A tail ESS warning against a local level model with an ordered logistic function

I’m encountering the following tail ESS warning. Other warnings disappeared after I had tried several changes in the codes (e.g. setting weakly informative priors), but still this warning remains. Does anyone know any solutions? I attached the R and STAN codes with accompanied data. Thank you for your cooperation.

Warning message:
Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
Running the chains for more iterations may help. See
Runtime warnings and convergence problems

STAN code

data {
  int<lower=1> N;
  int<lower=1> T;
  int<lower=1> T_pred;
  int<lower=1,upper=11> HA[N,T];

parameters {
  ordered[10] cutpoints;
  matrix[N,T] mu_raw; 
  matrix[N,T] phi_raw; 
  real mu_ori; 
  real<lower=0> s_mu; 
  real<lower=0> s_phi; 
  real<lower=0> s_ori; 

transformed parameters{
  matrix[N,T] mu; 
  matrix[N,T] phi; 
  mu[,1] = mu_ori + s_ori*mu_raw[,1] ; 
  for (t in 2:T)
      mu[,t] = mu[,t-1] + s_mu*mu_raw[,t]; 
  for (t in 1:T)
      phi[,t] = mu[,t] + s_phi*phi_raw[,t]; 

model {
  mu_ori ~ normal(0,1.5); 
  s_mu ~ exponential(3); 
  s_phi ~ lognormal(-0.5,0.5); 
  s_ori ~ lognormal(0,0.5); 
  for (t in 1:T){
      mu_raw[,t] ~ normal(0,1); 
      phi_raw[,t] ~ normal(0,1); 
      HA[,t] ~ ordered_logistic(phi[,t], cutpoints);

generated quantities {
  matrix[N,T+T_pred] mu_all;
  matrix[N,T+T_pred] phi_all;
  int<lower=1,upper=11> ha_all[N,T+T_pred];
  matrix[N,T] log_lik;
  for (t in 1:T)
    for (n in 1:N){
      mu_all[n,t] = mu[n,t];
      phi_all[n,t] = phi[n,t];
      log_lik[n,t] = ordered_logistic_lpmf(HA[n,t]|phi[n,t], cutpoints); 
  for (t in 1:T_pred)
    for (n in 1:N){
      mu_all[n,T+t] = normal_rng(mu_all[n,T+t-1], s_mu);
      phi_all[n,T+t] = normal_rng(mu_all[n,T+t], s_phi);
  for (t in 1:T+T_pred)
    for (n in 1:N)
      ha_all[n,t] = ordered_logistic_rng(phi_all[n,t], cutpoints);

R code

library(rstan); library(tidyverse); library(rlist); library(ggmcmc);library(bayesplot)
library(magrittr); library(pipeR); library(Hmisc); library(readxl); library(caret);
d <- read.csv(file='d.csv', header = TRUE)

year <- 2
data <- list(N=nrow(d),

stanmodel <- stan_model(file='test.stan')
fit <- sampling(
  data = data,
  seed =10,
  chains=4, iter=6000, warmup=1000, thin=5,
         mu_raw=matrix(rnorm(nrow(d)*year, mean = 0, sd = 1), nrow = nrow(d), ncol = year),
         phi_raw=matrix(rnorm(nrow(d)*year, mean = 0, sd = 1), nrow = nrow(d), ncol = year),

print(fit, pars=c("s_mu", "s_phi", "s_ori"))
pairs(fit, pars=c("s_mu", "s_phi", "s_ori"))
mcmc_combo(fit, pars=c("s_mu", "s_phi", "s_ori"))
monitor(rstan::extract(fit, permuted = FALSE, inc_warmup = FALSE, 
                       pars=c("s_mu", "s_phi", "s_ori")))

test.stan (1.4 KB)
test.R (1.1 KB)
d.csv (4.8 KB)

For which parameter? It seems your model is highly overparameterized, and it is likely that the posterior has thick tails that are not well explored by the sampling