Jay P. Hoffman

Remotely sensed ice motion is a crucial component in sea, lake, or river ice research. Over the past few decades, the ice movement has been detected and retrieved predominantly through the application of the Maximum Cross-Correlation (MCC) technique by analyzing the overlapped consecutive satellite images.Traditionally, ice motion products have been derived from daily averaged satellite imagery, commonly referred to as 'daily-map' (DM) ice motion. This DM ice motion product has gained widespread usage in sea ice studies due to its inherent timescale and extensive coverage.Recently, a new approach known as the swath-to-swath (S2S) method has emerged, deriving ice motion from individual satellite swath pairs. The S2S ice motion product has proven valuable in sea ice kinematics research, revealing a robust relationship between ice kinematics and thickness, characterized by its diverse timescale. Consequently, these two types of satellite-derived ice motion products contribute distinct perspectives to ice kinematics research.The latest generation of NOAA's Geostationary Operational Environmental Satellites (GOES), specifically the GOES-R Series, offers sea/lake/river ice observations at a relatively high resolution. A recent development involves the MCC approach generating a new DM ice motion product with a 2 km resolution using GOES-R reflectance imagery (0.5 km resolution). This ice motion dataset holds potential for final users engaged in analyzing small-scale sea/lake/river ice status and its changes.

This study presents a novel approach for the detection of contrails in satellite imagery using a convolutional neural network (CNN). Contrails are important to monitor because their contribution to climate change is uncertain and complex. Contrails are found to have a net warming effect because the clouds prevent terrestrial (longwave) radiation from escaping the atmosphere. Globally, this warming effect is greater than the cooling effect the clouds have in the reduction of solar (shortwave) radiation reaching the surface during the daytime. The detection of contrails in satellite imagery is challenging due to their similarity to natural clouds. In this study, a certain type of CNN, U-Net, is used to perform image segmentation in satellite imagery to detect contrails. U-Net can accurately detect contrails with an overall probability of detection of 0.51, a false alarm ratio of 0.46 and a F1 score of 0.52. These results demonstrate the effectiveness of using a U-Net for the detection of contrails in satellite imagery and could be applied to large-scale monitoring of contrail formation to measure their impact on climate change.

Sea ice leads, or fractures account for a small proportion of the Arctic Ocean surface area, but play a critical role in the energy and moisture exchanges between the ocean and atmosphere. As the sea ice area and volume in the Arctic has declined over the past few decades, changes in sea ice leads have not been studied as extensively. A recently developed approach uses artificial intelligence (AI) and satellite thermal infrared window data to build a twenty-year archive of sea ice lead detects with Moderate Resolution Imaging Spectroradiometer (MODIS) and later, an archive from Visible Infrared Imaging Radiometer Suite (VIIRS). The results are now available and show significant improvement over previously published methods. The AI method results have higher detection rates and a high level detection agreement between MODIS and VIIRS. Analysis over the winter season from 2002–2003 through to the 2021–2022 archive reveals lead detections have a small decreasing trend in lead area that can be attributed to increasing cloud cover in the Arctic. This work reveals that leads are becoming increasingly difficult to detect rather than less likely to occur. Although the trend is small and on the same order of magnitude as the uncertainty, leads are likely increasing at a rate of 3700 km² per year with a range of uncertainty of 3500 km² after the impact of cloud cover changes are removed.

Despite accounting for a small fraction of the surface area in the Arctic, long and narrow sea ice fractures, known as “leads”, play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over the past few decades, it is increasingly important to monitor the corresponding changes in sea ice leads. A novel approach has been developed using artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). In this new approach, a particular type of convolutional neural network, a U-Net, replaces a series of conventional image processing tests from our legacy algorithm. Results show the new approach has a high detection accuracy with F1 Scores on the order of 0.7. Compared to the legacy algorithm, the new algorithm shows improvement, with more true positives, fewer false positives, fewer false negatives, and better agreement between satellite instruments.

Sea ice leads (fractures) play a critical role in the exchange of mass and energy between the ocean and atmosphere in the polar regions. The thinning of Arctic sea ice over the last few decades will likely result in changes in lead distributions, so monitoring their characteristics is increasingly important. Here we present a methodology to detect and characterize sea ice leads using satellite imager thermal infrared window channels. A thermal contrast method is first used to identify possible sea ice lead pixels, then a number of geometric and image analysis tests are applied to build a subset of positively identified leads. Finally, characteristics such as width, length and orientation are derived. This methodology is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) observations for the months of January through April over the period of 2003 to 2018. The algorithm results are compared to other satellite estimates of lead distribution. Lead coverage maps and statistics over the Arctic illustrate spatial and temporal lead patterns.

Recently, global biomass-burning research has grown from what was primarily a climate field to include a vibrant air quality observation and forecasting community. While new fire monitoring systems are based on fundamental Earth Systems Science (ESS) research, adaptation to the forecasting problem requires special procedures and simplifications. In a reciprocal manner, results from the air quality research community have contributed scientifically to basic ESS. To help exploit research and data products in climate, ESS, meteorology and air quality biomass burning communities, the joint Navy, NASA, NOAA, and University Fire Locating and Modeling of Burning Emissions (FLAMBE) program was formed in 1999. Based upon the operational NOAA/NESDIS Wild-Fire Automated Biomass Burning Algorithm (WF_ABBA) and the near real time University of Maryland/NASA MODIS fire products coupled to the operational Navy Aerosol Analysis and Prediction System (NAAPS) transport model, FLAMBE is a combined ESS and operational system to study the nature of smoke particle emissions and transport at the synoptic to continental scales. In this paper, we give an overview of the FLAMBE system and present fundamental metrics on emission and transport patterns of smoke. We also provide examples on regional smoke transport mechanisms and demonstrate that MODIS optical depth data assimilation provides significant variance reduction against observations. Using FLAMBE as a context, throughout the paper we discuss observability issues surrounding the biomass burning system and the subsequent propagation of error. Current indications are that regional particle emissions estimates still have integer factors of uncertainty.

Since August 2000, the Wild fire Automated Biomass Burning Algorithm (WF_ABBA) has been generating half‐hourly fire hot spot analyses for the Western Hemisphere using GOES satellites to provide the Naval Research Laboratory (NRL) Aerosol Analysis and Prediction System (NAAPS) with near‐real‐time fire products. These are used to generate smoke particle fluxes for aerosol transport forecasting to benefit the scientific, weather, and regulatory communities. In South America, fire hot‐spot analysis is shown to be adequate for generating real‐time smoke source functions for aerosol forecast models. We present smoke coverage and flux estimates based on the WF_ABBA and NAAPS products. Modeled fluxes of emissions for 2001–2002 are ∼25 + 10 Tg yr⁻¹, similar to previous estimates. Correlations of optical depth with MODIS and AERONET show good agreement with observations. Comparisons of NAAPS aerosol fields with MODIS also show potential clear sky and other biases as smoke is transported into the Atlantic Ocean and the ITCZ.