Data archive for: Exploring the use of machine learning to improve vertical profiles of temperature and moisture

Vertical profiles of temperature and dewpoint are useful in predicting
deep convection that leads to severe weather that threatens property and
lives. Currently, forecasters rely on observations from radiosonde
launches and numerical weather prediction (NWP) models. Radiosonde
observations are, however, temporally and spatially sparse, and NWP models
contain inherent errors that influence short-term predictions of
high-impact events. This work explores using machine learning (ML) to
postprocess NWP model forecasts, combining them with satellite data to
improve vertical profiles of temperature and dewpoint. We focus on
different ML architectures, loss functions, and input features to optimize
predictions. Because we are predicting vertical profiles at 256 levels in
the atmosphere, this work provides a unique perspective at using ML for
1-D tasks. Compared to baseline profiles from the Rapid Refresh (RAP), ML
predictions offer the largest improvement for dewpoint, particularly in
the mid- and upper-atmosphere.  emperature improvements are
modest, but CAPE values are improved by up to 40%. Feature importance
analyses indicate that the ML models are primarily improving incoming RAP
biases. While additional model and satellite data offer some improvement
to the predictions, architecture choice is more important than feature
selection in fine-tuning the results. Our proposed deep residual UNet
performs the best by leveraging spatial context from the input RAP
profiles; however, the results are remarkably robust across model
architecture. Further, uncertainty estimates for every level are
well-calibrated and can provide useful information to forecasters.

温度和露点的垂直廓线对于预测导致强天气的深对流至关重要，此类强天气会威胁财产与生命安全。目前，预报员依赖无线电探空仪（radiosonde）施放获得的观测数据以及数值天气预报（NWP）模型。然而，无线电探空仪观测在时间和空间上都较为稀疏，而NWP模型存在固有误差，影响高影响事件的短期预测。本研究探索利用机器学习（ML）对NWP模型预报进行后处理，结合卫星数据以改进温度和露点的垂直廓线。我们重点研究不同的机器学习架构、损失函数和输入特征，以优化预测结果。由于我们要预测大气中256个层次的垂直廓线，本研究为将机器学习应用于一维任务提供了独特视角。与快速更新（RAP）的基准廓线相比，机器学习预测在露点方面的改进最为显著，尤其在中高层大气中。温度预测的改进较为温和，但对流有效位能（CAPE）值的改进幅度高达40%。特征重要性分析表明，机器学习模型主要改进了输入RAP廓线中的偏差。尽管额外的模型和卫星数据能对预测结果有所改进，但在微调结果时，架构选择比特征选择更为重要。我们提出的深度残差UNet模型通过利用输入RAP廓线中的空间上下文信息表现最佳；然而，在不同模型架构间，结果均展现出显著的鲁棒性。此外，各层次的不确定性估计校准良好，可为预报员提供有用信息。