Cv 5 option ： var_scaling * ： tuning factor of background error covariance for control variable

2. Cv 5 option： • var_scaling*：tuning factor of background error covariance for control variable • be_sub % val(:,:) = var_scaling * be_sub % val(:,:) • len_scaling*：tuning factor of scale-length • s(1:mz) = len_scaling * s(1:mz) • Cv 3 option： • as*:tuning factors for variance, horizontal and vertical scales for control variable • as*(1) first for variance, as*(2) second for horizontal scale, and as*(3) third for vertical scale • as= as*(1) • be % corz(i,j,k,n)=be % corz(i,j,k,n)*as(n) & • *samp/hwll(i,j,k,n)/vv(k,k)/global_fac(i,j) • as= as*(2) • hwll_avn(i,k,m)=hwll_avn(i,k,m)*as(m) • as= as*(3) • vztdq_avn(k,i,m)=vztdq_avn(k,i,m)*as(m) • Here, ‘*’behind the letters represent the different control variables

3. Random cv related parameters: alphacv_method: 1: ensemble perturbations in control variable space 2: ensemble perturbations in model variable space alpha_corr_type: 1: alpha_corr_type_exp 2: alpha_corr_type_soar 3: alpha_corr_type_gaussian (default) nj = 200 ! #Gaussian lats (even) corr(j) = exp(-d(j)) !exp or d(j) = 2.0 * d(j) corr(j) = (1.0 + d(j)) * exp(-d(j)) !soar or corr(j) = exp(-d(j) * d(j)) !gaussian alpha_corr_scale: 1500.0 km (default)

4. Q EnKFfinal analysis with adaptive inflation (up) / without inflation

5. T EnKF final analysis with adaptive inflation (up) / without inflation

6. U EnKF final analysis with adaptive inflation (up) / without inflation

7. V EnKF final analysis with adaptive inflation (up) / without inflation

8. The innovation vector

9. inflation taper(r)