EMDNA: Ensemble Meteorological Dataset for North America

Abstract

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).

EMDNA: an Ensemble Meteorological Dataset for North America

“…3 and 6) is caused by the inconsistent reporting time of stations. Daily precipitation from reanalysis products is accumulated from 00:00 to 24:00 UTC, while stations from different countries or regions usually have different UTC accumulation periods (Beck et al, 2019;Tang et al, 2020a). NRMSE is higher in central CONUS and Mexico compared to other regions.…”

“…The EMDNA dataset is available at https://doi.org/10.20383/101.0275 (Tang et al, 2020a) in netCDF format. Individual ensemble member; ensemble mean; and ensemble spread of precipitation, T mean , and T range are provided.…”

An ERA‐5 Derived CONUS‐Wide High‐Resolution Precipitation Dataset Based on a Refined Parametric Statistical Downscaling Framework

“…Current and future climate adaptation strategies are founded on the quality of hydrologic simulations, which are constrained by the characteristics of the precipitation forcing fields (see e.g., Chang et al., 2018; Das et al., 2008; Haddeland et al., 2002; Hagemann et al., 2013; Kaleris & Langousis, 2017; Langousis et al., 2018; Perra et al., 2018; Perra et al., 2020; Reshmidevi et al., 2018; Seyyedi et al., 2014, 2015; Smith et al., 2004; Sulis et al., 2011; Tang, Clark, Papalexiou, et al., 2020; Zehe et al., 2005, among others). In order to accurately assess hydroclimatic risk and the subsequent impacts of extreme events, such as flood inundation levels or infrastructure damage, multi‐year precipitation datasets at adequately high spatial and temporal resolutions are required (see e.g., Camici et al., 2014; Deidda et al., 2013; Johnson & Sharma, 2011; Kirchmeier‐Young et al., 2019; Langousis & Kaleris, 2014; Mamalakis et al., 2017; Maurer et al., 2016; Ochoa‐Rodriguez et al., 2015; Piras et al., 2014, 2016; Wilby & Harris, 2006).…”

“…Still, the temporal coverage of radar datasets is usually in the range from 15 to 18 years, which constitutes a significant constraint for water resources applications that call for extensive hydrologic modeling (see e.g., Boughton, 2007; Merwade et al., 2008). Moreover, it is well documented (see e.g., Anagnostou et al., 1998; Bales et al., 2006; Tang, Clark, Papalexiou, et al., 2020) that radar estimates are typically unreliable over complex terrain and mountainous areas, due to signal occlusion or beam overshooting. Notably, the Stage IV product tends to overestimate the rainfall intensity in the western US (see e.g., McGraw et al., 2019), when compared to NOAA’s raingauge records (National Centers for Environmental Information, 2017); see also Section 2 herein.…”

Leveraging ensemble meteorological forcing data to improve parameter estimation of hydrologic models

“…United States, Alaska and Hawaiian Islands Newman, Clark, Craig et al, 2020;Tang et al, 2020), the global ensemble HadCRUT4 temperature dataset (Morice et al, 2012) and the global ERA5 ensemble reanalysis (Hersbach et al, 2019). The choice of multiple determin- Two more advanced approaches utilizing a robust calibration algorithm are the work of Skinner et al (2015) and Clark et al (2006).…”