An Overview of the University of Arizona ATOMS Project

Herman, Benjamin M.; Feng, D.; Flittner, D. E.; Kursinski, E. R.; Syndergaard, Stig; Ward, D.

doi:10.1007/978-3-662-09041-1_18

Cited by 9 publications

(16 citation statements)

References 8 publications

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“…The LMO part of LMIO has substantial heritage from a range of studies over the recent decade (Kursinski et al, 2002Herman et al, 2004;Kirchengast and Hoeg, 2004;Kirchengast, 2005, 2007) and very recently a detailed LMO algorithm description and performance analysis was provided by Schweitzer et al (2011b). This heritage work established well the expected performance of LMO for accurate thermodynamic state profiling in the UTLS, which serves as the basis for the LIO-related GHG profiling introduced here.…”

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confidence: 77%

Greenhouse gas profiling by infrared-laser and microwave occultation: retrieval algorithm and demonstration results from end-to-end simulations

2011

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Abstract. Measuring greenhouse gas (GHG) profiles with global coverage and high accuracy and vertical resolution in the upper troposphere and lower stratosphere (UTLS) is key for improved monitoring of GHG concentrations in the free atmosphere. In this respect a new satellite mission concept adding an infrared-laser part to the already well studied microwave occultation technique exploits the joint propagation of infrared-laser and microwave signals between Low Earth Orbit (LEO) satellites. This synergetic combination, referred to as LEO-LEO microwave and infrared-laser occultation (LMIO) method, enables to retrieve thermodynamic profiles (pressure, temperature, humidity) and accurate altitude levels from the microwave signals and GHG profiles from the simultaneously measured infrared-laser signals. However, due to the novelty of the LMIO method, a retrieval algorithm for GHG profiling is not yet available. Here we introduce such an algorithm for retrieving GHGs from LEO-LEO infraredlaser occultation (LIO) data, applied as a second step after retrieving thermodynamic profiles from LEO-LEO microwave occultation (LMO) data. We thoroughly describe the LIO retrieval algorithm and unveil the synergy with the LMOretrieved pressure, temperature, and altitude information. We furthermore demonstrate the effective independence of the GHG retrieval results from background (a priori) information in discussing demonstration results from LMIO end-toend simulations for a representative set of GHG profiles, including carbon dioxide (CO 2 ), water vapor (H 2 O), methane (CH 4 ), and ozone (O 3 ). The GHGs except for ozone are well Correspondence to: V. Proschek (veronika.proschek@uni-graz.at) retrieved throughout the UTLS, while ozone is well retrieved from about 10 km to 15 km upwards, since the ozone layer resides in the lower stratosphere. The GHG retrieval errors are generally smaller than 1 % to 3 % r.m.s., at a vertical resolution of about 1 km. The retrieved profiles also appear unbiased, which points to the climate benchmarking capability of the LMIO method. This performance, found here for clear-air atmospheric conditions, is unprecedented for vertical profiling of GHGs in the free atmosphere and encouraging for future LMIO implementation. Subsequent work will examine GHG retrievals in cloudy air, addressing retrieval performance when scanning through intermittent upper tropospheric cloudiness.

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confidence: 77%

Greenhouse gas profiling by infrared-laser and microwave occultation: retrieval algorithm and demonstration results from end-to-end simulations

2011

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Section: Introductionmentioning

confidence: 99%

“…The microwave occultation technique using intersatellite centimeter‐ and millimeter‐wave signals between low Earth orbit (LEO) satellites [ Kursinski et al 2002, 2004; Herman et al , 2004; Kirchengast and Hoeg , 2004; Gorbunov and Kirchengast , 2005, 2007], termed LMO hereafter, enables us to jointly retrieve atmospheric profiles of temperature and humidity without auxiliary background information, in addition to profiles of refractivity, pressure, and geopotential height. LMO thus allows retrieval of all fundamental thermodynamic state variables (pressure, temperature, and humidity), as a function of height, and with added dedicated signals also other variables, in particular ozone [ Herman et al , 2004]. This is a key advancement compared to the highly successful radio occultation technique utilizing decimeter‐wave signals from the Global Navigation Satellite System (GNSS) received at LEO satellites [e.g., Ware et al , 1996; Kursinski et al , 1997; Steiner et al , 1999; Wickert et al , 2004; Healy and Thépaut , 2006; Anthes et al , 2008; Ho et al , 2009; Steiner et al , 2009], termed GRO hereafter (up to present the GNSS signals exploited were those from the U.S.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore there is sensitivity to both refraction and absorption, while the GRO frequencies in L band (1 to 2 GHz) are only sensitive to refraction (except in the lower troposphere toward the surface where some absorption may occur [e.g., Kursinski et al , 2000]). In particular, frequencies around the water vapor absorption lines at 22 GHz and 183 GHz, and the 195 GHz ozone line, have been investigated by Kursinski et al [2002], Herman et al [2004], and Kirchengast and Hoeg [2004], which are also the starting point for this study. The integrated absorption, and subsequently the absorption coefficient profile, is determined from the observed amplitude attenuation of each transmitted signal during the occultation event.…”

