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2019-07-19T19:20:02+02:00 (14 months ago)
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djlea
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#2297 Updates to OBS and ASM documentation

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  • NEMO/trunk/doc/latex/NEMO/subfiles/chap_OBS.tex

    r11151 r11316  
    88\label{chap:OBS} 
    99 
    10 Authors: D. Lea, M. Martin, K. Mogensen, A. Vidard, A. Weaver, A. Ryan, ...   % do we keep that ? 
    11  
    1210\minitoc 
    1311 
     12\vfill 
     13\begin{figure}[b] 
     14\subsubsection*{Changes record} 
     15\begin{tabular}{l||l|m{0.65\linewidth}} 
     16    Release   & Author        & Modifications \\ 
     17    {\em 4.0} & {\em D. J. Lea} & {\em \NEMO 4.0 updates}  \\ 
     18    {\em 3.6} & {\em M. Martin, A. Ryan} & {\em Add averaging operator, standalone obs oper} \\ 
     19    {\em 3.4} & {\em D. J. Lea, M. Martin, ...} & {\em Initial version}  \\ 
     20    {\em --\texttt{"}--} & {\em ... K. Mogensen, A. Vidard, A. Weaver} & {\em ---\texttt{"}---}  \\ 
     21\end{tabular} 
     22\end{figure} 
     23 
    1424\newpage 
    1525 
    16 The observation and model comparison code (OBS) reads in observation files 
    17 (profile temperature and salinity, sea surface temperature, sea level anomaly, sea ice concentration, and velocity) and calculates an interpolated model equivalent value at the observation location and nearest model timestep. 
     26The observation and model comparison code, the observation operator (OBS), reads in observation files 
     27(profile temperature and salinity, sea surface temperature, sea level anomaly, sea ice concentration, and velocity) and calculates an interpolated model equivalent value at the observation location and nearest model time step. 
    1828The resulting data are saved in a ``feedback'' file (or files). 
    1929The code was originally developed for use with the NEMOVAR data assimilation code, 
    2030but can be used for validation or verification of the model or with any other data assimilation system. 
    2131 
    22 The OBS code is called from \mdl{nemogcm} for model initialisation and to calculate the model equivalent values for observations on the 0th timestep. 
    23 The code is then called again after each timestep from \mdl{step}. 
    24 The code is only activated if the namelist logical \np{ln\_diaobs} is set to true. 
     32The OBS code is called from \mdl{nemogcm} for model initialisation and to calculate the model equivalent values for observations on the 0th time step. 
     33The code is then called again after each time step from \mdl{step}. 
     34The code is only activated if the \ngn{namobs} namelist logical \np{ln\_diaobs} is set to true. 
    2535 
    2636For all data types a 2D horizontal interpolator or averager is needed to 
     
    2838For {\em in situ} profiles, a 1D vertical interpolator is needed in addition to 
    2939provide model fields at the observation depths. 
    30 This now works in a generalised vertical coordinate system.  
     40This now works in a generalised vertical coordinate system. 
    3141 
    3242Some profile observation types (\eg tropical moored buoys) are made available as daily averaged quantities. 
     
    3646the observation operator code can calculate equivalent night-time average model SST fields by 
    3747setting the namelist value \np{ln\_sstnight} to true. 
    38 Otherwise the model value from the nearest timestep to the observation time is used. 
    39  
    40 The code is controlled by the namelist \textit{namobs}. 
     48Otherwise (by default) the model value from the nearest time step to the observation time is used. 
     49 
     50The code is controlled by the namelist \ngn{namobs}. 
    4151See the following sections for more details on setting up the namelist. 
    4252 
    43 \autoref{sec:OBS_example} introduces a test example of the observation operator code including 
     53In \autoref{sec:OBS_example} a test example of the observation operator code is introduced, including 
    4454where to obtain data and how to setup the namelist. 
    45 \autoref{sec:OBS_details} introduces some more technical details of the different observation types used and 
    46 also shows a more complete namelist. 
    47 \autoref{sec:OBS_theory} introduces some of the theoretical aspects of the observation operator including 
     55In \autoref{sec:OBS_details} some more technical details of the different observation types used are introduced, and we 
     56also show a more complete namelist. 
     57In \autoref{sec:OBS_theory} some of the theoretical aspects of the observation operator are described including 
    4858interpolation methods and running on multiple processors. 
    49 \autoref{sec:OBS_ooo} describes the offline observation operator code. 
    50 \autoref{sec:OBS_obsutils} introduces some utilities to help working with the files produced by the OBS code. 
     59In \autoref{sec:OBS_sao} the standalone observation operator code is described. 
     60In \autoref{sec:OBS_obsutils} we describe some utilities to help work with the files produced by the OBS code. 
    5161 
    5262% ================================================================ 
     
    5666\label{sec:OBS_example} 
    5767 
    58 This section describes an example of running the observation operator code using 
    59 profile data which can be freely downloaded. 
    60 It shows how to adapt an existing run and build of NEMO to run the observation operator. 
     68In this section an example of running the observation operator code is described using 
     69profile observation data which can be freely downloaded. 
     70It shows how to adapt an existing run and build of \NEMO to run the observation operator. Note also the observation operator and the assimilation increments code are run in the \np{ORCA2\_ICE\_OBS} SETTE test. 
    6171 
    6272\begin{enumerate} 
     
    6575\item Download some EN4 data from \href{http://www.metoffice.gov.uk/hadobs}{www.metoffice.gov.uk/hadobs}. 
    6676  Choose observations which are valid for the period of your test run because 
    67   the observation operator compares the model and observations for a matching date and time.  
    68  
    69 \item Compile the OBSTOOLS code using:  
     77  the observation operator compares the model and observations for a matching date and time. 
     78 
     79\item Compile the OBSTOOLS code in the \np{tools} directory using: 
    7080\begin{cmds} 
    71 ./maketools -n OBSTOOLS -m [ARCH]. 
     81./maketools -n OBSTOOLS -m [ARCH] 
    7282\end{cmds} 
    7383 
    74 \item Convert the EN4 data into feedback format:  
     84replacing \np{[ARCH]} with the build architecture file for your machine. Note the tools are checked out from a separate repository under \np{utils/tools}. 
     85 
     86\item Convert the EN4 data into feedback format: 
    7587\begin{cmds} 
    7688enact2fb.exe profiles_01.nc EN.4.1.1.f.profiles.g10.YYYYMM.nc 
    7789\end{cmds} 
    7890 
    79 \item Include the following in the NEMO namelist to run the observation operator on this data: 
     91\item Include the following in the \NEMO namelist to run the observation operator on this data: 
    8092\end{enumerate} 
    8193 
     
