Developed for short-term (0-48 h) wind power forecasting purposes, high-resolution meteorological forecasts for Eastern Canada are available from Environment Canada's Numerical Weather Prediction (NWP) model configured on a limited area (GEM-LAM). This paper uses 3 years of forecast data from this model for the region of North Cape (Prince Edward Island, Canada). Although the model resolution is relatively high (2.5 km), statistical analysis and site inspection reveal that the model does not have a sufficiently refined grid to properly represent the meteorological phenomena over this complex coastal site. To cope with such representation error, a generalized Geophysic Model Output Statistics (GMOS) module is developed and applied to reduce the forecast error of the NWP forecasts. GMOS differs from other Model Output Statistics (MOS) that are widely used by meteorological centres in the following aspects: (i) GMOS takes into account the surrounding geophysical parameters such as surface roughness, terrain height, etc., along with wind direction; (ii) GMOS can be directly applied for model output correction without any training.Compared with other methods, GMOS using a multiple grid point approach improves the GEM-LAM predictions root mean squared error by 1-5% for all time horizons and most meteorological conditions. Also, the topographic signature of the forecast error (uneven directional distribution of the forecast error related to the surface characteristics) due to misrepresentation issues is significantly reduced. The NWP forecasts combined with GMOS outperform the persistence model from a 2 h horizon, instead of 3 h using MOS. Finally, GMOS is applied and validated at two other sites located in New Brunswick, Canada. Similar improvements on the forecasts were observed, thus showing the general applicability of GMOS.
Two types of approaches are commonly used for estimating the impact of arbitrary subsets of observations on short-range forecast error. The first was developed for variational data assimilation systems and requires the adjoint of the forecast model. Comparable approaches were developed for use with the ensemble Kalman filter and rely on ensembles of forecasts. In this study, a new approach for computing observation impact is proposed for ensemble–variational data assimilation (EnVar). Like standard adjoint approaches, the adjoint of the data assimilation procedure is implemented through the iterative minimization of a modified cost function. However, like ensemble approaches, the adjoint of the forecast step is obtained by using an ensemble of forecasts. Numerical experiments were performed to compare the new approach with the standard adjoint approach in the context of operational deterministic NWP. Generally similar results are obtained with both approaches, especially when the new approach uses covariance localization that is horizontally advected between analysis and forecast times. However, large differences in estimated impacts are obtained for some surface observations. Vertical propagation of the observation impact is noticeably restricted with the new approach because of vertical covariance localization. The new approach is used to evaluate changes in observation impact as a result of the use of interchannel observation error correlations for radiance observations. The estimated observation impact in similarly configured global and regional prediction systems is also compared. Overall, the new approach should provide useful estimates of observation impact for data assimilation systems based on EnVar when an adjoint model is not available.
It is still common to neglect the spatial error correlations of assimilated observations in numerical weather prediction systems because no practical approach is available to account for them when the number of observations with correlated error is large or when these observations are non‐uniformly distributed. Instead, it is common practice to inflate observation error variances to avoid overfitting large scales and spatially thin observations to reduce error correlations between remaining observations, although both methods generally sacrifice small‐scale information. Inspired by previous work on assimilating the difference between adjacent observations (so‐called spatial difference observations), this study aims at combining direct observations with spatial difference observations in the assimilation to extract both large‐ and smaller‐scale information from observations with spatially correlated errors, while still neglecting these error correlations. Experiments performed in a simplified 1D context over a periodic domain show that the combined approach is numerically equivalent to directly assimilating observations available at every grid point using non‐diagonal observation error covariances based on a first‐order autoregressive correlation function. In a case where observation error correlations have a different structure (e.g. Gaussian), the true observation error correlations are not perfectly taken into account by the combined approach, but it still efficiently extracts information to correct the scales that have the most errors. Combining direct observations with spatial difference observations proves complementary and experimental results show lower analysis errors for both large and intermediate scales. More specifically, while neglecting spatial observation error correlations, the combined approach provides results with lower analysis error than the direct approach, especially when spatial error correlations are large and when direct observations are spatially thinned.
The exigencies of the global community toward Earth system science will increase in the future as the human population, economies, and the human footprint on the planet continue to grow. This growth, combined with intensifying urbanization, will inevitably exert increasing pressure on all ecosystem services. A unified interdisciplinary approach to Earth system science is required that can address this challenge, integrate technical demands and long-term visions, and reconcile user demands with scientific feasibility. Together with the research arms of the World Meteorological Organization, the Young Earth System Scientists community has gathered early-career scientists from around the world to initiate a discussion about frontiers of Earth system science. To provide optimal information for society, Earth system science has to provide a comprehensive understanding of the physical processes that drive the Earth system and anthropogenic influences. This understanding will be reflected in seamless prediction systems for environmental processes that are robust and instructive to local users on all scales. Such prediction systems require improved physical process understanding, more high-resolution global observations, and advanced modeling capability, as well as high-performance computing on unprecedented scales. At the same time, the robustness and usability of such prediction systems also depend on deepening our understanding of the entire Earth system and improved communication between end users and researchers. Earth system science is the fundamental baseline for understanding the Earth’s capacity to accommodate humanity, and it provides a means to have a rational discussion about the consequences and limits of anthropogenic influence on Earth. Without its progress, truly sustainable development will be impossible.
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