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Cheat sheet for using a Mosaic grid with MOM6 output
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cheat sheet for using a Mosaic grid with MOM6 output\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import netCDF4, numpy, matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.xkcd();"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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qV6+OqlWr4vHjx5g8eTLi4+MxcuTI8havRPKbBAoErzqvnRL4+OOP8cUXX0iu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\nDWMIEBFpGEOAiEjDGAJERBr2f+HfCLuxo2eaAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbe7f9e04a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Figure 1\n",
"plt.axis((0,100,0,100)); plt.plot([10,90,90,10,10],[10,10,90,90,10],'k')\n",
"text_opts={'horizontalalignment':'center','verticalalignment':'center','backgroundcolor':'w'}\n",
"plt.text(50,50,'h$_{i,j}$',text_opts)\n",
"plt.text(10,10,'q$_{i-1,j-1}$',text_opts); plt.text(10,90,'q$_{i-1,j}$',text_opts)\n",
"plt.text(90,10,'q$_{i,j-1}$',text_opts); plt.text(90,90,'q$_{i,j}$',text_opts)\n",
"plt.text(10,50,'u$_{i-1,j}$',text_opts); plt.text(90,50,'u$_{i,j}$',text_opts)\n",
"plt.text(50,10,'v$_{i,j-1}$',text_opts); plt.text(50,90,'v$_{i,j}$',text_opts)\n",
"plt.axis('off'); plt.title('Fig 1: Arakawa C-grid of variables around an\\nh-cell with North-East indexing convention');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The FMS indexing convention (horizontal indices)\n",
"\n",
"The MOM6 model uses an **Arakawa C-grid** for staggering/locating model variables and a **North-East indexing convention**, illustrated in the Fig. 1 above.\n",
"- This indexing concerns the horizontal directions only.\n",
"- The North-East convention means that all variables with an `i,j` index are either co-located with, or are placed to the north, east or north-east of, the $h_{i,j}$ point.\n",
"- An `h`-point is at the \"center\" of a continuity cell which governs the budget for mass and tracers. All advected tracers (e.g. potential temperature, salinity, bio-geochemical tracers, ...) are thus co-located with cell thickness, $h$."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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oNBpUqVIF3bp1w5o1a160ODZeMPv27UPHjh0RHR2Nrl27vmhxyiQ/Px9KpfKl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\nIBAIBF2Y/wd58mg7sfkbDwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbe7d5ccf28>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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AixcvlvUTqX/nXJjExsayV69eQnADJpXyTz/9lDqdjlevXhXqydJ/Tk5O/PLL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\nb9U+chqHFlB65JFHoFQq8dZbb8W8PmnSJEycODFGSL788ksMGjQIR44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HYty4cdixY0ez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\nf7ATQ3ED20OXAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbe7d51a2b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Figure 2\n",
"plt.axis((0,100,0,100)); plt.plot([10,90,90,10,10],[10,10,90,90,10],'k')\n",
