Global daily 1 km land surface precipitation based on cloud cover-informed downscaling

Abstract

High-resolution climatic data are essential to many questions and applications in environmental research and ecology. Here we develop and implement a new semi-mechanistic downscaling approach for daily precipitation estimate that incorporates high resolution (30 arcsec, ≈1 km) satellite-derived cloud frequency. The downscaling algorithm incorporates orographic predictors such as wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. We apply the method to the ERA5 precipitation archive and MODIS monthly cloud cover frequency to develop a daily gridded precipitation time series in 1 km resolution for the years 2003 onward. Comparison of the predictions with existing gridded products and station data from the Global Historical Climate Network indicates an improvement in the spatio-temporal performance of the downscaled data in predicting precipitation. Regional scrutiny of the cloud cover correction from the continental United States further indicates that CHELSA-EarthEnv performs well in comparison to other precipitation products. The CHELSA-EarthEnv daily precipitation product improves the temporal accuracy compared with a large improvement in the spatial accuracy especially in complex terrain.

Global climate-related predictors at kilometre resolution for the past and future

“…First-order variables are directly measurable properties, including near-surface temperature (daily means and extrema), precipitation rates, near-surface relative humidity, cloud area fraction, solar radiation, and near-surface wind speed. While downscaled climatologies and time-series of temperature and precipitation rates have been made available previously (Karger et al, 2017(Karger et al, , 2021c(Karger et al, , 2020, here we generated corresponding layers for the remaining first-order variables total cloud cover (clt), near-surface (10m) wind speed (sfcWind), and near-surface relative humidity (hurs). Directly based on these first-order variables, we have generated time series and climatologies for six biologically meaningful second-order variables, including frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), vapour pressure deficit (vpd), and surface downwelling shortwave radiation (rsds).…”

“…For past conditions, forcing from ERA5 (Hersbach et al, 2020) with a GPCC bias correction (Ziese et al, 2018) was used, as well as an air temperature algorithm that builds on an atmospheric lapse rate-based downscaling (Karger et al, 2017). Precipitation rates (pr) are based on a mechanistic downscaling that takes orographic effects into account (Karger et al, 2021c). Surface downwelling shortwave radiation (rsds) in CHELSA V2.1 based on a terrain-specific mechanistic model (Böhner and Antonic, 2009).…”

“…A popular repository for climate data is hosted by the climatologies at high resolution for the Earth's land surface areas (CHELSA) initiative (Karger et al, 2017(Karger et al, , 2021c(Karger et al, , 2020, which provides information on temperature and precipitation globally at kilometre resolution. Originally, the CHELSA initiative offered climate data primarily as climatologies, i.e., as monthly and seasonal statistics typically averaged over a representative period of 30 years or longer (Arguez and Vose, 2011), initially from 1979-2013 A key set of such climatologies are the 19 bioclimatic variables (Hijmans et al, 2005) that represent annual statistics of precipitation and temperature and are widely used as predictors in macroecology.…”

“…Moreover, extreme weather events such as droughts can be identified and better linked to consequential disturbances like wildfires and forest diebacks. While for temperature and precipitation such time series of high temporal (daily) resolution data have recently been published (Karger et al, 2020(Karger et al, , 2021c, global time-series at kilometre resolution are hardly available for other climate-related variables.…”

The Role of Citrus Groves in Rainfall-Triggered Landslide Hazards in Uwajima, Japan

“…Landslide modeling and prediction of rainfall-triggered landslides requires rainfall distribution data [21]. These data can be obtained from the global rainfall database, with a resolution of >1 km [22,23] or developed by spatial interpolation of ground-based observations [24,25]. Therefore, although rainfall is highly heterogeneous, rainfall distribution data with high resolution are not generally available.…”