This article investigates the use of an updated observation-error covariance matrix for the Infrared Atmospheric Sounding Interferometer (IASI) in the European Centre for Medium-Range Weather Forecasts (ECMWF) system. The new observation-error covariance matrix is based on observation-space diagnostics and includes interchannel error correlations, but also assigns significantly altered error standard deviations. The update is investigated in detail in assimilation experiments, including an assessment of the role of error inflation and taking interchannel error correlations into account.The updated observation-error covariance leads to a significant improvement in the use of IASI data, especially in the Tropics and the stratosphere and particularly for humidity and ozone. The benefits are especially strong for short-range forecasts, whereas the impact in the medium range is less pronounced.The study highlights the benefits of taking interchannel error correlations into account, which allows the use of an observation-error covariance for IASI that is overall more consistent with departure statistics. At the same time, the study also demonstrates that error inflation can be used to compensate partially, though not fully, for neglected error correlations. Adjustments such as scaling of the originally diagnosed observation-error estimates are also found to be beneficial when the diagnosed interchannel error correlations are taken into account.

The operational assimilation of theCross‐track Infrared Sounder (CrIS) radiances in a fourdimensional variational framework at the European Centre for Medium‐range Weather Forecasts (ECMWF) is presented. Currently onboard the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite, CrIS provides radiance data on 1305 channels in three spectral bands. Based on the experience of radiance assimilation using earlier infrared sounders, the use of CrIS radiances relies strongly on CO2‐sensitive channels in the 15 μm absorption band. The overwhelming majority of data is assimilated assuming no significant contamination from cloud or aerosol, and use of data over land is limited to channels with primary sensitivity in the stratosphere. Observation‐error covariance specification is based on diagnostic methods and inter‐channel error correlations are explicitly accounted for. The use of CrIS radiances is demonstrated to have a consistent beneficial impact on shortand medium‐range forecasts, especially in the Extratropics.

The operational assimilation of Infrared Atmospheric Sounding Interferometer (IASI) radiances at the European Centre for Medium-range Weather Forecasts (ECMWF) relies primarily on the use of clear data, either in completely cloud-free locations or restricting the assimilation to channels that are insensitive to underlying cloud. Prior to the data assimilation, cloud-contaminated channels are identified and rejected in cloud detection, i.e. in a screening process based on observation minus background departure data. Background errors have the potential to confuse the cloud detection. On the one hand, a false alarm occurs when a background error is incorrectly interpreted as a cloud. On the other hand, cloud is missed if the background error compensates for the cloud radiative effect. This article outlines a method to improve the cloud detection by making additional use of collocated imager data from the Advanced Very High Resolution Radiometer (AVHRR).An independent cloud-detection scheme, based only on the AVHRR data, is formulated and compared with the departure-based scheme currently in operational use at ECMWF. The intercomparison reveals a considerable number of discrepancies, with only one of the two schemes suggesting the presence of cloud. Combining the two schemes results in an imager-assisted scheme, where the AVHRR data are used to set an additional requirement before allowing an IASI field of view to be diagnosed completely clear of clouds. In data assimilation experiments, using the imager-assisted scheme results in systematic lower tropospheric warming in the winter hemispheres, particularly over the Arctic sea ice. The modified cloud detection is shown to have a modestly positive impact on independent observation departure statistics and forecast scores.

Tropospheric delay affects the propagation of the microwave signals broadcast by the Global Positioning System (GPS) satellites. Geodetic processing software enable estimation of this effect on the slanted signal paths connecting the satellites with the ground-based receivers. These estimates are called slant delay (SD) observations and they are potentially of benefit in numerical weather prediction.The three-dimensional variational data assimilation system of the High-Resolution Limited-Area Model (HIRLAM 3D-Var) has been modified for data assimilation of the SD observations. This article describes the ground-based GPS observing system, the SD observation operator, the estimation of the observation-and background-error standard deviations, the methodology of accounting for the observation-error correlation, and the tuning of the background quality control for this observation type.The SD data assimilation scheme is evaluated with experiments utilizing hypothetical observations from a single receiver station, as well as a single-case experiment utilizing real observations from a regional GPS receiver network. The ability of the data assimilation system to extract the asymmetric information from the SD observations is confirmed. In terms of analysis increment structure and magnitude, SD observations are found to be comparable with other observation types currently in use, provided that the observation-error correlation is taken into account.

Doppler radars provide measurements of the radar radial wind component at high spatial and temporal resolution. The variational data-assimilation framework enables direct use of these measurements in numerical weather prediction models. Bias estimation of Doppler-radar radial winds requires special attention because of the azimuthal scanning strategy. Calculation of the bias statistic over all azimuthal directions results in a near-zero value even in the presence of systematic differences between the measured and modelled wind speed and direction. This paper describes a method for bias estimation of Doppler-radar radial-wind observations. Intercomparison between the estimated biases of Doppler-radar and radiosonde wind observations reveals that the wind speed and direction biases are of comparable magnitude.