C-------------------------------------------------------------------- C Copyright (C) 2002, Space Science and Engineering Center, C University C of Wisconsin-Madison, Madison WI. C C This program is free software; you can redistribute it C and/or modify it under the terms of the GNU General C Public License as published by the Free Software Foundation; C either version 2 of the License, or (at your option) any C later version. C C This program is distributed in the hope that it will be C useful, but WITHOUT ANY WARRANTY; without even the implied C warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. C See the GNU General Public License for more details. C C You should have received a copy of the GNU General Public C License along with this program; if not, write to the Free C Software Foundation, Inc., 59 Temple Place, Suite 330, C Boston, MA 02111-1307 USA C-------------------------------------------------------------------- C subroutine extem101(tt,slat) c----------------------------------------------------------------------- c c!F77 c c!Description: c Extrapolate a temperature profile defined at the 101 standard c pressure levels up to 0.005 mb. c Woolf version of 24.04.02 c c!Input Parameters: c TT Temperature profile (K) with -1.0 at missing levels c SLAT Latitude (deg) c c!Output Parameters: c EXTEMP Temperature profile with missing values filled c c!Revision History: c c!Team-unique Header: c c!End c c----------------------------------------------------------------------- implicit none save c Parameter statements integer nx parameter (nx = 9) integer ny parameter (ny = 35) integer nz parameter (nz = 3) c Argument variables real tt(*), slat c Local variables real tx(nx), lx(nx), cc(nz,0:nx,ny), plat(nz), cfl(0:nx), * coef(0:nx,ny,nz) real x1, x2, x3, y1, y2, y3, alat, xx, cy, sum, c1, c2, c3 integer init, j, i, k, l c Common statements common /extcoeff/ coef c Data statements data init /1/ ,lx /36,40,45,51,56,64,70,76,86/ data x1 /75./, x2 /45./, x3/15./ if(init.ne.0) then do j=1,ny do i=0,nx y1=coef(i,j,1) y2=coef(i,j,2) y3=coef(i,j,3) call cofit3(x1,x2,x3,y1,y2,y3,c1,c2,c3) cc(1,i,j)=c1 cc(2,i,j)=c2 cc(3,i,j)=c3 enddo enddo init=0 endif alat=abs(slat) xx=1. do k=1,nz plat(k)=xx xx=xx*alat enddo c Load the predictor array do i=1,nx l=lx(i) tx(i)=tt(l) enddo do j=1,ny if(tt(j).gt.0.) return do i=0,nx cy=0. do k=1,nz cy=cy+cc(k,i,j)*plat(k) enddo cfl(i)=cy enddo sum=cfl(0) do i=1,nx sum=sum+cfl(i)*tx(i) enddo tt(j)=sum enddo return end block data extrap_coeffs integer nx parameter (nx = 9) integer ny parameter (ny = 35) integer nz parameter (nz = 3) integer i real coef(0:nx,ny,nz) common /extcoeff/ coef c Temperature-Extrapolation Coefficients ... c derived from NESDIS PROF1200 c +++ Zone 1 c * 0.0050 mb: data (coef(i, 