Model output for "Numerically consistent budgets of potential temperature, momentum, and moisture in Cartesian coordinates: application to the WRF model"

These data were produced with WRFlux v1.2.1 (https://github.com/matzegoebel/WRFlux/) from a numerical simulation with the community model WRF. Simulations represent the evolution of a convective boundary layer in the atmosphere over an idealized 2D mountain ridge. The data are published in connection with the article "Numerically consistent budgets of potential temperature, momentum and moisture in Cartesian coordinates: Application to the WRF model" in "Geoscientific Model Development" (https://doi.org/10.5194/gmd-15-669-2022). Three-dimensional (x, z, t) fields of five prognostic variables are provided: Potential temperature (T), water vapor mixing ratio (Q), cross-mountain (U), along-mountain (V), and vertical windspeed (W). All fields are averaged in time (30 min averaging interval) and in the along-mountain direction y. The repository contains the following files: grid.nc : variables related to the WRF numerical grid, air density<br> [U,W,T,Q]_flux.nc : resolved and subgrid-scale fluxes<br> [U,W,T,Q]_tendency.nc : resolved and subgrid-scale tendency components<br> UVWT_MEAN.nc : averaged values of the variables themselves<br> plotting.py : python script to approximately reproduce the figures of the paper. Requires the python packages matplotlib, xarray, and netcdf4. Figure 6 in the paper cannot be accurately reproduced with these data since the original figure uses 4D (x, y, z, t) output. For details on the simulation, refer to the article.本数据集依托WRFlux v1.2.1版本（https://github.com/matzegoebel/WRFlux/），基于通用社区数值模式WRF开展数值模拟生成。模拟聚焦于理想化二维山脊上空大气对流边界层的演化过程。本数据集与发表于《Geoscientific Model Development》的论文《笛卡尔坐标系下位温、动量与水汽的数值协调收支：在WRF模式中的应用》（https://doi.org/10.5194/gmd-15-669-2022）配套发布。数据集包含5个预报变量的三维（x、z、t）场：位温（Potential temperature, T）、水汽混合比（water vapor mixing ratio, Q）、跨山脊分量风速（cross-mountain, U）、沿山脊分量风速（along-mountain, V）以及垂直风速（vertical windspeed, W）。所有场数据均沿山脊方向y与时间维度进行平均处理，时间平均间隔为30分钟。该数据集仓库包含以下文件： 1. grid.nc：与WRF数值网格相关的变量、大气密度 2. [U,W,T,Q]_flux.nc：可解析尺度与次网格尺度通量 3. [U,W,T,Q]_tendency.nc：可解析尺度与次网格尺度倾向分量 4. UVWT_MEAN.nc：各变量自身的平均值场 5. plotting.py：用于近似复现论文配图的Python脚本，需依赖matplotlib、xarray与netcdf4 Python包。由于论文图6采用了四维（x,y,z,t）输出数据，因此无法通过本数据集精确复现该图。有关本次数值模拟的详细细节，请参阅配套论文。