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doi: 10.1002/qj.905pmid: N/A
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
doi: 10.1002/qj.953pmid: N/A
This article assesses current issues related to scattering radiative transfer (RT) in data assimilation (DA) and proposes possible ways to solve or mitigate these issues. Emphasis is put not so much on fundamental issues related to RT but on the practical application within a framework of operational numerical weather prediction and DA with their tight constraints on computational efficiency. In particular, three potentially critical open issues are studied: firstly, the trade‐off between speed and accuracy in RT schemes for DA. A numerically efficient method is proposed to determine beforehand whether scattering needs to be accounted for. Secondly, the impact of spectrally highly variable gaseous absorption coefficients within a given instrument's bandpass and its implications on scattering RT is studied. Results of this second part are also put in context with uncertainties caused by the lack of knowledge of scattering optical properties. Finally, model errors due to, for example, the assumption of plane‐parallel RT are studied. It is argued that errors caused by plane‐parallel RT will likely continue to dominate the error budget both in terms of biases and random errors. Copyright © 2011 Royal Meteorological Society
doi: 10.1002/qj.980pmid: N/A
Short‐range forecasts of cloud cover are considered an important part of providing a public weather service in the UK. These require accurate initial conditions. Satellite imagery and surface cloud reports provide observations of cloud but using these to initialize forecast models is not easy. Cloud data are not well suited to variational analysis schemes. This paper describes how the Met Office variational analysis has been adapted to use such data. Results show that cloud observations can be usefully assimilated. Copyright © 2011 British Crown copyright, the Met Office. Published by John Wiley & Sons Ltd.
doi: 10.1002/qj.928pmid: N/A
Since July 2008, Infrared Atmospheric Sounder Interferometer (IASI) radiances have been assimilated in the French global model Action de Recherche Petite Echelle Grande Echelle (ARPEGE) and since April 2010 at high density in the French convective‐scale model Applications of Research to Operations at MEsoscale (AROME). The impact of the assimilation of clear IASI data on forecast skill is found to be positive for both models. As many observed scenes are cloudy, several ways to characterize the clouds within the observed spectra are investigated. Firstly, a simple approach that enables the determination of both the cloud‐top pressure and the effective cloud amount of an equivalent single‐layer cloud is followed using a CO2‐slicing method. The first assimilation trials in overcast conditions lead to a small positive impact on forecast skill. Another approach would be to take advantage of the fact that cloud water variables are described at high resolution in the convective‐scale model AROME. Model cloud fields have to be used in conjunction with a cloudy radiative transfer model. The first simulations using this technique are performed and compared against observations. Copyright © 2011 Royal Meteorological Society
doi: 10.1002/qj.917pmid: N/A
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
doi: 10.1002/qj.802pmid: N/A
An ensemble assimilation, which is based on the operational cloud‐resolving model Applications de la Recherche à l’Opérationnel à Méso‐Echelle (AROME) and its 3D‐Var assimilation system, is used to diagnose background‐error covariances separately in areas with and without fog. The fog and haze analysis system Cartographies des Analyses du RIsque de BrOUillard (CARIBOU) is used as reference to calibrate the best fog predictor from model fields, which was found to be a low‐level nebulosity. It appears that the physical processes in fog layers lead to very specific balances between control variables as well as much shorter vertical correlation length‐scales at low levels in background‐error covariances. In order to spread the information from surface and satellite observations with adequate structures in fog areas, a binary heterogeneity based on the use of geographical masks is added to the background‐error covariances. After the elimination of discontinuities at the mask borders, the positive impact of this formalism on the analysis‐increment structure is discussed. Impact studies based on long‐term real cases indicate that the global impact is closely related to the quality of the fog mask, for which future improvements are awaited. Copyright © 2011 Royal Meteorological Society
doi: 10.1002/qj.833pmid: N/A
This article provides estimates of effective observation errors and their inter‐channel and spatial correlations for microwave imager radiances currently used in the European Centre for Medium‐Range Weather Forecasts (ECMWF) system. The estimates include the error contributions from the observation operator used in the assimilation system. We investigate how the estimates differ in clear and cloudy/rainy regions. The estimates are obtained using the Desroziers diagnostic. The results suggest considerable inter‐channel and spatial error correlations for current microwave imager radiances, with observation errors that are significantly higher than the measured instrument noise. Inter‐channel error correlations are even stronger for cloudy/rainy situations, where channels with the same frequency but different polarizations show error correlations larger than 0.9. The findings suggest that a large proportion of the observation error originates from errors of representativeness and errors in the observation operator. The latter includes the errors from the forecast model, which can be significant in the case of humidity or cloud and rain. Assimilation experiments with single SSM/I fields of view highlight how the filtering properties of a four‐dimensional variational assimilation system are changed when inter‐channel error correlations are taken into account in the assimilation. Depending on the first‐guess (FG) departures in the channels used, increments can be larger as well as smaller in comparison with the use of diagonal observation errors. Copyright © 2011 Royal Meteorological Society
doi: 10.1002/qj.830pmid: N/A
This article examines the first‐guess (FG) departures of microwave imager radiances assimilated in all‐sky conditions (i.e. clear, cloudy and precipitating). Agreement between FG and observations is good in clear skies, with error standard deviations around 2 K, but in heavy cloud or precipitation errors increase to 20 K. The forecast model is not good at predicting cloud and precipitation with exactly the right intensity or location. This leads to apparently non‐Gaussian behaviour, both heteroscedasticity, i.e. an increase in error with cloud amount, and boundedness, i.e. the size of errors is close to the geophysical range of the observations, which runs from clear to fully cloudy. However, the dependence of FG departure standard deviations on the mean cloud amount is predictable. Using this dependence to normalise the FG departures gives an error distribution that is close to Gaussian. Thus if errors are treated correctly, all‐sky observations can be assimilated successfully under the assumption of Gaussianity on which assimilation systems are based. This ‘symmetric’ error model can be used to provide a robust threshold quality‐control check and to determine the size of observation errors for all‐sky assimilation. In practice, however, this ‘observation’ error is being used to account for the model's difficulty in forecasting cloud, which really comes from errors in the background and in the forecast model. Hence in future it will be necessary to improve the representation of background and model error. Separately, symmetric cloud amount is recommended as a predictor for bias correction schemes, avoiding the sampling problems associated with ‘asymmetric’ predictors like the FG cloud amount. Copyright © 2011 Royal Meteorological Society
doi: 10.1002/qj.865pmid: N/A
In this paper, a comprehensive assessment of the impact of all‐sky radiance observations in the operational European Centre for Medium‐Range Weather Forecasts' assimilation and forecast system is presented using advanced diagnostic tools. In particular, the observation influence in the assimilation process and the related contribution to the short‐range forecast error of radiance observations from microwave sensors is evaluated with recently developed diagnostic tools based on the adjoint version of the model. Recent operational changes to the assimilation of these observations result in a more beneficial impact on the initial condition and the short‐range forecast. The largest information content is obtained for observations that are located in clear‐sky regions as indicated by the model short‐range forecast and the observations themselves. However, the largest decrease in the forecast error is provided by observations detected in cloudy regions. It is shown that the use of Special Sensor Microwave/Imager data helps to reduce model systematic errors in the central eastern equatorial Pacific, where the intertropical convergence zone is located, and the Arabian Sea, where the monsoon season occurs. Copyright © 2011 Royal Meteorological Society
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