Particle size spectrum analysis during Maria S. Merian cruise MSM26, spring 2013

A Laser In-Situ Scattering Transmissometer (LISST) was used to collect vertical distribution data of particles from 2.5 to 500 µm in size. The LISST uses a multi-ring detector to measure scattering light of particles from a laser diode. Particles are classified into 32 log-spaced bins and the concentration of each bin is calculated as micro-liters per liter (µl/l). The instrument is rated to a depth of 300 m, and also records temperature and pressure. The sample interval was set to record every second. The LISST was attached to the LOPC frame to conduct casts and allow for particle-size comparisons between the two instruments. The LOPC is rated to a depth of 2000 m, thus a short deployment to a depth of 300 m was first conducted with both instruments. The instruments were then returned to the deck and the LISST removed via a quick release bracket so deep LOPC casts could be continued at a station. Raw LISST size-spectrum data is presented as concentrations for each of the 32 size bins for every second of the cast.

Downscaled land surface temperatures obtained by a particle smoothing approach, with links to NetCDF files

This dataset contains continuous time series of land surface temperature (LST) at spatial resolution of 300m around the 12 experimental sites of the PAGE21 project (grant agreement number 282700, funded by the EC seventh Framework Program theme FP7-ENV-2011). This dataset was produced from hourly LST time series at 25km scale, retrieved from SSM/I data (André et al., 2015, doi:10.1016/j.rse.2015.01.028) and downscaled to 300m using a dynamic model and a particle smoothing approach. This methodology is based on two main assumptions. First, LST spatial variability is mostly explained by land cover and soil hydric state. Second, LST is unique for a land cover class within the low resolution pixel. Given these hypotheses, this variable can be estimated using a land cover map and a physically based land surface model constrained with observations using a data assimilation process. This methodology described in Mechri et al. (2014, doi:10.1002/2013JD020354) was applied to the ORCHIDEE land surface model (Krinner et al., 2005, doi:10.1029/2003GB002199) to estimate prior values of each land cover class provided by the ESA CCI-Land Cover product (Bontemps et al., 2013) at 300m resolution . The assimilation process (particle smoother) consists in simulating ensemble of LST time series for each land cover class and for a large number of parameter sets. For each parameter set, the resulting temperatures are aggregated considering the grid fraction of each land cover and compared to the coarse observations. Miniminizing the distance between the aggregated model solutions and the observations allow us to select the simulated LST and the corresponding parameter sets which fit the observations most closely. The retained parameter sets are then duplicated and randomly perturbed before simulating the next time window. At the end, the most likely LST of each land cover class are estimated and used to reconstruct LST maps at 300m resolution using ESA CCI-Land Cover. The resulting temperature maps on which ice pixels were masked, are provided at daily time step during the nine-year analysis period (2000-2009).