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 20171206T13:52:42+01:00 (5 years ago)
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branches/2017/dev_r8657_UKMO_OBSoper/DOC/TexFiles/Chapters/Chap_OBS.tex
r6997 r8919 27 27 is set to true. 28 28 29 For all data types a 2D horizontal interpolator is needed to interpolate the model fields to29 For all data types a 2D horizontal interpolator or averager is needed to interpolate/average the model fields to 30 30 the observation location. For {\em in situ} profiles, a 1D vertical interpolator is needed in 31 addition to provide model fields at the observation depths. Currently this only works in 32 zlevel model configurations, but is being developed to work with a generalised vertical 31 addition to provide model fields at the observation depths. This now works in a generalised vertical 33 32 coordinate system. 34 33 35 34 Some profile observation types (e.g. tropical moored buoys) are made available as daily averaged quantities. 36 The observation operator code can be setup to calculate dthe equivalent daily average model temperature fields35 The observation operator code can be setup to calculate the equivalent daily average model temperature fields 37 36 using the \np{nn\_profdavtypes} namelist array. Some SST observations are equivalent to a nighttime 38 37 average value and the observation operator code can calculate equivalent nighttime average model SST fields by … … 40 39 observation time is used. 41 40 42 The code is controlled by the namelist \textit{nam \_obs}. See the following sections for more41 The code is controlled by the namelist \textit{namobs}. See the following sections for more 43 42 details on setting up the namelist. 44 43 … … 560 559 \label{OBS_theory} 561 560 562 \subsection{Horizontal interpolation methods} 563 561 \subsection{Horizontal interpolation and averaging methods} 562 563 For most observation types, the horizontal extent of the observation is small compared to the model grid size 564 and so the model equivalent of the observation is calculated by interpolating from the four surrounding grid 565 points to the observation location. Some satellite observations (e.g. microwave satellite SST data, or SSS data) 566 have a footprint which is similar size or larger than the model grid size (particularly when the grid size is small). 567 In those cases the model counterpart should be calculated by averaging the model grid points over the same size as the footprint. 568 NEMO therefore has the capability to specify either an interpolation or an averaging (for surface observation types only). 569 570 The main namelist option associated with the interpolation/averaging is nn_2dint. This default option can be set to values from 0 to 6. 571 Values between 0 to 4 are associated with interpolation while values 5 or 6 are associated with averaging. 572 \begin{itemize} 573 \item n2dint=0: Distanceweighted interpolation 574 \item n2dint=1: Distanceweighted interpolation (small angle) 575 \item n2dint=2: Bilinear interpolation (geographical grid) 576 \item n2dint=3: Bilinear remapping interpolation (general grid) 577 \item n2dint=4: Polynomial interpolation 578 \item n2dint=5: Radial footprint averaging with radius specified in the namelist as rn_???_avglamscl in degrees or metres (set using ln_???_fp_indegs) 579 \item n2dint=6: Rectangular footprint averaging with E/W and N/S size specified in the namelist as rn_???_avglamscl and rn_???_avgphiscl in degrees or metres (set using ln_???_fp_indegs) 580 \end{itemize} 581 The ??? in the last two options indicate these options should be specified for each observation type for which the averaging is to be performed (see namelist example above). 582 The n2dint default option can be overridden for surface observation types using namelist values nn_2dint_??? where ??? is one of sla,sst,sss,sic. 583 584 Below is some more detail on the various options for interpolation and averaging available in NEMO. 585 586 \subsubsection{Horizontal interpolation} 564 587 Consider an observation point ${\rm P}$ with 565 588 with longitude and latitude $({\lambda_{}}_{\rm P}, \phi_{\rm P})$ and the … … 660 683 661 684 \end{enumerate} 685 686 \subsubsection{Horizontal averaging} 687 688 For each surface observation type: 689 \begin{itemize} 690 \item The standard gridsearching code is used to find the nearest model grid point to the observation location (see next subsection). 691 \item The maximum number of grid points is calculated in the local grid domain for which the averaging is likely need to cover. 692 \item The lats/longs of the grid points surrounding the nearest model grid box are extracted using existing mpi routines. 693 \item The weights for each grid point associated with each observation are calculated, either for radial or rectangular footprints. For grid points completely within the footprint, the weight is one; for grid points completely outside the footprint, the weight is zero. For grid points which are partly within the footprint the ratio between the area of the footprint within the grid box and the total area of the grid box is used as the weight. 694 \item The weighted average of the model grid points associated with each observation is calculated, and this is then given as the model counterpart of the observation. 695 \end{itemize} 696 697 %>>>>>>>>>>>>>>>>>>>>>>>>>>>> 698 \begin{figure} \begin{center} 699 \includegraphics[width=10cm,height=12cm]{Fig_OBS_avg_rec} 700 \caption{ \label{fig:obsavgrec} 701 Weights associated with each model grid box (blue lines and numbers) for an observation at 170.5E, 56.0N with a footprint of $1\deg \time 1\deg$} 702 \end{center} \end{figure} 703 %>>>>>>>>>>>>>>>>>>>>>>>>>>>> 704 705 706 %>>>>>>>>>>>>>>>>>>>>>>>>>>>> 707 \begin{figure} \begin{center} 708 \includegraphics[width=10cm,height=12cm]{Fig_OBS_avg_rad} 709 \caption{ \label{fig:obsavgrad} 710 As for figure \ref{obsavgrec} but for a radial footprint with diameter $1\deg$. 711 \end{center} \end{figure} 712 %>>>>>>>>>>>>>>>>>>>>>>>>>>>> 713 662 714 663 715 \subsection{Grid search}
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