A Preliminary Impact Study of Wind on Assimilation and Forecast Systems into the One-Dimensional Fog Forecasting Model COBEL-ISBA over Morocco

Driss Bari

doi:10.3390/atmos10100615

Abstract

The assimilation impact of wind data from aircraft measurements (AMDAR), surface synoptic observations (SYNOP) and 3D numerical weather prediction (NWP) mesoscale model, on short-range numerical weather forecasting (up to 12 h) and on the assimilation system, using the one-dimensional fog forecasting model COBEL-ISBA (Code de Brouillard à l’Échelle Locale-Interactions Soil Biosphere Atmosphere), is studied in the present work. The wind data are extracted at Nouasseur airport, Casablanca, Morocco, over a winter period from the national meteorological database. It is the first time that wind profiles (up to 1300 m) are assimilated in the framework of a single-column model. The impact is assessed by performing NWP experiments with data denial tests, configured to be close to the operational settings. The assimilation system estimates the flow-dependent background covariances for each run of the model and takes the cross-correlations between temperature, humidity and wind components into account. When assimilated into COBEL-ISBA with an hourly update cycle, the wind field has a positive impact on temperature and specific humidity analysis and forecasts accuracy. Thus, a superior fit of the analysis background fields to observations is found when assimilating AMDAR without NWP wind data. The latter has shown a detrimental impact in all experiments. Besides, wind assimilation gave a clear improvement to short-range forecasts of near-surface thermodynamical parameters. Although, assimilation of SYNOP and AMDAR wind measurements slightly improves the probability of detection of fog but also increases the false alarms ratio by a lower magnitude.

Highlights

Assimilation of wind observations plays an important role in numerical weather prediction (NWP)models to specify the atmospheric dynamics, at the mesoscale
Assimilation of synoptic observations (SYNOP) and AMDAR wind measurements slightly improves the probability of detection of fog and increases the false alarms ratio by a lower magnitude
As poor visibility conditions have a considerable influence on airport traffic, a need exists for accurate and updated fog and low cloud forecasts

Summary

Introduction

Assimilation of wind observations plays an important role in numerical weather prediction (NWP)models to specify the atmospheric dynamics, at the mesoscale. Assimilation of wind observations plays an important role in numerical weather prediction (NWP). The initialisation of such models through the assimilation of all available observations is found to be relevant for nowcasting and short-range forecasting of, among others, severe weather events such as fog and heavy rainfall (Strajnar et al, 2015 [1]; De Haan and Stoffelen, 2012 [2]). Showed that wind at the top of the nocturnal boundary layer plays a significant role during the bifurcation from formation to mature phases of fog layer development. As a result of an impact study on fog forecasting, Philip et al (2016) [4] found that high vertical resolution in a kilometric-scale 3D NWP model leads to stronger nocturnal jet and turbulence at the top of the nocturnal boundary layer. An improvement of the initial wind field will result in a better forecast of both wind and other meteorological parameters (e.g., temperature and humidity)

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