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total_cloudfrac.nco
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total_cloudfrac.nco
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// This will estimate the total cloud fraction of each grid cell
// according to the random overlap formula given by
// Hogan and Illingworth, "Deriving cloud overlap statistics from radar",
// Q. J. R. Meteorol. Soc. (2000), 126, 2903-2909.
// Eqn. 1-4 and Table 1 contain the relevant information.
// We will use the e-folding distance for 1 hr resolution and
// 720 m, as the level separation in the WRF output I'm using
// is closest to 700 m around where clouds start becoming prevalent.
// 1 hr time resolution could be argued either way; the output is
// sometimes 0.5 or 1 hour, but the boundary conditions are at 3 hr.
//
// This will need to loop over each level except the last and compare
// pairwise each level with the one above it.
print("Beginning total_cloudfrac\n");
// Get the dimensions to loop over
*sz_time=$Time.size;
*sz_south_north=$south_north.size;
*sz_west_east=$west_east.size;
*sz_bottom_top=$bottom_top.size;
// Store CLDFRA in ram to avoid too many disk reads
*tmp_cldfra=CLDFRA;
// Initialize the CLDFRA_TOTAL variable to 0. This'll be saved permanently later.
*CLDFRA_TOTAL[$Time, $south_north, $west_east]=0;
// Begin the loops
for(*tdx=0;tdx<sz_time;tdx++){
for(*idx=0;idx<sz_south_north;idx++){
print("Advancing idx...\n");
for(*jdx=0;jdx<sz_west_east;jdx++){
*ci=tmp_cldfra(tdx,0,idx,jdx);
for(*kdx=1;kdx<sz_bottom_top;kdx++){
*alpha=2.71^(zlev(tdx,kdx-1,idx,jdx)/1900);
*ci1=tmp_cldfra(tdx,kdx,idx,jdx);
*mc=0;
if(ci < ci1)
mc=ci;
else
mc=ci1;
*O=alpha*mc+(1-alpha)*ci*ci1;
ci=ci+ci1-O;
}
CLDFRA_TOTAL(tdx,idx,jdx)=ci;
}
}
}
CLDFRA_TOTAL.ram_write();