# Evaluating Satellite Sounding Temperature Observations for Cold Air Aloft Detection
## 3 Results
### Diagnosing Causes for True and False CAA Detection  
We evaluate diagnostic metrics within NUCAPS to better explain why NUCAPS may succeed or fail to detect a CAA event. As mentioned in Section 2.1, NUCAPS generates diagnostic retrieval metrics that quantify information content and cloud clearing uncertainty [4]. These include:  
* \"Degrees of freedom of temperature\" (_dof_temp_): The Trace of the AKM helps to diagnose the vertical resolution of the temperature retrieval. High values indicate high vertical resolution and may better capture CAA events, particularly when cold air layers are not very thick. Low values of _dof_temp_ indicate a lower vertical resolution and the resulting profiles may smooth the small-scale features in the temperature profile. While DOFs are useful diagnostic metrics, DOFs are total column values and do not describe the vertical resolution at specific pressure levels (e.g., between 250 and 100 hPa). * \"Lapse Rate\": The lapse rate is the change in temperature with height in the atmosphere. In general, NUCAPS has larger uncertainty when the lapse rates are small. We categorize lapse rate into two pressure ranges that we identified as contributing to CAA detection, one between 250-100 hPa (_lr250-100_) and 500-250 hPa (_lr500-250_). _lr250-100_ captures the atmospheric layer that typically contains the CAA, while _lr500-250_ is the \"background\" state and can influence the higher-level retrieval. * \"Cloud fraction\" (_cloud_frac_): NUCAPS retrieves Cloud fraction (with values ranging from 0 as 'cloud free\" to 1.0 as \"full cloud cover\") for each FOV from cloudy radiance measurements, then adds the \(3\times 3\) FOV fractions into a total cloud fraction for the FOR. As mentioned in Section 2.1, NUCAPS employs cloud clearing to retrieve temperature soundings for each FOR, which is successful in clear to partly cloudy scenes. To ensure confidence in the final retrieval, NUCAPS quantifies and propagates all known sources of uncertainty, which includes uncertainty due to clouds. Under some conditions, however, cloud uncertainty can be difficult to quantify and the retrieved temperature profile becomes cloud contaminated such that the portion of the profile underneath the cloud is cooler than it should be. Cloud fraction, alone, is not an indicator of cloud uncertainty or contamination. For instance, the quality of temperature profiles can be very good even in 85% cloudy FORs. We use cloud fraction in this study to improve our situational awareness of the atmospheric at the scenes in question and not as a measure of retrieval quality. * \"Chi-squared of cloud clearing\" (_eta_rej_). For all channels used in the retrieved variable, _eta_rej_ is the sum of the error in the cloud clearing radiance. _Eta_rej_ is a function of the inverse of the derivative of the plank function for the channel as well as the difference between the estimated clear-sky radiance and the radiance that is calculated after the final cloud clearing step in NUCAPS. Figure 5: Percentage of matchup profiles between January and March 2018 for the true positive, false positive, and false negative CAA detection states that are summarized in Table 1. Profiles categorized as true negative are the most numerous (N = 1026) but for clarity, are not shown. For scenes with high sensitivity (where _dof_temp_ is high), smaller _eta_rej_ values indicate that the cloud cleared radiance spectrum closely matches the estimated clear sky one and therefore likely has low cloud contamination (i.e., the radiative effects of clouds were accurately identified and removed). Values of _eta_rej_ are high when NUCAPS fails to accurately detect and remove clouds during cloud clearing so the retrieved profiles become cloud contaminated. Cloud contamination often happens over cold scenes where the radiance signal is low and has weak sensitivity to temperature at multiple layers. Cloud contamination also can occur where the temperature difference between cloud tops and the snow-covered Earth surface are equivalent and hamper cloud detection.