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170 results for “Quality model”
Fig. 5 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 5. Partial dependence plot for terrain roughness index (tri).
Fig. 7 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 7. Partial dependence plot for silt content (SLT).
Fig. 6 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 6. Partial dependence plot for pH water (phh2o).
Fertility_mini_statue_model_medium_quality
3D model made from high quality images of a souvenir mini statue representing the fertility symbol dated to 3000 BC (Chalcolithic period). The model was made and processed in Agisoft Metashape from 38 overlapping images captured using a Nikon D7100 with AF Micro Nikkor 60 mm 1:2.8 lens. The object was placed on a rotating table with white background and photographed in 360 degrees. Source: Objaverse 1.0 / Sketchfab
Microdata on vector abundance and IRS quality assurance (Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study)
<p>This repository contains the microdata on vector abundance and quality assurance of indoor residual spraying (IRS) that was used to estimate the impact of IRS on sandfly abundance and incidence of visceral leishmaniasis (VL) in India, as described in the paper "Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study" by Coffeng et al (<a href="https://doi.org/10.1016/S1473-3099(24)00420-1">https://doi.org/10.1016/S1473-3099(24)00420-1</a>). These data were collected as part of a BMGF-funded project led by dr. Michael Coleman at the Liverpool School for Tropical Medicine, as described in an earlier paper by Deb et al (<a href="https://doi.org/10.1371/journal.pntd.0009101">https://doi.org/10.1371/journal.pntd.0009101</a>).</p> <p>This repository does not include microdata on VL cases as these are owned by India's National Center for Vector Borne Disease Control (NCVBDC, <a href="https://ncvbdc.mohfw.gov.in/" target="_blank" rel="nofollow noreferrer noopener">https://ncvbdc.mohfw.gov.in/</a>).</p>
The dataset of the manuscript "GPU-HADVPPM4HIP V1.0: higher model accuracy on China's domestically GPU-like accelerator using heterogeneous compute interface for portability (HIP) technology to accelerate the piecewise parabolic method (PPM) in an air quality model (CAMx V6.10)"
<p><strong>bcfile.zip:</strong> the clean boundary condition files.</p> <p><strong>CAMxv6x_cpp.zip: </strong>the source code of CAMx-HIP version which coupled with HIP-HADVPPM scheme.</p> <p><strong>data.zip:</strong> final data tables used to plot figures.</p> <p><strong>emisfile.zip: </strong>the emission files.</p> <p><strong>icfile.zip:</strong> the clean initial condition files.</p> <p><strong>tuvfile.zip </strong>and <strong>o3mapfile.zip:</strong> the photolysis files.</p> <p><strong>outputfile.zip:</strong> the computation results outputted by CAMx model for Fortran version on the Intel Xeon E5-2682 v4 CPU, CUDA version on the NVIDIA K40m and V100 clusters, and HIP version on the China' s domestically heterogeneous cluster A.</p> <p><strong>wrfcamx.zip:</strong> the meteorological files.</p> <p><strong>offline_test_cuda.zip: </strong>the advection module code written in CUDA C language</p> <p><strong>offline_test_fortran.zip:</strong> the advection module code written in Fortran language</p> <p><strong>offline_test_hip.zip: </strong>the advection module code written in HIP C language</p>
CICE6 simulations: Quality Control for Community Based Sea Ice Model Development
<p>CICE6 simulations: Quality Control for Community Based Sea Ice Model Development<br> -----------------------------------------------------------------------------------</p> <p>Data from the CICE6 simulations by Elizabeth C. Hunke at Los Alamos National Laboratory described in the manuscript:</p> <p>Roberts, Hunke, Allard, Bailey, Craig, Lemieux and Turner (2018): Quality Control for Community Based Sea Ice Model Development.</p> <p>It was produced using the CICE sea ice model version 6.0.0.alpha with the baseline namelist:</p> <p>&setup_nml<br> days_per_year = 365<br> use_leap_years = .false.<br> year_init = 1990<br> istep0 = 0<br> dt = 3600.0<br> npt = 43800<br> ndtd = 1<br> runtype = 'continue'<br> ice_ic = '/usr/projects/climate/eclare/DATA/Consortium/CICE_data/ic/gx1/iced_gx1_v5.nc'<br> restart = .true.<br> restart_ext = .false.<br> use_restart_time = .true.<br> restart_format = 'nc'<br> lcdf64 = .false.<br> restart_dir = './restart/'<br> restart_file = 'iced'<br> pointer_file = './ice.restart_file'<br> dumpfreq = 'm'<br> dumpfreq_n = 1<br> dump_last = .false.<br> bfbflag = .false.<br> diagfreq = 24<br> diag_type = 'stdout'<br> diag_file = 'ice_diag.d'<br> print_global = .true.<br> print_points = .true.<br> latpnt(1) = 90.<br> lonpnt(1) = 0.<br> latpnt(2) = -65.<br> lonpnt(2) = -45.<br> dbug = .false.<br> histfreq = 'm','d','x','x','x'<br> histfreq_n = 1 , 1 , 1 , 1 , 1<br> hist_avg = .true.<br> history_dir = './history/'<br> history_file = 'iceh'<br> write_ic = .true.<br> incond_dir = './history/'<br> incond_file = 'iceh_ic'<br> /</p> <p>&grid_nml<br> grid_format = 'bin'<br> grid_type = 'displaced_pole'<br> grid_file = '/usr/projects/climate/eclare/DATA/Consortium/CICE_data/grid/gx1/grid_gx1.bin'<br> kmt_file = '/usr/projects/climate/eclare/DATA/Consortium/CICE_data/grid/gx1/kmt_gx1.bin'<br> gridcpl_file = 'unknown_gridcpl_file'<br> kcatbound = 0<br> /</p> <p>&domain_nml<br> nprocs = 32<br> processor_shape = 'slenderX2'<br> distribution_type = 'cartesian'<br> distribution_wght = 'latitude'<br> ew_boundary_type = 'cyclic'<br> ns_boundary_type = 'open'<br> maskhalo_dyn = .false.<br> maskhalo_remap = .false.<br> maskhalo_bound = .false.<br> /</p> <p>&tracer_nml<br> tr_iage = .true.<br> restart_age = .false.<br> tr_FY = .true.<br> restart_FY = .false.<br> tr_lvl = .true.<br> restart_lvl = .false.<br> tr_pond_cesm = .false.<br> restart_pond_cesm = .false.<br> tr_pond_topo = .false.<br> restart_pond_topo = .false.<br> tr_pond_lvl = .true.<br> restart_pond_lvl = .false.<br> tr_aero = .false.<br> restart_aero = .false.<br> /</p> <p>&thermo_nml<br> kitd = 1<br> ktherm = 2<br> conduct = 'bubbly'<br> a_rapid_mode = 0.5e-3<br> Rac_rapid_mode = 10.0<br> aspect_rapid_mode = 1.0<br> dSdt_slow_mode = -5.0e-8<br> phi_c_slow_mode = 0.05<br> phi_i_mushy = 0.85<br> /</p> <p>&dynamics_nml<br> kdyn = 1<br> ndte = 120<br> revised_evp = .false.