Les instruments de sondage des satellites météorologiques permettent la restitution opérationnelle de la température et l’humidité de l’atmosphère, variables essentielles pour la prévision météorologique et le suivi du climat. La combinaison de mesures infra-rouge et micro-onde permet cette restitution même en présence de nuages. Les nouveaux sondeurs infrarouge hyperspectraux donne accès à une information verticale beaucoup plus fine, élément clé notamment pour la prévision de la convection atmosphérique.

The Earth Energy Imbalance (EEI) is defined as the small difference between the incoming energy the Earth receives from the Sun and the outgoing energy lost by Earth to space. The EEI is accumulated in the Earth climate system and results in global temperature rise. Monitoring the EEI is of prime importance for a predictive understanding of climate change, and for estimating how well humankind is doing in implementing the Paris Climate Agreement.The current best estimates of the absolute value of the EEI, and of its long term variation are obtained from in situ observations. These observations can only be made over long time periods, typically a decade or longer. In contrast, with direct observations from space, the EEI can in principle be measured at the annual mean time scale. However, this strategy currently faces two fundamental challenges.The first challenge is that the EEI is the difference between two opposing terms of nearly equal amplitude. Currently, the Incoming Solar Radiation (ISR) and the Total Outgoing Radiation (TOR) are measured with separate instruments, which means that their calibration errors are added and overwhelm the signal to be measured. To make significant progress in this challenge, a differential measurement using identical intercalibrated radiometers to measure both the ISR and the TOR is needed.The second challenge is that the TOR has a systematic diurnal cycle. Currently, the TOR is sampled from the “morning” and “afternoon” Sun-synchronous orbits, complemented by narrowband geostationary imagers. Recently, the sampling from the morning orbit was abandoned. The sampling of the diurnal cycle can be improved, for example, by using two orthogonal 90° inclined orbits which give both global coverage, and a statistical sampling of the full diurnal cycle at seasonal time scale.For understanding the radiative forcing and climate feedback, mechanisms underlying changes in the EEI, and for climate model validation, it is necessary to separate the TOR spectrally into the Reflected Solar radiation (RSR) and Outgoing Longwave Radiation (OLR) and to map them at relatively high spatial resolution.The state-of-the-art observation of the OLR is provided by the CERES scanning 3-channel broadband radiometer aboard the Aqua, Suomi NPP and NOAA 20 satellites. We propose an innovative continuity of those measurements by replacing the radiometer by multispectral wide field of view (FOV) cameras. The wide FOV allows a full angular coverage, providing the potential for a significant reduction of the dominant angular conversion error. To realise this potential we propose to develop an innovative Deep Learning based angular conversion method. The multispectral bands of the camera should allow reconstructing the broadband OLR within the state of the art accuracy. The spatial resolution of the cameras should be sufficient to discriminate cloudy from clear-sky scenes.

