In this paper, we describe how researchers and weather forecasters work together to make satellite sounding data sets more useful in severe weather forecasting applications through participation in National Oceanic and Atmospheric Administration (NOAA)’s Hazardous Weather Testbed (HWT) and JPSS Proving Ground and Risk Reduction (PGRR) program. The HWT provides a forum for collaboration to improve products ahead of widespread operational deployment. We found that the utilization of the NOAA-Unique Combined Atmospheric Processing System (NUCAPS) soundings was improved when the product developer and forecaster directly communicated to overcome misunderstandings and to refine user requirements. Here we share our adaptive strategy for (1) assessing when and where NUCAPS soundings improved operational forecasts by using real, convective case studies and (2) working to increase NUCAPS utilization by improving existing products through direct, face-to-face interaction. Our goal is to discuss the lessons we learned and to share both our successes and challenges working with the weather forecasting community in designing, refining, and promoting novel products. We foresee that our experience in the NUCAPS product development life cycle may be relevant to other communities who can then build on these strategies to transition their products from research to operations (and operations back to research) within the satellite meteorological community.

The National Aeronautics and Space Administration (NASA) Short-term Prediction Research and Transition Center (SPoRT) has been part of a collaborative effort within the National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Proving Ground and Risk Reduction (PGRR) Program to develop gridded satellite sounding retrievals for the operational weather forecasting community. The NOAA Unique Combined Atmospheric Processing System (NUCAPS) retrieves vertical profiles of temperature, water vapor, trace gases, and cloud properties derived from infrared and microwave sounder measurements. A new, optimized method for deriving NUCAPS level 2 horizontally and vertically gridded products is described here. This work represents the development of approaches to better synthesize remote sensing observations that ultimately increase the availability and usability of NUCAPS observations. This approach, known as “Gridded NUCAPS”, was developed to more effectively visualize NUCAPS observations to aid in the quick identification of thermodynamic spatial gradients. Gridded NUCAPS development was based on operations-to-research feedback and is now part of the operational National Weather Service display system. In this paper, we discuss how Gridded NUCAPS was designed, how relevant atmospheric fields are derived, its operational application in pre-convective weather forecasting, and several emerging applications that expand the utility of NUCAPS for monitoring phenomena such as fire weather, the Saharan Air Layer, and stratospheric air intrusions.

Cloud movement and evolution signify the complex water and energy transport in the atmosphere-ocean-land system. Detecting, clustering, and tracking clouds as semicoherent clusters enable study of their evolution which can complement climate model simulations and enhance satellite retrieval algorithms, where there are gaps between overpasses. Using a cluster tracking algorithm, in this study we examine the trajectories, size, and brightness temperature of millions of cloud clusters over their lifespan, from infrared satellite observations at 30 min, 4 km resolution, for a period of 11 years. We found that the majority of cold clouds were both small and short lived and that their frequency and location are influenced by El Niño. Also, this large sample of individually tracked clouds shows their horizontal size and temperature evolution. Long-lived clusters tended to achieve their temperature and size maturity milestones at different times, while these stages often occurred simultaneously in short-lived clusters. On average, clusters with this lag also exhibited a greater rainfall contribution than those where minimum temperature and maximum size stages occurred simultaneously. Furthermore, by examining the diurnal cycle of cluster development over Africa and the Indian subcontinent, we observed differences in the local timing of the maximum occurrence at different life cycle stages. Over land there was a strong diurnal peak in the afternoon, while over the ocean there was a semidiurnal peak composed of longer-lived clusters in the early morning hours and shorter-lived clusters in the afternoon. Building on regional specific work, this study provides a global long-term survey of object-based cloud characteristics.

