how to reproduce your result on 80*40 resolution features?

[jasong-ovo]

Thanks for your amazing work! I want to reproduce your result on lower resolution but I get really bad result on 64*32. How should I change settings?

[jasong-ovo]

base config

full_field: &FULL_FIELD
loss: 'l2'
lr: 1E-3
scheduler: 'ReduceLROnPlateau'
num_data_workers: 4
dt: 1 # how many timesteps ahead the model will predict
n_history: 0 #how many previous timesteps to consider
prediction_type: 'iterative'
prediction_length: 41 #applicable only if prediction_type == 'iterative'
n_initial_conditions: 5 #applicable only if prediction_type == 'iterative'
ics_type: "default"
save_raw_forecasts: !!bool True
save_channel: !!bool False
masked_acc: !!bool False
maskpath: None
perturb: !!bool False
add_grid: !!bool False
N_grid_channels: 0
gridtype: 'sinusoidal' #options 'sinusoidal' or 'linear'
roll: !!bool False
max_epochs: 50
batch_size: 64

#afno hyperparams
num_blocks: 8
nettype: 'afno'
patch_size: 8
width: 56
modes: 32
img_size: [720, 1440]
#options default, residual
target: 'default'
in_channels: [0,1]
out_channels: [0,1] #must be same as in_channels if prediction_type == 'iterative'
normalization: 'zscore' #options zscore (minmax not supported)
train_data_path: '/mnt/petrelfs/gongjunchao/h5s' #'/pscratch/sd/j/jpathak/wind/train'
valid_data_path: '/mnt/petrelfs/gongjunchao/h5s' #'/pscratch/sd/j/jpathak/wind/test'
inf_data_path: '/pscratch/sd/j/jpathak/wind/out_of_sample' # test set path for inference
exp_dir: '/mnt/lustre/gongjunchao/FCN/results' #'/pscratch/sd/j/jpathak/ERA5_expts_gtc/wind'
time_means_path: '/pscratch/sd/j/jpathak/wind/time_means.npy'
global_means_path: '/pscratch/sd/j/jpathak/wind/global_means.npy'
global_stds_path: '/pscratch/sd/j/jpathak/wind/global_stds.npy'

orography: !!bool False
orography_path: None

log_to_screen: !!bool True
log_to_wandb: !!bool True
save_checkpoint: !!bool True

enable_nhwc: !!bool False
optimizer_type: 'FusedAdam'
crop_size_x: None
crop_size_y: None

two_step_training: !!bool False
plot_animations: !!bool False

add_noise: !!bool False
noise_std: 0

# type of dataset

ds_type: 'h5'

afno_backbone: &backbone
<<: *FULL_FIELD
log_to_wandb: !!bool False
lr: 5E-4
batch_size: 64 #64
max_epochs: 150
scheduler: 'CosineAnnealingLR'
in_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
out_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
orography: !!bool False
orography_path: None
exp_dir: '/mnt/lustre/gongjunchao/FCN/results' #'/pscratch/sd/j/jpathak/ERA5_expts_gtc/wind'
train_data_path: '/mnt/petrelfs/gongjunchao/h5s' #'/pscratch/sd/j/jpathak/wind/train'
valid_data_path: '/mnt/petrelfs/gongjunchao/h5s' #'/pscratch/sd/j/jpathak/wind/test'
inf_data_path: '/pscratch/sd/s/shas1693/data/era5/out_of_sample'
time_means_path: '/pscratch/sd/s/shas1693/data/era5/time_means.npy'
global_means_path: '/mnt/lustre/gongjunchao/global_means.npy' #'/pscratch/sd/s/shas1693/data/era5/global_means.npy'
global_stds_path: '/mnt/lustre/gongjunchao/global_stds.npy' #'/pscratch/sd/s/shas1693/data/era5/global_stds.npy'

afno_backbone_orography: &backbone_orography
<<: *backbone
orography: !!bool True
orography_path: '/pscratch/sd/s/shas1693/data/era5/static/orography.h5'

afno_backbone_finetune:
<<: *backbone
lr: 1E-4
batch_size: 64
log_to_wandb: !!bool True
max_epochs: 50
pretrained: !!bool True
two_step_training: !!bool True
pretrained_ckpt_path: '/pscratch/sd/s/shas1693/results/era5_wind/afno_backbone/0/training_checkpoints/best_ckpt.tar'

perturbations:
<<: *backbone
lr: 1E-4
batch_size: 64
max_epochs: 50
pretrained: !!bool True
two_step_training: !!bool True
pretrained_ckpt_path: '/pscratch/sd/j/jpathak/ERA5_expts_gtc/wind/afno_20ch_bs_64_lr5em4_blk_8_patch_8_cosine_sched/1/training_checkpoints/best_ckpt.tar'
prediction_length: 24
ics_type: "datetime"
n_perturbations: 100
save_channel: !bool True
save_idx: 4
save_raw_forecasts: !!bool False
date_strings: ["2018-01-01 00:00:00"]
inference_file_tag: " "
valid_data_path: "/pscratch/sd/j/jpathak/ "
perturb: !!bool True
n_level: 0.3

PRECIP

precip: &precip
<<: *backbone
in_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
out_channels: [0]
nettype: 'afno'
nettype_wind: 'afno'
log_to_wandb: !!bool True
lr: 2.5E-4
batch_size: 64
max_epochs: 25
precip: '/pscratch/sd/p/pharring/ERA5/precip/total_precipitation'
time_means_path_tp: '/pscratch/sd/p/pharring/ERA5/precip/total_precipitation/time_means.npy'
model_wind_path: '/pscratch/sd/s/shas1693/results/era5_wind/afno_backbone_finetune/0/training_checkpoints/best_ckpt.tar'
precip_eps: !!float 1e-5