A cloud scheme for data assimilation: Description and initial tests

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100

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.

Section: Infrared Radiancesmentioning

confidence: 99%

Satellite cloud and precipitation assimilation at operational NWP centres

Bauer

Auligné

Bell

et al. 2011

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100

“…The SVs targeted on TCs are calculated with the ECMWF linearized physics package included in the tangent linear and adjoint model . Linearized schemes for surface drag, vertical diffusion, subgrid-scale orographic effects, large-scale condensation and deep convection have been used in the SV computation (Mahfouf, 1999;Tompkins and Janisková, 2004;Lopez and Moreau, 2005). The SVs are optimized below 500 hPa (Puri et al, 2001).…”

Section: Singular Vectorsmentioning

confidence: 99%

Impact of perturbation methods in the ECMWF ensemble prediction system on tropical cyclone forecasts

Lang

Leutbecher

Jones

2012

In the operational configuration of the ensemble prediction system of the European Centre for Medium-Range Weather Forecasts, different methods are applied to account for initial condition and model uncertainties. Singular vectors and an ensemble of data assimilations are used to generate perturbations to the initial conditions. Two stochastic tendency perturbation schemes aim to mimic model errors: the stochastic kinetic-energy backscatter scheme and the stochastically perturbed parametrization tendency scheme. In this study, the impact of the different perturbation methods on the ensemble spread during tropical cyclone (TC) events is compared and the time evolution of the spatial perturbation structure is investigated. The structure of perturbations due to different methods initially is quite different. However, our results show that they converge toward a TC displacement and an intensity-change pattern during the first two days of the forecast, on average. The perturbations generated by the stochastic tendency perturbation schemes grow rapidly and effectively excite growing modes of the flow. After 48 hours, a large part of the total energy of the singular vector perturbations in the vicinity of the TCs can be explained by the perturbations of other methods. The perturbations by the ensemble of data assimilations dominate the ensemble spread for a rather short lead time (around 24 hours). In about 40% of cases, the perturbations due to the tendency perturbation schemes or those generated from singular vectors produce a larger TC track and central pressure spread than the perturbations by the ensemble of data assimilations after two days forecast time. If all methods are applied, the average TC track spread of the ensemble matches the average error of the ensemble-mean well. In addition, the ensemble captures the anisotropy in the position uncertainty of the TCs.

“…The 1D+4D-Var algorithm includes two parts: the 1D-Var that includes an optimal estimation procedure to retrieve the microphysical properties and TPW from SSM/I radiance observations, and the 4D-Var analysis (Rabier et al, 2000) that assimilates the TPW as a pseudo-observation. The observation operator includes three components: a convection scheme (Lopez and Moreau, 2005) that represents subgrid-scale processes and treats convection types defined as shallow, mid-level and deep convection in a unified way; a large-scale condensation scheme (Tompkins and Janisková, 2004) that uses the convective detrainment prescribed by the convection model with a similar precipitation generation formulation; and a multiple-scattering radiative-transfer model RTTOV-SCATT (Bauer et al, 2006a) with scattering calculated using the delta-Eddington approach. The advantage of the 1D-Var over ordinary variational retrievals is that it uses the same background state, background errors and moist physics package as the 4D-Var .…”

Section: The Ecmwf 1d+4d-var Algorithmmentioning

confidence: 99%

Comparing rain retrievals from GPROF with ECMWF 1D‐Var products

Wang

Kummerow

Geer

et al. 2012

Both the Goddard Profiling Algorithm (GPROF) and European Centre for MediumRange Weather Forecasts (ECMWF) one-dimensional + four-dimensional variational analysis (1D+4D-Var) rainfall retrievals are inversion algorithms based on Bayes' theorem. Differences stem primarily from the a priori information. The GPROF uses an observationally generated a priori database whereas ECMWF 1D-Var uses the model forecast first guess (FG) fields. The relative similarity in the two approaches means that comparisons can shed light on the differences that are produced by the a priori information. Case studies have found that differences can be classified into four categories based upon the agreement in the brightness temperatures (T b s) and in the microphysical properties of cloud water path (CWP) and rainwater path (RWP) space. A category of special interest is when both retrievals converge to similar T b through minimization procedures but produce different CWP and RWP. The similarity in T b can be attributed to comparable total water path (TWP) between the two retrievals while the disagreement in the microphysics is caused by their different degrees of constraint of the cloud/rain ratio by the observations. This situation occurs frequently and takes up 46.9% in the 1 month 1D-Var retrievals examined. The two retrievals produce similar spatial patterns but with different magnitude. The allocation of a large amount of CWP in the 1D-Var retrieval seems to be related to the stratiform portion of rain, which is produced by the large-scale condensation scheme. To attain better-constrained cloud/rain ratios and improved retrieval quality, this study suggests the implementation of higher microwave frequency channels in the 1D-Var algorithm.