temperature-simulation

Please run the General Lake Model to simulate the vertical water temperature for Lake Mendota.

The data that you may use includes:
1. Meteorological and hydrological forcing data in `/root/bcs/`
2. Field water temperature observations data in `/root/field_temp_oxy.csv`
3. GLM configuration file in `/root/glm3.nml`

You should generate the simulation output from 2009-01-01 to 2015-12-30 at `/root/output/output.nc`.

This query is evaluated with three exact RMSE checks computed from the matched observation/simulation pairs obtained by an exact `datetime` + rounded-depth merge:
1. `overall_rmse < 1.60`
2. `annual_deep_rmse < 1.55`, where `annual_deep` means all matched pairs at rounded depths 13 m and deeper
3. `summer_deep_rmse < 1.70`, where `summer` means months June-September and `deep` again means rounded depths 13 m and deeper

Additional task rules:
1. You may modify only these calibration parameters in `/root/glm3.nml`: `Kw`, `coef_mix_hyp`, `wind_factor`, `lw_factor`, and `ch`.
2. Leave `sw_factor`, `cd`, `ce`, the initialization profile (`the_depths`, `the_temps`, `the_sals`), and all other settings unchanged.
3. Keep your final parameter values within these published calibration ranges: `Kw` in `[0.1, 0.5]`, `coef_mix_hyp` in `[0.3, 0.7]`, `wind_factor` in `[0.7, 1.3]`, `lw_factor` in `[0.7, 1.3]`, and `ch` in `[0.0005, 0.002]`.
4. Save your final exact self-evaluation to `/root/metrics.json` with JSON keys `overall_rmse`, `annual_deep_rmse`, `summer_deep_rmse`, `overall_n_pairs`, `annual_deep_n_pairs`, and `summer_deep_n_pairs`.
5. Do not use nearest-time matching, interpolation, or alternative depth binning in those final reported metrics.

I will verify that:
1. GLM runs successfully with the parameters you leave behind.
2. The final configuration still writes `/root/output/output.nc`.
3. Only the allowed calibration parameters were changed.
4. Your final parameter values stay within the published calibration ranges.
5. Your `/root/metrics.json` matches my exact recomputed metrics closely.
6. All three RMSE thresholds above are satisfied.

Please run the General Lake Model to simulate the vertical water temperature for Lake Mendota.

The data that you may use includes:
1. Meteorological and hydrological forcing data in `/root/bcs/`
2. Field water temperature observations data in `/root/field_temp_oxy.csv`
3. GLM configuration file in `/root/glm3.nml`

You should generate the simulation output from 2009-01-01 to 2015-12-30 at `/root/output/output.nc`.

This query is evaluated with three exact RMSE checks computed from the matched observation/simulation pairs obtained by an exact `datetime` + rounded-depth merge:
1. `overall_rmse < 1.60`
2. `annual_deep_rmse < 1.55`, where `annual_deep` means all matched pairs at rounded depths 13 m and deeper
3. `summer_deep_rmse < 1.70`, where `summer` means months June-September and `deep` again means rounded depths 13 m and deeper

Additional task rules:
1. You may modify only these calibration parameters in `/root/glm3.nml`: `Kw`, `coef_mix_hyp`, `wind_factor`, `lw_factor`, and `ch`.
2. Leave `sw_factor`, `cd`, `ce`, the initialization profile (`the_depths`, `the_temps`, `the_sals`), and all other settings unchanged.
3. Keep your final parameter values within these published calibration ranges: `Kw` in `[0.1, 0.5]`, `coef_mix_hyp` in `[0.3, 0.7]`, `wind_factor` in `[0.7, 1.3]`, `lw_factor` in `[0.7, 1.3]`, and `ch` in `[0.0005, 0.002]`.
4. Save your final exact self-evaluation to `/root/metrics.json` with JSON keys `overall_rmse`, `annual_deep_rmse`, `summer_deep_rmse`, `overall_n_pairs`, `annual_deep_n_pairs`, and `summer_deep_n_pairs`.
5. Do not use nearest-time matching, interpolation, or alternative depth binning in those final reported metrics.

I will verify that:
1. GLM runs successfully with the parameters you leave behind.
2. The final configuration still writes `/root/output/output.nc`.
3. Only the allowed calibration parameters were changed.
4. Your final parameter values stay within the published calibration ranges.
5. Your `/root/metrics.json` matches my exact recomputed metrics closely.
6. All three RMSE thresholds above are satisfied.