1,1),i=0,nx)/ + 768.137268, -0.294463, -1.938120, 1.445014, 1.010484, + -1.501080, -0.333852, -0.460916, -0.289109, -0.185883/ c * 0.0161 mb: data (coef(i, 2,1),i=0,nx)/ + 729.598755, -0.048072, -2.192048, 1.556883, 1.041545, + -1.527906, -0.179924, -0.397472, -0.527085, -0.022882/ c * 0.0384 mb: data (coef(i, 3,1),i=0,nx)/ + 580.007996, 0.256773, -1.953103, 1.262224, 0.855626, + -1.226672, 0.021504, -0.272799, -0.672050, 0.167226/ c * 0.0769 mb: data (coef(i, 4,1),i=0,nx)/ + 378.418610, 0.481383, -1.547368, 0.949718, 0.557970, + -0.797499, 0.236666, -0.090441, -0.724859, 0.324352/ c * 0.1370 mb: data (coef(i, 5,1),i=0,nx)/ + 227.726013, 0.399807, -0.767517, 0.339597, 0.334890, + -0.304244, 0.242581, 0.200534, -0.710240, 0.359780/ c * 0.2244 mb: data (coef(i, 6,1),i=0,nx)/ + 131.540237, 0.116343, 0.181285, -0.404714, 0.194162, + 0.168128, 0.112673, 0.505991, -0.649249, 0.319506/ c * 0.3454 mb: data (coef(i, 7,1),i=0,nx)/ + 36.948456, -0.219132, 1.068293, -1.006580, 0.041164, + 0.596757, 0.016176, 0.724429, -0.524707, 0.280223/ c * 0.5064 mb: data (coef(i, 8,1),i=0,nx)/ + -47.064129, -0.502069, 1.832718, -1.521932, -0.102700, + 0.973025, -0.065917, 0.916755, -0.417642, 0.247873/ c * 0.7140 mb: data (coef(i, 9,1),i=0,nx)/ + -125.058113, -0.371252, 1.929798, -1.497271, -0.445293, + 1.206479, -0.047493, 1.051199, -0.411198, 0.284401/ c * 0.9753 mb: data (coef(i,10,1),i=0,nx)/ + -195.826279, -0.252554, 2.017885, -1.474895, -0.756146, + 1.418304, -0.030777, 1.173188, -0.405352, 0.317545/ c * 1.2972 mb: data (coef(i,11,1),i=0,nx)/ + -211.840836, -0.087453, 1.892584, -1.498378, -0.888045, + 1.557249, -0.013888, 1.153322, -0.274422, 0.228628/ c * 1.6872 mb: data (coef(i,12,1),i=0,nx)/ + -222.487411, 0.077679, 1.743851, -1.517234, -0.992018, + 1.676924, 0.003067, 1.119941, -0.144600, 0.140581/ c * 2.1526 mb: data (coef(i,13,1),i=0,nx)/ + -220.345123, 0.284957, 1.478203, -1.409153, -1.098716, + 1.732799, 0.005315, 1.062679, -0.051710, 0.083439/ c * 2.7009 mb: data (coef(i,14,1),i=0,nx)/ + -191.945847, 0.575167, 0.992410, -1.054349, -1.230327, + 1.674808, -0.024700, 0.961055, -0.019512, 0.071966/ c * 3.3398 mb: data (coef(i,15,1),i=0,nx)/ + -163.447739, 0.789576, 0.662758, -0.848763, -1.165136, + 1.486376, -0.005209, 0.856173, 0.036504, 0.000516/ c * 4.0770 mb: data (coef(i,16,1),i=0,nx)/ + -135.140701, 0.938526, 0.466544, -0.770585, -0.932123, + 1.188257, 0.055934, 0.750415, 0.112469, -0.122608/ c * 4.9204 mb: data (coef(i,17,1),i=0,nx)/ + -110.534798, 1.079643, 0.269122, -0.685459, -0.724919, + 0.927309, 0.105440, 0.666502, 0.180002, -0.240317/ c * 5.8776 mb: data (coef(i,18,1),i=0,nx)/ + -86.496407, 1.197924, 0.180591, -0.680802, -0.560434, + 0.725634, 0.114811, 0.524036, 0.254776, -0.292023/ c * 6.9567 mb: data (coef(i,19,1),i=0,nx)/ + -63.631668, 