EVALUATION OF RECENT UPDATES TO THE SPECTROSCOPY OF H 2 O, CO 2, AND CH 4 IN LBLRTM USING INFRARED OBSERVATIONS FROM IASI AND TES

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1 EVALUATION OF RECENT UPDATES TO THE SPECTROSCOPY OF H 2 O, CO 2, AND CH 4 IN LBLRTM USING INFRARED OBSERVATIONS FROM IASI AND TES M.J. Alvarado 1, V.H. Payne 2, K.E. Cady-Pereira 1, S.S. Kulawik 3, M.W. Shephard 4, E.J. Mlawer 1, and J.-L. Moncet 1 1 Atmospheric and Environmental Research (AER), 131 Hartwell Ave., Lexington, MA, USA 2 Jet Propulsion Laboratory/California Institute of Technology, Pasadena, CA, USA 3 Bay Area Environmental Research Institute, Mountain View, CA, USA 4 Environment Canada, Toronto, ON, Canada Abstract Modern data assimilation algorithms depend on accurate infrared spectroscopy in order to make use of the information related to temperature, water vapor (H 2 O), and other trace gases provided by satellite observations. Reducing the uncertainties in our knowledge of spectroscopic parameters is thus important to improving the application of satellite data to weather forecasting. Here we present the results of a rigorous validation of recent updates to the spectroscopic parameters for H 2 O, CO 2, and CH 4 in AER s line-by-line radiative transfer model LBLRTM against measurements from the Tropospheric Emission Spectrometer (TES) made during the HIAPER Pole-to-Pole Observations (HIPPO) of Carbon Cycle and Greenhouse Gases Study as well as from a global dataset of 120 clearsky, nighttime, ocean, near-nadir measurements from the Infrared Atmospheric Sounding Instrument (IASI). We find that the spectroscopy in the CO 2 ν 2 and ν 3 bands is significantly improved in LBLRTM v12.1 relative to LBLRTM v9.4, and that the spectroscopy of these two CO 2 bands is remarkably consistent in LBLRTM v12.1. The updated H 2 O spectroscopy in LBLRTM v12.1 substantially improved the residuals in the P-branch of the H 2 O ν 2 band, while the improvements in the R-branch are more modest. We also find that the LBLRTM v12.1 CH 4 spectroscopy reduces the positive bias in the CH 4 profiles retrieved by TES, although strong residual features remain above 1290 cm -1. The AER radiative transfer models and the associated databases (e.g., line parameters, continua, and molecular cross-sections) are publicly available from AER ( 1. INTRODUCTION Modern data assimilation algorithms for numerical weather prediction (NWP) make extensive use of the information related to temperature, water vapor (H 2 O), and other trace gases provided by satellite observations. The accuracy of the analyzed vertical profiles of temperature, H 2 O, and other trace gases from satellites depends on the accuracy of the radiative transfer model used in the data assimilation. Uncertainties in our knowledge of spectroscopic line parameters and continua are the primary limitations on the accuracy of computed absorption in leading edge radiative transfer models, so reducing these uncertainties is an important part of improving the application of satellite data to weather forecasting and climate studies. Radiance closure studies using high-spectral-resolution infrared radiance measurements allow us to assess the systematic differences between the calculated and measured spectral radiances, and provide a means to assess the consistency of the input spectroscopic parameters within different absorption bands of the same trace gas (e.g., Shephard et al., 2009) and for different gaseous absorbers. Here we present results of a rigorous validation of spectroscopic updates to an advanced radiative transfer model, the Line-By-Line Radiative Transfer Model (LBLRTM, Clough et al., 2005), with respect to a dataset of measurements from the Infrared Atmospheric Sounding Instrument (IASI,

