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Chandra M. Pasillas
,
Christian Kummerow
,
Michael Bell
, and
Steven D. Miller

Abstract

Meteorological satellite imagery is a critical asset for observing and forecasting weather phenomena. The Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) sensor collects measurements from moonlight, airglow, and artificial lights. DNB radiances are then manipulated and scaled with a focus on digital display. DNB imagery performance is tied to the lunar cycle, with best performance during the full moon and worst with the new moon. We propose using feed-forward neural networks models to transform brightness temperatures and wavelength differences in the infrared spectrum to a pseudo lunar reflectance value based on lunar reflectance values derived from observed DNB radiances. JPSS NOAA-20 and Suomi National Polar-orbiting Partnership (SNPP) satellite data over the North Pacific Ocean at night for full moon periods from December 2018 - November 2020 were used to design the models. The pseudo lunar reflectance values are quantitatively compared to DNB lunar reflectance, providing the first-ever lunar reflectance baseline metrics. The resulting imagery product, Machine Learning Night-time Visible Imagery (ML-NVI), is qualitatively compared to DNB lunar reflectance and infrared imagery across the lunar cycle. The imagery goal is not only to improve upon the consistency performance of DNB imagery products across the lunar cycle, but ultimately lay the foundation for transitioning the algorithm to geostationary sensors, making global continuous nighttime imagery possible. ML-NVI demonstrates its ability to provide DNB derived imagery with consistent contrast and representation of clouds across the full lunar cycle for night-time cloud detection.

Open access
Daphne S. LaDue
,
David Roueche
,
Frank Lombardo
, and
Lara Mayeux

Abstract

When a tornado strikes a permanent or mobile/manufactured home, occupants are at risk of injury and death from blunt force trauma caused by debris-loaded winds and failure of the structure. Mechanisms for these failures have been studied for the past few decades and identified common weaknesses in the structural load path. Also under study in recent decades, much has been learned about how people receive and understand warnings and determine how, when, and if they will shelter in advance. Recent research, for example, shows most people do not shelter until close to impact, after seeing, hearing, or feeling the approaching tornado. To advance beyond these innovations, a new, multi-disciplinary approach was fielded in nine Southeast U.S. tornadoes between 2019 and 2022. For each tornado, 1) wind engineering assessments documented near-surface wind fields, 2) structural engineering assessments documented the primary wind load path for each structure, and 3) social science interviews captured the survivor’s narrative and asked several follow-up questions to assure key items of interest were addressed in each interview. When possible, the team was multi-disciplinary during the interview, enabling survivors to ask questions and better understand their experiences. Most survivors became aware of the approaching tornado with at least a few minutes lead time and most were able to reach a place of refuge. Most survivors recalled sensory experiences during the tornado and about half could describe direction or temporal sequences of damage. A case study of the Cookeville, Tennessee, Tornado of 3 March 2020 illustrates the power of the integrated data assessment.

Open access
Free access
Free access
Xiangzhou Song
,
Xuehan Xie
,
Yunwei Yan
, and
Shang-Ping Xie

Abstract

Based on data collected from 14 buoys in the Gulf Stream, this study examines how hourly air–sea turbulent heat fluxes vary on subdaily time scales under different boundary layer stability conditions. The annual mean magnitudes of the subdaily variations in latent and sensible heat fluxes at all stations are 40 and 15 W m−2, respectively. Under near-neutral conditions, hourly fluctuations in air–sea humidity and temperature differences are the major drivers of subdaily variations in latent and sensible heat fluxes, respectively. When the boundary layer is stable, on the other hand, wind anomalies play a dominant role in shaping the subdaily variations in latent and sensible heat fluxes. In the context of a convectively unstable boundary layer, wind anomalies exert a strong controlling influence on subdaily variations in latent heat fluxes, whereas subdaily variations in sensible heat fluxes are equally determined by air–sea temperature difference and wind anomalies. The relative contributions by all physical quantities that affect subdaily variations in turbulent heat fluxes are further documented. For near-neutral and unstable boundary layers, the subdaily contributions are O(2) and O(1) W m−2 for latent and sensible heat fluxes, respectively, and they are less than O(1) W m−2 for turbulent heat fluxes under stable conditions.

