{"action":"created","issue":{"url":"https://api.github.com/repos/StrikeNP/trac_test/issues/740","repository_url":"https://api.github.com/repos/StrikeNP/trac_test","labels_url":"https://api.github.com/repos/StrikeNP/trac_test/issues/740/labels{/name}","comments_url":"https://api.github.com/repos/StrikeNP/trac_test/issues/740/comments","events_url":"https://api.github.com/repos/StrikeNP/trac_test/issues/740/events","html_url":"https://github.com/StrikeNP/trac_test/issues/740","id":334663939,"node_id":"MDU6SXNzdWUzMzQ2NjM5Mzk=","number":740,"title":"Calculations in preparation for a manuscript on flexible importance sampling (Trac #740)","user":{"login":"StrikeNP","id":2577059,"node_id":"MDQ6VXNlcjI1NzcwNTk=","avatar_url":"https://avatars0.githubusercontent.com/u/2577059?v=4","gravatar_id":"","url":"https://api.github.com/users/StrikeNP","html_url":"https://github.com/StrikeNP","followers_url":"https://api.github.com/users/StrikeNP/followers","following_url":"https://api.github.com/users/StrikeNP/following{/other_user}","gists_url":"https://api.github.com/users/StrikeNP/gists{/gist_id}","starred_url":"https://api.github.com/users/StrikeNP/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/StrikeNP/subscriptions","organizations_url":"https://api.github.com/users/StrikeNP/orgs","repos_url":"https://api.github.com/users/StrikeNP/repos","events_url":"https://api.github.com/users/StrikeNP/events{/privacy}","received_events_url":"https://api.github.com/users/StrikeNP/received_events","type":"User","site_admin":false},"labels":[{"id":971577014,"node_id":"MDU6TGFiZWw5NzE1NzcwMTQ=","url":"https://api.github.com/repos/StrikeNP/trac_test/labels/Migrated%20from%20Trac","name":"Migrated from Trac","color":"FFFFFF","default":false},{"id":971577274,"node_id":"MDU6TGFiZWw5NzE1NzcyNzQ=","url":"https://api.github.com/repos/StrikeNP/trac_test/labels/clubb_src","name":"clubb_src","color":"FFFFFF","default":false},{"id":971649570,"node_id":"MDU6TGFiZWw5NzE2NDk1NzA=","url":"https://api.github.com/repos/StrikeNP/trac_test/labels/raut@uwm.edu","name":"raut@uwm.edu","color":"FFFFFF","default":false},{"id":971577163,"node_id":"MDU6TGFiZWw5NzE1NzcxNjM=","url":"https://api.github.com/repos/StrikeNP/trac_test/labels/task","name":"task","color":"FFFFFF","default":false}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":{"url":"https://api.github.com/repos/StrikeNP/trac_test/milestones/8","html_url":"https://github.com/StrikeNP/trac_test/milestone/8","labels_url":"https://api.github.com/repos/StrikeNP/trac_test/milestones/8/labels","id":3442840,"node_id":"MDk6TWlsZXN0b25lMzQ0Mjg0MA==","number":8,"title":"Improve SILHS","description":null,"creator":{"login":"StrikeNP","id":2577059,"node_id":"MDQ6VXNlcjI1NzcwNTk=","avatar_url":"https://avatars0.githubusercontent.com/u/2577059?v=4","gravatar_id":"","url":"https://api.github.com/users/StrikeNP","html_url":"https://github.com/StrikeNP","followers_url":"https://api.github.com/users/StrikeNP/followers","following_url":"https://api.github.com/users/StrikeNP/following{/other_user}","gists_url":"https://api.github.com/users/StrikeNP/gists{/gist_id}","starred_url":"https://api.github.com/users/StrikeNP/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/StrikeNP/subscriptions","organizations_url":"https://api.github.com/users/StrikeNP/orgs","repos_url":"https://api.github.com/users/StrikeNP/repos","events_url":"https://api.github.com/users/StrikeNP/events{/privacy}","received_events_url":"https://api.github.com/users/StrikeNP/received_events","type":"User","site_admin":false},"open_issues":27,"closed_issues":54,"state":"open","created_at":"2018-06-21T18:03:51Z","updated_at":"2018-06-24T03:25:13Z","due_on":null,"closed_at":null},"comments":326,"created_at":"2018-06-21T21:26:10Z","updated_at":"2018-06-24T04:55:08Z","closed_at":null,"author_association":"OWNER","body":"\nWe'd like to write a paper on the new flexible importance sampling strategy.  What would we have to do in order to be ready to start writing a paper?\n\n * To date, all the analysis has been with 100 sample points. We should test out a more reasonable configuration like 8 sample points.\n\n * In the description of the algorithm, we would probably want to show a simple line diagram showing the 8 categories.  The diagram could also label which categories are associated with which processes (autoconversion, accretion, etc.).\n\n *  Let's choose cases that produce a reasonable comparison to LES.  Right now, in the RICO case, turbulent sedimentation and l_var_covar_src are turned off.  This allows the code to converge to the analytic solution, but it also produces strange results.  Perhaps we could reduce the time step and handle the turbulent sedimentation explicitly in both the analytic and SILHS code? We should also run at least one more case, such as DYCOMS-II RF02. \n\n *  We should check whether we can achieve convergence to the analytic solution in interactive tests.  