Tropospheric bromine monoxide radical vertical profiles and their differential slant column densities (DSCD) dataset based on forward simulations with an atmospheric radiative transfer model

This dataset was generated through simulation using the atmospheric radiative transfer model SCIATRAN v2.2, which includes three typical forms of BrO vertical profiles (exponential, Boltzmann, and Gaussian) and their corresponding differential slant column concentration (DSCD) data under different aerosol and geometric observation parameters. The profile generation is based on a parameter range obtained from literature research, covering different boundary layer heights, peak positions, and distribution widths, to simulate various distribution scenarios of BrO in the real atmosphere. Each set of profile data is combined with parameters such as aerosol extinction coefficient vertical profile, solar zenith angle (SZA), observed zenith angle (VZA), relative azimuth angle (RAA), etc., and input into the SCIATRAN model. By numerically solving the radiative transfer equation (RTE), the corresponding DSCD value under geometric conditions is calculated. The dataset contains approximately 50 million sets of samples, each containing a set of input parameters (aerosol extinction coefficient vertical profile, SZA, RAA, and a set of DSCD sequences) and an output data (BrO vertical profile). The data is stored in Parquet format, with each file containing multiple batches of data records, including column labels such as SZA, RAA, DSCD, Aerosol, BrO, etc. There were no missing values in the data, and all samples underwent integrity verification. Due to the fact that the data is generated through simulation, its errors mainly come from the numerical calculation errors of the radiative transfer model and the uncertainty of the parameterization scheme. It is suitable for machine learning model training and atmospheric composition inversion algorithm validation. This dataset is suitable for fields such as atmospheric remote sensing, machine learning inversion algorithm development, and BrO vertical distribution research. It is recommended to use Pandas, PyArrow, or PySpark in Python to read Parquet files. Relevant tools can be obtained through Anaconda or PyPI official channels.

本数据集通过大气辐射传输模型SCIATRAN v2.2仿真生成，包含三类典型的一氧化溴（BrO）垂直廓线形式——指数型、玻尔兹曼型与高斯型，以及不同气溶胶和几何观测参数下对应的差分斜柱浓度（differential slant column concentration, DSCD）数据。廓线生成基于文献调研得到的参数范围，涵盖不同边界层高度、峰值位置与分布宽度，以模拟真实大气中BrO的多种分布场景。每组廓线数据结合气溶胶消光系数垂直廓线、太阳天顶角（solar zenith angle, SZA）、观测天顶角（viewing zenith angle, VZA）、相对方位角（relative azimuth angle, RAA）等参数，输入至SCIATRAN模型中。通过数值求解辐射传输方程（radiative transfer equation, RTE），即可计算得到对应几何条件下的DSCD值。本数据集包含约5000万组样本，每组样本包含一组输入参数（气溶胶消光系数垂直廓线、SZA、RAA与一组DSCD序列）与一组输出数据（BrO垂直廓线）。数据以Parquet格式存储，每个文件包含多批次数据记录，列标签涵盖SZA、RAA、DSCD、Aerosol、BrO等内容。数据集无缺失值，所有样本均经过完整性校验。由于数据通过仿真生成，其误差主要来源于辐射传输模型的数值计算误差与参数化方案的不确定性。本数据集适用于机器学习模型训练与大气成分反演算法验证。本数据集可应用于大气遥感、机器学习反演算法开发以及BrO垂直分布研究等领域。建议使用Python中的Pandas、PyArrow或PySpark工具读取Parquet文件，相关工具可通过Anaconda或PyPI官方渠道获取。