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Nanjundiah, Ravi S.

Warming Causes Cooling! the Recent Cold Event over North America

Invisible in the Storm: The Role of Mathematics in Understanding Weather. Ian Roulstone and John Norbury

Representation of Physical Processes in Weather and Climate Models

Clouds, Microphysical Processes and Small-Scale Simulations

Evaluation of PM_2.5 Forecast using Chemical Data Assimilation in the WRF-Chem Model: A Novel Initiative Under the Ministry of Earth Sciences Air Quality Early Warning System for Delhi, India

Abstract Views :159 | PDF Views:74

Authors

Sachin D. Ghude ¹, Rajesh Kumar ², Chinmay Jena ¹, Sreyashi Debnath ¹, Rachana G. Kulkarni ¹, Stefano Alessandrini ², Mrinal Biswas ², Santosh Kulkrani ³, Prakash Pithani ¹, Saurab Kelkar ¹, Veeresh Sajjan ¹, D. M. Chate ¹, V. K. Soni ⁴, Siddhartha Singh ⁴, Ravi S. Nanjundiah ¹, M. Rajeevan ⁵

Affiliations
1 Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, IN
2 National Center for Atmospheric Research, Boulder, CO 80301, US
3 Centre for Development of Advanced Computing, Pune 411 008, IN
4 India Meteorological Department, Ministry of Earth Sciences, New Delhi 110 003, IN
5 Ministry of Earth Sciences, Government of India, New Delhi 110 003, IN

Source

Current Science, Vol 118, No 11 (2020), Pagination: 1803-1815

Abstract

Air quality has become one of the most important environmental concerns for Delhi, India. In this perspective, we have developed a high-resolution air quality prediction system for Delhi based on chemical data assimilation in the chemical transport model – Weather Research and Forecasting with Chemistry (WRF-Chem). The data assimilation system was applied to improve the PM2.5 forecast via assimilation of MODIS aerosol optical depth retrievals using threedimensional variational data analysis scheme. Near real-time MODIS fire count data were applied simultaneously to adjust the fire-emission inputs of chemical species before the assimilation cycle. Carbon monoxide (CO) emissions from biomass burning, anthropogenic emissions, and CO inflow from the domain boundaries were tagged to understand the contribution of local and non-local emission sources. We achieved significant improvements for surface PM2.5 forecast with joint adjustment of initial conditions and fire emissions.

Keywords

Air Quality, Particulate Matter, Chemical Data Assimilation, Aerosol Optical Depth, Fire Emissions.

Full Text

References

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Artificial intelligence and machine learning in earth system sciences with special reference to climate science and meteorology in South Asia

Abstract Views :164 | PDF Views:78

Authors

Manmeet Singh ¹, Bipin Kumar ², Rajib Chattopadhyay ², K. Amarjyothi ³, Anup K. Sutar ⁴, Sukanta Roy ⁴, Suryachandra A. Rao ², Ravi S. Nanjundiah ⁵

Affiliations
1 Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India; Jackson School of Geosciences, The University of Texas at Austin, Austin 78712, USA; IDP in Climate Studies, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, IN
2 Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, IN
3 National Centre for Medium Range Weather Forecasting, Ministry of Earth Sciences, Noida 201 309, IN
4 Borehole Geophysics Research Laboratory, Ministry of Earth Sciences, Karad 415 114, IN
5 Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India; Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru 560 012, India; Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, IN

Source

Current Science, Vol 122, No 9 (2022), Pagination: 1019-1030

Abstract

This study focuses on the current problems in earth system science (ESS), where machine learning (ML) algorithms can be applied. It provides an overview of previous studies, ongoing work at the Ministry of Earth Sciences, Government of India, and future applications of ML algorithms to some significant earth science problems. We compare previous studies, a mind map of multidimensional areas related to ML and Gartner’s hype cycle for ML in ESS. We mainly focus on the critical components in earth sciences, including studies on the atmosphere, oceans, biosphere, hydrogeology, human health and seismology. Various artificial intelligence (AI)/ML applications to problems in the core fields of earth sciences are discussed, in addition to gap areas and the potential for AI techniques.

Keywords

Artificial intelligence, climate science, earth sciences, machine learning, meteorology, mind map.

Full Text

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Current Science

Current Science

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Nanjundiah, Ravi S.

Warming Causes Cooling! the Recent Cold Event over North America

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Invisible in the Storm: The Role of Mathematics in Understanding Weather. Ian Roulstone and John Norbury

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Abstract

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Representation of Physical Processes in Weather and Climate Models

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Clouds, Microphysical Processes and Small-Scale Simulations

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Abstract

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Evaluation of PM2.5 Forecast using Chemical Data Assimilation in the WRF-Chem Model: A Novel Initiative Under the Ministry of Earth Sciences Air Quality Early Warning System for Delhi, India

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Abstract

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References

Artificial intelligence and machine learning in earth system sciences with special reference to climate science and meteorology in South Asia

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Evaluation of PM_2.5 Forecast using Chemical Data Assimilation in the WRF-Chem Model: A Novel Initiative Under the Ministry of Earth Sciences Air Quality Early Warning System for Delhi, India