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\documentclass[20pt,a4paper]{article}
\setlength{\parskip}{6pt}
\newcommand{\unit}[1]{\ensuremath{\, \mathrm{#1}}}
\usepackage{graphicx}
\usepackage[colorlinks, urlcolor=blue, linkcolor=black]{hyperref}
\begin{document}
\title{A comparison of AROME wind to the wind observations on the sea}
\author{Saeed Falahat}
\maketitle

\section{Introduction}
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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}.

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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 predicts the sea surface height, sea surface temperature, salinity and some other oceanic variables.   
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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.
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\section{Model data}
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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.
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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.
   
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Figure \ref{uwind}, showing  ${U}$ wind component, is an example of model data used in this study. The latitude of model data is in a range $53.5$-${67.725}$ and their respective ongitude lies between ${7.0}$-${27.958}$.
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\section{Observational data}
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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.   
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\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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\section{Conclusion and future perspective}
In this study, we compare the wind data from AROME atmospheric regional model to the observational data for the wind coming from mora database. This is done only for the stations located on the Baltic sea and the north sea which is very crucial for the NEMO ocean circulation model which is run operationally in SMHI. The model data is interpolated to the observational point using the inverse distance weight interpolation method. The comparsion between the model and observational data reveals that AROME does a good job in predicting the wind on the sea. The validation period was between January 1, 2018 and April 1, 2018. It means that we need to do a comparison for other years and also other times of a year. The best possible scenario is to look at the data for at least few years. That would be a motivation for the further study.     
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\section{Figures}

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\begin{figure}[h!]
\centering
\includegraphics[width=\textwidth]{uwind.png}
\caption{10 meter U wind from AROME model.}
\label{uwind}
\end{figure}

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\begin{figure}[h!]
\centering
\includegraphics[width=\textwidth]{stationmap.png}
\caption{WMO stations located on sea}
\label{station}
\end{figure}


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\end{document}