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.

Asheville, NC made a major contribution in providing special data summaries. The authors are indebted to Mr Keith Grant of the British Meteorological Office for his outstanding cooperation and many contributions of data. Companion documents to this work provide detailed electrooptical transmittance climatology and refractivity data for the Persian Gulf region; see USAFETAC/TN-88/003, Persian Gulf Transmittance Study, and USAFETAC/TN-88/004, Persian Gulf Refractivity Study. i Chapter 1 INTRODUCTION.

Since 2 June 2020, unusual heavy and continuous rainfall from the Asian summer monsoon rainy season caused widespread catastrophic floods in many Asian countries, including primarily the two most populated countries, China and India. To detect and monitor the floods and estimate the potentially affected population, data from sensors aboard the operational polar-orbiting satellites Suomi National Polar-Orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration (NOAA)-20 were used. The Visible Infrared Imaging Radiometer Suite (VIIRS) with a spatial resolution of 375 m available twice per day aboard these two satellites can observe floodwaters over large spatial regions. The flood maps derived from the VIIRS imagery provide a big picture over the entire flooding regions, and demonstrate that, in July, in China, floods mainly occurred across the Yangtze River, Hui River and their tributaries. The VIIRS 5-day composite flood maps, along with a population density dataset, were combined to estimate the population potentially exposed (PPE) to flooding. We report here on the procedure to combine such data using the Zonal Statistic Function from the ArcGIS Spatial Analyst toolbox. Based on the flood extend for July 2020 along with the population density dataset, the Jiangxi and Anhui provinces were the most affected regions with more than 10 million people in Jingdezhen and Shangrao in Jiangxi province, and Fuyang and Luan in Anhui province, and it is estimated that about 55 million people in China might have been affected by the floodwaters. In addition to China, several other countries, including India, Bangladesh, and Myanmar, were also severely impacted. In India, the worst inundated states include Utter Pradesh, Bihar, Assam, and West Bengal, and it is estimated that about 40 million people might have been affected by severe floods, mainly in the northern states of Bihar, Assam, and West Bengal. The most affected country was Bangladesh, where one third of the country was underwater, and the estimated population potentially exposed to floods is about 30 million in Bangladesh.

Among all the natural hazards throughout the world, floods occur most frequently. They occur in high latitude regions, such as: 82% of the area of North America; most of Russia; Norway, Finland, and Sweden in North Europe; China and Japan in Asia. River flooding due to ice jams may happen during the spring breakup season. The Northeast and North Central region, and some areas of the western United States, are especially harmed by floods due to ice jams and snowmelt. In this study, observations from operational satellites are used to map and monitor floods due to ice jams and snowmelt. For a coarse-to-moderate resolution sensor on board the operational satellites, like the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the National Polar-orbiting Partnership (NPP) and the Joint Polar Satellite System (JPSS) series, and the Advanced Baseline Imager (ABI) on board the GOES-R series, a pixel is usually composed of a mix of water and land. Water fraction can provide more information and can be estimated through mixed-pixel decomposition. The flood map can be derived from the water fraction difference after and before flooding. In high latitude areas, while conventional observations are usually sparse, multiple observations can be available from polar-orbiting satellites during a single day, and river forecasters can observe ice movement, snowmelt status and flood water evolution from satellite-based flood maps, which is very helpful in ice jam determination and flood prediction. The high temporal resolution of geostationary satellite imagery, like that of the ABI, can provide the greatest extent of flood signals, and multi-day composite flood products from higher spatial resolution imagery, such as VIIRS, can pinpoint areas of interest to uncover more details. One unique feature of our JPSS and GOES-R flood products is that they include not only normal flood type, but also a special flood type as the supra-snow/ice flood, and moreover, snow and ice masks. Following the demonstrations in this study, it is expected that the JPSS and GOES-R flood products, with ice and snow information, can allow dynamic monitoring and prediction of floods due to ice jams and snowmelt for wide-end users.

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