<br> advection = 'remap'<br> kstrength = 1<br> krdg_partic = 1<br> krdg_redist = 1<br> mu_rdg = 3<br> Cf = 17.<br> Ktens = 0.<br> e_ratio = 2.<br> basalstress = .false.<br> /</p> <p>&shortwave_nml<br> shortwave = 'dEdd'<br> albedo_type = 'default'<br> albicev = 0.78<br> albicei = 0.36<br> albsnowv = 0.98<br> albsnowi = 0.70 <br> ahmax = 0.3<br> R_ice = 0.<br> R_pnd = 0.<br> R_snw = 1.5<br> dT_mlt = 1.5<br> rsnw_mlt = 1500.<br> kalg = 0.6<br> /</p> <p>&ponds_nml<br> hp1 = 0.01<br> hs0 = 0.<br> hs1 = 0.03<br> dpscale = 1.e-3<br> frzpnd = 'hlid'<br> rfracmin = 0.15<br> rfracmax = 1.<br> pndaspect = 0.8<br> /</p> <p>&zbgc_nml<br> tr_brine = .false.<br> restart_hbrine = .false.<br> tr_zaero = .false.<br> modal_aero = .false.<br> skl_bgc = .false.<br> z_tracers = .false.<br> dEdd_algae = .false.<br> solve_zbgc = .false.<br> bgc_flux_type = 'Jin2006'<br> restore_bgc = .false.<br> restart_bgc = .false.<br> scale_bgc = .false.<br> solve_zsal = .false.<br> restart_zsal = .false.<br> bgc_data_dir = '/uknown_bgc_data_dir'<br> sil_data_type = 'default'<br> nit_data_type = 'default'<br> fe_data_type = 'default'<br> tr_bgc_Nit = .true.<br> tr_bgc_C = .true.<br> tr_bgc_chl = .false.<br> tr_bgc_Am = .true.<br> tr_bgc_Sil = .true.<br> tr_bgc_DMS = .false.<br> tr_bgc_PON = .true.<br> tr_bgc_hum = .true.<br> tr_bgc_DON = .false.<br> tr_bgc_Fe = .true. <br> grid_o = 0.006<br> grid_o_t = 0.006<br> l_sk = 0.024<br> grid_oS = 0.0<br> l_skS = 0.028<br> phi_snow = -0.3<br> initbio_frac = 0.8<br> frazil_scav = 0.8 <br> ratio_Si2N_diatoms = 1.8 <br> ratio_Si2N_sp = 0.0<br> ratio_Si2N_phaeo = 0.0<br> ratio_S2N_diatoms = 0.03 <br> ratio_S2N_sp = 0.03 <br> ratio_S2N_phaeo = 0.03<br> ratio_Fe2C_diatoms = 0.0033<br> ratio_Fe2C_sp = 0.0033<br> ratio_Fe2C_phaeo = 0.1<br> ratio_Fe2N_diatoms = 0.023 <br> ratio_Fe2N_sp = 0.023<br> ratio_Fe2N_phaeo = 0.7<br> ratio_Fe2DON = 0.023<br> ratio_Fe2DOC_s = 0.1<br> ratio_Fe2DOC_l = 0.033<br> fr_resp = 0.05<br> tau_min = 5200.0<br> tau_max = 173000.0<br> algal_vel = 0.0000000111<br> R_dFe2dust = 0.035<br> dustFe_sol = 0.005<br> chlabs_diatoms = 0.03<br> chlabs_sp = 0.01<br> chlabs_phaeo = 0.05<br> alpha2max_low_diatoms = 0.8<br> alpha2max_low_sp = 0.67<br> alpha2max_low_phaeo = 0.67<br> beta2max_diatoms = 0.018<br> beta2max_sp = 0.0025<br> beta2max_phaeo = 0.01<br> mu_max_diatoms = 1.44<br> mu_max_sp = 0.851<br> mu_max_phaeo = 0.851<br> grow_Tdep_diatoms = 0.06<br> grow_Tdep_sp = 0.06<br> grow_Tdep_phaeo = 0.06<br> fr_graze_diatoms = 0.0<br> fr_graze_sp = 0.1<br> fr_graze_phaeo = 0.1<br> mort_pre_diatoms = 0.007<br> mort_pre_sp = 0.007<br> mort_pre_phaeo = 0.007<br> mort_Tdep_diatoms = 0.03<br> mort_Tdep_sp = 0.03<br> mort_Tdep_phaeo = 0.03<br> k_exude_diatoms = 0.0<br> k_exude_sp = 0.0<br> k_exude_phaeo = 0.0<br> K_Nit_diatoms = 1.0<br> K_Nit_sp = 1.0<br> K_Nit_phaeo = 1.0<br> K_Am_diatoms = 0.3<br> K_Am_sp = 0.3<br> K_Am_phaeo = 0.3<br> K_Sil_diatoms = 4.0<br> K_Sil_sp = 0.0<br> K_Sil_phaeo = 0.0<br> K_Fe_diatoms = 1.0<br> K_Fe_sp = 0.2<br> K_Fe_phaeo = 0.1<br> f_don_protein = 0.6<br> kn_bac_protein = 0.03<br> f_don_Am_protein = 0.25<br> f_doc_s = 0.4<br> f_doc_l = 0.4<br> f_exude_s = 1.0<br> f_exude_l = 1.0<br> k_bac_s = 0.03<br> k_bac_l = 0.03<br> T_max = 0.0<br> fsal = 1.0<br> op_dep_min = 0.1<br> fr_graze_s = 0.5<br> fr_graze_e = 0.5<br> fr_mort2min = 0.5<br> fr_dFe = 0.3<br> k_nitrif = 0.0<br> t_iron_conv = 3065.0<br> max_loss = 0.9<br> max_dfe_doc1 = 0.2<br> fr_resp_s = 0.75<br> y_sk_DMS = 0.5<br> t_sk_conv = 3.0<br> t_sk_ox = 10.0<br> algaltype_diatoms = 0.0<br> algaltype_sp = 0.5<br> algaltype_phaeo = 0.5<br> nitratetype = -1.0<br> ammoniumtype = 1.0<br> silicatetype = -1.0<br> dmspptype = 0.5<br> dmspdtype = -1.0<br> humtype = 1.0<br> doctype_s = 0.5<br> doctype_l = 0.5<br> dontype_protein = 0.5<br> fedtype_1 = 0.5<br> feptype_1 = 0.5<br> zaerotype_bc1 = 1.0<br> zaerotype_bc2 = 1.0<br> zaerotype_dust1 = 1.0<br> zaerotype_dust2 = 1.0<br> zaerotype_dust3 = 1.0<br> zaerotype_dust4 = 1.0<br> ratio_C2N_diatoms = 7.0<br> ratio_C2N_sp = 7.0<br> ratio_C2N_phaeo = 7.0<br> ratio_chl2N_diatoms= 2.1<br> ratio_chl2N_sp = 1.1<br> ratio_chl2N_phaeo = 0.84<br> F_abs_chl_diatoms = 2.0<br> F_abs_chl_sp = 4.0<br> F_abs_chl_phaeo = 5.0<br> ratio_C2N_proteins = 7.0<br> /</p> <p>&forcing_nml<br> formdrag = .false.<br> atmbndy = 'default'<br> fyear_init = 1990<br> ycycle = 20<br> atm_data_format = 'bin'<br> atm_data_type = 'LYq'<br> atm_data_dir = '/usr/projects/climate/eclare/DATA/Consortium/CICE_data/forcing/gx1/COREII'<br> calc_strair = .true.<br> highfreq = .false.<br> natmiter = 5<br> calc_Tsfc = .true.<br> precip_units = 'mm_per_sec'<br> ustar_min = 0.0005<br> fbot_xfer_type = 'constant'<br> update_ocn_f = .false.<br> l_mpond_fresh = .false.<br> tfrz_option = 'mushy'<br> oceanmixed_ice = .true.<br> ocn_data_format = 'nc'<br> sss_data_type = 'ncar'<br> sst_data_type = 'ncar'<br> ocn_data_dir = '/usr/projects/climate/eclare/DATA/gx1v3/gx1v3/forcing/'<br> oceanmixed_file = 'oceanmixed_ice_depth.nc'<br> restore_sst = .false.<br> trestore = 90<br> restore_ice = .false.<br> /</p> <p>&icefields_nml<br> f_tmask = .true.<br> f_blkmask = .true.<br> f_tarea = .true.<br> f_uarea = .true.<br> f_dxt = .false.<br> f_dyt = .false.<br> f_dxu = .false.<br> f_dyu = .false.<br> f_HTN = .false.<br> f_HTE = .false.<br> f_ANGLE = .true.<br> f_ANGLET = .true.<br> f_NCAT = .true.<br> f_VGRDi = .false.<br> f_VGRDs = .false.<br> f_VGRDb = .false.<br> f_VGRDa = .true.<br> f_bounds = .false.