@@ -16,12 +16,13 @@ The objective of this study is to investigate how well AROME performs in predict

The model and observational data span from the January 1, 2018 to April 1, 2018 with a hourly resolution. Model data are in the grib format retrieved from mars archiving system.

\section{Model data}

AROME is a regional atmospheric model running operationally at SMHI. The current resolution of the model is ${2.5}$ km. AROME uses the lambert canonical conformal projection. We use the model wind data at ${10}$ meter. The model wind data is first interpolated to the regular grid and then their values at the observational points are obtained using the inverse distance weight interpolation method.

AROME is a regional atmospheric model running operationally at SMHI. The current resolution of the model is ${2.5}$ km. AROME uses the lambert canonical conformal projection. We use the model wind data at ${10}$ meter. The model wind data is first interpolated to the regular grid and then their values at the observational points are obtained using the inverse distance weight interpolation method. It should be mentioned that since the wind data are relative to the model grid, before doing the interpolation, we rotate them back to the geogrid, .i.e. west-east and south-north direction on the earth.

Inverse distance weighted (IDW) interpolation assumes that the points close to the observation point have more influence on that and the influence decreases further away from the observation point, determined by a defined weighted function such as the inverse of the distance between the observation point and the model point. The interpolation is done for both wind components in the west-east and south-north directions, namely ${u}$ and ${v}$ components.

Inverse distance weighted (IDW) interpolation assumes that the points close to the observation point have more influence on that and the influence decreases further away from the observation point, determined by a defined weighted function such as the inverse of the distance between the observation point and the model point. The interpolation is done for both wind components in the west-east and south-north directions, namely ${u}$ and ${v}$ components.

Figure \ref{uwind} is an example of model data used in this study. It shows ${U}$ wind component. The latitude of model data is in a range $53.5$-${67.725}$ and their respective ongitude lies between ${7.0}$-${27.958}$.

\section{Observational data}

Observational data comes from mora database. We only use WMO stations located on the sea or close to the coast. Figure \ref{station} shows the location of the mora stations on the sea. Note that not all these stations located exactly at the height 10 m above the ground. That is, we only use those stations having a height around${10}$ m. The data is retrieved from mora website in a json format.

Observational data comes from mora database. We only use WMO stations located on the sea or close to the coast. Figure \ref{station} shows the location of the 64 mora stations on the sea. Note that not all these stations measure the wind exactly at the height 10 m above the ground. That is, we only use those stations having a height comparable to$10$ m. The wind data is retrieved from mora website in a json format. The json data are then processed and converted to CSV format, making them suitable to be compared to the model data.

\section{Results}

In the comparison between the model and observation, we look at the time series of the wind component and also the scatter plots.

We also calculate the correlation coefficient between the model and observation. We only show the results of 5 stations as there is many stations.

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@@ -31,12 +32,6 @@ In this study, we compare the wind data from AROME atmospheric regional model to