SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018

Tang, Guoqiang; Clark, Martyn P.; Newman, Andrew J.; Wood, A. W.; Papalexiou, Simon Michael; Vionnet, Vincent; Whitfield, Paul H.

doi:10.5194/essd-12-2381-2020

Cited by 42 publications

(30 citation statements)

References 76 publications

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“…11b), because the observed precipitation samples from SCDNA increase during this period (Fig. 2 in Tang et al, 2020c). The increasing trend of BSS is particularly prominent from 2003 to 2009, during which precipitation samples in the USA experience the greatest increase (Tang et al, 2020c).…”

Section: Evaluation Of Probabilistic Estimatesmentioning

confidence: 90%

“…Station observations are often subject to temporal discontinuities caused by missing values and short record lengths (Kemp et al, 1983). This study uses station precipitation and minimum and maximum temperature data from the Serially Complete Dataset for North America (SCDNA; Tang et al, 2020c), which is an open-access dataset on Zenodo (https://doi.org/10.5281/zenodo.3735533). Serially complete datasets improve the quality of spatial interpolation estimates compared to raw station observations with data gaps (Longman et al, 2020;Tang et al, 2021).…”

Section: Datasetsmentioning

confidence: 99%

“…SCDNA uses reanalysis estimates as the auxiliary data to ensure temporal completeness in sparsely gauged regions. The production of SCDNA has nine steps: (1) matching reanalysis estimates and station data, (2) selecting qualified neighboring stations, (3) building empirical cumulative density functions (CDFs), (4) estimation based on 16 strategies for each day of the year, (5) independent validation, (6) merging estimates from the 16 strategies, (7) climatological bias correction, (8) evaluation of SCDNA, and (9) final quality control (Tang et al, 2020c). SCDNA covers the period from 1979 to 2018 and has 24 615 precipitation stations and 19 579 temperature stations.…”

Section: Datasetsmentioning

confidence: 99%

See 2 more Smart Citations

EMDNA: an Ensemble Meteorological Dataset for North America

Tang

Clark

Papalexiou

et al. 2021

Earth Syst. Sci. Data

Self Cite

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Abstract. Probabilistic methods are useful to estimate the uncertainty in spatial meteorological fields (e.g., the uncertainty in spatial patterns of precipitation and temperature across large domains). In ensemble probabilistic methods, “equally plausible” ensemble members are used to approximate the probability distribution, hence the uncertainty, of a spatially distributed meteorological variable conditioned to the available information. The ensemble members can be used to evaluate the impact of uncertainties in spatial meteorological fields for a myriad of applications. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 ensemble members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1∘ spatial resolution (approx. 10 km grids) from 1979 to 2018, derived from a fusion of station observations and reanalysis model outputs. The station data used in EMDNA are from a serially complete dataset for North America (SCDNA) that fills gaps in precipitation and temperature measurements using multiple strategies. Outputs from three reanalysis products are regridded, corrected, and merged using Bayesian model averaging. Optimal interpolation (OI) is used to merge station- and reanalysis-based estimates. EMDNA estimates are generated using spatiotemporally correlated random fields to sample from the OI estimates. Evaluation results show that (1) the merged reanalysis estimates outperform raw reanalysis estimates, particularly in high latitudes and mountainous regions; (2) the OI estimates are more accurate than the reanalysis and station-based regression estimates, with the most notable improvements for precipitation evident in sparsely gauged regions; and (3) EMDNA estimates exhibit good performance according to the diagrams and metrics used for probabilistic evaluation. We discuss the limitations of the current framework and highlight that further research is needed to improve ensemble meteorological datasets. Overall, EMDNA is expected to be useful for hydrological and meteorological applications in North America. The entire dataset and a teaser dataset (a small subset of EMDNA for easy download and preview) are available at https://doi.org/10.20383/101.0275 (Tang et al., 2020a).