"text_opts={'horizontalalignment':'center','verticalalignment':'center','backgroundcolor':'w'}\n",
"plt.text(50,50,'h$_{i,j}$',text_opts)\n",
"plt.text(10,10,'q',text_opts); plt.text(10,90,'q',text_opts)\n",
"plt.text(90,10,'q',text_opts); plt.text(90,90,'q',text_opts)\n",
"plt.text(13,50,'u$_{i-1,j}$',text_opts); plt.text(90,50,'u$_{i,j}$',text_opts)\n",
"plt.text(50,10,'v$_{i,j-1}$',text_opts); plt.text(50,90,'v$_{i,j}$',text_opts)\n",
"def myarrow(x1,x2,y1,y2,lab,a=0.5):\n",
" b=1-a; plt.arrow(a*x1+b*x2,a*y1+b*y2,x2-a*x1-b*x2,y2-a*y1-b*y2,length_includes_head=True,head_width=4)\n",
" plt.arrow(a*x1+b*x2,a*y1+b*y2,x1-a*x1-b*x2,y1-a*y1-b*y2,length_includes_head=True,head_width=4)\n",
" plt.text(a*x1+b*x2,a*y1+b*y2,lab,text_opts)\n",
"myarrow(10,90,40,40,'$\\Delta$x$_h$'); myarrow(40,40,10,90,'$\\Delta$y$_h$')\n",
"myarrow(4,4,10,90,'$\\Delta$y$_u$',a=.35); myarrow(96,96,10,90,'$\\Delta$y$_u$',a=.4)\n",
"myarrow(10,90,4,4,'$\\Delta$x$_v$'); myarrow(10,90,96,96,'$\\Delta$x$_v$',a=.6)\n",
"plt.axis('off'); plt.title('Fig 2a: Arakawa C-grid of variables around an\\nh-cell with labelled grid metrics');\n",
"plt.figure()\n",
"plt.axis((0,100,0,100)); plt.plot([10,90,90,10,10],[10,10,90,90,10],'k')\n",
"text_opts={'horizontalalignment':'center','verticalalignment':'center','backgroundcolor':'w'}\n",
"plt.text(50,50,'q$_{i,j}$',text_opts)\n",
"plt.text(10,10,'h',text_opts); plt.text(10,90,'h',text_opts)\n",
"plt.text(90,10,'h',text_opts); plt.text(90,90,'h',text_opts)\n",
"plt.text(10,50,'v$_{i,j}$',text_opts); plt.text(87,50,'v$_{i,j+1}$',text_opts)\n",
"plt.text(50,10,'u$_{i,j}$',text_opts); plt.text(50,90,'u$_{i+1,j}$',text_opts)\n",
"plt.axis('off'); plt.title('Fig 2b: Arakawa C-grid of variables around a q-cell\\nwith labelled grid metrics (shifted wrt Fig 2a)');\n",
"myarrow(10,90,40,40,'$\\Delta$x$_q$'); myarrow(40,40,10,90,'$\\Delta$y$_q$')\n",
"myarrow(4,4,10,90,'$\\Delta$y$_v$',a=.35); myarrow(96,96,10,90,'$\\Delta$y$_v$',a=.4)\n",
"myarrow(10,90,4,4,'$\\Delta$x$_u$'); myarrow(10,90,96,96,'$\\Delta$x$_u$',a=.7)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model, and analyses of model output, often require grid metrics that depend on locations within the staggered grid:\n",
"- Distance between the a variables location, e.g. distance between u-points, between v-points, ...;\n",
"- Area of cell centered on a variable, e.g. h-cell area which is that bounded by the q-points.\n",
"\n",
"The eight most widely used distances are shown in Fig. 2a and 2b. Note that Fig. 2b is shifted a half-cell diagnonally with respect to Fig. 2a.\n",
"\n",
"> ### Comment on why we use Mosaics\n",
"> Defining all of these metrics at each location led to the old grid-specification having numerous variables, with possibly ambiguous names, that meant subtlely different things, e.g. dx_u might have been the distance between u-points, which on a regular spherical grid would contain similar values to dx_h, the distance between h-points. In these old specification it was very easy to use the wrong variable just due to naming conventions. Further, naming metrics by variable location depends on whether a grid specificatino is to be used for a C-grid or a B-grid."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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hQwB531G8bG/FihVhY2PD7B00aBCGDx+Oa9euYdasWahfvz569uzJXCrDwsJQvXp11KtX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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbe7d9540b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Figure 3\n",