Monitoring the Earth Energy Imbalance (EEI) is of prime importance for a predictive understanding of climate change. Furthermore, monitoring of the EEI gives an early indication on how well mankind is doing in implementing the Paris Climate Agreement. EEI is defined as the small difference between the incoming energy the Earth receives from the Sun and the outgoing energy lost by Earth to space. The EEI is cumulated in the Earth climate system, particularly in the oceans, due to their substantial heat capacity, and results in global temperature rise. Currently the best estimates of the absolute value of the EEI, and of its long term variation are obtained from in situ observations, with a dominant contribution of the time derivative of the Ocean Heat Content (OHC). These in situ EEI observations can only be made over long time periods, typically a decade or longer. In contrast, with direct observations of the EEI from space, the EEI can be measured at the annual mean time scale. However, the EEI is currently poorly measured from space, due to two fundamental challenges. The first fundamental challenge is that the EEI is the difference between two opposing terms of nearly equal amplitude. Currently, the incoming solar radiation and outgoing terrestrial radiation are measured with separate instruments, which means that their calibration errors are added and overwhelm the signal to be measured. To make significant progress in this challenge, a differential measurement using identical intercalibrated instruments to measure both the incoming solar radiation and the outgoing terrestrial radiation is needed. The second fundamental challenge is that the outgoing terrestrial radiation has a systematic diurnal cycle. Currently, the outgoing terrestrial radiation is sampled from the so-called morning and afternoon Sun-synchronous orbits, complemented by narrow band geostationary imagers. Recently the sampling from the morning orbit was abandoned. The sampling of the diurnal cycle can be improved, for example, by using two orthogonal 90° inclined orbits which give both global coverage, and a statistical sampling of the full diurnal cycle at seasonal time scale. For understanding the radiative forcing – e.g. aerosol radiative forcing - and climate feedback – e.g. ice albedo feedback - mechanisms underlying changes in the EEI, and for climate model validation, it is necessary to separate the Total Outgoing Radiation (TOR) spectrally into the two components of the Earth Radiation Budget (ERB), namely the Reflected Solar radiation (RSR) and Outgoing Longwave Radiation (OLR) and to map them at relatively high spatial resolution. The Earth Climate Observatory (ECO) mission concept was recently selected by the European Space Agency as one of the 4 candidate Earth Explorer 12 missions, that will be further studied in Phase 0 until mid 2026. The current paper provides a broad overview of the ECO mission objectives, the mission requirements, and the key elements of a baseline mission concept. During Phase 0, the ECO mission concept will be further elaborated in two parallel industrial studies, which may or may not adopt or refine the elements of the baseline concept.

TRUTHS (Traceable Radiometry Underpinning Terrestrial- and Helio-Studies) is an operational climate mission, aiming to enhance, up to an order-of-magnitude, our ability to estimate the Earth radiation budget, spectrally resolved to support attribution. Through direct measurements of incoming total and spectrally resolved solar irradiances and Earth reflected radiances, spatially resolved, it establishes ‘benchmarks’ against which change/trends can be detected in as short a time as possible. These fiducial reference data sets can be combined with data from other sensors and also serve as ‘gold standard’ references to anchor and upgrade the performance of other space sensors through in-orbit calibration. TRUTHS will become a founding member of a new class of satellites called SITSats, SI-Traceable Satellites, with payloads explicitly designed to achieve and evidence an uncertainty, in-orbit, at a level commensurate with the exacting goals of long-time-base climate studies. SITSats also facilitate interoperability and enhanced trust in the data from the Earth observation system as a whole, helping to provide observational evidence-based confidence in actions addressing the climate emergency.  The unprecedented uncertainty of TRUTHS’ globally sampled hyperspectral data underpins many additional applications: Establishing an interoperable, harmonised Earth Observing system incorporating agency and commercial satellites: large and small Top and Bottom of atmosphere reflectances impacting carbon cycle (e.g. land cover, ocean colour, vegetation, methane etc together with similar applications of other hyper/multi-spectral missions). Low uncertainty also facilitates improvements in retrieval algorithms. Transferring radiometric reference values to existing Cal/Val infrastructure (e.g. RadCalNet, Pseudo-Invariant Calibration sites, In-situ ocean colour reference observations; selected surface reflectance test-sites (fluxnet, …), both nadir and multi-angular) and Moon observations. The mission comprises an “agile” satellite capable to point and image the Earth, Moon and Sun from a 90°polar orbit by the Hyperspectral Imaging Spectrometer (HIS). The HIS provides spectrally continuous observations from 320 to 2400 nm, with a spectral sampling between 2 and 6 nm and a spatial sampling of 50 m. The payload utilises a novel SI-traceable on-board calibration system (OBCS), comprising of the Cryogenic Solar Absolute Radiometer (CSAR), able to realise SI-traceability in space and also measure incoming solar radiation. Together with other optical elements the OBCS links the HIS observations to the CSAR with a target expanded uncertainty 0.3% (k=2). TRUTHS is implemented by the European Space Agency (ESA) as a UK-led Earth Watch mission in collaboration with Switzerland, Czech Republic, Greece, Romania and Spain. The mission was conceived by the UK national metrology institute, NPL, in response to challenges highlighted by the worlds space agencies, through bodies such as CEOS addressing observational needs of GCOS. The mission is under development by an industrial consortium led by Airbus Defence and Space UK, with a target launch date of 2030 and minimal operations life-time of 5 years with a goal of 8 yrs. Together with FORUM (ESA) and IASI-NG (CNES/EUMETSAT) it will provide spectrally resolved Earth radiance information from the UV to the Far-Infrared in the coming decade, and in partnership with CLARREO-Pathfinder (NASA) and CSRB (CMA) inaugurate a future constellation of SITSats.

