Uncertainty of atmospheric microwave absorption model: impact on ground-based radiometer simulations and retrievals

Abstract

Abstract. This paper presents a general approach to quantify the absorption model uncertainty due to uncertainty in underlying spectroscopic parameters. The approach is applied to radiative transfer calculations in the 20-60 GHz range, which is commonly exploited for atmospheric sounding by microwave radiometer (MWR). The approach however is not limited to any frequency range, observing geometry, or particular instrument. In the considered frequency range, relevant uncertainties come from water vapor and oxygen spectroscopic parameters. The uncertainty of the following parameters is 15 found to dominate: (for water vapor) self and foreign continuum absorption coefficients, line broadening by dry air, line intensity, temperature-dependence exponent for foreign continuum absorption, and line shift-to-broadening ratio; (for oxygen) line intensity, line broadening by dry air, line mixing, temperature-dependence exponent for broadening, zerofrequency line broadening in air, temperature-dependence coefficient for line mixing. The full uncertainty covariance matrix is then computed for the set of spectroscopic parameters with significant impact. The impact of the spectroscopic parameter 20 uncertainty covariance matrix on simulated downwelling microwave brightness temperatures (T B ) in the 20-60 GHz range is calculated for six atmospheric climatology conditions. The uncertainty contribution to simulated T B ranges from 0.30 K (sub-Arctic winter) to 0.92 K (tropical) at 22.2 GHz, and from 2.73 K (tropical) to 3.31 K (sub-Arctic winter) at 52.28 GHz.The uncertainty contribution is nearly zero at 55-60 GHz frequencies. Finally, the impact of spectroscopic parameter uncertainty on ground-based MWR retrievals of temperature and humidity profiles is discussed. 25

Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observations

“…Alternatively, a physical retrieval (PR) iterative approach can be used to retrieve vertical profiles of thermodynamic properties from the MWR and RASS observations in a synergistic manner (e.g., Maahn et al, 2020;Turner and Löhnert, 2021). In this case, an optimal estimation-based physical retrieval is initialized with a climatologically reasonable profile of temperature and water vapor and is iteratively repeated until the computed brightness temperatures match those observed by the MWR within the uncertainty of the observed brightness temperatures and the RASS virtual temperatures within their uncertainties (Rodgers, 2000;Turner and Löhnert, 2014;Cimini et al, 2018;Maahn et al, 2020).…”

Analysis of Water Vapor Absorption in the Far‐Infrared and Submillimeter Regions Using Surface Radiometric Measurements From Extremely Dry Locations

“…It is important to note that the adjustments made to TI‐sounding that are based on retrievals using radiometric measurements (hexagons in Figure ) are primarily performed in spectral regions in which we have more confidence in our spectroscopic knowledge than we have in the spectroscopy that we target in this study: Uncertainties are low for the spectroscopic parameters of the IR carbon dioxide bands, which are widely used for temperature retrievals from satellite observations. The properties of the 183‐GHz water vapor line and corresponding water vapor continuum also have modest uncertainties (Cimini et al, ; Payne et al, ; Payne et al, ) and measurements on this line have been relied upon in previous similar studies (e.g., Delamere et al, ). Water vapor line parameters and continuum from 400–550 cm −1 have been analyzed in Delamere et al () and improvements implemented, allowing this study to utilize this region with some confidence. …”

A dataset of temperature, humidity, and liquid water path retrievals from a network of ground-based microwave radiometers dedicated to fog investigation

“…The uncertainty for humidity profiles is also consistent with other studies, while reported biases are either smaller (Cimini et al 2006; or larger (Temimi et al 2020;Turner and Löhnert 2021) than those obtained here for the SOFOG3D dataset. One possible contribution to the observed humidity bias in the lowest layers may be the use of the lowest V-band channels, which are affected by larger uncertainties in absorption models (Cimini et al 2018). A bias correction of level1 data will be investigated in the future and may lead to a revised dataset (Tables 5 and 6).…”