(Banner image credit: NOAA)
Introduction Successful weather forecasts start from accurate estimates of the current state of the Earth system. Such estimates are obtained by combining model information with observations via data assimilation. Cloud‐affected satellite radiance observations have been at the forefront of recent advances in data assimilation (DA) at many operational centres. Prior efforts in cloud-affected (or all-sky) DA have focused primarily on microwave satellite observations, which provide information on the total distribution of rain and liquid cloud at a resolution of tens of kilometers. With the increase in computational capabilities, the ability to incorporate additional shorter wavelengths in DA systems, such as infrared (IR) and visible satellite radiances, has also improved.
Cloudy radiances in the visible and near-IR contain a wealth of information on clouds at much higher spatial and temporal resolution than microwave observations, but they have never been assimilated in global operational numerical weather prediction (NWP) models. Assimilating radiances in the visible part of the spectrum continues to pose many challenges due to the sensitivity to solar radiation and the details of the optical properties of atmospheric constituents. The high computational cost of the radiative transfer models used to convert model profiles into radiances further complicates this process.
Approach Existing methods for satellite data assimilation of all-weather visible and near-IR radiances face several unique challenges. The two primary categories requiring attention are the forward operators for visible radiances and their use within a data assimilation framework. The requirement of a fast and accurate operator is common to all centers, and largely independent of the type of assimilation method. However, there are specific considerations for variational systems, which also require inverse operators. Observation operators for visible radiance applications have already been developed.
Visible Frequency Forward Operator Assessment and Improvement The RTTOV forward model offers both the accurate but computationally expensive DISORT method and the fast MFASIS model. The Community Radiative Transfer Model (CRTM) simulates visible/near-IR scattering with an advanced adding-doubling method and cloud optical properties from various LUTs, making it highly efficient for operational applications. This effort will incorporate expertise from team members to ensure the latest knowledge of visible radiance simulation is available.
Data Assimilation Requirements NWP models have now reached a high level of maturity in describing cloud fields accurately. Along with fast forward operators for visible radiances, this is a prerequisite for successful assimilation. Centers like ECMWF and DWD have made significant progress in this area, using visible data from instruments such as OLCI and SEVIRI to evaluate and improve model performance.
Outcome The primary goal is to create a common set of standards/requirements for DA frameworks, focused on global NWP capabilities. Expected key outcomes include:
- Overview of the state-of-the-art in visible RT and DA methods for assimilating visible/near-IR radiances.
- Key recommendations to operational NWP centers, space agencies, and other stakeholders for further development of RT capabilities.
- Scientific support to groups already performing research in this area.
Conclusion Developing this framework is urgently needed to advance the use of Earth Observations from space in weather forecasting and NWP. This effort will support current and future operational meteorological missions and reanalysis and climate applications, leveraging the high spatial-temporal resolution of visible sensors.
Previous Meetings:
References
- Geer, A.J., et al., 2017. The growing impact of satellite observations sensitive to humidity, cloud, and precipitation, QJ Roy. Meteor. Soc., 143, 3189–3206.
- Saha, S., et al., 2010. The NCEP climate forecast system reanalysis. Bulletin of the American Meteorological Society, 91(8), pp.1015-1058.
- Saunders, R., et al., 2020. RTTOV-13 Science and Validation Report.