Satellite-based observations require independent sources of data to monitor and evaluate their precision and accuracy. For the temperature and water vapor profiles produced by satellite-based sounders, this typically results in comparisons to operational radiosonde observations. However, polar-orbiting satellite overpasses are frequently misaligned with the global synoptic launch times. The routine airborne in situ observations of temperature and water vapor from the Airborne Meteorological Data Relay (AMDAR) program and the Water Vapor Sensing System-II (WVSS-II) instrument greatly enhance opportunities to make precise matchups due to the far greater temporal frequency and spatial density of aircraft flights. The potential for the use of aircraft-based observations as a source of evaluation of tropospheric satellite sounder profiles is explored through a year-long intercomparison with the Infrared Atmospheric Sounding Interferometer (IASI) level-2 profiles produced from both the Metop-A and Metop-B satellites. Results using 1 h and 50 km match criteria indicating good agreement between the satellites and the aircraft-based observations with temperature, specific humidity, and relative humidity biases generally less than 0.5 K, 0.8 g kg−1, and 5 %, respectively; both IASI instruments perform nearly identically. While the intercomparisons are generally limited to the troposphere as aircraft typically reach their maximum height at the tropopause, the substantially larger number of intercomparison points enable characterization as a function of season, scan angle, and other characteristics heretofore unexplored due to a lack of sufficient validation data.

Upcoming SI-traceable satellite (SITSat) missions such as traceable radiometry underpinning terrestrial and helio studies (TRUTHS) aim to achieve unprecedented accuracy for SI-traceable measurements of the Earth-reflected radiation. These measurements will support the generation of low-uncertainty climate records and significantly improve the calibration of other sensors. In such a context, the calibration transfer rather than the reference sensor dominates the uncertainty budget. This study presents an end-to-end global intercalibration simulator capable of assessing the potential uncertainty for multiple scenarios that consider the interrelation of different error sources and match-ups. We first define the sensor-to-sensor match-ups through an orbital analysis that is followed by a top-of-atmosphere (TOA) radiance modeling of each match-up. Finally, we calculate the radiometric uncertainty based on different error sources combined globally. In this first implementation, we have calculated the match-ups of TRUTHS against observations by the Copernicus Sentinel-2A satellite over land areas throughout the year. We calculate the angular mismatch for both viewing differences and solar changes from different overpass times. We define multiple intercalibration scenarios based on temporal, angular, or cloud constraints. These first results show that considering overpasses up to 15-min difference, low cloud probability, and matching field-of-view (FoV), within 5 degrees, we sample most land areas with a mean error <0.1% and bias regression <0.5%. We have also restricted the sun zenith angle (SZA) to 60 degrees to minimize solar angle and view azimuthal dispersion over the poles. This also results in data gaps of several months that might be complemented with dedicated maneuvers or dedicated processing of these polar-region match-ups.

The potential of assimilating Infrared Atmospheric Sounding Interferometer (IASI) temperature and humidity retrievals with their scene-dependent observation operators, a product developed by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), has been investigated in the European Centre for Medium-Range Weather Forecasts (ECMWF) system. The results are compared with the corresponding radiance assimilation impact. The experiments are done in a depleted observing-system framework to emphasise the impact originating from the new data. The focus in the first assimilation experiments has been on retrievals over sea and in clear-sky scenes, as they have high and homogeneous quality. The results demonstrate a clear statistically significant positive impact on temperature, humidity, and wind forecasts. However, the impact is somewhat smaller in magnitude for the retrieval assimilation compared with the radiance assimilation above 700 hPa. The potential of assimilating Infrared Atmospheric Sounding Interferometer (IASI) temperature and humidity retrievals with their scene-dependent observation operators, a product developed by EUMETSAT, has been investigated in the ECMWF system. The results are compared with the corresponding radiance assimilation impact. The results demonstrate a clear statistically significant positive impact on temperature, humidity, and wind forecasts. However, the impact is somewhat smaller in magnitude for the retrieval assimilation compared with the radiance assimilation above 700 hPa. image

