Commit 2ef0b9ae authored by saeed's avatar saeed
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vindhavs is modified

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\maketitle
\section{Introduction}
In this study, we make a comparison between the wind data from the atmospheric model called AROME against the observation. The wind Observations stems from mora database. The information on mora database can be found \href{http://mora-apps/}{here}.
The objective of this study is to understand how well AROME performs in predicting the wind over the ocean and the coastal areas. This is of paramount importance in connection with the ocean circulation model such as NEMO when run in the forced mode.
The data spans 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.
In this study, we make a comparison between the wind data from the atmospheric model called AROME and the wind observation. The wind observations stem from mora database. The information on mora database can be found \href{http://mora-apps/}{here}.
The objective of this study is to investigate how well AROME performs in predicting the wind over the sea. This is of paramount importance in connection with the ocean circulation model such as NEMO ocean model when run in the forced mode. NEMO model is runs operationally in SMHI and give the sea surface, sea surface temperature, salinity and some other oceanic variables.
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. Inverse distance weighted (IDW) interpolation assumes that the points close to the observation point have more influence on that and the influence decreases further 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. It is done for both wind components in the west-east and south-north directions, namely ${u}$ and ${v}$ components.
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.
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.
\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.
......@@ -34,5 +38,13 @@ In this study, we compare the wind data from AROME atmospheric regional model to
\label{station}
\end{figure}
\begin{figure}[h!]
\centering
\includegraphics[width=\textwidth]{uwind.png}
\caption{10 meter U wind from AROME model.}
\label{uwind}
\end{figure}
\end{document}
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