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 is the surface-guided Lamb wave ( ≲ 0.01 Hz), which we observed propagating for four (+three antipodal) passages around the Earth over six days. Based on 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 (0.01–20 Hz), long-range (~10,000 km) 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. We highlight exceptional observations of the atmospheric waves.

Abstract. 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.

[1] Monitoring of atmospheric methane (CH 4 ) concentrations from space-based instruments such as the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and the Greenhouse Gases Observing Satellite (GOSAT) relies on observations of sunlight backscattered to space by the Earth's surface and atmosphere. Retrieval biases occur due to unaccounted scattering effects by aerosols and thin cirrus that modify the lightpath. Here, we evaluate the accuracy of two retrieval methods that aim at minimizing such scattering induced errors. The lightpath "proxy" method, applicable to SCIAMACHY and GOSAT, retrieves CH 4 and carbon dioxide (CO 2 ) simultaneously and uses CO 2 as a proxy for lightpath modification. The "physics-based" method, which we propose for GOSAT, aims at simultaneously retrieving CH 4 concentrations and scattering properties of the atmosphere. We evaluate performance of the methods against a trial ensemble of simulated aerosol and cirrus loaded scenes. More than 80% of the trials yield residual scattering induced CH 4 errors below 0.6% and 0.8% for the proxy and the physics-based approach, respectively. Very few cases result in errors greater than 2% for both methods. Advantages of the proxy approach are efficient and robust performance yielding more useful retrievals than the physics-based method which reveals some nonconvergent cases. The major disadvantage of the proxy method is the uncertainty of the proxy CO 2 concentration contributing to the overall error budget. Residual errors generally correlate with particle and surface properties and thus might impact inverse modeling of CH 4 sources and sinks.Citation: Butz, A., O. P. Hasekamp, C. Frankenberg, J. Vidot, and I. Aben (2010), CH 4 retrievals from space-based solar backscatter measurements: Performance evaluation against simulated aerosol and cirrus loaded scenes,

Abstract. 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 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.

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.

No abstract

[1] 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 mm), 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 mm. 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 mm (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.

[1] The fine particle abundance, i.e., particle matter (PM) concentration, is one of the indicators of air quality and is therefore subject to ground-based measurements. Complementary satellite aerosol remote sensing techniques provide one with maps of the aerosol optical thickness (AOT), which is sensitive to particle abundance. This paper investigates the problem of retrieving the PM concentration from the AOT, both on daily average values, on the basis of a large data set where data from the air quality networks are combined with ground-based measurements of the AOTs. It is found that a linear model fails at explaining the data well but that the performance may be significantly improved when such a linear relationship is conditioned on auxiliary parameters, mainly meteorological variables. The proposed model is expressed as an additive varying coefficient model (AVCM), which is defined as a linear model where the coefficients are additive functions of the auxiliary parameters. The model is represented using penalized smoothing splines, allowing for a proper control of the overall number of degrees of freedom via multiple smoothness parameters selection. The methodology is applied to data collected around Lille (France). The PM 10 concentrations are retrieved with an average uncertainty of less than 20%, leading to a correlation coefficient of 0.87 between fitted and expected PM 10 .Citation: Pelletier, B., R. Santer, and J. Vidot (2007), Retrieving of particulate matter from optical measurements: A semiparametric approach,