4.5 Article

Development and Evaluation of Ensemble Consensus Precipitation Estimates over High Mountain Asia

Journal

JOURNAL OF HYDROMETEOROLOGY
Volume 23, Issue 9, Pages 1469-1486

Publisher

AMER METEOROLOGICAL SOC
DOI: 10.1175/JHM-D-21-0196.1

Keywords

Asia; Forcing; Hydrologic cycle; Precipitation; Rainfall; Snowfall; Climate variability; Climatology; Ensembles

Funding

  1. National Aeronautics and Space Administration High Mountain Asia program
  2. [19-HMA19-0012]

Ask authors/readers for more resources

Precipitation estimates in complex regions like High Mountain Asia are highly uncertain. This study presents the development of a consensus ensemble precipitation product using three gridded precipitation datasets and a localized probability matched mean approach to improve accuracy and consistency.
Precipitation estimates are highly uncertain in complex regions such as High Mountain Asia (HMA), where ground measurements are very difficult to obtain and atmospheric dynamics poorly understood. Though gridded products derived from satellite-based observations and/or reanalysis can provide temporally and spatially distributed estimates of precipitation, there are significant inconsistencies in these products. As such, to date, there is little agreement in the community on the best and most accurate gridded precipitation product in HMA, which is likely area dependent because of HMA's strong heterogeneities and complex orography. Targeting these gaps, this article presents the development of a consensus ensemble precipitation product using three gridded precipitation datasets [the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), and the ECMWF reanalysis ERA5] with a localized probability matched mean (LPM) approach. We evaluate the performance of the LPM estimate along with a simple ensemble mean (EM) estimate to overcome the differences and disparities of the three selected constituent products on long-term averages and trends in HMA. Our analysis demonstrates that LPM reduces the high biases embedded in the ensemble members and provides more realistic spatial patterns compared to EM. LPM is also a good alternative for merging data products with different spatiotemporal resolutions. By filtering disparities among the individual ensemble members, LPM overcomes the problem of a certain product performing well only in a particular area and provides a consensus estimate with plausible temporal trends.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available