The beneficial impact of radio occultation observations on Australian region forecasts

Marshall, J. Le; Xiao, Yi; Norman, Robert; Zhang, Kefei; Rea, Anthony; Cucurull, Lídia; Seecamp, R; Steinle, Peter; Puri, Kamal; Le, Tan

doi:10.22499/2.6002.004

Cited by 29 publications

(18 citation statements)

References 16 publications

(18 reference statements)

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“…This technique reduces matrix A condition number and improves inversion stability. The design matrix A filtering process follows the methodology developed by Rohm (2013), based on the work of Xu (1998) andLynch (2005). In essence, matrix A is decomposed into three matrices (Strang and Borre, 1997):…”

Section: Kalman Filter Applicationmentioning

confidence: 99%

“…10) the stronger the amplification of observation uncertainty R R k . The last, useful singular value (s x ) considered in the processing is found using functional analysis of singular values function (Xu, 1998;Lynch, 2005;Hansen and O'Leary, 1993), this method to improve the matrix condition number is named truncated singular values decomposition (TSVD). The new filteredÃ matrix is obtained by composing the A matrix back according to the equation:…”

Section: Kalman Filter Applicationmentioning

confidence: 99%

“…The model is based on the UK Meteorological Office Unified Model, and a number of data sources are used to produce forecasts (e.g. COSMIC, AIRS, SYNOP) (Le Marshall et al, 2010). The model in the horizontal plane contains 229 nodes, with the grid spacing of 0.375 • (∼ 37.5 km) and the model utilises in the vertical direction terrain following hybrid (pressure/height) coordinates with 50 levels.…”

Section: Nwp Modelmentioning

confidence: 99%

“…These severe weather phenomena can potentially cause destructive damage to society and the economy and hence play a critical role in weather forecasting. Improving the understanding of WV distribution is important (Le Marshall et al, 2010), not only for meteorology, but also for an effective usage of the global navigation satellite system (GNSS) technology for precise positioning. For example, tropospheric effects are one of the important atmospheric errors that need to be removed or mitigated in both high-accuracy differential positioning and precise point positioning (Wielgosz et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…In this study, to overcome the ill-conditionedness of the inverse tomography problem without applying explicit constraints, the SVD method proposed by Xu (1998) and Lynch (2005) is used. The novelty in this approach is that the information provided in the observation matrix is used exclusively and singular values in the design matrix are sensibly selected.…”

Section: Introductionmentioning

confidence: 99%

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Limited constraint, robust Kalman filtering for GNSS troposphere tomography

2014

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Abstract. The mesoscale variability of water vapour (WV) in the troposphere is a highly complex phenomenon and modelling and monitoring the WV distribution is a very important but challenging task. Any observation technique that can reliably provide WV distribution is essential for both monitoring and predicting weather. The global navigation satellite system (GNSS) tomography technique is a powerful tool that builds upon the critical ground-based GNSS infrastructure (e.g. Continuous Operating Reference Station -CORS -networks) that can be used to sense the amount of WV. Previous research shows that the 3-D WV field from GNSS tomography has an uncertainty of 1 hPa. However, all the models used in GNSS tomography heavily rely on a priori information and constraints from non-GNSS measurements. In this study, 3-D GNSS tomography models are investigated based on a limited constrained approach -i.e. horizontal and vertical correlations between voxels were not introduced, instead various a priori information were added into the system. A case study is designed and the results show that proposed solutions are feasible by using a robust Kalman filtering technique and effective removal of linearly dependent observations and parameters. Discrepancies between reference wet refractivity data derived from the Australian Numerical Weather Prediction (NWP) model (ACCESS) and the GNSS tomography model using both simulated and real data are 4.2 ppm (mm km −1 ) and 6.2 ppm (mm km −1 ), respectively, which are essentially in the same order of accuracy.

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Section: Kalman Filter Applicationmentioning

confidence: 99%

Section: Kalman Filter Applicationmentioning

confidence: 99%

Section: Nwp Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Limited constraint, robust Kalman filtering for GNSS troposphere tomography

2014

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A new dynamic approach for statistical optimization of GNSS radio occultation bending angles for optimal climate monitoring utility

Kirchengast

Scherllin‐Pirscher

et al. 2013

JGR Atmospheres

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Global Navigation Satellite System (GNSS)‐based radio occultation (RO) is a satellite remote sensing technique providing accurate profiles of the Earth's atmosphere for weather and climate applications. Above about 30 km altitude, however, statistical optimization is a critical process for initializing the RO bending angles in order to optimize the climate monitoring utility of the retrieved atmospheric profiles. Here we introduce an advanced dynamic statistical optimization algorithm, which uses bending angles from multiple days of European Centre for Medium‐range Weather Forecasts (ECMWF) short‐range forecast and analysis fields, together with averaged‐observed bending angles, to obtain background profiles and associated error covariance matrices with geographically varying background uncertainty estimates on a daily updated basis. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.4 (OPSv5.4) algorithm, using several days of simulated MetOp and observed CHAMP and COSMIC data, for January and July conditions. We find the following for the new method's performance compared to OPSv5.4: 1.) it significantly reduces random errors (standard deviations), down to about half their size, and leaves less or about equal residual systematic errors (biases) in the optimized bending angles; 2.) the dynamic (daily) estimate of the background error correlation matrix alone already improves the optimized bending angles; 3.) the subsequently retrieved refractivity profiles and atmospheric (temperature) profiles benefit by improved error characteristics, especially above about 30 km. Based on these encouraging results, we work to employ similar dynamic error covariance estimation also for the observed bending angles and to apply the method to full months and subsequently to entire climate data records.

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