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The long term global temperature rise caused by increased greenhouse gas radiative forcing is partially masked by temporary aerosol radiative cooling, which remains poorly known.I present a new purely observation based estimate of Aerosol Radiative Forcing (ARF) due solely to the direct radiative effect of aerosols over clear sky ocean, and its time variation over the period 2003-20204 from combined MODIS and CERES aerosol, cloud, and radiation measurements. The resulting mean 2003-2020 ARF is -1.16 +/- 0.39 W/m2 , with no significant trend within an uncertainty of +/- 0.025 W/m2dec.Combining this ARF with the best estimate of the greenhouse gas and solar radiative forcing, and the most likely value of the Equilibrium Climate Sensitivity, produces a plausible Earth Energy Imbalance as a residual of the energy balance equation at the top of the atmosphere.

Observing the Earth radiation budget (ERB) at top of the atmosphere (TOA) from space is crucial for monitoring and understanding Earth’s climate. The accurate estimation of Earth’s outgoing radiative flux is of critical importance to studying ERB at TOA. The Moon-based wide field-of-view radiometer (MWFVR) can provide long-term, continuous full-disk broadband irradiance measurements, which provides an important data source for studying the ERB. Within this context, the lunar surface site 0° E 0° N is selected as the position of the Moon-based wide field-view radiometer, and based on the radiation transfer function, the entrance pupil irradiances time series are obtained by utilization of the CER_SYN1deg-1Hour_Edition4 data products and ERBE ADMs, which is used as the substitute for the truth of the measurements. In this work, the Earth outgoing radiative flux estimating model from the MWFVR measurements is established, and according to the framework, the entrance pupil irradiances are converted to full-disk LW and daytime SW outgoing radiative fluxes. By comparing the results from Moon-based radiometer measurements with those from NISTAR data and CERES SYN1deg data, the results show the moon-based data a much better agreement with those from the satellite data. Besides, The Moon-based SW fluxes oscillate around 194 and 205 W∙m-2, and the range of LW fluxes is 251 ~ 287 Wm−2. Therefore, the complementary advantages and cooperative work of platforms at different altitudes will be an important way for future research on the ERB.

Monitoring the Earth Radiation Budget (ERB) and in particular the EarthEnergy Imbalance (EEI), is of paramount importance for a predictiveunderstanding of global climate change. We propose the new EarthClimate Observatory (ECO) space mission concept for the monitoring ofthe EEI.The EEI is defined as the small difference between the two nearly equalterms of the incoming solar radiation, and the outgoing terrestrialradiation lost to space. Making a significant measurement of the EEIfrom space is very challenging, and requires a differential measurementwith one single instrument of both the incoming solar radiation and theoutgoing terrestrial radiation. The instrument that allows such adifferential measurement is an improved wide field of view electricalsubstitution cavity radiometer.The wide field of view radiometer will observe the earth from limb tolimb. A single measurement footprint is a circle with a diameter around6300 km. For the discrimination of cloudy and clear skies, a higherspatial resolution is needed. This will be obtained from two wide fieldof view cameras, a visible wide field of view camera for thecharacterisation of the spatial distribution of the reflected solarradiation, and a thermal infrared wide field of view camera for thecharacterisation of the spatial distribution of the emitted thermalradiation.

In July 2021, western Europe has been subject to extreme rainfall that lead to severe flooding, incurring heavy property losses and claiming dozens of people’s lives in both Germany and Belgium. This unfortunate disaster is a reminder that carrying out studies about extreme event forecasting is a matter of prime importance in the field of meteorology. The extreme rainfall from July 2021 invites us to study the performance of our deep learning method for precipitation estimation in case of extreme events.The main novelty of our method resides in its ability to merge different physical measurement modalities in order to improve precipitation estimation accuracy. In specific, the proposed method merges rain gauge measurements with a ground-based radar composite and thermal infrared satellite imagery. The proposed convolutional neural network design, composed of an encoder–decoder architecture, performs multiscale analysis of the three input modalities to simultaneously estimate the rainfall probability and the precipitation rate with a spatial resolution of 2 km. The training of our model and its performance evaluation are carried out on a dataset spanning 5 years from 2015 to 2019 and covering Belgium, the Netherlands, Germany and the North Sea. Once trained, we evaluate the performance of our model to estimate the extreme precipitation that happened in Belgium and Germany in July 2021 by comparing our results with the measurements from rain gauges and radar estimation.

