Regional Data Assimilation with the NCMRWF Unified Model (NCUM): Impact of Doppler Weather Radar Radial Wind

Dutta, Devajyoti; Routray, Aurobinda; Kumar, D. Preveen; George, John P.

doi:10.1007/s00024-019-02159-7

Cited by 9 publications

(2 citation statements)

References 41 publications

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“…In contrast, for regional simulation, scaling ML to km-scales is attractive. High-quality training data is abundant as many national weather agencies couple km-scale numerical weather models in a limited domain to coarser resolution global models [16] -a process called dynamical downscaling. Since these predictions are augmented by data assimilation from ground-based precipitation radar and other sensors, good estimates of regional km-scale atmospheric states exists [13].…”

Section: Introductionmentioning

confidence: 99%

Residual Diffusion Modeling for Km-scale Atmospheric Downscaling

Mardani,

Brenowitz,

Cohen

et al. 2024

Preprint

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Predictions of weather hazard require expensive km-scale simulations driven by coarser global inputs. Here, a cost-effective stochastic downscaling model is trained from a high-resolution 2-km weather model over Taiwan conditioned on 25-km ERA5 reanalysis. To address the multi-scale machine learning challenges of weather data, we employ a two-step approach Corrector Diffusion (CorrDiff), where a UNet prediction of the mean is corrected by a diffusion step. Akin to Reynolds decomposition in fluid dynamics, this isolates generative learning to the stochastic scales. CorrDiff exhibits skillful RMSE and CRPS and faithfully recovers spectra and distributions even for extremes. Case studies of coherent weather phenomena reveal appropriate multivariate relationships reminiscent of learnt physics: the collocation of intense rainfall and sharp gradients in fronts and extreme winds and rainfall bands near the eyewall of typhoons. Downscaling global forecasts successfully retains many of these benefits, foreshadowing the potential of end-to-end, global-to- km-scales machine learning weather predictions.

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Section: Introductionmentioning

confidence: 99%

Residual Diffusion Modeling for Km-scale Atmospheric Downscaling

Mardani,

Brenowitz,

Cohen

et al. 2024

Preprint

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“…With the development of high‐resolution numerical weather prediction (NWP) models and increased computing resources, the predictability of high‐impact weather events has been improved (Dutta et al ., 2018). It has been proved that the forecast skills of high‐resolution NWP models largely depend upon the quality of the initial conditions provided (Dutta et al ., 2019). Thus, the assimilation of observations at high temporal and spatial resolutions is required to provide an initial state that contains information at a suitable scale (Dance, 2004; Baxter et al ., 2011).…”

Section: Introductionmentioning

confidence: 99%

Assimilation of Doppler radar radial wind data in the GRAPES mesoscale model with observation error covariances tuning

Yin

Han

Gao

et al. 2021

Quart J Royal Meteoro Soc

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The accurate estimation of observation error statistics for high-density remote-sensing data is a fundamental step toward optimally utilizing such data, including radar data, for operational numerical weather prediction. Based on the high-resolution (3 × 3 km) GRAPES model together with the three-dimensional variational assimilation system (GRAPES_3DVAR) developed by the China Meteorological Administration, a set of assimilation processes for radar radial wind data is designed. First, a scheme is proposed to guarantee the quality of radar radial wind data by correcting the velocity ambiguity for the data. The observation error covariances of the radar data in the GRAPES_3DVAR assimilation system are re-estimated by using a diagnostic method based on analysis residuals, showing that the re-estimated radial wind observation error covariances increase with altitude. An optimal regularization parameter is selected by the L-curve method to constrain the radar observations that were assimilated using inaccurate observation error covariances. Furthermore, to verify the assimilation effects of the refined radial velocity observation error covariances and the constrained observations by the regularization parameter on the analysis and forecasting, a numerical study is carried out on typhoon Yunque, which occurred on 3 August 2018. The results show that the modified Doppler radar wind data assimilation using the re-estimated radial velocity observation error covariances enhances the wind analysis field and the water vapour transport, and accordingly obtains better short-term precipitation estimates. The radar wind data assimilation results of using the constrained observations also indicate an enhanced wind analysis field and water vapour transport, which leads to better short-term precipitation estimates.

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