An official website of the United States government
This paper describes, along with some validation results, the one-dimensional variational method (1D-Var) that is in use at the University Corporation for Atmospheric Research (UCAR) to retrieve atmospheric profiles of temperature, pressure, and humidity from the observation of the Global Navigation Satellite System (GNSS) radio occultation (RO). The retrieved profiles are physically consistent among the variables and statistically optimal as regards to a priori error statistics. Tests with idealized data demonstrate that the 1D-Var is highly effective in spreading the observational information and confirm that the method works as designed and expected, provided that correct input data are given. Tests for real-world data sets show that the retrieved profiles agree remarkably well with global weather analyses and collocated high vertical resolution radiosonde observations, and that the 1D-Var can produce value-added retrievals with respect to a priori profiles. We also find that the retrieved profiles are of exceptional long-term stability, suggesting that the 1D-Var can provide an excellent climate data record.
more »
« less
This content will become publicly available on June 26, 2026
Retrieval of thermodynamic profiles in the lower troposphere from GNSS radio occultation using deep learning
Abstract. Global Navigation Satellite Systems (GNSS) radio occultation (RO) is one of the most vital remote sensing techniques globally and of major importance for numerical weather prediction (NWP) and climate science. However, retrieving profiles of atmospheric quantities such as temperature or humidity from GNSS observations is not straightforward and dedicated algorithms still have their limitations. One of these limitations is the need for external meteorological data in the retrieval process. Various new RO missions have led to an enormous increase in data amounts and with over 10000 globally-distributed, daily profiles, RO can be considered big data nowadays. In this study, we make use of this fact by developing a new retrieval method based on a deep learning model, which only needs RO-specific quantities as an input to produce atmospheric profiles. The model is trained on almost a full year of data from COSMIC-2 and Spire RO missions, using vertical profiles of bending angle (BA) and other RO parameters as input features and operational results from a standard retrieval algorithm as target values for supervised learning. Initial results from both internal and external validation using reanalysis and radiosonde data suggest that this method produces results with an accuracy comparable to standard algorithms, while mitigating the need for external information in the retrieval process itself. These initial results serve as a starting point for further development of data-driven models for RO, which could significantly enhance the quality of RO products utilized in, e.g., climate sciences by mitigating external biases and increasing independence from other techniques.
more »
« less
- Award ID(s):
- 2054356
- PAR ID:
- 10615417
- Publisher / Repository:
- EGUsphere
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
We compare the random error statistics (uncertainties) of COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate, C1) and COSMIC-2 (C2) radio occultation (RO) bending angles and refractivities for the months of August 2006 and 2021 over the tropics and subtropics using the three-cornered hat method. The uncertainty profiles are similar for the two RO missions in the troposphere. However, a higher percentage of C2 profiles reach close to the surface in the moisture-rich tropics, an advantage of the higher signal-to-noise ratio (SNR) in C2. C2 uses signals from both GPS (Global Positioning System) and GLONASS Global Navigation System Satellites (GNSS). The GPS occultations show smaller uncertainties in the stratosphere and lower mesosphere (30–60 km) than the GLONASS occultations, a result of more accurate GPS clocks. Therefore, C2 (GPS) uncertainties are smaller than C1 uncertainties between 30–60 km while the C2 (GLONASS) uncertainties are larger than those of C1. The uncertainty profiles vary with latitude at all levels. We find that horizontal gradients in temperature and water vapor, and therefore refractivity, are the major cause of uncertainties in the tropopause region and troposphere through the violation of the assumption of spherical symmetry in the retrieval of bending angles and refractivity.more » « less
-
