Efficient dynamical downscaling of general circulation models using continuous data assimilation

Abstract

Continuous data assimilation (CDA) is successfully implemented for the first time for efficient dynamical downscaling of a global atmospheric reanalysis. A comparison of the performance of CDA with the standard grid and spectral nudging techniques for representing long‐ and short‐scale features in the downscaled fields using the Weather Research and Forecast (WRF) model is further presented and analysed. The WRF model is configured at 0.25° × 0.25° horizontal resolution and is driven by 2.5° × 2.5° initial and boundary conditions from NCEP/NCAR reanalysis fields. Downscaling experiments are performed over a one‐month period in January 2016. The similarity metric is used to evaluate the performance of the downscaling methods for large (2,000 km) and small (300 km) scales. Similarity results are compared for the outputs of the WRF model with different downscaling techniques, NCEP/NCAR reanalysis, and NCEP Final Analysis (FNL, available at 0.25° × 0.25° horizontal resolution). Both spectral nudging and CDA describe better the small‐scale features compared to grid nudging. The choice of the wave number is critical in spectral nudging; increasing the number of retained frequencies generally produced better small‐scale features, but only up to a certain threshold after which its solution gradually became closer to grid nudging. CDA maintains the balance of the large‐ and small‐scale features similar to that of the best simulation achieved by the best spectral nudging configuration, without the need of a spectral decomposition. The different downscaled atmospheric variables, including rainfall distribution, with CDA is most consistent with the observations. The Brier skill score values further indicate that the added value of CDA is distributed over the entire model domain. The overall results clearly suggest that CDA provides an efficient new approach for dynamical downscaling by maintaining better balance between the global model and the downscaled fields.

Towards an End-to-End Analysis and Prediction System for Weather, Climate, and Marine Applications in the Red Sea

“…we have further recently introduced and successfully tested a new dynamical downscaling algorithm with WRF, based on the Continuous Data Assimilation approach (Desamsetti et al 2019). Downscaling ensembles from the global operational centers remain computationally demanding in terms of both computational cost and storage.…”

Continuous data assimilation applied to a velocity-vorticity formulation of the 2D Navier-Stokes equations

“…Long-time accuracy of Algorithm 3.1. We now consider the difference between the solutions of (13) - (15) to the NSE solution. We will show that the algorithm solution converges to the true solution, up to an optimal O(∆t + h k+1 ) discretization error, independent of the initial condition, provided a restriction on the coarse mesh width and nudging parameters.…”

“…This stimulated a large amount of recent research on the CDA algorithm; see, e.g., [3,6,7,10,11,13,17,18,19,20,21,22,27,31,30,35,40,41] and the references therein. The recent paper [15] showed that CDA can be effectively used for weather prediction, showing that it can indeed be a powerful tool on practical large scale problems. Convergence of discretizations of CDA models was studied in [30,41,27,21] , and found results similar to those at the continuous level.…”

CMEMS-Based Coastal Analyses: Conditioning, Coupling and Limits for Applications

“…However, at high frequencies in-situ floating measurements have to be treated carefully since error profiles may show relevant peaks when the buoy resonance falls close to the driving metocean forcings. Model conditioning, based on assimilating RS/in-situ data as a Bayesian (conditional) estimation problem (Desamsetti et al, 2019), benefits from considering spatially distributed observations (for spatial structure) and high resolution time series (for temporal structure). The assimilation should consider bulk variables (e.g., significant wave height) and physically key properties, such as spectral tails, because well reproduced tails indicate a physically correct balance between generation and dissipation, adding reliability to the conditioning.…”