<br> f_aice = 'm' <br> f_hi = 'm'<br> f_hs = 'm' <br> f_Tsfc = 'm' <br> f_sice = 'm' <br> f_uvel = 'm' <br> f_vvel = 'm' <br> f_uatm = 'm' <br> f_vatm = 'm' <br> f_fswdn = 'm' <br> f_flwdn = 'm'<br> f_snowfrac = 'x'<br> f_snow = 'x' <br> f_snow_ai = 'm' <br> f_rain = 'x' <br> f_rain_ai = 'm' <br> f_sst = 'm' <br> f_sss = 'm' <br> f_uocn = 'm' <br> f_vocn = 'm' <br> f_frzmlt = 'm'<br> f_fswfac = 'm'<br> f_fswint_ai = 'm'<br> f_fswabs = 'x' <br> f_fswabs_ai = 'm' <br> f_albsni = 'm' <br> f_alvdr = 'x'<br> f_alidr = 'x'<br> f_alvdf = 'x'<br> f_alidf = 'x'<br> f_alvdr_ai = 'x'<br> f_alidr_ai = 'x'<br> f_alvdf_ai = 'x'<br> f_alidf_ai = 'x'<br> f_albice = 'x'<br> f_albsno = 'x'<br> f_albpnd = 'x'<br> f_coszen = 'x'<br> f_flat = 'x' <br> f_flat_ai = 'm' <br> f_fsens = 'x' <br> f_fsens_ai = 'm' <br> f_fswup = 'x' <br> f_flwup = 'x' <br> f_flwup_ai = 'm' <br> f_evap = 'x' <br> f_evap_ai = 'm' <br> f_Tair = 'm' <br> f_Tref = 'x' <br> f_Qref = 'x'<br> f_congel = 'm' <br> f_frazil = 'm' <br> f_snoice = 'm' <br> f_dsnow = 'x' <br> f_melts = 'm'<br> f_meltt = 'm'<br> f_meltb = 'm'<br> f_meltl = 'm'<br> f_fresh = 'x'<br> f_fresh_ai = 'm'<br> f_fsalt = 'x'<br> f_fsalt_ai = 'm'<br> f_fbot = 'm'<br> f_fhocn = 'x' <br> f_fhocn_ai = 'm' <br> f_fswthru = 'x' <br> f_fswthru_ai = 'm' <br> f_fsurf_ai = 'x'<br> f_fcondtop_ai = 'x'<br> f_fmeltt_ai = 'x' <br> f_strairx = 'm' <br> f_strairy = 'm' <br> f_strtltx = 'x' <br> f_strtlty = 'x' <br> f_strcorx = 'x' <br> f_strcory = 'x' <br> f_strocnx = 'x' <br> f_strocny = 'x' <br> f_strintx = 'x' <br> f_strinty = 'x'<br> f_taubx = 'x'<br> f_tauby = 'x'<br> f_strength = 'm'<br> f_divu = 'm'<br> f_shear = 'm'<br> f_sig1 = 'm' <br> f_sig2 = 'm' <br> f_dvidtt = 'm' <br> f_dvidtd = 'm' <br> f_daidtt = 'm'<br> f_daidtd = 'm' <br> f_dagedtt = 'm'<br> f_dagedtd = 'm' <br> f_mlt_onset = 'm'<br> f_frz_onset = 'm'<br> f_hisnap = 'd'<br> f_aisnap = 'd'<br> f_trsig = 'm'<br> f_icepresent = 'm'<br> f_iage = 'm'<br> f_FY = 'x'<br> f_aicen = 'x'<br> f_vicen = 'x'<br> f_vsnon = 'x'<br> f_snowfracn = 'x'<br> f_keffn_top = 'x'<br> f_Tinz = 'x'<br> f_Sinz = 'x'<br> f_Tsnz = 'x'<br> f_fsurfn_ai = 'x'<br> f_fcondtopn_ai = 'x'<br> f_fmelttn_ai = 'x'<br> f_flatn_ai = 'x'<br> f_fsensn_ai = 'x'<br> /</p> <p>&icefields_mechred_nml<br> f_alvl = 'm'<br> f_vlvl = 'm'<br> f_ardg = 'm'<br> f_vrdg = 'm'<br> f_dardg1dt = 'x'<br> f_dardg2dt = 'x'<br> f_dvirdgdt = 'x'<br> f_opening = 'x'<br> f_ardgn = 'x'<br> f_vrdgn = 'x'<br> f_dardg1ndt = 'x'<br> f_dardg2ndt = 'x'<br> f_dvirdgndt = 'x'<br> f_krdgn = 'x'<br> f_aparticn = 'x'<br> f_aredistn = 'x'<br> f_vredistn = 'x'<br> f_araftn = 'x'<br> f_vraftn = 'x'<br> /</p> <p>&icefields_pond_nml<br> f_apondn = 'x'<br> f_apeffn = 'x'<br> f_hpondn = 'x'<br> f_apond = 'm'<br> f_hpond = 'm'<br> f_ipond = 'm'<br> f_apeff = 'm'<br> f_apond_ai = 'm'<br> f_hpond_ai = 'm'<br> f_ipond_ai = 'm'<br> f_apeff_ai = 'm'<br> /</p> <p>&icefields_bgc_nml<br> f_faero_atm = 'x'<br> f_faero_ocn = 'x'<br> f_aero = 'x' <br> f_fbio = 'm'<br> f_fbio_ai = 'm'<br> f_zaero = 'x'<br> f_bgc_S = 'm'<br> f_bgc_N = 'm'<br> f_bgc_C = 'x'<br> f_bgc_DOC = 'm'<br> f_bgc_DIC = 'x'<br> f_bgc_chl = 'x'<br> f_bgc_Nit = 'm'<br> f_bgc_Am = 'm'<br> f_bgc_Sil = 'm'<br> f_bgc_DMSPp = 'x'<br> f_bgc_DMSPd = 'x'<br> f_bgc_DMS = 'x'<br> f_bgc_DON = 'x' <br> f_bgc_Fe = 'm' <br> f_bgc_hum = 'm' <br> f_bgc_PON = 'm'<br> f_bgc_ml = 'm'<br> f_upNO = 'm'<br> f_upNH = 'm'<br> f_bTin = 'm'<br> f_bphi = 'm' <br> f_iDi = 'm'<br> f_iki = 'm'<br> f_fbri = 'm' <br> f_hbri = 'm'<br> f_zfswin = 'm'<br> f_bionet = 'm'<br> f_biosnow = 'm'<br> f_grownet = 'm'<br> f_PPnet = 'm'<br> f_algalpeak = 'm'<br> f_zbgc_frac = 'm'<br> /</p> <p>&icefields_drag_nml<br> f_drag = 'x'<br> f_Cdn_atm = 'x'<br> f_Cdn_ocn = 'x'<br> /</p>
RASM simulations: Quality Control for Community Based Sea Ice Model Development
<p>RASM simulations: Quality Control for Community Based Sea Ice Model Development<br> -----------------------------------------------------------------------------------</p> <p>Data from the Regional Arctic System Model (RASM) simulations by Andrew F. Roberts at Naval Postgraduate School, described in the manuscript:</p> <p>Roberts, Hunke, Allard, Bailey, Craig, Lemieux and Turner (2018): Quality Control for Community Based Sea Ice Model Development.</p> <p>It was produced using RASM with CICE sea ice model version 5.2 with the namelist:</p> <p>&setup_nml<br> bfbflag = .false. <br> diagfreq = 24 <br> hist_avg = .true. <br> histfreq = 'h','x','x','x','x'<br> histfreq_n = 1,0,0,0,0<br> ice_ic = '/work4/projects/apcraig_dir/RACM/inputdata/ccsm4_init/R1008Gcaaa01a_loop2/1979-09-01/R1008Gcaaa01a_loop2.cice.r.1979-09-01-00000.nc'<br> latpnt = 90.0,-65.0 <br> lcdf64 = .true. <br> lonpnt = 0.0,-45.0 <br> ndtd = 1 <br> pointer_file = './rpointer.ice'<br> print_global = .true. <br> print_points = .false. <br> restart_ext = .false. <br> restart_format = 'pio'<br> year_init = 1 <br> /<br> &grid_nml<br> grid_file = '/work4/projects/apcraig_dir/RACM/inputdata/ice/cice/ar9v3_20101118.grid'<br> grid_format = 'bin'<br> grid_type = 'regional'<br> gridcpl_file = '/work4/projects/apcraig_dir/RACM/inputdata/ice/cice/domain.ocn.ar9v4.100920.nc'<br> kcatbound = 0 <br> kmt_file = '/work4/projects/apcraig_dir/RACM/inputdata/ice/cice/ar9v3_20110106.kmt'<br> /<br> &tracer_nml<br> restart_aero = .false. <br> restart_age = .false. <br> restart_fy = .false. <br> restart_lvl = .false. <br> restart_pond_cesm = .false. <br> restart_pond_lvl = .false. <br> restart_pond_topo = .false. <br> tr_aero = .false.<br> tr_fy = .true. <br> tr_iage = .true. <br> tr_lvl = .true. <br> tr_pond_cesm = .false. <br> tr_pond_lvl = .true. <br> tr_pond_topo = .false. <br> /<br> &thermo_nml<br> a_rapid_mode = 0.5e-03 <br> aspect_rapid_mode = 1.0 <br> conduct = 'bubbly'<br> dsdt_slow_mode = -1.5e-07 <br> kitd = 1 <br> ktherm = 2 <br> phi_c_slow_mode = 0.05 <br> phi_i_mushy = 0.85 <br> rac_rapid_mode = 10 <br> /<br> &dynamics_nml<br> advection = 'remap'<br> cf = 21.3<br> kdyn = 2 (EAP) or kdyn = 2 (EVP)<br> krdg_partic = 1<br> krdg_redist = 1<br> kstrength = 1 <br> mu_rdg = 3.0<br> ndte = 600 <br> revised_evp = .false. <br> /<br> &shortwave_nml<br> ahmax = 0.3 <br> albedo_type = 'default'<br> albicei = 0.45<br> albicev = 0.75<br> albsnowi = 0.73<br> albsnowv = 0.98<br> dt_mlt = 1.00 <br> kalg = 0.0 <br> r_ice = 0.0 <br> r_pnd = 0.0 <br> r_snw = 0.50 <br> rsnw_mlt = 1000. <br> shortwave = 'dEdd'<br> /<br> &ponds_nml<br> dpscale = 1.0e-3 <br> frzpnd = 'hlid'<br> hp1 = 0.01 <br> hs0 = 0.03 <br> hs1 = 0.03 <br> pndaspect = 0.8 <br> rfracmax = 0.85 <br> rfracmin = 0.15 <br> /<br> &forcing_nml<br> fbot_xfer_type = 'constant'<br> formdrag = .false.<br> highfreq = .true.<br> l_mpond_fresh = .false.<br> natmiter = 25 <br> tfrz_option = 'mushy'<br> /<br> &domain_nml<br> distribution_type = 'roundrobin'<br> ew_boundary_type = 'cyclic'<br> maskhalo_bound = .true.<br> maskhalo_dyn = .true.<br> maskhalo_remap = .true.