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In the past two decades, satellite measurements have been widely utilized for understanding and predicting weather and climate, and are now an essential component in global observing systems. Atmospheric and surface data observed from satellite, in-situ, and ground-based instruments are assimilated to produce a best representation of the atmospheric state on model grids. The gridded analysis data are derived from the scattered observations and a priori knowledge from the model forecasts. Through the assimilation of satellite data, the global medium-range (7−10 days) forecast score has been significantly improved, especially for the data-void Southern Hemisphere (Le Marshall et al., 2007). Also, the assimilation of satellite data in the Hurricane Weather and Forecast Model improves the prediction of hurricane tracks and intensity, as well as coastal precipitation (Zou et al., 2013). The data assimilation systems developed at major numerical weather prediction (NWP) centers have undergone more than 50 years of research. Optimal interpolation was initially utilized by NWP centers (until the early 1990s) to assimilate all the observations. Most of the ingested data (> 90%) into numerical models come from satellites, and the highest impacts of satellite data on global medium-range forecasts result from direct assimilation of satellite microwave and infrared sounding radiance data. Since the radiances are not atmospheric-state variables produced by forecast models, a relationship between the state variable and the satellite radiances is required and this relationship is based on the radiative transfer model or observation operator. In addition, the derivative of radiance relative to the state variable or radiance Jacobian is required in satellite data assimilation. Thus, it is important that the observation operator be executed fast and accurately in NWP models in order for a sheer volume of satellite data to be ingested within a limited assimilation time window. A fast radiative transfer model is effective for assimilation of satellite radiance data into NWP models. The United States and Europe have developed their respective fast models through satellite missions and NWP programs. The Community Radiative Transfer Model (CRTM) was proposed at the US Joint Center for Satellite Data Assimilation in the early 2000s (Weng et al., 2003; Weng, 2007) and has undergone nearly two decades of scientific and software-engineering improvements. The Radiative Transfer for TOVS (RTTOV) model was also developed for simulating satellite radiances at variable wavelengths (Saunders et al., 2018) and is now widely used in the NWP community. In the past, Chinese NWP development has benefited from using RTTOV in its data assimilation systems (Yu et al., 2019). Indeed, both CRTM and RTTOV are performing quite well for NOAA and for European satellite instruments. However, the scientific priority in upgrading CRTM and RTTOV is driven by the United States and European satellite missions. Thus, for the plethora of observation

advances in atmospheric sciences

Advanced Radiative Transfer Modeling System (ARMS): A New-Generation Satellite Observation Operator Developed for Numerical Weather Prediction and Remote Sensing Applications