In recent years, the interest has grown in satellite-derived hyperspectral radiance measurements for assessing the individual impact of climate drivers and their cascade of feedbacks on the outgoing longwave radiation (OLR). In this paper, we use 10 years (2008–2017) of reprocessed radiances from the infrared atmospheric sounding interferometer (IASI) to evaluate the linear trends in clear-sky spectrally resolved OLR (SOLR) in the range [645–2300] cm−1. Spatial inhomogeneities are observed in most of the analyzed spectral regions. These mostly reflected the natural variability of the atmospheric temperature and composition but long-term changes in greenhouse gases concentrations are also highlighted. In particular, the increase of atmospheric CO2 and CH4 led to significant negative trends in the SOLR of −0.05 to −0.3% per year in the spectral region corresponding to the ν2 and the ν3 CO2 and in the ν4 CH4 band. Most of the trends associated with the natural variability of the OLR can be related to the El Niño/Southern Oscillation activity and its teleconnections in the studied period. This is the case for the channels most affected by the temperature variations of the surface and the first layers of the atmosphere but also for the channels corresponding to the ν2 H2O and the ν3 O3 bands., SCOPUS: ar.j, info:eu-repo/semantics/published

Artificial Intelligence (AI) is an explosively growing field of computer technology, which is expected to transform many aspects of our society in a profound way. AI techniques are used to analyse large amounts of unstructured and heterogeneous data and discover and exploit complex and intricate relations among these data, without recourse to an explicit analytical treatment of those relations. These AI techniques are unavoidable to make sense of the rapidly increasing data deluge and to respond to the challenging new demands in Weather Forecast (WF), Climate Monitoring (CM) and Decadal Prediction (DP). The use of AI techniques can lead simultaneously to: (1) a reduction of human development effort, (2) a more efficient use of computing resources and (3) an increased forecast quality. To realise this potential, a new generation of scientists combining atmospheric science domain knowledge and state-of-the-art AI skills needs to be trained. AI should become a cornerstone of future weather and climate observation and modelling systems.

The Earth's Outgoing Longwave Radiation (OLR) is a key component in the study of climate. As part of the Earth's radiation budget, it reflects how the Earth-atmosphere system compensates the incoming solar radiation at the top of the atmosphere. At equilibrium, the two quantities compensate each other on average. Any variation of the climate drivers (e.g. greenhouse gases) causes an energy imbalance which leads to a climate response (e.g. surface temperature increase), with the effect of bringing the radiation budget back to equilibrium. Considerable improvements in our understanding of the Earth-atmosphere system and of its long-term changes have been achieved in the last four decades through the exploitation of measurements from dedicated broadband instruments. However, such instruments only provide spectrally integrated OLR over a broad spectral range and are therefore not well suited for tracking separately the impact of the different parameters affecting the OLR.Better constraints can, in principle, be obtained from spectrally resolved OLR (i.e. the integrand of broadband OLR, in units of W m-2 cm-1) derived from infrared hyperspectral sounders. Recently, a dedicated algorithm was developed to derive clear-sky spectrally resolved OLR from the Infrared Atmospheric Sounding Interferometer (IASI) at the 0.25 cm-1 native spectral sampling of the L1C spectra (Whitburn et al. 2020). Here, we analyze the changes in 10 years (2008-2017) of the IASI-derived OLR and we relate them to known changes in greenhouse gases concentrations (CO2, CH4, H2O, …) and climate phenomena activity such as El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO).Whitburn, S., Clarisse, L., Bauduin, S., George, M., Hurtmans, D., Safieddine, S., Coheur, P. F., and Clerbaux, C. (2020). Spectrally Resolved Fuxes from IASI Data: Retrieval algorithm for Clear-Sky Measurements. Journal of Climate. doi: 10.1175/jcli-d-19-0523.1

