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1. Reviews and syntheses: Ongoing and emerging opportunities to improve environmental science using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites

   https://doi.org/10.5194/bg-18-4117-2021  

   Khan, Anam M. ; Stoy, Paul C. ; Douglas, James T. ; Anderson, Martha ; Diak, George ; Otkin, Jason A. ; Hain, Christopher ; Rehbein, Elizabeth M. ; McCorkel, Joel ( January 2021 , Biogeosciences)

   null (Ed.)

   Abstract. Environmental science is increasingly reliant on remotely sensedobservations of the Earth's surface and atmosphere. Observations frompolar-orbiting satellites have long supported investigations on land coverchange, ecosystem productivity, hydrology, climate, the impacts ofdisturbance, and more and are critical for extrapolating (upscaling)ground-based measurements to larger areas. However, the limited temporalfrequency at which polar-orbiting satellites observe the Earth limits ourunderstanding of rapidly evolving ecosystem processes, especially in areaswith frequent cloud cover. Geostationary satellites have observed theEarth's surface and atmosphere at high temporal frequency for decades, andtheir imagers now have spectral resolutions in the visible and near-infrared regions that are comparable to commonly used polar-orbiting sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), or Landsat. These advances extend applications of geostationary Earth observations from weather monitoring to multiple disciplines in ecology and environmental science. We review a number of existing applications that use data from geostationary platforms and present upcoming opportunities for observing key ecosystem properties using high-frequency observations from the Advanced Baseline Imagers (ABI) on the Geostationary Operational Environmental Satellites (GOES), which routinely observe the Western Hemisphere every 5–15 min. Many of the existing applications in environmental science from ABI are focused on estimating land surface temperature, solar radiation, evapotranspiration, and biomass burning emissions along with detecting rapid drought development and wildfire. Ongoing work in estimating vegetation properties and phenology from other geostationary platforms demonstrates the potential to expand ABI observations to estimate vegetation greenness, moisture, and productivity at a high temporal frequency across the Western Hemisphere. Finally, we present emerging opportunities to address the relatively coarseresolution of ABI observations through multisensor fusion to resolvelandscape heterogeneity and to leverage observations from ABI to study thecarbon cycle and ecosystem function at unprecedented temporal frequency. 

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2. High Resolution 3D Mapping of Hurricane Flooding from Moderate-Resolution Operational Satellites

   https://doi.org/10.3390/rs14215445  

   Li, Sanmei ; Goldberg, Mitchell ; Kalluri, Satya ; Lindsey, Daniel T. ; Sjoberg, Bill ; Zhou, Lihang ; Helfrich, Sean ; Green, David ; Borges, David ; Yang, Tianshu ; et al ( November 2022 , Remote Sensing)

   Floods are often associated with hurricanes making landfall. When tropical cyclones/hurricanes make landfall, they are usually accompanied by heavy rainfall and storm surges that inundate coastal areas. The worst natural disaster in the United States, in terms of loss of life and property damage, was caused by hurricane storm surges and their associated coastal flooding. To monitor coastal flooding in the areas affected by hurricanes, we used data from sensors aboard the operational Polar-orbiting and Geostationary Operational Environmental Satellites. This study aims to apply a downscaling model to recent severe coastal flooding events caused by hurricanes. To demonstrate how high-resolution 3D flood mapping can be made from moderate-resolution operational satellite observations, the downscaling model was applied to the catastrophic coastal flooding in Florida due to Hurricane Ian and in New Orleans due to Hurricanes Ida and Laura. The floodwater fraction data derived from the SNPP/NOAA-20 VIIRS (Visible Infrared Imaging Radiometer Suite) observations at the original 375 m resolution were input into the downscaling model to obtain 3D flooding information at 30 m resolution, including flooding extent, water surface level and water depth. Compared to a 2D flood extent map at the VIIRS’ original 375 m resolution, the downscaled 30 m floodwater depth maps, even when shown as 2D images, can provide more details about floodwater distribution, while 3D visualizations can demonstrate floodwater depth more clearly in relative to the terrain and provide a more direct perception of the inundation situations caused by hurricanes. The use of 3D visualization can help users clearly see floodwaters occurring over various types of terrain conditions, thus identifying a hazardous flood from non-hazardous flood types. Furthermore, 3D maps displaying floodwater depth may provide additional information for rescue efforts and damage assessments. The downscaling model can help enhance the capabilities of moderate-to-coarse resolution sensors, such as those used in operational weather satellites, flood detection and monitoring. 