Section: Introductionmentioning

confidence: 99%

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Thermodynamic state retrieval from microwave occultation data and performance analysis based on end-to-end simulations

et al. 2011

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Microwave occultation using centimeter‐ and millimeter‐wave signals between low Earth orbit (LEO) satellites (LEO microwave occultation, LMO) is an advancement of GPS radio occultation (GRO) exploiting in addition to refraction also absorption of signals. Beyond the successful GRO refractivity profiling capability, which leaves a temperature‐humidity ambiguity in the troposphere where moisture cannot be neglected, LMO enables joint retrieval of pressure, temperature, and humidity profiles without auxiliary background information. Here we focus on the upper troposphere/lower stratosphere and advance the LMO method in two ways: (1) we introduce a new retrieval algorithm for processing LMO excess phase and amplitude data from multiple frequencies, complementing existing GRO retrieval algorithms, and (2) we employ the algorithm in an ensemble‐based end‐to‐end performance analysis and assess the accuracy of pressure, temperature, and humidity profiles retrieved from the LMO data. The end‐to‐end simulations were carried out under quasi‐realistic conditions for a day of LEO‐LEO occultation events, based on a high‐resolution atmospheric analysis of the European Centre for Medium‐Range Weather Forecasts (ECMWF) and accounting for scintillation noise from turbulence and instrumental errors. The new algorithm was found robust, fast, and versatile to adequately process LMO data under all conditions from dry and clear to moist and cloudy air as contained in the ECMWF analysis. The retrieved pressure, temperature, and humidity profiles were generally found unbiased and within target accuracy requirements, set by scientific objectives of atmosphere and climate research going to be supported by the data, of <0.2% (pressure), <0.5 K (temperature), and <10% (humidity). Extending a “minimum” LMO design with three frequencies near 22 GHz with two added frequencies near 183 GHz favorably provides humidity retrieval into the lower stratosphere but already the “minimum” design resolves the temperature‐humidity ambiguity of GRO in the upper troposphere (frequencies <15 GHz might extend this into the lower troposphere). The results are encouraging for future LMO implementation, both stand‐alone and combined with novel LEO‐LEO infrared laser occultation.

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“…More details on this technique, its capabilities and the quality of retrieval results can be found in Feng et al (2000); Herman et al (2004); Kursinski et al (2002Kursinski et al ( , 2004; Kirchengast and Høeg (2004); Kirchengast (2005, 2007); Kursinski et al (2009) ;Schweitzer et al (2011). A closely related occultation method is the GNSS-LEO radio occultation (GRO), which uses L-band signals from the US Global Positioning System (GPS) system.…”

Section: Introductionmentioning

confidence: 99%

Atmospheric influences on infrared-laser signals used for occultation measurements between Low Earth Orbit satellites

2011

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Abstract. LEO-LEO infrared-laser occultation (LIO) is a new occultation technique between Low Earth Orbit (LEO)satellites, which applies signals in the short wave infrared spectral range (SWIR) within 2 µm to 2.5 µm. It is part of the LEO-LEO microwave and infrared-laser occultation (LMIO) method that enables to retrieve thermodynamic profiles (pressure, temperature, humidity) and altitude levels from microwave signals and profiles of greenhouse gases and further variables such as line-of-sight wind speed from simultaneously measured LIO signals. Due to the novelty of the LMIO method, detailed knowledge of atmospheric influences on LIO signals and of their suitability for accurate trace species retrieval did not yet exist. Here we discuss these influences, assessing effects from refraction, trace species absorption, aerosol extinction and Rayleigh scattering in detail, and addressing clouds, turbulence, wind, scattered solar radiation and terrestrial thermal radiation as well. We show that the influence of refractive defocusing, foreign species absorption, aerosols and turbulence is observable, but can be rendered small to negligible by use of the differential transmission principle with a close frequency spacing of LIO absorption and reference signals within 0.5 %. The influences of Rayleigh scattering and terrestrial thermal radiation are found negligible. Cloud-scattered solar radiation can be observable under bright-day conditions, but this influence can be made negligible by a close time spacing (within 5 ms) of interleaved laser-pulse and background signals. Cloud extinction loss generally blocks SWIR signals, except very thin or sub-visible cirrus clouds, which can be addressed by retrieving a cloud layering profile and exploiting it in the trace species retrieval. Wind can have a small influence on the trace species absorption, which can be made negligible by using a simultaneously retrieved or a moderately accurate

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An Overview of the University of Arizona ATOMS Project

Cited by 9 publications

References 8 publications

Greenhouse gas profiling by infrared-laser and microwave occultation: retrieval algorithm and demonstration results from end-to-end simulations

Greenhouse gas profiling by infrared-laser and microwave occultation: retrieval algorithm and demonstration results from end-to-end simulations

Thermodynamic state retrieval from microwave occultation data and performance analysis based on end-to-end simulations

Atmospheric influences on infrared-laser signals used for occultation measurements between Low Earth Orbit satellites

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