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(Formerly CSIR Centre for Mathematical Modelling and Computer Simulation)

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Modelling for science, for a better future - some recent outcomes

by Toshiro INOUE, Kavirajan RAJENDRAN, Masaki SATOH, Hiroaki MIURA

Abstract: The dual peak semidiurnal variation in surface rainfall rate over the tropics, simulated using a 3.5-km-mesh Nonhydrostatic Icosahedral Atmospheric Model (NICAM) for 26–31 December 2006, is analyzed and compared with data from the 17 year winter precipitation climatology of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Precipitation Radar (PR), and the same 6 day data of Global Satellite Mapping of Precipitation, as well as infrared data from geostationary satellites.
 
We focus on land areas including southern Africa and the Amazon. Over these land areas, the NICAM simulation captures the primary peak in the afternoon and the secondary peak in the early morning, at similar times to those captured using TRMM data. In the PR observation, the primary peak of rainfall is mainly due to convective rain, whereas the secondary peak is due to stratiform rain. In the NICAM simulation, if a simple method is used for the classification of convective/stratiform rain, convective rain is dominant all day long, and the rainfall rate is generally higher than in the PR observation. Nevertheless, an analysis of deep convection (DC) areas indicates consistency between the observation and NICAM; the primary peak of rainfall rate occurs at the mature stage of the number of DC areas, whereas the secondary peak occurs when the mean size of DC areas is almost at its highest point. However, in the NICAM simulation, the relative magnitudes of the two peaks are not represented well, and the contribution of the stratiform rain is underestimated.
 
The present study indicates that a high-resolution global nonhydrostatic model like NICAM has the potential to overcome the limitations of coarse-resolution general circulation models by reproducing the semidiurnal variation of DC, although there is room for improvement.
 

by K Rajendran, Sajani Surendran, Stella Jes Varghese and A Chakraborty

Prediction for Indian summer monsoon rainfall (ISMR) is generated by integrating model from initial conditions (ICs) of weather at some time prior to season. We examine the factors responsible for the widely reported highest ISMR forecast skill for February ICs in climate forecast system (CFSv2) model. Skill for February ICs is highest only based on correlation between observed and predicted year-to-year variation of ISMR, whereas other skill scores indicate highest skill for late-April/early-May ICs having shorter yet useful forecast lead-time. Higher correlation for February ICs arises from correct forecasting of 1983 ISMR excess, which is however due to wrong forecast of La Niña and correlation drops to lower value than that for late-April/early-May ICs if 1983 is excluded. Forecast skill for sea surface temperature variation over equatorial central Pacific (ENSO) in Boreal summer is lowest for February ICs indicating role of dynamical drift induced by long forecast lead-time. Model deficiencies such as oversensitivity of ISMR to ENSO and unrealistic ENSO influence on variation of convection over equatorial Indian Ocean (EQUINOO) lead to errors in ISMR forecasting. In CFSv2, ISMR is mostly decided by ENSO whereas in observation it is influenced by ENSO and EQUINOO independently.

Source: https://www.currentscience.ac.in/Volumes/120/12/1863.pdf

by K. C. Gouda, Priya Singh, Nikhilasuma P, Mahendra Benke, Reshama Kumari, Geeta Agnihotri, Kiran M Hungund, Chandrika M, Kantha Rao B, Ramesh V & Himesh S 

The coronavirus disease 2019 (COVID-19), which became a global pandemic by March 2020, forced almost all countries over the world to impose the lockdown as a measure of social distancing to control the spread of infection. India also strictly implemented a countrywide lockdown, starting from 24 March to 12 May 2020. This measure resulted in the reduction of the sources of air pollution in general: industrial, commercial, and vehicular pollution in particular, with visible improvement in ambient air quality. In this study, the impact of COVID-19 lockdown on the ambient concentration of air pollutants over the city of Bangalore (India) is assessed using Continuous Ambient Air Quality Measurement (CAAQM) data from 10 monitoring stations spread across the city. The data was obtained from Central Pollution Control Board (CPCB) and Karnataka State Pollution Control Board (KSPCB). The analysis of the relative changes in the ambient concentration of six major air pollutants (NO, NO2, NOX, PM2.5, O3, and SO2) has been carried out for two periods: March–May 2020 (COVID-19 lockdown) and the corresponding period of 2019 during when there was no lockdown. The analysis revealed significant reduction in the concentration of ambient air pollutants at both daily and monthly intervals. This can be attributed to the reduction in sources of emission; vehicular traffic, industrial, and other activities. The average reduction in the concentration of NO, NO2, NOX, PM2.5, and O3 between 01 March and 12 May 2020 was found to be 63%, 48%, 48%, 18%, and 23% respectively when compared to the same period in 2019. Similarly, the comparative analysis of pollutant concentrations between pre-lockdown (01–23 March 2020) and lockdown (24 March–12 May 2020) periods has shown a huge reduction in the ambient concentration of air pollutants, 47.3% (NO), 49% (NO2), 49% (NOX), 10% (SO2), 37.7% (PM2.5), and 15.6% (O3), resulting in improved air quality over Bangalore during the COVID-19 lockdown period. It is shown that the strict lockdown resulted in a significant reduction in the pollution levels. Such lockdowns may be useful as emergency intervention strategies to control air pollution in megacities when ambient air quality deteriorates dangerously.

Source: https://doi.org/10.1007/s10661-021-09177-w

More Articles ...

  1. Spatio-temporal rainfall variability over different meteorological subdivisions in India: analysis using different machine learning techniques
  2. Does increasing the spatial resolution in dynamical downscaling impact climate change projection of Indian summer monsoon, population and GDP?
  3. Prediction of COVID‐19 cases using the weather integrated deep learning approach for India
  4. Historical extreme rainfall over the Bangalore city, India, on 14 and 15 August 2017: skill of sub-kilometer forecasts from WRF model
  5. Multiscale Forecasting of High-Impact Weather: Current Status and Future Challenges
  6. An optimum initial manifold for improved skill and lead in long-range forecasting of monsoon variability
  7. High‐resolution climate change projection of northeast monsoon rainfall over peninsular India
  8. Evaluation of the Skill of a Fully-Coupled Atmospheric–Hydrological Model in Simulating Extreme Hydrometeorological Event: A Case Study Over Cauvery River Catchment
  9. Insights from a Pan India Sero-Epidemiological survey (Phenome-India Cohort) for SARS-CoV2
  10. On the build-up of dust aerosols and possible indirect effect during Indian summer monsoon break spells using recent satellite observations of aerosols and cloud properties

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To synergize the Transdisciplinary Pan-CSIR expertise and build a unified platform that embodies a rich set of big data enabling technologies and services with optimized performance to facilitate data intensive scientific discovery in the country.

Mission:

To Pioneer data driven interdisciplinary research in diverse fields through state-of-the-art data science ecosystem and impactful industrial partnerships for the betterment of Society.

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To develop cutting edge data science products as a horizontal across the CSIR Themes and position as an Institute of Excellence in Bigdata and Artificial Intelligence.

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