सीएसआईआर-राष्ट्रीय डेटा विज्ञान एवं कृत्रिम बुद्धिमत्ता संस्थान

Investigation of background noise in the GNSS position time series using spectral analysis – A case study of Nepal Himalaya

by Jagat Dwipendra Ray, M. Sithartha Muthu Vijayan, Walyeldeen Godah, Ashok Kumar

 

Position time series from permanent Global Navigation Satellite System (GNSS)stations are commonly used for estimating secular velocities of discrete points on the Earth’s surface. An understanding of background noise in the GNSS position time series is essential to obtain realistic estimates of velocity uncertainties. The current study focuses on the investigation of background noise in position time series obtained from thirteen permanent GNSS stations located in Nepal Himalaya using the spectral analysis method. The power spectrum of the GNSS position time series has been estimated using the Lomb–Scargle method. The iterative nonlinear Levenberg–Marquardt (LM) algorithm has been applied to estimate the spectral index of the power spectrum. The power spectrum can be described by white noise in the high frequency zone and power law noise in the lower frequency zone. The mean and the standard deviation of the estimated spectral indices are 1.46±0.14; 1.39±0.16 and1.53±0.07 for north, east and vertical components, respectively. On average, the power law noise extends up to a period of ca. 21 days. For a shorter period, i.e. less than ca. 21days, the spectra are white. The spectral index corresponding to random walk noise (ca. –2) is obtained for a site located above the base of a seismogenic zone which can be due to the combined effect of tectonic and nontectonic factors rather than a spurious monumental motion. Overall, the usefulness of investigating the background noise in the GNSS position time series is discussed.

Ray, J. D., M. S. M. Vijayan, W. Godah, and A. Kumar (2019), Investigation of background noise in the GNSS position time series using spectral analysis – A case study of Nepal Himalaya, Geod. Cartogr., 68(No 2), 375–388, doi:10.24425/gac.2019.128468

Full Text: http://journals.pan.pl/dlibra/publication/128468/edition/112071/content

Water vapor study using MODIS and GPS data at 64 continuous GPS stations (2002–2017) in indian subcontinent

by SrideviJade, T.S. Shrungeshwara and Boddapati Anil

Precipitable Water Vapor (PWV) is estimated using MODIS (Moderate Resolution Imaging Spectro-radio-meter) Terra level 3 data with daily resolution i. e MOD08_D3 at 64 cGPS (continuous Global Positioning System) stations spatially spread over Indian subcontinent between geodetic latitude 5° to 35° N and geodetic longitude of 70° to 96° E. MODIS-PWV is compared with GPS-PWV estimated at these cGPS stations to check the validity of water vapor retrieved from MODIS data in Indian subcontinent. Correlation coefficient (R2) between daily values of MODIS and GPS water vapor is above 0.9 with RMSE (root mean square error) of 2–5 mm for 22 cGPS in peninsular India, above 0.9 with RMSE of 3–6 mm for 5 cGPS in northeast India and above 0.8 with RMSE of 1–9 mm for 26 cGPS in Himalayas. PWV time series at all the cGPS stations indicated distinct seasonal cycle for both MODIS and GPS PWV with high RMSE (~6 mm) in wet months and low RMSE (~3 mm) during dry months. Taking advantage of broad spatial spread of stations and long span of data, model for spatial variability of GPS-PWV for Indian subcontinent is proposed. Inter-annual and seasonal variability of GPS-PWV is discussed in detail for peninsular India, northeast India and Himalayas.

Source: https://doi.org/10.1016/j.jastp.2019.105138 

 

Seasonal rainfall forecast skill over Central Himalaya with an atmospheric general circulation model

by Sneh Joshi, K. C. Gouda and Prashant Goswami

Seasonal forecasts of monsoon at regional scales are critical for many applications but are rarely attempted as even the skill at all-India scale is not yet adequate. However, the conventional approach of evaluation of forecast skill for all-India seasonal monsoon rainfall implicitly assumes that the model performance is more or less spatially homogeneous. It is possible, however, that over a climatically diverse region (with large latitudinal extent), the model skill is dependent on geographical location. In particular, over land-locked regions with large orography, like the Himalayan region, the intrinsic dynamics may play the dominant role in interannual variability; this would imply that even a GCM without interannual variability in lower boundary forcing through SST may produce appreciable skill. We explore this hypothesis based on simulations for the period 1980–2003 with multiple initial conditions with an atmospheric GCM already validated at all-India scale. Multi-scale validation of seasonal forecasts is carried out at regional (Uttarakhand) to station scale over Central Himalaya with multi-source observations. In accordance with our hypothesis and for realizable forecast skill with an atmospheric GCM, the simulations are conducted with climatological monthly SST. At regional (Uttarakhand) scale, the interannual variability in composite observation and ensemble simulation are correlated at 99% significant level, with phase synchronization of about 75%. At station scale, also the skill is found to be non-trivial, especially with respect to gridded observations. Our results thus provide an effective methodology for seasonal forecasting at regional scale over certain geographical locations.

Source: https://link.springer.com/article/10.1007%2Fs00704-019-02971-0

Noise characteristics of GPS time series and their influence on velocity uncertainties

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