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3. Evaluating the Representations of Atmospheric Rivers and Their Associated Precipitation in Reanalyses With Satellite Observations

   https://doi.org/10.1029/2023JD038937  

   Ma, Weiming ; Chen, Gang ; Guan, Bin ; Shields, Christine A. ; Tian, Baijun ; Yanez, Emilio ( November 2023 , Journal of Geophysical Research: Atmospheres)

   Atmospheric rivers (ARs) are filaments of enhanced horizontal moisture transport in the atmosphere. Due to their prominent role in the meridional moisture transport and regional weather extremes, ARs have been studied extensively in recent years. Yet, the representations of ARs and their associated precipitation on a global scale remains largely unknown. In this study, we developed an AR detection algorithm specifically for satellite observations using moisture and the geostrophic winds derived from 3D geopotential height field from the combined retrievals of the Atmospheric Infrared Sounder and the Advanced Microwave Sounding Unit on NASA Aqua satellite. This algorithm enables us to develop the first global AR catalog based solely on satellite observations. The satellite‐based AR catalog is then combined with the satellite‐based precipitation (Integrated Muti‐SatellitE Retrievals for GPM) to evaluate the representations of ARs and AR‐induced precipitation in reanalysis products. Our results show that the spreads in AR frequency and AR length distribution are generally small across data sets, while the spread in AR width is relatively larger. Reanalysis products are found to consistently underestimate both mean and extreme AR‐related precipitation. However, all reanalyses tend to precipitate too often under AR conditions, especially over low latitude regions. This finding is consistent with the “drizzling” bias which has plagued generations of climate models. Overall, the findings of this study can help to improve the representations of ARs and associated precipitation in reanalyses and climate models.

    

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4. Mapping, Monitoring, and Prediction of Floods Due to Ice Jam and Snowmelt with Operational Weather Satellites

   https://doi.org/10.3390/rs12111865  

   Goldberg, Mitchell D. ; Li, Sanmei ; Lindsey, Daniel T. ; Sjoberg, William ; Zhou, Lihang ; Sun, Donglian ( June 2020 , Remote Sensing)

   null (Ed.)

   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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5. Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S.

   https://doi.org/10.3390/rs14236113  

   Ye, Xinxin ; Deshler, Mina ; Lyapustin, Alexi ; Wang, Yujie ; Kondragunta, Shobha ; Saide, Pablo ( December 2022 , Remote Sensing)

   Satellite remote sensing of aerosol optical depth (AOD) is essential for detection, characterization, and forecasting of wildfire smoke. In this work, we evaluate the AOD (550 nm) retrievals during the extreme wildfire events over the western U.S. in September 2020. Three products are analyzed, including the Moderate-resolution Imaging Spectroradiometers (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) product collections C6.0 and C6.1, and the NOAA-20 Visible Infrared Imaging Radiometer (VIIRS) AOD from the NOAA Enterprise Processing System (EPS) algorithm. Compared with the Aerosol Robotic Network (AERONET) data, all three products show strong linear correlations with MAIAC C6.1 and VIIRS presenting overall low bias (<0.06). The accuracy of MAIAC C6.1 is found to be substantially improved with respect to MAIAC C6.0 that drastically underestimated AOD over thick smoke, which validates the effectiveness of updates made in MAIAC C6.1 in terms of an improved representation of smoke aerosol optical properties. VIIRS AOD exhibits comparable uncertainty with MAIAC C6.1 with a slight tendency of increased positive bias over the AERONET AOD range of 0.5–3.0. Averaging coincident retrievals from MAIAC C6.1 and VIIRS provides a lower root mean square error and higher correlation than for the individual products, motivating the benefit of blending these datasets. MAIAC C6.1 and VIIRS are further compared to provide insights on their retrieval strategy. When gridded at 0.1° resolution, MAIAC C6.1 and VIIRS provide similar monthly AOD distribution patterns and the latter exhibits a slightly higher domain average. On daily scale, over thick plumes near fire sources, MAIAC C6.1 reports more valid retrievals where VIIRS tends to have retrievals designated as low or medium quality, which tends to be due to internal quality checks. Over transported smoke near scattered clouds, VIIRS provides better retrieval coverage than MAIAC C6.1 owing to its higher spatial resolution, pixel-level processing, and less strict cloud masking. These results can be used as a guide for applications of satellite AOD retrievals during wildfire events and provide insights on future improvement of retrieval algorithms under heavy smoke conditions. 

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