2 Chalon et al., 2001) aboard the EUMETSAT MetOp-A satellite, and the Tropospheric Emission Spectrometer (TES, Worden et al., 2012) aboard the NASA Aqua satellite. LBLRTM is an accurate and flexible radiative transfer model that can be used over the full spectral range from the microwave to the ultraviolet, providing the foundation for many radiative transfer applications. The main features of LBLRTM are described in Clough et al. (2005). Spectral line parameters used in LBLRTM v12.1 are based on the HITRAN 2008 compilation, with selected notable exceptions, made only after extensive validation. Exceptions to HITRAN in the thermal infrared include updated CO 2 line positions and intensities, updated line mixing coefficients for CO 2 and CH 4, and improvements to the H 2 O line positions and intensities. LBLRTM v12.1 incorporates the continuum model MT_CKD v2.5 (Mlawer et al., 2012), which includes self- and foreign-broadened water vapor continua as well as continua for CO 2, O 2, N 2, O 3, and extinction due to Rayleigh scattering. LBLRTM has a long and successful heritage at the leading edge of the field, and the model is continually updated and validated against high-resolution spectral measurements (e.g., Payne et al., 2008; Shephard et al., 2009; Delamere et al., 2010; Mlawer et al., 2012; Alvarado et al., 2013). LBLRTM calculations in the thermal infrared are recognized as a reference standard for model intercomparisons (e.g., Forster et al., 2011; Oreopoulos et al., 2012). LBLRTM is widely used as the foundation for operational forward models, including those of IASI (Tjemkes et al., 2003) and TES (Clough et al., 2006). In addition, LBLRTM has been used to derive the absorption coefficients for the fast radiation codes RRTM and RRTMG (Mlawer et al., 1997; Iacono et al., 2008), which are used for broadband flux and heating rate calculations in several General Circulation Models (GCMs). LBLRTM is also used to train fast radiative transfer models used in NWP assimilation systems, such as the OPTRAN (McMillin et al., 1979) and Optimal Spectral Sampling (OSS) (Moncet et al., 2008) models implemented in the Joint Center for Satellite Data Assimilation (JCSDA) Community Radiative Transfer Model (CRTM), as well as OPTRAN-Compact and the RRTOV model (Matricardi, 2009). Section 2 discusses our validation of LBLRTM v12.1 with a global dataset of IASI measurements, while Section 3 describes our evaluation of the CH 4 spectroscopy in LBLRTM v12.1 using TES observations during the first and second campaigns of the HIAPER Pole-to-Pole Observations (HIPPO) of Carbon Cycle and Greenhouse Gases Study (Wofsy et al., 2011). 2. IASI CLOSURE STUDY 2.1 Methodology The IASI closure study is described in detail in Alvarado et al. (2013). In this work, we used two versions of LBLRTM: LBLRTM v12.1 and a modified version of LBLRTM v9.4, here called v9.4+. LBLRTM v9.4+ has spectroscopy equivalent to v9.4, but incorporates the improvements to other parts of LBLRTM (e.g., instrument line shapes and analytic Jacobian calculations) made between v9.4 and v12.1. We chose v9.4+ for this study as this model version was released prior to the recent improvements in CO 2 spectroscopy, including the addition of P- and R-branch line coupling for all CO 2 bands (Shephard et al, 2009). Table 1 shows some important differences in the spectroscopy of LBLRTM v9.4+ and v12.1. LBLRTM v9.4+ LBLRTM v12.1 HITRAN Version H2000 H2008 H 2O Line Params. H2000 H Strengths and positions from Coudert et al. (2008) CO 2 Line Params. H Q-branch line coupling H Str. and pos. from Tashkun et al., P-, Q-, and R-branch line coupling from Lamouroux et al., Continuum MT_CKD 1.2 MT_CKD v2.5 (Mlawer et al., 2012) Table 1: Comparison of spectroscopy of LBLRTM v9.4+ and v12.1. IASI instrument characteristics are as described in Shephard et al. (2009) and Alvarado et al. (2013). Here we use a set of 120 near-nadir (i.e., off-nadir angle less than o ) spectra measured by IASI in April 2008, which are a subset of the profiles analyzed by Matricardi (2009). Only clear sky, ocean,

