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.

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.

Airborne dust has broad adverse effects on human activity, including aviation, human health, and agriculture. Remote sensing observations are used to detect dust and aerosols in the atmosphere using long established techniques. False color Red‐Green‐Blue (RGB) imagery using band differences sensitive to dust absorption (Dust RGB) is currently used operationally to assist forecasters and decision‐makers in identifying dust at night, but there are still limitations, subjectivity, and nuances to image interpretation making night‐time dust identification difficult even for experts. This study applies machine learning to the problem of night‐time dust detection with a simple random forest (RF) model using Geostationary Operational Environmental Satellite‐16 (GOES‐16) Advanced Baseline Imager (ABI) infrared imagery, band differences sensitive to dust absorption, and Dust RGB color components as inputs to the model. The RF model achieves an Area‐Under‐Curve (AUC) of 0.97 with a standard deviation of 0.04 for dust cases. For images with dust present, the model correctly labels 85% of dust pixels and 99.96% of no‐dust pixels for all dust images in the validation data set. The addition of a single null case to the training data set drastically reduces error in labeling no‐dust pixels as dust from 45% to 14.5%. Application of the machine learning model to the April 13–14, 2019 dust event demonstrates the ability of the model to identify dust during night‐time hours when visual dust detection is limited by the cooling ground surface characteristics.

In recent years, National Oceanic and Atmospheric Administration (NOAA) Unique Combined Atmospheric Processing System (NUCAPS) hyperspectral infrared satellite sounding retrievals derived from Joint Polar Satellite System (JPSS) polar‐orbiting satellites have been documented as observations that add value to weather forecasting applications. NUCAPS is currently the operational algorithm delivering JPSS satellite sounding retrievals to the NOAA National Weather Service (NWS) and is based on the heritage Atmospheric Infrared Sounder (AIRS) Science Team algorithm for processing vertical temperature, moisture, and trace gas retrievals. For the Special Collection on “Twenty Years of Observations from AIRS,” we highlight the methodology we implemented to develop a prototype science‐to‐applications system to enable real‐time processing of AIRS satellite sounding retrievals through the NUCAPS algorithm (i.e., NUCAPS‐Aqua) to support weather forecasting applications. The addition of NUCAPS‐Aqua to experimental real‐time pathways alongside operational JPSS NUCAPS soundings, facilitated assessment of NUCAPS‐Aqua at the 2022 Hazardous Weather Testbed (HWT) Spring Experiment. Development of NUCAPS‐Aqua described in this technical report includes preservation of microwave observations and calculation of a‐priori regression coefficients. Additionally, the real‐time processing and challenges with implementing a science‐to‐applications system are discussed. Two illustrative pre‐convective forecasting examples analyzed by NWS forecasters during the 2022 HWT Spring Experiment are highlighted to demonstrate the benefit of NUCAPS‐Aqua as (a) special afternoon soundings, (b) an additional observation to assess temporal trends using multiple satellites, and (c) a complement to observational and model analysis.

The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) began creating multispectral [i.e., red–green–blue (RGB)] composites in the early 2000s with the advent of the Meteosat-8 Spinning Enhanced Visible and Infrared Imager (SEVIRI). As new satellite sensors—for example, the Himawari-8 Advanced Himawari Imager (AHI) and the Geostationary Operational Environmental Satellite Advanced Baseline Imager (ABI)—become available, there is a need to adjust the EUMETSAT RGB standard thresholds (i.e., recipes) to account for differences in spectral characteristics, spectral response, and atmospheric absorption in order to maintain an interpretation consistent with legacy composites. For the purpose of comparing RGB composites derived from nonoverlapping geostationary sensors, an adjustment technique was applied to the Suomi National Polar-Orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) to create an intermediate reference sensor (i.e., SEVIRI proxy). Brightness temperature offset values between each AHI and SEVIRI proxy band centered near 3.9, 8.6, 11.0, and 12.0 µm were determined with this technique and through line-by-line radiative transfer model simulations. The relationship between measured brightness temperature of AHI and the SEVIRI proxy was determined though linear regression similar to research by the Japan Meteorological Agency. The linear regression coefficients were utilized to determine the RGB recipe adjustments. Adjusting the RGB recipes to account for the differences in spectral characteristics results in RGB composites consistent with legacy EUMETSAT composites. The methodology was applied to an example of the Nighttime Microphysics RGB, confirming the Japan Meteorological Agency adjustments and demonstrating a simple methodology to determine recipe adjustments for RGB composites derived with next-generation sensors.