1.308646, 0.105890, -0.683525, -0.407437, + 0.538658, 0.120171, 0.383015, 0.326700, -0.335437/ c * 8.1655 mb: data (coef(i,20,1),i=0,nx)/ + -53.883522, 1.446655, -0.084576, -0.584522, -0.321630, + 0.418490, 0.138251, 0.323507, 0.266197, -0.297677/ c * 9.5119 mb: data (coef(i,21,1),i=0,nx)/ + -45.056870, 1.579389, -0.270613, -0.486306, -0.242174, + 0.306220, 0.155973, 0.269677, 0.203612, -0.258671/ c * 11.0038 mb: data (coef(i,22,1),i=0,nx)/ + -37.774498, 1.605633, -0.336989, -0.423904, -0.179642, + 0.233162, 0.156346, 0.226620, 0.162179, -0.225690/ c * 12.6492 mb: data (coef(i,23,1),i=0,nx)/ + -31.381693, 1.580467, -0.344828, -0.379910, -0.126498, + 0.180357, 0.148424, 0.189608, 0.131713, -0.196274/ c * 14.4559 mb: data (coef(i,24,1),i=0,nx)/ + -25.256893, 1.556356, -0.352337, -0.337762, -0.075582, + 0.129767, 0.140834, 0.154147, 0.102526, -0.168092/ c * 16.4318 mb: data (coef(i,25,1),i=0,nx)/ + -19.332832, 1.532327, -0.358523, -0.296751, -0.023514, + 0.076359, 0.135129, 0.118184, 0.075598, -0.141048/ c * 18.5847 mb: data (coef(i,26,1),i=0,nx)/ + -13.621389, 1.508887, -0.364070, -0.257120, 0.027774, + 0.023140, 0.130262, 0.082869, 0.050141, -0.115058/ c * 20.9224 mb: data (coef(i,27,1),i=0,nx)/ + -10.552977, 1.475920, -0.355611, -0.209939, 0.047681, + -0.006736, 0.119757, 0.072153, 0.024709, -0.098524/ c * 23.4526 mb: data (coef(i,28,1),i=0,nx)/ + -11.404002, 1.427825, -0.325822, -0.150293, 0.020678, + -0.002060, 0.100506, 0.098321, -0.001260, -0.095886/ c * 26.1829 mb: data (coef(i,29,1),i=0,nx)/ + -12.431881, 1.380402, -0.295441, -0.093749, -0.004390, + 0.002642, 0.081415, 0.123695, -0.026677, -0.092560/ c * 29.1210 mb: data (coef(i,30,1),i=0,nx)/ + -13.700622, 1.333231, -0.263907, -0.040468, -0.027290, + 0.007437, 0.062283, 0.148370, -0.051712, -0.088305/ c * 32.2744 mb: data (coef(i,31,1),i=0,nx)/ + -12.049180, 1.288188, -0.233483, -0.020884, -0.028961, + 0.007528, 0.048806, 0.133084, -0.050300, -0.074679/ c * 35.6505 mb: data (coef(i,32,1),i=0,nx)/ + -9.317835, 1.244821, -0.204065, -0.014504, -0.022521, + 0.005827, 0.037740, 0.102881, -0.038848, -0.057748/ c * 39.2566 mb: data (coef(i,33,1),i=0,nx)/ + -6.672400, 1.202820, -0.175573, -0.008325, -0.016283, + 0.004179, 0.027023, 0.073628, -0.027756, -0.041350/ c * 43.1001 mb: data (coef(i,34,1),i=0,nx)/ + -4.107985, 1.162105, -0.147954, -0.002335, -0.010237, + 0.002583, 0.016633, 0.045271, -0.017004, -0.025454/ c * 47.1882 mb: data (coef(i,35,1),i=0,nx)/ + -1.620239, 1.122606, -0.121160, 0.003476, -0.004369, + 0.001033, 0.006554, 0.017761, -0.006572, -0.010032/ c +++ Zone 2 c * 0.0050 mb: data (coef(i, 1,2),i=0,nx)/ + 834.861145, -0.395518, -0.168292, -0.522989, 0.135368, + -0.648098, -0.120294, -0.432757, -0.616573, -0.049551/ c * 0.0161 mb: data (coef(i, 2,2),i=0,nx)/ + 