by Jagat Dwipendra Ray, M Sithartha Muthu Vijayan and Ashok Kumar
Accurate geodetic crustal deformation estimates with realistic uncertainties are essential to constrain geophysical models. A selection of appropriate noise model in geodetic data processing based on the characteristics of the geodetic time series being studied is the key to achieving realistic uncertainties. In this study, we report noise characteristics of a 12-yr long global positioning system (GPS) geodetic time series (2002–2013) obtained from 22 continuous mode GPS stations situated in north-east India, Nepal and Bhutan Himalayas which are one of the most complex tectonic regimes influenced by the largest hydrological loading and impacted with a load of the largest inland glaciers. A comparison of the maximum log likelihood estimates of three different noise models – (i) white plus power law (WPL), (ii) white plus flicker law (WFL) and (iii) white plus random walk noise – adopted to process the GPS time series reveals that among the three models, ∼74% of the time series can be better described either by WPL or WFL model. The results further showed that the horizontals in Nepal Himalayas and verticals in north-east India are highly correlated with time. The impact analysis of noise models on velocity estimation shows that the conventional way of assuming time uncorrelated noise models (white noise) for constraining the crustal deformation of this region severely underestimates rate uncertainty up to 14 times. Such simplistic assumption, being adopted in many geodetic crustal deformation studies, will completely mislead the geophysical interpretations and has the potential danger of identifying any inter/intra-plate tectonic quiescence as active tectonic deformation. Furthermore, the analysis on the effect of the time span of observations on velocity uncertainties suggests 3 yr of continuous observations as a minimum requirement to estimate the horizontal velocities with realistic uncertainties for constraining the tectonics of this region.
Ray, J. D., M. S. M. Vijayan, and A. Kumar (2019), Noise characteristics of GPS time series and their influence on velocity uncertainties, J. Earth Syst. Sci., 128(6), 146, doi:10.1007/s12040-019-1179-5.
Full-text: https://rdcu.be/bFkkC
by Sulochana Gadgil, K Rajendran and D S Pai
Abstract: Most of the studies of the observed variability of the Indian summer monsoon rainfall (ISMR), its prediction and of its impact have involved analysis of an index for ISMR derived by Parthasarathy et al. (1995) or the all-India rainfall during the summer monsoon, available from the India Meteorological Department (IMD) website. Both these indices are based on the average rainfall over the meteorological subdivisions of India. Rajeevan et al. (2006) first derived a gridded rainfall data set for the Indian region which was at a resolution of 1° and subsequently, Pai et al. (2014) have derived a finer resolution (0.25°) rainfall data set for the same region. At present, these data sets are widely used by modelers to generate the ‘observed’ ISMR for assessment of the skill of their models. However, in different studies, different regions are used for averaging the grid data to obtain the ‘observed’ ISMR. For proper assessment and comparison of the skill of the simulations/predictions by different models/versions, it is important that the same region be used for averaging the rainfall to obtain the observed ISMR in each case. Here, we suggest what we consider as the appropriate regions for averaging the rainfall in terms of the 1° and 0.25° to derive/represent ISMR, on the basis of the present understanding of the monsoonal regions and the Indian summer monsoon. We show that the interannual variation of the ISMR thus derived (by averaging rainfall over the regions identified in this study) from gridded data sets is largely consistent with the indices derived as the area weighted sub-divisional rainfall data used in the indices used earlier.
Citation: Sulochana Gadgil, K Rajendran and D S Pai (2019): A new rain-based index for the Indian summer monsoon rainfall, MAUSAM, 70 (3), 485-500
URL: https://metnet.imd.gov.in/mausamdocs/17036_F.pdf
