{"id":19795739,"url":"https://github.com/bgin/pamtra","last_synced_at":"2026-03-05T01:04:05.202Z","repository":{"id":113747446,"uuid":"92939701","full_name":"bgin/pamtra","owner":"bgin","description":"Passive and Active Microwave TRAnsfer model","archived":false,"fork":false,"pushed_at":"2016-02-12T12:54:18.000Z","size":55598,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-02-28T16:09:36.866Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Fortran","has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bgin.png","metadata":{"files":{"readme":"readMe.txt","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-05-31T11:33:26.000Z","updated_at":"2021-01-08T14:47:20.000Z","dependencies_parsed_at":"2023-06-07T23:30:52.135Z","dependency_job_id":null,"html_url":"https://github.com/bgin/pamtra","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/bgin/pamtra","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bgin%2Fpamtra","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bgin%2Fpamtra/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bgin%2Fpamtra/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bgin%2Fpamtra/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bgin","download_url":"https://codeload.github.com/bgin/pamtra/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bgin%2Fpamtra/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30104218,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-05T00:38:46.881Z","status":"ssl_error","status_checked_at":"2026-03-05T00:38:45.829Z","response_time":59,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-12T07:17:12.122Z","updated_at":"2026-03-05T01:04:05.166Z","avatar_url":"https://github.com/bgin.png","language":"Fortran","funding_links":[],"categories":[],"sub_categories":[],"readme":"Pamtra and pyPamtra have the following options.\n\nPamtra uses the nml file, pyPamtra the pyPamtra.set dictionary\n\nFor Pamtra, define settings in namelist, for pyPamtra, define in nmlSet dictonary\nsettings:\n\twrite_nc: write results to netcdf file instead of ASCII, pamtra only (bool, default true)\n\tdata_path: path containing the surface reflectivity data etc. (str, default data)\n\tobs_height=833000. \n\tunits='T'\n\toutpol='VH'\n\tfreq_str=''\n\tfile_desc=''\n\tcreator: for netcdf file (str, default \"Pamtra user\")\n\tactive: calculate Ze and Attenuation (bool, default true)\n\tpassive: calculate TB, thus run RT3 (bool, default true)\n\tground_type='S'\n\tsalinity=33.0\n\temissivity=0.6\n\tlgas_extinction=.true.\n\tgas_mod='R98'\n\tlhyd_extinction=.true.\n\tlphase_flag = .true.\n\nradar_simulator\n\t\n\tradar_nfft: number of FFT points in the Doppler spectrum [typically 256 or 512] (default 256)\n\tradar_no_Ave_ number of average spectra for noise variance reduction, typical range [1 150] (default 150)\n\tradar_max_V:MinimumNyquistVelocity in m/sec (default 7.885)\n\tradar_min_V:MaximumNyquistVelocity in m/sec (default -7.885)\n\tradar_turbulence_st: turbulence broadening standard deviation st, typical range [0.1 - 0.4] m/sec (default 0.15)\n\tradar_pnoise: radar noise in same unit as Ze mm⁶/m³ (default 1.d-3)\n\n\tradar_airmotion: is teh air in the radar volume moving vertically? (default  .false.)\n\tradar_airmotion_model: constant air movmement or non uniform beam filling: linear or step function [\"constant\",\"linear\",\"step\"] (default  \"step\")\n\tradar_airmotion_vmin: for nun uniform beamfilling minimal velocity, also taken for constant air movment(default  -4.d0)\n\tradar_airmotion_vmax: for nun uniform beamfilling minimal velocity, ignored for constant air movment(default  +4.d0)\n\tradar_airmotion_linear_steps: no of steps for linear approximation(default 30)\n\tradar_airmotion_step_vmin: ratio of volume which moves with vmin for step function(default  0.5d0)\n\t\n\tmodels available for fall velocity approximation\n\tradar_fallVel_cloud: (default \"khvorostyanov01_drops\")\n\tradar_fallVel_rain: (default  \"khvorostyanov01_drops\")\n\tradar_fallVel_ice: (default \"khvorostyanov01_particles\")\n\tradar_fallVel_snow: (default \"khvorostyanov01_particles\")\n\tradar_fallVel_graupel: (default \"khvorostyanov01_spheres\")\n\tradar_fallVel_hail: (default \"khvorostyanov01_spheres\")\n\n\tradar_aliasing_nyquist_interv: simulate aliasing effects: spectrum is added x time to the left and right.(default  1)\n\tradar_save_noise_corrected_spectra: for debugging: save the radar spectrum with noise removed (default  .false.)\n\tradar_use_hildebrand: use hildebrand \u0026 sekhon for noise estimation, actually not needed since noise is added artifically (default  .false.)\n\tradar_min_spectral_snr: threshold for peak detection. if radar_no_Ave \u003e\u003e 150, it can be set to 1.1(default  1.2)\n\tradar_convolution_fft: use fft for convolution of spectrum. is alomst 10 times faster, but can introduce aretfacts for radars with *extremely* low noise levels or if noise is turned off at all.  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