Error evaluating the log probability at the initial value

Modeling
1

I am getting

Rejecting initial value:
  Error evaluating the log probability at the initial value.
Exception: lognormal_lpdf: Random variable[3] is -0.150929, but must be >= 0!  (in 'modela6b869eb4ad3_LorenzStan' at line 51)

and eventually

Initialization between (-2, 2) failed after 100 attempts. 
 Try specifying initial values, reducing ranges of constrained values, or reparameterizing the model.
[1] "Error in sampler$call_sampler(args_list[[i]]) : Initialization failed."

The offending line (51) is

  x[1,] ~ lognormal(log(1.0), 0.2);

The full model is below. Aren’t I just asking the prior for x[1,] to be sampled from a log normal distribution? But it looks like negative values are being sampled.

functions {
  real[] ds_dt(real t,       // time
               real[] s,     // system state
               real[] theta, // parameters
               real[] x_r,   // data
               int[] x_i)    // unused integer data
  {
    real x = s[1];
    real y = s[2];
    real z = s[3];

    real alpha = theta[1];

    real rho = x_r[1];
    real beta = x_r[2];

    real dx_dt = alpha * (y - x);
    real dy_dt = x * (rho - z) - y;
    real dz_dt = x * y - beta * z;

    return { dx_dt, dy_dt, dz_dt };
  }
}

data {
  int<lower = 1> N; // Number of measurements
  real ts[N];       // Times of the measurements
  real y[N];        // The measurements themselves
}

transformed data {
  real rho  = 45.92;
  real beta =  4.00;
  real rdummy[2];
  rdummy[1] = rho;
  rdummy[2] = beta;
}

parameters {
  real x[N,3];
  real ln_alpha[N];
  real mu_alpha;
  real<lower = 0> sigma_alpha;
}

model {
  real parms[1];
  real deltaT;
  mu_alpha ~ uniform(12.0, 20.0);
  sigma_alpha ~ uniform(0.0, 0.5);
  x[1,] ~ lognormal(log(1.0), 0.2);
  ln_alpha[1] ~ normal(log(mu_alpha), sigma_alpha);
  for (i in 2:N) {
    parms[1] = exp(ln_alpha[i-1]);
    target += normal_lpdf(x[i,] | (integrate_ode_rk45(ds_dt, x[i-1,], ts[i-1], ts[i:i], parms, rdummy, rep_array(0, 0)))[1], 1.0E-3);
    deltaT = ts[i] - ts[i-1];
    target += normal_lpdf(ln_alpha[i] | ln_alpha[i - 1] - deltaT * sigma_alpha * sigma_alpha / 2, sqrt(deltaT) * sigma_alpha);
  }
  y ~ normal(x[,1], 0.2);
}