3 night-time cases were selected in order to minimize uncertainties associated with cloud, surface emissivity and non-local thermodynamic equilibrium (non-lte) effects. We then use retrievals of temperature and trace gases to optimally adjust our specification of the atmospheric state prior to analysis of the spectral residuals, as done in Shephard et al. (2009). Using the observed spectra to provide an optimal estimate of the atmospheric state can reduce the impact of collocation errors and any systematic in situ observation or model biases on the residuals, allowing the systematic issues in the spectroscopy to be more easily discerned. Thus, we first minimize the errors in the specification of the atmospheric state by using each of the two versions of LBLRTM discussed above to retrieve best-fit specifications of the atmospheric state for all cases in the data set. The retrieval methodology is illustrated in Figure 1 and described in detail in Alvarado et al. (2013). Systematic residuals that remain after this procedure indicate errors in the spectroscopy. Figure 1. Schematic of the retrieval procedure. The dashed arrows show additional retrievals performed to assess the consistency of CO 2 in the IASI spectral range. 2.2 Results and Discussion Figure 2 shows the mean of the final brightness temperature residuals in the CO 2 ν 2 band for all 120 IASI scans considered here for both LBLRTM v12.1 (Figure 2b) and v9.4+ (Figure 2c). (Errors in these mean residuals from IASI instrument noise are generally too small to be seen on the same scale as the residuals and so are not plotted.) The CO 2 ν 2 band atmospheric temperature retrieval window is shown in red in both panels, along with the mean and RMS of the residuals within this window. The updated CO 2 spectroscopy in LBLRTM v12.1 clearly improves the residuals on either side of the CO 2 Q-branch at 720 cm -1. The major remaining a posteriori residual features in LBLRTM v12.1 are negative residuals of ~0.5 K in the 667 and 720 cm -1 Q-branches and a positive offset of ~0.2 K between 755 and 770 cm -1. This improvement in the residuals is primarily from the addition of P- and R-branch line coupling to LBLRTM (and the associated recalculation of the CO 2 continuum in MT_CKD). In our study, we assessed the consistency of the spectroscopy between the CO 2 ν 2 and ν 3 bands in two ways. First, we used the temperature profile retrieved using the CO 2 ν 2 band to simulate the radiances in the ν 3 band. (We discuss our second assessment method, comparing the temperature profiles retrieved using each band, further below.) Figure 3 shows the mean of the final and a priori residuals for both model versions in the CO 2 ν 3 band for the 120 IASI spectra. These residuals are plotted in radiance units, as the low radiance values in this region (from the fall off of the Planck function) make a small change in radiance appear as a large change in brightness temperature. Figure 3 shows that the spectroscopy in the CO 2 ν 3 band has been greatly improved in LBLRTM v12.1, especially in the region past the bandhead ( cm -1 ). However, a small systematic residual near the bandhead remains in LBLRTM v12.1. In addition, the large negative residuals between cm -1 in both Figure 3b and 3c suggest that the optical depth in this region is still

4 largely underestimated in LBLRTM v12.1. This is likely due to remaining errors in the N 2 O, CO 2, and/or H 2 O spectroscopy in this spectral region, though we note that the updated CO 2, N 2 O, and H 2 O spectroscopy in LBLRTM v12.1 has reduced the residuals in this region. Figure 2: (a) IASI observed brightness temperature spectrum in the ν 2 band of CO 2 for an example profile with 1.5 cm PWV. (b+c) Mean of the final (a posteriori) brightness temperature residuals for 120 spectra using (b) LBLRTM v12.1 and (c) LBLRTM v9.4+. Figure 3: As in Figure 2, but for the ν 3 band of CO 2. A second test of the consistency of the spectroscopy in the CO 2 ν 2 and ν 3 bands is to evaluate the consistency of the atmospheric temperature profiles retrieved using each band after appropriate smoothing has been applied (Rodgers and Connor, 2003). Figure 4 shows the mean and standard deviation of the differences between the ν 3 and ν 2 temperature retrievals. The mean differences between the temperature profiles are substantially reduced in LBLRTM v12.1, especially in the stratosphere. However, significant differences (-0.7±1.6 K) still exist between the two retrieved

