Intercomparison of Gridded Precipitation Datasets over a Sub-Region of the Central Himalaya and the Southwestern Tibetan Plateau

Abstract

Precipitation is a central quantity of hydrometeorological research and applications. Especially in complex terrain, such as in High Mountain Asia (HMA), surface precipitation observations are scarce. Gridded precipitation products are one way to overcome the limitations of ground truth observations. They can provide datasets continuous in both space and time. However, there are many products available, which use various methods for data generation and lead to different precipitation values. In our study we compare nine different gridded precipitation products from different origins (ERA5, ERA5-Land, ERA-interim, HAR v2 10 km, HAR v2 2 km, JRA-55, MERRA-2, GPCC and PRETIP) over a subregion of the Central Himalaya and the Southwest Tibetan Plateau, from May to September 2017. Total spatially averaged precipitation over the study period ranged from 411 mm (GPCC) to 781 mm (ERA-Interim) with a mean value of 623 mm and a standard deviation of 132 mm. We found that the gridded products and the few observations, with few exceptions, are consistent among each other regarding precipitation variability and rough amount within the study area. It became obvious that higher grid resolution can resolve extreme precipitation much better, leading to overall lower mean precipitation spatially, but higher extreme precipitation events. We also found that generally high terrain complexity leads to larger differences in the amount of precipitation between products. Due to the considerable differences between products in space and time, we suggest carefully selecting the product used as input for any research application based on the type of application and specific research question. While coarse products such as ERA-Interim or ERA5 that cover long periods but have coarse grid resolution have previously shown to be able to capture long-term trends and help with identifying climate change features, this study suggests that more regional applications, such as glacier mass-balance modeling, require higher spatial resolution, as is reproduced, for example, in HAR v2 10 km.

Characteristics of Historical Precipitation in High Mountain Asia Based on a 15-Year High Resolution Dynamical Downscaling

“…The results also suggest precipitation observations from valleys are not always representative of the magnitude, frequency, or seasonality of nearby high elevation regions. In addition, this further supports previous work [21][22][23]26] that highlight the importance of model resolution in capturing the magnitude, frequency and seasonality of precipitation in complex terrain. While higher-resolution models do not automatically result in improved accuracy of precipitation statistics, they have improved terrain resolution which allows for more detailed analyses of the relationship between precipitation and topography.…”

“…The HAR version 2 (HARv2) is now available and provides a larger 10-km domain and improved spatial distribution of seasonal mean precipitation [25]. In an intercomparison of gridded precipitation datasets over the central Himalaya and southwestern Tibetan Plateau from May to September 2017, Hamm et al [26] found that precipitation produced by the HARv2 and a version of the HARv2 run with 2-km grid spacing (HARv2 2 km) produced a better match compared to ground-based precipitation observations than other gridded datasets. The HARv2 2-km output exhibited a slight improvement relative to observational data, including improved daily precipitation statistics due in part to the explicit rather than parameterized convection.…”

Atmospheric triggering conditions and climatic disposition of landslides in Kyrgyzstan and Tajikistan at the beginning of the 21st century

“…Using a certain threshold as a binary classifier, LTEs and NLTEs were categorized into true positive (TP), true negative (TN), false positive (FP), and false negative (FN). The Peirce skill score (PSS) (Hanssen and Kuipers, 1965) was applied as the measure of the predictive performance because it is trailindependent, which means it is unbiased even when the numbers of LTEs and NLTEs are not equally presented (Woodcock, 1976). The PSS is also known as the Hanssen-Kuiper skill score and the true skill statistic.…”

Comment on nhess-2020-418

“…It was generated by dynamical downscaling of the ERA5 reanalysis data using the Weather Research and Forecasting model (WRF). It is the only gridded atmospheric data set over High Mountain Asia with high resolution and accuracy (Hamm et al, 2020). Detailed modeling strategies of the HAR v2 are described in Wang et al (2021).…”