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.

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.

A TRhlM Precipitation Radar (PR) standard algorithm for classifying precipitation types is designated as the algorithm 2A-23. This algorithm classifies precipitation type into three categories: stratiform, convective, and other. In the case of convective precipitation, a further examination is made to determine whether it is warm rain or not. The algorithrn 2A-23 also detects bright band and determines the height of briglit band when it is detected. OBJECTIVESMain ol)ject,ivcs of the 'Fropica1 Rainfall Measuring Missioii (TRMM) Precipitation Radar (PR) standard algoritlirn 2.4-23 are as follows: 0 Detection of briglit band I(BB) 0 Determination of the height of BB when it exists 0 Classification of rain type into three categories 0 Determination of warm raiii In 2A-23, two algorithms ai-e installed for classifying precipitation type; they are called respectively as a vertical profile method (V-Inethod) arid a horizontal pattern Inethod (H-method). Both methods classify precipitation into three categories: stratiforin, corivective, and othier. Since the results are different for two methods, a merged precipitation type is written in the output file of 2A-23. A strategy for the merge of preciipitation type is explained latcr. VERTICAL PROFILE METHODThe V-method tries to detelct the existence of bright band (BB) first. When BB is detected, the precipitation is basically classified as stratifoimi. Then the V-method goes on to the detection of convective precipitation, whiich is cliaracterizetl by a strong ratliar echo. When the precipitation type is neither stratiforin nor convective, it is classified as other type.The detection of BB is c a I r i d out using the following spatial filter: 'Supported by NASDA 11irorigli 'I'IUvlM JltA contract.where the row indicates the direction of antenna scan angle and the column the range direction. Since adjacent three antenna scans of data are applied to the spatial filter, it can detect BB which usually extends soniewliat uniformly in the horizontal direction.The above spatial filter is based on the following second derivative of Z factor with respective to the range from tlie satellite, d2Z ---(-Z(r -Ar) + 2Z(r) -Z(r + Ar))/(Ar)2 (2) dr2 -where r stands for the range, and Ar stands for the range resolution (=0.25 ktn in the case of TRMM PR).When the output of the spatial filter satisfies certain conditions, it is concluded that the bright band (BB) is detected. The followings are rnairi conditions for the existence of BB:(1) Near the height of BB, the output of the spatial f i l k r exceeds a given threshold,(2) Above the height of BB (Hbb), the value of Z decreases appreciably,(3) The height Hbb must appear almost at the same height, (4) The height Hbb must be within the BB window region, which is estimated from a climatological surface terripeiature.In the above, the spatial filter is applied to thc real value of Z, but not to the dB value of Z, in order to detect the maxiniuni value of Z in its vertical profile. With ronditions (2) and (3), we car1 discriminate the BB peak ...

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.