    8799This can be expensive, particularly for large numbers of observations, 
    88100setting \np{ln\_grid\_search\_lookup} allows the use of a lookup table which 
    89 is saved into an ``xypos`` file (or files). 
     101is saved into an \np{cn\_gridsearch} file (or files). 
    90102This will need to be generated the first time if it does not exist in the run directory. 
    91103However, once produced it will significantly speed up future grid searches. 
    92104Setting \np{ln\_grid\_global} means that the code distributes the observations evenly between processors. 
    93105Alternatively each processor will work with observations located within the model subdomain 
    94 (see section~\autoref{subsec:OBS_parallel}). 
     106(see \autoref{subsec:OBS_parallel}). 
    95107 
    96108A number of utilities are now provided to plot the feedback files, convert and recombine the files. 
    97 These are explained in more detail in section~\autoref{sec:OBS_obsutils}. 
    98 Utilites to convert other input data formats into the feedback format are also described in 
    99 section~\autoref{sec:OBS_obsutils}. 
     109These are explained in more detail in \autoref{sec:OBS_obsutils}. 
     110Utilities to convert other input data formats into the feedback format are also described in 
     111\autoref{sec:OBS_obsutils}. 
    100112 
    101113\section{Technical details (feedback type observation file headers)} 
     
    110122%------------------------------------------------------------------------------------------------------------- 
    111123 
    112 The observation operator code uses the "feedback" observation file format for all data types. 
     124The observation operator code uses the feedback observation file format for all data types. 
    113125All the observation files must be in NetCDF format. 
    114126Some example headers (produced using \mbox{\textit{ncdump~-h}}) for profile data, sea level anomaly and 
    115127sea surface temperature are in the following subsections. 
    116128 
    117 \subsection{Profile feedback} 
     129\subsection{Profile feedback file} 
    118130 
    119131\begin{clines} 
     
    271283\end{clines} 
    272284 
    273 \subsection{Sea level anomaly feedback} 
     285\subsection{Sea level anomaly feedback file} 
    274286 
    275287\begin{clines} 
     
    395407\end{clines} 
    396408 
    397 The mean dynamic topography (MDT) must be provided in a separate file defined on 
     409To use Sea Level Anomaly (SLA) data the mean dynamic topography (MDT) must be provided in a separate file defined on 
    398410the model grid called \ifile{slaReferenceLevel}. 
    399411The MDT is required in order to produce the model equivalent sea level anomaly from the model sea surface height. 
     
    417429\end{clines} 
    418430 
    419 \subsection{Sea surface temperature feedback} 
     431\subsection{Sea surface temperature feedback file} 
    420432 
    421433\begin{clines} 
     
    546558In those cases the model counterpart should be calculated by averaging the model grid points over 
    547559the same size as the footprint. 
    548 NEMO therefore has the capability to specify either an interpolation or an averaging 
    549 (for surface observation types only).  
     560\NEMO therefore has the capability to specify either an interpolation or an averaging 
     561(for surface observation types only). 
    550562 
    551563The main namelist option associated with the interpolation/averaging is \np{nn\_2dint}. 
     
    559571\item \np{nn\_2dint}\forcode{ = 4}: Polynomial interpolation 
    560572\item \np{nn\_2dint}\forcode{ = 5}: Radial footprint averaging with diameter specified in the namelist as 
    561   \np{rn\_???\_avglamscl} in degrees or metres (set using \np{ln\_???\_fp\_indegs}) 
     573  \np{rn\_[var]\_avglamscl} in degrees or metres (set using \np{ln\_[var]\_fp\_indegs}) 
    562574\item \np{nn\_2dint}\forcode{ = 6}: Rectangular footprint averaging with E/W and N/S size specified in 
    563   the namelist as \np{rn\_???\_avglamscl} and \np{rn\_???\_avgphiscl} in degrees or metres 
    564   (set using \np{ln\_???\_fp\_indegs}) 
     575  the namelist as \np{rn\_[var]\_avglamscl} and \np{rn\_[var]\_avgphiscl} in degrees or metres 
     576  (set using \np{ln\_[var]\_fp\_indegs}) 
    565577\end{itemize} 
    566 The ??? in the last two options indicate these options should be specified for each observation type for 
     578Replace \np{[var]} in the last two options with the observation type (sla, sst, sss or sic) for 
    567579which the averaging is to be performed (see namelist example above). 
    568580The \np{nn\_2dint} default option can be overridden for surface observation types using 
    569 namelist values \np{nn\_2dint\_???} where ??? is one of sla,sst,sss,sic. 
     581namelist values \np{nn\_2dint\_[var]} where \np{[var]} is the observation type. 
    570582 
    571583Below is some more detail on the various options for interpolation and averaging available in NEMO. 
     
    573585\subsubsection{Horizontal interpolation} 
    574586 
    575 Consider an observation point ${\mathrm P}$ with with longitude and latitude $({\lambda_{}}_{\mathrm P}, \phi_{\mathrm P})$ and 
     587Consider an observation point ${\mathrm P}$ with longitude and latitude (${\lambda_{}}_{\mathrm P}$, $\phi_{\mathrm P}$) and 
    576588the four nearest neighbouring model grid points ${\mathrm A}$, ${\mathrm B}$, ${\mathrm C}$ and ${\mathrm D}$ with 
    577589longitude and latitude ($\lambda_{\mathrm A}$, $\phi_{\mathrm A}$),($\lambda_{\mathrm B}$, $\phi_{\mathrm B}$) etc. 
    578 All horizontal interpolation methods implemented in NEMO estimate the value of a model variable $x$ at point $P$ as 
     590All horizontal interpolation methods implemented in \NEMO estimate the value of a model variable $x$ at point $P$ as 
    579591a weighted linear combination of the values of the model variables at the grid points ${\mathrm A}$, ${\mathrm B}$ etc.: 
     592 
    580593\begin{align*} 
    581   {x_{}}_{\mathrm P} & \hspace{-2mm} = \hspace{-2mm} & 
    582                                                    \frac{1}{w} \left( {w_{}}_{\mathrm A} {x_{}}_{\mathrm A} + 
    583                                                    {w_{}}_{\mathrm B} {x_{}}_{\mathrm B} + 
    584                                                    {w_{}}_{\mathrm C} {x_{}}_{\mathrm C} + 
    585                                                    {w_{}}_{\mathrm D} {x_{}}_{\mathrm D} \right) 
     594  {x_{}}_{\mathrm P} = 
     595\frac{1}{w} \left( {w_{}}_{\mathrm A} {x_{}}_{\mathrm A} + 
     596{w_{}}_{\mathrm B} {x_{}}_{\mathrm B} + 
     597{w_{}}_{\mathrm C} {x_{}}_{\mathrm C} + 
     598{w_{}}_{\mathrm D} {x_{}}_{\mathrm D} \right) 
    586599\end{align*} 
     600 
    587601where ${w_{}}_{\mathrm A}$, ${w_{}}_{\mathrm B}$ etc. are the respective weights for the model field at 
    588602points ${\mathrm A}$, ${\mathrm B}$ etc., and $w = {w_{}}_{\mathrm A} + {w_{}}_{\mathrm B} + {w_{}}_{\mathrm C} + {w_{}}_{\mathrm D}$. 
     
