# Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed
## 3 Results
### Forecasters Feedback to NUCAPS Developers  
The weekly questionnaire allows forecasters to make more general statements about the utility of NUCAPS. Figure 8 shows that most forecasters would use NUCAPS in the future, particularly the modified product. This again reinforces the need to accurately represent the boundary layer to increase usability for severe weather forecasting. Most forecasters said they would 'usually' access the gridded NUCAPS product, which was introduced to enhance situational awareness. Interestingly, forecasters said they would'sometimes' or 'usually' use the NUCAPS-Forecast product. In past HWTs, forecasters wanted model independence. However, their willingness to use a hybrid model-satellite product (albeit with significant revision to the product) shows a promising new area for continued development in the context of operations. At the end of week, we asked if forecasters had any suggestions for future improvement. Sixty percent of respondents requested more data. Geostationary datasets have the advantage over CONUS, where forecasters can make observations continuously. While it is not possible to increase the temporal sampling from a single polar-orbiting satellite, sounders are currently available on six JPSS, NASA, and European partner satellites. Full utilization of these resources would increase spatial and temporal availability of soundings. Presently, NWS has requirements to use a single satellite source, which as of July 2019 was switched from Suomi NPP to NOAA-20. A challenge on the part of the NWS is a saturation of data through their available channels. However, the files are distributed from the operational Environmental Data Record (EDR) which has extra data that are not used by AWIPS or the NWS. It is possible that a thinned file that contains multiple satellite swaths could be developed without increasing (and perhaps reducing) the file size. While this feedback is consistent with qualitative survey responses from past HWTs, the message was particularly strong in the 2019 questionnaire. Figure 8: Reponses to the question “How often would you use NUCAPS in the future?”. However, we also received new feedback. In personal correspondence, forecasters requested a blended model-observation dataset. This could lead to the boundary layer being replaced or convolved with model analysis, where models are stronger than satellites, and then observations used at the mid to upper levels, which is where they excel. An opportunity arises here where the forecast and modified soundings could be blended into a single product, with more aggressive boundary layer correction. Forecasters verbally expressed dissatisfaction with the AWIPS visualization of soundings, for NUCAPS, model, and radiosondes. Many access sounding data through web-based visualizations or other software tools, such as BUFKIT ([[https://training.weather.gov/wdtd/tools/BUFKIT/](https://training.weather.gov/wdtd/tools/BUFKIT/)]([https://training.weather.gov/wdtd/tools/BUFKIT/](https://training.weather.gov/wdtd/tools/BUFKIT/))) and SHARPpy ([[http://sharp.weather.ou.edu/dev/](http://sharp.weather.ou.edu/dev/)]([http://sharp.weather.ou.edu/dev/](http://sharp.weather.ou.edu/dev/))). Providing multiple avenues to access NUCAPS would give forecasters more options to display the data and can lead to feedback for updates to the baseline AWIPS. A unique challenge of working with polar-orbiting data over geostationary is the intermittent nature of data availability. While researchers are familiar with the orbital dynamics of low-earth orbit satellites, forecasters need to make quick decisions and do not have time to consider when and where data will be in the future. One forecaster suggested tools to provide an alert when an overpass is available in their forecast area of responsibility. Products within AWIPS that bring attention to potential hazards, such as ProbSevere [37; 38] (which was also tested in the HWT), were very useful to forecasters who must analyze significant amounts of data [11].