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afe |
1.1 |
subroutine greg_sqrt(xens,yo,H,ngp,mobs,RRs,nens) |
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c implicit none |
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include 'mpif.h' |
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integer MASTER, FROM_MASTER, FROM_WORKER |
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parameter (MASTER = 0) |
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parameter (FROM_MASTER = 1) |
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parameter (FROM_WORKER = 2) |
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integer taskid, ierr, numtasks, numworkers, snumworkers |
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integer source, dest, mtype, num, avenum, extra, offset |
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integer status(MPI_STATUS_SIZE) |
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! Arguments |
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integer, intent(in) :: nens, mobs, ngp |
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real, intent(inout) :: xens(ngp,nens) |
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real, intent(in) :: yo(mobs), RRs(mobs), H(mobs,ngp) |
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! Local Variables |
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integer :: rad, xob(mobs), yob(mobs), ind, nx, ny |
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integer :: k, j, i, r2, kk, jj, ko, jo, kj, g |
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real :: PHT(ngp), HPHT, Ks(ngp), Khat(ngp), alpha |
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real :: xp(ngp), xa(ngp), zp(ngp,nens), R, Rs, boost |
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real, parameter :: eps = 1.e-6 ! corresponds to verr = 0.001 |
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! Filter Stuff |
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integer :: d, fexp |
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real :: dr, fcoef, filt |
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call MPI_COMM_RANK(MPI_COMM_WORLD,taskid,ierr) |
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call MPI_COMM_SIZE(MPI_COMM_WORLD,numtasks,ierr) |
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if (taskid.eq.MASTER) then |
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write(6,*) 'a' |
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nx = 33 |
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ny = 65 |
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Rs = RRs(1) |
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R = sqrt(Rs) |
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rad = 20 |
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rad = 100 |
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r2 = rad*rad |
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fexp = 2 |
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fcoef = 0.05 |
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boost = 1.05 |
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boost =1.0 |
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zp = xens |
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!*** Transform 2D state vector into 1D vector.... |
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! do j = 1, nens |
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! do k = 1, ny |
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! zp(nx*(k-1)+1:k*nx,j) = xens(:,k,j) |
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! end do |
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! end do |
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write(6,*) 'b' |
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!*** Find the initial ensemble mean |
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do j = 1, ngp |
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xp(j) = sum(zp(j,:))/float(nens) |
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end do |
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!*** Apply inflation factor to initial ensemble |
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do k = 1, nens |
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zp(:,k) = boost*(zp(:,k) - xp) + xp |
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end do |
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write(6,*) 'c' |
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!*** Find the xob and yob arrays from H |
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do j = 1, ngp |
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do k = 1, mobs |
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if ( H(k,j) == 1. ) then |
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xob(k) = mod(j-1,nx) + 1 |
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yob(k) = (j-1)/nx + 1 |
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end if |
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end do |
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end do |
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write(6,*) 'd' |
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!*** Now process each observation sequentially abiding by cut-off radius |
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do j = 1, mobs |
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ind = nx*( yob(j) - 1 ) + xob(j) |
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write(6,*) 'e' |
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!*** Find PH' first |
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PHT = 0.0 |
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do jj = yob(j)-rad, yob(j)+rad |
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do kk = xob(j)-rad, xob(j)+rad |
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jo = jj |
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ko = kk |
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write(6,*) 'f', j, jo, ko |
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!*** Point is within block of radius, but it may not be within the |
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! basin boundaries |
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if ( ko>0 .and. ko<=nx .and. jo>0 .and. jo<=ny ) then |
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!*** Since we've sequestered a square of side 2*rad and the |
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! cut-off radius assumes a circle, we need to check to |
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! make sure the point we're considering is actually |
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! within the circle. |
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write(6,*) 'g' |
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d = (ko - xob(j))**2 + (jo - yob(j))**2 |
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write(6,*) 'h' |
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if ( d <= r2 ) then |
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dr = sqrt( float( d ) ) |
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!*** The element of interest in the 1D vector according to addresses |
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! kk and jj is: |
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kj = nx*(jo-1) + ko |
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write(6,*) 'i' |
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!*** Evaluate the filter coefficient based on distance from center d |
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filt = 1.0 - exp( -fcoef*( (float(rad) - dr)**fexp ) ) |
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filt = 1.0 |
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write(6,*) 'j' |
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!*** Now contribute to PHT sum |
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do g = 1, nens |
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PHT(kj) = PHT(kj) + filt*(zp(kj,g) - xp(kj))* |
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& (zp(ind,g) - xp(ind)) |
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end do |
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write(6,*) 'k' |
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end if |
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end if |
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end do |
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end do |
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PHT = PHT/float(nens - 1) |
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!*** Now find HPH' from PH'. Because of cut-off radius, there is a |
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! (good) chance that HPH' will be zero. |
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HPHT = PHT(ind) |
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!*** Evaluate Ks |
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Ks = PHT/( HPHT + Rs ) |
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!*** Update all effected elements in the mean |
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xa = xp + Ks*( yo(j) - xp(ind) ) |
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!*** Now update all ensemble members as perturbations about mean |
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alpha = 1.0/( 1.0 + sqrt( Rs/( HPHT + Rs ) ) ) |
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Khat = alpha*Ks |
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do g = 1, nens |
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zp(:,g) = ((zp(:,g) - xp) - Khat*( zp(ind,g) - |
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& xp(ind) )) + xa |
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end do |
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!*** Use analysis ensemble as the background for the next observation |
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xp = xa |
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end do |
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print*, 'EnSRF:: Done Mobs Loop' |
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xens = zp |
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endif |
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return |
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end subroutine greg_sqrt |
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