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.
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.
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.
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.
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.
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