This work builds on the analyses made within the EUMETSAT ComboCloud project (contract EUM/CO/19/4600002352/THH) whose purpose was to develop AI-based solutions to infer key cloud parameters exploiting the combination of innovative features offered by upcoming satellite sensors, namely the Next Generation Atmospheric Sounding Interferometer (IASI-NG), and the Microwave Sounder (MWS). We present the potential of the developed solutions applied to real observations, from the instruments flying onboard the EUMETSAT MetOp satellites such as the Atmospheric Sounding Interferometer (IASI), the Advanced Microwave Sounding Unit (AMSU), and the Microwave Humidity Sounder (MHS) and validated against cloud products from an independent dataset of real observations. The validation demonstrated good agreement between reference and retrieved cloud key parameters, showing consistent range and spatial patterns.

Observations from spaceborne microwave (MW) and infrared (IR) passive sensors are the backbone of current satellite meteorology, essential for data assimilation into modern numerical weather prediction and for climate benchmarking. While MW and IR observations from space offer complementary features with respect to cloud properties, their synergy for cloud investigation is currently underexplored, despite the presence of both MW and IR sensors on operational meteorological satellites such as the EUMETSAT Polar System (EPS) MetOp series. As such, several key cloud microphysical properties are not part of the operational products available from EPS MetOp sensors. In addition, the EPS Second Generation (EPS-SG) series, scheduled for launch starting from 2024 onward, will carry sensors such as the Microwave Sounder (MWS) and IASI Next Generation (IASI-NG), enhancing spatial and spectral resolutions and thus capacity to retrieve cloud properties. This article presents the Combined MWS and IASI-NG Soundings for Cloud Properties (ComboCloud) project, funded by EUMETSAT with the overall objective to specify, prototype, and validate algorithms for the retrieval of cloud microphysical properties (e.g., water content and drop effective radius) from the synergy of passive MW and IR observations. The article presents the synergy rationale, the algorithm design, and the results obtained exploiting simulated observations from EPS and EPS-SG sensors, quantifying the benefits to be expected from the MW-IR synergy and the new generation sensors.

This dataset is published in support of a tentative journal publication in a peer-reviewed journal. The full data record is scheduled for release under DOI:10.15770/EUM_SEC_CLM_0086

EUMETSAT generates operational water-vapour and temperature ‘all-sky’ products from the infrared (IASI) and microwave (AMSU, MHS) sounders onboard EPS/Metop satellites (EUMETSAT Polar System). The atmospheric profiles are available to the regional users within 15 to 30 minutes from sensing, via EUMETSAT EARS-IASI L2 service1. The potential for the prediction and monitoring of severe storms of such satellite–based thermodynamic profiles -and their derived nowcasting-relevant parameters, e.g. CAPE- complementary to numerical forecasts has been established in dedicated severe storm test beds2 and nowcasting studies3. The operational baseline of the future sounding missions –IASI-NG and MTG-IRS- directly builds on the experience made with IASI, implementing the same machine-learning all-sky retrieval approach, namely the piece-wise linear regression (PWLR)4. It is essential to characterise and document the precision of the satellite products in particular in severe weather precursor conditions. The quality of the satellite sounding products is routinely assessed against radiosondes for validation and long-term monitoring purposes5. Unfortunately, the satellite overpass times rarely match the in situ measurements from the synoptic sondes. The 3h difference tolerated in building the validation match-ups (satellite-sonde) can incur large collocation uncertainties especially in the boundary layer. This and the fact that radiosonde sites are relatively scarce, makes it difficult to evaluate the satellite products in pre-convective situations with large statistical significance. To circumvent this and evaluate satellite products specifically in pre-convective environments, we studied the potential of routine in situ measurements acquired from commercial airlines. These are coordinated under WMO auspices in the AMDAR (Aircraft Meteorological Data Relay) programme. The AMDAR data have the decisive advantage of higher spatio-temporal density than radiosondes, which ensures numerous and more representative collocations to satellite products. We present here the preliminary results, confirming that satellite sounders are capable of quantifying atmospheric instability. These results also tend to quantitatively identify a dry bias in unstable situations, which was previously suspected, while confirming the relative robustness and accuracy of temperature soundings in the free and lower troposhere. The next generation of EUMETSAT imagers, e.g. MTG-FCI and EPS-SG/METimage, will operate channels around 0.9 µm. This near-infrared channel has unique sensitivity to atmospheric moisture in the boundary layer while infrared sounders have their maximum sensitivity in the mid-troposphere. We will also present the status and plans to retrieve atmospheric humidity from the optical imagers (TCWV first, and then study in the boundary layer) and their complementarity with passive sounders, which will provide key information for storm prediction. 1 https://www.eumetsat.int/ears-iasi 2 https://www.eumetsat.int/severe-storm-forecasting-lab 3 https://www.eumetsat.int/hyperspectral-instability-monitoring-using-iasi 4 https://www.eumetsat.int/IASI-PWLR 5 https://www.eumetsat.int/iasi-level-2-geophysical-products-monitoring-reports