    597611  For example, the weight given to the field ${x_{}}_{\mathrm A}$ is specified as the product of the distances 
    598612  from ${\mathrm P}$ to the other points: 
    599   \begin{align*} 
     613 
     614  \begin{alignat*}{2} 
    600615    {w_{}}_{\mathrm A} = s({\mathrm P}, {\mathrm B}) \, s({\mathrm P}, {\mathrm C}) \, s({\mathrm P}, {\mathrm D}) 
    601   \end{align*} 
    602   where  
    603   \begin{align*} 
    604     s\left ({\mathrm P}, {\mathrm M} \right )  
    605      & \hspace{-2mm} = \hspace{-2mm} &  
    606       \cos^{-1} \! \left\{  
     616  \end{alignat*} 
     617 
     618  where 
     619 
     620  \begin{alignat*}{2} 
     621    s\left({\mathrm P}, {\mathrm M} \right) & = & \hspace{0.25em} \cos^{-1} \! \left\{ 
    607622               \sin {\phi_{}}_{\mathrm P} \sin {\phi_{}}_{\mathrm M} 
    608              + \cos {\phi_{}}_{\mathrm P} \cos {\phi_{}}_{\mathrm M}  
    609                \cos ({\lambda_{}}_{\mathrm M} - {\lambda_{}}_{\mathrm P})  
     623             + \cos {\phi_{}}_{\mathrm P} \cos {\phi_{}}_{\mathrm M} 
     624               \cos ({\lambda_{}}_{\mathrm M} - {\lambda_{}}_{\mathrm P}) 
    610625                   \right\} 
    611    \end{align*} 
     626   \end{alignat*} 
     627 
    612628   and $M$ corresponds to $B$, $C$ or $D$. 
    613629   A more stable form of the great-circle distance formula for small distances ($x$ near 1) 
    614630   involves the arcsine function (\eg see p.~101 of \citet{daley.barker_bk01}: 
    615    \begin{align*} 
    616      s\left( {\mathrm P}, {\mathrm M} \right) & \hspace{-2mm} = \hspace{-2mm} & \sin^{-1} \! \left\{ \sqrt{ 1 - x^2 } \right\} 
    617    \end{align*} 
     631 
     632   \begin{alignat*}{2} 
     633     s\left( {\mathrm P}, {\mathrm M} \right) = \sin^{-1} \! \left\{ \sqrt{ 1 - x^2 } \right\} 
     634   \end{alignat*} 
     635 
    618636   where 
    619    \begin{align*} 
    620      x & \hspace{-2mm} = \hspace{-2mm} & 
    621                                          {a_{}}_{\mathrm M} {a_{}}_{\mathrm P} + {b_{}}_{\mathrm M} {b_{}}_{\mathrm P} + {c_{}}_{\mathrm M} {c_{}}_{\mathrm P} 
    622    \end{align*} 
    623    and  
    624    \begin{align*} 
    625       {a_{}}_{\mathrm M} & \hspace{-2mm} = \hspace{-2mm} & \sin {\phi_{}}_{\mathrm M}, \\ 
    626       {a_{}}_{\mathrm P} & \hspace{-2mm} = \hspace{-2mm} & \sin {\phi_{}}_{\mathrm P}, \\ 
    627       {b_{}}_{\mathrm M} & \hspace{-2mm} = \hspace{-2mm} & \cos {\phi_{}}_{\mathrm M} \cos {\phi_{}}_{\mathrm M}, \\ 
    628       {b_{}}_{\mathrm P} & \hspace{-2mm} = \hspace{-2mm} & \cos {\phi_{}}_{\mathrm P} \cos {\phi_{}}_{\mathrm P}, \\ 
    629       {c_{}}_{\mathrm M} & \hspace{-2mm} = \hspace{-2mm} & \cos {\phi_{}}_{\mathrm M} \sin {\phi_{}}_{\mathrm M}, \\ 
    630       {c_{}}_{\mathrm P} & \hspace{-2mm} = \hspace{-2mm} & \cos {\phi_{}}_{\mathrm P} \sin {\phi_{}}_{\mathrm P}. 
    631   \end{align*} 
     637 
     638   \begin{alignat*}{2} 
     639     x = {a_{}}_{\mathrm M} {a_{}}_{\mathrm P} + {b_{}}_{\mathrm M} {b_{}}_{\mathrm P} + {c_{}}_{\mathrm M} {c_{}}_{\mathrm P} 
     640   \end{alignat*} 
     641 
     642   and 
     643 
     644   \begin{alignat*}{3} 
     645   & {a_{}}_{\mathrm M} & = && \quad \sin {\phi_{}}_{\mathrm M}, \\ 
     646   & {a_{}}_{\mathrm P} & = && \quad \sin {\phi_{}}_{\mathrm P}, \\ 
     647   & {b_{}}_{\mathrm M} & = && \quad \cos {\phi_{}}_{\mathrm M} \cos {\phi_{}}_{\mathrm M}, \\ 
     648   & {b_{}}_{\mathrm P} & = && \quad \cos {\phi_{}}_{\mathrm P} \cos {\phi_{}}_{\mathrm P}, \\ 
     649   & {c_{}}_{\mathrm M} & = && \quad \cos {\phi_{}}_{\mathrm M} \sin {\phi_{}}_{\mathrm M}, \\ 
     650   & {c_{}}_{\mathrm P} & = && \quad \cos {\phi_{}}_{\mathrm P} \sin {\phi_{}}_{\mathrm P}. 
     651   \end{alignat*} 
    632652 
    633653\item[2.] {\bfseries Great-Circle distance-weighted interpolation with small angle approximation.} 
    634654  Similar to the previous interpolation but with the distance $s$ computed as 
    635   \begin{align*} 
     655  \begin{alignat*}{2} 
    636656    s\left( {\mathrm P}, {\mathrm M} \right) 
    637     & \hspace{-2mm} = \hspace{-2mm} & 
    638                                       \sqrt{ \left( {\phi_{}}_{\mathrm M} - {\phi_{}}_{\mathrm P} \right)^{2} 
     657    & = & \sqrt{ \left( {\phi_{}}_{\mathrm M} - {\phi_{}}_{\mathrm P} \right)^{2} 
    639658                                      + \left( {\lambda_{}}_{\mathrm M} - {\lambda_{}}_{\mathrm P} \right)^{2} 
    640659                                      \cos^{2} {\phi_{}}_{\mathrm M} } 
    641   \end{align*} 
     660  \end{alignat*} 
    642661  where $M$ corresponds to $A$, $B$, $C$ or $D$. 
    643662 
     