"nan = numpy.nan\n",
"plt.axis((0,100,0,100)); plt.plot([10,90,90,10,10,nan,10,90,nan,50,50],[10,10,90,90,10,nan,50,50,nan,10,90],'k')\n",
"text_opts={'horizontalalignment':'center','verticalalignment':'center','backgroundcolor':'w'}\n",
"for y in [10,50,90]:\n",
" for x in [10,50,90]:\n",
" plt.text(x,y,'x,y',text_opts)\n",
" for x in [30,70]:\n",
" plt.text(x,y,'$\\Delta$x',text_opts)\n",
"for y in [30,70]:\n",
" for x in [10,50,90]:\n",
" plt.text(x,y,'$\\Delta$y',text_opts)\n",
" for x in [30,70]:\n",
" plt.text(x,y,'A',text_opts)\n",
"plt.axis('off'); plt.title('Fig 3: Four mosaic cells corresponding\\nto a single model h-cell');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mosaic grid specification\n",
"\n",
"A mosaic grid specification makes use of a \"supergrid\" which is a simple grid which is an integer refinement of the target model grid. The grid specification provides the bare minimum metrics for the supergrid cells which can be combined by aggregation to obtain the appropriate metrics for the model grid. A logically rectangular grid (LRG) mosaic specification will contain\n",
"- `ni` - Number of columns in x-direction\n",
"- `nj` - Number of rows in y-direction\n",
"- `x[nj+1,ni+1]` - x-coordinate of the grid vertices\n",
"- `y[nj+1,ni+1]` - y-coordinate of the grid vertices\n",
"- `area[nj,ni]` - area of each cell\n",
"- `dx[nj+1,ni]` - length of the edges of constant j-index\n",
"- `dy[nj,ni+1]` - length of the edges of constant -index\n",
"\n",
"Fig. 3 shows a super grid which overlays the model grid in Figs 1 and 2a with a refinement factor of 2. For orthogonal grids, we can see that the quantities in Fig. 2a can be found by summing pairs of quantities in Fig. 3.\n",
"\n",
"Notes:\n",
"- The data has different shapes depending on location which means that an indexing convention is not needed.\n",
"- The is no redundant data, except:\n",
" - For an x-periodic domain, the `x,y` and $\\Delta y$ data along the left and right edges should be identical data.\n",
" - Similar for a y-periodic domain (unusual in global models), the `x,y` and $\\Delta x$ data along the top and bottom edges should be identical data.\n",
"\n",
"### Mosaic index relationship to model index\n",
"\n",
"The refined resolution of a mosaic with respect to a model grid means one needs to calculate relative indices. Using a **python** index convention that starts at 0, netcdf index-order (j,i) and assuming a refinement factor of 2:\n",
"\n",
"- The `x,y` location of `h[j,i]` is at `x[2j+1,2i+1],y[2j+1,2i+1]`\n",
" - so that `h[0,0]` is at `x[1,1],y[1,1]`, `h[0,1]` is at `x[1,3],y[1,3]`, etc.\n",
"- The `x,y` location of `q[j,i]` is at `x[2j,2i],y[2j,2i]`\n",
" - so that `q[0,0]` is at `x[0,0],y[0,0]`, `q[0,1]` is at `x[0,2],y[0,2]`, etc.\n",
