Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements
<p>Deriving large-scale and high-quality precipitation products from satellite remote-sensing spectral data is always challenging in quantitative precipitation estimation (QPE), and limited studies have been conducted even using China's latest Fengyun-4A (FY-4A) geostationary satellite. T...
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Copernicus Publications
2021
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oai:doaj.org-article:254c2f80c54043a69ef84571b60fd7332021-11-05T10:20:14ZLeveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements10.5194/amt-14-7007-20211867-13811867-8548https://doaj.org/article/254c2f80c54043a69ef84571b60fd7332021-11-01T00:00:00Zhttps://amt.copernicus.org/articles/14/7007/2021/amt-14-7007-2021.pdfhttps://doaj.org/toc/1867-1381https://doaj.org/toc/1867-8548<p>Deriving large-scale and high-quality precipitation products from satellite remote-sensing spectral data is always challenging in quantitative precipitation estimation (QPE), and limited studies have been conducted even using China's latest Fengyun-4A (FY-4A) geostationary satellite. Taking three rainstorm events over South China as examples, a machine-learning-based regression model was established using the random forest (RF) method to derive QPE from FY-4A observations, in conjunction with cloud parameters and physical quantities. The cross-validation results indicate that both daytime (DQPE) and nighttime (NQPE) RF algorithms performed well in estimating QPE, with the bias score, correlation coefficient and root-mean-square error of DQPE (NQPE) of 2.17 (2.42), 0.79 (0.83) and 1.77 <span class="inline-formula">mm h<sup>−1</sup></span> (2.31 <span class="inline-formula">mm h<sup>−1</sup></span>), respectively. Overall, the algorithm has a high accuracy in estimating precipitation under the heavy-rain level or below. Nevertheless, the positive bias still implies an overestimation of precipitation by the QPE algorithm, in addition to certain misjudgements from non-precipitation pixels to precipitation events. Also, the QPE algorithm tends to underestimate the precipitation at the rainstorm or even above levels. Compared to single-sensor algorithms, the developed QPE algorithm can better capture the spatial distribution of land-surface precipitation, especially the centre of strong precipitation. Marginal difference between the data accuracy over sites in urban and rural areas indicate that the model performs well over space and has no evident dependence on landscape. In general, our proposed FY-4A QPE algorithm has advantages for quantitative estimation of summer precipitation over East Asia.</p>X. LiY. YangJ. MiX. BiY. ZhaoZ. HuangC. LiuL. ZongW. LiCopernicus PublicationsarticleEnvironmental engineeringTA170-171Earthwork. FoundationsTA715-787ENAtmospheric Measurement Techniques, Vol 14, Pp 7007-7023 (2021) |
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DOAJ |
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DOAJ |
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Environmental engineering TA170-171 Earthwork. Foundations TA715-787 |
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Environmental engineering TA170-171 Earthwork. Foundations TA715-787 X. Li Y. Yang J. Mi X. Bi Y. Zhao Z. Huang C. Liu L. Zong W. Li Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements |
| description |
<p>Deriving large-scale and high-quality precipitation products from satellite
remote-sensing spectral data is always challenging in quantitative
precipitation estimation (QPE), and limited studies have been conducted even
using China's latest Fengyun-4A (FY-4A) geostationary satellite. Taking
three rainstorm events over South China as examples, a machine-learning-based regression model was established using the random forest (RF) method to derive
QPE from FY-4A observations, in conjunction with cloud parameters and physical
quantities. The cross-validation results indicate that both daytime (DQPE) and
nighttime (NQPE) RF algorithms performed well in estimating QPE, with the bias
score, correlation coefficient and root-mean-square error of DQPE (NQPE) of
2.17 (2.42), 0.79 (0.83) and 1.77 <span class="inline-formula">mm h<sup>−1</sup></span> (2.31 <span class="inline-formula">mm h<sup>−1</sup></span>), respectively. Overall, the
algorithm has a high accuracy in estimating precipitation under the heavy-rain
level or below. Nevertheless, the positive bias still implies an
overestimation of precipitation by the QPE algorithm, in addition to certain
misjudgements from non-precipitation pixels to precipitation events. Also, the
QPE algorithm tends to underestimate the precipitation at the rainstorm or
even above levels. Compared to single-sensor algorithms, the developed QPE
algorithm can better capture the spatial distribution of land-surface
precipitation, especially the centre of strong precipitation. Marginal
difference between the data accuracy over sites in urban and rural areas
indicate that the model performs well over space and has no evident dependence
on landscape. In general, our proposed FY-4A QPE algorithm has advantages for
quantitative estimation of summer precipitation over East Asia.</p> |
| format |
article |
| author |
X. Li Y. Yang J. Mi X. Bi Y. Zhao Z. Huang C. Liu L. Zong W. Li |
| author_facet |
X. Li Y. Yang J. Mi X. Bi Y. Zhao Z. Huang C. Liu L. Zong W. Li |
| author_sort |
X. Li |
| title |
Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements |
| title_short |
Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements |
| title_full |
Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements |
| title_fullStr |
Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements |
| title_full_unstemmed |
Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements |
| title_sort |
leveraging machine learning for quantitative precipitation estimation from fengyun-4 geostationary observations and ground meteorological measurements |
| publisher |
Copernicus Publications |
| publishDate |
2021 |
| url |
https://doaj.org/article/254c2f80c54043a69ef84571b60fd733 |
| work_keys_str_mv |
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