arXiv subjects

Bedartha Goswami

Publications and source records attributed to Bedartha Goswami.

7 recordsLinked to original sources

arXiv2025

A Hybrid Deep-Learning Model for El Niño Southern Oscillation in the Low-Data Regime

While deep-learning models have demonstrated skillful El Niño Southern Oscillation (ENSO) forecasts up to one year in advance, they are predominantly trained on climate model simulations that provide thousands of years of training data at the expense of introducing climate model biases. Simpler Linear Inverse Models (LIMs) trained on the much shorter observational record also make skillful ENSO predictions but do not capture predictable nonlinear processes. This motivates a hybrid approach, combining the LIMs modest data needs with a deep-learning non-Markovian correction of the LIM. For O(100 yr) datasets, our resulting Hybrid model is more skillful than the LIM while also exceeding the skill of a full deep-learning model. Additionally, while the most predictable ENSO events are still identified in advance by the LIM, they are better predicted by the Hybrid model, especially in the western tropical Pacific for leads beyond about 9 months, by capturing the subsequent asymmetric (warm versus cold phases) evolution of ENSO.

cs.LG↗

arXiv2024

Contributions of El Niño Southern Oscillation (ENSO) Diversity to Low-Frequency Changes in ENSO Variance

El Niño Southern Oscillation (ENSO) diversity is characterized based on the longitudinal location of maximum sea surface temperature anomalies (SSTA) and amplitude in the tropical Pacific, as Central Pacific (CP) events are typically weaker than Eastern Pacific (EP) events. SSTA pattern and intensity undergo low-frequency modulations, affecting ENSO prediction skill and remote impacts. Yet, how different ENSO types contribute to these decadal variations and long-term variance trends remain uncertain. Here, we decompose the low-frequency changes of ENSO variance into contributions from ENSO diversity categories. We propose a fuzzy clustering of monthly SSTA to allow for non-binary event category memberships. Our approach identifies two La Niña and three El Niño categories and shows that the shift of ENSO variance in the mid-1970s is associated with an increasing likelihood of strong La Niña and extreme El Niño events.

physics.ao-ph↗

arXiv2024

Intraseasonal synchronization of extreme rainfalls between North India and the Sahel

The Indian Summer Monsoon (ISM) and the West African Monsoon (WAM) are dominant drivers of boreal summer precipitation variability in tropical and subtropical regions. Although the regional precipitation dynamics in these two regions have been extensively studied, the intraseasonal interactions between the ISM and WAM remain poorly understood. Here, we employ a climate network approach based on extreme rainfall events to uncover synchronously occurring extreme rainfall patterns across the two monsoon systems. We reveal strong synchronization of extreme rainfall events during the peak monsoon period in July and August, linking heavy rainfall over North India to that over the Sahel with a lag of around 12 days. We find that La Niña-like conditions in combination with the Boreal Summer Intraseasonal Oscillation and an enhanced Tropical Easterly Jet (TEJ) foster the synchronization between the ISM and the WAM. Convective clouds are transported by an intensified TEJ from southwestern Asia toward North Africa, supporting anomalous deep convection over the Sahel region.

physics.ao-ph↗

arXiv2024

Inductive biases in deep learning models for weather prediction

Deep learning has gained immense popularity in the Earth sciences as it enables us to formulate purely data-driven models of complex Earth system processes. Deep learning-based weather prediction (DLWP) models have made significant progress in the last few years, achieving forecast skills comparable to established numerical weather prediction models with comparatively lesser computational costs. In order to train accurate, reliable, and tractable DLWP models with several millions of parameters, the model design needs to incorporate suitable inductive biases that encode structural assumptions about the data and the modelled processes. When chosen appropriately, these biases enable faster learning and better generalisation to unseen data. Although inductive biases play a crucial role in successful DLWP models, they are often not stated explicitly and their contribution to model performance remains unclear. Here, we review and analyse the inductive biases of state-of-the-art DLWP models with respect to five key design elements: data selection, learning objective, loss function, architecture, and optimisation method. We identify the most important inductive biases and highlight potential avenues towards more efficient and probabilistic DLWP models.

physics.ao-ph↗

arXiv2023

Extreme rainfall propagation within Boreal Summer Intraseasonal Oscillation modulated by Pacific sea surface temperature

Intraseasonal variations of the South Asian Summer Monsoon (SASM) contain alternating extreme rainfall (active) and low rainfall (break) phases impacting agriculture and economies. Their timing and spatial location are dominated by the Boreal Summer Intraseasonal Oscillation (BSISO), a quasi-periodic movement of convective precipitation from the equatorial Indian Ocean to the Western Pacific. However, observed deviations from the BSISO's canonical north-eastward propagation are poorly understood. Utilizing climate networks to characterize how active phases propagate within the SASM domain and using clustering analysis, we reveal three distinct modes of BSISO propagation: north-eastward, eastward-blocked, and stationary. We further show that Pacific sea surface temperatures modulate the modes - with El Niño- (La Niña-) like conditions favoring the stationary (eastward-blocked) - by changing local zonal and meridional overturning circulations and the BSISO Kelvin wave component. Using these insights, we demonstrate the potential for early warning signals of extreme rainfall until four weeks in advance.

physics.ao-ph↗

arXiv2022

Pitfalls of Climate Network Construction: A Statistical Perspective

Network-based analyses of dynamical systems have become increasingly popular in climate science. Here we address network construction from a statistical perspective and highlight the often ignored fact that the calculated correlation values are only empirical estimates. To measure spurious behaviour as deviation from a ground truth network, we simulate time-dependent isotropic random fields on the sphere and apply common network construction techniques. We find several ways in which the uncertainty stemming from the estimation procedure has major impact on network characteristics. When the data has locally coherent correlation structure, spurious link bundle teleconnections and spurious high-degree clusters have to be expected. Anisotropic estimation variance can also induce severe biases into empirical networks. We validate our findings with ERA5 reanalysis data. Moreover we explain why commonly applied resampling procedures are inappropriate for significance evaluation and propose a statistically more meaningful ensemble construction framework. By communicating which difficulties arise in estimation from scarce data and by presenting which design decisions increase robustness, we hope to contribute to more reliable climate network construction in the future.

cs.LG↗

arXiv2022

Teleconnection patterns of different El Niño types revealed by climate network curvature

The diversity of El Niño events is commonly described by two distinct flavors, the Eastern Pacific (EP) and Central Pacific (CP) types. While the remote impacts, i.e. teleconnections, of EP and CP events have been studied for different regions individually, a global picture of their teleconnection patterns is still lacking. Here, we use Forman-Ricci curvature applied on climate networks constructed from 2-meter air temperature data to distinguish regional links from teleconnections. Our results confirm that teleconnection patterns are strongly influenced by the El Niño type. EP events have primarily tropical teleconnections whereas CP events involve tropical-extratropical connections, particularly in the Pacific. Moreover, the central Pacific region does not have many teleconnections, even during CP events. It is mainly the eastern Pacific that mediates the remote influences for both El Niño types.

physics.ao-ph↗