Assimilation Of Meteorological Observations Research Articles

Absorbing aerosols over Asia – an inter-model and model-observation comparison study using CAM5.3-Oslo

Aerosol absorption constitutes a significant component of the total radiative effect of aerosols, and hence its representation in general circulation models is crucial to radiative forcing estimates. We use here multiple observations to evaluate the performance of CAM5.3-Oslo with respect to its aerosol representation. CAM5.3-Oslo is the atmospheric component of the earth system model NorESM1.2 and shows on average an underestimation of aerosol absorption in the focus region over East and South Asia and a strong aerosol absorption overestimation in desert and arid regions compared to observations and other AeroCom phase III models. We explore the reasons of the model spread and find that it is related to the column burden and residence time of absorbing aerosols, in particular black carbon and dust. We conduct further sensitivity simulations with CAM5.3-Oslo to identify processes which are most important for modelled aerosol absorption. The sensitivity experiments target aerosol optical properties, and contrast their impact with effects from changes in emissions and deposition processes, and the driving meteorology. An improved agreement with observations was found with the use of a refined emission data set, transient emissions and assimilation of meteorological observations. Changes in optical properties of absorbing aerosols can also reduce the under- and overestimation of aerosol absorption in the model. However, changes in aerosol absorption strength between the sensitivity experiments are small compared to the inter-model spread among the AeroCom phase III models.

COVID-19 Pandemic Imperils Weather Forecast.

Weather forecasts play essential parts in economic activity. Assimilation of meteorological observations from aircraft improves forecasts greatly. However, global lockdown during the COVID‐19 pandemic (March to May 2020) has eliminated 50‐75% aircraft observations and imperiled weather forecasting. Here, we verify global forecasts against reanalysis to quantify the impact of the pandemic. We find a large deterioration in forecasts of surface meteorology over regions with busy air flights, such as North America, southeast China, and Australia. Forecasts over remote regions are also substantially worse during March to May 2020 than 2017–2019, and the deterioration increases for longer‐term forecasts. This could handicap early warning of extreme weather and cause additional economic damage on the top of that from the pandemic. The impact over Western Europe is buffered by the high density of conventional observations, suggesting that introduction of new observations in data‐sparse regions would be needed to minimize the impact of global emergencies on weather forecasts.

Variational assimilation of meteorological observations with the adjoint vorticity equation. II: Numerical results

Variational assimilation of meteorological observations with the adjoint vorticity equation. II: Numerical results

Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects

Two general algorithms for solving constrained minimization problems are presented and discussed in the context of analysis and assimilation of meteorological observations. In both algorithms, the original constrained problem is transformed by appropriate modifications into one unconstrained problem, or into a sequence of unconstrained problems. The main advantage of proceeding in this way is that the new unconstrained problems can be solved by classical descent algorithms, thus avoiding the need of directly solving the Euler-Lagrange equations of the original constrained problem. The first algorithm presented in the augmented lagrangian algorithm. It generalizes the more classical penalty and duality algorithms. The second algorithm, inspired from optimal control techniques, is based on an appropriate use of an adjoint dynamical equation, and seems to be particularly well adapted to the assimilation of observations distributed in time. Simple numerical examples show the ability of these algorithms to solve non-linear minimization problems of the type encountered in meteorology. Their possible use in more complex situations is discussed, in particular in terms of their computational cost. DOI: 10.1111/j.1600-0870.1986.tb00459.x

Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects

Two general algorithms for solving constrained minimization problems are presented and discussed in the context of analysis and assimilation of meteorological observations. In both algorithms, the original constrained problem is transformed by appropriate modifications into one unconstrained problem, or into a sequence of unconstrained problems. The main advantage of proceeding in this way is that the new unconstrained problems can be solved by classical descent algorithms, thus avoiding the need of directly solving the Euler-Lagrange equations of the original constrained problem. The first algorithm presented in the augmented lagrangian algorithm. It generalizes the more classical penalty and duality algorithms. The second algorithm, inspired from optimal control techniques, is based on an appropriate use of an adjoint dynamical equation, and seems to be particularly well adapted to the assimilation of observations distributed in time. Simple numerical examples show the ability of these algorithms to solve non-linear minimization problems of the type encountered in meteorology. Their possible use in more complex situations is discussed, in particular in terms of their computational cost. DOI: 10.1111/j.1600-0870.1986.tb00459.x