تقرير
Detail Enhancement of AIRS/AMSU Temperature and Moisture Profiles Using a 3D Deep Neural Network
| العنوان: | Detail Enhancement of AIRS/AMSU Temperature and Moisture Profiles Using a 3D Deep Neural Network |
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| المؤلفون: | Adam B Milstein, Joseph A Santanello Jr, William J Blackwell |
| المصدر: | Artificial Intelligence for the Earth System. 2(2) |
| بيانات النشر: | United States: NASA Center for Aerospace Information (CASI), 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Meteorology and Climatology |
| الوصف: | In recent decades, spaceborne microwave and hyperspectral infrared sounding instruments have significantly benefited weather forecasting and climate science. However, existing retrievals of lower troposphere temperature and humidity profiles have limitations in vertical resolution, and often cannot accurately represent key features such as the mixed layer thermodynamic structure and the inversion at the planetary boundary layer (PBL) top. Because of the existing limitations in PBL remote sensing from space, there is a compelling need to improve routine, global observations of the PBL and enable advances in scientific understanding and weather and climate prediction. To address this, we have developed a new 3D deep neural network (DNN) which enhances detail and reduces noise in Level 2 granules of temperature and humidity profiles from the Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit (AMSU) sounder instruments aboard NASA’s Aqua spacecraft. We show that the enhancement improves accuracy and detail including key features such as capping inversions at the top of the PBL over land, resulting in improved accuracy in estimations of PBL height. |
| نوع الوثيقة: | Report |
| اللغة: | English |
| تدمد: | 2769-7525 |
| DOI: | 10.1175/AIES-D-22-0037.1 |
| URL الوصول: | https://ntrs.nasa.gov/citations/20230000538 |
| ملاحظات: | 981698.01.04.51.05.60.17 SPEC5722 |
| رقم الأكسشن: | edsnas.20230000538 |
| قاعدة البيانات: | NASA Technical Reports |
| تدمد: | 27697525 |
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| DOI: | 10.1175/AIES-D-22-0037.1 |