This article presents SPARE‐ICE, the Synergistic Passive Atmospheric Retrieval Experiment‐ICE. SPARE‐ICE is the first Ice Water Path (IWP) product combining infrared and microwave radiances. By using only passive operational sensors, the SPARE‐ICE retrieval can be used to process data from at least the NOAA 15 to 19 and MetOp satellites, obtaining time series from 1998 onward. The retrieval is developed using collocations between passive operational sensors (solar, terrestrial infrared, microwave), the CloudSat radar, and the CALIPSO lidar. The collocations form a retrieval database matching measurements from passive sensors against the existing active combined radar‐lidar product 2C‐ICE. With this retrieval database, we train a pair of artificial neural networks to detect clouds and retrieve IWP. When considering solar, terrestrial infrared, and microwave‐based measurements, we show that any combination of two techniques performs better than either single‐technique retrieval. We choose not to include solar reflectances in SPARE‐ICE, because the improvement is small, and so that SPARE‐ICE can be retrieved both daytime and nighttime. The median fractional error between SPARE‐ICE and 2C‐ICE is around a factor 2, a figure similar to the random error between 2C‐ICE ice water content (IWC) and in situ measurements. A comparison of SPARE‐ICE with Moderate Resolution Imaging Spectroradiometer (MODIS), Pathfinder Atmospheric Extended (PATMOS‐X), and Microwave Surface and Precipitation Products System (MSPPS) indicates that SPARE‐ICE appears to perform well even in difficult conditions. SPARE‐ICE is available for public use.

The High-resolution Infrared Radiation Sounder (HIRS) has been flown on 17 polar-orbiting satellites between the late 1970s and the present day. HIRS applications require accurate characterisation of uncertainties and inter-channel error correlations, which has so far been lacking. Here, we calculate error correlation matrices by accumulating count deviations for sequential sets of calibration measurements, and then correlating deviations between channels (for a fixed view) or views (for a fixed channel). The inter-channel error covariance is usually assumed to be diagonal, but we show that large error correlations, both positive and negative, exist between channels and between views close in time. We show that correlated error exists for all HIRS and that the degree of correlation varies markedly on both short and long timescales. Error correlations in excess of 0.5 are not unusual. Correlations between calibration observations taken sequentially in time arise from periodic error affecting both calibration and Earth counts. A Fourier spectral analysis shows that, for some HIRS instruments, this instrumental effect dominates at some or all spatial frequencies. These findings are significant for application of HIRS data in various applications, and related information will be made available as part of an upcoming Fundamental Climate Data Record covering all HIRS channels and satellites.