"anim1" <- function(N=200, dt=.02){ yr<- c(-8,15) xr<- c(1,40) plot( xr, yr, type="n", xlab="LONGITUDE", ylab="VARIABLE") loc<- 1:40 xl<- seq( 1,40,,300) temp<- L96() lines( xl, splint(1:40, temp, xl),col=2, lwd=1.5) tt<- -dt for ( k in 1:N){ tt<- tt + dt text( 3,-8, round( tt,2), col=4, cex=2, adj=0) lines( xl, splint(1:40, temp, xl),col="#FFFFFF", lwd=1.5) temp<- L96(temp, dt=dt) lines( xl, splint(1:40, temp, xl), col=4, lwd=1.5) junk<- 0 for ( j in 1:10000){junk <- sqrt(j)} xline( 10, col=3) text( 3,-8, round( tt,2), col="#FFFFFF", cex=2, adj=0) #system( "sleep 0") } } "anim2" <- function(N=15, MM=5, dt=.02){ # MM is number of ensemble members set.seed( 123) yr<- c(-8,15) xr<- c(1,40) plot( xr, yr, type="n", xlab="LONGITUDE", ylab="VARIABLE") loc<- 1:40 xl<- seq( 1,40,,150) temp<- L96() # generate MM perturbations from starting state. X<-matrix( temp, ncol=MM, nrow=40) + matrix( rnorm(MM*40)*.05, nrow=40) # # tt<- -dt for ( k in 1:N){ tt<- tt + dt text( 3,-8, round( tt,2), col=4, cex=2, adj=0) for ( jj in 1: MM){ lines( xl, splint(1:40, X[,jj], xl),col="#FFFFFF", lwd=1) } lines( xl, splint(1:40, temp, xl),col="#FFFFFF", lwd=1) X<- L96(X, dt=dt) temp<- L96( temp, dt=dt) for ( jj in 1: MM){ lines( xl, splint(1:40, X[,jj], xl),col=2, lwd=1) } lines( xl, splint(1:40, temp, xl),col=4, lwd=1) junk<- 0 #for ( jj in 1:1000) { junk<- sqrt(jj)} if( k ==1) { system( "sleep 1")} system( "sleep 0.2") text( 3,-8, round( tt,2), col="#FFFFFF", cex=2, adj=0) } } "EKF" <- function(N=25, MM=15, dt=.2/5, sige=.5,tempo=T){ set.seed( 123) yr<- c(-8,15) xr<- c(1,40) plot( xr, yr, type="n", xlab="LONGITUDE", ylab="VARIABLE") loc<- 1:40 ind<- ( 1:20)*2 xl<- seq( 1,40,,300) temp<- L96() X<-matrix( temp, ncol=MM, nrow=40) + matrix( rnorm(MM*40)*.5, nrow=40) tt<- 0 # # for ( k in 1:N){ tt<- tt + dt text( 3,-8, round( tt,2), col=4, cex=2, adj=0) for ( jj in 1: MM){ lines( xl, splint(1:40, X[,jj], xl),col="#FFFFFF", lwd=1) } lines( xl, splint(1:40, temp, xl),col="#FFFFFF", lwd=2) X<- L96(X, dt=dt) temp<- L96( temp, dt=dt) cat (k, " ") for ( jj in 1: MM){ lines( xl, splint(1:40, X[,jj], xl),col=2, lwd=1) } lines( xl, splint(1:40, temp, xl),col=4, lwd=2) if( (k %% 5)==0){ y <- temp[ind] + rnorm( 20)*sige update( X, y, ind=ind, sige=sige)-> Xnew points( ind, y, cex=2, pch="o") if (tempo) system( "sleep 1") for ( jj in 1: MM){ lines( xl, splint(1:40, Xnew[,jj], xl),col=3, lwd=1) } lines( xl, splint(1:40, temp, xl),col=4, lwd=2) points( ind, y, cex=2, pch="o") if (k hold abline( coefficients(hold), col=4, lwd=2) plot( X[,2], X[,3], xlab="X2", ylab="X3") points( xtrue[kk,2], xtrue[kk,3], pch="+", col=3, cex=2) lines( sreg( X[,2], X[,3], df=400/25) $predicted, col=2, lwd=2) lm( X[,3]~ X[,2])-> hold abline( coefficients(hold), col=4, lwd=2) } "L96" <- function( z0=z0.start.L96,dt=.4,F=8, delta=.001){ MM <- nrow( z0) nstep <- round( dt/delta) delta <- dt/nstep ind <- (1:MM)+2 for( k in 1:nstep){ z0 <- rbind( z0[MM-1,], z0[MM,], z0,z0[1,]) z0 <- matrix(z0[ind,]+delta*((z0[ind+1,]-z0[ind-2,])*z0[ind-1,]-z0[ind,] +F), nrow=MM) } z0 } "sim.EKF" <- function( N=25,true.state=z0.start.L96,X=NULL, dt=.2, sige=.5, M=15, ind= (1:20)*2, alpha=1.0) { if( is.null( X)){ n<- nrow( true.state) X<- matrix( true.state, nrow=n, ncol=M) + .5*sige*matrix( rnorm(M*n), nrow=n,ncol=M) } n<- nrow(X) set.seed( 123) xhat<- (X%*% rep( 1/M, M)) rmse<- sqrt(mean( ( xhat- true.state)**2) ) cat( rmse, fill=T) for ( k in 1:N){ # update/analysis y<- true.state[ind] + rnorm( length( ind))*sige update( X, y, ind=ind, sige=sige, alpha=alpha)-> X xhat<- stats( t(X))[2,] rmse1<- sqrt(mean( ( xhat- true.state)**2) ) # forecast true.state<- L96( true.state, dt=dt) X<- L96(X, dt=dt) xhat<- stats( t(X))[2,] rmse2<- sqrt(mean( (xhat- true.state)**2) ) rmse3<- sqrt(mean(stats( t( X))[3,]**2)) cat( k,rmse1,rmse2, rmse3,fill=T) } list( true.state= true.state, X=X) } "update" <- function(X,y, ind,sige=2, window=20, alpha =1.0){ R<- diag( sige**2, 20) M<- ncol( X) dd<- c(0:19,20:1) phi<- matrix( dd, ncol=40, nrow=40) for ( k in 1:40){ phi[k,]<- dd dd<- c( dd[40], dd[1:39]) } M<- ncol( X) #taper phi<- exp( -(dd/window)**2) # Sigma<- var( t(X))*phi N<- length( ind) #perturbed obs. Y<- matrix( y, ncol=M, nrow=N) + matrix( sige*rnorm(M*N), nrow=N, ncol=M) # #print(stats( sqrt(diag( Sigma)))) X + Sigma[,ind] %*% solve( alpha*Sigma[ind, ind] + R)%*%(Y - X[ind,]) } "Xstart" <- structure(c(3.5886628854236, 9.00624508024815, 0.91216532710636, -2.49452610267489, 0.376274678815257, 6.31940649415671, 10.0424654460794, 1.69970472610699, 4.55080130549106, 6.19769977130194, -1.39848173920306, 1.70244951165189, 5.24763939220236, -0.511615517190156, -0.899444456883006, -1.14336384883213, 8.13135492877022, 0.459242460002726, -2.27360643666785, 0.81155124031871, 8.11450833383125, -1.56879461907277, 2.70517443310065, 2.05655129540688, 3.8827495075211, 5.91599581311795, -3.94825514073419, 0.470819911388634, -0.388112956206747, -0.0300068555728339, 1.51659977098393, 10.3267825060747, -0.0540411642573238, -1.59363333849404, 4.36169331576214, 3.24393303614042, 3.65986208977533, 7.42651621834744, 2.59956625568726, -1.16906381111526, 3.57938889507908, 8.96848718654208, 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