# Evaluating Satellite Sounding Temperature Observations for Cold Air Aloft Detection
## 5 Summary and Conclusions  
NUCAPS globally retrieves temperature, water vapor, and trace gas profiles for operational weather forecasting. Through developer and user partnerships, new and innovative uses of the data have been discovered, such as CAA detection in aviation weather. NUCAPS has minimal reliance on non-sounder data sources and, as a result, retrievals are independent of NWP models. Thus, it is feasible for forecasters use NUCAPS, alongside traditional observations such as radiosondes and AMDAR, to compare with NWP model forecasts to monitor CAA. NUCAPS was designed to be globally relevant and not tailored for specific applications, so it is important to evaluate NUCAPS skill for specific phenomena like CAA detection for aviation forecasts. In this study, we compared NUCAPS with COSMIC profiles from January to March 2018 to see if CAA was detected in either profile. Four detection states (true positive, true negative, false positive, and false negative) were used to categorize the collocated profiles. When compared with COSMIC data, we found NUCAPS successfully detected CAA for 48.2% of profiles (true positive), false detectedCAA for 17.2% of profiles (false positive) and did not capture CAA for 34.7% of profiles (false negative). These results were consistent with forecaster inspection of the data over the same time period. To determine some of the underlying conditions that led to these outcomes, we examined several diagnostic metrics produced in the operational NUCAPS file (the degrees of freedom of temperature, cloud fraction, chi squared of cloud clearing, and lapse rates) using a multinomial logistic regression. We found that diagnostic metrics that indicated successful cloud clearing (where the radiative effects of clouds are removed) increased the odds of the true positive and false negative states, whereas diagnostics that lead to failed cloud clearing and low information content may contribute to an increase in false positive cases. As the false positive cases represented the fewest matchups, including more sounder orbits may help improve situational awareness of CAA. Between satellite overpasses, the underlying conditions may change rapidly and become more favorable to the retrieval. While up to five additional satellites could be utilized for CAA monitoring, only one satellite is presently available in the SBN, which is the primary means of data access for the NWS. For severe weather forecasting, 60% of forecasters requested more satellite sounders to improve situations [8]. So, increasing sounding availability can support other NWS needs. Finally, our results show that the diagnostics metrics were different for each for the four detection states. We propose that in the future, NUCAPS developers collaborate with forecasters to develop a CAA-specific quality control flag. While the diagnostic metrics shown in this study are useful for developers, NWS forecasters work in a fast-paced environment and do not have time to perform a complex assessments of data quality. A simplified quality control flag can increase the utility of NUCAPS for CAA detection and can help increase forecaster confidence in the product. The results in this study suggest that a simplified quality control flag is feasible and is an example of how developers can refine data products to meet aviation forecaster needs. Conceptualization, R.E., N.S., and M.S. ; methodology, R.E. and M.S. ; software, R.E. ; writing--original draft preparation, R.E., N.S., and C.B. ; writing--review and editing, R.E., N.S., C.B., and M.S. ; visualization, R.E. ; supervision, N.S. ; project administration, N.S. ; funding acquisition, M.S. and C.B. All authors have read and agreed to the published version of the manuscript. This research was funded through the Joint Polar Satellite System (JPSS) Proving Ground and Risk Reduction Program (PGRR) and by the United States Air Force. The publication costs were paid for by Science and Technology Corp.