{"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":330468760,"node_id":"MDU6SXNzdWUzMzA0Njg3NjA=","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":958019852,"node_id":"MDU6TGFiZWw5NTgwMTk4NTI=","url":"https://api.github.com/repos/StrikeNP/trac_test/labels/Migrated%20from%20Trac","name":"Migrated from Trac","color":"FFFFFF","default":false},{"id":958042830,"node_id":"MDU6TGFiZWw5NTgwNDI4MzA=","url":"https://api.github.com/repos/StrikeNP/trac_test/labels/clubb_src","name":"clubb_src","color":"FFFFFF","default":false},{"id":958114258,"node_id":"MDU6TGFiZWw5NTgxMTQyNTg=","url":"https://api.github.com/repos/StrikeNP/trac_test/labels/raut@uwm.edu","name":"raut@uwm.edu","color":"FFFFFF","default":false},{"id":958042692,"node_id":"MDU6TGFiZWw5NTgwNDI2OTI=","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":3408876,"node_id":"MDk6TWlsZXN0b25lMzQwODg3Ng==","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":"2018-06-07T19:59:06Z","updated_at":"2018-06-10T05:32:45Z","due_on":null,"closed_at":null},"comments":312,"created_at":"2018-06-07T23:21:22Z","updated_at":"2018-06-10T07:03:17Z","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 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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/396026336","html_url":"https://github.com/StrikeNP/trac_test/issues/740#issuecomment-396026336","issue_url":"https://api.github.com/repos/StrikeNP/trac_test/issues/740","id":396026336,"node_id":"MDEyOklzc3VlQ29tbWVudDM5NjAyNjMzNg==","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":"2018-06-10T07:03:17Z","updated_at":"2018-06-10T07:03:17Z","author_association":"OWNER","body":"Trac update at 2015/07/29 at 04:13\n\nvlarson@uwm.edu commented:\n\nReplying to [comment:200 raut@…]:\n> Replying to [comment:191 raut@…]:\n> > Okay. If I use the following values of f, I get a maximum weight of about 2.56 in RICO at the 800^th^ timestep:\n> > \n> > \n\n```text\n> > [ 0.3494, 0.13982, 0.23602, 0.06008, 0.14282, 0.06986, 0.001, 0.001 ]\n> > ```\n> > \n> > I'll start up a run with these values for both RICO and DYCOMS, and we'll see what happens!\n> \n> Let's use the following values of f for the final plots:\n> \n\n```text\n> [ 0.35, 0.14, 0.24, 0.06, 0.14, 0.07, 0.0, 0.0 ]\n> ```\n> \n> I vote we leave the last two categories in the hands of the weight limiter.\n\nThe values of f and the use of the weight limiter sound fine.\n\n"}}
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