An official website of the United States government
Abstract. The Radio Occultation Modeling EXperiment (ROMEX) is an international collaboration to test the impact of varying numbers of radio occultation (RO) profiles in operational numerical weather prediction (NWP) models. An average of 35,000 RO profiles per day for September–November 2022 from 13 different missions are being used in experiments at major NWP centers. This paper evaluates properties of ROMEX data, with emphasis on the three largest datasets: COSMIC-2 (C2), Spire, and Yunyao. The penetration rates (percent of profiles reaching different levels above the surface) of most of the ROMEX datasets are similar, with more than 80 % of all occultations reaching 2 km or lower and more than 50 % reaching 1 km or lower. The relative uncertainties of the C2, Spire, and Yunyao bending angles and refractivities are estimated using the three-cornered hat method. They are similar on the average in the region of overlap (45° S–45° N). Larger uncertainties occur in the tropics compared to higher latitudes below 20 km. Relatively small variations in longitude exist. The assimilation of ROMEX data caused small degradations in biases in several NWP models. We investigate biases in the observations by comparing them to each other and to models. C2 bending angles appear to be biased by about +0.1–0.15 % compared to Spire and other ROMEX data. These apparent biases, some of which are representativeness or sampling differences, are caused by the different orbits of C2 and other ROMEX missions around the non-spherical Earth and the associated varying radii of curvature (radius of a sphere that best fits the Earth’s surface curvature at a given location and orientation of the RO occultation plane and is used in the RO BA retrievals).
more »
« less
Estimating Individual Radio Occultation Uncertainties Using the Observations and Environmental Parameters
Abstract Estimation of uncertainties (random error statistics) of radio occultation (RO) observations is important for their effective assimilation in numerical weather prediction (NWP) models. Average uncertainties can be estimated for large samples of RO observations and these statistics may be used for specifying the observation errors in NWP data assimilation. However, the uncertainties of individual RO observations vary, and so using average uncertainty estimates will overestimate the uncertainties of some observations and underestimate those of others, reducing their overall effectiveness in the assimilation. Several parameters associated with RO observations or their atmospheric environments have been proposed to estimate individual RO errors. These include the standard deviation of bending angle (BA) departures from either climatology in the upper stratosphere and lower mesosphere (STDV) or the sample mean between 40 and 60 km (STD4060), the local spectral width (LSW), and the magnitude of the horizontal gradient of refractivity (|∇HN|). In this paper we show how the uncertainties of two RO datasets, COSMIC-2 and Spire BA, as well as their combination, vary with these parameters. We find that the uncertainties are highly correlated with STDV and STD4060 in the stratosphere, and with LSW and |∇HN| in the lower troposphere. These results suggest a hybrid error model for individual BA observations that uses an average statistical model of RO errors modified by STDV or STD4060 above 30 km, and LSW or |∇HN| below 8 km. Significance StatementThese results contribute to the understanding of the sources of uncertainties in radio occultation observations. They could be used to improve the effectiveness of these observations in their assimilation into numerical weather prediction and reanalysis models by improving the estimation of their observational errors.
more »
« less
- Award ID(s):
- 2054356
- PAR ID:
- 10474461
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of Atmospheric and Oceanic Technology
- Volume:
- 40
- Issue:
- 11
- ISSN:
- 0739-0572
- Format(s):
- Medium: X Size: p. 1461-1474
- Size(s):
- p. 1461-1474
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract. The international collaborative Radio Occultation Modeling EXperiment (ROMEX) project marks the first time using a large volume of real data to assess the impact of increased Global Navigation Satellite System (GNSS) radio occultation (RO) observations beyond current operational levels, moving past previous theoretical simulation-based studies. The ROMEX project enabled the use of approximately 35,000 RO profiles– nearly triple the number typically available to operational centers, which is about 8,000 to 12,000 per day. This study investigates the impact of increased RO profiles on numerical weather prediction (NWP) with the Joint Effort for Data assimilation Integration (JEDI) and the global forecast system (GFS), as part of the ROMEX effort. A series of experiments were conducted assimilating varying amounts of RO data along with a common set of other key observations. The results confirm that assimilating additional RO data further improves forecasts across all major meteorological fields, including temperature, humidity, geopotential height, and wind speed, for most of vertical levels. These improvements are significantly evident in verification against both critical observations and the European Center for Medium-Range Weather Forecasts (ECMWF) analyses, with beneficial impacts lasting up to five days. Conversely, withholding RO data resulted in forecast degradations. The results also suggest that forecast improvements scale approximately logarithmically with the number of assimilated profiles, and no evidence of saturation was observed. Biases in the forecast of temperature and geopotential height over the lower stratosphere are discussed, and they are consistent with findings from other studies in the ROMEX community.more » « less
-
Abstract The international radio occultation (RO) community is conducting a collaborative effort to explore the impact of a large number of RO observations on numerical weather prediction (NWP). This effort, the Radio Occultation Modeling Experiment (ROMEX), has been endorsed by the International Radio Occultation Working Group, a scientific working group under the auspices of the Coordination Group for Meteorological Satellites (CGMS). ROMEX seeks to inform strategies for future RO missions and acquisitions. ROMEX is planned to consist of at least one three-month period during which all available RO data are collected, processed, archived, and made available to the global community free of charge for research and testing. Although the primary purpose is to test the impact of varying numbers of RO observations on NWP, the three months of RO observations during the first ROMEX period (ROMEX-1, September-November 2022) will be a rich data set for research on many atmospheric phenomena. The RO data providers have sent their data to EUMETSAT for processing. The total number of RO profiles averages between 30,000 and 40,000 per day for ROMEX-1. The processed data (phase, bending angle, refractivity, temperature, and water vapor) will be distributed to ROMEX participants by the Radio Occultation Meteorology Satellite Applications Facility (ROM SAF). The data will also be processed independently by the UCAR COSMIC Data Analysis and Archive Center (CDAAC) and available via ROM SAF. The data are freely available to all participants who agree to the conditions that the providers be acknowledged and the data are not used for commercial or operational purposes.more » « less
-
Abstract The FORMOSAT‐7/COSMIC‐2 (F7/C2) satellite mission was launched on 25 June 2019 with six low‐Earth‐orbit satellites and can provide thousands of daily radio occultation (RO) soundings in the low‐latitude and midlatitude regions. This study shows the preliminary results of space weather data products based on F7/C2 RO sounding: global ionospheric specification (GIS) electron density and Ne‐aided Abel and Abel electron density profiles. GIS is the ionospheric data assimilation product based on the Gauss‐Markov Kalman filter, assimilating the ground‐based Global Positioning System and space‐based F7/C2 RO slant total electron content, providing continuous global three‐dimensional electron density distribution. The Ne‐aided Abel inversion implements four‐dimensional climatological electron density constructed from previous RO observations, which has the advantage of providing altitudinal information on the horizontal gradient to reduce the retrieval error due to the spherical symmetry assumption of the Abel inversion. The comparisons show that climatological structures are consistent with each other above 300 km altitude. Both the Abel electron density profiles and GIS detect electron density variations during a minor geomagnetic storm that occurred within the study period. Moreover, GIS is further capable of reconstructing the variation of equatorial ionization anomaly crests. Detailed validations of all the three products are carried out using manually scaled digisondeNmF2(hmF2), yielding correlation coefficients of 0.885 (0.885) for both Abel inversions and 0.903 (0.862) for GIS. The results show that both GIS and Ne‐aided Abel are reliable products in studying ionosphere climatology, with the additional advantage of GIS for space weather research and day‐to‐day variations.more » « less
-
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
