{"action":"created","issue":{"url":"https://api.github.com/repos/StrikeNP/trac_test/issues/752","repository_url":"https://api.github.com/repos/StrikeNP/trac_test","labels_url":"https://api.github.com/repos/StrikeNP/trac_test/issues/752/labels{/name}","comments_url":"https://api.github.com/repos/StrikeNP/trac_test/issues/752/comments","events_url":"https://api.github.com/repos/StrikeNP/trac_test/issues/752/events","html_url":"https://github.com/StrikeNP/trac_test/issues/752","id":332200100,"node_id":"MDU6SXNzdWUzMzIyMDAxMDA=","number":752,"title":"Evaluation and improvement of the new SILHS sampling scheme (Trac #752)","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":963997231,"node_id":"MDU6TGFiZWw5NjM5OTcyMzE=","url":"https://api.github.com/repos/StrikeNP/trac_test/labels/Migrated%20from%20Trac","name":"Migrated from Trac","color":"FFFFFF","default":false},{"id":963997516,"node_id":"MDU6TGFiZWw5NjM5OTc1MTY=","url":"https://api.github.com/repos/StrikeNP/trac_test/labels/clubb_src","name":"clubb_src","color":"FFFFFF","default":false},{"id":963997043,"node_id":"MDU6TGFiZWw5NjM5OTcwNDM=","url":"https://api.github.com/repos/StrikeNP/trac_test/labels/enhancement","name":"enhancement","color":"a2eeef","default":true},{"id":964076735,"node_id":"MDU6TGFiZWw5NjQwNzY3MzU=","url":"https://api.github.com/repos/StrikeNP/trac_test/labels/raut@uwm.edu","name":"raut@uwm.edu","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":3423184,"node_id":"MDk6TWlsZXN0b25lMzQyMzE4NA==","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":24,"closed_issues":57,"state":"open","created_at":"2018-06-13T20:03:15Z","updated_at":"2018-06-16T08:28:59Z","due_on":null,"closed_at":null},"comments":77,"created_at":"2018-06-13T23:26:05Z","updated_at":"2018-06-16T08:31:34Z","closed_at":null,"author_association":"OWNER","body":"**Introduction**\n\nIn ticket:736, we implemented a new importance sampling scheme in SILHS that allowed a flexible number of points to be placed in each category. Through various unit tests and assertion checks, I am somewhat confident that the new code is working.\n\nI started a new ticket here dedicated to evaluating and/or improving the new scheme.\n\n(Note: some discussion on this is in ticket:736.)\n\n**Important Tasks**\n\n1. How do profiles look in the new scheme vs. the old scheme? Compare plots for both single timesteps and time-averaged output.\n\n2. How do interactive runs look when using the new scheme vs. the old scheme? Time series such as liquid water path are of particular interest to me.\n\n3. How do other cases and microphysics schemes look with the new scheme vs. the old scheme? So far, my analysis has been limited to RICO with KK microphysics.\n\nAttachments:\n\n[prescribed_prob_profile.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/prescribed_prob_profile.maff)\n[rrm_evap_k_lh_start.jpg](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/rrm_evap_k_lh_start.jpg)\n[rico_lh_profile_convergence_silhs.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/rico_lh_profile_convergence_silhs.maff)\n[rrm_evap_profile.jpg](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/rrm_evap_profile.jpg)\n[single_timestep_profile_compare.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/single_timestep_profile_compare.maff)\n[single_timestep_maximal_overlap_profile_compare.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/single_timestep_maximal_overlap_profile_compare.maff)\n[single_timestep_max_ovlap_unweighted_frac.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/single_timestep_max_ovlap_unweighted_frac.maff)\n[cloud_k_timestep_profile.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/cloud_k_timestep_profile.maff)\n[cloud_k_timestep_profile_maxonly.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/cloud_k_timestep_profile_maxonly.maff)\n[cloud_k_timeaverage_profile.