706.697998, -0.564473, 0.061239, -0.490616, 0.279786, + -0.515674, -0.074882, -0.145787, -0.887622, 0.150642/ c * 0.0384 mb: data (coef(i, 3,2),i=0,nx)/ + 456.640137, -0.515665, 0.238243, -0.325252, 0.306180, + -0.235547, -0.028916, 0.203108, -0.970324, 0.303589/ c * 0.0769 mb: data (coef(i, 4,2),i=0,nx)/ + 196.650543, -0.580368, 0.469164, -0.167309, 0.389823, + 0.028119, 0.028069, 0.512286, -0.967577, 0.455387/ c * 0.1370 mb: data (coef(i, 5,2),i=0,nx)/ + 55.473385, -0.577106, 0.510232, -0.037092, 0.481069, + 0.095268, 0.059780, 0.602277, -0.792924, 0.480604/ c * 0.2244 mb: data (coef(i, 6,2),i=0,nx)/ + 3.620781, -0.523341, 0.420839, 0.076334, 0.556607, + 0.033327, 0.063618, 0.596428, -0.589134, 0.438588/ c * 0.3454 mb: data (coef(i, 7,2),i=0,nx)/ + -45.342590, -0.570448, 0.387072, 0.196123, 0.643398, + -0.025933, 0.053534, 0.719213, -0.558915, 0.459968/ c * 0.5064 mb: data (coef(i, 8,2),i=0,nx)/ + -88.503006, -0.608382, 0.353316, 0.301524, 0.715639, + -0.073664, 0.042024, 0.832244, -0.535806, 0.479966/ c * 0.7140 mb: data (coef(i, 9,2),i=0,nx)/ + -120.005302, -0.541255, 0.223263, 0.373417, 0.655807, + 0.010448, -0.035588, 1.041446, -0.612080, 0.524942/ c * 0.9753 mb: data (coef(i,10,2),i=0,nx)/ + -148.589035, -0.480346, 0.105258, 0.438649, 0.601519, + 0.086768, -0.106009, 1.231265, -0.681287, 0.565751/ c * 1.2972 mb: data (coef(i,11,2),i=0,nx)/ + -161.973740, -0.371849, 0.008552, 0.486533, 0.557450, + 0.112235, -0.175632, 1.253408, -0.627670, 0.561722/ c * 1.6872 mb: data (coef(i,12,2),i=0,nx)/ + -173.125641, -0.266075, -0.083040, 0.532244, 0.515987, + 0.132649, -0.241772, 1.260636, -0.568181, 0.555640/ c * 2.1526 mb: data (coef(i,13,2),i=0,nx)/ + -173.081696, -0.094634, -0.220959, 0.567489, 0.477990, + 0.120870, -0.283719, 1.244042, -0.533282, 0.546141/ c * 2.7009 mb: data (coef(i,14,2),i=0,nx)/ + -150.789734, 0.220479, -0.455112, 0.578471, 0.446871, + 0.041562, -0.277308, 1.177748, -0.543711, 0.525773/ c * 3.3398 mb: data (coef(i,15,2),i=0,nx)/ + -130.673721, 0.435536, -0.553035, 0.536432, 0.402999, + -0.011230, -0.249925, 1.061322, -0.498618, 0.491557/ c * 4.0770 mb: data (coef(i,16,2),i=0,nx)/ + -112.541351, 0.564179, -0.533735, 0.448692, 0.348486, + -0.040873, -0.204408, 0.902095, -0.405841, 0.445350/ c * 4.9204 mb: data (coef(i,17,2),i=0,nx)/ + -96.168854, 0.685395, -0.516674, 0.364636, 0.299450, + -0.066547, -0.160329, 0.753376, -0.318513, 0.400629/ c * 5.8776 mb: data (coef(i,18,2),i=0,nx)/ + -121.107452, 0.789144, -0.466781, 0.308981, 0.265531, + -0.041420, -0.096691, 0.627374, -0.172447, 0.330783/ c * 6.9567 mb: data (coef(i,19,2),i=0,nx)/ + -148.557831, 0.886490, -0.416297, 0.258454, 0.234542, + -0.012946, -0.034285, 0.509281, -0.027978, 0.261965/ c * 8.1655 mb: data (coef(i,20,2),i=0,nx)/ + -146.946228, 0.924881, -0.351371, 