arXiv subjects

Sagar K. Tamang

Publications and source records attributed to Sagar K. Tamang.

3 recordsLinked to original sources

arXiv2021

Ensemble Riemannian Data Assimilation over the Wasserstein Space

In this paper, we present an ensemble data assimilation paradigm over a Riemannian manifold equipped with the Wasserstein metric. Unlike the Eulerian penalization of error in the Euclidean space, the Wasserstein metric can capture translation and difference between the shapes of square-integrable probability distributions of the background state and observations -- enabling to formally penalize geophysical biases in state-space with non-Gaussian distributions. The new approach is applied to dissipative and chaotic evolutionary dynamics and its potential advantages and limitations are highlighted compared to the classic variational and filtering data assimilation approaches under systematic and random errors.

stat.ME↗

arXiv2020

Regularized Variational Data Assimilation for Bias Treatment using the Wasserstein Metric

This paper presents a new variational data assimilation (VDA) approach for the formal treatment of bias in both model outputs and observations. This approach relies on the Wasserstein metric stemming from the theory of optimal mass transport to penalize the distance between the probability histograms of the analysis state and an a priori reference dataset, which is likely to be more uncertain but less biased than both model and observations. Unlike previous bias-aware VDA approaches, the new Wasserstein metric VDA (WM-VDA) dynamically treats systematic biases of unknown magnitude and sign in both model and observations through assimilation of the reference data in the probability domain and can fully recover the probability histogram of the analysis state. The performance of WM-VDA is compared with the classic three-dimensional VDA (3D-Var) scheme on first-order linear dynamics and the chaotic Lorenz attractor. Under positive systematic biases in both model and observations, we consistently demonstrate a significant reduction in the forecast bias and unbiased root mean squared error.

stat.ME↗

arXiv2019

On Changes of Global Wet-bulb Temperature and Snowfall Regimes

To properly interpret the observed shrinkage of the Earth's cryosphere it is important to understand global changes of snowfall dominant regimes. To document these changes, three different reanalysis products of wet-bulb temperature together with observationally-based data sets are processed from 1979 to 2017. It is found that over the Northern Hemisphere (NH), the annual mean wet-bulb temperature has increased at a rate of 0.34$^\circ$C per decade (pd) over land and 0.35$^\circ$C pd over ocean, resulting in a reduction of the annual mean potential areas of snowfall dominant regimes by 0.52/0.34 million km$^2$pd over land/ocean. However, the changes in the Southern Hemisphere (SH) are less conclusive and more uncertain. Among the K$ö$ppen-Geiger climate classes, the highest warming trend is observed over the NH polar climate regimes. Over studied mountain regions, the Alps are warming at a faster rate compared to the Rockies, Andes and High Mountain Asia (HMA). Due to such warming, potential snowfall areas over the Alps is reducing at 3.64% pd followed by Rockies at 2.81 and HMA at 1.85% pd. On average, these mountain ranges have lost 0.02 million km$^2$pd of potential snowfall areas. The NH potential snowfall areas is retracting towards the North pole over the Central Asia and Europe at a rate of 0.45 and 0.7 degree pd. Furthermore, terrestrial regions over the NH including the Great Plains in the United States, Canadian provinces around the Hudson Bay, Central Siberian and Tibetan Plateaus, are losing as much as 4% of the solid proportion of the annual precipitation amount pd.

stat.AP↗