NASA Technical Reports Server (NTRS) 20180000636: Using Neural Networks To Improve The Performance Of Radiative Transfer Modeling Used For Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations - Info and Reading Options
By NASA Technical Reports Server (NTRS)
"NASA Technical Reports Server (NTRS) 20180000636: Using Neural Networks To Improve The Performance Of Radiative Transfer Modeling Used For Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations" and the language of the book is English.
“NASA Technical Reports Server (NTRS) 20180000636: Using Neural Networks To Improve The Performance Of Radiative Transfer Modeling Used For Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations” Metadata:
- Title: ➤ NASA Technical Reports Server (NTRS) 20180000636: Using Neural Networks To Improve The Performance Of Radiative Transfer Modeling Used For Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations
- Author: ➤ NASA Technical Reports Server (NTRS)
- Language: English
Edition Identifiers:
- Internet Archive ID: NASA_NTRS_Archive_20180000636
AI-generated Review of “NASA Technical Reports Server (NTRS) 20180000636: Using Neural Networks To Improve The Performance Of Radiative Transfer Modeling Used For Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations”:
"NASA Technical Reports Server (NTRS) 20180000636: Using Neural Networks To Improve The Performance Of Radiative Transfer Modeling Used For Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations" Description:
The Internet Archive:
Surface Lambertian-equivalent reflectivity (LER) is important for trace gas retrievals in the direct calculation of cloud fractions and indirect calculation of the air mass factor. Current trace gas retrievals use climatological surface LER's. Surface properties that impact the bidirectional reflectance distribution function (BRDF) as well as varying satellite viewing geometry can be important for retrieval of trace gases. Geometry Dependent LER (GLER) captures these effects with its calculation of sun normalized radiances (I/F) and can be used in current LER algorithms (Vasilkov et al. 2016). Pixel by pixel radiative transfer calculations are computationally expensive for large datasets. Modern satellite missions such as the Tropospheric Monitoring Instrument (TROPOMI) produce very large datasets as they take measurements at much higher spatial and spectral resolutions. Look up table (LUT) interpolation improves the speed of radiative transfer calculations but complexity increases for non-linear functions. Neural networks perform fast calculations and can accurately predict both non-linear and linear functions with little effort.
Read “NASA Technical Reports Server (NTRS) 20180000636: Using Neural Networks To Improve The Performance Of Radiative Transfer Modeling Used For Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations”:
Read “NASA Technical Reports Server (NTRS) 20180000636: Using Neural Networks To Improve The Performance Of Radiative Transfer Modeling Used For Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations” by choosing from the options below.
Available Downloads for “NASA Technical Reports Server (NTRS) 20180000636: Using Neural Networks To Improve The Performance Of Radiative Transfer Modeling Used For Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations”:
"NASA Technical Reports Server (NTRS) 20180000636: Using Neural Networks To Improve The Performance Of Radiative Transfer Modeling Used For Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations" is available for download from The Internet Archive in "texts" format, the size of the file-s is: 0.89 Mbs, and the file-s went public at Wed Jun 29 2022.
Legal and Safety Notes
Copyright Disclaimer and Liability Limitation:
A. Automated Content Display
The creation of this page is fully automated. All data, including text, images, and links, is displayed exactly as received from its original source, without any modification, alteration, or verification. We do not claim ownership of, nor assume any responsibility for, the accuracy or legality of this content.
B. Liability Disclaimer for External Content
The files provided below are solely the responsibility of their respective originators. We disclaim any and all liability, whether direct or indirect, for the content, accuracy, legality, or any other aspect of these files. By using this website, you acknowledge that we have no control over, nor endorse, the content hosted by external sources.
C. Inquiries and Disputes
For any inquiries, concerns, or issues related to the content displayed, including potential copyright claims, please contact the original source or provider of the files directly. We are not responsible for resolving any content-related disputes or claims of intellectual property infringement.
D. No Copyright Ownership
We do not claim ownership of any intellectual property contained in the files or data displayed on this website. All copyrights, trademarks, and other intellectual property rights remain the sole property of their respective owners. If you believe that content displayed on this website infringes upon your intellectual property rights, please contact the original content provider directly.
E. Fair Use Notice
Some content displayed on this website may fall under the "fair use" provisions of copyright law for purposes such as commentary, criticism, news reporting, research, or educational purposes. If you believe any content violates fair use guidelines, please reach out directly to the original source of the content for resolution.
Virus Scanning for Your Peace of Mind:
The files provided below have already been scanned for viruses by their original source. However, if you’d like to double-check before downloading, you can easily scan them yourself using the following steps:
How to scan a direct download link for viruses:
- 1- Copy the direct link to the file you want to download (don’t open it yet). (a free online tool) and paste the direct link into the provided field to start the scan.
- 2- Visit VirusTotal (a free online tool) and paste the direct link into the provided field to start the scan.
- 3- VirusTotal will scan the file using multiple antivirus vendors to detect any potential threats.
- 4- Once the scan confirms the file is safe, you can proceed to download it with confidence and enjoy your content.
Available Downloads
- Source: Internet Archive
- All Files are Available: Yes
- Number of Files: 5
- Number of Available Files: 5
- Added Date: 2022-06-29 11:29:27
- Scanner: Internet Archive Python library 2.0.3
Available Files:
1- Text PDF
- File origin: original
- File Format: Text PDF
- File Size: 0.00 Mbs
- File Name: 20180000636.pdf
- Direct Link: Click here
2- Metadata
- File origin: original
- File Format: Metadata
- File Size: 0.00 Mbs
- File Name: NASA_NTRS_Archive_20180000636_files.xml
- Direct Link: Click here
3- Metadata
- File origin: original
- File Format: Metadata
- File Size: 0.00 Mbs
- File Name: NASA_NTRS_Archive_20180000636_meta.sqlite
- Direct Link: Click here
4- Metadata
- File origin: original
- File Format: Metadata
- File Size: 0.00 Mbs
- File Name: NASA_NTRS_Archive_20180000636_meta.xml
- Direct Link: Click here
5- Archive BitTorrent
- File origin: metadata
- File Format: Archive BitTorrent
- File Size: 0.00 Mbs
- File Name: NASA_NTRS_Archive_20180000636_archive.torrent
- Direct Link: Click here
Search for “NASA Technical Reports Server (NTRS) 20180000636: Using Neural Networks To Improve The Performance Of Radiative Transfer Modeling Used For Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations” downloads:
Visit our Downloads Search page to see if downloads are available.
Find “NASA Technical Reports Server (NTRS) 20180000636: Using Neural Networks To Improve The Performance Of Radiative Transfer Modeling Used For Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations” in Libraries Near You:
Read or borrow “NASA Technical Reports Server (NTRS) 20180000636: Using Neural Networks To Improve The Performance Of Radiative Transfer Modeling Used For Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations” from your local library.
Buy “NASA Technical Reports Server (NTRS) 20180000636: Using Neural Networks To Improve The Performance Of Radiative Transfer Modeling Used For Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations” online:
Shop for “NASA Technical Reports Server (NTRS) 20180000636: Using Neural Networks To Improve The Performance Of Radiative Transfer Modeling Used For Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations” on popular online marketplaces.
- Ebay: New and used books.