    649668  a cell with coordinates (0,0), (1,0), (0,1) and (1,1). 
    650669  This method is based on the \href{https://github.com/SCRIP-Project/SCRIP}{SCRIP interpolation package}. 
    651    
     670 
    652671\end{enumerate} 
    653672 
     
    658677\item The standard grid-searching code is used to find the nearest model grid point to the observation location 
    659678  (see next subsection). 
    660 \item The maximum number of grid points is calculated in the local grid domain for which 
    661   the averaging is likely need to cover. 
    662 \item The lats/longs of the grid points surrounding the nearest model grid box are extracted using 
    663   existing mpi routines. 
     679\item The maximum number of grid points required for that observation in each local grid domain is calculated. Some of these points may later turn out to have zero weight depending on the shape of the footprint. 
     680\item The longitudes and latitudes of the grid points surrounding the nearest model grid box are extracted using 
     681  existing MPI routines. 
    664682\item The weights for each grid point associated with each observation are calculated, 
    665683  either for radial or rectangular footprints. 
     
    673691 
    674692Examples of the weights calculated for an observation with rectangular and radial footprints are shown in 
    675 Figs.~\autoref{fig:obsavgrec} and~\autoref{fig:obsavgrad}. 
     693\autoref{fig:obsavgrec} and~\autoref{fig:obsavgrad}. 
    676694 
    677695%>>>>>>>>>>>>>>>>>>>>>>>>>>>> 
     
    696714      Weights associated with each model grid box (blue lines and numbers) 
    697715      for an observation at -170.5\deg{E}, 56.0\deg{N} with a radial footprint with diameter 1\deg. 
    698     }  
     716    } 
    699717  \end{center} 
    700718\end{figure} 
     
    704722\subsection{Grid search} 
    705723 
    706 For many grids used by the NEMO model, such as the ORCA family, the horizontal grid coordinates $i$ and $j$ are not simple functions of latitude and longitude. 
     724For many grids used by the \NEMO model, such as the ORCA family, the horizontal grid coordinates $i$ and $j$ are not simple functions of latitude and longitude. 
    707725Therefore, it is not always straightforward to determine the grid points surrounding any given observational position. 
    708 Before the interpolation can be performed, a search algorithm is then required to determine the corner points of  
     726Before the interpolation can be performed, a search algorithm is then required to determine the corner points of 
    709727the quadrilateral cell in which the observation is located. 
    710 This is the most difficult and time consuming part of the 2D interpolation procedure.  
     728This is the most difficult and time consuming part of the 2D interpolation procedure. 
    711729A robust test for determining if an observation falls within a given quadrilateral cell is as follows. 
    712730Let ${\mathrm P}({\lambda_{}}_{\mathrm P} ,{\phi_{}}_{\mathrm P} )$ denote the observation point, 
    713731and let ${\mathrm A}({\lambda_{}}_{\mathrm A} ,{\phi_{}}_{\mathrm A} )$, ${\mathrm B}({\lambda_{}}_{\mathrm B} ,{\phi_{}}_{\mathrm B} )$, 
    714732${\mathrm C}({\lambda_{}}_{\mathrm C} ,{\phi_{}}_{\mathrm C} )$ and ${\mathrm D}({\lambda_{}}_{\mathrm D} ,{\phi_{}}_{\mathrm D} )$ 
    715 denote the bottom left, bottom right, top left and top right corner points of the cell, respectively.  
    716 To determine if P is inside the cell, we verify that the cross-products  
     733denote the bottom left, bottom right, top left and top right corner points of the cell, respectively. 
     734To determine if P is inside the cell, we verify that the cross-products 
    717735\begin{align*} 
    718736  \begin{array}{lllll} 
     
    750768be searched for on a regular grid. 
    751769For each observation position, the closest point on the regular grid of this position is computed and 
    752 the $i$ and $j$ ranges of this point searched to determine the precise four points surrounding the observation.  
     770the $i$ and $j$ ranges of this point searched to determine the precise four points surrounding the observation. 
    753771 
    754772\subsection{Parallel aspects of horizontal interpolation} 
     
    757775For horizontal interpolation, there is the basic problem that 
    758776the observations are unevenly distributed on the globe. 
    759 In numerical models, it is common to divide the model grid into subgrids (or domains) where 
     777In \NEMO the model grid is divided into subgrids (or domains) where 
    760778each subgrid is executed on a single processing element with explicit message passing for 
    761779exchange of information along the domain boundaries when running on a massively parallel processor (MPP) system. 
    762 This approach is used by \NEMO. 
    763  
    764 For observations there is no natural distribution since the observations are not equally distributed on the globe.  
     780 
     781For observations there is no natural distribution since the observations are not equally distributed on the globe. 
    765782Two options have been made available: 
    7667831) geographical distribution; 
     
    784801the domain of the grid-point parallelization. 
    785802\autoref{fig:obslocal} shows an example of the distribution of the {\em in situ} data on processors with 
    786 a different colour for each observation on a given processor for a 4 $\times$ 2 decomposition with ORCA2.  
     803a different colour for each observation on a given processor for a 4 $\times$ 2 decomposition with ORCA2. 
    787804The grid-point domain decomposition is clearly visible on the plot. 
    788805 
    789806The advantage of this approach is that all information needed for horizontal interpolation is available without 
    790807any MPP communication. 
    791 Of course, this is under the assumption that we are only using a $2 \times 2$ grid-point stencil for 
     808This is under the assumption that we are dealing with point observations and only using a $2 \times 2$ grid-point stencil for 
    792809the interpolation (\eg bilinear interpolation). 
    793810For higher order interpolation schemes this is no longer valid. 
     