If not, we'll have to make it converge or else just focus on non-interactive tests.\n\n * We should compare time series of the new and old sampling methods at k_lh_start in order to give a visual impression of the improvement in noise.  \n\n *  We should create plots of profiles that illustrate that the sampling method works well away from the k_lh_start grid level.  Perhaps compare with the old sampling method?\n\n * Using the old code, we might also want to show profiles or surface time series from an ensemble of interactive runs, each member of which has a different random seed.  Then repeat for the new code.  Hopefully, the new code will show less variability among the ensemble members.   \n\n *  We should show a plot that illustrates that the method converges as 1/N.  I.e., it could show the RMS error as a function of sample points.  We could also overplot results from the old sampling method.\n\n *  We should show that as the sample points are moved to different categories, the noise in rrm_auto, rrm_accr, and rrm_evap increases or decreases as expected.  This would illustrate the flexibility of the method.  As part of this, for the new method, we could show the 8 categories and how many samples are in each box, along with labels showing which processes are important in which boxes.  We could show the same for the old method.  Then we could illustrate to the reader that putting more sample points in the important boxes decreases the noise.\n\nAttachments:\n\n[plot_explicit_ta_configs.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/trac_test/822/plot_explicit_ta_configs.maff)\n[plot_new_pdf_config_1_plot_2.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/trac_test/822/plot_new_pdf_config_1_plot_2.maff)\n[plot_combo_pdf_run_3.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/trac_test/822/plot_combo_pdf_run_3.maff)\n[plot_input_fields_rtp3_thlp3_1.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/trac_test/822/plot_input_fields_rtp3_thlp3_1.maff)\n[plot_new_pdf_20180522_test_1.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/trac_test/822/plot_new_pdf_20180522_test_1.maff)\n[plot_attempts_8_10.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/trac_test/822/plot_attempts_8_10.maff)\n[plot_attempt_8_only.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/trac_test/822/plot_attempt_8_only.maff)\n[plot_beta_1p3.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/trac_test/822/plot_beta_1p3.maff)\n[plot_beta_1p3_all.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/trac_test/822/plot_beta_1p3_all.maff)\n\nMigrated from http://carson.math.uwm.edu/trac/clubb/ticket/740\n```json\n{\n    \"status\": \"closed\", \n    \"changetime\": \"2015-11-23T22:56:22\", \n    \"description\": \"\\nWe'd like to write a paper on the new flexible importance sampling strategy.  What would we have to do in order to be ready to start writing a paper?\\n\\n * To date, all the analysis has been with 100 sample points. We should test out a more reasonable configuration like 8 sample points.\\n\\n * In the description of the algorithm, we would probably want to show a simple line diagram showing the 8 categories.  The diagram could also label which categories are associated with which processes (autoconversion, accretion, etc.).\\n\\n *  Let's choose cases that produce a reasonable comparison to LES.  Right now, in the RICO case, turbulent sedimentation and l_var_covar_src are turned off.  This allows the code to converge to the analytic solution, but it also produces strange results.  Perhaps we could reduce the time step and handle the turbulent sedimentation explicitly in both the analytic and SILHS code? We should also run at least one more case, such as DYCOMS-II RF02. \\n\\n *  We should check whether we can achieve convergence to the analytic solution in interactive tests.  If not, we'll have to make it converge or else just focus on non-interactive tests.\\n\\n * We should compare time series of the new and old sampling methods at k_lh_start in order to give a visual impression of the improvement in noise.  \\n\\n *  We should create plots of profiles that illustrate that the sampling method works well away from the k_lh_start grid level.  Perhaps compare with the old sampling method?\\n\\n * Using the old code, we might also want to show profiles or surface time series from an ensemble of interactive runs, each member of which has a different random seed.  Then repeat for the new code.  Hopefully, the new code will show less variability among the ensemble members.   \\n\\n *  We should show a plot that illustrates that the method converges as 1/N.  I.e., it could show the RMS error as a function of sample points.  We could also overplot results from the old sampling method.\\n\\n *  We should show that as the sample points are moved to different categories, the noise in rrm_auto, rrm_accr, and rrm_evap increases or decreases as expected.  