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

Abstract

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.

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

“…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.…”

“…The EGOPS system development started for GRO end-to-end simulations more than a decade ago (Kirchengast, 1996(Kirchengast, , 1998Ramsauer and Kirchengast, 2000;Kirchengast et al, 2002), was then extended to also enable LMO simulations Fritzer et al, 2009), and proved useful in a myriad of GRO and LMOrelated studies, including the recent study by Schweitzer et al (2011b). The complementary xEGOPS system (Fritzer et al, 2010) was developed more recently and extends EGOPS by LIO end-to-end simulation capabilities.…”

“…The complementary xEGOPS system (Fritzer et al, 2010) was developed more recently and extends EGOPS by LIO end-to-end simulation capabilities. We use EGOPS/xEGOPS here in the same way and logic for LMIO simulations as Schweitzer et al (2011b) used EGOPS for LMO simulations.…”

“…We used LEO satellites in sun-synchronous orbits, two transmitters and two counterrotating receivers, with the transmitters at an orbital height of 800 km and the receivers at 650 km, yielding about 230 globally well distributed occultation events per day (same as Schweitzer et al, 2011b). The MAP calculates occultation event locations and related positions of the transmitter and receiver satellites during the event.…”

“…We implemented the LIO retrieval as a complementary, subsequent part to the LMO retrieval of Schweitzer et al (2011b), completing the LMIO retrieval algorithm introduced in a brief overview form in Kirchengast and Schweitzer (2011). We describe in this study the detailed steps of the algorithm, which can be applied for single species (single-line trace species retrieval, SSR) or a set of several trace species (multi-line trace species retrieval, MSR), and include a demonstration of its capabilities by endto-end performance simulation results.…”