One of the challenges in studying desert dust aerosol along with its numerous interactions and impacts is the paucity of direct in situ measurements, particularly in the areas most affected by dust storms. Satellites typically provide column-integrated aerosol measurements, but observationally constrained continuous 3D dust fields are needed to assess dust variability, climate effects and impacts upon a variety of socio-economic sectors. Here, we present a high-resolution regional reanalysis data set of desert dust aerosols that covers Northern Africa, the Middle East and Europe along with the Mediterranean Sea and parts of central Asia and the Atlantic and Indian oceans between 2007 and 2016. The horizontal resolution is 0.1^∘ latitude × 0.1^∘ longitude in a rotated grid, and the temporal resolution is 3 h. The reanalysis was produced using local ensemble transform Kalman filter (LETKF) data assimilation in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) developed at the Barcelona Supercomputing Center (BSC). The assimilated data are coarse-mode dust optical depth retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue Level 2 products. The reanalysis data set consists of upper-air variables (dust mass concentrations and the extinction coefficient), surface variables (dust deposition and solar irradiance fields among them) and total column variables (e.g. dust optical depth and load). Some dust variables, such as concentrations and wet and dry deposition, are expressed for a binned size distribution that ranges from 0.2 to 20 µm in particle diameter. Both analysis and first-guess (analysis-initialized simulation) fields are available for the variables that are diagnosed from the state vector. A set of ensemble statistics is archived for each output variable, namely the ensemble mean, standard deviation, maximum and median. The spatial and temporal distribution of the dust fields follows well-known dust cycle features controlled by seasonal changes in meteorology and vegetation cover. The analysis is statistically closer to the assimilated retrievals than the first guess, which proves the consistency of the data assimilation method. Independent evaluation using Aerosol Robotic Network (AERONET) dust-filtered optical depth retrievals indicates that the reanalysis data set is highly accurate (mean bias = −0.05, RMSE = 0.12 and r = 0.81 when compared to retrievals from the spectral de-convolution algorithm on a 3-hourly basis). Verification statistics are broadly homogeneous in space and time with regional differences that can be partly attributed to model limitations (e.g. poor representation of small-scale emission processes), the presence of aerosols other than dust in the observations used in the evaluation and differences in the number of observations among seasons. Such a reliable high-resolution historical record of atmospheric desert dust will allow a better quantification of dust impacts upon key sectors of society and economy, including health, solar energy production and transportation. The reanalysis data set (Di Tomaso et al., 2021) is distributed via Thematic Real-time Environmental Distributed Data Services (THREDDS) at BSC and is freely available at http://hdl.handle.net/21.12146/c6d4a608-5de3-47f6-a004-67cb1d498d98 (last access: 10 June 2022).

中文翻译：

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北非、中东和欧洲沙漠尘埃气溶胶的MONARCH高分辨率再分析（2007-2016）

研究沙漠沙尘气溶胶及其众多相互作用和影响的挑战之一是缺乏直接的原位测量，特别是在受沙尘暴影响最严重的地区。卫星通常提供柱集成气溶胶测量，但需要观察受限的连续 3D 尘埃场来评估尘埃变异性、气候影响以及对各种社会经济部门的影响。在这里，我们展示了 2007 年至 2016 年期间覆盖北非、中东和欧洲以及地中海和中亚部分地区以及大西洋和印度洋的沙漠尘埃气溶胶高分辨率区域再分析数据集。水平分辨率是 0.1 ^∘纬度 ×  0.1 ^∘旋转网格中的经度，时间分辨率为 3  h. 再分析是在巴塞罗那超级计算中心 (BSC) 开发的多尺度在线非静水大气化学模型 (MONARCH) 中使用局部集合变换卡尔曼滤波器 (LETKF) 数据同化产生的。同化数据是从中分辨率成像光谱仪 (MODIS) 深蓝 2 级产品中检索到的粗模尘埃光学深度。再分析数据集由高空变量（尘埃质量浓度和消光系数）、地表变量（其中尘埃沉降和太阳辐照场）和总柱变量（如尘埃光学深度和载荷）组成。一些粉尘变量，例如浓度和干湿沉降，表示为 0.2 到 20  µm的分级粒度分布在粒径。分析和初步猜测（分析初始化模拟）字段都可用于从状态向量诊断的变量。为每个输出变量存档一组整体统计数据，即整体均值、标准差、最大值和中值。沙尘场的时空分布遵循众所周知的由气象和植被覆盖的季节变化控制的沙尘循环特征。该分析在统计上比第一次猜测更接近同化检索，这证明了数据同化方法的一致性。使用气溶胶机器人网络 (AERONET) 灰尘过滤光学深度检索的独立评估表明，再分析数据集是高度准确的（平均偏差 =  - 0.05，RMSE =  0.12 和r  =  0.81，与以 3 小时为基础的光谱反卷积算法检索相比）。验证统计在空间和时间上大体上是同质的，区域差异可部分归因于模型限制（例如小规模排放过程的代表性不足）、评估中使用的观测中存在除尘埃以外的气溶胶以及季节之间的观察次数。这种可靠的高分辨率大气沙漠尘埃历史记录将有助于更好地量化尘埃对社会和经济关键部门的影响，包括健康、太阳能生产和运输。再分析数据集（Di Tomaso et al., 2021）通过 BSC 的主题实时环境分布式数据服务 (THREDDS) 分发，可在 http://hdl.handle.net/21.12146/c6d4a608-5de3-47f6-a004-67cb1d498d98免费获得（最后访问时间：2022 年 6 月 10 日） .