Using AIRS and ARM SGP Clear‐Sky Observations to Evaluate Meteorological Reanalyses: A Hyperspectral Radiance Closure Approach

Abstract

Using the ground-based measurements from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site and spectral radiance from the Atmospheric Infrared Sounder (AIRS) on National Aeronautics and Space Administration Aqua, we evaluate the temperature and humidity profiles from European Center for Medium Range Weather Forecasting ERA-Interim and Modern-Era Retrospective analysis for Research and Applications Version 2 reanalyses. Four sets of synthetic AIRS spectra are calculated using 51 clear-sky sounding profiles from the ARM SGP observations, the collocated AIRS L2 retrievals and the two reanalyses, respectively. A subset of AIRS channels sensitive to temperature, CO 2 , or H 2 O but not to other trace gases is chosen and further categorized into different groups according to the peak altitudes of their weighting functions. Synthetic radiances are then compared to the observed AIRS radiances for each group. For all groups, the observed AIRS radiances agree well with the synthetic ones based on the ARM SGP soundings or the AIRS L2 retrievals. The brightness temperature (BT) differences are within ±0.5 K. For two reanalyses, BT differences in all temperature-sensitive groups are generally within ±0.5 K; but the mean BT differences in all groups sensitive to both T and H 2 O are negative. Together, they suggest a wet bias in the free troposphere in both reanalyses. Moreover, such BT differences can be seen in the analysis of AIRS clear-sky radiances over the entire 30-40°N zone. A grid-search retrieval suggests that 9-30% reduction for reanalysis humidity between 200 and 800 hPa is needed to correct such wet bias.

The Spectral Dimension of Arctic Outgoing Longwave Radiation and Greenhouse Efficiency Trends From 2003 to 2016

“…Based on the PCRTM, Chen, Huang, and Liu () developed a simulator that can calculate spectral radiance or flux using global‐scale meteorological fields from climate model simulations, reanalyses, and satellite retrievals in a computationally affordable way. The details of the simulator can be found in Chen, Huang, and Liu (), and it has been used in other published studies (e.g., Bantges et al, ; Chen et al, ; Huang, Chen, Soden, et al, ; Pan et al, ). The same simulator package is used in this study.…”

Retrieval of Surface Spectral Emissivity in Polar Regions Based on the Optimal Estimation Method

“…As demonstrated by several validation studies of the atmospheric temperature and humidity retrievals (Boylan et al., 2015; Divakarla et al., 2006, 2014; Milstein & Blackwell, 2016; Nalli et al., 2018; Pougatchev et al., 2009), atmospheric temperature profiles from data assimilation or reanalysis are generally in good agreement with those inverted from hyperspectral soundings or directly observed by radiosondes, and the RMSEs are around or less than 1.5K in the troposphere. In contrast, state‐of‐the‐art humidity retrievals from hyperspectral sounders still can have an RMSE as large as 20% in the troposphere (Milstein & Blackwell, 2016; Nalli et al., 2018), and reanalysis humidity fields bear similar uncertainty (Chen et al., 2018; Jakobson et al., 2012; Nygård et al., 2016). Therefore, the uncertainties in the humidity profiles likely can affect the surface emissivity retrievals more than those in the temperature profiles.…”

“…More details about the PCRTM can be found in Liu et al (2006). Chen et al (2013) also developed a PCRTM-based simulator that can conveniently interface with reanalysis profiles and has been subsequently used in several studies (Bantges et al, 2016;Chen et al, 2018;Huang et al, 2014;Pan et al, 2017). This PCRTMbased simulator is also used in this study as the forward model in the retrieval scheme.…”

Joint Use of Far‐Infrared and Mid‐Infrared Observation for Sounding Retrievals: Learning From the Past for Upcoming Far‐Infrared Missions

“…In addition, it has been showed by Chen et al (2013) that the RMSE of brightness temperature between the PCRTM V2.1 and the LBLRTM (Line-By-Line Radiative Transfer Model) 12.0 (Clough et al, 2005) can be 0.67 K under clear-sky conditions, while the PCRTM requires approximately only 0.02% of the computation time that LBLRTM takes. A PCRTM-based simulator has been developed by Chen et al (2013) and successfully applied in multiple studies (Bantges et al, 2016;Chen et al, 2018;Huang et al, 2014;. It is applied in our study as the forward model to calculate spectral radiances and Jacobians in clear-sky conditions.…”

“…A PCRTM‐based simulator has been developed by Chen et al. (2013) and successfully applied in multiple studies (Bantges et al., 2016; Chen et al., 2018; Huang et al., 2014; Xie, Huang, Chen, L’Ecuyer, Drouin, et al., 2022). It is applied in our study as the forward model to calculate spectral radiances and Jacobians in clear‐sky conditions.…”