e under threat of photoinhibition) An important aspect of our w

e. under threat of photoinhibition). An important aspect of our work to date aiming to construct an effective SatBałtyk PD0325901 datasheet operational system included the successful attempts to expand the applicability of the earlier DESAMBEM algorithms by linking them

up with the packet of algorithms from the BALTFOS Forecasting System. The latter are based on forecasting models and procedures for their calibration by the assimilation of satellite data and other data obtained using the diagnostic subalgorithms of the DESAMBEM (see Figure 1 in Part 1 of Woźniak et al. (2011), in this issue). As we have already stated, this is essential in the case of the Baltic, where frequent cloudiness partially or entirely precludes the use of satellite sensors for recording radiation in the visible and thermal infra-red bands for diagnosing various parameters of the marine environment (including chlorophyll concentration and SST). In such IWR-1 mw cases, interpolation (between points in time-space) of measurements remotely sensed in cloud-free areas is often resorted to. Our trials

with respect to SST interpolations in cloudy areas have shown that such geostatic methods would not be very effective in an operational system for the Baltic, because of the long periods for which cloudiness persists there. In our opinion, the most effective and reliable approach would be to use data generated by prognostic hydrodynamic and eco-hydrodynamic models, which assimilate data calibrated with data from satellite estimates and/or data generated using the DESAMBEM algorithm. This is shown by the results of filling

Celecoxib in the SST map of the Baltic carried out in various ways for 28 April 2009 (11:52 UTC), shown in Figure 9. The SST maps are drawn with the aid of a NLSST algorithm ( Walton et al. 1998, Krężel et al. 2005) for cloudless areas on the basis of satellite data recorded with an AVHRR sensor (TIROS-N/NOAA). On that day most of the Baltic Sea area was overcast, and estimating SST from satellite data and using diagnostic algorithms was possible for only small areas of the sea (see Figure 9b). The area overcast on that day had been ‘seen’ by the satellite four days earlier, i.e. on 25 April 2009 at 19:15 UTC (see the SST distribution in Figure 9a). Kriging interpolation with the aid of linear regression was applied to these data to make up the missing SST data on the cloudy 28 April 2009 (see the SST distribution in Figure 9d). Another way of filling in gaps in SST fields in overcast areas is to use prognostic models. Figure 9e shows the remotely sensed distribution of SST in which overcast areas ( Figure 9b) have been replaced by results supplied by the M3D hydrodynamic model ( Kowalewski 1997, Kowalewski & Kowalewska-Kalkowska 2011).

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