Global 3-Hourly 0.1 Bias-Corrected Meteorological Data Including Near-Real-Time Updates and Forecast Ensembles

Hylke E. Beck*, Albert I.J.M. van Dijk, Pablo R. Larraondo, Tim R. McVicar, Ming Pan, Emanuel Dutra, Diego G. Miralles

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

We present Multi-Source Weather (MSWX), a seamless global gridded near-surface meteorological product featuring a high 3-hourly 0.1 resolution, near-real-time updates (~3-h latency), and bias-corrected medium-range (up to 10 days) and long-range (up to 7 months) forecast ensembles. The product includes 10 meteorological variables: precipitation, air temperature, daily minimum and maximum air temperature, surface pressure, relative and specific humidity, wind speed, and downward shortwave and longwave radiation. The historical part of the record starts 1 January 1979 and is based on ERA5 data bias corrected and downscaled using high-resolution reference climatologies. The data extension to within ~3 h of real time is based on analysis data from GDAS. The 30-member medium-range forecast ensemble is based on GEFS and updated daily. Finally, the 51-member long-range forecast ensemble is based on SEAS5 and updated monthly. The near-real-time and forecast data are statistically harmonized using running-mean and cumulative distribution function-matching approaches to obtain a seamless record covering 1 January 1979 to 7 months from now. MSWX presents new and unique opportunities for hydrological modeling, climate analysis, impact studies, and monitoring and forecasting of droughts, floods, and heatwaves (within the bounds of the caveats and limitations discussed herein). The product is available at www.gloh2o.org/mswx.

Original languageEnglish
Pages (from-to)E710-E732
JournalBulletin of the American Meteorological Society
Volume103
Issue number3
DOIs
Publication statusPublished - 1 Mar 2022

Keywords

  • Climate records
  • Climate services
  • Data science
  • Downscaling
  • Operational forecasting
  • Reanalysis data

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