<br> ns_boundary_type = 'open'<br> processor_shape = 'null'<br> /<br> &zbgc_nml<br> bgc_data_dir = 'unknown_bgc_data_dir'<br> bgc_flux_type = 'Jin2006'<br> nit_data_type = 'unknown'<br> phi_snow = 0.5 <br> restart_bgc = .false. <br> restart_hbrine = .false. <br> restore_bgc = .false. <br> sil_data_type = 'unknown'<br> skl_bgc = .false. <br> tr_bgc_am_sk = .false. <br> tr_bgc_c_sk = .false. <br> tr_bgc_chl_sk = .false. <br> tr_bgc_dms_sk = .false. <br> tr_bgc_dmspd_sk = .false. <br> tr_bgc_dmspp_sk = .false. <br> tr_bgc_sil_sk = .false. <br> tr_brine = .false. <br> /<br> &icefields_bgc_nml<br> f_aero = 'mxxxx'<br> f_aeron = 'xxxxx'<br> f_bgc_am_ml = 'xxxxx'<br> f_bgc_am_sk = 'xxxxx'<br> f_bgc_c = 'xxxxx'<br> f_bgc_c_sk = 'xxxxx'<br> f_bgc_chl = 'xxxxx'<br> f_bgc_chl_sk = 'xxxxx'<br> f_bgc_dms = 'xxxxx'<br> f_bgc_dms_ml = 'xxxxx'<br> f_bgc_dms_sk = 'xxxxx'<br> f_bgc_dmsp_ml = 'xxxxx'<br> f_bgc_dmspd = 'xxxxx'<br> f_bgc_dmspd_sk = 'xxxxx'<br> f_bgc_dmspp = 'xxxxx'<br> f_bgc_dmspp_sk = 'xxxxx'<br> f_bgc_n = 'xxxxx'<br> f_bgc_n_sk = 'xxxxx'<br> f_bgc_nh = 'xxxxx'<br> f_bgc_nit_ml = 'xxxxx'<br> f_bgc_nit_sk = 'xxxxx'<br> f_bgc_no = 'xxxxx'<br> f_bgc_s = 'xxxxx'<br> f_bgc_sil = 'xxxxx'<br> f_bgc_sil_ml = 'xxxxx'<br> f_bgc_sil_sk = 'xxxxx'<br> f_bphi = 'xxxxx'<br> f_btin = 'xxxxx'<br> f_faero_atm = 'mxxxx'<br> f_faero_ocn = 'mxxxx'<br> f_fbri = 'xxxxx'<br> f_fn = 'xxxxx'<br> f_fn_ai = 'xxxxx'<br> f_fnh = 'xxxxx'<br> f_fnh_ai = 'xxxxx'<br> f_fno = 'xxxxx'<br> f_fno_ai = 'xxxxx'<br> f_fsil = 'xxxxx'<br> f_fsil_ai = 'xxxxx'<br> f_grownet = 'xxxxx'<br> f_hbri = 'xxxxx'<br> f_ppnet = 'xxxxx'<br> /<br> &icefields_drag_nml<br> f_cdn_atm = 'hxxxx'<br> f_cdn_ocn = 'hxxxx'<br> f_drag = 'hxxxx'<br> /<br> &icefields_mechred_nml<br> f_alvl = 'hxxxx'<br> f_aparticn = 'xxxxx'<br> f_araftn = 'xxxxx'<br> f_ardg = 'hxxxx'<br> f_ardgn = 'hxxxx'<br> f_aredistn = 'xxxxx'<br> f_dardg1dt = 'xxxxx'<br> f_dardg1ndt = 'xxxxx'<br> f_dardg2dt = 'xxxxx'<br> f_dardg2ndt = 'xxxxx'<br> f_dvirdgdt = 'xxxxx'<br> f_dvirdgndt = 'xxxxx'<br> f_krdgn = 'xxxxx'<br> f_opening = 'hxxxx'<br> f_vlvl = 'hxxxx'<br> f_vraftn = 'xxxxx'<br> f_vrdg = 'hxxxx'<br> f_vrdgn = 'hxxxx'<br> f_vredistn = 'xxxxx'<br> /<br> &icefields_pond_nml<br> f_apeff = 'hxxxx'<br> f_apeff_ai = 'hxxxx'<br> f_apeffn = 'hxxxx'<br> f_apond = 'hxxxx'<br> f_apond_ai = 'hxxxx'<br> f_apondn = 'hxxxx'<br> f_hpond = 'hxxxx'<br> f_hpond_ai = 'hxxxx'<br> f_hpondn = 'hxxxx'<br> f_ipond = 'hxxxx'<br> f_ipond_ai = 'hxxxx'<br> /<br> &icefields_nml<br> f_a11 = 'hxxxx'<br> f_a12 = 'hxxxx'<br> f_aice = 'hxxxx'<br> f_aicen = 'hxxxx'<br> f_aisnap = 'hxxxx'<br> f_albice = 'hxxxx'<br> f_albpnd = 'hxxxx'<br> f_albsni = 'hxxxx'<br> f_albsno = 'hxxxx'<br> f_alidf = 'hxxxx'<br> f_alidr = 'hxxxx'<br> f_alvdf = 'hxxxx'<br> f_alvdr = 'hxxxx'<br> f_angle = .true.<br> f_anglet = .true.<br> f_blkmask = .true.<br> f_bounds = .false.<br> f_congel = 'hxxxx'<br> f_coszen = 'hxxxx'<br> f_daidtd = 'hxxxx'<br> f_daidtt = 'hxxxx'<br> f_divu = 'hxxxx'<br> f_dsnow = 'hxxxx'<br> f_dvidtd = 'hxxxx'<br> f_dvidtt = 'hxxxx'<br> f_dxt = .false.<br> f_dxu = .false.<br> f_dyt = .false.<br> f_dyu = .false.<br> f_e11 = 'xxxxx'<br> f_e12 = 'xxxxx'<br> f_e22 = 'xxxxx'<br> f_evap = 'hxxxx'<br> f_evap_ai = 'hxxxx'<br> f_fcondtop_ai = 'hxxxx'<br> f_fcondtopn_ai = 'hxxxx'<br> f_fhocn = 'hxxxx'<br> f_fhocn_ai = 'hxxxx'<br> f_flat = 'hxxxx'<br> f_flat_ai = 'hxxxx'<br> f_flatn_ai = 'hxxxx'<br> f_flwdn = 'hxxxx'<br> f_flwup = 'hxxxx'<br> f_flwup_ai = 'hxxxx'<br> f_fmeltt_ai = 'hxxxx'<br> f_fmelttn_ai = 'hxxxx'<br> f_frazil = 'hxxxx'<br> f_fresh = 'hxxxx'<br> f_fresh_ai = 'hxxxx'<br> f_frz_onset = 'hxxxx'<br> f_frzmlt = 'hxxxx'<br> f_fsalt = 'hxxxx'<br> f_fsalt_ai = 'hxxxx'<br> f_fsens = 'hxxxx'<br> f_fsens_ai = 'hxxxx'<br> f_fsensn_ai = 'hxxxx'<br> f_fsurf_ai = 'hxxxx'<br> f_fsurfn_ai = 'hxxxx'<br> f_fswabs = 'hxxxx'<br> f_fswabs_ai = 'hxxxx'<br> f_fswdn = 'hxxxx'<br> f_fswfac = 'hxxxx'<br> f_fswint_ai = 'xxxxx'<br> f_fswthru = 'hxxxx'<br> f_fswthru_ai = 'hxxxx'<br> f_fy = 'hxxxx'<br> f_hi = 'hxxxx'<br> f_hisnap = 'xxxxx'<br> f_hs = 'hxxxx'<br> f_hte = .false.<br> f_htn = .false.<br> f_iage = 'hxxxx'<br> f_icepresent = 'hxxxx'<br> f_keffn_top = 'xxxxx'<br> f_meltb = 'hxxxx'<br> f_meltl = 'hxxxx'<br> f_melts = 'hxxxx'<br> f_meltt = 'hxxxx'<br> f_mlt_onset = 'hxxxx'<br> f_ncat = .true.<br> f_qref = 'hxxxx'<br> f_rain = 'hxxxx'<br> f_rain_ai = 'hxxxx'<br> f_s11 = 'hxxxx'<br> f_s12 = 'hxxxx'<br> f_s22 = 'hxxxx'<br> f_shear = 'hxxxx'<br> f_sice = 'hxxxx'<br> f_sig1 = 'hxxxx'<br> f_sig2 = 'hxxxx'<br> f_sinz = 'xxxxx'<br> f_snoice = 'hxxxx'<br> f_snow = 'hxxxx'<br> f_snow_ai = 'hxxxx'<br> f_sss = 'hxxxx'<br> f_sst = 'hxxxx'<br> f_strairx = 'hxxxx'<br> f_strairy = 'hxxxx'<br> f_strcorx = 'hxxxx'<br> f_strcory = 'hxxxx'<br> f_strength = 'hxxxx'<br> f_strintx = 'hxxxx'<br> f_strinty = 'hxxxx'<br> f_strocnx = 'hxxxx'<br> f_strocny = 'hxxxx'<br> f_strtltx = 'hxxxx'<br> f_strtlty = 'hxxxx'<br> f_tair = 'hxxxx'<br> f_tarea = .true.<br> f_tinz = 'xxxxx'<br> f_tmask = .true.<br> f_tref = 'hxxxx'<br> f_trsig = 'hxxxx'<br> f_tsfc = 'hxxxx'<br> f_tsnz = 'xxxxx'<br> f_uarea = .true.<br> f_uatm = 'hxxxx'<br> f_uocn = 'hxxxx'<br> f_uvel = 'hxxxx'<br> f_vatm = 'hxxxx'<br> f_vgrdb = .true.<br> f_vgrdi = .true.<br> f_vgrds = .true.<br> f_vicen = 'hxxxx'<br> f_vocn = 'hxxxx'<br> f_vsnon = 'hxxxx'<br> f_vvel = 'hxxxx'<br> f_yieldstress11 = 'hxxxx'<br> f_yieldstress12 = 'hxxxx'<br> f_yieldstress22 = 'hxxxx'<br> /<br> &ice_prescribed_nml<br> prescribed_ice = .false.<br> /</p>
GOFS simulations: Quality Control for Community Based Sea Ice Model Development
<p>GOFS simulations: Quality Control for Community Based Sea Ice Model Development<br> -----------------------------------------------------------------------------------</p> <p>Data from the Global Ocean Forecast System (GOFS 3.1) from the Naval Research Laboratory, curated by Richard Allard, and described in the manuscript:</p> <p>Roberts, Hunke, Allard, Bailey, Craig, Lemieux and Turner (2018): Quality Control for Community Based Sea Ice Model Development.</p> <p>It was produced using GOFS with CICE sea ice model version 4.0 with the namelist:</p> <p>&setup_nml<br> days_per_year= 366<br> , year_init = 1901<br> , istep0 = 0<br> , dt = 225.0<br> , npt = -1 <br> , ndyn_dt = 1<br> , inserthr = 0<br> , runtype = 'continue'<br> , ice_ic = 'default'<br> , restart = .true.<br> , restart_dir = './'<br> , restart_file = 'cice.restart'<br> , pointer_file = './cice.restart_file'<br> , histfreq = 'h'<br> , histfreq_n = 12<br> , dumpfreq = '5'<br> , dumpfreq_n = 1<br> , diagfreq = 8<br> , diag_type = 'stdout'<br> , diag_file = 'ice.diag.d'<br> , print_global = .true.<br> , print_points = .true.<br> , latpnt(1) = 72.011<br> , lonpnt(1) = -149.987<br> , latpnt(2) = 90.