5 temperature profiles in the middle troposphere. This appears to be caused by the fact that, in the ν 3 temperature retrieval, the bandhead is the only region sensitive to middle tropospheric temperatures. Thus, this remaining discrepancy in middle tropospheric temperatures appears consistent with the small, systematic residual feature near the bandhead seen in Figure 3b. Figure 4: Mean and std. dev. of the differences between the retrieved temperature profiles. The ν 2 retrieval was smoothed with the ν 3 averaging kernel and retrieval (Rodgers and Connor, 2003). Figure 5 shows the mean of the a posteriori brightness temperature residuals in the H 2 O ν 2 band for both LBLRTM v12.1 (Figure 5b) and v9.4+ (Figure 5c). The P-branch and R-branch H 2 O retrieval windows ( cm -1 and cm -1, respectively) are shown in red in these panels, which also show the mean and root mean square of the residuals in each window. The updated H 2 O spectroscopy in LBLRTM v12.1 substantially reduces the RMS of the final, a posteriori residuals in the H 2 O P-branch (to 0.27 K from 0.34K) and R-branch (to 0.31 K from 0.34 K). While the R-branch improvement is more modest, the updated spectroscopy has reduced several features that were present in the LBLRTM v9.4+ R-branch residuals (e.g., the positive spikes near 1920 cm -1 ). These changes are due to a combination of H 2 O spectroscopy changes and the temperature profile changes (themselves caused by CO 2 spectroscopy changes), with the H 2 O spectroscopy changes more important in the P-branch and the temperature changes more important in the R-branch. 3. TES CH 4 Retrieval Evaluation 3.1 Methodology In this work, we evaluated TES CH 4 retrievals that used two versions of the absorption coefficient (ABSCO) tables produced with different versions of spectroscopic line parameters for H 2 O and CH 4. The first (called tes_v_1.4) was used to produce V005 of the TES retrieval products. In this version, the H 2 O and CH 4 ABSCO tables were produced using LBLRTM v11.3, which used the H 2 O and CH 4 spectroscopic parameters from the HITRAN 2004 compilation. In the second version (called tes_v_1.8), the H 2 O and CH 4 line parameters were updated to those in LBLRTM v12.1. The H 2 O line parameters in LBLRTM v12.1 are as in Table 1 above, and the CH 4 line parameters in v12.1 are from HITRAN 2008 with the line mixing coefficients from Tran et al. (2006). TES is a nadir-viewing Fourier-transform infrared (FTIR) spectrometer aboard the NASA Aura spacecraft with a high spectral resolution of 0.06 cm 1 and a nadir footprint of 5.3 km 8.3 km. Here we use TES observations collocated with the first and second HIPPO campaigns of the HIAPER Pole-

6 to-pole Observations (HIPPO) of Carbon Cycle and Greenhouse Gases Study, which were performed in January and November 2009, respectively (Wofsy et al., 2011). TES CH 4 retrievals were performed for these collocations using both sets of ABSCO tables described above following the retrieval algorithm of Worden et al. (2012). These retrieved CH 4 values were then evaluated against the HIPPO CH 4 observations following the methods of Wecht et al. (2012). Figure 5: As in Figure 2, but for the ν 2 band of H 2O. 3.2 Results and Discussion Figure 6 shows the average brightness temperature residuals in the TES CH 4 retrieval windows for both versions of the ABSCO tables. The tes_v_1.8 tables, based on LBLRTM v12.1, show substantially reduced residuals in the cm -1 range relative to tes_v_1.4 (based on LBLRTM v11.3), but the residuals in the windows with wavenumbers above 1290 cm -1 appear worse in LBLRTM v12.1. Figure 7 shows the difference between the TES retrieved CH 4 profiles and the HIPPO measured profiles using both versions of the ABSCO tables. Both profiles are plotted using the representative tropospheric volume mixing ratio (RTVMR, Payne et al., 2009). We can see that the bias is substantially reduced using the tes_v_1.8 tables, showing that the updated H 2 O and CH 4 spectroscopy in LBLRTM v12.1 improves TES retrievals of CH 4. Note that this is the result of a physically-based, spectroscopic approach to improving the CH 4 forward model calculations, in contrast to tuning absorption coefficients to match the HIPPO observations, as tuning may only work under the specific conditions it is derived for. In both cases, the bias south of 30 ºN is relatively constant with latitude, while the decrease in the bias north of 30 ºN is not due to an improvement in the retrieval, but rather to the loss of signal due to reduced thermal contrast during northern fall and winter. 4. CONCLUSIONS We find that the CO 2 spectroscopy in LBLRTM v12.1 is remarkably consistent between the CO 2 ν 2 and ν 3 bands. Systematic residuals remain in the CO 2 ν 2 Q-branches and at the ν 3 bandhead. The H 2 O spectroscopy is improved in the ν 2 band in LBLRTM v12.1, with the P-branch improvements mainly from H 2 O spectroscopy and the R-branch improvements mainly from the temperature profile. We also find that the LBLRTM v12.1 CH 4 spectroscopy improves TES CH 4 retrievals, reducing the CH 4 positive bias relative to HIPPO observations from 39.5 ppb to 22.6 ppb.

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