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

Abstract

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.

Diverging Trends in Rain‐On‐Snow Over High Mountain Asia

“…Because of the differences in the definitions of "runoff" between our study (which is the water available for runoff) and Ghiggi et al (2021), we only compared the sign of the trends, not their magnitudes. Note that extensive evaluations of the precipitation data used in this study have been presented in Maina, Kumar, Dollan, and Maggioni (2022), which demonstrates that the ensemble precipitation allows for a better representation of the ground measured precipitation, particularly in the yearly trends and averages.…”

“…Noah-MP allows simulations with various options compared to the previous versions of the model Noah and includes the choice of multiple options for the snowpack, vegetation, infiltration, and runoff physics computations (Niu et al, 2011). The Noah-MP model simulations are driven with an ensemble precipitation data set generated by Maina, Kumar, Dollan, and Maggioni (2022) using a localized probability matched method (Clark, 2017) with three gridded precipitation products (IMERG, CHIRPS, and ERA5) while the other meteorological forcing (temperature, shortwave, and longwave radiation, wind speed, relative humidity, etc.) are generated by downscaling ERA5 following Xue et al (2019Xue et al ( , 2022.…”

“…Due to the complexities of the atmospheric and land surface processes in HMA, accurate estimation of precipitation and other meteorological forcing is difficult and different forcings lead to different results. This issue has also been widely discussed in the literature (Maina, Kumar, Dollan, & Maggioni, 2022;Yoon et al, 2019;You et al, 2015). The uncertainties from the forcing meteorology are reduced by using an ensemble generated with reasonable gridded precipitation datasets, described in detail in Maina, Kumar, Dollan, and Maggioni (2022).…”

“…In this study, we assess the changes of ROS in HMA over the two past decades from 2001 to 2018 based on land surface model simulations informed by high quality precipitation inputs and remote sensing information on snow conditions. Specifically, the land surface model Noah-MultiParameterization (Noah-MP, Niu et al, 2011) is driven with an ensemble precipitation product (Maina, Kumar, Dollan, & Maggioni, 2022) generated using the best available gridded precipitation products that includes the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement IMERG (Huffman et al, 2015), the Climate Hazards group Infrared Precipitation with Stations CHIRPS (Funk et al, 2015), and the ECMWF Reanalysis ERA5 (Hersbach et al, 2020). These precipitation products were found to better represent the precipitation averages and trends in HMA (Maina, Kumar, Dollan, & Maggioni, 2022).…”

“…Specifically, the land surface model Noah-MultiParameterization (Noah-MP, Niu et al, 2011) is driven with an ensemble precipitation product (Maina, Kumar, Dollan, & Maggioni, 2022) generated using the best available gridded precipitation products that includes the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement IMERG (Huffman et al, 2015), the Climate Hazards group Infrared Precipitation with Stations CHIRPS (Funk et al, 2015), and the ECMWF Reanalysis ERA5 (Hersbach et al, 2020). These precipitation products were found to better represent the precipitation averages and trends in HMA (Maina, Kumar, Dollan, & Maggioni, 2022). The land surface model Noah-MP allows for a physical representation of the key HMA's dynamics such as snow dynamics, vegetation dynamics, and hydrodynamics by physically solving the water and energy budgets.…”