Commit 4306c85a authored by saeed's avatar saeed
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parent 999daff8
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\setlength{\parskip}{6pt}
\newcommand{\unit}[1]{\ensuremath{\, \mathrm{#1}}}
\usepackage{graphicx}
\usepackage[utf8]{inputenc}
\usepackage[colorlinks, urlcolor=blue, linkcolor=black]{hyperref}
\begin{document}
\title{A comparison of AROME wind to the wind observations on the sea}
......@@ -18,7 +19,7 @@ The model and observational data span from the January 1, 2018 to April 1, 2018
\section{Model data}
AROME is a regional atmospheric model running operationally for the short range forecast at SMHI. The current resolution of the model is ${2.5}$ km. AROME uses the lambert canonical conformal projection. A more detailed information on AROME is given by Lisa Bengstson et al (2017), which can be obtained from \href{https://journals.ametsoc.org/doi/pdf/10.1175/MWR-D-16-0417.1}{here}.
We use the model wind data at ${10}$ meter. Model data in the GRIB format are retrieved for the period January 1, 2018 and April 1, 2018 from mars archiving system. They have an hourly temporal resolution. Note that we only use the hourly average wind not the gust wind. The effect gust wind on the average wind are very interesting but it is beyond the scope of this study.
We use the model wind data at ${10}$ meter. Model data in the GRIB format are retrieved for the period January 1, 2018 and April 1, 2018 from mars archiving system. They have an hourly temporal resolution. Note that we only use the hourly average wind not the gust wind. The effect of the gust wind on the average wind are very interesting but it is beyond the scope of this study.
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.
......@@ -38,7 +39,7 @@ In this study, we have compared the wind data from AROME atmospheric regional mo
The model data is interpolated to the observational point using the inverse distance weight interpolation method. The validation period was between January 1, 2018 and April 1, 2018. The comparison between the model and observational data in terms of the time series, scatter plots and correlation coefficients reveals that AROME model does a good job in predicting the wind on the sea.
A follow up to this study is to do a comparison for other years than 2018 and also other periods of a year. The best possible scenario is to look at the data for at least few consecutive years. Also, it is interesting to find out how the interpolation method can affect the results. For instance, we can use the bilinear or the nearest point interpolation method. These warrant a motivation for the follow up study.
\clearpage
\section{Figures}
\begin{figure}[h!]
......@@ -296,16 +297,53 @@ A follow up to this study is to do a comparison for other years than 2018 and al
\end{figure}
\clearpage
\pagebreak
%station Experiment RMS RMSD Correlation
% OBSERVATION 5.979 0.0 1.0
%NYHAMN AROME 5.704 1.552 0.966
%station Experiment RMS RMSD Correlation
% OBSERVATION 5.478 0.0 1.0
%ULKOKALLA AROME 5.401 1.561 0.959
%station Experiment RMS RMSD Correlation
%KOKKOLATANKAR OBSERVATION 5.033 0.0 1.0
%KOKKOLATANKAR AROME 4.909 1.592 0.949
%station Experiment RMS RMSD Correlation
%PORITAHKOLUOTO OBSERVATION 5.237 0.0 1.0
%PORITAHKOLUOTO AROME 4.814 1.341 0.968
%station Experiment RMS RMSD Correlation
%KIRKKONUMMIMAKILUOTO OBSERVATION 5.647 0.0 1.0
%KIRKKONUMMIMAKILUOTO AROME 5.445 1.597 0.959
%station Experiment RMS RMSD Correlation
%PERNAJAORRENGRUND OBSERVATION 5.557 0.0 1.0
%PERNAJAORRENGRUND AROME 5.432 1.501 0.963
%station Experiment RMS RMSD Correlation
%SöderarmA OBSERVATION 5.929 0.0 1.0
%SöderarmA AROME 5.988 1.797 0.955
\section{Tables}
\begin{table}[h!]
\centering
\begin{tabular}{c|c}
Station& Correlation \\
stn1 & 0.92 \\
\begin{tabular}{l|c}
Station& Correlation Coefficient \\
\hline
NYHAMN & 0.966 \\
\hline
ULKOKALLA & 0.959 \\
\hline
KOKKOLA TANKAR & 0.949 \\
\hline
PORI TAHKOLUOTO & 0.968 \\
\hline
KIRKKONUMMI MAKILUOTO & 0.959 \\
\hline
PERNAJA ORRENGRUND & 0.963 \\
\hline
stn2 & 0.92 \\
SöderarmA & 0.955\\
\hline
\end{tabular}
\caption{Correlation table for some stations over the sea}
\caption{Correlation coefficient between ${U}$ wind component of model and the observation for some stations over the sea}
\label{tabstation}
\end{table}
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