Climate on Earth is determined by the Earth Radiation Budget (ERB), which quantifies the incoming and outgoing radiative energy fluxes at the top-of-atmosphere (TOA). The ERB can be monitored from space by non-scanning wide field-of-view radiometers (WFOV), or by scanning narrow field-of-view radiometers. Recently, WFOV radiometers have gained renewed interest as illustrated by the development of the RAVAN and SIMBA 3U CubeSats. RAVAN uses a Vertically Aligned Carbon Nanotubes (VACNT) coating, while the SIMBA CubeSat uses a novel cavity-based geometry with a Black Velvet coating. Both VACNT and Black Velvet are diffuse coatings, but when applied to flat sensors, the VACNT coating has a significantly lower reflectivity in comparison to classic diffuse or specular black coating materials. When used on a cavity radiometer, it is currently unclear if a VACNT coating would improve the measurement accuracy compared to other diffuse coatings, such as Black Velvet. In this paper, we therefore investigate the potential benefits of using the VACNT coating as an alternative for Black Velvet, in our in-house developed radiometer. Our analysis includes the evaluation of the influence of the cavity geometry as well as the coating absorption factor. The comparison of the VACNT with the Black Velvet coating is based on the absorption factor of the cavity that is determined using radiation view factor calculations. Scattering and stray-light analyses are carried out using commercially available ray-tracing software (ASAP®R, Breault Research). We evaluated whether the coating or the geometry is the main contributing factor to the performance of the radiometer. As a conclusion, we observed that for cavity-type radiometers, the difference in the cavity absorption factor between Black Velvet and VACNT becomes negligible, favoring the use of Black Velvet, since Black Velvet has a long space heritage and appears more user friendly from a fabrication point of view, as it can be deposited in an easier and more reproducible manner on the radiometer cavity walls, including non flat surfaces.

In the coming years, Artificial Intelligence (AI), for which Deep Learning (DL) is an essential component, is expected to transform society in a way that is compared to the introduction of electricity or the introduction of the internet. The high expectations are founded on the many impressive results of recent DL studies for AI tasks (e.g. computer vision, text translation, image or text generation...). Also for weather and climate observations, a large potential for AI application exists. We present the results of the recent paper [Moraux et al, 2019], which is one of the first demonstrations of the application of cutting edge deep learning techniques to a practical weather observation problem. We developed a multiscale encoder-decoder convolutional neural network using the three most relevant SEVIRI/MSG spectral images at 8.7, 10.8 and 12.0 micron and in situ rain gauge measurements as input. The network is trained to reproduce precipitation measured by rain gauges in Belgium, the Netherlands and Germany. Precipitating pixels are detected with a POD of 0.75 and a FAR of 0.3. Instantaneous precipitation rate is estimated with a RMSE of 1.6 mm/h. Reference:[Moraux et al, 2019] Moraux, A.; Dewitte, S.; Cornelis, B.; Munteanu, A. Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements. Remote Sens. 2019, 11, 2463.

Climate on Earth is determined by the Earth Radiation Budget (ERB), which quantifies the incoming and outgoing radiative energy fluxes. The ERB can be monitored by non-scanning wide field-of-view radiometers, or by scanning narrow field-of-view radiometers. We propose an enhanced design for the wide field-of-view radiometer, with as key features the use of a near-spherical cavity to obtain a uniform angular sensitivity and the integration of the shuttered electrical substitution principle, eliminating long term drifts of the radiometer and improving its time response. The target absolute accuracy is 1 W/m 2 and the target stability is 0.1 W/m 2 per decade for the measurement of the total outgoing Earth’s radiation. In order to increase the spatial resolution and to separate the total outgoing radiation into reflected Solar and emitted thermal radiation, we propose the joint use of the radiometer with wide field-of-view Shortwave (400−900 nm) and Longwave (8−14 μm) cameras. This paper presents the concept and design of the novel wide field-of-view radiometer, including simulations and analyses of its expected performance. We focus on mechanical design and the measurement characteristics based on optical and thermal analyses. In combination with the cameras, we obtain an estimated accuracy of 0.44 W/m 2 .

Since the early 1980s, the geostationary Meteosat instruments have been observing the earth at a high temporal and spatial resolution. The data record contains more than 30 years of observations and has become very useful for climate monitoring. With a compilation of 10 satellites, it is crucial to reach the highest possible consistency between the instruments. However, the quality of the level 1 data records is still far from what is needed to generate homogeneous climate data records, due to both temporal ageing as well as discontinuities between successive instruments. In this letter, a method is proposed to assess the pre-launch Meteosat-7 visible spectral response curve and a model of its ageing. The results not only validate the ageing model, but also provide evidence that the official pre-launch spectral response curve of Meteosat-7 poorly represents the sensitivity of the instrument in the blue part of the visible spectrum. Until the Meteosat-7 visible spectral response curve has been reassessed, ...