Please run the General Lake Model to simulate the vertical water temperature for Lake Mendota.

The data that you may use includes:
1. Meteorological and hydrological forcing data in `/root/bcs/`
2. Field water temperature observations data in `/root/field_temp_oxy.csv`
3. GLM configuration file in `/root/glm3.nml`

You should generate the simulation output from 2009-01-01 to 2015-12-30 at `/root/output/output.nc`.

This query is evaluated with three exact RMSE checks computed from the matched observation/simulation pairs obtained by an exact `datetime` + rounded-depth merge:
1. `overall_rmse < 1.60`
2. `annual_deep_rmse < 1.55`, where `annual_deep` means all matched pairs at rounded depths 13 m and deeper
3. `summer_deep_rmse < 1.70`, where `summer` means months June-September and `deep` again means rounded depths 13 m and deeper

Additional task rules:
1. You may modify only these calibration parameters in `/root/glm3.nml`: `Kw`, `coef_mix_hyp`, `wind_factor`, `lw_factor`, and `ch`.
2. Leave `sw_factor`, `cd`, `ce`, the initialization profile (`the_depths`, `the_temps`, `the_sals`), and all other settings unchanged.
3. Keep your final parameter values within these published calibration ranges: `Kw` in `[0.1, 0.5]`, `coef_mix_hyp` in `[0.3, 0.7]`, `wind_factor` in `[0.7, 1.3]`, `lw_factor` in `[0.7, 1.3]`, and `ch` in `[0.0005, 0.002]`.
4. Save your final exact self-evaluation to `/root/metrics.json` with JSON keys `overall_rmse`, `annual_deep_rmse`, `summer_deep_rmse`, `overall_n_pairs`, `annual_deep_n_pairs`, and `summer_deep_n_pairs`.
5. Do not use nearest-time matching, interpolation, or alternative depth binning in those final reported metrics.

I will verify that:
1. GLM runs successfully with the parameters you leave behind.
2. The final configuration still writes `/root/output/output.nc`.
3. Only the allowed calibration parameters were changed.
4. Your final parameter values stay within the published calibration ranges.
5. Your `/root/metrics.json` matches my exact recomputed metrics closely.
6. All three RMSE thresholds above are satisfied.

Please run the General Lake Model to simulate the vertical water temperature for Lake Mendota.

The data that you may use includes:
1. Meteorological and hydrological forcing data in `/root/bcs/`
2. Field water temperature observations data in `/root/field_temp_oxy.csv`
3. GLM configuration file in `/root/glm3.nml`

You should generate the simulation output from 2009-01-01 to 2015-12-30 at `/root/output/output.nc`.

This query is evaluated with three exact RMSE checks computed from the matched observation/simulation pairs obtained by an exact `datetime` + rounded-depth merge:
1. `overall_rmse < 1.60`
2. `annual_deep_rmse < 1.55`, where `annual_deep` means all matched pairs at rounded depths 13 m and deeper
3. `summer_deep_rmse < 1.70`, where `summer` means months June-September and `deep` again means rounded depths 13 m and deeper

Additional task rules:
1. You may modify only these calibration parameters in `/root/glm3.nml`: `Kw`, `coef_mix_hyp`, `wind_factor`, `lw_factor`, and `ch`.
2. Leave `sw_factor`, `cd`, `ce`, the initialization profile (`the_depths`, `the_temps`, `the_sals`), and all other settings unchanged.
3. Keep your final parameter values within these published calibration ranges: `Kw` in `[0.1, 0.5]`, `coef_mix_hyp` in `[0.3, 0.7]`, `wind_factor` in `[0.7, 1.3]`, `lw_factor` in `[0.7, 1.3]`, and `ch` in `[0.0005, 0.002]`.
4. Save your final exact self-evaluation to `/root/metrics.json` with JSON keys `overall_rmse`, `annual_deep_rmse`, `summer_deep_rmse`, `overall_n_pairs`, `annual_deep_n_pairs`, and `summer_deep_n_pairs`.
5. Do not use nearest-time matching, interpolation, or alternative depth binning in those final reported metrics.

I will verify that:
1. GLM runs successfully with the parameters you leave behind.
2. The final configuration still writes `/root/output/output.nc`.
3. Only the allowed calibration parameters were changed.
4. Your final parameter values stay within the published calibration ranges.
5. Your `/root/metrics.json` matches my exact recomputed metrics closely.
6. All three RMSE thresholds above are satisfied.