by Chiranjeevi G. Vivek, T. S. Shrungeshwara and Sridevi Jade
This study evaluates the impact of multi-GNSS (Global Navigation Satellite System) signals on the estimation of precise position with millimetre accuracy. Compared to standalone satellite systems like the Global Positioning System (GPS), multi-GNSS improves start-up time, performance, satellite visibility, accuracy, spatial geometry and reliability. However, on the flip side it increases the noise, signal interference, hardware complexity of the receiver, intersystem interference and computation complexity which may degrade its performance. Though GNSS is similar at fundamental levels, differences exist in signal structures, reference frames and timing standards. Compatibility and interoperability between the different constellations of the highest order is required to achieve the best results. At present, GPS and Glonass navigation systems are fully functional with global coverage and comparable precision. Glonass satellite constellation, signal structure is slightly different when compared to GPS, whereas major differences exist in the reference frame and epoch time. Combined GPS–Glonass solution significantly improves the accuracy in navigation applications with increased satellite signal observations and spatial distribution of visible satellites. For precise geodetic studies using static post-processing, combined solution may degrade the accuracy, if these differences are not handled carefully. Currently for geodetic studies, only GPS observations are majorly used worldwide. For the first time, daily precise position is estimated for continuous GNSS stations located in India using static postprocessing with standalone GPS, Glonass as well as combined GPS–Glonass to study the impact of multiGNSS signals for geodetic studies.
Source: https://www.currentscience.ac.in/Volumes/119/09/1503.pdf
by Govindan Kutty, Rekha Gogoi, V Rakesh & M Pateria
This study compares the performance of hybrid ensemble transform Kalman filter – three dimensional variational data assimilation (HYBRID) system and three dimensional variational (3DVAR) data assimilation system in Weather Research and Forecasting Model (WRF) in simulating tropical cyclones (TC) formed over the Bay of Bengal. An Ensemble Transform Kalman Filter (ETKF) system updates the ensemble system that provides flow-evolving background error covariance for HYBRID data assimilation system. Results indicate that use of flow-evolving ensemble error covariance in 3DVAR system has systematically reduced the TC position and intensity errors in the analysis; however, adding more weights to the ensemble error covariance term in 3DVAR cost function has not made any significant impact. The 3DVAR analysis depicts a stronger TC vortex with a well pronounced warm core structure as compared to that in HYBRID analysis. The forecasts from HYBRID analysis outperform that from 3DVAR in reducing TC track forecast error. The relative improvement in TC landfall position is 43% and 49% for variously configured HYBRID experiments. The forecasts initiated from HYBRID analysis has higher skill in quantitative precipitation forecasts during TC landfall compared to 3DVAR, which may be attributed to improved track prediction in the HYBRID experiments.
Source: https://link.springer.com/article/10.1007/s12040-020-01497-8