Abstract Climate services are largely supported by climate reanalyses and by satellite Fundamental (Climate) Data Records (F(C)DRs). This paper demonstrates how the development and the uptake of F(C)DR benefit from radiance simulations, using reanalyses and radiative transfer models. We identify three classes of applications, with examples for each application class. The first application is to validate assumptions during F(C)DR development. Hereto we show the value of applying advanced quality controls to geostationary European (Meteosat) images. We also show the value of a cloud mask to study the spatio‐temporal coherence of the impact of the Mount Pinatubo volcanic eruption between Advanced Very High Resolution Radiometer (AVHRR) and the High‐resolution Infrared Radiation Sounder (HIRS) data. The second application is to assess the coherence between reanalyses and observations. Hereto we show the capability of reanalyses to reconstruct spectra observed by the Spektrometer Interferometer (SI‐1) flown on a Soviet satellite in 1979. We also present a first attempt to estimate the random uncertainties from this instrument. Finally, we investigate how advanced bias correction can help to improve the coherence between reanalysis and Nimbus‐3 Medium‐Resolution Infrared Radiometer (MRIR) in 1969. The third application is to inform F(C)DR users about particular quality aspects. We show how simulations can help to make a better‐informed use of the corresponding F(C)DR, taking as examples the Nimbus‐7 Scanning Multichannel Microwave Radiometer (SMMR), the Meteosat Second Generation (MSG) imager, and the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Water Vapor Profiler (SSM/T‐2).

This study proposes an Artificial Neural Network approach for the detection of optically thin cirrus using observations from the Infrared Atmospheric Sounding Interferometer - New Generation (IASI-NG) and from its predecessor, IASI. The Thin Cirrus Detection Algorithm applies a Feedforward Neural Network (NN) to IASI/IASI-NG samples previously declared as clear by a cloud detection algorithm. The NN training, test and validation datasets are generated from a set of ECMWF 5-generation reanalysis (ERA5) processed with the σ-IASI radiative transfer model to simulate IASI/IASI-NG radiances. The IASI and IASI-NG Thin Cirrus detection algorithms were validated against an independent dataset showing better performances for the IASI-NG thin-cirrus-detection algorithm. Moreover, IASI thin-cirrus-detection algorithm outputs were compared against Cloudsat/CPR and SEVIRI cloud products, showing good probability of detection: 0.84 for SEVIRI and 0.77 for CPR/Cloudsat.

