This paper gives an update of the RTTOV (Radiative Transfer for TOVS) fast radiative transfer model, which is widely used in the satellite retrieval and data assimilation communities. RTTOV is a fast radiative transfer model for simulating top-of-atmosphere radiances from passive visible, infrared and microwave downward-viewing satellite radiometers. In addition to the forward model, it also optionally computes the tangent linear, adjoint and Jacobian matrix providing changes in radiances for profile variable perturbations assuming a linear relationship about a given atmospheric state. This makes it a useful tool for developing physical retrievals from satellite radiances, for direct radiance assimilation in NWP models, for simulating future instruments, and for training or teaching with a graphical user interface. An overview of the RTTOV model is given, highlighting the updates and increased capability of the latest versions, and it gives some examples of its current performance when compared with more accurate line-by-line radiative transfer models and a few selected observations. The improvement over the original version of the model released in 1999 is demonstrated.

The 15 January 2022 climactic eruption of Hunga volcano, Tonga, produced an explosion in the atmosphere of a size that has not been documented in the modern geophysical record. The event generated a broad range of atmospheric waves observed globally by various ground-based and spaceborne instrumentation networks. Most prominent was the surface-guided Lamb wave (≲0.01 hertz), which we observed propagating for four (plus three antipodal) passages around Earth over 6 days. As measured by the Lamb wave amplitudes, the climactic Hunga explosion was comparable in size to that of the 1883 Krakatau eruption. The Hunga eruption produced remarkable globally detected infrasound (0.01 to 20 hertz), long-range (~10,000 kilometers) audible sound, and ionospheric perturbations. Seismometers worldwide recorded pure seismic and air-to-ground coupled waves. Air-to-sea coupling likely contributed to fast-arriving tsunamis. Here, we highlight exceptional observations of the atmospheric waves.

Airborne measurements in an Arctic mixed‐phase nimbostratus cloud were conducted in Spitsbergen on 21 May 2004 during the international Arctic Study of Tropospheric Aerosol, Clouds and Radiation (ASTAR) campaign. The in situ instrument suite aboard the Alfred Wegener Institute Polar 2 aircraft included a polar nephelometer (PN), a cloud particle imager (CPI), a Nevzorov probe, and a standard PMS 2DC probe to measure the cloud particle single‐scattering properties (at a wavelength of 0.8 μm), and the particle morphology and size, as well as the in‐cloud partitioning of ice/water content. The main objective of this work is to present a technique based on principal component analysis and light‐scattering modeling to link the microphysical properties of cloud particles to their optical characteristics. The technique is applied to the data collected during the 21 May case study where a wide variety of ice crystal shapes and liquid water fractions were observed at temperatures ranging from −1°C to −12°C. CPI measurements highlight the presence of large supercooled water droplets with diameters close to 500 μm. Although the majority of ice particles were found to have irregular shapes, columns and needles were the prevailing regular habits between −3°C and −6°C while stellars and plates were observed at temperatures below −8°C. The implementation of the principal component analysis of the PN scattering phase function measurements revealed representative optical patterns that were consistent with the particle habit classification derived from the CPI. This indicates that the synergy between the CPI and the PN can be exploited to link the microphysical and shape properties of cloud particles to their single‐scattering characteristics. Using light‐scattering modeling, we have established equivalent microphysical models based on a limited set of free parameters (roughness, mixture of idealized particle habits, and aspect ratio of ice crystals) that reproduce the main optical features assessed for cloud regions with different particle geometries and liquid water fractions. However, the retrieved bulk microphysical parameters can substantially differ from the measurements (by several times for the effective size and up to 3 orders of magnitude for the number concentration). Several possible explanations for these discrepancies are discussed. The retrievals show that the optical contribution of small particles with sizes lower than 50 μm (droplets and ice crystals) is significant, always exceeding 50% of the total scattering signal, and thus needs to be more accurately quantified. The shattering of large ice crystals on the shrouded inlet of the PN could also strongly affect the retrieved microphysical parameters.

A new ice cloud optical property database in the thermal infrared has been parameterized for the RTTOV radiative transfer model. The Self‐Consistent Scattering Model (SCSM) database is based on an ensemble model of ice crystals and a parameterization of the particle size distribution. This convolution can predict the radiative properties of cirrus without the need of a priori information on the ice particle shape and an estimate of the ice crystal effective dimension. The ice cloud optical properties are estimated through linear parameterizations of ambient temperature and ice water content. We evaluate the new parameterization against existing parameterizations used in RTTOV. We compare infrared observations from Imaging Infrared Radiometer, on board CALIPSO, against RTTOV simulations of the observations. The simulations are performed using two different products of ice cloud profiles, retrieved from the synergy between space‐based radar and lidar observations. These are the 2C‐ICE and DARDAR products. We optimized the parameterization by testing different SCSM databases, derived from different shapes of the particle size distribution, and weighting the volume extinction coefficient of the ensemble model. By selecting a large global data set of ice cloud profiles of visible optical depths between 0.03 and 4, we found that the simulations, based on the optimized SCSM database parameterization, reproduces the observations with a mean bias of only 0.43 K and a standard deviation of 6.85 K. The optimized SCSM database parameterization can also be applied to any other radiative transfer model.