0.173360, 0.243558, + -0.023024, -0.043987, 0.511167, -0.028737, 0.240058/ c * 9.5119 mb: data (coef(i,21,2),i=0,nx)/ + -144.347504, 0.959377, -0.288869, 0.090871, 0.253623, + -0.034051, -0.055879, 0.517345, -0.034760, 0.220859/ c * 11.0038 mb: data (coef(i,22,2),i=0,nx)/ + -136.725281, 0.967216, -0.250049, 0.038883, 0.268749, + -0.045579, -0.034804, 0.465370, -0.010830, 0.193404/ c * 12.6492 mb: data (coef(i,23,2),i=0,nx)/ + -126.862816, 0.962160, -0.223352, 0.002548, 0.285976, + -0.057106, 0.001579, 0.386704, 0.026907, 0.162579/ c * 14.4559 mb: data (coef(i,24,2),i=0,nx)/ + -117.413834, 0.957316, -0.197774, -0.032263, 0.302480, + -0.068150, 0.036436, 0.311337, 0.063062, 0.133046/ c * 16.4318 mb: data (coef(i,25,2),i=0,nx)/ + -108.387199, 0.951026, -0.170067, -0.067915, 0.319109, + -0.079884, 0.071843, 0.243068, 0.098536, 0.099678/ c * 18.5847 mb: data (coef(i,26,2),i=0,nx)/ + -99.728241, 0.944342, -0.142208, -0.103052, 0.335396, + -0.091603, 0.106632, 0.179040, 0.132930, 0.065653/ c * 20.9224 mb: data (coef(i,27,2),i=0,nx)/ + -90.187675, 0.953354, -0.131290, -0.116374, 0.326715, + -0.094256, 0.128964, 0.132450, 0.141525, 0.044614/ c * 23.4526 mb: data (coef(i,28,2),i=0,nx)/ + -79.100502, 0.986260, -0.145695, -0.097073, 0.280154, + -0.083285, 0.132998, 0.111127, 0.111379, 0.042697/ c * 26.1829 mb: data (coef(i,29,2),i=0,nx)/ + -68.699837, 1.017853, -0.157891, -0.083327, 0.241075, + -0.074152, 0.137479, 0.089334, 0.082811, 0.041165/ c * 29.1210 mb: data (coef(i,30,2),i=0,nx)/ + -59.048897, 1.048161, -0.167399, -0.076558, 0.211126, + -0.067266, 0.142594, 0.066655, 0.055907, 0.040111/ c * 32.2744 mb: data (coef(i,31,2),i=0,nx)/ + -48.262180, 1.062744, -0.160901, -0.062914, 0.173613, + -0.056035, 0.123367, 0.051704, 0.041447, 0.034117/ c * 35.6505 mb: data (coef(i,32,2),i=0,nx)/ + -37.250641, 1.071060, -0.148434, -0.046917, 0.133942, + -0.043364, 0.095243, 0.039982, 0.032004, 0.026359/ c * 39.2566 mb: data (coef(i,33,2),i=0,nx)/ + -26.585524, 1.079114, -0.136360, -0.031422, 0.095519, + -0.031093, 0.068005, 0.028629, 0.022858, 0.018845/ c * 43.1001 mb: data (coef(i,34,2),i=0,nx)/ + -16.247211, 1.086921, -0.124655, -0.016402, 0.058273, + -0.019197, 0.041600, 0.017624, 0.013991, 0.011561/ c * 47.1882 mb: data (coef(i,35,2),i=0,nx)/ + -6.217585, 1.094496, -0.113301, -0.001831, 0.022139, + -0.007657, 0.015985, 0.006947, 0.005390, 0.004494/ c +++ Zone 3 c * 0.0050 mb: data (coef(i, 1,3),i=0,nx)/ + 253.099335, -0.059435, -0.113692, -0.076584, 0.018085, + 0.109696, -0.245047, 0.207798, -0.158821, -0.005658/ c * 0.0161 mb: data (coef(i, 2,3),i=0,nx)/ + 393.724976, -0.159516, -0.305137, -0.205541, 0.048536, + 0.294412, -0.657678, 0.557706, -0.426257, -0.015186/ c * 0.0384 mb: data (coef(i, 3,3),i=0,nx)/ + 488.316467, -0.220589, -0.421964, -0.284236, 