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On the life and achievements of Avul Pakir Jainulabudeen Abdul Kalam, 1931-2015, former President of India and architect of missile technology in India.

China and India, the world's two most populous countries, will exercise increasing influence in international affairs in the coming decades. This document assesses the relative prospects of China and India through 2025 in four domains: demography, macroeconomics, science and technology, and defense spending and procurement. In each domain, the authors try to answer the following questions: Who is ahead? By how much? and Why?

On the life and achievements of Avul Pakir Jainulabudeen Abdul Kalam, 1931-2015, former President of India and architect of missile technology in India.

The ascension of China and India will be the outstanding development of the 21st century, raising fundamental questions about both the structure of the world economy and the balance of global geopolitical power. How aggressive a superpower will China be? And what about India, whose vast population and economic prospects appear to guarantee prosperity? Economist David Smith analyzes in depth the rapid eastward shift in global power to Beijing and Delhi — and its enormous ramifications for the west.

\"The amazing true story of the man behind modern weather prediction. Consider a world without weather prediction. How would we know when to evacuate communities ahead of fires or floods, or figure out what to wear tomorrow? Until 40 years ago, we couldn't forecast weather conditions beyond ten days. Renowned climate scientist Dr. Jagadish Shukla is largely to thank for modern weather forecasting. Born in rural India with no electricity, plumbing, or formal schools, he attended classes that were held in a cow shed. Shukla grew up amid turmoil: overwhelming monsoons, devastating droughts, and unpredictable crop yields. His drive brought him to the Indian Institute of Tropical Meteorology, despite little experience. He then followed an unlikely path to MIT and Princeton, and the highest echelons of climate science. His work, which has enabled us to predict weather farther into the future than previously thought possible, allows us to feed more people, save lives, and hold on to hope in a warming world. Paired with his philanthropic endeavors and extreme dedication to the field, Dr. Shukla has been lauded internationally for his achievements, including a shared Nobel Peace Prize with Al Gore for his governmental research on climate change. A Billion Butterflies is a wondrous insider's account of climate science and an unbelievable memoir of his life. Understanding dynamical seasonal prediction will change the way you experience a thunderstorm or interpret a forecast; understanding its origins and the remarkable story of the man who discovered it will change the way you see our world\"-- Provided by publisher.

Streamflow prediction is crucial for flood monitoring and early warning, which often hampered by bias and uncertainties arising from nonlinear processes, model parameterization, and errors in meteorological forecast. We examined the utility of multiple hydrological models (VIC, H08, CWatM, Noah‐MP, and CLM) and machine learning (ML) methods to improve streamflow simulations and prediction. The hydrological models (HMs) were forced with observed meteorological data from the India Meteorological Department (IMD) and meteorological forecast from the Global Ensemble Forecast System (GEFS) to simulate flood peaks and flood inundation areas. We used Multiple Linear Regression, Random Forest (RF), Extreme Gradient Boosting (XGB), and Long Short‐Term Memory (LSTM) for the post‐processing of simulated streamflow from HMs. Considering the influence of dams is crucial for the effectiveness of HMs and ML methods for improving streamflow simulations and predictions. In addition, ML‐based multi‐model ensemble streamflow from HMs performs better than individual models, highlighting the need for multi‐model‐based streamflow forecast systems. The post‐processing of streamflow simulated by the hydrological models using ML significantly improved overall streamflow simulations, with limited improvement in high‐flow conditions. The combination of physics‐based hydrological models, observed climate data, and ML methods improve streamflow predictions for flood magnitude, timing, and inundated area, which can be valuable for developing flood early warning systems in India. Plain Language Summary Streamflow is a key variable in effective flood monitoring and developing early warning systems. However, accurate streamflow prediction is challenging due to limitations in existing hydrological models (HMs), errors in meteorological forecasts, and initial hydrological conditions. We used a combination of HMs and machine learning (ML) methods to improve streamflow simulations. We applied ML methods to take an ensemble of multi‐models and to improve the prediction skills of streamflow in individual HMs. We show that ML methods significantly improved the prediction of normal and high flow magnitude, timing, and flood‐inundated areas. Moreover, the inclusion of human interventions in the hydrological modeling framework is essential in the river basins that are significantly influenced by dams and reservoirs. Overall, the combination of multiple hydrological models and ML methods improves streamflow simulations that can assist the flood early warning systems. Key Points Integration of multi‐model hydrological predictions with machine learning (ML)‐based post‐processing techniques Incorporating the influence of dams improves streamflow simulations from hydrological models Multi‐model ensemble generated using ML outperforms individually post‐processed hydrological models

\"Every year at the World Economic Forum in Davos, there's a star. Not a person but a country. One country impresses the gathering of global leaders because of a particularly smart Finance minister or a compelling tale of reform or even a glamorous gala. This year there was no contest. In the decade that I've been going to Davos, no country has captured the imagination of the conference and dominated the conversation as India in 2006. It was not a matter of chance. As you got off the plane in Zurich, there were large billboards extolling INCREDIBLE INDIA. Davos itself was plastered with signs. WORLD'S FASTEST GROWING FREE MARKET DEMOCRACY! proclaimed the town's buses. When you got to your room, you found an iPod Shuffle loaded with Bollywood songs, and a pashmina shawl, gifts from the Indian delegation. When you entered the meeting rooms, you were likely to hear an Indian voice, one of the dozens of CEOs of world-class Indian companies. And then there were the government officials, India's 'Dream Team,' all intelligent and articulate, and all selling their country.\" (Newsweek) This article discusses the growing Indian economy and how it has impacted the country and the world.

