Satellites provide a wealth of observations of the Earth’s atmosphere and surface, giving a global coverage. More than 15GB of data are now received every day at operational weather centres in near real time and this data explosion is set to continue as more satellites are launched. Until the 1960s, observations of the Earth’s atmosphere and surface were all made by in situ measurements from weather stations on land, ships and radiosondes. The first satellites that observed the atmosphere were the Television and Infra-Red Observation Satellite (TIROS) class, which imaged the Earth from a low orbit, showing the position of clouds in rather fuzzy images. TIROS-1 was launched in 1960 and was the first dedicated meteorological satellite but only covered the tropics and mid-latitudes, providing images of the same location twice a day. Polar orbiting satellites, generally at around 800km altitude, were developed for meteorological applications with the National Oceanic and Atmospheric Administration (NOAA) series, and regular launches were made from 1970 with increasingly sophisticated instrumentation on board. The latest, NOAA-20, was launched in 2017 and provides imagery for forecasters and sounding data for NWP models in the early morning and afternoon overpasses. Europe started launching its own polar orbiter series, Metop, in 2006, to cover the mid-morning/late evening orbit. China launched the FY-3 series of meteorological satellites starting in 2008, and Japan and India also have several meteorological satellites making a contribution to the observing system. In addition to the operational satelThe use of satellite data in numerical weather prediction

A new configuration of the European Centre for Medium-Range Weather Forecasts (ECMWF) incremental 4D-Var data assimilation (DA) system is introduced which builds upon the quasi-continuous DA concept proposed in the mid-1990s. Rather than working with a fixed set of observations, the new 4D-Var configuration exploits the near-continuous stream of incoming observations by introducing recently arrived observations at each outer loop iteration of the assimilation. This allows the analysis to benefit from more recent observations. Additionally, by decoupling the start time of the DA calculations from the observational data cut-off time, real-time forecasting applications can benefit from more expensive analysis configurations that previously could not have been considered. In this work we present results of a systematic comparison of the performance of a Continuous DA system against that of two more traditional baseline 4D-Var configurations. We show that the quality of the analysis produced by the new, more continuous configuration is comparable to that of a conventional baseline that has access to all of the observations in each of the outer loops, which is a configuration not feasible in real-time operational numerical weather prediction. For real-time forecasting applications, the Continuous DA framework allows configurations which clearly outperform the best available affordable non-continuous configuration. Continuous DA became operational at ECMWF in June 2019 and led to significant 2 to 3% reductions in medium-range forecast root mean square errors, which is roughly equivalent to 2-3 hr of additional predictive skill.

The European Centre for Medium‐range Weather Forecasts (ECMWF) 4D‐Var data assimilation system has been modified to allow the direct assimilation of Principal Component (PC) scores derived from spectra measured by the Infrared Atmospheric Sounding Interferometer (IASI). Testing of a prototype system where 165 IASI radiances are replaced by just 20 PC scores shows significant computational savings with no detectable loss of skill in the resulting analyses or forecasts. Indeed in some respects the assimilation of PC scores leads to marginal improvements over the traditional radiance‐based assimilation.

As a step toward the assimilation of cloud‐affected infrared radiances in multi‐layer cloud conditions, this study evaluates cloud effects on model first‐guess simulations (background) and observations using the Infrared Atmospheric Sounding Interferometer (IASI) radiances. It is found from an extensive statistical analysis that over oceans the magnitude of observation‐minus‐background departures (O–B) – even in the most cloud‐sensitive window channels – is typically less than 10 K for 85% of all‐sky IASI data. A parameter has been developed to express the magnitude of the cloud effect based upon observed and simulated cloudy radiances. It is shown that the variations in the standard deviation (SD) of O–B departures can be described (and thus predicted) by this cloud effect parameter – such that the probability density function (PDF) of O–B normalized with predicted O–B SD exhibits a near‐Gaussian form. It is argued that the predicted cloud effect can be used in an assimilation context to define cloud‐dependent quality controls and aid observation error assignment. Simple linear estimation theory is used to simulate the possible benefits of state‐dependent observation errors according to cloud effect.