<br> , lonpnt(2) = 0.<br> , dbug = .false.<br> , incond_dir = <br> , incond_file = './cice'<br> /<br> &grid_nml<br> grid_format = 'bin'<br> , grid_type = 'hycom'<br> , grid_file = 'regional.cice.r'<br> , kmt_file = 'kmt'<br> , kcatbound = 0 <br> /<br> &domain_nml<br> nprocs = 1800<br> , processor_shape = 'slenderX4'<br> , distribution_type = 'cartesian'<br> , distribution_wght = 'latitude'<br> , ew_boundary_type = 'cyclic'<br> , ns_boundary_type = 'tripole'<br> /<br> &tracer_nml<br> tr_iage = .false.<br> , restart_age = .false.<br> , tr_pond = .false.<br> , restart_pond = .false.<br> /<br> &ice_nml<br> kitd = 1<br> , kdyn = 1<br> , ndte = 120<br> , kstrength = 1<br> , krdg_partic = 1<br> , krdg_redist = 1<br> , advection = 'remap'<br> , heat_capacity = .true.<br> , shortwave = 'default'<br> , albedo_type = 'default'<br> , albicev = 0.78<br> , albicei = 0.36<br> , albsnowv = 0.98<br> , albsnowi = 0.70 <br> , R_ice = 0.0<br> , R_pnd = 0.0<br> , R_snw = 0.0<br> , atmbndy = 'default'<br> , fyear_init = 1911 <br> , ycycle = 1 <br> , atm_data_format = 'bin'<br> , atm_data_type= 'cfsr'<br> , atm_data_dir = './cice.'<br> , calc_strair = .true.<br> , calc_Tsfc = .true.<br> , precip_units = 'mm_per_month'<br> , Tfrzpt = 'linear_S'<br> , update_ocn_f = .false.<br> , oceanmixed_ice = .true.<br> , ocn_data_format = 'bin'<br> , sss_data_type= 'cfsr'<br> , sst_data_type= 'cfsr'<br> , ocn_data_dir = 'COUPLED'<br> , oceanmixed_file= 'pop_frc.gx1v3.051202.nc'<br> , restore_sst = .true.<br> , insert_ssmi = .true.<br> , insert_sih = .false.<br> , trestore = 1 <br> , atm_netrad = .false.<br> , albnetrad = 0.00<br> /<br> &icefields_nml<br> f_tmask = .false.<br> , f_tarea = .false.<br> , f_uarea = .false.<br> , f_dxt = .false.<br> , f_dyt = .false.<br> , f_dxu = .false.<br> , f_dyu = .false.<br> , f_HTN = .false.<br> , f_HTE = .false.<br> , f_ANGLE = .false.<br> , f_ANGLET = .false.<br> , f_bounds = .false.<br> , f_hi = .true.<br> , f_hs = .true. <br> , f_Tsfc = .true. <br> , f_aice = .true. <br> , f_uvel = .true. <br> , f_vvel = .true. <br> , f_fswdn = .true. <br> , f_flwdn = .true.<br> , f_snow = .true. <br> , f_snow_ai = .true. <br> , f_rain = .false. <br> , f_rain_ai = .true. <br> , f_sst = .true. <br> , f_sss = .true. <br> , f_uocn = .true. <br> , f_vocn = .true. <br> , f_frzmlt = .true.<br> , f_fswabs = .true. <br> , f_fswabs_ai = .true. <br> , f_albsni = .true. <br> , f_alvdr = .false.<br> , f_alidr = .false.<br> , f_flat = .false. <br> , f_flat_ai = .true. <br> , f_fsens = .false. <br> , f_fsens_ai = .true. <br> , f_flwup = .false. <br> , f_flwup_ai = .true. <br> , f_evap = .false. <br> , f_evap_ai = .true. <br> , f_Tair = .true. <br> , f_Tref = .false. <br> , f_Qref = .false.<br> , f_congel = .true. <br> , f_frazil = .true. <br> , f_snoice = .true. <br> , f_meltt = .true.<br> , f_meltb = .true.<br> , f_meltl = .true.<br> , f_fresh = .false.<br> , f_fresh_ai = .true.<br> , f_fsalt = .false.<br> , f_fsalt_ai = .true.<br> , f_fhocn = .false. <br> , f_fhocn_ai = .true. <br> , f_fswthru = .false. <br> , f_fswthru_ai= .true. <br> , f_fsurf_ai = .false.<br> , f_fcondtop_ai= .false.<br> , f_fmeltt_ai = .false. <br> , f_strairx = .true. <br> , f_strairy = .true. <br> , f_strtltx = .false. <br> , f_strtlty = .false. <br> , f_strcorx = .true. <br> , f_strcory = .true. <br> , f_strocnx = .true. <br> , f_strocny = .true. <br> , f_strintx = .false. <br> , f_strinty = .false.<br> , f_strength = .true.<br> , f_divu = .true.<br> , f_shear = .false.<br> , f_sig1 = .false. <br> , f_sig2 = .false. <br> , f_dvidtt = .false. <br> , f_dvidtd = .false. <br> , f_daidtt = .false.<br> , f_daidtd = .false. <br> , f_mlt_onset = .false.<br> , f_frz_onset = .false.<br> , f_dardg1dt = .false.<br> , f_dardg2dt = .false.<br> , f_dvirdgdt = .false.<br> , f_opening = .true.<br> , f_hisnap = .false.<br> , f_aisnap = .false.<br> , f_trsig = .false.<br> , f_icepresent= .false.<br> , f_iage = .false.<br> , f_aicen = .false.<br> , f_vicen = .false.<br> , f_fsurfn_ai = .false.<br> , f_fcondtopn_ai = .false.<br> , f_fmelttn_ai = .false.<br> , f_flatn_ai = .false.<br> /</p> <p> </p> <p> </p>
ECCC simulations: Quality Control for Community Based Sea Ice Model Development
<p>ECCC simulations: Quality Control for Community Based Sea Ice Model Development<br> -----------------------------------------------------------------------------------</p> <p>Data from the Environment and Climate Change Canada (ECCC) simulation by Jean-François Lemieux at Environment and Climate Change Canada, described in the manuscript:</p> <p>Roberts, Hunke, Allard, Bailey, Craig, Lemieux and Turner (2018): Quality Control for Community Based Sea Ice Model Development.</p> <p>It was produced using ECCC with CICE sea ice model version 4.0 with the namelist:</p> <p>&setup_nml<br> days_per_year = 365<br> , year_init = 2001100100<br> , istep0 = 0<br> , dt = 600<br> , npt = 2880<br> , ndyn_dt = 1<br> , runtype = 'continue'<br> , ice_ic = 'default'<br> , restart = .true.<br> , restart_dir = 'not_used_at_cmc'<br> , restart_file = 'not_used_at_cmc'<br> , pointer_file = './ice.restart_file'<br> , dumpfreq = 's'<br> , dumpfreq_n = 2880<br> , diagfreq = 144<br> , diag_type = 'file'<br> , diag_file = 'ice_stdout'<br> , print_global = .false.<br> , print_points = .false.<br> , latpnt(1) = 90.<br> , lonpnt(1) = 0.<br> , latpnt(2) = -65.<br> , lonpnt(2) = -45.<br> , dbug = .false.<br> , histfreq = 's' <br> , histfreq_n = 144<br> , hist_avg = .false.<br> , history_dir = './history/'<br> , history_file = 'iceh'<br> , history_format = 'nc'<br> , write_ic = .false.<br> , incond_dir = './history/'<br> , incond_file = 'iceh_ic'<br> /</p> <p>&grid_nml<br> grid_format = 'cmc'<br> , grid_type = 'cmc'<br> , grid_file = 'not_used_at_cmc'<br> , kmt_file = 'not_used_at_cmc'<br> , kcatbound = 2<br> /</p> <p>&domain_nml<br> nprocs = 72<br> , processor_shape = 'square-pop'<br> , distribution_type = 'cartesian'<br> , distribution_wght = 'block'<br> , ew_boundary_type = 'closed'<br> , ns_boundary_type = 'closed'<br> /</p> <p>&tracer_nml<br> tr_iage = .false.<br> , restart_age = .false.<br> , tr_pond = .false.<br> , restart_pond = .false.<br> /</p> <p>&ice_nml<br> kitd = 1<br> , kdyn = 1<br> , ndte = 900<br> , kstrength = 0<br> , Pstar = 27.5e3<br> , floediam = 300.0<br> , hfrazilmin = 0.05 <br> , iceruf = 0.0004<br> , krdg_partic = 1<br> , krdg_redist = 1<br> , advection = 'remap'<br> , heat_capacity = .true.