# Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed
## 1 Introduction
From written survey responses in the testbed, forecasters communicated that satellite soundings were valuable for interrogating uncertainty in Numerical Weather Prediction (NWP) models. Secondly, satellite soundings provide forecasters with data between radiosondes launch times. Radiosondes are launched around 00 and 12 UTC from 70 locations across the United States, making them sparse in time and space [9]. For comparison, radiosondes provide a total of 140 profiles each day, whereas NUCAPS can produce 120 profiles in 30 seconds across a swath that is 2200 km wide. Globally, 324,000 profiles are produced daily, during both day and night. Moreover, forecasters have communicated that these wide swaths improve situational awareness before hazards develop. From forecaster feedback, we learned that increasing the temporal frequency of sounding observations is critical to nowcasting severe weather [10; 11]; one satellite is not enough. Now in the HWT, NUCAPS profiles are available from all NOAA operational satellite platforms (Suomi National-Polar-orbiting Partnership (Suomi NPP), NOAA-20, MetOp-A, and MetOp-B). We utilized over 300 scenarios since 2014 to determine how and when forecasters use NUCAPS soundings in their decision making during severe weather outbreaks [11]. More recently, forecasters communicated the novel combinations of NUCAPS soundings and other satellite, model, and in-situ data sources that helped them to predict severe weather. Upon introduction into AWIPS and by participating in the HWT, we were able to respond to make improvements based on forecaster needs, understand NUCAPS strengths and weaknesses through specific examples provided by forecasters, and discover how to pair NUCAPS with the latest observation and model datasets in operations. As developers, we have learned a few valuable lessons about product design and development for operational use. For example, in feedback from 2015 and 2016 HWTs, forecasters favored models and observations that are produced independently of each other [12]. However, since then, the quantity of model data and satellite products available to forecasters has only increased over time. In 2019, AWIPS contained over 35 numerical models, gridded data, and point data sources; each of the data sources contain a variety of variables and can be displayed in multiple projections ([[http://unidata.github.io/awips2/cave/d2d-gridded-models](http://unidata.github.io/awips2/cave/d2d-gridded-models)]([http://unidata.github.io/awips2/cave/d2d-gridded-models](http://unidata.github.io/awips2/cave/d2d-gridded-models))). For nowcasting thunderstorms and tornadoes, forecasters are highly time-limited and must focus their attention only on the most relevant products [11]. Data that blends both models and observations allow forecasters to inspect two critical datasets simultaneously. This is contrary to some of our earlier findings that valued clear separation. So, one \"lesson learned\" is that improvements do not necessarily evolve incrementally but more like a random walk process. This paper describes how we refined NUCAPS soundings and derived products within a NOAA testbed environment to more readily meet forecaster needs for severe weather prediction. Each year our approach has evolved and our experience in the HWT is unique. As we introduce new NUCAPS-derived data sets, we have an opportunity to apply our lessons learned and tailor NUCAPS for the specific applications. We will describe how our efforts improved NUCAPS utility for severe weather forecasting and provide guidance on how to transition satellite retrievals from research to operations, both in a testbed and real forecast environment. By doing so, we hope to prevent other valuable products from becoming \"skeletons in the valley of death\" [13].