This article compares different methods of deriving cloud properties in the footprint of the Infrared Atmospheric Sounding Interferometer (IASI), onboard the European MetOp satellite. Cloud properties produced by ten operational schemes are assessed and an intercomparison of the products for a 12 h global acquisition is presented. Clouds cover a large part of the Earth, contaminating most of the radiance data. The estimation of cloud top height and effective amount within the sounder footprint is an important step towards the direct assimilation of cloud‐affected radiances. This study first examines the capability of all the schemes to detect and characterize the clouds for all complex situations and provides some indications of confidence in the data. Then the dataset is restricted to thick overcast single layers and the comparison shows a significant agreement between all the schemes. The impact of the retrieved cloud properties on the residuals between calculated cloudy radiances and observations is estimated in the long‐wave part of the spectrum. Copyright © 2011 Royal Meteorological Society, Crown in the right of Canada, and British Crown copyright, the Met Office

ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF. Copyright (C) 2011 Royal Meteorological Society

Methodologies are discussed for the efficient representation of observations from high-resolution infrared sounders for the purposes of assimilation into numerical weather prediction models. The use of principal component analysis is explored and it is noted that, while the available information in the observations is stored efficiently, the non-locality of the Jacobians that arise may cause practical problems in an operational assimilation system. Reconstructing radiance spectra from the principal components appears to be a more realistic approach in the near term. However, initial experiments with reconstructed radiances do not appear to give significant improvements in forecast skill above that already demonstrated with the current use of advanced sounder data. Copyright (C) 2010 Royal Meteorological Society and Crown Copyright.

An analysis of ozone that is constrained only by observations of ultraviolet backscatter has an obvious limitation. These data are not available at night‐time. A system has been developed to exploit ozone information from infrared radiances measured by IASI which suffer no such sampling problems related to the position of the sun. It has been found that, relative to a baseline system that has no ozone observations, the use of IASI significantly improves the fit to independent ozone estimates from the Aura MLS. Indeed this improvement is, in some areas, comparable to or better than that obtained when more established ozone estimates from UV sensors are assimilated. One area where the assimilation of IASI is found to be clearly beneficial is in the winter high latitudes and southern polar night. Copyright © 2010 Royal Meteorological Society

A system has been developed to make use of infrared radiance data in cloudy conditions. The central strategy is to extend the 4D‐Var analysis control vector to include parameters which describe the cloud conditions and simultaneously estimate these parameters together with temperature and humidity inside the main analysis. In the current prototype configuration of the system, the extra cloud variables are decoupled from the model physics and only cloudy data in completely overcast conditions are used. Early experiments with the new system have produced some encouraging results. Additional use of overcast radiance data improves the analysis fit to independent radiosonde observations and results in some small, but statistically significant, improvements in forecast quality. Copyright © 2009 Royal Meteorological Society

The assimilation of Infrared Atmospheric Sounding Interferometer (IASI) radiances at the European Centre for Medium-Range Weather Forecasts (ECMWF) is described. The configuration of the IASI radiance assimilation system relies heavily on the experience obtained from using Atmospheric InfraRed Sounder (AIRS) data over the previous five years. IASI observations are found to be of high quality, particularly in the important region of the 15 mu m CO2 temperature-sounding band. The choice of channels to be actively assimilated is focused on this 15 mu m band and experiments have shown that the additional use of IASI data produces a statistically significant positive impact on forecast quality. Copyright (C) 2009 Royal Meteorological Society

The impact of geostationary clear‐sky radiances (CSRs) on 4D‐Var wind analyses has been investigated by running a set of observing system experiments. Analysis scores have been calculated to measure the ability of individual satellite datasets to improve the wind analysis, starting from a no‐satellite baseline. In this context, the assimilation of CSRs from the two water‐vapour channels on Meteosat‐9 has been found to improve the wind analysis throughout the troposphere, with the strongest signal at 300 and 500 hPa. Indeed, for the Northern Hemisphere and the Tropics, the CSR impact at these levels exceeds that of the Meteosat‐9 atmospheric motion vectors (AMVs), the sampling of the latter in the assimilation being rather limited. Conversely, the impact of AMVs exceeds that of CSRs in the lower troposphere, where the latter provide very little direct information. This demonstrates the complementarity of the two datasets in the operational 4D‐Var wind analysis. The mechanisms through which the assimilation of CSRs can impact wind analyses have been isolated. The dominant effect is that of humidity‐tracer advection, by which the wind field is adjusted in order to fit observed humidity features via the minimization of the 4D‐Var cost function. Other mechanisms, such as balance constraints and the cycling of the forecast model that links the humidity and wind variables, have been found to play a minor role. The benefit of having frequent CSR images within the assimilation window has also been demonstrated. Copyright © 2009 Royal Meteorological Society