"- The `x,y` location of `u[j,i]` is at `x[2j+1,2i],y[2j+1,2i]`\n",
" - so that `u[0,0]` is at `x[1,0],y[1,0]`, `u[0,1]` is at `x[1,2],y[1,2]`, etc.\n",
"- The `x,y` location of `v[j,i]` is at `x[2j,2i+1],y[2j,2i+1]`\n",
" - so that `v[0,0]` is at `x[0,1],y[0,1]`, `v[0,1]` is at `x[0,3],y[0,3]`, etc.\n",
"- The distance between u-points, centered at `h[j,i]` is `dxh[j,i] = dx[2j+1,2i] + dx[2j+1,2i+1]`\n",
"- The distance between v-points, centered at `h[j,i]` is `dyh[j,i] = dy[2j,2i+1] + dy[2j+1,2i+1]`\n",
"- The distance between q-points, centered at `v[j,i]` is `dxCv[j,i] = dx[2j,2i] + dx[2j,2i+1]`\n",
"- The distance between q-points, centered at `u[j,i]` is `dyCu[j,i] = dy[2j,2i] + dy[2j+1,2i]`\n",
"- The distance between h-points, centered at `u[j,i]` is `dxCu[j,i] = dx[2j+1,2i-1] + dx[2j+1,2i]`\n",
" - (note periodic wrapping of i-index is needed)\n",
"- The distance between h-points, centered at `v[j,i]` is `dyCv[j,i] = dy[2j-1,2i+1] + dx[2j,2i+1]`\n",
" - (note periodic wrapping of j-index is needed)\n",
"- The distance between v-points, centered at `q[j,i]` is `dxBu[j,i] = dx[2j,2i-1] + dx[2j,2i]`\n",
" - (note periodic wrapping of i-index is needed)\n",
"- The distance between u-points, centered at `q[j,i]` is `dyBu[j,i] = dy[2j-1,2i] + dx[2j,2i]`\n",
" - (note periodic wrapping of j-index is needed)\n",
"- The area of an h-cell, centered at `h[j,i]` is `areah[j,i] = area[2j,2i] + area[2j,2i+1] + area[2j+1,2i] + area[2j+1,2i+1]`\n",
"- The area of a q-cell, centered at `q[j,i]` is `areaBu[j,i] = area[2j-1,2i-1] + area[2j-1,2i] + area[2j,2i-1] + area[2j,2i]`\n",
" - (note periodic wrapping of i- and j-index is needed)\n",
"- The area of a u-cell, centered at `u[j,i]` is `areaCu[j,i] = area[2j,2i-1] + area[2j,2i] + area[2j+1,2i-1] + area[2j+1,2i]`\n",
" - (note periodic wrapping of i-index is needed)\n",
"- The area of a v-cell, centered at `v[j,i]` is `areaCv[j,i] = area[2j-1,2i] + area[2j-1,2i+1] + area[2j,2i] + area[2j,2i+1]`\n",
" - (note periodic wrapping of j-index is needed)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# For the OM4_025 grid (CM4 ocean component)\n",
"path_to_mosaic_dir = '/archive/gold/datasets/OM4_025/mosaic.v20140610.unpacked/'\n",
"\n",
"# T point locations\n",
"xt = netCDF4.Dataset(path_to_mosaic_dir+'/ocean_hgrid.nc').variables['x'][1::2,1::2]\n",
"yt = netCDF4.Dataset(path_to_mosaic_dir+'/ocean_hgrid.nc').variables['y'][1::2,1::2]\n",
"\n",
"# Corner point locations\n",
"xq = netCDF4.Dataset(path_to_mosaic_dir+'/ocean_hgrid.nc').variables['x'][::2,::2]\n",
"yq = netCDF4.Dataset(path_to_mosaic_dir+'/ocean_hgrid.nc').variables['y'][::2,::2]\n",
"\n",
"# U point locations\n",
"xu = netCDF4.Dataset(path_to_mosaic_dir+'/ocean_hgrid.nc').variables['x'][1::2,::2]\n",
"yu= netCDF4.Dataset(path_to_mosaic_dir+'/ocean_hgrid.nc').variables['y'][1::2,::2]\n",
"\n",
"# V point locations\n",
"xv = netCDF4.Dataset(path_to_mosaic_dir+'/ocean_hgrid.nc').variables['x'][::2,1::2]\n",
"yv= netCDF4.Dataset(path_to_mosaic_dir+'/ocean_hgrid.nc').variables['y'][::2,1::2]\n",
"\n",
"# Wet/dry mask\n",
"wet = netCDF4.Dataset(path_to_mosaic_dir+'/ocean_mask.nc').variables['mask'][:,:]\n",