For more than 30 years, the Meteosat satellites have been in a geostationary orbit around the earth. Because of the high temporal frequency of the data and the long time period, this database is an excellent candidate for fundamental climate data records (FCDRs). One of the prerequisites to create FCDRs is an accurate and stable calibration over the full data period. Because of the presence of contamination on the instrument in space, a degradation of the visible band of the instruments has been observed. Previous work on the Meteosat First Generation satellites, together with results from other spaceborne instruments, led to the idea that there is a spectral component to this degradation. This paper describes the model that was created to correct the Meteosat-7 visible (VIS) channel for these spectral aging effects. The model assumes an exponential temporal decay for the gray part of the degradation and a linear temporal decay for the wavelength-dependent part. The effect of these two parts of the model is tuned according to three parameters; 253 clear-sky stable earth targets with different surface types are used together with deep convective cloud measurements to fit these parameters. The validation of the model leads to an overall stability of the Meteosat-7 reflected solar radiation data record of about 0.66 W m−2 decade−1.

International audience; We present the lessons learned about the degradation observed in several space solar missions, based on contributions at the Workshop about On-Orbit Degradation of Solar and Space Weather Instruments that took place at the Solar Terrestrial Centre of Excellence (Royal Observatory of Belgium) in Brussels on 3 May 2012. The aim of this workshop was to open discussions related to the degradation observed in Sun-observing instruments exposed to the effects of the space environment. This article summarizes the various lessons learned and offers recommendations to reduce or correct expected degradation with the goal of increasing the useful lifespan of future and ongoing space missions.

Current measurements from DIARAD/VIRGO, PMO6V/VIRGO and ACRIM3 radiometers are of the same order of magnitude, but differ from TIM/SORCE by about 4.5 W m −2 . This difference is higher than the sum of the claimed individual absolute uncertainties of the instruments. In this context, the SOLAR payload on the International Space Station embarks the SOVIM package. We give the results of the differential absolute radiometer DIARAD/SOVIM and discuss its associated uncertainties. Compared to DIARAD/VIRGO, all possible efforts have been made to improve the absolute accuracy. Substantial progress has been made in the aperture area and electrical power measurements. The measured TSI value from the left channel of DIARAD/SOVIM during three days of June 2008 is 1364.50 ± 1.38 W m −2 (Total) or ±0.49 W m −2 (if we combine the individual contributions in quadrature). The right channel gives 1364.75 W m −2 with the same uncertainties. These values are about 1.2 W m −2 lower than DIARAD/VIRGO and about 4 W m −2 higher than TIM/SORCE. The difference between the left and right channels measurements is as low as 0.25 W m −2 which is within the improved uncertainty limits.

The authors present a new algorithm to retrieve aerosol optical depth (AOD) over a desert using the window channels centered at 8.7, 10.8, and 12.0 μm of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the Meteosat Second Generation satellite. The presence of dust aerosols impacts the longwave outgoing radiation, allowing the aerosols over the desert surfaces to be detected in the thermal infrared (IR) wavelengths. To retrieve the aerosol properties over land, the surface contribution to the satellite radiance measured at the top of the atmosphere has to be taken into account. The surface radiation depends on the surface temperature, which is characterized by a strong diurnal variation over the desert, and the surface emissivity, which is assumed to be constant over a time span of 24 h. The surface emissivity is based on clear-sky observations that are corrected for atmospheric absorption and emission. The clear-sky image is a composite of pixels that is characterized by the highest brightness temperature (BT) of the SEVIRI channel at 10.8 μm, and by a negative BT difference between the channels at 8.7 and 10.8 μm. Because of the lower temperatures of clouds and aerosols compared to clear-sky conditions, the authors assume that the selected pixel values are obtained for a clear-sky day. A forward model is used to simulate the thermal IR radiation transfer in the dust layer. The apparent surface radiation for the three window channels in the presence of aerosols is calculated as a function of the surface emissivity and the surface temperature, the aerosol layer temperature, and the AOD for different aerosol loadings. From these simulations two emissivity ratios, which are stored in lookup tables (LUT), are calculated. The retrieval algorithm consists of processing the clear-sky image and computing the surface emissivity, processing the instantaneous image, and computing the apparent surface radiation for the three window channels. The two emissivity ratios are computed using the radiances at 8.7 and 10.8 μm and at 8.7 and 12.0 μm, respectively. The SEVIRI AOD is obtained by the inversion of these emissivity ratios using the corresponding LUT. The algorithm is applied to a minor dust event over the Sahara between 19 and 22 June 2007. For the validation the SEVIRI AOD is compared with the AOD from the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) along the satellite track.

