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
by P Ajilesh, V Rakesh, Sanjeeb K Sahoo and S Himesh
In this study, 32 rainfall events spanning from 2012 to 2014 over the urban Indian city, Bangalore were simulated using the Weather Research and Forecast (WRF) model. Model simulations were carried out with a four‐nested domain initialized with Global Forecast System (GFS) data and the forecast was generated on an hourly basis. The forecasted rainfall at hobli‐level (Bangalore has 34 hobli divisions with an area of each hobli of the order of ~10 km2) was evaluated in terms of their intensity and pattern of spatial distribution by comparing with corresponding rain‐gauge observations. Also, the rainfall forecast skill of the model was evaluated statistically by computing Root Mean Square Error (RMSE), Bias, and Mean Absolute Error (MAE). Thermodynamic variables like Equivalent Potential Temperature, Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), K Index (KI), Lifted Index (LI), and Total Totals Index (TTI) were also derived from simulated model parameters for all the events and verified against corresponding observations. Results showed that the WRF model could simulate the rainfall events and associated thermodynamic features qualitatively; however, there were few hoblis where the relative errors in the forecast were more than 100%. The forecast errors were relatively lower for cases during the south‐west monsoon season compared to other seasons. It was found that the model underestimated thermodynamic indices like CAPE, dew point depression and the simulated LI were positive; these were indicative of model's limitation in simulating intense convection and a possible reason for underpredicted rainfall simulations.