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FIGURE 6. Male sternite IX in Male secondary sexual characters resolve taxonomic uncertainty: five new species and a review of the formerly monotypic rove beetle genus Mimosticus Sharp (Coleoptera: Staphylinidae: Staphylininae)

FIGURE 6. Male sternite IX of Mimosticus viridipennis, Sharp (A), M. tenuiformis Brunke and Solodovnikov (B), M. aeneipennis Brunke and Solodovnikov (C), M. sharpi Brunke and Solodovnikov (D), M. pseudosharpi Brunke and Solodovnikov (E) and M. latens Brunke and Solodovnikov (F). Scale bars = 0.2 mm.

FIGURE 2. Antennomeres 4–11 in Male secondary sexual characters resolve taxonomic uncertainty: five new species and a review of the formerly monotypic rove beetle genus Mimosticus Sharp (Coleoptera: Staphylinidae: Staphylininae)

FIGURE 2. Antennomeres 4–11: Mimosticus viridipennis, Sharp (A), M. tenuiformis Brunke and Solodovnikov (B), M. aeneipennis Brunke and Solodovnikov (C) and M. sharpi Brunke and Solodovnikov (D). Ventral forebody of M. tenuiformis (E). Mesotrochanter and mesofemur of M. tenuiformis (F). Hindwing of M. aeneipennis, vein MP4 fused to CuA (G). Scale bars = 1 mm.

FIGURE 10 in Male secondary sexual characters resolve taxonomic uncertainty: five new species and a review of the formerly monotypic rove beetle genus Mimosticus Sharp (Coleoptera: Staphylinidae: Staphylininae)

FIGURE 10. Distribution of Mimosticus aeneipennis Brunke and Solodovnikov, M. latens Brunke and Solodovnikov, M pseudosharpi Brunke and Solodovnikov, and M. viridipennis Sharp (A); and M. tenuiformis Brunke and Solodovnikov, and M. sharpi (B).

FIGURE 1 in Male secondary sexual characters resolve taxonomic uncertainty: five new species and a review of the formerly monotypic rove beetle genus Mimosticus Sharp (Coleoptera: Staphylinidae: Staphylininae)

FIGURE 1. Dorsal habitus of Mimosticus viridipennis, Sharp (A), M. tenuiformis Brunke and Solodovnikov (B), M. aeneipennis Brunke and Solodovnikov (C) and M. sharpi Brunke and Solodovnikov (D). Scale bars = 2 mm.

FIGURE 8 in Male secondary sexual characters resolve taxonomic uncertainty: five new species and a review of the formerly monotypic rove beetle genus Mimosticus Sharp (Coleoptera: Staphylinidae: Staphylininae)

FIGURE 8. Aedeagus of Mimosticus viridipennis Sharp (A–D), M. tenuiformis Brunke and Solodovnikov (E) and M. sharpi Brunke and Solodovnikov (F). Parameral view (A–B, E–F), lateral view (C–D). Internal sac not everted (A, C, E, F), internal sac everted (B, D). Scale bars = 0.5 mm.

FIGURE 4 in Male secondary sexual characters resolve taxonomic uncertainty: five new species and a review of the formerly monotypic rove beetle genus Mimosticus Sharp (Coleoptera: Staphylinidae: Staphylininae)

FIGURE 4. Male sternite VIII of Mimosticus viridipennis, Sharp (A), M. tenuiformis Brunke and Solodovnikov (B), M. aeneipennis Brunke and Solodovnikov (C), M. sharpi Brunke and Solodovnikov (D), M. pseudosharpi Brunke and Solodovnikov (E) and M. latens Brunke and Solodovnikov (F). Scale bars = 0.5 mm.

