Add optional initial temperature guess #128
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132: Add data collection mechanism r=charleskawczynski a=charleskawczynski One concern we've had recently is whether our tolerance for saturation adjustment is reasonable or not because it's a bit difficult to reason about. If the tolerance is occasionally poor, we could have convergence issues with reasonable inputs and, at the moment, it's a bit difficult to know that since the inputs are not in more intuitive variables like temperature. #128 is an attempt to make sure that we start with a reasonable guess, however, knowing whether this is a good idea or not requires collecting data from a real-world run, and we don't have a clean way to do that at the moment. This PR adds a module dedicated to collecting data, to help us better understand statistics of some important information: - Maximum number of iterations performed - Average number of iterations performed - Number of converged and non-converged calls (if / when we set `TD.error_on_non_convergence() = false`) Here's a simple script for running the moist baroclinic wave in ClimaAtmos: ```julia using Revise; include("examples/hybrid/cli_options.jl"); dict = parsed_args_per_job_id(); parsed_args = dict["sphere_baroclinic_wave_rhoe_equilmoist"]; parsed_args["enable_threading"] = false import Thermodynamics import RootSolvers Thermodynamics.solution_type() = RootSolvers.VerboseSolution() include("examples/hybrid/driver.jl") Thermodynamics.DataCollection.print_summary() ``` At the moment, this produces ```julia julia> Thermodynamics.DataCollection.print_summary() ┌ Info: Thermodynamics saturation_adjustment statistics: │ max_iter = 1 │ call_counter = 15904225 │ average_max_iter = 6.287637404526156e-8 │ converged_counter = 15904225 └ non_converged_counter = 0 ``` Which seems pretty good, however, this is running at coarse resolution, for only 6 days (1152 `step!`s). Runtime was about 4 min, so we can definitely crank things up. Co-authored-by: Charles Kawczynski <kawczynski.charles@gmail.com>
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132: Add data collection mechanism r=charleskawczynski a=charleskawczynski One concern we've had recently is whether our tolerance for saturation adjustment is reasonable or not because it's a bit difficult to reason about. If the tolerance is occasionally poor, we could have convergence issues with reasonable inputs and, at the moment, it's a bit difficult to know that since the inputs are not in more intuitive variables like temperature. #128 is an attempt to make sure that we start with a reasonable guess, however, knowing whether this is a good idea or not requires collecting data from a real-world run, and we don't have a clean way to do that at the moment. This PR adds a module dedicated to collecting data, to help us better understand statistics of some important information: - Maximum number of iterations performed - Average number of iterations performed - Number of converged and non-converged calls (if / when we set `TD.error_on_non_convergence() = false`) Here's a simple script for running the moist baroclinic wave in ClimaAtmos: ```julia using Revise; include("examples/hybrid/cli_options.jl"); dict = parsed_args_per_job_id(); parsed_args = dict["sphere_baroclinic_wave_rhoe_equilmoist"]; parsed_args["enable_threading"] = false import Thermodynamics import RootSolvers Thermodynamics.solution_type() = RootSolvers.VerboseSolution() include("examples/hybrid/driver.jl") Thermodynamics.DataCollection.print_summary() ``` At the moment, this produces ```julia julia> Thermodynamics.DataCollection.print_summary() ┌ Info: Thermodynamics saturation_adjustment statistics: │ max_iter = 1 │ call_counter = 15904225 │ average_max_iter = 6.287637404526156e-8 │ converged_counter = 15904225 └ non_converged_counter = 0 ``` Which seems pretty good, however, this is running at coarse resolution, for only 6 days (1152 `step!`s). Runtime was about 4 min, so we can definitely crank things up. Co-authored-by: Charles Kawczynski <kawczynski.charles@gmail.com>
