Combined IASI-NG and MWS observations for the retrieval of cloud liquid and ice water path: a deep learning artificial intelligence approach

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/m 2 and 1.18 10 −2 kg/m 2 , 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/m 2 ) 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/m 2 ) 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

Potential for the use of reconstructed IASI radiances in the detection of atmospheric trace gases

Principal component (PC) analysis has received considerable attention as a technique for the extraction of meteorological signals from hyperspectral infra-red sounders such as the Infrared Atmospheric Sounding Interferometer (IASI) and the Atmospheric Infrared Sounder (AIRS). In addition to achieving substantial bit-volume reductions for dissemination purposes, the technique can also be used to generate reconstructed radiances in which random instrument noise has been reduced. Studies on PC analysis of hyperspectral infrared sounder data have been undertaken in the context of numerical weather prediction, instrument monitoring and geophysical variable retrieval, as well as data compression. This study examines the potential of PC analysis for chemistry applications.A major concern in the use of PC analysis for chemistry is that the spectral features associated with trace gases may not be well represented in the reconstructed spectra, either due to deficiencies in the training set or due to the limited number of PC scores used in the radiance reconstruction. In this paper we show examples of reconstructed IASI radiances for several trace gases: ammonia, sulphur dioxide, methane and carbon monoxide. It is shown that care must be taken in the selection of spectra for the initial training set: an iterative technique, in which outlier spectra are added to a base training set, gives the best results. For the four trace gases examined, key features of the chemical signatures are retained in the reconstructed radiances, whilst achieving a substantial reduction in instrument noise.A new regional re-transmission service for IASI is scheduled to start in 2010, as part of the EUMETSAT Advanced Retransmission Service (EARS). For this EARS-IASI service it is intended to include PC scores as part of the data stream. The paper describes the generation of the reference eigenvectors for this new service

Cloud detection from IASI radiance for climate analysis purposes

International audienc

Assessing The Impact of Aerosol on the Accuracy of IASI SST

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A CO₂-independent cloud mask from Infrared Atmospheric Sounding Interferometer (IASI) radiances for climate applications

International audienceWith more than 15 years of continuous and consis- tent measurements, the Infrared Atmospheric Sounding In- terferometer (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 mea- surements 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 ab- sorption 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 prodWith more than 15 years of continuous and consis- tent measurements, the Infrared Atmospheric Sounding In- terferometer (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 mea- surements 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 ab- sorption lines and those corresponding to the ν2 H2O ab- sorption band. As a reference dataset for the training, ver- sion 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 some- times large differences in the mean cloud amount (up to 10 %) due to the specificities of each of the different prod-With more than 15 years of continuous and consis- tent measurements, the Infrared Atmospheric Sounding In- terferometer (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 mea- surements 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 ab- sorption lines and those corresponding to the ν2 H2O ab- sorption band. As a reference dataset for the training, ver- sion 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 some- times 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

Cloud detection from IASI radiance for climate analysis purposes

Clouds are an essential component in our Earth system because of their importance for the weather, the water cycle and the Earth radiation budget. To better understand the climate, its past and future evolution, the development of long coherent time series of cloud properties is needed. In addition, as the clouds strongly impact the radiance at the top of the atmosphere, the detection of clear-sky scenes is a major preprocessing step for most climate and atmospheric satellite applications, such as trace gas retrieval or to derive the Earth Outgoing Longwave Radiation (OLR) budget. The Infrared Atmospheric Sounding Interferometer (IASI), flying on board the suite of Metop satellites for more than 15 years, has shown an excellent stability over its entire lifespan and a very good consistency between the three instruments (on board Metop-A, -B and -C). This makes the IASI dataset an excellent climate data record. For the detection and the characterization of clouds, the current IASI operational Level 2 product is highly performant. However, since it was first released in 2007, the L2 cloud data have undergone a series of updates which have not yet been reprocessed back in time. This leads to discontinuities in the data record which makes it very difficult for use in long-term studies. Even in the event of a complete reprocessing of the L2, there would also be no guarantee on the homogeneity of the futures versions. Other cloud products exist (e.g. the AVHRR-L1C, the cloud_cci, the CIRS-LMD) but those are usually either less accurate or sensitive to cloud detection or are not available in near-real-time. These limitations in the existing products triggered the development of a sensitive and coherent IASI cloud detection dataset, using the IASI radiances. Here we present a new cloud detection algorithm for the IASI measurements based on a Neural Network (NN). The input data consists of a set of 45 IASI channels. Those were selected outside the regions affected by CO2, CH4, CFC-11 and CFC-12 absorptions to avoid any long-term bias in the detection as their concentrations are evolving over time in the atmosphere. As a reference dataset, we use the current version (v6.6) of the IASI L2 cloud product. The IASI-derived NN cloud product appears to be both accurate in the cloud detection and coherent over the whole IASI period and between the three versions of the instrument. To illustrate this, we show global distributions and time series of the cloud fractions and we assess the quality of the cloud mask by comparing the NN product against several other cloud products. We also evaluate the capabilities of our NN cloud detection product to correctly distinguish cloud from dust plumes. Finally, we analyze the trends in the cloud amount for different regions of the globe.info:eu-repo/semantics/nonPublishe