Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed

Abstract

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.

Observation of the Tropical Cyclone Diurnal Cycle Using Hyperspectral Infrared Satellite Sounding Retrievals

“…The combined infrared and microwave retrievals (IR+MW) can fail in regions of uniform cloudiness (e.g., where the cloud fraction within the field of regard is above 85%) and precipitation. In partly-cloudy regions, errors in clear-column radiances, estimated skin temperature, and surface emissivity can also reduce the retrieval quality (Iturbide-Sanchez et al 2018;Esmaili et al 2020). While the NUCAPS algorithm performs best in clear and partly-cloudy conditions, microwave profiles are often still retrieved in cloudy or lightly-precipitating regions where IR+MW retrievals fail (Nalli et al 2013(Nalli et al , 2018; in heavily-precipitating regions, such as the eyewall of TCs, both microwave and infrared retrievals typically fail.…”

“…NUCAPS produces vertical profiles of temperature and moisture, trace gases, and cloud properties globally using a combination of both infrared and microwave sounding instrumentation (Gambacorta 2013). This algorithm is capable of retrieving atmospheric profiles over nearly 80% of the globe under clear to partly cloudy (non-precipitating) conditions (Nalli et al 2013(Nalli et al , 2018Susskind et al 2003) (Weaver et al 2019); observing the pre-convective environment (Iturbide-Sanchez et al 2018;Esmaili et al 2020); and diagnosing extratropical transition (Berndt and Folmer 2018).…”

Satellite imagery and products of the 16–17 February 2020 Saharan Air Layer dust event over the eastern Atlantic: impacts of water vapor on dust detection and morphology

“…Layer precipitable water (LPW) is the depth of condensed water vapor that exists between two given pressure levels. An initial LPW product (Forsythe et al, 2015;LeRoy et al, 2016) was developed at CIRA, which employed a fusion of the NOAA MIRS (Boukabara et al, 2011) water vapor profile retrievals from seven LEO satellites. Satellites used in the initial LPW study were S-NPP, NOAA-19/20, MetOp-A/B, and Defense Meteorological Satellite Program F17/18.…”

“…One of the uses of microwave data is the retrieval of water vapor. As stated above, Forsythe et al (2015) developed a methodology to retrieve values of water vapor in layers of the troposphere, referred to as the ALPW product. Due to the use of LEO sensors, imagery for the ALPW was not necessarily available as often as ABI data from GOES-16.…”

“…Results of the 16-17 February 2020 observational study serve to support Banks et al (2019) and Miller et al (2019) by showing that reduced values of water vapor allowed dust associated with the SAL to be both detected and tracked over the eastern Atlantic Ocean. Observational datasets include (1) a simple difference in infrared imagery, (2) a microwave retrieval of layer precipitable water known as the Advected Layer Precipitable Water (ALPW) product (Forsythe et al, 2015;LeRoy et al, 2016), and (3) an infrared retrieval of total precipitable water (TPW; Gambacorta and Barnet, 2013;, all of which addressed water vapor in the environment of dust within the SAL.…”