Evaluation of a Linear Phase Observation Operator with CHAMP Radio Occultation Data and High-Resolution Regional Analysis

2005 ◽  
Vol 133 (10) ◽  
pp. 3053-3059 ◽  
Author(s):  
S. Sokolovskiy ◽  
Y-H. Kuo ◽  
W. Wang
Keyword(s):  
High Resolution ◽  
Radio Occultation ◽  
Regional Analysis ◽  
Observation Error ◽  
Computationally Efficient ◽  
Observation Operator ◽  
Simple Implementation ◽  
Excess Phase ◽  
Occultation Data ◽  
Radio Occultation Data

Abstract In this study a nonlocal, linear observation operator for assimilating radio occultation data is evaluated. The operator consists of modeling the excess phase, that is, integrating the refractivity along straight lines tangent to rays, below a certain height. The corresponding observable is the excess phase integrated through the Abel-retrieved refractivity, along the same lines, below the same height. The operator allows very simple implementation (computationally efficient) while accurately accounting for the horizontal refractivity gradients. This is due to significant cancellation of the linearization and discretization errors when modeling the observable. Evaluation of the operator with Challenging Minisatellite Payload (CHAMP) radio occultation data and grid refractivity fields from high-resolution regional analysis over the continental United States showed reduction of the observation error in the troposphere (below 7 km) 1.5–2 times, compared to the error of local refractivity. The operator is useful for the assimilation of radio occultation data by high-resolution weather models in the troposphere.

Assessing the Accuracy of a Linearized Observation Operator for Assimilation of Radio Occultation Data: Case Simulations with a High-Resolution Weather Model

2005 ◽  
Vol 133 (8) ◽  
pp. 2200-2212 ◽  
Author(s):  
Sergey Sokolovskiy ◽  
Ying-Hwa Kuo ◽  
Wei Wang
Keyword(s):  
High Resolution ◽  
Radio Occultation ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Linear Modeling ◽  
Observation Operator ◽  
Phase Path ◽  
Excess Phase

Abstract Assimilation into numerical weather models of the refractivity, Abel-retrieved from radio occultations, as the local refractivity at ray tangent point may result in large errors in the presence of strong horizontal gradients (atmospheric fronts, strong convection). To reduce these errors, other authors suggested modeling the Abel-retrieved refractivity as a nonlocal linear function of the 3D refractivity, which can be used as a linear observation operator for assimiliation. The authors of this study introduce their approach for the nonlocal linear observation operator, which consists of modeling the excess phase path, calculated along certain trajectories below the top of an atmospheric model. In this study (not aimed at development of an observation operator for any specific atmospheric model), both approaches are validated by assessing the accuracy of both linearized observation operators by numerical simulations with the high-resolution Weather Research and Forecasting (WRF) model and comparing them to the accuracy of interpretation of the Abel-retrieved refractivity as local. Improvement of the accuracy of about an order of magnitude is found with the nonlocal refractivity and further improvement is found with the excess phase path. The effect of horizontal resolution of an atmospheric model on the accuracy of modeling local and nonlocal linear observables is also investigated, and it is demonstrated that the nonlocal linear modeling of radio occultation observables is especially important for weather prediction models with sufficiently high horizontal resolution, grid size <100 km (mesoscale models).

A global 3-D electron density reconstruction model based on radio occultation data and neural networks

2021 ◽  
pp. 105702
Author(s):  
John Bosco Habarulema ◽  
Daniel Okoh ◽  
Dalia Burešová ◽  
Babatunde Rabiu ◽  
Mpho Tshisaphungo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Electron Density ◽  
Radio Occultation ◽  
Model Based ◽  
Occultation Data ◽  
Reconstruction Model ◽  
Radio Occultation Data

Parallelization Strategies for the GPS Radio Occultation Data Assimilation with a Nonlocal Operator in the Weather Research and Forecasting Model