    827844At the bottom boundary, this is done using the land-ocean mask. 
    828845 
     846For profile observation types we do both vertical and horizontal interpolation. \NEMO has a generalised vertical coordinate system this means the vertical level depths can vary with location. Therefore, it is necessary first to perform vertical interpolation of the model value to the observation depths for each of the four surrounding grid points. After this the model values, at these points, at the observation depth, are horizontally interpolated to the observation location. 
     847 
    829848\newpage 
    830849 
    831850% ================================================================ 
    832 % Offline observation operator documentation 
     851% Standalone observation operator documentation 
    833852% ================================================================ 
    834853 
    835854%\usepackage{framed} 
    836855 
    837 \section{Offline observation operator} 
    838 \label{sec:OBS_ooo} 
     856\section{Standalone observation operator} 
     857\label{sec:OBS_sao} 
    839858 
    840859\subsection{Concept} 
    841860 
    842 The obs oper maps model variables to observation space. 
    843 It is possible to apply this mapping without running the model. 
    844 The software which performs this functionality is known as the \textbf{offline obs oper}. 
    845 The obs oper is divided into three stages. 
    846 An initialisation phase, an interpolation phase and an output phase. 
    847 The implementation of which is outlined in the previous sections. 
    848 During the interpolation phase the offline obs oper populates the model arrays by 
    849 reading saved model fields from disk. 
    850  
    851 There are two ways of exploiting this offline capacity. 
     861The observation operator maps model variables to observation space. This is normally done while the model is running, i.e. online, it is possible to apply this mapping offline without running the model with the \textbf{standalone observation operator} (SAO). The process is divided into an initialisation phase, an interpolation phase and an output phase. 
     862During the interpolation phase the SAO populates the model arrays by 
     863reading saved model fields from disk. The interpolation and the output phases use the same OBS code described in the preceding sections. 
     864 
     865There are two ways of exploiting the standalone capacity. 
    852866The first is to mimic the behaviour of the online system by supplying model fields at 
    853867regular intervals between the start and the end of the run. 
    854868This approach results in a single model counterpart per observation. 
    855 This kind of usage produces feedback files the same file format as the online obs oper. 
    856 The second is to take advantage of the offline setting in which 
    857 multiple model counterparts can be calculated per observation. 
     869This kind of usage produces feedback files the same file format as the online observation operator. 
     870The second is to take advantage of the ability to run offline by calculating 
     871multiple model counterparts for each observation. 
    858872In this case it is possible to consider all forecasts verifying at the same time. 
    859 By forecast, I mean any method which produces an estimate of physical reality which is not an observed value. 
    860 In the case of class 4 files this means forecasts, analyses, persisted analyses and 
    861 climatological values verifying at the same time. 
    862 Although the class 4 file format doesn't account for multiple ensemble members or 
    863 multiple experiments per observation, it is possible to include these components in the same or multiple files. 
     873By forecast, we mean any method which produces an estimate of physical reality which is not an observed value. 
    864874 
    865875%-------------------------------------------------------------------------------------------------------- 
    866 % offline_oper.exe  
     876% sao.exe 
    867877%-------------------------------------------------------------------------------------------------------- 
    868878 
    869 \subsection{Using the offline observation operator} 
     879\subsection{Using the standalone observation operator} 
    870880 
    871881\subsubsection{Building} 
    872882 
    873 In addition to \emph{OPA\_SRC} the offline obs oper requires the inclusion of the \emph{OOO\_SRC} directory. 
    874 \emph{OOO\_SRC} contains a replacement \mdl{nemo} and \mdl{nemogcm} which 
     883In addition to \emph{OPA\_SRC} the SAO requires the inclusion of the \emph{SAO\_SRC} directory. 
     884\emph{SAO\_SRC} contains a replacement \mdl{nemo} and \mdl{nemogcm} which 
    875885overwrites the resultant \textbf{nemo.exe}. 
    876 This is the approach taken by \emph{SAS\_SRC} and \emph{OFF\_SRC}. 
     886Note this a similar approach to that taken by the standalone surface scheme \emph{SAS\_SRC} and the offline TOP model \emph{OFF\_SRC}. 
    877887 
    878888%-------------------------------------------------------------------------------------------------------- 
    879 % Running  
     889% Running 
    880890%-------------------------------------------------------------------------------------------------------- 
    881891\subsubsection{Running} 
    882892 
    883 The simplest way to use the executable is to edit and append the \textbf{ooo.nml} namelist to 
    884 a full NEMO namelist and then to run the executable as if it were nemo.exe.  
    885  
    886 \subsubsection{Quick script} 
    887  
    888 A useful Python utility to control the namelist options can be found in \textbf{OBSTOOLS/OOO}. 
    889 The functions which locate model fields and observation files can be manually specified. 
    890 The package can be installed by appropriate use of the included setup.py script. 
    891  
    892 Documentation can be auto-generated by Sphinx by running \emph{make html} in the \textbf{doc} directory. 
     893The simplest way to use the executable is to edit and append the \textbf{sao.nml} namelist to 
     894a full \NEMO namelist and then to run the executable as if it were nemo.exe. 
    893895 
    894896%-------------------------------------------------------------------------------------------------------- 
    895897% Configuration section 
    896898%-------------------------------------------------------------------------------------------------------- 
    897 \subsection{Configuring the offline observation operator} 
    898 The observation files and settings understood by \textbf{namobs} have been outlined in the online obs oper section. 
    899 In addition there are two further namelists wich control the operation of the offline obs oper. 
    900 \textbf{namooo} which controls the input model fields and \textbf{namcl4} which 
    901 controls the production of class 4 files.  
     899\subsection{Configuring the standalone observation operator} 
     900The observation files and settings understood by \ngn{namobs} have been outlined in the online observation operator section. 
     901In addition is a further namelist \ngn{namsao} which used to set the input model fields for the SAO 
    902902 
    903903\subsubsection{Single field} 
    904904 
    905 In offline mode model arrays are populated at appropriate time steps via input files. 
    906 At present, \textbf{tsn} and \textbf{sshn} are populated by the default read routines.  
     905In the SAO the model arrays are populated at appropriate time steps via input files. 
     906At present, \textbf{tsn} and \textbf{sshn} are populated by the default read routines. 
    907907These routines will be expanded upon in future versions to allow the specification of any model variable. 
    908908As such, input files must be global versions of the model domain with 
    909909\textbf{votemper}, \textbf{vosaline} and optionally \textbf{sshn} present. 
    910910 
    911 For each field read there must be an entry in the \textbf{namooo} namelist specifying 
     911For each field read there must be an entry in the \ngn{namsao} namelist specifying 
    912912the name of the file to read and the index along the \emph{time\_counter}. 
    913913For example, to read the second time counter from a single file the namelist would be. 
     
    915915\begin{forlines} 
    916916!---------------------------------------------------------------------- 
    917 !       namooo Offline obs_oper namelist 
     917!       namsao Standalone obs_oper namelist 
    918918!---------------------------------------------------------------------- 
    919 !   ooo_files    specifies the files containing the model counterpart 
    920 !   nn_ooo_idx   specifies the time_counter index within the model file 
    921 &namooo 
    922    ooo_files = "foo.nc" 
    923    nn_ooo_idx = 2 
     919!   sao_files    specifies the files containing the model counterpart 
     920!   nn_sao_idx   specifies the time_counter index within the model file 
     921&namsao 
     922   sao_files = "foo.nc" 
     923   nn_sao_idx = 2 
    924924/ 
    925925\end{forlines} 
     
    927927\subsubsection{Multiple fields per run} 
    928928 
    929 Model field iteration is controlled via \textbf{nn\_ooo\_freq} which 
     929Model field iteration is controlled via \textbf{nn\_sao\_freq} which 
    930930specifies the number of model steps at which the next field gets read. 
    931931For example, if 12 hourly fields are to be interpolated in a setup where 288 steps equals 24 hours. 
     
