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首页> 外文期刊>Atmospheric chemistry and physics >Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting
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Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting

机译：集团海军气溶胶分析预测系统（eNAAPS）的发展及其应用数据同化研究试验（DART）在气溶胶预测支持中的支持下

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An ensemble-based forecast and data assimilation system has been developed for use in Navy aerosol forecasting. The system makes use of an ensemble of the Navy Aerosol Analysis Prediction System (ENAAPS) at 1 x 1A degrees, combined with an ensemble adjustment Kalman filter from NCAR's Data Assimilation Research Testbed (DART). The base ENAAPS-DART system discussed in this work utilizes the Navy Operational Global Analysis Prediction System (NOGAPS) meteorological ensemble to drive offline NAAPS simulations coupled with the DART ensemble Kalman filter architecture to assimilate bias-corrected MODIS aerosol optical thickness (AOT) retrievals. This work outlines the optimization of the 20-member ensemble system, including consideration of meteorology and source-perturbed ensemble members as well as covariance inflation. Additional tests with 80 meteorological and source members were also performed. An important finding of this work is that an adaptive covariance inflation method, which has not been previously tested for aerosol applications, was found to perform better than a temporally and spatially constant covariance inflation. Problems were identified with the constant inflation in regions with limited observational coverage. The second major finding of this work is that combined meteorology and aerosol source ensembles are superior to either in isolation and that both are necessary to produce a robust system with sufficient spread in the ensemble members as well as realistic correlation fields for spreading observational information. The inclusion of aerosol source ensembles improves correlation fields for large aerosol source regions, such as smoke and dust in Africa, by statistically separating freshly emitted from transported aerosol species. However, the source ensembles have limited efficacy during long-range transport. Conversely, the meteorological ensemble generates sufficient spread at the synoptic scale to enable observational impact through the ensemble data assimilation. The optimized ensemble system was compared to the Navy's current operational aerosol forecasting system, which makes use of NAVDAS-AOD (NRL Atmospheric Variational Data Assimilation System for aerosol optical depth), a 2-D variational data assimilation system. Overall, the two systems had statistically insignificant differences in root-mean-squared error (RMSE), bias, and correlation relative to AERONET-observed AOT. However, the ensemble system is able to better capture sharp gradients in aerosol features compared to the 2DVar system, which has a tendency to smooth out aerosol events. Such skill is not easily observable in bulk metrics. Further, the ENAAPS-DART system will allow for new avenues of model development, such as more efficient lidar and surface station assimilation as well as adaptive source functions. At this early stage of development, the parity with the current variational system is encouraging.
机译：基于集合的预测和数据同化系统已经开发用于海军气溶胶预测。该系统利用100度的海军气溶胶分析预测系统（eNAAPS）的集合，与NCAR的数据同化研究试验用过的集合调整卡尔曼滤波器相结合（DART）。本工作中讨论的基本enaaps-Dart系统利用海军运营全局分析预测系统（Nogaps）气象集合来驱动脱机Naaps模拟，耦合与飞镖合奏卡尔曼滤波器架构，以同化偏置校正的Modis气溶胶光学厚度（AOT）检索。这项工作概述了20成员集合系统的优化，包括考虑气象和源扰动的集合成员以及协方差通胀。还进行了80个气象和来源成员的额外测试。该作品的重要发现是，发现尚未测试过气溶胶应用的自适应协方差膨胀方法，以优于时间上和空间恒定的协方差膨胀。在具有有限的观察覆盖率的区域中持续通胀识别出问题。这项工作的第二个主要发现是，组合气象和气溶胶源集合在隔离中优于，这两者都是生产具有足够普及的强大系统所必需的，以及用于传播观测信息的现实相关领域。纳入气溶胶源集合可以通过统计分离从运输的气溶胶物种的统计分离，改善了大型气溶胶源区的相关领域，例如非洲的烟雾和灰尘。然而，在远程运输期间，源集合的功效有限。相反，气象合奏会产生足够的扩展规模的展开，以通过集合数据同化来实现观测的影响。将优化的集合系统与海军的当前运营气溶胶预测系统进行了比较，这是利用NavDAS-AOD（NRL大气变分数据同化系统进行气溶胶光学深度），是一个二维变分数据同化系统。总体而言，这两个系统在与AeroNet观察到的AOT相比具有统计学上的无关紧要的根本平均误差（RMSE），偏差和相关性。然而，与2DVAR系统相比，集合系统能够更好地捕获气溶胶特征中的尖锐梯度，这具有平滑气溶胶事件的趋势。在批量指标中，此类技能不容易观察。此外，ENAAPS-DART系统将允许模型开发的新途径，例如更有效的激光雷达和表面站同化以及自适应源功能。在这个早期的发展阶段，与当前变分系统的平等令人鼓舞。

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