Climatology of OPGs in HMA

Climatology of Orographic Precipitation Gradients over High Mountain Asia Derived from Dynamical Downscaling

Within High Mountain Asia (HMA), the annual melting of glaciers and snowpack provides vital freshwater to populations living downstream. Precipitation over HMA can directly affect the freshwater availability in this region by altering the mass balance of glaciers and snowpack. However, available reanalyses and downscaling simulations lack the resolution required to understand important glacier-scale variations in precipitation. This study aimed to determine the current characteristics of Orographic Precipitation Gradients (OPG) by curve-fitting daily precipitation as a function of elevation from a 15-year, 4-km grid spaced Weather Research and Forecasting (WRF) model simulation focused on the Himalayan, Karakoram, and Hindu-Kush mountain ranges. To facilitate precipitation curve-fitting, the WRF model grid points were separated into regions of similar orientation, referred to as facets. Akaike Information Criterion-corrected values and an F-Test p-value identified the need for a curvature term to account for a varying OPG with elevation. Regions with similar seasonal variability were found using $k$-means clustering of the monthly mean OPG coefficients. The central Himalayan slope's intra-seasonal variability of OPG depended on synoptic scale conditions, in which cyclonically-forced heavy-precipitation events produced strong sublinear increases in precipitation with elevation. Initial testing of precipitation estimates using monthly coefficients showed promising results in downscaling daily WRF precipitation; the daily mean absolute error at each grid point had a lower magnitude than the daily mean precipitation total, on average. Results provide a physically-based context for machine learning algorithms being developed to predict OPG and downscale precipitation output from global climate models over HMA.

Support for this project was provided by the NASA High Mountain Asia Team (HiMAT; Award 80NSSC20K1594).

Getting Started

Many of these scripts require the following datasets/open-source code.

• Country Borders from Natural Earth:
  https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/cultural/ne_50m_admin_0_countries.zip
  
• Export High-Resolution and Transparent Figures:
  https://www.mathworks.com/matlabcentral/fileexchange/23629-export_fig
  
• M_MAP: A Mapping Package for MATLAB:
  https://www.eoas.ubc.ca/~rich/map.html

Folder Structure

.
├── correlation             # Find atmospheric grid point of highest correlation to OPG
├── curveFitting            # Evaluate, visualize, and quantify the relationship between precipitation and elevation.
├── facetingAlgorithm       # Group terrain into facets based on orientation. (Gibson et al. 1997)
├── kMeansClustering        # K-Means clustering of OPG coefficients
├── nclColormaps            # NCL Colormaps converted into MATLAB files.
├── opgVariability          # Evaluation of the seasonal variability on the Central Himalayan Slope
├── post-processingWRF      # Post-processing the WRF output files
└── README.md