The identification of optically thin cirrus is crucial for their accurate parameterization in climate and Earth’s system models. This study exploits the characteristics of the infrared atmospheric sounding interferometer—new generation (IASI-NG) to develop an algorithm for the detection of optically thin cirrus. IASI-NG has been designed for the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar system second-generation program to continue the service of its predecessor IASI from 2024 onward. A thin-cirrus detection algorithm (TCDA) is presented here, as developed for IASI-NG, but also in parallel for IASI to evaluate its performance on currently available real observations. TCDA uses a feedforward neural network (NN) approach to detect thin cirrus eventually misidentified as clear sky by a previously applied cloud detection algorithm. TCDA also estimates the uncertainty of “clear-sky” or “thin-cirrus” detection. NN is trained and tested on a dataset of IASI-NG (or IASI) simulations obtained by processing ECMWF 5-generation reanalysis (ERA5) data with the $\sigma $ -IASI radiative transfer model. TCDA validation against an independent simulated dataset provides a quantitative statistical assessment of the improvements brought by IASI-NG with respect to IASI. In fact, IASI-NG TCDA outperforms IASI TCDA by 3% in probability of detection (POD), 1% in bias, and 2% in accuracy, and the false alarm ratio (FAR) passes from 0.02 to 0.01. Moreover, IASI TCDA validation against state-of-the-art cloud products from Cloudsat/CPR and CALIPSO/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) real observations reveals a tendency for IASI TCDA to underestimate the presence of thin cirrus (POD = 0.47) but with a low FAR (0.07), which drops to 0.0 for very thin cirrus.

The EUMETSAT Central Facility retrieves and disseminates several near-real time geophysical products from both geostationary and polar VIS/IR imagers. The primary scope of these missions is to serve Numerical Weather Prediction (NWP), nowcasting and climate monitoring. In this contribution, we focus on the cloud and Water Vapor (WV) imaging products from the new generation EUMETSAT imagers i.e., the Flexible Combined Imager (FCI) on board of Meteosat Third Generation (MTG-I, launched in Dec 2022) and METimage on board EUMETSAT Polar System Second Generation (EPS-SG, expected 2024+). These instruments provide unprecedented spatial resolution (down to 500 m at Nadir), temporal sampling (ten minutes for the geostationary FCI), and wider spectral range (approximately 0.4 μm to 13 μm) including WV (approximately 0.9 μm, 1.38 μm, approximately 6.7 μm, approximately 7.3 μm), O2 A-band (0.762 μm), and CO2 (approximately 13.3 μm) absorption channels. We present the retrieval and validation approach chosen for these products and the challenges presented by the near-real time operational processing. We explore, in particular, the expected improvements based on the enhanced instrument's capabilities (i.e., more accurate cloud detection, layering, altitude and spatial inhomogeneity), while maintaining continuity with the legacy products from their predecessor satellites. In particular, the new approximately 0.9μm channel allows improved daytime estimates of WV amount near the surface. We show preliminary cloud and WV products retrieved from early FCI measurements, including their validation strategy against independent cloud observations from the ground-based ACTRIS network and humidly measurements from IGRA radiosondes.

Abstract. With more than 15 years of continuous and consistent measurements, the Infrared Atmospheric Sounding Interferometer (IASI) radiance dataset is becoming a reference climate data record. To be exploited to its full potential, it requires a cloud filter that is both accurate, unbiased over the full IASI lifespan, and strict enough to be used in satellite data retrieval schemes. Here, we present a new cloud detection algorithm which combines (1) a high sensitivity, (2) a good consistency over the whole IASI time series and between the different copies of the instrument flying on board the suite of Metop satellites and (3) simplicity in its parametrization. The method is based on a supervised neural network (NN) and relies, as input parameters, on the IASI radiance measurements only. The robustness of the cloud mask over time is ensured in particular by avoiding the IASI channels that are influenced by CO2, N2O, CH4, CFC-11 and CFC-12 absorption lines and those corresponding to the ν2 H2O absorption band. As a reference dataset for the training, the latest version of the operational IASI Level 2 (L2) cloud product is used. We provide different illustrations of the NN cloud product, including comparisons with other existing products. We find a very good agreement overall with the last version of the operational IASI L2 with an identical mean annual cloud amount and a pixel-by-pixel correspondence of about 87 %. The comparison with the other cloud products shows a good correspondence in the main cloud regimes but with sometimes large differences in the mean cloud amount (up to 10 %) due to the specificities of each of the different products. We also show the good capability of the NN product to differentiate clouds from dust plumes.