<br> , shortwave = 'default'<br> , albedo_type = 'default'<br> , albicev = 0.85<br> , albicei = 0.40<br> , albsnowv = 0.98<br> , albsnowi = 0.70 <br> , R_ice = 0.<br> , R_pnd = 0.<br> , R_snw = 0.<br> , atmbndy = 'default'<br> , fyear_init = 1000<br> , ycycle = 1<br> , atm_data_format = 'not_used_at_cmc'<br> , atm_data_type = 'cmc'<br> , atm_data_dir = 'not_used_at_cmc'<br> , calc_strair = .true.<br> , calc_Tsfc = .true.<br> , precip_units = 'mks'<br> , Tfrzpt = 'nonlin'<br> , update_ocn_f = .true.<br> , oceanmixed_ice = .false.<br> , oceanmixed_frc = .false.<br> , ocn_data_format = 'not_used_at_cmc'<br> , sss_data_type = 'cmcint'<br> , sst_data_type = 'cmcint'<br> , ocn_data_dir = 'not_used_at_cmc'<br> , oceanmixed_file = 'not_used_at_cmc'<br> , restore_sst = .false.<br> , trestore = 0<br> , dirassm_ice = .false.<br> , restore_ice = .false.<br> , restore_nul = .false.<br> , l_thermo = .true.<br> , l_dynmcs = .true.<br> , z0io = 0.0165<br> , l_dragio_r = .false.<br> , l_basalstress = .true.<br> , k1 = 8.0<br> , k2 = 15.0<br> , u0 = 5e-5<br> , CC = 20.0<br> , Ktens = 0.05<br> , e_ratio = 1.4<br> /</p> <p>&namcmc_nml<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! CMC grid parameters (cmc_ln_gemgrid = T ==> grtyp=Z, else grtyp=X)<br> ! The buffer zone is not output <br> ! cmc_ln_bdyopn=T ==> open boundaries including buffer zone<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> cmc_ln_gemgrid = .false.<br> , cmc_ln_nemogrid = .true.<br> , cmc_nbuff_zone = 0<br> , cmc_ln_bdyopn = .false.<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! CMC atmospheric forcing parameters<br> cmc_dt_frcd = -9<br> , cmc_dt_prec = -9<br> , cmc_ln_intrp = .true.<br> , cmc_dateo = '20011001.000000' <br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! Input varables ! Long name (doc only) ! nomvar ! Constant (C) or Field (F) !<br> ! ...! Value (if C) ! Binary (B) or Integer (I) (if F) !<br> ! ...! ip1_1 ! ip1_2 ! scaling ! offset !<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! Grid ...<br> cmc_ulat = 'u-Lat(deg)','ULAT','F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_ulon = 'u-Lon(deg)','ULON','F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_tlat = 't-Lat(deg)','TLAT','F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_tlon = 't-Lon(deg)','TLON','F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_kmt = 'Mask (0-1)','KMT', 'F',-999.0,'I',-1 ,-1 , 1., 0.<br> cmc_hte = 'HTE (m) ','HTE' ,'F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_htn = 'HTN (m) ','HTN' ,'F',-999.0,'B',-1 ,-1 , 1., 0.<br> ! Ice-ocean state ...<br> cmc_ln_gslcat = .false.<br> cmc_ln_wmocat = .false.<br> cmc_ln_avgcat = .true.<br> cmc_ln_itdspr = .true.<br> cmc_gl = 'GL de CMC ','GL' ,'F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_i8 = 'I8 de CMC ','I8' ,'F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_uice0 = 'U ice Vel.','null','C', 0.0,' ',-999999999,-999999999, -999., -999.<br> cmc_vice0 = 'V ice Vel.','null','C', 0.0,' ',-999999999,-999999999, -999., -999.<br> ! Ocean forcing ...<br> cmc_ln_frcsss = .false.<br> cmc_ln_frcsst = .false.<br> cmc_ln_frcuvo = .false.<br> ! Atm forcing ... (for ice I. C. internal temp override by restart)<br> cmc_tair = 'Air temp ','TT', 'C', 273.15,' ',999999999,999999999, 999., 999.<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! Mixed layer parameters<br> cmc_frzeps = 0.0005<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! Output parameters (cmc_ofmt='F' or 'H', Forecast or Hindcast style)<br> cmc_ofmt = 'H'<br> cmc_npako = -32<br> cmc_ln_stdrsti = .true.<br> cmc_ln_stdrsto = .true.<br> cmc_npakri = -64<br> cmc_npakro = -64<br> cmc_etiket = 'CREG_HCST'<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> /</p> <p>&icefields_nml<br> f_tmask = .false.<br> , f_tarea = .false.<br> , f_uarea = .false.<br> , f_dxt = .false.<br> , f_dyt = .false.<br> , f_dxu = .false.<br> , f_dyu = .false.<br> , f_HTN = .false.<br> , f_HTE = .false.<br> , f_ANGLE = .false.<br> , f_ANGLET = .false.<br> , f_bounds = .false.<br> , f_hi = .true.<br> , f_hs = .false. <br> , f_Tsfc = .false. <br> , f_aice = .true. <br> , f_uvel = .false. <br> , f_vvel = .false. <br> , f_fswdn = .false. <br> , f_flwdn = .false.<br> , f_snow = .false. <br> , f_snow_ai = .false. <br> , f_rain = .false. <br> , f_rain_ai = .false. <br> , f_sst = .false. <br> , f_sss = .false. <br> , f_uocn = .false. <br> , f_vocn = .false. <br> , f_frzmlt = .false.<br> , f_fswfac = .false.<br> , f_fswabs = .false. <br> , f_fswabs_ai = .false. <br> , f_albsni = .false. <br> , f_alvdr = .false.<br> , f_alidr = .false.<br> , f_albice = .false.<br> , f_albsno = .false.<br> , f_albpnd = .false.<br> , f_coszen = .false.<br> , f_flat = .false. <br> , f_flat_ai = .false. <br> , f_fsens = .false. <br> , f_fsens_ai = .false. <br> , f_flwup = .false. <br> , f_flwup_ai = .false. <br> , f_evap = .false. <br> , f_evap_ai = .false. <br> , f_Tair = .false. <br> , f_Tref = .false. <br> , f_Qref = .false.<br> , f_congel = .false. <br> , f_frazil = .false. <br> , f_snoice = .false. <br> , f_meltt = .false.<br> , f_meltb = .false.<br> , f_meltl = .false.<br> , f_fresh = .false.<br> , f_fresh_ai = .false.<br> , f_fsalt = .false.<br> , f_fsalt_ai = .false.<br> , f_fhocn = .false. <br> , f_fhocn_ai = .false. <br> , f_fswthru = .false. <br> , f_fswthru_ai = .false. <br> , f_fsurf_ai = .false.<br> , f_fcondtop_ai = .false.<br> , f_fmeltt_ai = .false. <br> , f_strairx = .false. <br> , f_strairy = .false. <br> , f_strtltx = .false. <br> , f_strtlty = .false. <br> , f_strcorx = .false. <br> , f_strcory = .false. <br> , f_strocnx = .false. <br> , f_strocny = .false. <br> , f_strintx = .false. <br> , f_strinty = .false.<br> , f_strength = .false.<br> , f_divu = .false.<br> , f_shear = .false.<br> , f_sig1 = .false. <br> , f_sig2 = .false. <br> , f_sigI = .false. <br> , f_dvidtt = .false. <br> , f_dvidtd = .false. <br> , f_daidtt = .false.<br> , f_daidtd = .false. <br> , f_mlt_onset = .false.<br> , f_frz_onset = .false.<br> , f_dardg1dt = .false.<br> , f_dardg2dt = .false.<br> , f_dvirdgdt = .false.<br> , f_opening = .false.<br> , f_hisnap = .false.<br> , f_aisnap = .false.<br> , f_trsig = .false.<br> , f_icepresent = .false.<br> , f_iage = .false.<br> , f_aicen = .false.<br> , f_vicen = .false.<br> , f_fsurfn_ai = .false.<br> , f_fcondtopn_ai = .false.<br> , f_fmelttn_ai = .false.<br> , f_flatn_ai = .false.<br> , f_apondn = .false.<br> , f_tau_bu = .false.<br> , f_tau_bv = .false.<br> /</p> <p> </p>
