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| DOI | 10.1175/JAMC-D-23-0117.1 |
| Improved Diagnosis of Precipitation Type with LightGBM Machine Learning | |
| 发表日期 | 2024 |
| ISSN | 1558-8424 |
| EISSN | 1558-8432 |
| 起始页码 | 63 |
| 结束页码 | 3 |
| 卷号 | 63期号:3 |
| 英文摘要 | Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitationtype diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h-1, as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs. SIGNIFICANCE STATEMENT: Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h-1. Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models. |
| 英文关键词 | Surface observations; Forecast verification/skill; Hindcasts; Classification; Decision trees; Machine learning |
| 语种 | 英语 |
| WOS研究方向 | Meteorology & Atmospheric Sciences |
| WOS类目 | Meteorology & Atmospheric Sciences |
| WOS记录号 | WOS:001183614000001 |
| 来源期刊 | JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/305741 |
| 作者单位 | Cornell University; National Center Atmospheric Research (NCAR) - USA; Cornell University |
| 推荐引用方式 GB/T 7714 | . Improved Diagnosis of Precipitation Type with LightGBM Machine Learning[J],2024,63(3). |
| APA | (2024).Improved Diagnosis of Precipitation Type with LightGBM Machine Learning.JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY,63(3). |
| MLA | "Improved Diagnosis of Precipitation Type with LightGBM Machine Learning".JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY 63.3(2024). |
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