Cold Air Aloft (CAA) can impact commercial flights when cold air descends below 12,192 m (40,000 ft) and temperatures drop dramatically. A CAA event is identified when air temperature falls below −65 °C, which decreases fuel efficiency and poses a safety hazard. This manuscript assesses the performance of the National Oceanic and Atmospheric Administration Unique Combined Atmospheric Processing System (NUCAPS) in detecting CAA events using sounders on polar-orbiting satellites. We compare NUCAPS air temperature profiles with those from Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) for January–March 2018. Of 1311 collocated profiles, 236 detected CAA. Our results showed that NUCAPS correctly detects CAA in 48.1% of profiles, while 17.2% are false positives and 34.7% are false negatives. To identify the reason for these detection states, we used a logistic regression trained on NUCAPS diagnostic parameters. We found that cloud cover can impact the skill even at higher vertical levels. This work indicates that a CAA-specific quality flag is feasible and may be useful to help forecasters to diagnose NUCAPS in real-time. Furthermore, the inclusion of an additional sounder data source (e.g., NOAA-20) may increase CAA forecast accuracy. Cloud scenes change rapidly, so additional observations provide more opportunities for correct detection.

Tropical cyclones can form over open ocean where in situ observations are limited, so forecasters rely on satellite observations to monitor their development and track. We explore the utility of an operational satellite sounding product for tropical forecasting by characterizing the products retrieval skill during research flights. Scientists from both the NOAA-Unique Combined Atmospheric Processing System (NUCAPS) research team and tropical cyclone communities collaborated to target relevant tropical cyclones during the campaign. This effort produced 130 dropsondes that are well-timed with satellite sounder overpasses over three different tropical cyclones and one Saharan Air Layer outbreak. For the combined infrared and microwave retrieval, the NUCAPS temperature has a root mean square error (RMSE) of 1.2 K near the surface (1000–600 mb) and 0.8 K in the mid-troposphere (600–300 mb), which is in line with global product requirements. The water vapor mixing ratio RMSE was 26% near the surface and 46% in the mid-troposphere. NUCAPS microwave-only retrievals can also be useful for cloudy scenes, with surface RMSE values of 4 K (temperature) and 23% (water vapor). Using information content analysis, we estimated that the vertical resolution near the surface was 1.7 km for the temperature retrievals and 2.2 km for the water vapor retrievals in this study. We discuss the feasibility of implementing NUCAPS in an operational forecasting setting, which requires rapid data delivery to forecaster software tools.

Dust outflows off Northwest Africa often propagate westward across the North Tropical Atlantic Basin (NTAB) into the greater Caribbean and US. From a health perspective, weather forecasters in these regions often monitor hazardous air quality associated with this dust. However, forecasters can be constrained by sparse data observations upwind over the Atlantic of the impacted populated areas. Global satellite sounding retrievals can potentially augment and enhance the operational forecasting toolkit for monitoring Saharan dust episodes. The focus of this paper was to examine the skill of the NOAA Unique Combined Atmospheric Processing System (NUCAPS) temperature and water vapor profiles within the dust and non-dust conditions during the March 2019 NOAA Aerosols and Ocean Science Expedition (AEROSE). During this time, the NOAA Ron Brown research ship launched radiosondes to coincide with satellite overpasses that served as independent ground truth data for evaluating NUCAPS. Compared to RAOBs from the Ron Brown, the SNPP and NOAA-20 NUCAPS-derived soundings showed skill in profiling atmospheric conditions supporting Saharan dust monitoring. Outside of dust regions, the NOAA-20 NUCAPS surface temperature bias peaks at 2.0 K; the surface water vapor bias is minimal (~1000 hPa), with a small cold bias that peaks at −50% between 742 and 790 hPa. Corresponding temperature RMS values are less than 2.0 K; water vapor RMS values are generally below 70%. Within the dust regions, NOAA-20 NUCAPS temperature soundings show a cold bias peak of 2.6 K at 918 hPa and 113% of a moist bias peak at the same level. Corresponding temperature RMS values maximize at 3.5 K at 945 hPa; the water vapor RMS shows a peak value of 106% at the same level. Weather forecasters can apply NUCAPS across the NTAB in issuing timely and accurate hazardous air quality warnings and visibility alerts to health officials and the general public.