2SPT Test Time Series: Quality Control for Community Based Sea Ice Model Development
<p>2SPT Test Time Series: Quality Control for Community Based Sea Ice Model Development</p> <p>------------------------------------------------------------------------------------</p> <p>This dataset comes from Regional Arctic System Model (RASM) simulations by Andrew F. Roberts at Naval Postgraduate School, described and used in Figure 3 in the manuscript:</p> <p>Roberts, Hunke, Allard, Bailey, Craig, Lemieux and Turner (2018): Quality Control for Community Based Sea Ice Model Development.</p> <p>Files for the series in Figure 3 as they appear in that paper to demonstrate the Two-Stage Paired Thickness Test:</p> <p>Figure 3a:</p> <p>R1009RBRcevp01a.cice.h.hi.1996_2000.North_Pole.nc</p> <p>R1009RBRcevp01a_2.cice.h.hi.1996_2000.North_Pole.nc</p> <p> </p> <p>Figure 3b:</p> <p>R1009RBRcevp01a.cice.h.hi.1996_2000.North_Pole.nc</p> <p>R1009RBRceap01a.cice.h.hi.1996_2000.North_Pole.nc</p> <p> </p> <p>Figure 3c:</p> <p>R1009RBRcevp01a.cice.h.hi.1996_2000.North_Bathurst_Island.nc</p> <p>R1009RBRceap01a.cice.h.hi.1996_2000.North_Bathurst_Island.nc</p> <p> </p> <p>Where the name "evp" or "eap" in the file name corresponds to the EVP and EAP simulations as described in the manuscript. These are selected timeseries from 1996 to 2000 from the RASM simasimulations described in section 2 of the above manuscript. CICE namelist settings are identical to the description for the dataset:</p> <p>Roberts, A. (2018). RASM simulations: Quality Control for Community Based Sea Ice Model Development [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1308236</p> <p> </p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Model Simulation Data
<p>WRF-Chem simulated daily mean PM2.5 concentrations for:</p> <p>1) with fires </p> <p>2) without fires</p> <p>simulations. </p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Purple Air data
<p>Daily mean PM2.5 concentrations collected by Purple Air sensors between 2023-08-16 and 2023-12-01. Concentrations have been RH adjusted using the Nilson et al (2022) adjustment. </p>
A Model-Driven, Metrics-Based Approach to Assessing Support for Quality Aspects in MLOps System Architectures: Replication Package
<div> <p><strong>Title:</strong> A Model-Driven, Metrics-Based Approach to Assessing Support for Quality Aspects in MLOps System Architectures: Replication Package</p> <p><strong>Authors:</strong> Stephen John Warnett; Uwe Zdun</p> <p><strong>About:</strong> This is the replication package artefact for the paper entitled "A Model-Driven, Metrics-Based Approach to Assessing Support for Quality Aspects in MLOps System Architectures".</p> <p><strong>Paper Abstract:</strong> In machine learning (ML) and machine learning operations (MLOps), automation serves as a fundamental pillar, streamlining the deployment of ML models and representing an architectural quality aspect. Support for automation is especially relevant when dealing with ML deployments characterised by the continuous delivery of ML models. Taking automation in MLOps systems as an example, we present novel metrics that offer reliable insights into support for this vital quality attribute, validated by ordinal regression analysis. Our method introduces novel, technology-agnostic metrics aligned with typical Architectural Design Decisions (ADDs) for automation in MLOps. Through systematic processes, we demonstrate the feasibility of our approach in evaluating automation-related ADDs and decision options. Our approach can itself be automated within continuous integration/continuous delivery pipelines. It can also be modified and extended to evaluate any relevant architectural quality aspects, thereby assisting in enhancing compliance with non-functional requirements and streamlining development, quality assurance and release cycles.</p> </div>
Data from: Voice efficiency for different voice qualities combining experimentally derived sound signals and numerical modeling of the vocal tract
<p>This dataset contains Stereo-Lithographic (STL) surface models of a human vocal tract, derived Finite-Element-Models, numerical results, and scripts for analyzing these results and (re-)running the computation.</p> <p> </p> <p><strong>In the main folder, this dataset contains:</strong></p> <p>1) Python files (*fig*.py) for the creation of figures and tables (*tab*.py)</p> <p>2) Python files (*.py) for analyzing Finite-Element (FE) calculations (x_resonances.py, x_libs.py, x_fem2excel.py)</p> <p>3) Python-files (*.py) for analyzing stl-data (x_analyzeSTL.py)</p> <p>4) Python files (*.py) for deriving Infinite-Impulse-Response (IIR) filter and their impulse responses (x_IIR.py)</p> <p>5) Excel files (*.xlsx) containing Volume-velocity-transfer-functions (Vlg.xlsx), Pressure-transfer-functions at the lips (Hlg.xlsx), and the glottis (Hgg.xlsx) based on FE, the sound spectra of audio signals (sound_spectra.xlsx), the polynomials describing the IIR (IIR_polynomial.xlsx) and their impulse responses (IIR_impulse_responses.xlsx), and glottal waveforms (glottal_waveform.xlsx) and spectra (glottal_spectra.xlsx)</p> <p>6) Several figures (*.pdf)</p> <p> </p> <p><strong>In folder „x_fenics/x_Subject-1“ (and sub-folders), this data set contains:</strong></p> <p>1) Surface models of the human vocal tract for different voice qualities (glottis.stl, wall.stl, lips.stl)</p> <p>2) Sub-volumes of the vocal tract cavities (*ET.stl, *HPl.stl, *HPu.stl, *OPf.stl, *OPr.stl, *SP.stl, *.VV.stl)</p> <p>3) Derived gmsh volume meshes (*.msh) (www.gmsh.info)</p> <p>3) Derived volume models applicable to FE-Solvers (*.h5, *.xdmf)</p> <p>4) Results of the FE-calculation (*pvtf*.txt, *vvtf*.txt, *pglottis*.txt)</p> <p>5) Formant frequencies computed by inverse filtering (*.for)</p> <p> </p> <p><strong>In folder „x_fenics/x_misc“ the data set contains:</strong></p> <p>1) Python-files (*.py) for (re-)running the calculations using the FE-Method</p>
Diet quality impairs male and female reproductive performance and affects the opportunity for selection in an insect model
