Estimating global precipitation fields by interpolating rain  gauge observations using the local ensemble transform  Kalman filter and reanalysis precipitation

Muto, Yuka; Kotsuki, Shunji

doi:https://doi.org/10.5194/hess-28-5401-2024

Articles | Volume 28, issue 24

https://doi.org/10.5194/hess-28-5401-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-28-5401-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 28, issue 24

Research article

|

17 Dec 2024

Research article |

| 17 Dec 2024

Estimating global precipitation fields by interpolating rain gauge observations using the local ensemble transform Kalman filter and reanalysis precipitation

Yuka Muto and Shunji Kotsuki

Data sets

ERA5 hourly data on single levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.adbb2d47

GPCC Full data monthly product Version 2022 at 0.5: Monthly land-surface precipitation from rain-gauges built on GTS-based and historical data U. Schneider et al. https://doi.org/10.5676/DWD_GPCC/FD_M_V2022_050

CPC Global Unified Gauge-based Analysis of Daily Precipitation M. Chen et al. https://ftp.cpc.ncep.noaa.gov/precip/CPC_UNI_PRCP/

Short summary

It is crucial to improve global precipitation estimates to understand water-related disasters and water resources. This study proposes a new methodology to interpolate global precipitation fields from ground rain gauge observations using ensemble data assimilation and the precipitation of a numerical weather prediction model. Our estimates agree better with independent rain gauge observations than existing precipitation estimates, especially in mountainous or rain-gauge-sparse regions.