5.1. Sources of Analysis Discrepancies
 Fetzer et al.  present a discussion of the error analysis associated with remotely sensed soundings, and specifically on AIRS validation studies. The influences on AIRS data for resolving fine-scale atmospheric structures are T lapse rate, surface-atmospheric thermal contrast, the number of and specific IR channels used in the retrieval algorithm (i.e., spectral resolution), instrument noise, retrieval algorithm constraints, and the broadness of weighting functions [Maddy and Barnet, 2008]. As a result, overly smooth AIRS vertical profiles are derived, and low-level features are undetected even though strict tests are applied in lower tropospheric T retrievals [Susskind, 2006]. These uncertainties are reflected in the error estimates that accompany retrieved AIRS products. Sounding quality control, among others, is eventually dependent on the use of these error estimates, especially for the determination of PBest.
 There are several limiting factors that may have introduced errors into the analyses presented here. First, cloud contamination in the IR pixel was unavoidable in some cases due to the presence of broken cloud cover at the time of the Aqua overpass, leading to cold T biases in AIRS retrievals. Second, the case study selection process was made difficult due to the limited amount of 1800 UTC RAOBs, the presence of broken cloud cover at the time of an Aqua overpass, and the limited number of times that an Aqua overpass occurred within 2 h of a RAOB launch (an optimal sampling difference is <30 min). Third, Sun et al.  studied the expected errors when collocation comparisons are imperfect; standard deviation errors for T are 0.35–0.42 K, and for RH are 3.1–3.3%, for collocation mismatches ≤3 h and 100 km. Our collocation strategies used may therefore have introduced errors. Fourth, previous studies have delineated the performance of AIRS soundings over land and over ocean [Fetzer et al., 2003; Divakarla et al., 2006], however this study did not examine the performance of AIRS over oceans even though 10 of the 76 soundings were retrieved over ocean or coastline. Last, the definition of a T inversion used for this study will differ from those used by others, specifically because only the layer at which the inversion begins, and not the inversion strength, was determined.
5.2. Main Conclusions
 The main conclusions drawn from this study are the following:
 1. AIRS T and Td profiles are smooth compared to collocated RAOB profiles, however there is fairly good agreement between AIRS and RAOB T, demonstrating the AIRS instrument's ability to measure atmospheric T with good accuracy in the pre-convective environment.
 2. Height-dependent errors and bias exist in AIRS, of which the most pronounced are found in the Td and q profiles. In the q profiles, a 2.25 g kg−1 dry bias is seen near the surface, and a 1.8 g kg−1 dry bias from 850 to 900 hPa.
 3. AIRS T indicates a RMS difference of 1.5 K at 850 hPa. The heights of the T discrepancies correspond with the heights of the largest WV discrepancies, namely above 900 hPa.
 4. The errors and biases in low-level AIRS T and WV observations had a pronounced effect on the stability indices and PW derived from AIRS. The near-surface and ∼850 hPa dry biases resulted in an underestimation of the instability, as well as in PW. However, as atmospheric instability is also affected by the mid-tropospheric T, the slight warm bias at 500 hPa seen in AIRS will also contribute to the underestimation of instability.
 5. The RMS error in AIRS CAPE increases with increasing true CAPE. Similarly, the negative bias in CAPE and positive bias in LI derived from AIRS also increases with increasing true CAPE values (up to 3000 J kg−1). The lowest bias for PW, and in four out of the five stability indices, was seen for cases with true CAPE 600-1200 J kg−1. Therefore, the AIRS soundings agree better with truth/RAOB data in environments with low to moderate CAPE. This is most likely related to the more accurate AIRS T lapse rate of the mid-troposphere in such environments. (See Divakarla et al.  for discussion of height dependence on AIRS T soundings.) If the AIRS instrument is unable to sense cooler mid-levels, larger discrepancies between AIRS and the RAOB data will occur in large-CAPE environments.
 6. PBest can vary significantly for AIRS soundings within a 2 × 2° display window. It is suggested that further work be done to explore the relation between PBest and cloud fraction, as well as the height of low-level clouds. PBestmax values do not appear correlated to the difference magnitude between AIRS and RAOB CAPE, however this does not eliminate the possibility that PBestmax can be used as an indicator of cloud height.
 7. RUC seems to overestimate WV, especially in mid-levels above 600 hPa with Td biases up to 6 K. Biases in q however are mostly small, except below 900 hPa, which is opposite that of AIRS. An insignificant negative T bias is seen in RUC above 600 hPa (≤1 K).
 8. RUC performs similarly to AIRS for CIN, PW, KI and TT, especially in CAPE categories 3–5, whereas biases in RUC LI and CAPE are overall smaller compared to AIRS.
 For conclusions 1–3, and as noted above, cloud contamination may be a significant contributing factor by producing either moist or dry sampling biases depending on the cloud type in the IR FOV, which in turn would affect the quality of AIRS-derived stability indices. The pre-convective environment is usually not cloud free given the high likelihood of sub-pixel scale (500 m to ∼2 km wide) cumulus clouds before convective storms initiate. The heights of the largest biases in Td and q correspond well with the heights of low-level clouds and T inversions, suggesting issues associated with the broadening of weighting functions and/or some amount of cloud contamination around 850 hPa. These are speculative statements, all requiring follow-up analysis in order to quantify, and a suggested direction for this research.
 In the 76 soundings, 19 included a robust T inversion below 500 hPa, as seen in the corresponding RAOBs. As distinct increases in T with height are not seen in AIRS, the minimum in T lapse rate is regarded as the location of the AIRS inversion. On average, the minimum lapse rate can be found at ∼854 hPa, while the average of the lowest height of RAOB inversions was 815 hPa, giving an inversion height bias of +39 hPa. According to our definition of an AIRS inversion, it is concluded that they are on average located too close to the surface. A simple, subjective method (Figure 10 and Table 3) is presented for reconstructing a RAOB-like inversion (in terms of magnitude and altitude) within AIRS soundings, hence developing more representative RAOB-like soundings that can help benefit the operational forecaster.