The Satellite Application Facility on Climate Monitoring (CM-SAF) aims at the provision of satellite-derived geophysical parameter data sets suitable for climate monitoring. CM-SAF provides climatologies for Essential Climate Variables (ECV), as required by the Global Climate Observing System implementation plan in support of the UNFCCC. Several cloud parameters, surface albedo, radiation fluxes at the top of the atmosphere and at the surface as well as atmospheric temperature and humidity products form a sound basis for climate monitoring of the atmosphere. The products are categorized in monitoring data sets obtained in near real time and data sets based on carefully intercalibrated radiances. The CM-SAF products are derived from several instruments on-board operational satellites in geostationary and polar orbit as the Meteosat and NOAA satellites, respectively. The existing data sets will be continued using data from the instruments on-board the new joint NOAA/EUMETSAT Meteorological Operational Polar satellite. The products have mostly been validated against several ground-based data sets both in situ and remotely sensed. The accomplished accuracy for products derived in near real time is sufficient to monitor variability on diurnal and seasonal scales. The demands on accuracy increase the longer the considered time scale is. Thus, interannual variability or trends can only be assessed if the sensor data are corrected for jumps created by instrument changes on successive satellites and more subtle effects like instrument and orbit drift and also changes to the spectral response function of an instrument. Thus, a central goal of the recently started Continuous Development and Operations Phase of the CM-SAF (2007–2012) is to further improve all CM-SAF data products to a quality level that allows for studies of interannual variability.

article Whenever possible, intercomparisons of Earth radiation budget data from different spaceborne instruments should be made as they are important steps in the overall validation process. Intercomparisons are also key elements to compile long-term climate datasets by merging data from several instruments. In this study the GERB Edition-1 and CERES Edition-2 data are compared for June and December 2004. The comparisons concern shortwave and longwave radiance and flux at the top-of-atmosphere. Three different GERB level-2 data products with differing space-time characteristics are compared with data from the 4 CERES instruments. In general, the GERB unfiltered radiances and fluxes are found to be 5.9% and 7.5% higher than CERES for the shortwave. The GERB longwave radiance and flux are 1.3% lower than CERES. Analysis separated by scene type reveals differences between the GERB products. These differences should be taken into account by the user of the GERB data. The LW flux intercomparison shows angular dependency problem affecting the GERB dataset in cloudy and aerosol regions.

The method used to estimate the unfiltered shortwave broadband radiance from the filtered radiances measured by the Geostationary Earth Radiation Budget (GERB) instrument is presented. This unfiltering method is used to generate the first released edition of the GERB-2 dataset. The method involves a set of regressions between the unfiltering factor (i.e., the ratio of the unfiltered and filtered broadband radiances) and the narrowband observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument. The regressions are theoretically derived from a large database of simulated spectral radiance curves obtained by radiative transfer computations. The generation of the database is fully described. Different sources of error that may affect the GERB unfiltering have been identified and the associated error magnitudes are assessed on this database. For most of the earth–atmosphere conditions, the error introduced during the unfiltering process is below 1%. In some conditions (e.g., low sun elevation above the horizon) the error can present a higher relative value, but the absolute error value remains well under the accuracy goal of 1% of the full instrument scale (2.4 W m−2 sr−1). To increase the confidence level, the edition 1 unfiltered radiances of GERB-2 are validated by cross comparison with collocated and coangular Clouds and the Earth’s Radiant Energy System (CERES) observations for different scene types. In addition to an overall offset between the two instruments, the intercomparisons indicate a scene-type dependency up to 4% in unfiltered radiance. Further studies are required to confirm the cause, but an insufficiently accurate characterization of the shortwave spectral response of the GERB instrument in the visible part of the spectrum is one area under further investigation.