"\n",
"# Super grid data\n",
"area = netCDF4.Dataset(path_to_mosaic_dir+'/ocean_hgrid.nc').variables['area'][:,:]\n",
"dx = netCDF4.Dataset(path_to_mosaic_dir+'/ocean_hgrid.nc').variables['dx'][:,:]\n",
"dy = netCDF4.Dataset(path_to_mosaic_dir+'/ocean_hgrid.nc').variables['dy'][:,:]\n",
"\n",
"# T cell area (sum of four supergrid cells)\n",
"areat = ( ( area[::2,::2] + area[1::2,1::2] ) + ( area[::2,1::2] + area[::2,1::2] ) )*wet\n",
"\n",
"# x-distance between U points\n",
"dxh = dx[1::2,::2] + dx[1::2,1::2]\n",
"\n",
"# x-distance between corner points\n",
"dxCv = dx[2::2,::2] + dx[2::2,1::2]\n",
"\n",
"# x-distance between T points\n",
"dxCu = dx[1::2,::2]\n",
"dxCu = dxCu + numpy.roll( dx[1::2,:-1:2], -1, axis=-1 )\n",
"\n",
"# x-distance between V points\n",
"dxBu = dx[2::2,1::2]\n",
"dxBu = dxBu + numpy.roll( dx[2::2,::2], -1, axis=-1 )\n",
"\n",
"# y-distance between U points\n",
"dyh = dy[::2,1::2] + dy[1::2,1::2]\n",
"\n",
"# y-distance between corner points\n",
"dyCu = dy[::2,2::2] + dy[1::2,2::2]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Variable Type Data/Info\n",
"---------------------------------------------\n",
"area MaskedArray [[8589462.30372283 858683<...>197276 151284.56904635]]\n",
"areat MaskedArray [[0. 0. 0. ... 0. 0. 0.]\\<...> [0. 0. 0. ... 0. 0. 0.]]\n",
"dx MaskedArray [[2435.52726818 2435.6992<...>.04492222 6080.93976971]]\n",
"dxBu MaskedArray [[ 4904.41778454 4905.07<...>24152524 12161.87953942]]\n",
"dxCu MaskedArray [[ 4887.84552672 4888.51<...>44001297 12149.98489123]]\n",
"dxCv MaskedArray [[ 4904.08501162 4904.74<...>59119873 12149.98469193]]\n",
"dxh MaskedArray [[ 4887.67711374 4888.35<...>59179234 12149.98489123]]\n",
"dy MaskedArray [[3526.73624965 3525.4066<...>.87848483 0. ]]\n",
"dyCu MaskedArray [[7044.18397056 7038.9377<...>.31884562 0. ]]\n",
"dyh MaskedArray [[7046.83060878 7041.5529<...>.68712416 49.75677292]]\n",
"myarrow function <function myarrow at 0x2b7925888170>\n",
"nan float nan\n",
"netCDF4 module <module 'netCDF4' from '/<...>ges/netCDF4/__init__.py'>\n",
"numpy module <module 'numpy' from '/ne<...>kages/numpy/__init__.py'>\n",
"path_to_mosaic_dir str /archive/gold/datasets/OM<...>osaic.v20140610.unpacked/\n",
"plt module <module 'matplotlib.pyplo<...>es/matplotlib/pyplot.py'>\n",
"text_opts dict n=3\n",
"wet MaskedArray [[0. 0. 0. ... 0. 0. 0.]\\<...> [0. 0. 0. ... 0. 0. 0.]]\n",
"x int 70\n",
"xq MaskedArray [[-299.83624028 -299.5883<...>. 60. ]]\n",
"xt MaskedArray [[-299.7183397 -299.4703<...>.99846427 59.9994881 ]]\n",
"xu MaskedArray [[-299.84232357 -299.5943<...>.99897619 60. ]]\n",
"xv MaskedArray [[-299.71229562 -299.4644<...>. 60. ]]\n",
"y int 70\n",
"yq MaskedArray [[-79.83914959 -79.837644<...>6810724\\n 64.05895973]]\n",
"yt MaskedArray [[-79.80674269 -79.805269<...>2251977\\n 64.11358646]]\n",
"yu MaskedArray [[-79.80748594 -79.806003<...>6810633\\n 64.05895973]]\n",
"yv MaskedArray [[-79.83839463 -79.836898<...>2252114\\n 64.11358691]]\n"
]
}
],
"source": [
"whos"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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