0.067118, + 0.407132, -0.909481, 0.771234, -0.589456, -0.021001/ c * 0.0769 mb: data (coef(i, 4,3),i=0,nx)/ + 571.788940, -0.274628, -0.525335, -0.353867, 0.083561, + 0.506871, -1.132283, 0.960168, -0.733859, -0.026146/ c * 0.1370 mb: data (coef(i, 5,3),i=0,nx)/ + 564.131775, -0.280373, -0.502551, -0.299793, 0.065189, + 0.444018, -1.027926, 0.888957, -0.690901, -0.011248/ c * 0.2244 mb: data (coef(i, 6,3),i=0,nx)/ + 496.362030, -0.249809, -0.394284, -0.179865, 0.027459, + 0.290057, -0.717002, 0.634090, -0.503432, 0.004950/ c * 0.3454 mb: data (coef(i, 7,3),i=0,nx)/ + 432.359314, -0.211118, -0.286078, -0.105599, -0.000052, + 0.184573, -0.442425, 0.356257, -0.266806, -0.011249/ c * 0.5064 mb: data (coef(i, 8,3),i=0,nx)/ + 375.968506, -0.173903, -0.194453, -0.041546, -0.020229, + 0.090298, -0.207287, 0.122826, -0.062737, -0.026678/ c * 0.7140 mb: data (coef(i, 9,3),i=0,nx)/ + 335.498596, -0.064613, -0.226756, -0.032027, 0.072557, + -0.012652, -0.217699, 0.255347, -0.032882, -0.068283/ c * 0.9753 mb: data (coef(i,10,3),i=0,nx)/ + 298.778290, 0.034553, -0.256067, -0.023391, 0.156749, + -0.106065, -0.227148, 0.375591, -0.005793, -0.106034/ c * 1.2972 mb: data (coef(i,11,3),i=0,nx)/ + 320.508484, 0.055737, -0.215899, -0.126142, 0.126488, + -0.119170, -0.163850, 0.312299, 0.057370, -0.183508/ c * 1.6872 mb: data (coef(i,12,3),i=0,nx)/ + 338.109192, 0.077756, -0.167569, -0.228182, 0.081726, + -0.109376, -0.114042, 0.249524, 0.124284, -0.253638/ c * 2.1526 mb: data (coef(i,13,3),i=0,nx)/ + 330.158142, 0.125628, -0.127452, -0.284897, 0.018188, + -0.073173, -0.058458, 0.164222, 0.202521, -0.283837/ c * 2.7009 mb: data (coef(i,14,3),i=0,nx)/ + 288.525238, 0.208034, -0.113437, -0.262148, -0.070341, + -0.019104, 0.050349, -0.000575, 0.297252, -0.249445/ c * 3.3398 mb: data (coef(i,15,3),i=0,nx)/ + 247.402054, 0.288580, -0.105926, -0.234689, -0.143902, + 0.044019, 0.059252, -0.048267, 0.325860, -0.189348/ c * 4.0770 mb: data (coef(i,16,3),i=0,nx)/ + 207.174408, 0.365898, -0.103597, -0.202744, -0.204937, + 0.116567, -0.020110, 0.001814, 0.300148, -0.106910/ c * 4.9204 mb: data (coef(i,17,3),i=0,nx)/ + 172.771362, 0.425351, -0.097642, -0.168375, -0.266588, + 0.200371, -0.115398, 0.021846, 0.299176, -0.026388/ c * 5.8776 mb: data (coef(i,18,3),i=0,nx)/ + 170.819962, 0.377608, 0.003744, -0.188331, -0.277131, + 0.162590, -0.022318, -0.064583, 0.305203, -0.023369/ c * 6.9567 mb: data (coef(i,19,3),i=0,nx)/ + 171.848145, 0.322555, 0.108887, -0.212190, -0.282633, + 0.115752, 0.083180, -0.156449, 0.311570, -0.027389/ c * 8.1655 mb: data (coef(i,20,3),i=0,nx)/ + 178.900009, 0.317759, 0.072287, -0.147714, -0.288088, + 0.079424, 0.109033, -0.207073, 0.293082, -0.003934/ c * 9.5119 mb: data (coef(i,21,3),i=0,nx)/ + 185.850937, 0.315015, 