Assimilation of Atmospheric Radio Refractivity Using a Nonhydrostatic Adjoint Model

Zou, Xiaolei; Kuo, Ying‐Hwa; Guo, Yong-run

doi:10.1175/1520-0493(1995)123<2229:aoarru>2.0.co;2

Cited by 118 publications

(71 citation statements)

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“…The variational method combines the occultation measurements (e.g., refractivity) with the apriori (or background) atmospheric condition in a statistically optimal way (Healy and Eyre, 2000;Zou et al, 1995). For example, the optimal solution to the state vectors (e.g., T, Pw and P) can be found by adjusting the state vector elements in a way that is consistent with the estimated background errors, to produce simulated measurement values that fit the 10 observations to within their expected observational errors (Healy and Eyre, 2000).…”

Section: Cosmic Ro Retrieval Algorithm Difference Between Jpl and Ucamentioning

confidence: 99%

Evaluating the Lower Tropospheric COSMIC GPS Radio Occultation Sounding Quality over the Arctic

Xie¹,

Ao²

2017

Preprint

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Abstract. Lower tropospheric moisture and temperature measurements are crucial for understanding weather predication and 10 climate change. Global Positioning System radio occultation (GPS RO) has been demonstrated as a high-quality observation with high-vertical-resolution and sub-Kelvin temperature precision from the upper troposphere to the stratosphere. In the tropical lower troposphere, particularly the lowest 2 km, the quality of RO retrievals is known to be degraded and is a topic of active research. However, it is not clear whether similar problems exist in the high latitudes, particularly over the Artic, which is characterized by smooth ocean surface and often negligible moisture in the atmosphere. In this study, three-year 15 (2008-2010) GPS RO soundings from COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) over the Arctic (65°N-90°N) show uniform spatial sampling with average penetration depth within 300 m above the ocean surface. Over 70% soundings penetrate below 300 m in all non-summer seasons but only about 50-60% in summer, when near-surface moisture and its variation increase. Both structural and parametric uncertainties of GPS RO soundings were also analyzed. The structural uncertainty (due to different data processing approaches) is estimated to be within 0.07% 20 in refractivity, 0.72 K in temperature and -0.029 g/kg in specific humidity below 10 km, which is derived by comparing RO retrievals from two independent data processing centers. The parametric uncertainty (internal uncertainty of RO sounding) is quantified by comparing GPS RO with near-coincident radiosonde and ECMWF ERA-Interim profiles. A systematic negative bias up to ~1% in refractivity below 2 km is only seen in the summer, which confirms the moisture impact on GPS RO quality. 25

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Section: Cosmic Ro Retrieval Algorithm Difference Between Jpl and Ucamentioning

confidence: 99%

Evaluating the Lower Tropospheric COSMIC GPS Radio Occultation Sounding Quality over the Arctic

Xie¹,

Ao²

2017

Preprint

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“…In recent years, these advantages have fostered the implementation of variational data assimilation systems in limited area models (Zou et al, 1995;Barker et al, 2004;Huang et al, 2009;Zupanski et al, 2005). These systems replaced previously used schemes such as optimal interpolation (Parrish and Derber, 1992;Rabier et al, 2000).…”

Section: S Federico: Implementation Of a 3d-var Systemmentioning

confidence: 99%

“…Nowadays, increased computing power coupled with greater access to real-time a-synoptic data is paving the way toward a new generation of high-resolution (10 km or less in the horizontal plane) operational mesoscale analysis and forecasting systems (Zou et al, 1995;Sun and Crook, 1997;Lazarus et al, 2002;Kalnay, 2003;Barker et al, 2004;Zupanski et al, 2005;Huang et al, 2009). Moreover, better initial conditions are increasingly considered as of the utmost importance for a range of NWP applications, in particular at the short range (0-12 h, Zhang et al, 2005;Schenkman et al, 2011;Sun et al, 2012;Wang et al, 2013;Sun and Wang, 2013).…”

Section: Introductionmentioning

confidence: 99%

Implementation of a 3D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

Federico

2013

Atmos. Meas. Tech.

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Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system (3D-Var). The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height.Important features of the data assimilation system are the use of incremental formulation of the cost function, and the representation of the background error by recursive filters and the eigenmodes of the vertical component of the background error covariance matrix. This matrix is estimated by the National Meteorological Center (NMC) method.The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network.Results show the validity of the analyses because they are closer to the observations (lower root mean square error (RMSE)) compared to the background (higher RMSE), and the differences of the RMSEs are in agreement with the data assimilation settings.To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of three-hours forecasts of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3D-Var as initial conditions, then it is driven by the ECMWF forecast.The improvement is quantified by considering the horizontal components of the wind, which are measured at asynoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range. The results are in agreement with the set-up of the numerical experiment.

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“…In 1993 the University Corporation for Atmospheric Research (UCAR) organized a proof-of-concept experiment on a LEO satellite (the Microlab-1 satellite) at a nearly circular orbit of ~735 km altitude and a ~70° inclination angle to receive GPS signals and to demonstrate active limb sounding of the Earth's atmosphere and ionosphere by the radio occultation technique. The main objectives of this GPS-LEO occultation mission were numerical weather prediction and long-term monitoring of the Earth's climate and thus it was termed the GPS/Meteorology (GPS/MET) program (Zou et al 1995;Ware et al 1996;Kursinski et al 1997;Rocken et al 1997). GPS/MET has also been able to sound the ionosphere from the LEO satellite orbit altitude to ~60 km below the D and E regions and provide profiles of ionospheric electron density from measurements of ray-path bending angle or total electron content (TEC).…”

Section: Introductionmentioning

confidence: 99%

Improvement of GPS/MET Ionospheric Profiling and Validation Using the Chung-Li Ionosonde Measurements and the IRI model

Tsai¹,

Tsai²

2004

Terr. Atmos. Ocean. Sci.

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Assimilation of Atmospheric Radio Refractivity Using a Nonhydrostatic Adjoint Model

Cited by 118 publications

References 0 publications

Evaluating the Lower Tropospheric COSMIC GPS Radio Occultation Sounding Quality over the Arctic

Evaluating the Lower Tropospheric COSMIC GPS Radio Occultation Sounding Quality over the Arctic

Implementation of a 3D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

Improvement of GPS/MET Ionospheric Profiling and Validation Using the Chung-Li Ionosonde Measurements and the IRI model

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