<p><span>Environmental factors can have profound effects on the strength and direction of selection and recent studies suggest that such environment-dependent selection can be sex-specific. Sexual selection theory predicts that male fitness is more condition dependent compared to female fitness, suggesting that male fitness is more sensitive to environmental stress. However, our knowledge about the effect of environmental factors on sex-specific reproductive performance and on sex differences in the opportunity for selection is still very limited. In the present study, we investigated the sex-specific effects of diet quality (yeast deprivation and flour type) in the red flour beetle <em>Tribolium castaneum</em>. Specifically, we manipulated yeast supplementation in wheat and whole-wheat flour in competition assays allowing to test for sex-specific effects of food quality (i) on reproductive success and (ii) on the opportunity for selection. Our data show that yeast deprivation in wheat flour had significantly negative effects on body mass and reproductive success of both sexes, while high quality flour (whole-wheat flour) was able to buffer the negative impact to a large extent. Importantly, our data suggest no sex-specific effect of dietary stress on reproductive success because the magnitude of the negative effect of yeast deprivation was similar for males and females. Moreover, our study demonstrates that low food quality inflated the opportunity for selection and did not differ between sexes neither under benign nor stressful dietary conditions. We discuss the implications of our findings for the adaptation to stressful environments.</span></p>
Non-Conventional Time Domain (TD)-NMR Approaches for Food Quality: Case of Gelatin-Based Candies as a Model Food
<p>The TD-NMR technique mostly involves the use of T1 (spin-lattice) and T2 (spin-spin)<br> relaxation times to explain the changes occurring in food systems. However, these relaxation times<br> are affected by many factors and might not always be the best indicators to work with in food related<br> TD-NMR studies. In this study, the non-conventional TD-NMR approaches of Solid Echo<br> (SE)/Magic Sandwich Echo (MSE) and Spin Diffusion in food systems were used for the first time. Soft<br> confectionary gelatin gels were formulated and conventional (T1) and non-conventional (SE, MSE and<br> Spin Diffusion) TD-NMR experiments were performed. Corn syrups with different glucose/fructose<br> compositions were used to prepare the soft candies. Hardness, °Brix (°Bx), and water activity (aw)<br> measurements were also conducted complementary to NMR experiments. Relaxation times changed<br> (p < 0.05) with respect to syrup type with no obvious trend. SE/MSE experiments were performed to<br> calculate the crystallinity of the samples. Samples prepared with fructose had the lowest crystallinity<br> values (p < 0.05). Spin Diffusion experiments were performed by using Goldman–Shen pulse sequence<br> and the interface thickness (d) was calculated. Interface thickness values showed a wide range of<br> variation (p < 0.05). Results showed that non-conventional NMR approaches had a high potential to be<br> utilized in food systems for quality control purposes.</p>
Supplemental data for: Longitudinal, multi-platform metagenomics yields a high-quality genomic catalog and guides an in vitro model for cheese communities
<p><span>Microbiomes are intricately intertwined with human health, geochemical cycles, and food production. While many microbiomes of interest are highly complex and experimentally intractable, cheese rind microbiomes have proven powerful model systems for the study of microbial interactions. To provide a more comprehensive view of the genomic potential and temporal dynamics of cheese rind communities, we combine longitudinal, multi-platform metagenomics of three ripening washed-rind cheeses with whole genome sequencing of community isolates. Sequencing-based approaches revealed a highly reproducible microbial succession in each cheese, co-existence of closely related <em>Psychrobacter</em> species, and enabled the prediction of plasmid and phage diversity and their host associations. Combined with culture-based approaches, we established a genomic catalog and a paired 16-member in vitro washed rind cheese system. The combination of multi-platform metagenomic time-series data and an <em>in vitro</em> model provides a rich resource for further investigation of cheese rind microbiomes both computationally and experimentally. </span></p>
Data used to create figures and tables in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains all simulation output and observational data of ground-based/satellite-retrieved meteorological and air quality for computing statistical metrics in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. Simulation and observational results of meteorological and air quality including four folders:</p> <p> Day_PBLH: Daily PBLH data</p> <p> Hour_air: Hourly air quality data regarding PM2.5, O3, SO2, NO2 and CO</p> <p> Hour_met: Hourly meteorological data regarding T2, Q2, RH2, WS10 and precipitation</p> <p> Hour_radiation: Hourly surface radiation data</p> <p>2. Simulation and satellite-retrieved results of meteorological and air quality including nine folders:</p> <p> AOD: Yearly and seasonal AOD data</p> <p> CF: Yearly and seasonal CF data</p> <p> CO: Yearly and seasonal CO data</p> <p> LWP: Yearly and seasonal LWP data</p> <p> NO2: Yearly and seasonal NO2 data</p> <p> O3: Yearly and seasonal O3 data</p> <p> Precipitation: Yearly and seasonal precipitation data</p> <p> Radiation: Yearly and seasonal radiation data</p> <p> SO2: Yearly and seasonal SO2 data</p>
Data used to simulations in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains input data of simulations by WRF-CMAQ, WRF-Chem and WRF-CHIMERE in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. WRF-CMAQ input data including emission, ICs and lateral BCs of meteorology and air quality:</p> <p>YYYYMM.zip represents the input data for each month for simulations. Due to the large size of the compressed file containing input data each month, there may be interruptions when uploading it to Zenodo. Therefore, we will split each compressed file into 50MB. If users want to browse the file, they can download the segmented files, and then merge them into the YYYYMM.zip file using the Linux command line "unzip 'YYYYMM.zip.*' -d combined"</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.