2014 ◽  
Vol 31 (9) ◽  
pp. 2008-2014 ◽  
Author(s):  
Xin Zhang ◽  
Ying-Hwa Kuo ◽  
Shu-Ya Chen ◽  
Xiang-Yu Huang ◽  
Ling-Feng Hsiao
Keyword(s):  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Observation Operator ◽  
Gps Radio Occultation ◽  
Phase Observation ◽  
Excess Phase ◽  
Weather Research

Abstract The nonlocal excess phase observation operator for assimilating the global positioning system (GPS) radio occultation (RO) sounding data has been proven by some research papers to produce significantly better analyses for numerical weather prediction (NWP) compared to the local refractivity observation operator. However, the high computational cost and the difficulties in parallelization associated with the nonlocal GPS RO operator deter its application in research and operational NWP practices. In this article, two strategies are designed and implemented in the data assimilation system for the Weather Research and Forecasting Model to demonstrate the capability of parallel assimilation of GPS RO profiles with the nonlocal excess phase observation operator. In particular, to solve the parallel load imbalance problem due to the uneven geographic distribution of the GPS RO observations, round-robin scheduling is adopted to distribute GPS RO observations among the processing cores to balance the workload. The wall clock time required to complete a five-iteration minimization on a demonstration Antarctic case with 106 GPS RO observations is reduced from more than 3.5 h with a single processing core to 2.5 min with 106 processing cores. These strategies present the possibility of application of the nonlocal GPS RO excess phase observation operator in operational data assimilation systems with a cutoff time limit.

Analysis of selected Solar events with Mars Express radio occultation data 

2021 ◽  
Author(s):  
Özgür Karatekin ◽  
Ananya Krishnan ◽  
Nayeem Ebrahimkutty ◽  
Greg Henry ◽  
Ahmed El Fadhel ◽  
...  
Keyword(s):  
Radio Occultation ◽  
Mars Express ◽  
Solar Events ◽  
Occultation Data ◽  
Radio Occultation Data

Analyzing the Diurnal Cycle by Bayesian Interpolation on a Sphere for Mapping GNSS Radio Occultation Data

2021 ◽  
Vol 38 (5) ◽  
pp. 951-961
Author(s):  
Stephen S. Leroy ◽  
Chi O. Ao ◽  
Olga P. Verkhoglyadova ◽  
Mayra I. Oyola
Keyword(s):  
Diurnal Cycle ◽  
Radio Occultation ◽  
Basis Functions ◽  
Atmospheric Tides ◽  
Spherical Harmonic Degree ◽  
Construct Maps ◽  
Bayesian Interpolation ◽  
Occultation Data ◽  
High Degree ◽  
Radio Occultation Data

AbstractBayesian interpolation has previously been proposed as a strategy to construct maps of radio occultation (RO) data, but that proposition did not consider the diurnal dimension of RO data. In this work, the basis functions of Bayesian interpolation are extended into the domain of the diurnal cycle, thus enabling monthly mapping of radio occultation data in synoptic time and analysis of the atmospheric tides. The basis functions are spherical harmonics multiplied by sinusoids in the diurnal cycle up to arbitrary spherical harmonic degree and diurnal cycle harmonic. Bayesian interpolation requires a regularizer to impose smoothness on the fits it produces, thereby preventing the overfitting of data. In this work, a formulation for the regularizer is proposed and the most probable values of the parameters of the regularizer determined. Special care is required when obvious gaps in the sampling of the diurnal cycle are known to occur in order to prevent the false detection of statistically significant high-degree harmonics of the diurnal cycle in the atmosphere. Finally, this work probes the ability of Bayesian interpolation to generate a valid uncertainty analysis of the fit. The postfit residuals of Bayesian interpolation are dominated not by measurement noise but by unresolved variability in the atmosphere, which is statistically nonuniform across the globe, thus violating the central assumption of Bayesian interpolation. The problem is ameliorated by constructing maps of RO data using Bayesian interpolation that partially resolve the temporal variability of the atmosphere, constructing maps for approximately every 3 days of RO data.

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