FIGURE 3 in Male secondary sexual characters resolve taxonomic uncertainty: five new species and a review of the formerly monotypic rove beetle genus Mimosticus Sharp (Coleoptera: Staphylinidae: Staphylininae)

FIGURE 3. Forebody of Mimosticus viridipennis, Sharp (A), M. tenuiformis Brunke and Solodovnikov (B), M. aeneipennis Brunke and Solodovnikov (C) and M. sharpi Brunke and Solodovnikov (D). Scale bars = 1 mm. a—anterior frontal puncture, b—oculomarginal puncture, c—posterior frontal puncture, d—vertical puncture.

FIGURE 7. Male tergite X in Male secondary sexual characters resolve taxonomic uncertainty: five new species and a review of the formerly monotypic rove beetle genus Mimosticus Sharp (Coleoptera: Staphylinidae: Staphylininae)

FIGURE 7. Male tergite X of Mimosticus viridipennis, Sharp (A), M. tenuiformis Brunke and Solodovnikov (B), M. aeneipennis Brunke and Solodovnikov (C), M. sharpi Brunke and Solodovnikov (D), M. pseudosharpi Brunke and Solodovnikov (E) and M. latens Brunke and Solodovnikov (F). Scale bars = 0.5 mm.

opencc-by-4.0Sep 2024View details →

Data of paper "Grid ,Hydrodynamic boundary and Uncertainty analysis of 2D-SWEs in the context of digital twins: Taking numerical simulation of river networksas an example"

论文数据 “数字孪生背景下2D-SWEs的网格、水动力边界和不确定性分析:以河流网络数值模拟为例”

opencc-by-4.0Dec 2020View details →

Data for MWR article "The consequences of surface-exchange coefficient uncertainty on an otherwise highly predictable major hurricane"

This repository contains the model code, initial and boundary conditions and the namelist settings needed to reproduce the results of "The consequences of surface-exchange coefficient uncertainty on an otherwise highly predictable major hurricane".

opencc-by-4.0Oct 2023View details →

Code for ESS publication - Quantifying the uncertainty of ice-crystal-related parameters to simulated winter precipitation over the Korean Peninsula

In this repository, we include the source codes used in the ESS publication "Quantifying the uncertainty of ice-crystal-related parameters to simulated winter precipitation over the Korean Peninsula"There are WDM6 codes and parameter sets in "Model_codes" for WRF simulation, model output files in "Model_outputs", AWS datas in "AWS", Scripts for calculating statistical values in a table in "Table", and "Figures" has a scripts for the figure of the manuscript.In "Model_codes", the text file starting with LHS is 50 parameters sets generated by the Latin hypercube sampling method, and the text files starting with the case are the set of parameters used for the SEN experiment for each case. There are two WDM6 codes, the CTL code uses the parameter set in Table 1 as CTL, and the LHS code uses the 50 parameters set in the text file.In "Figures", there is a script corresponding to each figure of the paper.

Evaluation and uncertainty analysis of the land surface hydrology in LS3MIP models over China

The attached is the dataset assoicated with the paper titled "Evaluation and uncertainty analysis of the land surface hydrology in LS3MIP models over China" which was submitted to Journal of Earth and Space Science.The Land Surface, Snow and Soil moisture Model Intercomparison Project (LS3MIP) offers valuable land surface hydrology products from the land modules of current Earth System Models (ESMs). In this paper, historical LS3MIP hydrological variables including precipitation (PR), evapotranspiration (ET), soil moisture (SM), total runoff (Ro), and snow cover fraction (SCF) were extensively evaluated with various high-quality reference datasets over Chinese mainland. The six ESMs in LS3MIP were driven by four meteorological forcing datasets. The results indicated that the LS3MIP multi-model means (MMEs) of most variables are underestimated overall, while they show high spatial consistency in term of linear trends, with the percentage area ranging 56% ~ 85% between simulations and reference datasets. After computing and ranking multi statistical metrics (bias, correlation coefficient, normalized standard deviation, and unbiased root-mean-square biases), it is found that the CESM2 model produces the best performance of land surface hydrological variables, while as the meteorological forcing dataset GSWP3 exhibits the highest quality. Furthermore, the analysis of variance method (ANOVA) was then used to trace sources of the uncertainty of the LS3MIP hydrological variables for 1900–2012 (1948–2012 for Ro). In ANOVA, the simulation uncertainties may be decomposed into three sources: model, atmospheric forcing datasets and their interactions. In LS3MIP historical hydrological variables over China, model uncertainty is the dominant factor overall although it shows regional differences, and the dependence of uncertainty on the model differs among hydrological regimes. This highlights the urgent requirements to improve the land surface model representation in future research.

