Deriving Bias and Uncertainty in MERRA-2 Snowfall Precipitation Over High Mountain Asia

Abstract

A Bayesian approach to estimate bias and uncertainty in snowfall precipitation from MERRA-2 and other precipitation products was applied over High Mountain Asia (HMA), using a newly developed snow reanalysis method. Starting from an "uninformed" prior probability distribution, a posterior scaling factor applied to MERRA-2 snowfall was derived by constraining model-based estimates of seasonal snow accumulation and ablation over the water year (WY) with fractional snow covered area (fSCA) measurements derived from Landsat and MODIS (MODSCAG). Several subdomains (nine representative 1 • by 1 • tiles) across HMA were examined over the period WYs 2001-2015 and compiled into an uncertainty parameterization where a lognormal distribution was fitted to the empirical posterior distribution with a mean of 1.54 (median of 1.19) and coefficient of variation (CV) of 0.83, indicating that MERRA-2 underestimates snowfall on average by ∼54% with sizeable uncertainty. For reference, the uncertainties in snowfall precipitation from the ERA5 and APHRODITE-2 precipitation products were also evaluated, and these products were found to underestimate snowfall, on average by a factor around 1.78 and 3.34 (with median scaling factors of 1.42 and 2.51), respectively. The results indicate that snowfall precipitation at high-elevations dominated by snowfall is underestimated in most existing products, especially in the gauge-based APHRODITE-2 product, where the biases were also found to exhibit geographical variations with the largest underestimation in monsoon-influenced high-elevation tiles. The derived MERRA-2 uncertainty model is being used to develop a full domain-wide HMA snow reanalysis, which will shed further light onto the space-time variations in snowfall biases in these products.

Spatiotemporal distribution of seasonal snow water equivalent in High-Mountain Asia from an 18-year Landsat-MODIS era snow reanalysis dataset

“…In HMASR, prior surface meteorological inputs were obtained from MERRA-2 at its raw resolution (0.5° by 0.625° latitude-longitude), including precipitation, air temperature, solar radiation, specific humidity, surface pressure and wind speed. The uncertainty models and their parameters used to perform bias-correction and uncertainty perturbation are specified in Margulis et al (2019) for the HMA region, except that prior ensemble precipitation is perturbed by a lognormal distribution with mean of 1.54 and coefficient of variation (CV) of 0.83 based on the results from Liu and Margulis (2019).…”

Observing Snow Cover and Water Resource Changes in the High Mountain Asia Region in Comparison with Global Mountain Trends over 2000–2018

“…In this area, it is particularly relevant to assimilate ground data to consider the transient snowpack [46]. On the other side, Liu and Margulis (2019) [47] pointed out that MERRA2 data, as well as other reanalysis products, show an underestimation of snowfall precipitation over HMA areas. It is worthwhile mentioning that the complex orography and the lack of in-situ data at high elevations remain as the two main limitations in the accuracy assessment of these data.…”

Snowmelt and Snow Sublimation in the Indus Basin

“…This reanalysis is based on an energy balance model, which computes sublimation unlike previous studies [14]. Another advantage is that this reanalysis also corrects biases in the precipitation input data by assimilating remote sensing observations of the snow covered area [15]. Hence, this dataset provides the opportunity to reevaluate snowmelt and to evaluate snow sublimation in the Indus basin.…”