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Section: Evaluation Of Probabilistic Estimatesmentioning

confidence: 90%

Section: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

EMDNA: an Ensemble Meteorological Dataset for North America

Tang

Clark

Papalexiou

et al. 2021

Earth Syst. Sci. Data

Self Cite

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show abstract

“…A recent addition to the reanalysis landscape is the serially complete data for North America (SCDNA) product that provides a 10 km reanalysis of precipitation and minimum and maximum temperature from 1979 to 2018 across all of North America [21]. Through the Canadian Centre for Climate Modelling and Analysis, there is improved dissemination of 10 km regridded and downscaled climate data for Canadian watersheds to standardize future analyses and improve intercomparison [22].…”

Section: Emerging Continental Datasetsmentioning

confidence: 99%

Canadian Continental-Scale Hydrology under a Changing Climate: A Review

Stadnyk

Déry

2021

Water

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Canada, like other high latitude cold regions on Earth, is experiencing some of the most accelerated and intense warming resulting from global climate change. In the northern regions, Arctic amplification has resulted in warming two to three times greater than global mean temperature trends. Unprecedented warming is matched by intensification of wet and dry regions and hydroclimatic cycles, which is altering the spatial and seasonal distribution of surface waters in Canada. Diagnosing and tracking hydrologic change across Canada requires the implementation of continental-scale prediction models owing the size of Canada’s drainage basins, their distribution across multiple eco- and climatic zones, and the scarcity and paucity of observational networks. This review examines the current state of continental-scale climate change across Canada and the anticipated impacts to freshwater availability, including the role of anthropogenic regulation. The review focuses on continental and regional-scale prediction that underpins operational design and long-term resource planning and management in Canada. While there are significant process-based changes being experienced within Canadian catchments that are equally—if not more so—critical for community water availability, the focus of this review is on the cumulative effects of climate change and anthropogenic regulation for the Canadian freshwater supply.

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“…Station observations often have missing values and short record lengths (Kemp et al, 1983). This study uses station precipitation and minimum/maximum temperature data from the Serially Complete Dataset for North America (Tang et al, 2020c), which is open-access on Zenodo (https://doi.org/10.5281/zenodo.3735533; Access Date: July 25, 2020).…”

Section: Datasetsmentioning

confidence: 99%

EMDNA: Ensemble Meteorological Dataset for North America

Tang¹,

Clark²,

Papalexiou³

et al. 2020

Preprint

Self Cite

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Abstract. Probabilistic methods are very useful to estimate the spatial variability in meteorological conditions (e.g., spatial patterns of precipitation and temperature across large domains). In ensemble probabilistic methods, equally plausible ensemble members are used to approximate the probability distribution, hence uncertainty, of a spatially distributed meteorological variable conditioned on the available information. The ensemble can be used to evaluate the impact of the uncertainties in a myriad of applications. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1° spatial resolution from 1979 to 2018, derived from a fusion of station observations and reanalysis model outputs. The station data used in EMDNA are from a serially complete dataset for North America (SCDNA) that fills gaps in precipitation and temperature measurements using multiple strategies. Outputs from three reanalysis products are regridded, corrected, and merged using the Bayesian Model Averaging. Optimal Interpolation (OI) is used to merge station- and reanalysis-based estimates. EMDNA estimates are generated based on OI estimates and spatiotemporally correlated random fields. Evaluation results show that (1) the merged reanalysis estimates outperform raw reanalysis estimates, particularly in high latitudes and mountainous regions; (2) the OI estimates are more accurate than the reanalysis and station-based regression estimates, with the most notable improvement for precipitation occurring in sparsely gauged regions; and (3) EMDNA estimates exhibit good performance according to the diagrams and metrics used for probabilistic evaluation. We also discuss the limitations of the current framework and highlight that persistent efforts are needed to further develop probabilistic methods and ensemble datasets. Overall, EMDNA is expected to be useful for hydrological and meteorological applications in North America. The whole dataset and a teaser dataset (a small subset of EMDNA for easy download and preview) are available at https://doi.org/10.20383/101.0275 (Tang et al., 2020a).

show abstract

SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018

Cited by 42 publications

References 76 publications

EMDNA: an Ensemble Meteorological Dataset for North America

EMDNA: an Ensemble Meteorological Dataset for North America

Canadian Continental-Scale Hydrology under a Changing Climate: A Review

EMDNA: Ensemble Meteorological Dataset for North America

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