Variability and change of cloudiness diurnal cycle over the past 30 years: a global analysis based on polar orbiting satellites
is funded by the National Science Centre, Poland (NCN) via POLONEZ grant. The primary goal of this study is to render the satellite-derived dataset suitable for climate analysis. Cloud property datasets derived from passive sensors onboard a series of polar orbiting satellites (such as NOAA and MetOp) have a global coverage and now span a climatological time period (30 years). However, changes in a number of simultaneously operating satellites, drift of their orbits and varying equatorial crossing times of consecutive satellite missions lead to a different frequency of image acquisitions per day, and to their different local time. When observing the atmospheric state of a distinct diurnal cycle, such as cloud formation, the changing number and local time of measurements lead to uncertainty in cloud characteristics in the same order of magnitude as the GCOS requirements on decadal stability of cloud fraction data (3%).
Specifically we aim to: 1) develop and validate a method for statistical reconstruction of cloud cover diurnal cycle, 2) create a 30-year global cloud fraction climatology (1°×1°) suitable for trend analysis by correcting the satellite orbital drift issue, 3) quantify global changes in cloud cover distribution and in diurnal cycle of cloud formation over the last 30 years.


ESA Cloud_cci
The primary objective of the project is to provide the long-term (~ 30 y) coherent cloud property global dataset by exploiting different Earth observation missions. In the first phase of the project (in 2013) I was responsible for validation of the cloud physical properties dataset in the alpine and polar regions. In the second phase (2014-2016) I lead two work packages which aim at evaluating climatological stability and homogeneity, and analyzing diurnal cycle of the AVHRR and MODIS cloud cover by use of a Meteosat-based cloud climatology (CM SAF) and SYNOP. This should lead to de-trended and de-biased cloud cover climatology over Central Europe. General information can be found on the project website, and the MeteoSwiss contribution is decribed here.

CM SAF (EUMETSAT) CDOP II Meteosat cloud mask generation
The project aims at deriving a cloud mask covering the Meteosat satellite's visible disc. The final time series will extend over 30 years by using data from Meteosat first and second generation satellites (1983-present). I am involved in validation activities of the dataset based on ground-based cloud amount observations and measurements. The MeteoSwiss contribution is decribed here.

Quantifying solar radiation at the Earth surface with meteorological and satellite data Within the project I have carried out a comprehensive accuracy assessment and rigorous inter-comparison of solar radiation datasets for Europe; these were derived from satellite observations, empirical solar radiation models, and weather prediction models. Based on project results I provided guidelines on how a long-term seamless gridded time series of daily solar radiation may be constructed for Europe from two currently available products that are derived from geostationary satellites, the European Reanalysis (ERA-Interim) and weather station data.

Semi-automatic crop growth monitoring system for Poland I co-developed a crop growth monitoring system, which was based on the AVHRR and MODIS satellite images. The system has been programmed in R and it included: reading the AVHRR data derived from image receiving station, radiometric corrections, processing, generating vegetation indices, similarity analysis, qualitative assessment of crop conditions and mapping results. There were some specific tools developed for this system, i.e. cloud masking algorithm, method for noise reduction in the NDVI time series, downscaling method for arable land mask.

Global burnt surfaces In the Global Environment Monitoring Unit of the Joint Research Centre I carried out the project aiming at concatenating of the existing global burnt surfaces products to obtain a long-term and homogeneous dataset. A part of this work was presented at UseR! conference in Dortmund: Using R for time series analysis and the spatio-temporal distribution of the global burnt surface multi-year product. I presented R applications such as: fire probability extension algorithm, PCA with 3D interactive visualization or mapping of spatio-temporal distribution of global data.

Atmospheric correction For my master thesis I carried out a project about the implementation of the atmospheric correction algorithm in GRASS environment. The scientific objective was to carry out an analysis of sensibility to the change of the input parameters in the 6S model. Whole processing of satellite images, atmospheric correction and statistical analyses were performed in GRASS and R.