To study the effect of aerosols on the Earth's radiation budget (ERB), the Royal Meteorological Institute of Belgium (RMIB) has integrated spectral aerosol optical depth (AOD) measurements over the ocean from the Spinning Enhanced Visible and Infra-Red Scanner (SEVIRI) into its Geostationary Earth's Radiation Budget, or GERB, processing system referred to as the RGP. Aerosols affect the ERB both directly (when radiation interacts with an aerosol particle) and indirectly (when aerosols act as cloud condensation nuclei). Quantifying the indirect effect is challenging as it requires accurate aerosol retrievals in the close proximity to clouds, where aerosol retrievals may be biased due to leakages from the cloud mask (CM). The initial focus of the RGP project was on the direct effect using confidently clear scenes. A single channel CM exploiting the SEVIRI temporal sampling was developed at the RMIB for the use in the RGP project. In this study, that single channel mask was evaluated against two multi-channel CMs, one from the Meteorological Products Extraction Facility (MPEF) at the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the other from the Satellite Application Facility for Supporting NoWCasting and Very Short Range Forecasting (SAFNWC), respectively. The NOAA/NESDIS Advanced Very High Resolution Radiometer (AVHRR) single channel aerosol algorithm was adjusted to SEVIRI spectral bands and consistently applied to the pixels identified as cloud-free. The aerosol products corresponding to the three CMs were compared, and the RMIB CM was found to be sufficiently accurate and conservative, for RGP applications. Comparisons with independent AODs derived from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites show that the RMIB CM-based SEVIRI aerosol product compares well with its MODIS counterpart. However, a small fraction of cloud-contaminated pixels may still remain in the SEVIRI AOD imagery, chiefly within one to two SEVIRI pixels of the cloud boundary, thus limiting its use for indirect forcing studies. Also, the RMIB CM may screen high AOD non-dust aerosol events (e.g., smoke from biomass burning) as cloud. The potential of the new SEVIRI aerosol product is illustrated by generating 9 km-resolution seasonal maps of AODs and ´AǺngstrom Exponents, and by using the GERB radiative flux measurements for a preliminary quick assessment of the direct aerosol forcing.

Because space-borne radiometers do not measure the Earth’s outgoing fluxes directly, angular distribution models (ADMs) are required to relate actual radiance measurement to flux at given solar angle, satellite-viewing geometries, surface, and atmospheric conditions. The conversion of one footprint broad-band radiance into the corresponding flux requires therefore one to first characterize each footprint in terms of surface type and cloud cover properties to properly select the adequate ADM. A snow (and sea-ice) retrieval technique based on spectral measurements from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat 8 is presented. It has been developed to improve the scene identification and thus the ADM selection in the near-real time processing of the Geostationary Earth Radiation Budget (GERB) data at the Royal Meteorological Institute of Belgium. The improvement in the GERB short wave flux estimations over snow covered scene types resulting from angular conversion using dedicated snow ADMs (e.g., empirical snow ADMs and/or pre-computed theoretical snow ADM) instead of empirical snow-free ADMs is discussed.

The Geostationary Earth Radiation Budget (GERB) instrument is the first to measure the earth radiation budget from a geostationary orbit. This allows a full sampling of the diurnal cycle of radiation and clouds – which is important for climate studies, as well as detailed process studies, e.g. the lifecycle of clouds or particular aerosol events such as desert storms. GERB data is now for the first time released as Edition 1 data for public scientific use. In this paper we summarise the algorithms used for the Edition 1 GERB data processing and the main validation results. Based on the comparison with the independent CERES instrument, the Edition 1 GERB accuracy is 5% for the reflected solar radiances and 2% for the emitted thermal radiances.

During solar cycle 23, which is now close to its end, variations of the total solar irradiance were measured by six different instruments, providing four independent time series of the irradiance variation over the complete solar cycle. A new composite time series constructed using five of these six instruments provides unprecedented instrument stability for the study of the open question of solar irradiance variations between minima. An independent analysis of the different composite time series is performed through an empirical proxy model fit. The new composite is fitted with 0.96 correlation (R2=93%) and RMS error of 0.15 W m−2, thus reaching the limit of the individual instrument stabilities. Both the measurements and the model indicate that for the current cycle the minimum irradiance level has not yet been reached. Therefore we use the model to extrapolate measurements up to 2008 when the minimum irradiance level is expected. If we assume that there will be no changes in the solar irradiance from 2006 to 2008 that are not captured by the regression model, it can be predicted that there will be no variation of the solar minimum irradiance level during cycle 23 with an uncertainty of ±0.14 W m−2.