| Presentation Title: | إطار عمل هجين للتعلم الآلي لتحسين التنبؤ الآني بهطول الأمطار |
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| Full Name: | لوكا ديلي موناكي |
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| Affiliation / Institution: | CW3E/SIO/UCSD |
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| Co-Author |
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| Full Name: | Duncan Axisa |
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| Affiliation / Institution: | CW3E/SIO/UCSD |
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| Co-Author 2 |
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| Full Name: | Vesta Afzali Gorooh |
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| Affiliation / Institution: | CW3E/SIO/UCSD |
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| Co-Author 3 |
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| Full Name: | Zhenhai Zhang |
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| Affiliation / Institution: | CW3E/SIO/UCSD |
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| Co-Author 4 |
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| Full Name: | Mohammadvaghef Ghazvinian |
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| Affiliation / Institution: | CW3E/SIO/UCSD |
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| Co-Author 5 |
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| Full Name: | Chandrasekaran Venkatachalam |
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| Affiliation / Institution: | Colorado State University |
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| Co-Author 6 |
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| Full Name: | Kim Eun Yeol |
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| Affiliation / Institution: | Colorado State University |
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| Co-Author 7 |
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| Full Name: | Chandrasekar Radhakrishnan |
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| Affiliation / Institution: | Colorado State University |
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| Co-Author 8 |
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| Full Name: | Ernesto Damiani |
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| Affiliation / Institution: | جامعة خليفة |
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| Co-Author 9 |
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| Full Name: | Marco Anisetti |
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| Affiliation / Institution: | جامعة خليفة |
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| Abstract: | Operational rainfall enhancement programs rely heavily on the accuracy of precipitation
forecasts to improve cloud targeting, which guides the action of inserting seeding material at the
right place and at the right time in the vicinity of suitable clouds. As part of the UAE Research
Program for Rain Enhancement Science (UAEREP) we are developing artificial intelligence (AI)
algorithms to improve cloud seeding operations over the UAE. We have built a novel AI
framework leveraging satellite observations, ground-based weather radar data, rain gauges,
aircraft data, and numerical weather prediction estimates to extract features and generate
products to determine optimal cloud seeding timing and location, and to generate more
accurate quantitative precipitation estimation to support the UAE rainfall enhancement
program. An advanced deep learning algorithm is proposed to learn from several thousands of
examples from historical data how to effectively extract and extrapolate inputs and the required
cloud features important to define cloud patches that are seedable. These features and inputs,
along with extrapolated satellite and radar data, as well as numerical weather prediction data
and rain gauges, are utilized as input to an AI-based model to generate precipitation predictions
six hours in the future. We will describe the methods developed under this project to support
cloud seeding operations and provide an assessment of their performance. |
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