Accession Number:

AD1166514

Title:

Planetary Boundary Layer (PBL)

Descriptive Note:

[Technical Report, Final Report]

Corporate Author:

MASSACHUSETTS INST OF TECH LEXINGTON

Personal Author(s):

• Milstein, Adam B.

Report Date:

2022-03-17

Pagination or Media Count:

15

Abstract:

Improved understanding of thermodynamics within the Planetary Boundary Layer PBL, including its structure and PBL height PBLH over land and water as a function of time of day, is of great importance to NASA, as recommended by the National Academy of Sciences in the 2017 Decadal Survey for Earth Science and Applications from Space ESAS 2017 1, 2 and subsequently by the NASA PBL Incubation Study Team Report STR 3. During the ESAS 2017 process, improved PBL monitoring from space was identified as a high priority across multiple interdisciplinary panels and science and application questions, leading to the current NASA PBL Decadal Survey Incubation DSI program that will invest in future spaceborne PBL mission development. In recent decades, spaceborne microwave and hyperspectral infrared HIR sounding instruments on Aqua, Suomi NPP, and JPSS have significantly improved weather forecasting 4, 5. However, existing retrievals of lower troposphere temperature T and water vapor q profiles from HIR and microwave sounders have limitations in vertical resolution, and often cannot accurately represent key features such as the mixed layer thermodynamic structure and the inversion at the PBL top, the latter of which appears as a sharp gradient in q or potential temperature Tpot as illustrated in Figure 1. With the mixed layer itself being 1-3 km thick, previously reported AIRS T and q profile resolution and resultant PBLH errors on the order of 1-2 km 6 are not sufficient, and, alone, fall well short of the ESAS recommendation of 100-300 m vertical resolution for new PBL observing systems. Because of the existing limitations in PBL remote sensing from space, there is an urgent need to improve routine, global observations of the PBL and enable advances in scientific understanding and weather and climate prediction.

Descriptors:

• artificial intelligence
• neural networks
• water vapor
• algorithms
• boundary layer
• ecology
• machine learning
• measurement
• radiative transfer
• remote sensing
• research facilities
• image processing
• materials
• space systems
• united states
• air force
• atmospheric temperature

Subject Categories:

• Meteorology
• Optical Detection and Detectors

Distribution Statement:

[A, Approved For Public Release]