The global analysis and forecast impact of observed humidity has been assessed by means of observing system experiments with the ECMWF 4D-Var data assimilation system. It is found that humidity data have a significant impact extending into the medium range (5-6 day forecasts), with a marked impact also on the wind and temperature fields. This contradicts some previous studies that have shown insignificant impact of humidity observations in general. The current, greater benefit of the humidity analysis may be due to improved model and data assimilation methods, and vastly increased availability of atmospheric moisture observations. The results show that each tested data type provides benefit to the analysis and forecast performance, which indicates that the humidity analysis is effective in extracting information from a wide variety of humidity observations. Data from the microwave sounding instruments (SSMI and AMSU-B) dominate the humidity analysis over the sea, whereas radiosondes, surface stations (SYNOP) and AMSU-B dominate over land. The infrared sounders (GOES, HIRS and AIRS) dominate in the upper troposphere, at 200-300 hPa.The lack of absolutely calibrated humidity data makes dealing with biases in observations and model one of the main issues for determining the global moisture distribution and a balanced hydrological cycle. In these experiments, SSMI adds water in the subtropical subsidence areas due to a bias with respect to the model. In several locations over land, radiosondes and SYNOP have opposite bias impacts in the boundary layer, resulting in local influence on precipitation when either dataset is withheld. The SYNOP data are biased wet and the radiosondes are biased dry with respect to the model. Copyright (C) 2007 Royal Meteorological Society.

An interaction between the quality control (QC) and the bias correction of satellite radiances has been identified. If the bias correction is recalibrated intermittently, or if it is adaptive, a feedback process is possible. Indeed, the bias is calculated over a population of quality-controlled observations. Since QC usually acts upon bias-corrected observed-minus-first-guess departures, the value of the bias correction influences the next population that passes the QC, and so on.Two situations that can trigger a feedback are described: residual outliers that have not been detected by the QC; and an asymmetric QC that selects a sub-population of the good dataset. In both cases, the bias correction is strongly influenced by the feedback, and the performance of the QC is also degraded.The use of a new metric for the bias correction (called the 'pseudo-mode'), approximating the mode of the radiance-departure distribution, significantly reduces the feedback due to outliers and asymmetric QC. The variational bias correction scheme VarBC, which updates the bias inside the analysis, also constrains the feedback triggered by an asymmetric QC, but it has limited skill for outliers in a population of infrared window channel observations. A combination of VarBC with the pseudo-mode benefits from the advantages of both approaches. The bias correction is less sensitive to the QC, and more robust with respect to residual outliers. Copyright (C) 2007 Royal Meteorological Society.

Adaptive bias corrections for satellite radiances need to separate the observation bias from the systematic errors in the background in order to prevent the analysis from drifting towards its own climate. The variational bias correction scheme (VarBC) is a particular adaptive scheme that is embedded inside the assimilation system.VarBC is compared with an offline adaptive and a static bias correction scheme. In simulation, the three schemes are exposed to artificial shifts in the observations and the background. The VarBC scheme can be considered as a good compromise between the static and the offline adaptive schemes. It shows some skill in distinguishing between the background-error and the observation biases when other unbiased observations are available to anchor the system. Tests of VarBC in a real numerical weather prediction (NWP) environment show a significant reduction in the misfit with radiosonde observations (especially in the stratosphere) due to NWP model error. The scheme adapts to an instrument error with only minimal disruption to the analysis.In VarBC, the bias is constrained by the fit to observations - such as radiosondes - that are not bias-corrected to the NWP model. In parts of the atmosphere where no radiosonde observations are available, the radiosonde network still imposes an indirect constraint on the system, which can be enhanced by applying a mask to VarBC. Copyright (C) 2007 Royal Meteorological Society.

The development of an assimilation system for radiance data from the Atmospheric InfraRed Sounder (AIRS) is described, in particular the identification Of Cloud contamination. bias correction and the characterization of errors in the measured radiances and radiative-transfer model. The results of assimilation experiments are presented. These show that a conservative use of AIRS radiance data (in a system already extensively observed with other satellite data) results in a small, but consistent. improvement in the quality of analyses and forecasts. Larger impacts of AIRS are found in hypothetical experiments that test the use of radiances from only a single sounding instrument. In these, the use of AIRS is found to outperform the use of data either from a single Advanced Microwave Sounding Unit-A (AMSU-A) or front a single High-resolution InfraRed Sounder (HIRS). In this hypothetical context the relative forecast performance of each sensor is found to correlate with the size and vertical scale of increments caused by the assimilation of the radiances.