The Community Radiative Transfer Model (CRTM) Community-Focused Collaborative Model Development Accelerating Research to Operations

Benjamin T. Johnson, Cheng Dang, Patrick Stegmann, Quanhua Liu, Isaac Moradi, Thomas Auligne

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

The Joint Center for Satellite Data Assimilation (JCSDA) Community Radiative Transfer Model (CRTM) is a fast, 1D radiative transfer model used in numerical weather prediction, calibration/validation, etc., across multiple federal agencies and universities. The key benefit of the CRTM is that it is a satellite simulator. It provides a highly accurate representation of satellite radiances by using the specific sensor response functions convolved with a Line-by-Line Radiative Transfer Model (LBLRTM). CRTM covers the spectral ranges consistent with all present operational and most research satellites, from visible to microwave. The capability to simulate ultraviolet radiances and support space-based radar sensors is being added over the next 2 years in CRTM version 3.0. In addition to simulated radiances, the CRTM also provides Jacobian outputs needed to interpret satellite observations for numerical weather prediction. The Jacobian estimates how changes in geophysical parameters affect simulated measurements from satellite sensors. Using the Jacobian in modeling and weather prediction improves the accuracy and efficiency of data analysis, leading to better weather predictions. The CRTM model’s success and growth depend on community contributions and evaluation. To facilitate this, we have made the CRTM highly accessible through modular programming, clear documentation and tutorials, public domain licensing, unfettered public access via GitHub, and a clear path to operational implementation for innovative research. We encourage and welcome contributions from the community to help us continue to improve the CRTM.

Original languageEnglish
Pages (from-to)E1817-E1830
JournalBulletin of the American Meteorological Society
Volume104
Issue number10
DOIs
StatePublished - Oct 2023
Externally publishedYes

Keywords

  • Aerolsols/ particulates
  • Atmospheric chemistry
  • Data assimilation
  • Numerical weather prediction/ forecasting
  • Radiative transfer
  • Satellite observations

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