A regional‐scale fully coupled data assimilation (DA) system based on the ensemble Kalman filter is developed for a high‐resolution coupled atmosphere‐ocean model. Through the flow‐dependent covariance both within and across the oceanic and atmospheric domains, the fully coupled DA system is capable of updating both atmospheric and oceanic state variables simultaneously by assimilating either atmospheric and/or oceanic observations. The potential impacts of oceanic observations, including sea‐surface temperature, sea‐surface height anomaly, and sea‐surface current, in addition to the observation of the minimum surface pressure at the storm center (HPI), on tropical cyclone analysis and prediction are examined through observing system simulation experiments of Hurricane Florence (2018). Results show that assimilation of oceanic observations not only resulted in better analysis and forecast of the oceanic variables but also considerably reduced analysis and forecast errors in the atmospheric fields, including the intensity and structure of Florence. Compared to weakly coupled DA in which the analysis update is performed separately for the atmospheric and oceanic domains, fully coupled DA reduces the forecast errors of tropical cyclone track and intensity. Results show promise in potential further improvement in tropical cyclone prediction through assimilation of both atmospheric and oceanic observations using the ensemble‐based fully coupled DA system.
Lack of high‐resolution observations in the inner‐core of tropical cyclones remains a key issue when constructing an accurate initial state of the storm structure. The major implication of an improper initial state is the poor predictability of the future state of the storm. The size and associated hazard from strong winds at the inner‐core make it impossible to sample this region entirely. However, targeting regions of the inner‐core where forecasted atmospheric measurements have high uncertainty can significantly improve the accuracy of measurements for the initial state of the storm. This study provides a scheme for targeted high‐resolution observations for small Unmanned Aircraft Systems (sUAS) platforms (e.g., Coyote sUAS) to improve the estimates of the atmospheric measurement in the inner‐core structure. The benefit of observation is calculated based on the high‐fidelity state‐of‐the‐art hurricane ensemble data assimilation system. Potential locations with the most
- Award ID(s):
- 1910397
- NSF-PAR ID:
- 10375030
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Earth and Space Science
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2333-5084
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Airborne Doppler radar provides detailed and targeted observations of winds and precipitation in weather systems over remote or difficult-to-access regions that can help to improve scientific understanding and weather forecasts. Quality control (QC) is necessary to remove nonweather echoes from raw radar data for subsequent analysis. The complex decision-making ability of the machine learning random-forest technique is employed to create a generalized QC method for airborne radar data in convective weather systems. A manually QCed dataset was used to train the model containing data from the Electra Doppler Radar (ELDORA) in mature and developing tropical cyclones, a tornadic supercell, and a bow echo. Successful classification of ∼96% and ∼93% of weather and nonweather radar gates, respectively, in withheld testing data indicate the generalizability of the method. Dual-Doppler analysis from the genesis phase of Hurricane Ophelia (2005) using data not previously seen by the model produced a comparable wind field to that from manual QC. The framework demonstrates a proof of concept that can be applied to newer airborne Doppler radars.
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Significance Statement The results of this study show the effectiveness of utilizing kriging with external drift (KED) in merging gauge and radar rainfall data to produce highly accurate, reliable rainfall estimates in mountainous tropical regions, such as O‘ahu. The validated KED dataset, with its high temporal and spatial resolutions, offers a valuable resource for various types of rainfall-related research, particularly for extreme weather response and rainfall intensity analyses in Hawai’i. Our findings improve the accuracy of rainfall estimates and contribute to a deeper understanding of the performance of various rainfall estimation methods under different storm types and rainfall structures in a mountainous tropical setting.
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