MME-only models trained with clean data for JAMES paper "Machine-learned uncertainty quantification is not magic"

This tar file contains all 100 trained models in the MME-only ensemble from Experiment 1 (i.e., those trained with clean data, not with lightly perturbed data).  To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.

MME-only models trained with lightly perturbed data for JAMES paper "Machine-learned uncertainty quantification is not magic"

This tar file contains all 100 trained models in the MME-only ensemble from Experiment 2 (i.e., those trained with lightly perturbed data).  To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.

MME/CRPS models trained with clean data for JAMES paper "Machine-learned uncertainty quantification is not magic"

This tar file contains all 100 trained models in the MME/CRPS ensemble from Experiment 1 (i.e., those trained with clean data, not with lightly perturbed data).  To pare the ensemble down to 50 models, we randomly select 50.  To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.

MME/CRPS models trained with lightly perturbed data for JAMES paper "Machine-learned uncertainty quantification is not magic"

This tar file contains all 100 trained models in the MME/CRPS ensemble from Experiment 2 (i.e., those trained with lightly perturbed data).  To pare the ensemble down to 50 models, we randomly select 50.  To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.

Input data and some models (all except multi-model ensembles) for JAMES paper "Machine-learned uncertainty quantification is not magic"

The tar file contains two directories: data and models.  Within "data," there are 4 subdirectories: "training" (the clean training data -- without perturbations), "training_all_perturbed_for_uq" (the lightly perturbed training data), "validation_all_perturbed_for_uq" (the moderately perturbed validation data), and "testing_all_perturbed_for_uq" (the heavily perturbed validation data).  The data in these directories are unnormalized.  The subdirectories "training" and "training_all_perturbed_for_uq" each contain a normalization file.  These normalization files contain parameters used to normalize the data (from physical units to z-scores) for Experiment 1 and Experiment 2, respectively.  To do the normalization, you can use the script normalize_examples.py in the code library (ml4rt) with the argument input_normalization_file_name set to one of these two file paths.  The other arguments should be as follows:--uniformize=1--predictor_norm_type_string="z_score"--vector_target_norm_type_string=""--scalar_target_norm_type_string="" Within the directory "models," there are 6 subdirectories: for the BNN-only models trained with clean and lightly perturbed data, for the CRPS-only models trained with clean and lightly perturbed data, and for the BNN/CRPS models trained with clean and lightly perturbed data.  To read the models into Python, you can use the method neural_net.read_model in the ml4rt library.

WAM-IPE uncertainty quantification data

In each file, one or several specific WAM-IPE outputs (quantities of interest, QoI) and the associated parameters used in latent space to generate synthetic drivers. The parameters in latent space and the QoIs can be used to build polynomial chaos expansion based surrogate model.

opencc-by-4.0Jul 2023View details →

Data and code for manuscript ``Insights on the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty''

Supporting data and code for manuscript:Seroussi, H., Verjans, V., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., Van Katwyk, P., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: Insights into the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty, The Cryosphere, 17, 5197–5217, https://doi.org/10.5194/tc-17-5197-2023, 2023. It contains the code to prepare the datasets, to create the figures and the data for the analysis, and the scalar values computed for the 198 Antarctic glaciers stored by ice flow models.The files Glacier_XX contain the data to emulate the results for individual glaciers.The files Antarctica and AntarcticaWithCtrl contain the data to emulate the results for the Antarctic runs without and with the ctrl_proj experiment.The files GROUP_ICEFLOW contain the ice flow model data for all the experiments recomputed for the 198 glaciers in Antarctica.