A neural network (NN) approach is proposed to combine future infrared (IASI-NG) and microwave (MWS) observations to retrieve cloud liquid and ice water path. The methodology is applied to simulated IASI-NG and MWS observations in the period January–October 2019. IASI-NG and MWS observations are simulated globally at synoptic hours (00:00, 06:00, 12:00, 18:00 UTC) and on a regular spatial grid (0.125° × 0.125°) from ECMWF 5-generation reanalysis (ERA5). The state-of-the-art σ-IASI and RTTOV radiative transfer codes are used to simulate IASI-NG and MWS observations, respectively, from the earth's state vector given by ERA5. A principal component analysis of the simulated IASI-NG observations is performed. Accordingly, a NN is developed to retrieve cloud liquid and ice water path from a combination of 24 MWS channels and 30 IASI-NG PCs. Validation indicates that this combination results in liquid and ice water path retrievals with overall accuracy of 1.85 10−2 kg/m2 and 1.18 10−2 kg/m2, respectively, and 0.97 correlation with respect to reference values. The root-mean-square error (RMSE) for CLWP results in about 30% of the mean value (5.91 10−2 kg/m2) and 22% of the variability (1-sigma). Similarly, the RMSE for CIWP results in about 41% of the mean value (2.91 10−2 kg/m2) and 22% of the variability. Two more NN are developed, retrieving cloud liquid and ice water path from microwave observations only (24 MWS channels) and infrared observations only (30 IASI-NG PCs), demonstrating quantitatively the advantage of using the combination of infrared and microwave observations with respect to either one alone.

The main objective of the study is to evaluate the feasibility and benefits of assimilating satellite temperature and humidity soundings (aka Level 2 or L2 profiles), instead of radiances, from the EUMETSAT Advanced Retransmission Service (EARS) into the AROME-France data assimilation system. The satellite soundings are operational forecast-independent retrievals that used the infrared sounder IASI in synergy with its companion microwave instruments AMSU-A and MHS on board the MetOp platforms. In this assimilation study, L2 profiles are used as pseudoradiosondes, discarding vertical error correlations and the instrument vertical sensitivity in the observation operator due to the lack of available averaging kernels. Three assimilation experiments were performed, the baseline (including all satellite radiances except those from IASI, AMSU-A, and MHS sounders), the control (with observations from the baseline plus IASI, AMSU-A, and MHS radiances), and the L2 experiment (with observations from the baseline and L2 temperature and humidity profiles). The assimilation runs cover the periods of the winter 2017 and summer 2018. The forecast skills of the three experiments are gauged against independent analyses and observations. Despite that the vertical observation operator is not accounted for in this study, it is found that L2 profile assimilation does not have a negative impact on 1-h temperature and humidity forecasts, especially in the midtroposphere. Their impacts are comparable in magnitude to the radiance ones in the operational AROME framework, except in terms of temperature and wind fields during winter where the impact is more negative than positive. These findings encourage further investigations.

With more than 15 years of continuous and consistent measurements, the Infrared Atmospheric Sounding Interferometer (IASI) radiance dataset is becoming a reference climate data record. To be exploited to its full potential, it requires a cloud filter that is accurate, unbiased over the full IASI life span and strict enough to be used in satellite data retrieval schemes. Here, we present a new cloud detection algorithm which combines (1) a high sensitivity, (2) a good consistency over the whole IASI time series and between the different copies of the instrument flying on board the suite of Metop satellites, and (3) simplicity in its parametrization. The method is based on a supervised neural network (NN) and relies, as input parameters, on the IASI radiance measurements only. The robustness of the cloud mask over time is ensured in particular by avoiding the IASI channels that are influenced by CO2, N2O, CH4, CFC-11 and CFC-12 absorption lines and those corresponding to the ν2 H2O absorption band. As a reference dataset for the training, version 6.5 of the operational IASI Level 2 (L2) cloud product is used. We provide different illustrations of the NN cloud product, including comparisons with other existing products. We find very good agreement overall with version 6.5 of the operational IASI L2 with an identical mean annual cloud amount and a pixel-by-pixel correspondence of about 87 %. The comparison with the other cloud products shows a good correspondence in the main cloud regimes but with sometimes large differences in the mean cloud amount (up to 10 %) due to the specificities of each of the different products. We also show the good capability of the NN product to differentiate clouds from dust plumes.