    933933\begin{forlines} 
    934934!---------------------------------------------------------------------- 
    935 !       namooo Offline obs_oper namelist 
     935!       namsao Standalone obs_oper namelist 
    936936!---------------------------------------------------------------------- 
    937 !   ooo_files    specifies the files containing the model counterpart 
    938 !   nn_ooo_idx   specifies the time_counter index within the model file 
    939 !   nn_ooo_freq  specifies number of time steps between read operations 
    940 &namooo 
    941    ooo_files = "foo.nc" "foo.nc" 
    942    nn_ooo_idx = 1 2 
    943    nn_ooo_freq = 144 
     937!   sao_files    specifies the files containing the model counterpart 
     938!   nn_sao_idx   specifies the time_counter index within the model file 
     939!   nn_sao_freq  specifies number of time steps between read operations 
     940&namsao 
     941   sao_files = "foo.nc" "foo.nc" 
     942   nn_sao_idx = 1 2 
     943   nn_sao_freq = 144 
    944944/ 
    945945\end{forlines} 
     
    952952%\end{framed} 
    953953 
    954 It is easy to see how a collection of fields taken fron a number of files at different indices can be combined at 
     954A collection of fields taken from a number of files at different indices can be combined at 
    955955a particular frequency in time to generate a pseudo model evolution. 
    956 As long as all that is needed is a single model counterpart at a regular interval then 
    957 namooo is all that needs to be edited. 
    958 However, a far more interesting approach can be taken in which multiple forecasts, analyses, persisted analyses and 
    959 climatologies are considered against the same set of observations. 
    960 For this a slightly more complicated approach is needed. 
    961 It is referred to as \emph{Class 4} since it is the fourth metric defined by the GODAE intercomparison project. 
    962  
    963 %-------------------------------------------------------------------------------------------------------- 
    964 % Class 4 file section 
    965 %-------------------------------------------------------------------------------------------------------- 
    966 \subsubsection{Multiple model counterparts per observation a.k.a Class 4} 
    967  
    968 A generalisation of feedback files to allow multiple model components per observation. 
    969 For a single observation, as well as previous forecasts verifying at the same time 
    970 there are also analyses, persisted analyses and climatologies.  
    971  
    972  
    973 The above namelist performs two basic functions. 
    974 It organises the fields given in \textbf{namooo} into groups so that observations can be matched up multiple times. 
    975 It also controls the metadata and the output variable of the class 4 file when a write routine is called. 
    976  
    977 %\begin{framed} 
    978 \textbf{Note: ln\_cl4} must be set to \forcode{.true.} in \textbf{namobs} to use class 4 outputs. 
    979 %\end{framed} 
    980  
    981 \subsubsection{Class 4 naming convention} 
    982  
    983 The standard class 4 file naming convention is as follows. 
    984  
    985 \noindent 
    986 \linebreak 
    987 \textbf{\$\{prefix\}\_\$\{yyyymmdd\}\_\$\{sys\}\_\$\{cfg\}\_\$\{vn\}\_\$\{kind\}\_\$\{nproc\}}.nc 
    988  
    989 \noindent 
    990 \linebreak 
    991 Much of the namelist is devoted to specifying this convention. 
    992 The following namelist settings control the elements of the output file names. 
    993 Each should be specified as a single string of character data. 
    994  
    995 \begin{description} 
    996 \item[cl4\_prefix] 
    997   Prefix for class 4 files \eg class4 
    998 \item[cl4\_date] 
    999   YYYYMMDD validity date 
    1000 \item[cl4\_sys] 
    1001   The name of the class 4 model system \eg FOAM 
    1002 \item[cl4\_cfg] 
    1003   The name of the class 4 model configuration \eg orca025 
    1004 \item[cl4\_vn] 
    1005   The name of the class 4 model version \eg 12.0 
    1006 \end{description} 
    1007  
    1008 \noindent 
    1009 The kind is specified by the observation type internally to the obs oper. 
    1010 The processor number is specified internally in NEMO.  
    1011  
    1012 \subsubsection{Class 4 file global attributes} 
    1013  
    1014 Global attributes necessary to fulfill the class 4 file definition. 
    1015 These are also useful pieces of information when collaborating with external partners. 
    1016  
    1017 \begin{description} 
    1018 \item[cl4\_contact] 
    1019   Contact email for class 4 files. 
    1020 \item[cl4\_inst] 
    1021   The name of the producers institution. 
    1022 \item[cl4\_cfg] 
    1023   The name of the class 4 model configuration \eg orca025 
    1024 \item[cl4\_vn] 
    1025   The name of the class 4 model version \eg 12.0 
    1026 \end{description} 
    1027  
    1028 \noindent 
    1029 The obs\_type, creation date and validity time are specified internally to the obs oper. 
    1030  
    1031 \subsubsection{Class 4 model counterpart configuration} 
    1032  
    1033 As seen previously it is possible to perform a single sweep of the obs oper and 
    1034 specify a collection of model fields equally spaced along that sweep. 
    1035 In the class 4 case the single sweep is replaced with multiple sweeps and 
    1036 a certain ammount of book keeping is needed to ensure each model counterpart makes its way to 
    1037 the correct piece of memory in the output files. 
    1038  
    1039 \noindent 
    1040 \linebreak 
    1041 In terms of book keeping, the offline obs oper needs to know how many full sweeps need to be performed. 
    1042 This is specified via the \textbf{cl4\_match\_len} variable and 
    1043 is the total number of model counterparts per observation. 
    1044 For example, a 3 forecasts plus 3 persistence fields plus an analysis field would be 7 counterparts per observation. 
    1045  
    1046 \begin{forlines} 
    1047    cl4_match_len = 7 
    1048 \end{forlines} 
    1049  
    1050 Then to correctly allocate a class 4 file the forecast axis must be defined. 
    1051 This is controlled via \textbf{cl4\_fcst\_len}, which in out above example would be 3. 
    1052  
    1053 \begin{forlines} 
    1054    cl4_fcst_len = 3 
    1055 \end{forlines} 
    1056  
    1057 Then for each model field it is necessary to designate what class 4 variable and index along 
    1058 the forecast dimension the model counterpart should be stored in the output file. 
    1059 As well as a value for that lead time in hours, this will be useful when interpreting the data afterwards.  
    1060  
    1061 \begin{forlines} 
    1062    cl4_vars = "forecast" "forecast" "forecast" "persistence" "persistence" 