Gauging appropriate physical parameterization schemes for any numerical weather prediction model is indispensable for obtaining high accuracy in tropical cyclone forecasting. In this study, combinations of five microphysics, three cumulus convection, and two planetary boundary layer (PBL) schemes are investigated with respect to track, intensity, and time of landfall to determine an optimal combination of physical schemes of the weather research and forecasting (WRF) model (version 4.0) with advanced research WRF (ARW) core. All sensitivity experiments are carried out by taking the initial and boundary conditionsfrom the National Centers for Environmental Prediction Global Forecast System (NCEP-GFS). The simulated track, intensity, and landfall time are compared with the Indian Meteorological Department (IMD) observations.The sensitivity experiments reveal that the KF cumulus is performing better with YSU PBL along with WSM6, Ferrier (new eta), and Thompson microphysics for the track (position and time), and intensity with the least errors. Furthermore, we examined the performance of the model with the above combination of schemes on four severe landfalling cyclones (Bulbul, Hudhud, Aila, and Sidr). The root mean square error (RMSE) for central pressure gives the least value in the range of 0.4 to 8 hPa and 0.2 to 3.7 ms−1 for maximum surface wind (MSW) during landfall with YSU-KF- Ferrier combination. The equivalent potential temperature shows strong vertical mixing up to 500 hPa in the case of YSU-KF-Ferrier, which enhances the formation of warm-core, which further explains the intensity of cyclones. Overall, the track, intensity, and rainfall forecasts for the extreme cyclones considered in this study are consistent with IMD observations using YSU PBL, KF cumulus convection, and Ferrier microphysics.

Hydrological data is crucial for accurate forecasting of precipitation which can be used for water resources planning and management. The purpose of this study is to develop a seasonal rainfall forecast model, using a hybrid wavelet-artificial neural network (WANN) model based on regression analysis to predict  seasonal  rainfall in Almora, Lansdown, Kashipur and Mukteswar region in Uttarakhand (India).The statistical results shows that the mean maximum rainfall was found to be 746.82 mm, 1586.58 mm, 1060.53 mm and 964.43 mm for Almora, Lansdown, Kashipur and Mukteswar, respectively. The models WANN-03 (Network 4–8-1), WANN-10 (Network 4–7-1), WANN-10 (Network 4–7-1) and WANN-15 (Network 4–8-1) were found to be the most efficient models for Mukteswar, Lansdown, Kashipur and Almora, based on the high coefficient of determination (R 2 ) and coefficient of efficiency (CE) values and low root mean square error (RMSE) values that were obtained using each model. For each season, four WANN modelshave been developed (total of sixteen models) by varying the number of hidden neurons. The results shows that only one WANN model was not sufficient to predict the rainfall of all stations. Every station has a specific networked model which could model the data more precisely preciously. The findings illustrated that the hybrid model of WANN having Network (4–7-1) was found most superior model (R 2  = 0.857, RMSE = 32.192 and CE = 0.846) for the Lansdown stations among all the stations.

Accurate and reliable air temperature forecasts are necessary for predicting and responding to thermal disasters such as heat strokes. Forecasts from Numerical Weather Prediction (NWP) models contain biases which require post-processing. Studies assessing the skill of probabilistic post-processing techniques (PPTs) on temperature forecasts in India are lacking. This study aims to evaluate probabilistic post-processing approaches such as Nonhomogeneous Gaussian Regression (NGR) and Bayesian Model Averaging (BMA) for improving daily temperature forecasts from two NWP models, namely, the European Centre for Medium Range Weather Forecasts (ECMWF) and the Global Ensemble Forecast System (GEFS), across the Indian subcontinent. Apart from that, the effect of probabilistic PPT on heatwave prediction skills across India is also evaluated. Results show that probabilistic PPT comprehensively outperform traditional approaches in forecasting temperatures across India at all lead times. In the Himalayan regions where the forecast skill of raw forecasts is low, the probabilistic techniques are not able to produce skillful forecasts even though they perform much better than traditional techniques. The NGR method is found to be the best performing PPT across the Indian region. Post-processing Tmax forecasts using the NGR approach was found to considerably improve the heatwave prediction skill across highly heatwave prone regions in India. The outcomes of this study will be helpful in setting up improved heatwave prediction and early warning systems in India.