Advances in data assimilation methods for weather and environmental forecasts and climate simulations

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In atmospheric and oceanic sciences, models and observations are combined in modern data assimilation systems to produce analyses which are our best estimate of the state of the atmosphere at any given time. Historically, the analyses were needed to provide initial conditions to numerical weather prediction (NWP) models as the error made on weather forecasts were attributed to error in the initial conditions. It was soon realized that comparing model simulations to observations offers information to pinpoint weaknesses in the model which helped to improve the NWP model. Much of the advances made in recent years in data assimilation methods aimed at being able to use the vast amount of data now obtained from several satellite-based instruments on top of those from ground-based instrments. Millions of data are used every 24-h to produce analyses because of the significant advances made in data assimilation methods. The author conducted the R&D which led to the implementation of a 4D assimilation system, the so-called "4D-Var", which is capable to extract information from observations sampling the atmosphere in both time and space. Wind information can be inferred from observations of atmospheric constituents. Precursors to the development of meteorological weather events are better resolved which resulted in a reduction of missed forecasts of significant severe weather systems.*For twenty years, the author worked on the development and implementation of new advanced variational methods which have been part of the operational analysis and forecast suite of Environment Canada first in 1997, for the first stage of the project which was completed in 2005 with the implementation of 4D-Var which is still used to this day. Building on the success obtained in NWP, the same data assimilation systems were used to redo the analyses of the recent past, the so-called reanalyses which are now a key component of the validation of climate models. My own view is that climate models should be used to produce short-term forecasts used as an a priori to do the subsequent analysis. This cycle is repeated and this is through this constant comparison to observations that information can be obtained and used to better understand physical processes and their interactions and validate this against observations.*The research presented in this proposal is concerned with specific aspects of data assimilation methods which could impact the quality of the analyses. The results will help to make an assessment of the impact this may have on the end result: improving weather forecasts and analyses. This supports the long term view of improving the observation and modelling of the Earth system to better understand its evolution, improve high impact weather forecasts and assess climate changes through modelling consistent with observations. This can now be tested with a quasi-operational data assimilation and forecast system which is now running on Compute Canada platforms, an effort led by the author for the last six years. This opens a wide range of possibilities for research projects in universities which were only possible within operational NWP research centres like that of Environment Canada, Météo-France or the European Centre for Medium-range Weather Forecasts with which the author collaborates. The specific questions will be addressed concerns the ability of ensemble approaches to capture correctly the temporal dimension that was responsible for the success of 4D-Var. As forecasting the development of significant weather events is extremely important, the observability of precursors to atmospheric instability will be examined with a view of finding out how to design an observation strategy that could detect them and improve the forecast of such events.