    1063               "persistence" "best_estimate" 
    1064    cl4_fcst_idx = 1 2 3 1 2 3 1 
    1065    cl4_leadtime = 12 36 60  
    1066 \end{forlines} 
    1067  
    1068 In terms of files and indices of fields inside each file the class 4 approach makes use of 
    1069 the \textbf{namooo} namelist. 
    1070 If our fields are in separate files with a single field per file our example inputs will be specified. 
    1071  
    1072 \begin{forlines} 
    1073    ooo_files = "F.1.nc" "F.2.nc" "F.3.nc" "P.1.nc" "P.2.nc" "P.3.nc" "A.1.nc" 
    1074    nn_ooo_idx = 1 1 1 1 1 1 1 
    1075 \end{forlines} 
    1076  
    1077 When we combine all of the naming conventions, global attributes and i/o instructions the class 4 namelist becomes. 
    1078  
    1079 \begin{forlines} 
    1080 !---------------------------------------------------------------------- 
    1081 !       namooo Offline obs_oper namelist 
    1082 !---------------------------------------------------------------------- 
    1083 !   ooo_files    specifies the files containing the model counterpart 
    1084 !   nn_ooo_idx   specifies the time_counter index within the model file 
    1085 !   nn_ooo_freq  specifies number of time steps between read operations 
    1086 &namooo 
    1087    ooo_files = "F.1.nc" "F.2.nc" "F.3.nc" "P.1.nc" "P.2.nc" "P.3.nc" "A.1.nc" 
    1088    nn_ooo_idx = 1 1 1 1 1 1 1 
    1089 / 
    1090 !---------------------------------------------------------------------- 
    1091 !       namcl4 Offline obs_oper class 4 namelist 
    1092 !---------------------------------------------------------------------- 
    1093 ! 
    1094 !  Naming convention 
    1095 !  ----------------- 
    1096 !  cl4_prefix    specifies the output file prefix 
    1097 !  cl4_date      specifies the output file validity date 
    1098 !  cl4_sys       specifies the model counterpart system 
    1099 !  cl4_cfg       specifies the model counterpart configuration 
    1100 !  cl4_vn        specifies the model counterpart version 
    1101 !  cl4_inst      specifies the model counterpart institute 
    1102 !  cl4_contact   specifies the file producers contact details 
    1103 ! 
    1104 !  I/O specification 
    1105 !  ----------------- 
    1106 !  cl4_vars      specifies the names of the output file netcdf variable 
    1107 !  cl4_fcst_idx  specifies output file forecast index 
    1108 !  cl4_fcst_len  specifies forecast axis length 
    1109 !  cl4_match_len specifies number of unique matches per observation 
    1110 !  cl4_leadtime  specifies the forecast axis lead time  
    1111 ! 
    1112 &namcl4 
    1113    cl4_match_len = 7 
    1114    cl4_fcst_len = 3 
    1115    cl4_fcst_idx = 1 2 3 1 2 3 1 
    1116    cl4_vars = "forecast" "forecast" "forecast" "persistence" "persistence" 
    1117               "persistence" "best_estimate" 
    1118    cl4_leadtime = 12 36 60 
    1119    cl4_prefix = "class4" 
    1120    cl4_date = "20130101" 
    1121    cl4_vn = "12.0" 
    1122    cl4_sys = "FOAM" 
    1123    cl4_cfg = "AMM7" 
    1124    cl4_contact = "example@example.com" 
    1125    cl4_inst = "UK Met Office" 
    1126 / 
    1127 \end{forlines} 
    1128  
    1129 \subsubsection{Climatology interpolation} 
    1130  
    1131 The climatological counterpart is generated at the start of the run by 
    1132 restarting the model from climatology through appropriate use of \textbf{namtsd}. 
    1133 To override the offline observation operator read routine and to take advantage of the restart settings, 
    1134 specify the first entry in \textbf{cl4\_vars} as "climatology". 
    1135 This will then pipe the restart from climatology into the output class 4 file. 
    1136 As in every other class 4 matchup the input file, input index and output index must be specified. 
    1137 These can be replaced with dummy data since they are not used but 
    1138 they must be present to cycle through the matchups correctly.  
    1139  
    1140 \subsection{Advanced usage} 
    1141  
    1142 In certain cases it may be desirable to include both multiple model fields per observation window with 
    1143 multiple match ups per observation. 
    1144 This can be achieved by specifying \textbf{nn\_ooo\_freq} as well as the class 4 settings. 
    1145 Care must be taken in generating the ooo\_files list such that the files are arranged into 
    1146 consecutive blocks of single match ups. 
    1147 For example, 2 forecast fields of 12 hourly data would result in 4 separate read operations but 
    1148 only 2 write operations, 1 per forecast. 
    1149  
    1150 \begin{forlines} 
    1151    ooo_files = "F1.nc" "F1.nc" "F2.nc" "F2.nc" 
    1152 ... 
    1153    cl4_fcst_idx = 1 2 
    1154 \end{forlines} 
    1155  
    1156 The above notation reveals the internal split between match up iterators and file iterators. 
    1157 This technique has not been used before so experimentation is needed before results can be trusted. 
     956If all that is needed is a single model counterpart at a regular interval then 
     957the standard SAO is all that is required. 
     958However, just to note, it is possible to extend this approach by comparing multiple forecasts, analyses, persisted analyses and 
     959climatologies with the same set of observations. 
     960This approach is referred to as \emph{Class 4} since it is the fourth metric defined by the GODAE intercomparison project. This requires multiple runs of the SAO and running an additional utility (not currently in the \NEMO repository) to combine the feedback files into one class 4 file. 
    1158961 
    1159962\newpage 
     
    1162965\label{sec:OBS_obsutils} 
    1163966 
    1164 Some tools for viewing and processing of observation and feedback files are provided in 
    1165 the NEMO repository for convenience. 
    1166 These include OBSTOOLS which are a collection of \fortran programs which are helpful to deal with feedback files. 
     967For convenience some tools for viewing and processing of observation and feedback files are provided in 
     968the \NEMO repository. 
     969These tools include OBSTOOLS which are a collection of \fortran programs which are helpful to deal with feedback files. 
    1167970They do such tasks as observation file conversion, printing of file contents, 
    1168971some basic statistical analysis of feedback files. 
    1169 The other tool is an IDL program called dataplot which uses a graphical interface to 
     972The other main tool is an IDL program called dataplot which uses a graphical interface to 
    1170973visualise observations and feedback files. 
    1171974OBSTOOLS and dataplot are described in more detail below. 
     
