PyTroll (http://pytroll.org) is a suite of open-source easy-to-use Python packages to facilitate processing and efficient sharing of Earth Observation (EO) satellite data. The PyTroll software is intended for both 24/7 real-time operations as well as research and development. PyTroll grew out of the need to provide a resilient and agile platform that can respond quickly to new user needs and new data sources. PyTroll, being open source, stimulates international collaboration, which is vital with the rapid increase of satellite information availability. The PyTroll software development is strongly user driven and has grown over the past eight years from a collaborative effort between the Danish and Swedish national meteorological services to encompass a worldwide community with active contributors. PyTroll is being used at least operationally in the national meteorological services of Denmark, Norway, Sweden, Finland, Germany, Switzerland, Italy, Estonia, and Latvia. However, given its simplicity, minimal demand on user resources, and community-driven approach, it also encourages and facilitates usage of EO data for individual applications. While PyTroll was originally developed to cater to the needs of the atmospheric remote sensing community, it could be equally useful for land and ocean applications and within hydrology. This article provides an overview of PyTroll, with examples showing the capability of some of the core packages.
A sea ice thermal microwave emission model for 50 GHz was developed under EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSI SAF) programme. The model is based on correlations between the surface brightness temperature at 18, 36 and 50 GHz. The model coefficients are estimated using simulated data from a combined thermodynamic and emission model. The intention with the model is to provide a first guess sea ice surface emissivity estimate for atmospheric temperature sounding applications in the troposphere in numerical weather prediction (NWP) models assimilating Advanced Microwave Sounding Unit (AMSU) and Special Sensor Microwave Imager/Sounder (SSMIS) data. The spectral gradient ratio is defined as the difference over the sum of the SSMIS brightness temperatures at 18 and 36 GHz vertical linear polarisation (GR1836). The GR1836 is related to the emissivity at the atmospheric temperature sounding channels at around 50 GHz. Furthermore, the brightness temperatures and the polarisation ratio (PR) at the neighbouring 18, 36 and 50 GHz channels are highly correlated. Both the gradient ratio at 18 and 36 GHz and the PR at 36 GHz measured by SSMIS are input into the model predicting the 50 GHz emissivity for horizontal and vertical linear polarisations and incidence angles between 0° and 60° The simulated emissivity is compared to the emissivity derived with alternative methods. The fit to real AMSU observations is investigated using the different emissivity estimates for simulating the observations with atmospheric data from a regional weather prediction model
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