Remote-sensed flood monitoring is rapidly advanced by the growing abundance of satellite data. This study presents the progress in building an operational system that harnesses the power of spatially high-resolution (HR), multi-source remote sensing data for event-based flood extent and depth mapping. By integrating pioneering extent retrieval—Radar Produced Inundation Diary (RAPID), Self-supervised Waterbody Detection (SWD), and depth retrieval processors, Global-LOCAL Solvers Integration Algorithm (GLOCAL), and the Emulated Flood Recession Algorithm (EFRA)—our proposed framework marks a significant leap in flood monitoring capabilities. The upgraded RAPID addresses the complexities of Synthetic Aperture Radar (SAR) flood mapping in diverse environments, including snow-covered and arid regions, enhancing adaptability and consistency across multiple SAR satellites such as ESA Sentinel-1, CSA RADARSAT Constellation Mission (RCM), MDA RADARSAT-2 (RS2), and Capella. Meanwhile, SWD brings a method to automatically map flood extents from high-resolution optical images, including Planet, Sentinel-2, and Landsat, during clear weather conditions. GLOCAL and EFRA are tailored for depth estimation from the generated HR remotely sensed flood extents, and HR or VHR topography. Both algorithms tolerate the error from extent and topography, with EFRA physically considering the influence of micro-topography. The proposed framework has proven to produce consistent flood extent results across multi-source, high-resolution satellite images and demonstrates robust depth estimation with various input DEMs. Validation against USGS high water masks yields 1.01 m RMSE when 1 m resolution DEM is used. With continuous development and integration into NOAA's near real-time operational platform, these approaches do not only improve the resolution and dependability of operational flood monitoring globally but also significantly bolster emergency response strategies.

Instruments on board Joint Polar Satellite System (JPSS) satellites (and other polar orbiters) provide unique operational capabilities for tropical cyclones to United States National Weather Service (NWS) forecasters throughout the Pacific basin. Data from these satellites are also used as input to forecast models that are used globally for tropical cyclone guidance. Specific products include visible imagery at night (from the Visible-Infrared Imaging Radiometer Suite -- VIIRS -- Day Night Band (DNB) that flies on the Suomi-NPP and NOAA-20 satellites), information in the microwave part of the electromagnetic spectrum as sensed by the Advanced Technology Microwave Sounder (ATMS) on Suomi-NPP and NOAA-20 and the Advanced Microwave Sounding Unit (AMSU) and Microwave Humidity Sounder (MHS) instruments on NOAA-19 (and also on EUMETSAT's MetOp series of satellites). JPSS data from ATMS and from the Cross-track Infrared Sounder (CrIS, also on board Suomi-NPP and NOAA-20) instruments are used to create vertical profiles of temperature in moisture via HEAP (Hyper-spectral Enterprise Algorithm Package; formerly NOAA-Unique Combined Atmospheric Processing System -- NUCAPS). These data sources afford a forecaster with important analysis techniques for center-fixing a tropical cyclone (TC), estimating the size of the eye (a metric that influences peak speeds) and for assessing the environment in and around the storm.

Current operational weather satellites from the United States provide high temporal (GOES), spatial, spectral, and radiometric resolution data and derived products for fire detection and monitoring. These include GOES-16/17 for geostationary missions and Suomi NPP and NOAA-20 for low earth orbiting satellites. The Advanced Baseline Imager (ABI) on the latest geostationary satellites provide 16 channels, including a dedicated fire detection channel along with near-infrared channels which can aid with fire detection at night [1], [2].

Disaster response to flood events requires, amongst others, data on flood inundation. Flood forecast systems, such as the Global Flood Awareness System (GloFAS), provide such data by coupling streamflow forecasts to a catalogue of flood inundation scenarios. Inaccuracies in either the streamflow forecasts or the flood inundation catalogue can result in misses or false positives. To date however, no formal assessment of the accuracy of the GloFAS flood inundation estimates has been conducted. In this study we assess the accuracy of the GloFAS flood inundation estimates against imagery from VIIRS for the widespread flooding in China and India during the monsoon season of 2020. Results indicate that GloFAS captures the larger scale patterns of flood inundation but misses finer details. This affects the results of the exposure estimates. Further work could expand the assessment to use the full archive of global VIIRS data to conduct a global skill assessment of GloFAS.

Current operational weather satellites from the United States provide high temporal (GOES), spatial, spectral, and radiometric resolution data and derived products for fire detection and monitoring. These include GOES-16/17 for geostationary missions and Suomi NPP and NOAA-20 for low earth orbiting satellites. The Advanced Baseline Imager (ABI) on the latest geostationary satellites provide 16 channels, including a dedicated fire detection channel along with near-infrared channels which can aid with fire detection at night [1]. A similar sensor is currently being used on Himawari-8/9 satellites as well as on other geostationary satellites, offering similar capabilities as the GOES-East (16) and GOES-West (17) satellites.

In this presentation, we present details on ways that the JPSS Program uses its investments in environmental satellites to benefit not only the NOAA mission but other countries as well. The data, products, and capabilities from the NOAA-20 satellite has been well integrated into the global observation network following its launch on November 18, 2017. The Suomi National Polar-orbiting Partnership (Suomi NPP) continues to provide critical data as its sensors maintain their health well past their expected life-time.

In this presentation, we present details on various newsworthy storms that have received support from the data and products from the Joint Polar Satellite System (JPSS) satellites. With the launch of the NOAA-20 satellite on November 18, 2017 and the continued technical health of the Suomi NPP satellite, multiple sources of data and products are available for decisionmakers as they face weather disasters around the world. This value of these satellites is continually reinforced as the JPSS Program is frequently called on to provide tailored products in response to these disasters. The operational application of JPSS data and products comes about in three primary ways.

In the last several years, the new generation of weather satellites, including GOES-16 and NOAA-20, have gone operational, providing higher temporal, spatial, spectral, and radiometric resolution data and derived products. In particular, the latest generation of United States polar satellites offer unique sensitivity capabilities. The focus of this discussion is the Day/Night Band (DNB)-a low-light sensor on the Visible/Infrared Imaging Radiometer Suite (VIIRS), that continues to expand the range possibilities for characterizing the nocturnal environment. VIIRS is carried on the Joint Polar Satellite System (JPSS) program's Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites. The DNB allows for both daytime visible imagery as well as the unique ability to detect extremely low-levels of visible and near-infrared light at night ( Miller et al., 2013 ).

As a close proxy of the GOES-R Advanced Baseline Imager (ABI) instrument, the on-board 16-band Advanced Himawari Imager (AHI) brings an unprecedented opportunity to exercise the ABI algorithms developed for GOES-R in the STAR Algorithm Processing Framework (SAPF). STAR has been collaborating with JMA and NASA since AHI's post launch checkout and has been acquiring the full resolution AHI data form JMA's Cloud Service. Because AHI has almost identical channels as ABI, most of the ABI algorithms can be easily adapted to run with AHI data. This presentation describes the process to update the GOES-R ABI baseline algorithms for AHI data, including the update of SAPF system and the ABI algorithm software. The benefits and lessons learned from the AHI data exercise will also be discussed.