This would illustrate the flexibility of the method.  As part of this, for the new method, we could show the 8 categories and how many samples are in each box, along with labels showing which processes are important in which boxes.  We could show the same for the old method.  Then we could illustrate to the reader that putting more sample points in the important boxes decreases the noise.\", \n    \"reporter\": \"vlarson@uwm.edu\", \n    \"cc\": \"vlarson@uwm.edu\", \n    \"resolution\": \"fixed\", \n    \"_ts\": \"1448319382831859\", \n    \"component\": \"clubb_src\", \n    \"summary\": \"Calculations in preparation for a manuscript on flexible importance sampling\", \n    \"priority\": \"major\", \n    \"keywords\": \"\", \n    \"time\": \"2014-10-01T18:12:55\", \n    \"milestone\": \"Improve SILHS\", \n    \"owner\": \"raut@uwm.edu\", \n    \"type\": \"task\"\n}\n```\n"},"comment":{"url":"https://api.github.com/repos/StrikeNP/trac_test/issues/comments/399729338","html_url":"https://github.com/StrikeNP/trac_test/issues/740#issuecomment-399729338","issue_url":"https://api.github.com/repos/StrikeNP/trac_test/issues/740","id":399729338,"node_id":"MDEyOklzc3VlQ29tbWVudDM5OTcyOTMzOA==","user":{"login":"StrikeNP","id":2577059,"node_id":"MDQ6VXNlcjI1NzcwNTk=","avatar_url":"https://avatars0.githubusercontent.com/u/2577059?v=4","gravatar_id":"","url":"https://api.github.com/users/StrikeNP","html_url":"https://github.com/StrikeNP","followers_url":"https://api.github.com/users/StrikeNP/followers","following_url":"https://api.github.com/users/StrikeNP/following{/other_user}","gists_url":"https://api.github.com/users/StrikeNP/gists{/gist_id}","starred_url":"https://api.github.com/users/StrikeNP/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/StrikeNP/subscriptions","organizations_url":"https://api.github.com/users/StrikeNP/orgs","repos_url":"https://api.github.com/users/StrikeNP/repos","events_url":"https://api.github.com/users/StrikeNP/events{/privacy}","received_events_url":"https://api.github.com/users/StrikeNP/received_events","type":"User","site_admin":false},"created_at":"2018-06-24T04:55:08Z","updated_at":"2018-06-24T04:55:08Z","author_association":"OWNER","body":"**Trac update on 2015/10/04 at 04:45**\n**vlarson@uwm.edu commented:**\n\nReplying to [comment:214 raut@…]:\n> Replying to [comment:213 vlarson@…]:\n> > This is the wrong ticket for this comment, because the flexible importance sampling manuscript has already been submitted, and I'm not suggesting that we do any of the following for the initial manuscript submission, but to follow up on prior comments, perhaps it would avoid some spikes to artificially make the no-cloud, no-precip, component-2 region a bit smaller.  E.g., suppose a spike is caused by a no-cloud sample drifting into cloud.  Instead of placing the category boundary at `cloud_frac`, place it at `min(1,cloud_frac*1.1)`.  Then we'll be wasting densely-spaced sample points on the clear air near clouds, but a large-weight (no-cloud) sample point will be less likely to find itself within cloud if `cloud_frac` drifts with height.  Ditto for the `d+1` and `d+2` variates.  If this formula for category boundary proves to be oversimplified, as is likely, we could make it fancier.  E.g., we could look up and down the profile and find the largest `cloud_frac`, and (artificially) set the category boundary at that largest `cloud_frac`.  Would some approach in this spirit address the root cause of the spikes?\n>\n> \n> This is an interesting idea. It certainly seems like it would help. Better yet, it wouldn't bias the solution, as far as I can tell.\n> \n> > P.S.  If the `d+1` variate is the main cause of the spikes, would it be more robust to cluster components 1 and 2?  E.g., should we forgo importance sampling on the mixture components?\n>\n> \n> Even if we don't explicitly do importance sampling on mixture fraction, in a case like RICO, I think we'd implicitly be doing importance sampling on mixture fraction. For example, in RICO (at least in the simulations I've been looking at), if a sample point is in cloud, it is almost guaranteed to be in mixture component 1. So, if cloud is sampled often (by doing importance sampling on cloud), then mixture component 1 will be sampled often, and a sample point in mixture component 2 will have a larger weight as usual. Ditto for precipitation fraction.\n> \n> > P.P.S.  Is this worth a new ticket?\n>\n> \n> Probably. Do I get to be the owner?! :D\n\nFeel free to create a ticket if you're interested.  I still think that getting the nCat method to work in CAM is the first order of business, but if we suspect that spikes are degrading the CAM-CLUBB solutions, it might be worth looking again at ways to reduce the number of spikes.\n\n"}}
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