Evaluation of radiative transfer schemes for mesoscale model data assimilation: a case study

Memmo, A.; Faccani, C.; Ferretti, Rossella; Michele, Sabatino Di; Marzano, Frank S.

doi:10.5194/adgeo-7-193-2006

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…The possible causes of the bias in the 3D-Var RT model have been analysed elsewhere (Memmo et al, 2006). The aim is to compare the 3D-Var RT model with a fairly detailed RT scheme implementing both extinction and multiple scattering processes due to sphere-equivalent hydrometeors in various thermodynamic phases (Smith et al, 2002;Tassa et al, 2003).…”

Section: Improvement Factormentioning

confidence: 99%

Three‐dimensional variational assimilation of Special Sensor Microwave/Imager data into a mesoscale weather‐prediction model: A case study

Faccani

Cimini

Marzano

et al. 2007

Quart J Royal Meteoro Soc

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Assimilation of data from the Special Sensor Microwave/Imager (SSM/I) is performed in order to improve the forecast of a heavy-precipitation case (IOP2b, 20-21 September 1999) of the Mesoscale Alpine Programme 1999. The three-dimensional variational data assimilation technique of the MM5 model is used. Either brightness temperatures or precipitable water and surface wind speed are assimilated. The sensitivity of the model to SSM/I data is also tested by selectively excluding SSM/I frequencies and changing the size of the thinning box. All the experiments are performed using the European Center for Medium range Weather Forecasting (ECMWF) analysis on pressure level.The new initial conditions show considerable underestimation of the surface wind component V , and, even more, of the surface water vapour mixing ratio. This last error is partially corrected by assimilation of precipitable water alone, although these data produce a large increase in the mean error of the other surface variables (U , V and T ). However, the forecast with this new set of initial conditions shows a good agreement (high correlation coefficient) with the rain gauge observations for the 1 h accumulated precipitation 3 h after the initial time. With a doubled box size, there is low sensitivity to the density of the observations used. In this case, the effect of the SSM/I data is slight, and the rainfall pattern produced is comparable to that obtained without any data assimilation. The model performance is also degraded if the 22 GHz brightness temperatures are removed from the assimilated measurements: the correlation coefficient for the precipitation is lower than in the case where all the frequencies are assimilated, and it decreases over time. In general, the use of precipitable water and surface wind speed affects the early stages (3 h) of the rainfall forecast, reducing the model spin-up. Brightness temperatures affect the forecast at a longer range (10 h).

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Section: Improvement Factormentioning

confidence: 99%