A Dynamic Blending Scheme to Mitigate Large‐Scale Bias in Regional Models

Abstract

Several blending methods have been developed in dynamic downscaling and rapid cycled data assimilation. Blending the large‐scale part of the global model (GM) analysis or forecast has led to improvement in regional model (RM) simulations. However, in previous studies the blended waveband of the GM has generally been determined using a fixed, arbitrarily chosen cutoff wave number. Here we introduce a new dynamic blending (DB) scheme with a dynamic cutoff wave number computed according to the spectral characteristics of GM forecast quality and the spectral distribution of errors in the RM. The DB scheme is described and applied to eight‐day summertime and seven‐day wintertime cycled Weather Research and Forecasting Model forecasts over a regional domain in the continental United States. The scheme can determine a cutoff wave number with significant temporal variations. The temporal variation results from the error growth property of the RM and has a clear diurnal oscillation, suggesting that fewer (more) GM waves should be introduced into the RM at noon (night). The cutoff wave number difference between the two periods indicates seasonal variation of the cutoff wave number with larger day‐to‐day change in winter. Comparison among no blending experiment, two fixed wave number blending experiments, and two DB experiments with and without vertically varying cutoff wave number suggests that the DB scheme with vertically averaged but temporally varying cutoff wave number results in less model bias and less disturbance to the RM dynamic balance. By reducing the forecast background error, the DB scheme can potentially provide improved first guess for a rapid‐update‐cycle weather forecast system.

Reducing the spin‐up of a regional NWP system without data assimilation

“…As in other work (e.g., Wang et al ., 2014; Feng et al ., 2020), we adopt the sixth‐order implicit tangent filter described by Raymond (1988; referred to as the Raymond filter hereafter), which is particularly suitable for use in limited‐area models. At a given wavenumber $$$ k $$$, the amplitude response of the Raymond filter is $$$$ \hat{\alpha}(k)={\left[1+\epsilon {\tan}^6\left(\frac{k\Delta}{2}\right)\right]}^{-1}, $$$$ where $$$$ \epsilon ={\tan}^{-6}\left(\frac{k_{\mathrm{c}}\Delta}{2}\right), $$$$ $$$ \Delta $$$ is the model grid spacing, and $$$ {k}_{\mathrm{c}} $$$ is a characteristic wavenumber at which the Raymond filter possesses half power, $$$ \hat{\alpha}\left({k}_{\mathrm{c}}\right)=0.5 $$$.…”

“…As in other work (e.g., Wang et al, 2014;Feng et al, 2020), we adopt the sixth-order implicit tangent filter described by Raymond (1988; referred to as the Raymond filter hereafter), which is particularly suitable for use in limited-area models. At a given wavenumber k, the amplitude response of the Raymond filter is…”

Improving Surface Wind Speed Forecasts Using an Offline Surface Multilayer Model With Optimal Ground Forcing

“…Negative biases occur in a few windy mountainous areas of Xinjiang, northern Inner Mongolia, and the Qinghai-Tibet Plateau (Lew, 2000). Previous studies have reported similar SWS prediction errors in other regions (Duan et al, 2018;Feng, Sun, & Zhang, 2020;Misaki et al, 2019;Pan et al, 2021;Shimada et al, 2011;Wyszogrodzki et al, 2013).…”

On Applying Large-Scale Correction to Limited-Area Numerical Weather Prediction Models

“…Despite the inconsistent x b and K, background blending has been demonstrated to significantly improve the forecast quality in different parts of the world [5,12,15,16]. This suggests that the errors due to incorrect large-scale information in LAMs are perhaps more significant than the errors due to the aforementioned inconsistency.…”