    1173976\subsection{Obstools} 
    1174977 
    1175 A series of \fortran utilities is provided with NEMO called OBSTOOLS. 
    1176 This are helpful in handling observation files and the feedback file output from the NEMO observation operator. 
    1177 The utilities are as follows 
    1178  
    1179 \subsubsection{c4comb} 
    1180  
    1181 The program c4comb combines multiple class 4 files produced by individual processors in 
    1182 an MPI run of NEMO offline obs\_oper into a single class 4 file. 
    1183 The program is called in the following way: 
    1184  
    1185  
    1186 \footnotesize 
    1187 \begin{cmds} 
    1188 c4comb.exe outputfile inputfile1 inputfile2 ... 
    1189 \end{cmds} 
     978A series of \fortran utilities is provided with \NEMO called OBSTOOLS. 
     979This are helpful in handling observation files and the feedback file output from the observation operator. A brief description of some of the utilities follows 
    1190980 
    1191981\subsubsection{corio2fb} 
    1192982 
    1193983The program corio2fb converts profile observation files from the Coriolis format to the standard feedback format. 
    1194 The program is called in the following way: 
    1195  
    1196 \footnotesize 
     984It is called in the following way: 
     985 
    1197986\begin{cmds} 
    1198987corio2fb.exe outputfile inputfile1 inputfile2 ... 
     
    1202991 
    1203992The program enact2fb converts profile observation files from the ENACT format to the standard feedback format. 
    1204 The program is called in the following way: 
    1205  
    1206 \footnotesize 
     993It is called in the following way: 
     994 
    1207995\begin{cmds} 
    1208996enact2fb.exe outputfile inputfile1 inputfile2 ... 
     
    12121000 
    12131001The program fbcomb combines multiple feedback files produced by individual processors in 
    1214 an MPI run of NEMO into a single feedback file. 
    1215 The program is called in the following way: 
    1216  
    1217 \footnotesize 
     1002an MPI run of \NEMO into a single feedback file. 
     1003It is called in the following way: 
     1004 
    12181005\begin{cmds} 
    12191006fbcomb.exe outputfile inputfile1 inputfile2 ... 
     
    12231010 
    12241011The program fbmatchup will match observations from two feedback files. 
    1225 The program is called in the following way: 
    1226  
    1227 \footnotesize 
     1012It is called in the following way: 
     1013 
    12281014\begin{cmds} 
    12291015fbmatchup.exe outputfile inputfile1 varname1 inputfile2 varname2 ... 
     
    12341020The program fbprint will print the contents of a feedback file or files to standard output. 
    12351021Selected information can be output using optional arguments. 
    1236 The program is called in the following way: 
    1237  
    1238 \footnotesize 
     1022It is called in the following way: 
     1023 
    12391024\begin{cmds} 
    12401025fbprint.exe [options] inputfile 
     
    12461031     -B            Select observations based on QC flags 
    12471032     -u            unsorted 
    1248      -s ID         select station ID   
     1033     -s ID         select station ID 
    12491034     -t TYPE       select observation type 
    1250      -v NUM1-NUM2  select variable range to print by number  
     1035     -v NUM1-NUM2  select variable range to print by number 
    12511036                      (default all) 
    1252      -a NUM1-NUM2  select additional variable range to print by number  
     1037     -a NUM1-NUM2  select additional variable range to print by number 
    12531038                      (default all) 
    1254      -e NUM1-NUM2  select extra variable range to print by number  
     1039     -e NUM1-NUM2  select extra variable range to print by number 
    12551040                      (default all) 
    12561041     -d            output date range 
     
    12621047 
    12631048The program fbsel will select or subsample observations. 
    1264 The program is called in the following way: 
    1265  
    1266 \footnotesize 
     1049It is called in the following way: 
     1050 
    12671051\begin{cmds} 
    12681052fbsel.exe <input filename> <output filename> 
     
    12721056 
    12731057The program fbstat will output summary statistics in different global areas into a number of files. 
    1274 The program is called in the following way: 
    1275  
    1276 \footnotesize 
     1058It is called in the following way: 
     1059 
    12771060\begin{cmds} 
    12781061fbstat.exe [-nmlev] <filenames> 
     
    12831066The program fbthin will thin the data to 1 degree resolution. 
    12841067The code could easily be modified to thin to a different resolution. 
    1285 The program is called in the following way: 
    1286  
    1287 \footnotesize 
     1068It is called in the following way: 
     1069 
    12881070\begin{cmds} 
    12891071fbthin.exe inputfile outputfile 
     
    12931075 
    12941076The program sla2fb will convert an AVISO SLA format file to feedback format. 
    1295 The program is called in the following way: 
    1296  
    1297 \footnotesize 
     1077It is called in the following way: 
     1078 
    12981079\begin{cmds} 
    12991080sla2fb.exe [-s type] outputfile inputfile1 inputfile2 ... 
     
    13061087 
    13071088The program vel2fb will convert TAO/PIRATA/RAMA currents files to feedback format. 
    1308 The program is called in the following way: 
    1309  
    1310 \footnotesize 
     1089It is called in the following way: 
     1090 
    13111091\begin{cmds} 
    13121092vel2fb.exe outputfile inputfile1 inputfile2 ... 
     
    13201100 
    13211101An IDL program called dataplot is included which uses a graphical interface to 
    1322 visualise observations and feedback files. 
     1102visualise observations and feedback files. Note a similar package has recently developed in python (also called dataplot) which does some of the same things that the IDL dataplot does. Please contact the authors of the this chapter if you are interested in this. 
     1103 
    13231104It is possible to zoom in, plot individual profiles and calculate some basic statistics. 
    13241105To plot some data run IDL and then: 
    1325 \footnotesize 
     1106 
    13261107\begin{minted}{idl} 
    13271108IDL> dataplot, "filename" 
     
    13311112for example multiple feedback files from different processors or from different days, 
    13321113the easiest method is to use the spawn command to generate a list of files which can then be passed to dataplot. 
    1333 \footnotesize 
     1114 
    13341115\begin{minted}{idl} 
    13351116IDL> spawn, 'ls profb*.nc', files 
     
    13501131The plotting colour range can be changed by clicking on the colour bar. 
    13511132The title of the plot gives some basic information about the date range and depth range shown, 
    1352 the extreme values, and the mean and rms values. 
     1133the extreme values, and the mean and RMS values. 
    13531134It is possible to zoom in using a drag-box. 
    13541135You may also zoom in or out using the mouse wheel. 
     
    13621143observation minus background value. 
    13631144The next group of radio buttons selects the map projection. 
    1364 This can either be regular latitude longitude grid, or north or south polar stereographic. 
     1145This can either be regular longitude latitude grid, or north or south polar stereographic. 
    13651146The next group of radio buttons will plot bad observations, switch to salinity and 
    13661147plot density for profile observations. 
Note: See TracChangeset for help on using the changeset viewer.