Besides their strong contribution to weather forecast improvement through data assimilation, thermal infrared sounders onboard polar-orbiting platforms are now playing a key role for monitoring atmospheric composition changes. The Infrared Atmospheric Sounding Interferometer (IASI) instrument developed by the French space agency (CNES) and launched by EUMETSAT onboard the Metop satellite series is providing essential inputs for weather forecasting and pollution/climate monitoring owing to its smart combination of large horizontal swath, good spectral resolution and high radiometric performance. EUMETSAT is currently preparing the next polar-orbiting program (EPS-SG) with the Metop-SG satellite series that should be launched around 2020. In this framework, CNES is studying the concept of a new instrument, the IASI-New Generation (IASI-NG), characterized by an improvement of both spectral and radiometric characteristics as compared to IASI, with three objectives: (i) continuity of the IASI/Metop series; (ii) improvement of vertical resolution; and (iii) improvement of the accuracy and detection threshold for atmospheric and surface components. In this paper, we show that an improvement of spectral resolution and radiometric noise fulfil these objectives by leading to (i) a better vertical coverage in the lower part of the troposphere, thanks to the increase in spectral resolution; and (ii) an increase in the accuracy of the retrieval of several thermodynamic, climate and chemistry variables, thanks to the improved signal-to-noise ratio as well as less interference between the signatures of the absorbing species in the measured radiances. The detection limit of several atmospheric species is also improved. We conclude that IASI-NG has the potential to strongly benefit the numerical weather prediction, chemistry and climate communities now connected through the European GMES/Copernicus initiative.

An approach to make use of Atmospheric Infrared Sounder (AIRS) cloud-affected infrared radiances has been developed at Météo-France in the context of the global numerical weather prediction model. The method is based on (i) the detection and the characterization of clouds by the CO2-slicing algorithm and (ii) the identification of clear–cloudy channels using the ECMWF cloud-detection scheme. Once a hypothetical cloud-affected pixel is detected by the CO2-slicing scheme, the cloud-top pressure and the effective cloud fraction are provided to the radiative transfer model simultaneously with other atmospheric variables to simulate cloud-affected radiances. Furthermore, the ECMWF scheme flags each channel of the pixel as clear or cloudy. In the current configuration of the assimilation scheme, channels affected by clouds whose cloud-top pressure ranges between 600 and 950 hPa are assimilated over sea in addition to clear channels. Results of assimilation experiments are presented. On average, 3.5% of additional pixels are assimilated over the globe but additional assimilated channels are much more numerous for mid- to high latitudes (10% of additional assimilated channels on average). Encouraging results are found in the quality of the analyses: background departures of AIRS observations are reduced, especially for surface channels, which are globally 4 times smaller, and the analysis better fits some conventional and satellite data. Global forecasts are slightly improved for the geopotential field. These improvements are significant up to the 72-h forecast range. Predictability improvements have been obtained for a case study: a low pressure system that affected the southeastern part of Italy in September 2006. The trajectory, intensity, and the whole development of the cyclogenesis are better predicted, whatever the forecast range, for this case study.

We address the problem of how to take advantage, in a limited‐area variational assimilation, of the previously‐performed global data analysis of the coupling system. We expect this information to add value to the limited‐area analysis, since, for example, the global analysis can deal with observations close to but outside the domain of interest. The global analysis is treated as an extra source of information, and we derive the full least‐square error problem for the augmented information vector. We are left with the specifications of a global analysis‐error covariance matrix and extra cross‐covariance terms, expressed in a lower‐resolution grid covering all of the coupled domain. Simplifications are proposed to make the problem algorithmically more tractable, especially in order to benefit from the advances in the ‘modelization’ of the background‐error covariance matrix. Further, we obtain a more classical minimization problem for the sum of three, instead of two, additive cost functions.We have tested the formulation in the framework of the coupled ARPÈGE 4D‐Var and ALADIN‐France 3D‐Var systems. Objective scores with respect to radiosonde and aircraft observations give neutral or slightly positive results for the proposed method. The information‐augmented assimilation cycle produces background 6 h forecasts that are slightly closer to the verifying observations. Subjective case‐by‐case verification does not reveal a visible systematic beneficial impact on the quality of the forecasts, especially for small‐scale parameters such as precipitation. We nevertheless propose to apply the method in future to very high‐resolution assimilation systems, which are likely to be operated on fairly small domains. Copyright © 2008 Royal Meteorological Society

