2013 ◽  
Vol 118 (24) ◽  
pp. 13,443-13,448 ◽  
Author(s):  
D. W. Shin ◽  
Steve Cocke

2014 ◽  
Vol 111 (24) ◽  
pp. 8776-8781 ◽  
Author(s):  
M. Glotter ◽  
J. Elliott ◽  
D. McInerney ◽  
N. Best ◽  
I. Foster ◽  
...  

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 260
Author(s):  
Mario Raffa ◽  
Alfredo Reder ◽  
Marianna Adinolfi ◽  
Paola Mercogliano

Recently, the European Centre for Medium Range Weather Forecast (ECMWF) has released a new generation of reanalysis, acknowledged as ERA5, representing at the present the most plausible picture for the current climate. Although ERA5 enhancements, in some cases, its coarse spatial resolution (~31 km) could still discourage a direct use of precipitation fields. Such a gap could be faced dynamically downscaling ERA5 at convection permitting scale (resolution < 4 km). On this regard, the selection of the most appropriate nesting strategy (direct one-step against nested two-step) represents a pivotal issue for saving time and computational resources. Two questions may be raised within this context: (i) may the dynamical downscaling of ERA5 accurately represents past precipitation patterns? and (ii) at what extent may the direct nesting strategy performances be adequately for this scope? This work addresses these questions evaluating two ERA5-driven experiments at ~2.2 km grid spacing over part of the central Europe, run using the regional climate model COSMO-CLM with different nesting strategies, for the period 2007–2011. Precipitation data are analysed at different temporal and spatial scales with respect to gridded observational datasets (i.e., E-OBS and RADKLIM-RW) and existing reanalysis products (i.e., ERA5-Land and UERRA). The present work demonstrates that the one-step experiment tendentially outperforms the two-step one when there is no spectral nudging, providing results at different spatial and temporal scales in line with the other existing reanalysis products. However, the results can be highly model and event dependent as some different aspects might need to be considered (i.e., the nesting strategies) during the configuration phase of the climate experiments. For this reason, a clear and consolidated recommendation on this topic cannot be stated. Such a level of confidence could be achieved in future works by increasing the number of cities and events analysed. Nevertheless, these promising results represent a starting point for the optimal experimental configuration assessment, in the frame of future climate studies.


2021 ◽  
Vol 9 (2) ◽  
pp. 208
Author(s):  
Valentina Vannucchi ◽  
Stefano Taddei ◽  
Valerio Capecchi ◽  
Michele Bendoni ◽  
Carlo Brandini

A 29-year wind/wave hindcast is produced over the Mediterranean Sea for the period 1990–2018. The dataset is obtained by downscaling the ERA5 global atmospheric reanalyses, which provide the initial and boundary conditions for a numerical chain based on limited-area weather and wave models: the BOLAM, MOLOCH and WaveWatch III (WW3) models. In the WW3 computational domain, an unstructured mesh is used. The variable resolutions reach up to 500 m along the coasts of the Ligurian and Tyrrhenian seas (Italy), the main objects of the study. The wind/wave hindcast is validated using observations from coastal weather stations and buoys. The wind validation provides velocity correlations between 0.45 and 0.76, while significant wave height correlations are much higher—between 0.89 and 0.96. The results are also compared to the original low-resolution ERA5 dataset, based on assimilated models. The comparison shows that the downscaling improves the hindcast reliability, particularly in the coastal regions, and especially with regard to wind and wave directions.


2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Bin Mu ◽  
Bo Qin ◽  
Shijin Yuan ◽  
Xiaoyun Qin

Climate downscaling is a way to provide finer resolution data at local scales, which has been widely used in meteorological research. The two main approaches for climate downscaling are dynamical and statistical. The traditional dynamical downscaling methods are quite time- and resource-consuming based on general circulation models (GCMs). Recently, more and more researchers construct a statistical deep learning model for climate downscaling motivated by the single-image superresolution (SISR) process in computer vision (CV). This is an approach that uses historical climate observations to learn a low-resolution to high-resolution mapping and produces great enhancements in terms of efficiency and effectiveness. Therefore, it has provided an appreciable new insight and successful downscaling solution to multiple climate phenomena. However, most existing models only make a simple analogy between climate downscaling and SISR and ignore the underlying dynamical mechanisms, which leads to the overaveraged downscaling results lacking crucial physical details. In this paper, we incorporate the a priori meteorological knowledge into a deep learning formalization for climate downscaling. More specifically, we consider the multiscale spatial correlations and the chaos in multiple climate events. Depending on two characteristics, we build up a two-stage deep learning model containing a stepwise reconstruction process and ensemble inference, which is named climate downscaling network (CDN). It can extract more local/remote spatial dependencies and provide more comprehensive captures of extreme conditions. We evaluate our model based on two datasets: climate science dataset (CSD) and benchmark image dataset (BID). The results demonstrate that our model shows the effectiveness and superiority in downscaling daily precipitation data from 2.5 degrees to 0.5 degrees over Asia and Europe. In addition, our model exhibits better performance than the other traditional approaches and state-of-the-art deep learning models.


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