0.032178, -0.082944, -0.293293, + 0.045129, 0.130805, -0.253892, 0.274527, 0.019458/ c * 11.0038 mb: data (coef(i,22,3),i=0,nx)/ + 181.033875, 0.342789, -0.008198, -0.050784, -0.276437, + 0.028043, 0.139765, -0.267162, 0.249229, 0.043197/ c * 12.6492 mb: data (coef(i,23,3),i=0,nx)/ + 170.696304, 0.384559, -0.047857, -0.034871, -0.249395, + 0.019533, 0.142417, -0.264131, 0.221237, 0.066608/ c * 14.4559 mb: data (coef(i,24,3),i=0,nx)/ + 160.792282, 0.424578, -0.085853, -0.019626, -0.223487, + 0.011380, 0.144958, -0.261227, 0.194418, 0.089037/ c * 16.4318 mb: data (coef(i,25,3),i=0,nx)/ + 150.499008, 0.461093, -0.122477, -0.003129, -0.196627, + 0.000506, 0.147210, -0.251144, 0.167289, 0.109366/ c * 18.5847 mb: data (coef(i,26,3),i=0,nx)/ + 140.299698, 0.495449, -0.157735, 0.013451, -0.170036, + -0.011132, 0.149301, -0.238611, 0.140674, 0.128437/ c * 20.9224 mb: data (coef(i,27,3),i=0,nx)/ + 133.652267, 0.528523, -0.174538, 0.028105, -0.154952, + -0.021779, 0.150992, -0.227335, 0.124328, 0.123706/ c * 23.4526 mb: data (coef(i,28,3),i=0,nx)/ + 132.214737, 0.560411, -0.163866, 0.040182, -0.156891, + -0.031171, 0.152119, -0.217697, 0.123112, 0.082945/ c * 26.1829 mb: data (coef(i,29,3),i=0,nx)/ + 130.282120, 0.591975, -0.157004, 0.055184, -0.159140, + -0.038202, 0.157241, -0.213196, 0.124561, 0.042131/ c * 29.1210 mb: data (coef(i,30,3),i=0,nx)/ + 127.686752, 0.623526, -0.154959, 0.074146, -0.161817, + -0.042282, 0.167575, -0.215254, 0.129460, 0.000725/ c * 32.2744 mb: data (coef(i,31,3),i=0,nx)/ + 108.781189, 0.698895, -0.147134, 0.068980, -0.139383, + -0.037117, 0.145935, -0.184685, 0.111908, -0.009194/ c * 35.6505 mb: data (coef(i,32,3),i=0,nx)/ + 84.030518, 0.789494, -0.137261, 0.054729, -0.107821, + -0.028531, 0.112540, -0.142284, 0.086147, -0.006936/ c * 39.2566 mb: data (coef(i,33,3),i=0,nx)/ + 60.058582, 0.877243, -0.127698, 0.040927, -0.077253, + -0.020216, 0.080196, -0.101216, 0.061195, -0.004749/ c * 43.1001 mb: data (coef(i,34,3),i=0,nx)/ + 36.821239, 0.962303, -0.118428, 0.027547, -0.047621, + -0.012155, 0.048844, -0.061407, 0.037009, -0.002629/ c * 47.1882 mb: data (coef(i,35,3),i=0,nx)/ + 14.277436, 1.044823, -0.109435, 0.014568, -0.018874, + -0.004335, 0.018428, -0.022788, 0.013544, -0.000572/ end subroutine cofit3(xx1,xx2,xx3,yy1,yy2,yy3, c0,c1,c2) c Obtain coefficients for 3-point parabolic fit implicit none c Argument variables real xx1, xx2, xx3, yy1, yy2, yy3, c0, c1, c2 c Local variables real x1, x2, x3, y1, y2, y3, x12, x22, x32, t1, t2, t3, * det x1=xx1 x2=xx2 x3=xx3 y1=yy1 y2=yy2 y3=yy3 x12=x1*x1 x22=x2*x2 x32=x3*x3 t1=x2*x32-x3*x22 t2=-(x1*x32-x3*x12) t3=x1*x22-x2*x12 det=t1+t2+t3 c0=(y1*t1+y2*t2+y3*t3)/det c1=((y2*x32-y3*x22)-(y1*x32-y3*x12)+(y1*x22-y2*x12))/det c2=((x2*y3-x3*y2)-(x1*y3-x3*y1)+(x1*y2-x2*y1))/det return end