scholarly journals Quasi-stationary temperature structure in the upper troposphere over the tropical Indian Ocean inferred from radio occultation data

2010 ◽  
Vol 115 (D14) ◽  
Author(s):  
Noriyuki Nishi ◽  
Eriko Nishimoto ◽  
Hiroo Hayashi ◽  
Masato Shiotani ◽  
Hisahiro Takashima ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Radio Occultation ◽  
Tropical Indian Ocean ◽  
Temperature Structure ◽  
Upper Troposphere ◽  
Occultation Data ◽  
Radio Occultation Data

scholarly journals Observing upper troposphere–lower stratosphere climate with radio occultation data from the CHAMP satellite

2007 ◽  
Vol 31 (1) ◽  
pp. 49-65 ◽  
Author(s):  
Ulrich Foelsche ◽  
Michael Borsche ◽  
Andrea K. Steiner ◽  
Andreas Gobiet ◽  
Barbara Pirscher ◽  
...  
Keyword(s):  
Radio Occultation ◽  
Lower Stratosphere ◽  
Upper Troposphere ◽  
Champ Satellite ◽  
Occultation Data ◽  
Radio Occultation Data

scholarly journals GNSS Radio Occultation Constellation Observing System Experiments

2014 ◽  
Vol 142 (2) ◽  
pp. 555-572 ◽  
Author(s):  
Peter Bauer ◽  
Gábor Radnóti ◽  
Sean Healy ◽  
Carla Cardinali
Keyword(s):  
Radio Occultation ◽  
Lower Stratosphere ◽  
Satellite System ◽  
Model Bias ◽  
Upper Troposphere ◽  
Aircraft Observations ◽  
Data Volume ◽  
Occultation Data ◽  
Global Navigation Satellite ◽  
Radio Occultation Data

Abstract Observing system experiments within the operational ECMWF data assimilation framework have been performed for summer 2008 when the largest recorded number of Global Navigation Satellite System (GNSS) radio occultation observations from both operational and experimental satellites were available. Constellations with 0%, 5%, 33%, 67%, and 100% data volume were assimilated to quantify the sensitivity of analysis and forecast quality to radio occultation data volume. These observations mostly constrain upper-tropospheric and stratospheric temperatures and correct an apparent model bias that changes sign across the upper-troposphere–lower-stratosphere boundary. This correction effect does not saturate with increasing data volume, even if more data are assimilated than available in today’s analyses. Another important function of radio occultation data, namely, the anchoring of variational radiance bias corrections, is demonstrated in this study. This effect also does not saturate with increasing data volume. In the stratosphere, the anchoring by radio occultation data is stronger than provided by radiosonde and aircraft observations.

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

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.

Sign in / Sign up

Export Citation Format

Share Document