Significance Statement

High-resolution buoy observations of air–sea variables in the Gulf Stream provide the opportunity to investigate the physical factors that determine subdaily variations in air–sea turbulent heat fluxes. This study addresses two key points. First, the observed subdaily amplitudes of heat fluxes are related to various processes, including wind fields and air–sea thermal effect differences. Second, the global sea surface heat budget is known to not be in near-zero balance and it ranges from several to tens of watts per square meter. Therefore, consideration of the relatively strong influence of subdaily variability in air–sea turbulent heat fluxes could provide a new strategy for solving the global heat budget balance problem.

Open access
Free access
Da Fan
,
Steven J. Greybush
,
Eugene E. Clothiaux
, and
David John Gagne II

Abstract

Convective initiation (CI) nowcasting remains a challenging problem for both numerical weather prediction models and existing nowcasting algorithms. In this study, an object-based probabilistic deep learning model is developed to predict CI based on multichannel infrared GOES-16 satellite observations. The data come from patches surrounding potential CI events identified in Multi-Radar Multi-Sensor Doppler weather radar products over the Great Plains region from June and July 2020 and June 2021. An objective radar-based approach is used to identify these events. The deep learning model significantly outperforms the classical logistic model at lead times up to 1 hour, especially on the false alarm ratio. Through case studies, the deep learning model exhibits dependence on the characteristics of clouds and moisture at multiple altitudes. Model explanation further reveals that the contribution of features to model predictions is significantly dependent on the baseline, a reference point against which the prediction is compared. Under a moist baseline, moisture gradients in the lower and middle troposphere contribute most to correct CI forecasts. In contrast, under clear-sky baselines, correct CI forecasts are dominated by cloud-top features, including cloud-top glaciation, height, and cloud coverage. Our study demonstrates the advantage of using different baselines in further understanding model behavior and gaining scientific insights.

Open access
Ayumi Fujisaki-Manome
,
Haoguo Hu
,
Jia Wang
,
Joannes J. Westerink
,
Damrongsak Wirasaet
,
Guoming Ling
,
Mindo Choi
,
Saeed Moghimi
,
Edward Myers
,
Ali Abdolali
,
Clint Dawson
, and
Carol Janzen

Abstract

In Alaska’s coastal environment, accurate information of sea ice conditions is desired by operational forecasters, emergency managers, and responders. Complicated interactions among atmosphere, waves, ocean circulation, and sea ice collectively impact the ice conditions, intensity of storm surges, and flooding, making accurate predictions challenging. A collaborative work to build the Alaska Coastal Ocean Forecast System established an integrated storm surge, wave, and sea ice model system for the coasts of Alaska, where the verified model components are linked using the Earth System Modeling Framework and the National Unified Operational Prediction Capability. We present the verification of the sea ice model component based on the Los Alamos Sea Ice Model, version 6. The regional, high-resolution (3 km) configuration of the model was forced by operational atmospheric and ocean model outputs. Extensive numerical experiments were conducted from December 2018 to August 2020 to verify the model’s capability to represent detailed nearshore and offshore sea ice behavior, including landfast ice, ice thickness, and evolution of air–ice drag coefficient. Comparisons of the hindcast simulations with the observations of ice extent presented the model’s comparable performance with the Global Ocean Forecast System 3.1 (GOFS3.1). The model’s skill in reproducing landfast ice area significantly outperformed GOFS3.1. Comparison of the modeled sea ice freeboard with the Ice, Cloud, and Land Elevation Satellite-2 product showed a mean bias of −4.6 cm. Daily 5-day forecast simulations for October 2020–August 2021 presented the model’s promising performance for future implementation in the coupled model system.

Significance Statement

Accurate sea ice information along Alaska’s coasts is desired by the communities for preparedness of hazardous events, such as storm surges and flooding. However, such information, in particular predicted conditions, remains to be a gap. This study presents the verification of the state-of-art sea ice model for Alaska’s coasts for future use in the more comprehensive coupled model system where ocean circulation, wave, and sea ice models are integrated. The model demonstrates comparable performance with the existing operational ocean–ice coupled model product in reproducing overall sea ice extent and significantly outperformed it in reproducing landfast ice cover. Comparison with the novel satellite product presented the model’s ability to capture sea ice freeboard in the stable ice season.

Open access