Science, Met Office, Exeter, EX1 3PB, United Kingdom
  • 2Ocean Forecasting Research & Development, Weather Science, Met Office, Exeter, EX1 3PB, United Kingdom

Abstract. A feature-based verification method, commonly used for atmospheric model applications, has been applied to Chlorophyll-a (Chl-a) concentration forecasts from the Met Office Atlantic Margin Model at 7 km resolution (AMM7) North West European Shelf Seas model, and compared against gridded satellite observations of Chl-a concentration from the Copernicus Marine Environmental Monitoring Service (CMEMS) catalogue. A significant concentration bias was found between the model and observations. Two variants of quantile mapping were used to mitigate against the impact of this bias on feature identification (determined by threshold exceedance). Forecast and observed Chl-a objects for the 2019 bloom season (March 1 to 31 July), were analysed, firstly in space only, and secondly as space-time objects, incorporating concepts of onset, duration and demise. It was found that forecast objects tend to be too large spatially, with lower object numbers produced by the forecasts compared to those observed. Based on an analysis of the space-time objects the onset of Chl-a blooming episodes at the start of the season is almost a month too late in the forecasts, whilst several forecast blooms did not materialise in the observations. Whilst the model does produce blooms in the right places, they may not be at the right time. There was very little variation in forecasts and results as a function of lead time. A pre-operational AMM7 analysis, which assimilates Chl-a concentrations was also assessed, and found to behave more like the observations, suggesting that forecasts driven from these analyses could improve both timing errors and the bias.

Marion Mittermaier et al.

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Marion Mittermaier et al.

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Short summary
Regions of enhanced Chlorophyll-a concentrations can be identified by applying a threshold to the concentration value to a forecast and observed field (or analysis). These regions can then be treated and analysed as features using diagnostic techniques to consider of the evolution of the chlorophyll-a blooms in space and time. This allows us to understand whether the biogeochemistry in the model has any skill in predicting these blooms, their location, intensity, onset, duration and demise.
Regions of enhanced Chlorophyll-a concentrations can be identified by applying a threshold to...
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