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Arnout Feijt, Paul de Valk, and Sibbo van der Veen

Abstract

The cloud detection algorithm of the Royal Netherlands Meteorological Institute (KNMI) Meteosat Cloud Detection and Characterization KNMI (Metclock) scheme is introduced. The algorithm analyzes the Meteosat infrared and visual channel measurements over an area from about 25°W to 25°E and from 35° to 70°N, encompassing Europe and a small part of northern Africa. The scheme utilizes surface temperatures from a numerical weather prediction model. Synoptic observations are used to adjust the model surface temperatures to represent satellite brightness temperatures for cloud-free conditions. The measured reflected sunlight is analyzed using a minimum reflectivity atlas. Comparison of cloud detection results with synoptic observations of cloud cover at about 800 synoptic stations over land and 50 over sea were made on a 3-h basis for 1997. In total, two million synoptic observations were used to evaluate the detection method. Of the reported cloud cover, Metclock detected 89% during daytime and 73% during nighttime over land and 86% during daytime and 80% during nighttime over sea. The fraction of pixels labeled as cloud free in reported cloud-free conditions was 92% for daytime and 90% for nighttime over land and 94% during daytime and 90% during nighttime over sea. The largest contribution to the cloud detection capability is the threshold comparison of the satellite brightness temperatures with the adjusted model surface temperatures. The cloud detection method is used for the initialization of a short-term cloud prediction model and testing of cloud parameterizations of atmospheric models that will be used as an aid to meteorologists in analyzing Meteosat data.

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Siebren de Haan, Gert-Jan Marseille, Paul de Valk, and John de Vries

Abstract

Denial experiments, also denoted observing system experiments (OSEs), are used to determine the impact of an observing system on the forecast quality of a numerical weather prediction (NWP) model. When the impact is neutral or positive, new observations from this observing system may be admitted to an operational forecasting system based on that NWP model. A drawback of the method applied in most denial experiments is that it neglects the operational time constraint on the delivery of observations. In a 10-week twin experiment with the operational High-Resolution Limited-Area Model (HIRLAM) at KNMI, the impact of additional ocean surface wind observations from the Advanced Scatterometer (ASCAT) on the forecast quality of the model has been verified under operational conditions. In the experiment, the operational model was used as reference, parallel to an augmented system in which the ASCAT winds were assimilated actively. Objective verification of the forecast with independent wind observations from moored buoys and ASCAT winds revealed a slight improvement in forecast skill as measured by a decrease in observation-minus-forecast standard deviation in the wind components for the short range (up to 24 h). A subjective analysis in a case study showed a realistic deepening of a low pressure system over the North Atlantic near the coast of Ireland through the assimilation of scatterometer data that were verified with radiosonde observations over Ireland. Based on these results, the decision was made to include ASCAT in operations at the next upgrade of the forecasting system.

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Germán Delgado, Paul de Valk, Ángel Redaño, Sibbo van der Veen, and Jerónimo Lorente

Abstract

A validation of a very short-range forecast model is presented: the Meteosat Cloud Advection System (METCAST). The model forecasts IR (10.8 μm) images based on Meteosat Second Generation (MSG) data and uses ouput from the Royal Netherlands Meteorological Institute’s [Koninklijk Nederlands Meteorologisch Instituut (KNMI)] NWP model, the High Resolution Limited Area Model (HIRLAM). METCAST advects clouds and takes into account the evaporation–condensation processes in the atmosphere. To assimilate the satellite images into METCAST, an MSG image is converted to a modified image with coarser resolution. The relative performance of METCAST is evaluated, comparing the model results with persistence and a second nowcasting model called CineSat.

Two statistical techniques are used to evaluate the forecasts: (a) the computation of the BIAS, RMSE, and Hanssen and Kuiper (HK) discriminant for a cloud mask selected in the modified and forecast images and (b) the contiguous rain areas (CRAs) technique, which permits a decomposition of the mean-squared error (MSE) of cloud clusters in three components: displacement, intensity, and shape.

Five months of data, from June to November 2006 (August was not available), are used for this study. METCAST BIAS shows poor skill in comparison to CineSat and persistence. METCAST performs better in terms of the RMSE and HK discriminant. The CRA application reveals that although METCAST has a greater MSE volume component than CineSat, its displacement error component is smaller. Two interesting conclusions can be drawn: METCAST performs well when advecting cloudy pixels, but improvement in the atmospheric physics of the nowcast model may be required.

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