Related papers: Spatio-temporal Patterns of Indian Monsoon Rainfal…
Precipitation exceedance probabilities are widely used in engineering design, risk assessment, and floodplain management. While common approaches like NOAA Atlas 14 assume that extreme precipitation characteristics are stationary over time,…
Modeling precipitation and its accumulation over time and space is essential for flood risk assessment. In this paper, we analyze rainfall data collected over several years through a micro-scale precipitation sensor network in Montpellier,…
In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial Neural Network models. In formulating the Artificial Neural Network based predictive model, three layered…
In this diagnostic study we analyze changes of rainfall seasonality and dry spells by the end of the twenty-first century under the most extreme IPCC5 emission scenario (RCP8.5) as projected by twenty-four coupled climate models…
The analysis of spatio-temporal data has been the object of research in several areas of knowledge. One of the main objectives of such research is the need to evaluate the behavior of climate effects in certain regions across a period of…
We develop a flexible spline-based Bayesian hidden Markov model stochastic weather generator to statistically model daily precipitation over time by season at individual locations. The model naturally accounts for missing data (considered…
Present study aims to develop a predictive model for the average summer monsoon rainfall amount over India. The dataset made available by the Indian Institute of Tropical Meteorology, Pune, has been explored. To develop the predictive…
In contrast to the wet gets wetter and dry gets drier paradigm, here, using observations and climate model simulations, we show that the mean rainfall over the semi-arid northwest parts of India and Pakistan has increased by 10 to 50…
For over a century, raindrop size distributions have been a subject of extensive scientific study, typically described by models including the Marshall-Palmer exponential equation, gamma, Weibull, lognormal, and other mathematical…
What is the role of soil moisture in maintaining the land ITCZ during the active phase of the monsoon? This question has been addressed in this study by using ERA5 reanalysis datasets, and then we evaluate the question in the CFS model-free…
Modelling the intermittency of wind speed has got significant relevance on many fields, including wind power generation and distribution. Most of the available modelling techniques assume the temporal fluctuations in the wind is stochastic…
This study investigates how conditional normalizing flows can be applied to remote sensing data products in climate science for spatio-temporal prediction. The method is chosen due to its desired properties such as exact likelihood…
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…
We address the problem of predicting spatio-temporal processes with temporal patterns that vary across spatial regions, when data is obtained as a stream. That is, when the training dataset is augmented sequentially. Specifically, we…
The formation of Middle Tropospheric Cyclones (MTCs) that are responsible for a large portion of annual precipitation and extreme rainfall events over western India is studied using an unsupervised machine learning algorithm and cyclone…
Understanding the temporal dependence of precipitation is key to improving weather predictability and developing efficient stochastic rainfall models. We introduce an information-theoretic approach to quantify memory effects in discrete…
Skilful prediction of the seasonal Indian summer monsoon (ISM) rainfall (ISMR) at least one season in advance has great socio-economic value. It represents a lifeline for about a sixth of the world's population. The ISMR prediction remained…
Statistical modeling of monthly, seasonal, or annual rainfall data is an important research area in meteorology. These models play a crucial role in rainfed agriculture, where a proper assessment of the future availability of rainwater is…
Modeling a precipitation field is challenging due to its intermittent and highly scale-dependent nature. Motivated by the features of high-frequency precipitation data from a network of rain gauges, we propose a threshold space-time $t$…
Observations of soil moisture (SM) during excess and deficit monsoon seasons between 2000 to 2021 present a unique opportunity to understand the soil water dynamics (SWD) over core monsoon zone (CMZ) of India. This study aims to analyse SWD…