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

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-hour latency), and bias-corrected medium-range (up to 10 days) and long-range (up to 7 months) forecast ensembles. The product includes ten 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 January 1, 1979, and is based on ERA5 data bias-corrected and downscaled using high-resolution reference climatologies. The data extension to within ~3 hours 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 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.

Satellite‐based evidence highlights a considerable increase of urban tree cooling benefits from 2000 to 2015

“…VPD (kPa) in this study is calculated aswhere T dew is dewpoint temperature (°C), e s is saturation water vapor (kPa), and e a is actual water vapor (kPa). We also used the Multi‐Source Weather (MSWX) historical records (MSWX‐Past) (Beck et al, 2022) as a supplementary to ERA5‐Land. The meteorological product is based on ERA5 but bias‐corrected using high‐resolution reference climatology.…”

Effects of Observational Uncertainty and Models Similarity on Climate Change Projections

“…We inter-compared annual and seasonal climatological spatial distributions, pattern correlations, and interannual variabilities to gauge the differences among the seven observational rainfall and temperature datasets (Table S1). We used the Multi-Source Weather (MSWX) datasets as a reference for both precipitation and temperature because of i) their high resolution and bias correction with qualitycontrolled ground and satellite observations 17 and ii) both MSWX temperature and rainfall datasets exhibited high correlation (> 0.98) with the averages of all the observations (Table S2). We generated differences between the other observations and the MSWX datasets for the intercomparison.…”

Likely impacts of the 2022 heatwave on India’s wheat production

“…There are multiple weather data products, some publicly available, that are updated real-time to provide near-term weather forecasts. For example, the Multi-Source Weather data product (Beck et al 2022) provides a 51-member daily weather forecast ensemble for the next 7 months globally at a high spatial resolution. Similarly, government organizations also provide regular weather forecasts.…”