4th workshop on assimilating satellite cloud and precipitation observations for NWP

The Joint Center for Satellite Data Assimilation (JCSDA) Community Radiative Transfer Model (CRTM) is a fast, 1-D 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, in that it provides a highly accurate representation of satellite...

Nowadays, satellite microwave (MW) observations are gaining weight in weather and climate applications. The upcoming Ice Cloud Imager (ICI) mission covering frequencies between 183 and 670 GHz aims at improving the representation of cloud ice in models. Ultimately, ICI will extend the scope of MW assimilation. In stand-alone retrievals and data assimilation, several simplifications are still...

Cloudy infrared observations are not currently fully exploited in operational NWP models. There are many reasons for this and the one we are interested in is the need to have an accurate and fast radiative transfer model to simulate cloud scattering in infrared. In most NWP centers, cloudy infrared observations are assimilated using the grey-cloud approximation. In this approximation clouds...

Visible satellite images provide high-resolution information on
clouds. However, so far they have not been assimilated directly for
operational purposes, as multiple scattering dominates in the visible
spectral range and makes radiative transfer computations with standard
methods complex and slow. Only recently, sufficiently fast and
accurate forward operators have become available. Here...

The assimilation of all-sky radiance data requires a fast and accurate radiative transfer model capable of simulating the scattering and absorption effects of hydrometeors. In the past decade, complex Discrete Dipole Approximation (DDA) scattering calculations have demonstrated that commonly used simplifications in frozen particle shape (i.e., sphere and spheroids) are insufficient for...