Long-lead predictions of early winter precipitation over South Korea using a SST anomaly pattern in the North Atlantic Ocean

Noh, Gyu-Ho; Ahn, Kuk‐Hyun

doi:10.1007/s00382-021-06109-9

Cited by 8 publications

(3 citation statements)

References 60 publications

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“…In particular, K-Hidra is the closest relative (in terms of data construction) to the chosen reference stations over South Korea. Other studies have mentioned its high quality for our study area [84,85]. Also, from the results, we find that a reliable precipitation product is not necessarily the best for representing temperature (e.g., MERRA).…”

Section: Discussionmentioning

confidence: 66%

Assessment of Suitable Gridded Climate Datasets for Large-Scale Hydrological Modelling over South Korea

Lee

Ahn

2022

Remote Sensing

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There is a large number of grid-based climate datasets available which differ in terms of their data source, estimation procedures, and spatial and temporal resolutions. This study evaluates the performance of diverse meteorological datasets in terms of representing spatio-temporal climate variabilities based on a national-scale domain over South Korea. Eleven precipitation products, including six satellite-based data (CMORPH, MSWEP, MERRA, PERSIANN, TRMM, and TRMM-RT) and five reanalysis-based data (ERA5, JRA-55, CPC-U, NCEP-DOE, and K-Hidra) and four temperature products (MERRA, ERA5, CPC-U, and NCEP-DOE) are investigated. In addition, the hydrological performance of forty-four input combinations of climate datasets are explored by using the Variable Infiltration Capacity (VIC) macroscale model. For this analysis, the VIC model is independently calibrated for each combination of input and the response to each combination is then evaluated with in situ streamflow data. Our results show that the gridded datasets perform differently particularly in representing precipitation variability. When a diverse combination of the datasets are used to represent spatio-temporal variability of streamflow through the hydrological model, K-Hidra and CPC-U performed best for precipitation and temperature, followed by the MERRA and ERA5 datasets, respectively. Lastly, we obtain only marginal improvement in the hydrological performance when utilizing multiple climate datasets after comparing it to a single hydrological simulation with the best performing climate dataset. Overall, our results indicate that the hydrological performance may vary considerably based on the selection of climate datasets, emphasizing the importance of regional evaluation studies for meteorological datasets.

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Section: Discussionmentioning

confidence: 66%

Assessment of Suitable Gridded Climate Datasets for Large-Scale Hydrological Modelling over South Korea

Lee

Ahn

2022

Remote Sensing

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“…To our knowledge, whereas many studies have analysed the possibility of producing statistical seasonal forecasts for South Korea at either national or regional scale (Jin et al, 2005; Kim et al, 2020a, 2020b; Kim & Kug, 2018; Lee & Julien, 2016; Noh & Ahn, 2022; Son et al, 2015), there has been no previous attempt to assess the skill of seasonal precipitation forecasts from GCMs across catchments in South Korea. Few studies (Hyun et al, 2020; Kim et al, 2021; Lee et al, 2016) described the GCM‐based forecasting systems run by Korea Meteorology Administration (KMA) and the APEC (Asia‐Pacific Economic Cooperation) Climate Centre (APCC), but they mainly focused on the comparison between different versions of the operational forecasting system and the skill of weather variables at a global scale.…”

Section: Introductionmentioning

confidence: 99%

Catchment‐scale skill assessment of seasonal precipitation forecasts across South Korea

Lee

Peñuela

Pianosi

et al. 2023

Intl Journal of Climatology

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Climate change is expected to make droughts more frequent and severe all over the world. South Korea is no exception and is already suffering from extreme droughts such as one that prolonged from 2013 to 2015 and caused nation‐wide damage. To mitigate drought damages, better management of existing water infrastructure is essential. A promising opportunity to improve operational decisions is to make use of seasonal weather forecasts that are provided by general circulation models and can be downscaled to the catchment scale. This study hence assesses the skill of seasonal forecasts over 20 catchments in South Korea, where the largest reservoirs are located. Datasets from four weather forecasting centres (ECMWF, UK Met Office, Météo France and DWD) have been evaluated over the period 2011–2020, and their skill quantified using the Continuous Ranked Probability Skill Score (CRPSS). We analyse how skill varies across the seasons and years, and if it can be linked to catchments characteristics. In doing so, we develop a methodology and a Python package to implement it, which is freely available for future applications to other regions. As for the study case, our results showed that among the four forecasting centres, ECMWF's forecasts were the most skilful in South Korea. In particular, seasonal forecasts outperform the climatology for 2 months of lead time and are more skilful during the wet season and in dry years. Linear bias correction is found to be useful to correct systematic seasonal biases, whereas we found no significant correlation between the catchment characteristics and forecast skill. We also investigated the possibility of anticipating dry years from seasonal forecasts and/or ENSO indices but found no significant link.

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“…With the rise in global warming and technological growth, the Arctic has become a strategically important region for countries around the world and is of great importance in terms of economic and trade flows, military, and tourism [1][2][3]. However, Arctic sea ice, an important factor in the global climate system, is steadily melting [4,5], which affects the ecological and climatic environment of the Arctic region [6][7][8]. From other perspectives, the melting of the Arctic sea ice also facilitates the opening of the Arctic corridor and helps to save trade miles and navigation costs, which has important military and trade implications [9].…”

Section: Introductionmentioning

confidence: 99%

Ice Identification with Error-Accumulation Enhanced Neural Dynamics in Optical Remote Sensing Images

Xiong,

Wang,

et al. 2023

Remote Sensing

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Arctic sea ice plays an important role in Arctic-related research. Therefore, how to identify Arctic sea ice from remote sensing images with high quality in an unavoidable noise environment is an urgent challenge to be solved. In this paper, a constrained energy minimization (CEM) method is applied for Arctic sea ice identification, which only requires the target spectrum. Moreover, an error-accumulation enhanced neural dynamics (EAEND) model with strong noise immunity and high computing accuracy is proposed to aid with the CEM method for Arctic sea ice identification. With the theoretical analysis, the proposed EAEND model possesses a small steady-state error in noisy environments. Finally, compared with other existing models, the proposed EAEND model can not only complete sea ice identification in excellent fashion, but also has the advantages of high efficiency and noise immunity.

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Long-lead predictions of early winter precipitation over South Korea using a SST anomaly pattern in the North Atlantic Ocean

Cited by 8 publications

References 60 publications

Assessment of Suitable Gridded Climate Datasets for Large-Scale Hydrological Modelling over South Korea

Assessment of Suitable Gridded Climate Datasets for Large-Scale Hydrological Modelling over South Korea

Catchment‐scale skill assessment of seasonal precipitation forecasts across South Korea

Ice Identification with Error-Accumulation Enhanced Neural Dynamics in Optical Remote Sensing Images

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