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/cloud_k_timeaverage_profile.maff)\n[cldk_maxoverlap_old_vs_new.maff](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/cldk_maxoverlap_old_vs_new.maff)\n[rrm_auto_categories.svg](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/rrm_auto_categories.svg)\n[auto_opt.svg](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/auto_opt.svg)\n[rrm_auto.svg](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/rrm_auto.svg)\n[rrm_auto_evenprobs_only.svg](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/rrm_auto_evenprobs_only.svg)\n[rrm_accr.svg](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/rrm_accr.svg)\n[rrm_evap.svg](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/rrm_evap.svg)\n[rrm_mc.svg](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/rrm_mc.svg)\n[rrm_auto_variance.svg](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/rrm_auto_variance.svg)\n[rrm_mc_variance_spectrum.svg](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/rrm_mc_variance_spectrum.svg)\n[r7512_large_sample_variance.diff](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/r7512_large_sample_variance.diff)\n[rrm_mc_sqrt_variance_spectrum.svg](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/rrm_mc_sqrt_variance_spectrum.svg)\n[r7523_large_sample_opt.diff](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/r7523_large_sample_opt.diff)\n[r7523_large_sample_opt.svg](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/r7523_large_sample_opt.svg)\n[r7525_default_vs_optimal_allocation.svg](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/r7525_default_vs_optimal_allocation.svg)\n[thlm_timeseries_compare.svg](https://github.com/larson-group/trac_attachment_archive/blob/master/clubb/thlm_timeseries_compare.svg)\n\nMigrated from http://carson.math.uwm.edu/trac/clubb/ticket/752\n```json\n{\n    \"status\": \"closed\", \n    \"changetime\": \"2016-07-27T19:46:08\", \n    \"description\": \"'''Introduction'''\\n\\nIn ticket:736, we implemented a new importance sampling scheme in SILHS that allowed a flexible number of points to be placed in each category. Through various unit tests and assertion checks, I am somewhat confident that the new code is working.\\n\\nI started a new ticket here dedicated to evaluating and/or improving the new scheme.\\n\\n(Note: some discussion on this is in ticket:736.)\\n\\n'''Important Tasks'''\\n\\n1. How do profiles look in the new scheme vs. the old scheme? Compare plots for both single timesteps and time-averaged output.\\n\\n2. How do interactive runs look when using the new scheme vs. the old scheme? Time series such as liquid water path are of particular interest to me.\\n\\n3. How do other cases and microphysics schemes look with the new scheme vs. the old scheme? So far, my analysis has been limited to RICO with KK microphysics.\", \n    \"reporter\": \"raut@uwm.edu\", \n    \"cc\": \"vlarson@uwm.edu\", \n    \"resolution\": \"fixed\", \n    \"_ts\": \"1469648768420580\", \n    \"component\": \"clubb_src\", \n    \"summary\": \"Evaluation and improvement of the new SILHS sampling scheme\", \n    \"priority\": \"major\", \n    \"keywords\": \"SILHS flexible sampling\", \n    \"time\": \"2014-10-31T00:43:30\", \n    \"milestone\": \"Improve SILHS\", \n    \"owner\": \"raut@uwm.edu\", \n    \"type\": \"enhancement\"\n}\n```\n"},"comment":{"url":"https://api.github.com/repos/StrikeNP/trac_test/issues/comments/397797587","html_url":"https://github.com/StrikeNP/trac_test/issues/752#issuecomment-397797587","issue_url":"https://api.github.com/repos/StrikeNP/trac_test/issues/752","id":397797587,"node_id":"MDEyOklzc3VlQ29tbWVudDM5Nzc5NzU4Nw==","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":"2018-06-16T08:31:34Z","updated_at":"2018-06-16T08:31:34Z","author_association":"OWNER","body":"**Trac update on 2015/04/02 at 01:46**\n**raut@uwm.edu commented:**\n\nCommit comment from clubb:r7609:\n\nI am implementing a new \"probability transference\" scheme that transfers extra prescribed probability mass from a category with negligible PDF probability to the other categories based on the relative PDF probabilities of the other categories.\n\nThe algorithm is described toward the bottom of https://github.com/larson-group/clubb/issues/752#comment:51. The algorithm was devised from analyzing SCAM-CLUBB-SILHS runs where the sum of the sample point weights was very far off from the expected value of `num_samples`. This improved algorithm should hopefully keep the sample point weights away from extremes, perhaps increasing the stability of CAM-CLUBB-SILHS.\n\nOutput is very very likely to change for every case that uses SILHS, though I'd expect the difference to be \"minor\" for most cases.\n\n\n\n"}}
{"actor":{"display_login":"StrikeNP"}}