run2-glm-evaluation

name	run2_glm-evaluation
description	Robust GLM evaluation methods for deep and summer water temperatures.

Robust GLM Output Evaluation

Evaluation must precisely match observation times and rounded depths.

Depth Handling in GLM Output

In GLM, layers are defined by their height z from the bottom. To find the temperature at a specific depth from the surface:

1. Surface Height: $H_{surface}(t) = z(t, \text{num_layers}-1)$
2. Layer Depths: $D_{i}(t) = H_{surface}(t) - z_{i}(t)$
3. Interpolation: Since GLM uses discrete layers, linear interpolation at the target depth $d$ within the $(D_i, T_i)$ profile provides a more accurate value than choosing the nearest layer.

RMSE Calculation Requirements

According to task rules:

• Overall RMSE: Filter for available pairs across all times and depths.
• Annual Deep RMSE: Filter for rounded depths $\ge 13$ m.
• Summer Deep RMSE: Filter for months June (6) to September (9) AND rounded depths $\ge 13$ m.

Example Python snippets using netCDF4 and pandas:

import netCDF4
import pandas as pd
import numpy as np

def get_sim_temp_profile(nc_vars, time_idx):
    num_layers = int(nc_vars['NS'][time_idx])
    z = nc_vars['z'][time_idx, :num_layers]
    temp = nc_vars['temp'][time_idx, :num_layers]
    
    # Ensure 1D arrays and handle masking
    if hasattr(z, 'compressed'): z = z.compressed()
    if hasattr(temp, 'compressed'): temp = temp.compressed()
    
    surface_height = z[-1]
    depths = surface_height - z
    return depths, temp

def match_obs_sim(obs_csv, nc_path):
    # Load and preprocess
    obs = pd.read_csv(obs_csv)
    obs['datetime'] = pd.to_datetime(obs['datetime']).dt.strftime('%Y-%m-%d %H:%M:%S')
    obs['rounded_depth'] = obs['depth'].round().astype(int)
    
    # ... logic to apply interpolation for each observation row ...

Avoid nearest-time matching or alternative depth binning.