Abstract Observationally, a major source of uncertainty in evaluation of climate models arises from the difficulty in obtaining globally distributed, fine scale profiles of temperature, pressure and water vapor, that probe through convective precipitating clouds, from the boundary layer to the upper levels of the free troposphere. In this manuscript, a two-year analysis of data from the Radio Occultations through Heavy Precipitation (ROHP) polarimetric RO demonstration mission onboard the Spanish PAZ spacecraft is presented. ROHP measures the difference in the differential propagation phase delay (Δ 𝜙 ) between two orthogonal polarization receive states that is induced from the presence of non-spherically shaped hydrometeors along the Global Navigation Satellite System (GNSS) propagation path, complementing the standard RO thermodynamic profile. Since Δφ is a net path-accumulated depolarization and does not resolve the precipitation structure along the propagation path, orbital coincidences between ROHP and the Global Precipitation Measurement (GPM) constellation passive MW radiometers are identified to provides three-dimensional precipitation context to the RO thermodynamic profile. Passive MW-derived precipitation profiles are used to simulate the Δ φ along the ROHP propagation paths. Comparison between the simulated and observed Δ φ are indicative of the ability of ROHP to detect threshold levels of ray path-averaged condensed water content, as well as to suggest possible inferences on the average ice phase hydrometeor non-sphericity. The use of the polarimetric RO vertical structure is demonstrated as a means to condition the lower tropospheric humidity by the top-most height of the associated convective cloud structure.more » « less
-
Abstract Global Navigation Satellite System (GNSS) Radio Occultation (RO) missions, such as the Formosa Satellite‐3/Constellation Observing System for Meteorology, Ionosphere, and Climate (FORMOSAT‐3/COSMIC) and the upcoming FORMOSAT‐7/COSMIC‐2, provide valuable profiling of the ionized atmosphere for the monitoring of space weather. This study shows that the FORMOSAT‐3/COSMIC and FORMOSAT‐7/COSMIC‐2 missions' ability to monitor highly variable ionospheric weather can be considerably extended with the help of data assimilation. The Gridpoint Statistical Interpolation (GSI) Ionosphere is a new data assimilation system designed specifically for the low‐latitude and midlatitude ionosphere. The capability of the GSI Ionosphere is first demonstrated with actual FORMOSAT‐3/COSMIC RO total electron content (TEC) data for January 2013. Features of the ionospheric equatorial ionization anomaly in a coupled plasmasphere ionosphere thermosphere model become more consistent with the TEC maps created with independent ground‐based GPS data. The consistency has improved by assimilation of FORMOSAT‐3/COSMIC RO data up to about 50% in comparison to the control simulation case without data assimilation. To evaluate the impact of future RO missions on ionospheric weather specification, comparative Observing System Simulation Experiments (OSSEs) are carried out with synthetic RO TEC data. An OSSE of FORMOSAT‐7/COSMIC‐2 shows that the GSI Ionosphere can improve the ionospheric specification within ±30° geomagnetic latitude by 67% over the control case, which is comparable to the improvement yielded by FORMOSAT‐3/COSMIC for 2009 (61%). These results indicate a great potential for improving the monitoring of realistic ionospheric weather with the help of FORMOSAT‐7/COSMIC‐2 RO TEC data.more » « less
-
Abstract The transition to deep convection and associated precipitation is often studied in relationship to the associated column water vapor owing to the wide availability of these data from various ground or satellite-based products. Based on radiosonde and ground-based Global Navigation Satellite System (GNSS) data examined at limited locations and model comparison studies, water vapor at different vertical levels is conjectured to have different relationships to convective intensity. Here, the relationship between precipitation and water vapor in different free tropospheric layers is investigated using globally distributed GNSS radio occultation (RO) temperature and moisture profiles collocated with GPM IMERG precipitation across the tropical latitudes. A key feature of the RO measurement is its ability to directly sense in and near regions of heavy precipitation and clouds. Sharp pickups (i.e. sudden increases) of conditionally averaged precipitation as a function of water vapor in different tropospheric layers are noted for a variety of tropical ocean and land regions. The layer-integrated water vapor value at which this pickup occurs has a dependence on temperature that is more complex than constant RH, with larger subsaturation at warmer temperatures. These relationships of precipitation to its thermodynamic environment for different layers can provide a baseline for comparison with climate model simulations of the convective onset. Furthermore, vertical profiles before, during, and after convection are consistent with the hypothesis that the lower troposphere plays a causal role in the onset of convection, while the upper troposphere is moistened by de-trainment from convection.more » « less
