Academic Journal
VIIRS Radiance Cluster Analysis in CrIS Observations for Enhanced Data Assimilation in NWP Models.
| Τίτλος: | VIIRS Radiance Cluster Analysis in CrIS Observations for Enhanced Data Assimilation in NWP Models. |
|---|---|
| Συγγραφείς: | Wang, Likun, Zhou, Lihang, Sun, Haibin, Burrow, Chris, Yan, Banghua, Kalluri, Satya |
| Πηγή: | Earth & Space Science; Oct2025, Vol. 12 Issue 10, p1-14, 14p |
| Θεματικοί όροι: | Data assimilation, Numerical weather forecasting, Infrared radiometry, Meteorological research, Radiance, K-means clustering |
| Περίληψη: | The Cross‐track Infrared Sounder (CrIS) radiance data plays a crucial role in numerical weather prediction (NWP) models by providing essential atmospheric sounding information through data assimilation. However, challenges arise in handling subpixel cloud contamination within CrIS fields of view (FOVs), which can impact the accuracy of radiance simulations. To address this, the Visible Infrared Imaging Radiometer Suite (VIIRS) Radiances Cluster analysis within the CrIS FOVs is developed to characterize subpixel scene homogeneity. This paper describes the algorithms and data processing procedures for this cluster analysis. A fast and accurate collocation method was developed to directly align VIIRS measurements within CrIS FOVs using line‐of‐sight (LOS) pointing vectors. This method supports both terrain‐corrected and non‐terrain‐corrected VIIRS geolocation data sets as inputs. The K‐means clustering method is used to group collocated VIIRS radiance within CrIS FOVs into seven (7) clusters based on their radiance values. The mean, standard deviation, and coverage of each cluster are output for each CrIS FOV. Comparisons with the Infrared Atmospheric Sounding Interferometer cluster analysis demonstrate similar performance, confirming the validity of the CrIS‐VIIRS approach. Data assimilation experiments at the European Centre for Medium‐Range Weather Forecasts indicate that the VIIRS radiance cluster data can be effectively integrated into NWP models, aiding in cloud detection and improving data quality. These findings highlight the potential of CrIS‐VIIRS clustering for enhancing data thinning, quality control, and assimilation of cloudy radiance observations in operational NWP systems. Plain Language Summary: To support data assimilations of numerical weather predictions models, the Visible Infrared Imaging Radiometer Suite Radiances Cluster analysis within the Cross‐track Infrared Sounder Fields of Views is developed to characterize subpixel scene homogeneity. This paper describes the underlying algorithms, data processing methods, and potential applications. Key Points: Cross‐track Infrared Sounder (CrIS) radiance data support weather models by providing key atmospheric profiles through data assimilationVisible Infrared Imaging Radiometer Suite radiances cluster analysis within the CrIS is developed to characterize subpixel scene homogeneityThis paper describes the underlying algorithms, data processing methods, and potential applications [ABSTRACT FROM AUTHOR] |
| Copyright of Earth & Space Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
καταχωρήστε σχόλιο πρώτοι!