Tue, Dec 03, 2024
Text Size

CSIR Fourth Paradigm Institute

(Formerly CSIR Centre for Mathematical Modelling and Computer Simulation)

A constituent laboratory of Council of Scientific & Industrial Research (CSIR).

Ministry of Science and Technology, Government of India.

CSIR Portals

Events/Seminars @CSIR-4PI

Past Events/Seminars

Notifications/Announcements

News

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

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

by Rani Devi, K. C. Gouda & S. Lenka 

The extreme temperature events are a concern in recent years due to climate variability particularly in India as there is an increase in the temperature intensity, frequency, and duration. This study represents stationary temperature-duration-frequency (TDF) analysis over two mega cities in India Delhi (north) and Bengaluru (south) using the daily maximum temperatures at meteorological stations for the period 1969–2016 observed by India Meteorological Department (IMD).The interannual variability of maximum temperature and the maximum daily recorded value indicates the increasing trend in both the cities. The study investigates the extreme analysis of the maximum temperature using two distributions, i.e., Gumbel’s Extreme Value Type 1 (GEVT) and Log Pearson Type III (LPT), for return periods 2, 5, 10, 25, 50, and 100 years at both the locations and the positive temporal trend is observed. The TDF curves were build using annual maximum temperature values for total 8 durations (different days) of 48 years analyzed and results show the increasing trend of maximum temperature at lower duration and high return period values. The TDF is also used for prediction of the maximum temperature for the 2 hottest years in India, i.e., 2012 and 2015, and it is comparable with the observed maximum temperature. Similarly, the predictions for 11 years, i.e. 2006 to 2016, over both the cities are simulated using both the GEVT-I and LPT-III and the models have better potential skill in predicting the extreme maximum temperature. These results can be useful for the sectors like health, energy, agriculture, urban management, and ecology management and can help the policy decision makers and disaster managers in the mitigation and adoption steps to face the extreme temperature disaster at city scale.

Source: https://doi.org/10.1007/s00704-021-03824-5

by Imtiyaz A.Parvez

Earthquakes constitute one of the most feared natural disasters that occur with no warning and can result in great destruction along with loss of lives, particularly the seismically earthquake-prone countries like India. One way to mitigate the destructive impact of such earthquakes is to conduct a seismic hazard and risk assessment and take remedial measures. This chapter aims at demonstrating significant contributions to the application of neo-deterministic seismic hazard assessment (NDSHA) in India and neighboring region. NDSHA has been built on a rigorous theoretical basis and exploits the currently available computational resources that permit to compute realistic synthetic seismograms. Parvez et al. (2003) published the first-ever neo-deterministic seismic hazard map of India by computing synthetic seismograms with input data set consisting of structural models, seismogenic zones, focal mechanisms, and earthquake catalogs. Later, using updated input data and advanced computational tools, Parvez et al. (2017) produced a revised neo-deterministic seismic hazard map of India. High-resolution NDSHA has also been applied at the regional and local scale in India, particularly in Gujarat state, Kolkata, and the capital city Delhi. This chapter also discusses how peak ground acceleration estimates alone are not sufficient for the adequate design of buildings and infrastructures, since displacements play a critical role and the dynamical analysis of the structure response requires scenario-based complete time series of ground motion. The applications discussed in this chapter can be used as a preventive definition of the seismic hazard without waiting for the great earthquakes and also help those earthquake and civil engineers to avail realistically estimated ground motion database for the safe design of buildings and other infrastructure in the country.

Source: https://doi.org/10.1016/B978-0-12-823503-4.00021-X

More Articles ...

  1. Sinking Slab Stress and Seismo-Tectonics of the Indo-Burmese Arc: A Reappraisal
  2. On the Semidiurnal Variation in Surface Rainfall Rate over the Tropics in a Global Cloud-Resolving Model Simulation and Satellite Observations
  3. Do seasonal forecasts of Indian summer monsoon rainfall show better skill with February initial conditions?
  4. Assessment of air pollution status during COVID-19 lockdown (March–May 2020) over Bangalore City in India
  5. Spatio-temporal rainfall variability over different meteorological subdivisions in India: analysis using different machine learning techniques
  6. Does increasing the spatial resolution in dynamical downscaling impact climate change projection of Indian summer monsoon, population and GDP?
  7. Prediction of COVID‐19 cases using the weather integrated deep learning approach for India
  8. Historical extreme rainfall over the Bangalore city, India, on 14 and 15 August 2017: skill of sub-kilometer forecasts from WRF model
  9. Multiscale Forecasting of High-Impact Weather: Current Status and Future Challenges
  10. An optimum initial manifold for improved skill and lead in long-range forecasting of monsoon variability

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.

Mandate: 

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.

Student Programme for Advancement in Research Knowledge (SPARK)

SPARK is intended to provide a unique opportunity to bright and motivated students of reputed Universities to carry out their major project/thesis work and advance their research knowledge in mathematical modelling and simulation of complex systems. The programme is intended to increase the interaction between scientists and faculty members of academic institutes along with their students towards a long term research collaboration. Click here to apply for SPARK.

A FAQ on SPARK is available here.