Atmospheric Modeling, Data Assimilation and Predictability

By Eugenia Kalnay. 2002

3,277 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/73452deffbdc7636502756e3961ab657ddb0ccc2

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Citations in the corpus

Impact of Atmosphere-Ocean Coupling on the Predictability of Monsoon Intraseasonal Oscillations (2007, background): “From the historical view in the progress of weather forecast (Kalnay 2003), we learned that both statistical and dynamical models significantly contribute to the improvement of forecast.”
Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part I: Observation Operators for Reflectivity and Polarimetric Variables (2008, positive): “Modern data assimilation (DA) techniques such as 3D and 4D variational data assimilation (3DVAR and 4DVAR, respectively), and ensemble Kalman filter (EnKF) methods are able to assimilate observations directly using the forward observation operators that link the model state variables to the observat”
The Observed State of the Energy Budget in the Early Twenty-First Century (2015, methodology): “Using well-documented variational methods (Rodgers 2000; Kalnay 2003), atmospheric and surface energy and water cycle closure equations are incorporated as soft constraints, yielding balanced energy budgets on continental scales with modest adjustments to each of the component fluxes based on…”
Data Assimilation for Numerical Weather Prediction: A Review (2009, methodology): “D VAR are enumerated in Kalnay (2003).”
A review of numerical methods for nonlinear partial differential equations (2012, positive): “Instead, there is a growing role for nonlinear stochastic PDEs [112, 146, 42] with a wide range of applications, from ﬁnancial models and data assimilation in atmospheric sciences to material sciences and biological models; see [118, 185, 116, 121, 154, 169, 166] and the references therein.”
Data assimilation: making sense of Earth Observation (2014, methodology): “Kalnay (2003) reviews the fundamental concepts of chaotic systems.”
Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, background): “For instance, one promising direction is to forecast future events using ML and continuously updating model states by incorporating ideas from data assimilation [124].”
Calibration/Data Assimilation Approach for Integrating GRACE Data into the WaterGAP Global Hydrology Model (WGHM) Using an Ensemble Kalman Filter: First Results (2013, methodology): “Variational algorithms employ advanced numerical methods to minimize an optimization functional directly, while they usually require the generation of adjoint code to do so (Kalnay 2003).”
Effects of stochastic parametrizations in the Lorenz '96 system (2005, background): “Finally, an attribute of interest is the potential for ensemble dispersion to predict accuracy of the ensemble-mean forecast, in order to ‘forecast forecast skill’ (e.g. Kalnay and Dalcher 1987; Houtekamer et al. 1996; Buizza and Palmer 1998).”
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators (2022, methodology): “ERA5 is an atmospheric reanalysis [Kalnay et al., 1996] dataset and is the result of an optimal combination of observations from various measurement sources and the output of a numerical model using a Bayesian estimation process called data-assimilation [Kalnay, 2003].”
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere (2023, methodology): “It is the result of reanalysis, a process that uses data-assimilation (Carrassi et al., 2018; Kalnay, 2003) to combine modern numerical weather forecasting models with historical observational records to produce an estimate of the historical ocean-atmosphere system.”
A Dual-Weighted Approach to Order Reduction in 4DVAR Data Assimilation (2008, methodology): “An ensemble of model forecasts initialized at t0 with perturbations that capture the main directions of variability of the model, such as the bred vectors and singular vectors of the tangent linear model (Kalnay 2003), may be used to generate snapshots taken from multiple state calculations.”
Further improvements to the Statistical Hurricane Intensity Prediction Scheme (SHIPS) (2005, methodology): “The SHIPS model utilizes a " perfect prog " approach (Kalnay 2003) where the predictors are calculated from analyses along the best-track positions in the dependent sample to develop the coefficients but applied using the NCEP global model along a forecast track in real time.”