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RSS FeedsRemote Sensing, Vol. 11, Pages 2886: Variational Assimilation of Radio Occultation Observations into Numerical Weather Prediction Models: Equations, Strategies, and Algorithms (Remote Sensing)

 
 

4 december 2019 14:00:50

 
Remote Sensing, Vol. 11, Pages 2886: Variational Assimilation of Radio Occultation Observations into Numerical Weather Prediction Models: Equations, Strategies, and Algorithms (Remote Sensing)
 




We review different approaches to the variational assimilation of radio occultation (RO) observations into models of global atmospheric circulation. We derive the general equation for the bending angle that reduces to the Abel integral for a spherically layered atmosphere. We review the full 3-D observation operator for bending angles, which provides the strictest solution, but is also most computationally expensive. Commonly used is the 2-D approximation that allows treating rays as plane curve. We discuss a simple 1-D approach to the assimilation of bending angles. The observation operator based on the standard form of the Abel integral has a disadvantage, because it cannot account for waveguides. Alternative approaches use 1-D ray-tracing. The most straightforward way is to use the same framework as for the 3-D observation operator, with the refractivity field reduced to a single profile independent from the horizontal coordinates. An alternative 1-D ray-tracing approach uses the form of ray equation in a spherically layered medium that uses an invariant. The assimilation of refractivity has also 1-D and 3-D options. We derive a new simple form of the refractivity-mapping operator. We present the results of numerical tests of different 3-D and 1-D observation operators, based on Spire data.


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44 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 2887: Differential Ground-Based Radar Interferometry for Slope and Civil Structures Monitoring: Two Case Studies of Landslide and Bridge (Remote Sensing)
Remote Sensing, Vol. 11, Pages 2885: Focusing High-Squint Synthetic Aperture Radar Data Based on Factorized Back-Projection and Precise Spectrum Fusion (Remote Sensing)
 
 
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