This article reviews developments towards assimilating cloud‐ and precipitation‐ affected satellite radiances at operational forecasting centres. Satellite data assimilation is moving beyond the “clear‐sky” approach that discards any observations affected by cloud. Some centres already assimilate cloud‐ and precipitation‐affected radiances operationally and the most popular approach is known as “all‐sky,” which assimilates all observations directly as radiances, whether they are clear, cloudy or precipitating, using models (for both radiative transfer and forecasting) that are capable of simulating cloud and precipitation with sufficient accuracy. Other frameworks are being tried, including the assimilation of humidity retrieved from cloudy observations using Bayesian techniques. Although the all‐sky technique is now proven for assimilation of microwave radiances, it has yet to be demonstrated operationally for infrared radiances, though several centres are getting close. Assimilating frequently available all‐sky infrared observations from geostationary satellites could give particular benefit for short‐range forecasting. More generally, assimilating cloud‐ and precipitation‐affected satellite observations improves forecasts in the medium range globally and can also improve the analysis and shorter‐range forecasting of otherwise poorly observed weather phenomena as diverse as tropical cyclones and wintertime low cloud.

Japan’s new geostationary satellite Himawari-8, the first of a series of the third-generation geostationary meteorological satellites including GOES-16, has been operational since July 2015. Himawari-8 produces high-resolution observations with 16 frequency bands every 10 min for full disk, and every 2.5 min for local regions. This study aims to assimilate all-sky every-10-min infrared (IR) radiances from Himawari-8 with a regional numerical weather prediction model and to investigate its impact on real-world tropical cyclone (TC) analyses and forecasts for the first time. The results show that the assimilation of Himawari-8 IR radiances improves the analyzed TC structure in both inner-core and outer-rainband regions. The TC intensity forecasts are also improved due to Himawari-8 data because of the improved TC structure analysis.

The status of current efforts to assimilate cloud‐ and precipitation‐affected satellite data is summarised with special focus on infrared and microwave radiance data obtained from operational Earth observation satellites. All global centres pursue efforts to enhance infrared radiance data usage due to the limited availability of temperature observations in cloudy regions where forecast skill is estimated to strongly depend on the initial conditions. Most systems focus on the sharpening of weighting functions at cloud top providing high vertical resolution temperature increments to the analysis, mainly in areas of persistent high and low cloud cover. Microwave radiance assimilation produces impact on the deeper atmospheric moisture structures as well as cloud microphysics and, through control variable and background‐error formulation, also on temperature but to lesser extent than infrared data. Examples of how the impacts of these two observation types are combined are shown for subtropical low‐level cloud regimes. The overall impact of assimilating such data on forecast skill is measurably positive despite the fact that the employed assimilation systems have been constructed and optimized for clear‐sky data. This leads to the conclusion that a better understanding and modelling of model processes in cloud‐affected areas and data assimilation system enhancements through inclusion of moist processes and their error characterization will contribute substantially to future forecast improvement. Copyright © 2011 Royal Meteorological Society, Crown in the right of Canada, and British Crown copyright, the Met Office

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 study examines the advantages of infrared all‐sky radiance (ASR) assimilation over traditional clear‐sky radiance (CSR) assimilation using a mesoscale LETKF data assimilation system. To effectively assimilate ASR data from the Himawari‐8 geostationary satellite, a cloud‐dependent quality‐control procedure and an observation error model were developed. A single humidity band to be assimilated and thinning distance were determined based on observation error statistics. The operational pre‐processing and parameter settings, such as observation errors and an adaptive bias correction for CSR, were incorporated into the LETKF data assimilation system.A comparison of the impacts of assimilating ASRs and CSRs was accomplished using single‐cycle and 10‐day cycle assimilation experiments. Study results revealed that ASR assimilation provided a higher degree of improvement in the first‐guess fit for conventional observations and satellite retrievals with respect to temperature, moisture and wind. Furthermore, ASR assimilation displayed a more stable improvement in the prediction of a severe rainfall event because it has more universal data coverage than CSR. Adaptive bias correction schemes with two different sets of predictors for ASRs were tested and revealed the difficulty in extracting additional information for the assimilation of no‐bias corrected ASR due to complicated bias factors. This was in contrast to the CSR assimilation, where bias correction had a positive impact.