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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.

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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 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 U.C Dumka, D.G Kaskaoutis, Pradeep Khatri, Shantikumar S. Ningombam, Rahul Sheoran, Sridevi Jade, T. S. Shrungeshwara, Maheswar Rupakheti

We analyze long-term aerosol and precipitable water vapour (PWV) properties at two high-altitude sites (Nainital and Hanle) over the central Himalayan and western Trans-Himalayan region from 2008 to 2018. First-time assessment of the seasonality and variation in combined aerosol and water vapour radiative effects are also attempted, aiming to investigate the atmospheric effect on solar radiation over the Himalayan range that is especially important for the regional climate. A synergy of ground-based measurements from sun photometers, GPS (Global Positioning Systems) observations, radiosondes, along with satellite and reanalysis data was used to examine inter-annual and seasonal variability of PWV and specific humidity over both sites. The PWV is highest in monsoon and much lower during the dry winter season with slightly higher values at Nainital compared to Hanle. This is due to the lower altitude (∼2 km amsl) of Nainital, which is also directly affected by the Indian summer monsoon, compared to the Trans-Himalayan region. The vertical profiles of PWV from satellite and reanalysis data reveal a great consistency on a seasonal basis. The PWV is considered as one of the main greenhouse gases that exhibits a positive radiative effect at the Top of the Atmosphere (TOA) in the order of about 10 W m−2 at Nainital and 7.4 W m−2 at Hanle. The atmospheric radiative effect due to water vapour is about 3–4 times higher compared to aerosols, resulting in atmospheric heating rates of 0.94 and 0.96 K Day−1 at Nainital and Hanle, respectively. The results highlight the importance of water vapour and aerosol radiative effects in the climate sensitive Himalayan range.

Source: https://doi.org/10.1016/j.apr.2021.101303

More Articles ...

  1. Evaluation of long-term variability of ionospheric total electron content from IRI-2016 model over the Indian sub-continent with a latitudinal chain of dual-frequency geodetic GPS observations during 2002 to 2019
  2. Imaging subsurface geological complexity (2D/3D) beneath the Greater Srinagar region of the Kashmir basin, Northwest Himalaya
  3. Do seasonal forecasts of Indian summer monsoon rainfall show better skill with February initial conditions?
  4. Temperature-duration-frequency analysis over Delhi and Bengaluru city in India
  5. Sinking Slab Stress and Seismo-Tectonics of the Indo-Burmese Arc: A Reappraisal
  6. Application of neo-deterministic seismic hazard assessment to India
  7. Does increasing the spatial resolution in dynamical downscaling impact climate change projection of Indian summer monsoon, population and GDP?
  8. Assessment of air pollution status during COVID-19 lockdown (March–May 2020) over Bangalore City in India
  9. Spatio-temporal rainfall variability over different meteorological subdivisions in India: analysis using different machine learning techniques
  10. Historical extreme rainfall over the Bangalore city, India, on 14 and 15 August 2017: skill of sub-kilometer forecasts from WRF model

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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.

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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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