# Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed
## 3 Results
### Assessing Limitations for Storm Prediction  
Figure 7 shows a breakdown of situations that impede utilization of NUCAPS. This list of possible limitations was compiled from written responses to qualitive survey questions from previous HWTs. In the following discussion, we will examine each category. In previous HWTs, data latency was a barrier to applying NUCAPS to severe weather forecasting [10; 34]. Figure 7 shows that \"observations too late\" was less of a limitation for profiles than the other data sets. This is due to the way the data were fed into the HWT. NOAA-20 was not yet operational in AWIPS during the HWT, so it was provided via L  
Figure 5: Reponses to the question “Which of the following NUCAPS profiles did you use?”. Figure 6: Reponses to the question “If convection initiated, did NUCAPS-Forecast provide skill in determining the eventual convective intensity, convective mode, and type of severe weather produced?”. from the NESDIS Production Distribution and Access (PDA). Other datasets are derived from baseline NUCAPS, and modified soundings must wait until RTMA and GOES observations are available before processing the data and delivery. Gridded NUCAPS was also pushed after processing from the regular NUCAPS soundings, and the gridding process took 10-15 min. Additionally, NUCAPS-Forecast required parallel-processing to derive the product with HYSPLIT and deliver them with minimal latency. Thus, not all products were available at the same time. Fewer forecasters are limited by the baseline profile availability than the other products; modified NUCAPS, gridded NUCAPS, and NUCAPS-Forecast products were often only delayed by 10 min. This data staggering is an artifact of being in an experimental environment and the additional latency inherent in downloading, processing, and delivery of pre-processed products. While this was not a planned means of evaluating the impact of latency on nowcasting, result indicates how time sensitive the data are to forecasters. It is encouraging to see that for the regular sounding products, the latency was not particularly prohibitive. NOAA-20 replaced Suomi NPP in AWIPS as the operational satellite in July 2019, and the data transfer latency was reduced from 90-240 min to 40-60 min, due to the fact NOAA-20 is downlinked every half orbit. Soundings, gridded, and modified datasets have no missing values within the swath, but have gaps in overpasses from the same satellite, which are large over the southern United States, where significant convective activity takes place. By utilizing both Suomi NPP and NOAA-20, all orbit-related gaps are filled within a 12-h period. NUCAPS-Forecast ranked less favorable because projections were made using only those profiles that passed the microwave and infrared physical retrieval step. This is where the needs of the operational community strongly differ from the research community. Researchers often want to see the \"best quality\" data, which are as close to the original observation, not data that has been interpolated [35]. However, generating the best quality data requires strict quality controls in the processing methodology, which may take too much time for the data to be operationally viable. Weather forecasters prefer good, high-quality data. However, the shorter time constraints necessary to make decisions in the operational environment requires them to use potentially lower-quality, quickly-deliverable data. To assess quality, they will rely on training and other datasets. For severe weather applications, three categories of limitations in Figure 7 stem from boundary layer depictions: the unrealistic low quality, unrealistic high quality, and unrealistic CAPE or lapse rates. Forecasters wrote that CAPE values, which typically range from 2500-3500 J kg\({}^{-1}\) for a highly unstable atmosphere, derived from NUCAPS had a difference of \(\pm\)500 J kg\({}^{-1}\) compared to models and radiosondes.