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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 Siva Sai KumarRajana, T.S.Shrungeshwara, Chiranjeevi G.Vivek, Sampad Kumar Panda and Sridevi Jade

We evaluate the performance of the latest version of International Reference Ionosphere (IRI-2016) by comparing the estimated Total Electron Content (TEC) with the observed values from four geodetic Global Positioning System (GPS) receivers, latitudinally aligned from the equator to low latitudes (−5° to 20° Geomagnetic) in the Indian longitudes (75–95°) from 2002 to 2019. It is observed that IRI-2016 model underestimates and overestimates the GPS TEC depending on the season, solar activity, and geographic location of observation. On a decadal scale, the monthly mean of IRI TEC and GPS TEC show distinct seasonal variation trends for all years with seasonal asymmetry. The bias and RMSE values are low in the ascending and descending phases of solar cycles 23 and 24 compared to high values with significant fluctuations observed during the peak solar activity phase. For the declining phase of solar cycle 24, IRI TEC underestimates the GPS TEC values at EIA crest regions. On an annual scale, IRI TEC agrees better with GPS TEC during the low solar activity years (2010 and 2019) than the high solar activity years (2002 and 2014). Moreover, IRI model is unable to manifest the winter anomaly characteristics during the high solar activity years. On a diurnal scale, the performance of IRI model is poor with high RMSE during daytime hours and it underestimates the GPS TEC at equatorial and low latitude regions during the high solar activity phases. On a seasonal scale, high bias and RMSE are observed during the spring equinox compared to other seasons under the high solar activity phases. On a spatial scale, the bias and RMSE are high with low yearly coefficient of determination () in the EIA crest regions as compared to the equatorial and low latitude regions. High underestimation of IRI model was also observed in November 2011 due to high solar indices. The proportionality relationship between RMSE and solar indices is observed in all the phases of solar activity. Additionally, the mean annual RMSE and  values indicate solar activity predominantly affecting the performance of IRI model. This study suggests that the influence of solar and magnetic indices inputs in the present IRI model could be revisited for reflecting the solar activity effects distinctly in the model outputs. In addition, IRI-2016 model requires further improvements for equatorial, low latitude, and EIA crest regions of the Indian sub-continent, specifically during the high solar activity phases.

Source: https://doi.org/10.1016/j.asr.2021.12.005

 

by S. Vishal Gupta, Imtiyaz A. Parvez and Prosanta K. Khan

A high-resolution microtremor measurement in Greater Srinagar city of the Kashmir valley has been analysed to image 2D and 3D subsurface geological complexities. This region is located in the highly seismogenic Himalayan belt and sits atop a deep sedimentary lake bed with a laterally varying thickness of soft sediments. Srinagar region is a major economic centre and the capital city of the Kashmir valley with 2 million inhabitants living at high seismic risk. To assess the subsurface complexity beneath the city, we present: (1) high-resolution subsurface shear wave velocity Vs structure using the horizontal-to-vertical spectral ratio inversion; (2) shear wave velocity for top 30 metres of soil column (VS30) map with National Earthquake Hazards Reduction Program site classification; (3) comparison of VS30 maps calculated from horizontal-to-vertical spectral ratio inversion and topographic slope methods; and (4) azimuthal behaviour of horizontal-to-vertical spectral ratio peaks, all of which unravel the subsurface spatial heterogeneity and suitability for the building of engineering structures in the study area. In addition, a new matlab code is applied to generate 3D subsurface Vs slices in the study region in different directions using its pre-generated 2D Vs profile data. The presented potentiality of microtremor horizontal-to-vertical spectral ratio technique in Srinagar region, which lies on the eastern edge of the basin with significant topographic irregularities, indicates an uneven distribution of local site effects (primary and secondary) in the case of strong ground motion. The comprehensive results can be promising in engineering analyses of local ground and structural responses in order to mitigate the impact of earthquake occurrence and seismic risk in the city and adjoining regions.

Source: https://doi.org/10.1002/nsg.12186

by M.Sithartha Muthu Vijayan and K.Shimna

Ionospheric perturbations induced by tsunamis and earthquakes can be used for tsunami early warning and remote sensing of earthquakes, provided the perturbations are characterized properly to distinguish them from the ones caused by other sources. The ionospheric perturbations are increasingly being obtained from Global Positioning System (GPS) based Total Electron Content (TEC) measurements sampled at uniform time intervals. However, the sampling is not uniform in space. The nonuniform spatial sampling along the GPS satellite tracks introduces aliasing if it is not accounted while computing the ionospheric perturbations. All the methods hitherto used to detect the co-seismic and tsunamigenic ionospheric perturbations did not account the nonuniform spatial sampling while computing these perturbations. In addition, the residual approach used to obtain the perturbations by detrending the TEC time series using high-order polynomial fit introduces artifacts. These aliasing and artifacts corrupt amplitude, Signal-to-Noise Ratio (SNR), phase, and frequency of ionospheric perturbations which are vital to distinguish the perturbations induced by tsunamis and earthquakes from the rest. We show that Spatio-Periodic Leveling Algorithm (SPLA) successfully removes such aliasing and artifacts. The efficiency of SPLA in removing the aliases and artifacts is validated under two simulated scenarios, and using GPS observations carried out during two natural disasters – the 2004 Indian Ocean tsunami and the 2015 Nepal-Gorkha earthquake. We, further, studied the severity of aliasing and artifacts on co-seismic and tsunamigenic perturbations by analyzing its characteristics employing SNR, spatiotemporal, and wavelet analyses. The results reveal that removal of aliasing and artifacts using SPLA i) increases the SNR up to ∼149% compared to the residual method and ∼39% compared to the differential method, ii) distinctly resolves signals from sharp static variations, and iii) detects 50% more co-seismic ionospheric perturbations and 25% more tsunami-induced ionospheric perturbations in the two events studied. Cross-correlation of the perturbation time series obtained using the residual method and SPLA reveals that aliasing and artifacts shift the time of occurrence by −7.64 minutes to +4.21 minutes. Further, the results show that the SPLA efficiently detects the ionospheric perturbations at low elevation angles, thereby removes the need of applying elevation cut-off and increases the area of ionospheric exploration of a GPS receiver.

Source : https://doi.org/10.1016/j.asr.2021.10.040

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  6. Assessment of air pollution status during COVID-19 lockdown (March–May 2020) over Bangalore City in India
  7. Spatio-temporal rainfall variability over different meteorological subdivisions in India: analysis using different machine learning techniques
  8. Does increasing the spatial resolution in dynamical downscaling impact climate change projection of Indian summer monsoon, population and GDP?
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  10. Historical extreme rainfall over the Bangalore city, India, on 14 and 15 August 2017: skill of sub-kilometer forecasts from WRF model

Vision: 

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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