in a data assimilation approach to assess natural hazards such as landslides. Given the abundance of spacecraft data for other planets in the solar system, data assimilation is now also applied beyond the Earth to obtain re-analyses of the atmospheric state of extraterrestrial planets. Mars is the only extraterrestrial planet to which data assimilation has been applied so far. Available spacecraft data include, in particular, retrievals of temperature and dust/water/ice optical thicknesses from the Thermal Emission Spectrometer onboard NASA's Mars Global Surveyor and the Mars Climate Sounder onboard NASA's Mars Reconnaissance Orbiter. Two methods of data assimilation have been applied to these datasets: an Analysis Correction scheme and two Ensemble Kalman Filter schemes, both using a global circulation model of the martian atmosphere as forward model. The Mars Analysis Correction Data Assimilation (MACDA) dataset is publicly available from the British Atmospheric Data Centre. Data assimilation is a part of the challenge for every forecasting problem. Dealing with biased data is a serious challenge in data assimilation. Further development of methods to deal with biases will be of particular use. If there are several instruments observing the same variable then intercomparing them using probability distribution functions can be instructive. The numerical forecast models are becoming of higher resolution due to the increase of computational power, with operational atmospheric models now running with horizontal resolutions of order of 1 km (e.g. at the German National Meteorological Service, the Deutscher Wetterdienst (DWD) and Met Office in the UK). This increase in horizontal resolutions is starting to allow to resolve more chaotic features of the non-linear models, e.g. to resolve convection on the grid scale, or clouds, in the atmospheric models. This increasing non-linearity in the models and observation operators poses a new problem in the data assimilation. The existing data assimilation methods such as many variants of ensemble Kalman filters and variational methods, well established with linear or near-linear models, are being assessed on non-linear models. Many new methods are being developed, e.g. particle filters for high-dimensional problems, and hybrid data assimilation methods. Other uses include trajectory estimation for the Apollo program, GPS, and atmospheric chemistry. See also Calibration References Further reading External links Examples of how variational assimilation is implemented weather forecasting at: Other examples of assimilation: CDACentral (an example analysis from Chemical Data Assimilation) PDFCentral (using PDFs to examine biases and representativeness) OpenDA – Open Source Data Assimilation package PDAF –