Accurate, long‐term, consistent data are fundamental to climate science and satellite observations are the key to obtain such data globally in the Earth's atmosphere. Current methods are unable to jointly and consistently observe essential climate variables including thermodynamic ones (temperature, pressure, humidity), wind, and greenhouse gases. Here we introduce a method that profiles these variables over the upper troposphere and lower stratosphere and beyond as consistent benchmark dataset (e.g., monthly‐mean temperature accurate to 0.1 K, wind to 0.5 m s−1, carbon dioxide concentration to within 1 ppm). It combines microwave and infrared‐laser occultation between satellites in low Earth orbit for thermodynamic state, greenhouse gas and line‐of‐sight wind profiling. With adequate scaling it can also be applied beyond Earth's atmosphere such as in planetary atmospheres. The method may become an authoritative reference standard for global monitoring of greenhouse gases and climate change in Earth's free atmosphere over the 21st century.

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.

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.

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

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, recently introduced by Kirchengast and Schweitzer (2011), that enables to retrieve thermodynamic profiles (pressure, temperature, humidity) and accurate 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. For enabling trace species retrieval based on differential transmission, the LIO signals are spectrally located as pairs, one in the centre of a suitable absorption line of a target species (absorption signal) and one close by but outside of any absorption lines (reference signal). 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 the atmospheric influences on the transmission and differential transmission of LIO signals. Refraction effects, trace species absorption (by target species, and cross-sensitivity to foreign species), aerosol extinction and Rayleigh scattering are studied in detail. The influences of clouds, turbulence, wind, scattered solar radiation and terrestrial thermal radiation are discussed as well. We show that the influence of defocusing, foreign species absorption, aerosols and turbulence is observable, but can be rendered small to negligible by use of the differential transmission principle and by a design with close frequency spacing of absorption and reference signals within 0.5 %. The influences of Rayleigh scattering and thermal radiation on the received signal intensities are found negligible. Cloud-scattered solar radiation can be observable under bright-day conditions but this influence can be as well made negligible by a design with 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 a design allowing retrieval of a cloud layering profile from reference signals and its use in trace species retrieval when scanning through intermittent upper tropospheric cloudiness. Wind can have a small influence via Doppler shift resulting in a slightly modified trace species absorption in comparison to calm air, which can be made negligible by using a simultaneously retrieved wind speed profile or a moderately accurate (to about 10 m s⁻¹) background wind profile. Considering all these influences, we conclude that the set of SWIR channels proposed for implementing the LMIO method (Kirchengast et al., 2010; Kirchengast and Schweitzer, 2011) provides adequate sensitivity to accurately retrieve eight greenhouse gas/isotope trace species...