Introduction
The planetary boundary layer (PBL), is a significant component of our atmosphere that greatly impacts our weather conditions and climate. This layer, which resides closest to Earth’s surface, has surfaced as an intriguing area of scientific inquiry in the 2018 planetary science decadal survey due to its potential to bolster storm forecasting and elevate climate projection accuracy. Let’s see how Deep Learning helps, in this article.
The Vital Role of the PBL
Technical staff member of Lincoln Laboratory’s Applied Space Systems Group, Adam Milstein, explains that the PBL is vital not only for our climate and severe weather development, but also for our daily lives. This air-seasoned layer interacts directly with the Earth’s surface, mobilizing exchanges of moisture and heat that can trigger severe weather events and instigate climate change. The PBL also houses the human populace and constitutes the crucial thoroughfare for aerosols, which greatly influence air quality and, consequently, public health.
Laying Bare the Subtleties of PBL
Being the key for understanding weather and climate, the defining factors of the PBL like its height, pose a formidable challenge to unfold with existing technology. Lincoln Laboratory researchers have expended considerable efforts over the last four years studying the PBL. They focused on utilizing machine learning to craft 3D-scanned profiles of the atmosphere and refining the vertical structure visibility of the atmosphere to upgrade drought prediction.
A Glimpse into Our Instrumental Past
The research performed building on a decade-long work on efficient, operational neural network algorithms by Lincoln Laboratory innovated for NASA missions. This involves missions like the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission, and Aqua, a satellite providing premier data about Earth’s water cycle. These pioneering algorithms extract temperature and humidity data from satellite instruments, thereby significantly amplifying the precision and global coverage of the observations beyond previous methodologies.
The Evolution of AI Algorithms with Deep Learning
The operational algorithms developed for TROPICS and Aqua, based on conventional “shallow” neural networks, have expedited speed while simplifying complexities to provide one-dimensional vertical atmospheric profiles. However, Lincoln Laboratory researchers discerned the need for modern “deep” learning techniques that treat the atmosphere over select regions as a three-dimensional image to further refine PBL details.
FAQ
1. What is the Planetary Boundary Layer (PBL)?
Answer: The PBL is the lowest layer of the troposphere and the one that significantly influences weather near the Earth’s surface where it interacts with the atmosphere, leading to exchanges of moisture and heat that affect severe weather and climate change.
2. Why is the PBL important to study?
Answer: The PBL plays a crucial role in climate change, severe weather development and significantly influences the air quality influencing human health. Therefore, understanding the PBL can lead to accurate climate projections and stronger storm forecasting.
3. Can PBL study improve drought predictions?
Answer: Yes, studying the vertical structure of the atmosphere more clearly can greatly improve the ability to predict future drought conditions.
4. What are some of the challenges in studying the PBL?
Answer: Defining factors of the PBL such as its height are difficult to comprehend with current technology.
5. What role does the NASA play in the study of the PBL?
Answer: Efforts by NASA in collaboration with Lincoln Laboratory has significantly improved the understanding of the PBL, resulting in more robust climate and weather predictions.
6. How is Artificial Intelligence leveraged in the study of PBL?
Answer: AI and deep learning techniques are used to improve the statistical representation of 3D temperature and humidity imagery of the atmosphere.
7. Can the PBL research have future implications?
Answer: Yes, The improved neural network approach holds the promise to demonstrate drought prediction that can exceed the capabilities of existing indicators, and be a tool that scientists can rely on for decades to come.
8. What’s the importance of aerosols in the PBL?
Answer: Aerosols present in the PBL significantly influence air quality, hence affecting human health.
9. What is the future of this research?
Answer: The researchers plan to compare the deep learning results to datasets collected directly in the PBL using radiosondes to quantify the accuracy of the developed techniques.
10. What are the key instruments used for this research?
Answer: The key instruments used for research include various satellite data and instruments, including those from the TROPICS mission as well as the Aqua spacecraft.
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