A comprehensive analysis of the water budget over the Dome C (Concordia, Antarctica) station has been performed during the austral summer 2018–2019 as part of the Year of Polar Prediction (YOPP) international campaign. Thin (∼100 m deep) supercooled liquid water (SLW) clouds have been detected and analysed using remotely sensed observations at the station (tropospheric depolarization lidar, the H₂O Antarctica Microwave Stratospheric and Tropospheric Radiometer (HAMSTRAD), net surface radiation from the Baseline Surface Radiation Network (BSRN)), radiosondes, and satellite observations (CALIOP, Cloud-Aerosol LIdar with Orthogonal Polarization/CALIPSO, Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations) combined with a specific configuration of the numerical weather prediction model: ARPEGE-SH (Action de Recherche Petite Echelle Grande Echelle – Southern Hemisphere). The analysis shows that SLW clouds were present from November to March, with the greatest frequency occurring in December and January when ∼50 % of the days in summer time exhibited SLW clouds for at least 1 h. Two case studies are used to illustrate this phenomenon. On 24 December 2018, the atmospheric planetary boundary layer (PBL) evolved following a typical diurnal variation, which is to say with a warm and dry mixing layer at local noon thicker than the cold and dry stable layer at local midnight. Our study showed that the SLW clouds were observed at Dome C within the entrainment and the capping inversion zones at the top of the PBL. ARPEGE-SH was not able to correctly estimate the ratio between liquid and solid water inside the clouds with the liquid water path (LWP) strongly underestimated by a factor of 1000 compared to observations. The lack of simulated SLW in the model impacted the net surface radiation that was 20–30 W m⁻² higher in the BSRN observations than in the ARPEGE-SH calculations, mainly attributable to the BSRN longwave downward surface radiation being 50 W m⁻² greater than that of ARPEGE-SH. The second case study took place on 20 December 2018, when a warm and wet episode impacted the PBL with no clear diurnal cycle of the PBL top. SLW cloud appearance within the entrainment and capping inversion zones coincided with the warm and wet event. The amount of liquid water measured by HAMSTRAD was ∼20 times greater in this perturbed PBL than in the typical PBL. Since ARPEGE-SH was not able to accurately reproduce these SLW clouds, the discrepancy between the observed and calculated net surface radiation was even greater than in the typical PBL case, reaching +50 W m⁻², mainly attributable to the downwelling longwave surface radiation from BSRN being 100 W m⁻² greater than that of ARPEGE-SH. The model was then run with a new partition function favouring liquid water for temperatures below −20 down to −40 ^∘C. In this test mode, ARPEGE-SH has been able to generate SLW clouds with modelled LWP and net surface radiation consistent with observations during the typical case, whereas, during the perturbed case, the modelled LWP was 10 times less than the observations and the modelled net surface radiation remained lower than the observations by ∼50 W m⁻². Accurately modelling the presence of SLW clouds appears crucial to correctly simulate the surface energy budget over the Antarctic Plateau.

Validation of ozone profiles measured from a nadir looking satellite instrument over Antarctica is a challenging task due to differences in their vertical sensitivity with ozonesonde measurements. In this paper, ozone observations provided by the Infrared Atmospheric Sounding Interferometer (IASI) instrument onboard the polar-orbiting satellite MetOp are compared with ozone profiles collected between August and October 2010 at McMurdo Station, Antarctica, during the Concordiasi measurement campaign. The main objective of the campaign was the satellite data validation. With this aim 20 zero-pressure sounding balloons carrying ozonesondes were launched during this period when the MetOp satellite was passing above McMurdo. This makes the dataset relevant for comparison, especially because the balloons covered the entire altitude range of IASI profiles. The validation methodology and the collocation criteria vary according to the availability of global positioning system auxiliary data with each electro-chemical cell ozonesonde observation. The relative mean difference is shown to depend on the vertical range investigated. The analysis shows a good agreement in the troposphere (below 10 km) and middle stratosphere (25–40 km), where the differences are lower than 10%. However a significant positive bias of about 10–26% is estimated in the lower stratosphere at 10–25 km, depending on altitude. The positive bias in the 10–25 km range is consistent with previously reported studies comparing in situ data with thermal infrared satellite measurements. This study allows for a better characterization of IASI-retrieved ozone over the polar region during ozone depletion/recovery processes.