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132: Add data collection mechanism r=charleskawczynski a=charleskawczynski One concern we've had recently is whether our tolerance for saturation adjustment is reasonable or not because it's a bit difficult to reason about. If the tolerance is occasionally poor, we could have convergence issues with reasonable inputs and, at the moment, it's a bit difficult to know that since the inputs are not in more intuitive variables like temperature. #128 is an attempt to make sure that we start with a reasonable guess, however, knowing whether this is a good idea or not requires collecting data from a real-world run, and we don't have a clean way to do that at the moment. This PR adds a module dedicated to collecting data, to help us better understand statistics of some important information: - Maximum number of iterations performed - Average number of iterations performed - Number of converged and non-converged calls (if / when we set `TD.error_on_non_convergence() = false`) Here's a simple script for running the moist baroclinic wave in ClimaAtmos: ```julia using Revise; include("examples/hybrid/cli_options.jl"); dict = parsed_args_per_job_id(); parsed_args = dict["sphere_baroclinic_wave_rhoe_equilmoist"]; parsed_args["enable_threading"] = false import Thermodynamics import RootSolvers Thermodynamics.solution_type() = RootSolvers.VerboseSolution() include("examples/hybrid/driver.jl") Thermodynamics.DataCollection.print_summary() ``` At the moment, this produces ```julia julia> Thermodynamics.DataCollection.print_summary() ┌ Info: Thermodynamics saturation_adjustment statistics: │ max_iter = 1 │ call_counter = 15904225 │ average_max_iter = 6.287637404526156e-8 │ converged_counter = 15904225 └ non_converged_counter = 0 ``` Which seems pretty good, however, this is running at coarse resolution, for only 6 days (1152 `step!`s). Runtime was about 4 min, so we can definitely crank things up. Co-authored-by: Charles Kawczynski <kawczynski.charles@gmail.com>
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132: Add data collection mechanism r=charleskawczynski a=charleskawczynski One concern we've had recently is whether our tolerance for saturation adjustment is reasonable or not because it's a bit difficult to reason about. If the tolerance is occasionally poor, we could have convergence issues with reasonable inputs and, at the moment, it's a bit difficult to know that since the inputs are not in more intuitive variables like temperature. #128 is an attempt to make sure that we start with a reasonable guess, however, knowing whether this is a good idea or not requires collecting data from a real-world run, and we don't have a clean way to do that at the moment. This PR adds a module dedicated to collecting data, to help us better understand statistics of some important information: - Maximum number of iterations performed - Average number of iterations performed - Number of converged and non-converged calls (if / when we set `TD.error_on_non_convergence() = false`) Here's a simple script for running the moist baroclinic wave in ClimaAtmos: ```julia using Revise; include("examples/hybrid/cli_options.jl"); dict = parsed_args_per_job_id(); parsed_args = dict["sphere_baroclinic_wave_rhoe_equilmoist"]; parsed_args["enable_threading"] = false import Thermodynamics import RootSolvers Thermodynamics.solution_type() = RootSolvers.VerboseSolution() include("examples/hybrid/driver.jl") Thermodynamics.DataCollection.print_summary() ``` At the moment, this produces ```julia julia> Thermodynamics.DataCollection.print_summary() ┌ Info: Thermodynamics saturation_adjustment statistics: │ max_iter = 1 │ call_counter = 15904225 │ average_max_iter = 6.287637404526156e-8 │ converged_counter = 15904225 └ non_converged_counter = 0 ``` Which seems pretty good, however, this is running at coarse resolution, for only 6 days (1152 `step!`s). Runtime was about 4 min, so we can definitely crank things up. Co-authored-by: Charles Kawczynski <kawczynski.charles@gmail.com>
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128: Add optional initial temperature guess r=charleskawczynski a=charleskawczynski This PR: - Adds an optional initial temperature guess - Adds appropriate docs - Fixes some docs `@tapios,` I've added a test that I think may be useful for us to review interactively. Closes #32. Co-authored-by: Charles Kawczynski <kawczynski.charles@gmail.com>
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WIP Revert tolerance change Exercise T_guess in test suite
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Codecov ReportBase: 93.63% // Head: 93.10% // Decreases project coverage by
Additional details and impacted files@@ Coverage Diff @@
## main #128 +/- ##
==========================================
- Coverage 93.63% 93.10% -0.53%
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Files 9 9
Lines 1053 1074 +21
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+ Hits 986 1000 +14
- Misses 67 74 +7
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This PR:
@tapios, I've added a test that I think may be useful for us to review interactively.
Closes #32.