@article{Li-2020-Object-based,
title = "Object-based tracking of precipitation systems in western Canada: the importance of temporal resolution of source data",
author = "Li, Lintao and
Li, Yanping and
Li, Zhenhua",
journal = "Climate Dynamics, Volume 55, Issue 9-10",
volume = "55",
number = "9-10",
year = "2020",
publisher = "Springer Science and Business Media LLC",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G20-13001",
doi = "10.1007/s00382-020-05388-y",
pages = "2421--2437",
abstract = "Object-based algorithm provides additional spatiotemporal information of precipitation, besides traditional aspects such as amount and intensity. Using the Method for Object-based Diagnostic Evaluation with Time Dimension (MODE-TD, or MTD), precipitation features in western Canada have been analyzed comprehensively based on the Canadian Precipitation Analysis, North American Regional Reanalysis, Multi-Source Weighted-Ensemble Precipitation, and a convection-permitting climate model. We found light precipitation occurs frequently in the interior valleys of western Canada while moderate to heavy precipitation is rare there. The size of maritime precipitation system near the coast is similar to the continental precipitation system on the Prairies for moderate to heavy precipitation while light precipitation on the Prairies is larger in size than that occurs near the coast. For temporal features, moderate to heavy precipitation lasts longer than light precipitation over the Pacific coast, and precipitation systems on the Prairies generally move faster than the coastal precipitation. For annual cycle, the west coast has more precipitation events in cold seasons while more precipitation events are identified in warm seasons on the Prairies due to vigorous convection activities. Using two control experiments, the way how the spatiotemporal resolution of source data influences the MTD results has been examined. Overall, the spatial resolution of source data has little influence on MTD results. However, MTD driven by dataset with coarse temporal resolution tend to identify precipitation systems with relatively large size and slow propagation speed. This kind of precipitation systems normally have short track length and relatively long lifetime. For a typical precipitation system (0.7 ∼ 2 {\mbox{$\times$}} 104 km2 in size) in western Canada, the maximum propagation speed that can be identified by 6-h data is approximately 25 km h−1, 33 km h−1 for 3-h, and 100 km h−1 for hourly dataset. Since the propagation speed of precipitation systems in North America is basically between 0 and 80 km h−1, we argue that precipitation features can be identified properly by MTD only when dataset with hourly or higher temporal resolution is used.",
}
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<abstract>Object-based algorithm provides additional spatiotemporal information of precipitation, besides traditional aspects such as amount and intensity. Using the Method for Object-based Diagnostic Evaluation with Time Dimension (MODE-TD, or MTD), precipitation features in western Canada have been analyzed comprehensively based on the Canadian Precipitation Analysis, North American Regional Reanalysis, Multi-Source Weighted-Ensemble Precipitation, and a convection-permitting climate model. We found light precipitation occurs frequently in the interior valleys of western Canada while moderate to heavy precipitation is rare there. The size of maritime precipitation system near the coast is similar to the continental precipitation system on the Prairies for moderate to heavy precipitation while light precipitation on the Prairies is larger in size than that occurs near the coast. For temporal features, moderate to heavy precipitation lasts longer than light precipitation over the Pacific coast, and precipitation systems on the Prairies generally move faster than the coastal precipitation. For annual cycle, the west coast has more precipitation events in cold seasons while more precipitation events are identified in warm seasons on the Prairies due to vigorous convection activities. Using two control experiments, the way how the spatiotemporal resolution of source data influences the MTD results has been examined. Overall, the spatial resolution of source data has little influence on MTD results. However, MTD driven by dataset with coarse temporal resolution tend to identify precipitation systems with relatively large size and slow propagation speed. This kind of precipitation systems normally have short track length and relatively long lifetime. For a typical precipitation system (0.7 ∼ 2 \times 104 km2 in size) in western Canada, the maximum propagation speed that can be identified by 6-h data is approximately 25 km h−1, 33 km h−1 for 3-h, and 100 km h−1 for hourly dataset. Since the propagation speed of precipitation systems in North America is basically between 0 and 80 km h−1, we argue that precipitation features can be identified properly by MTD only when dataset with hourly or higher temporal resolution is used.</abstract>
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%0 Journal Article
%T Object-based tracking of precipitation systems in western Canada: the importance of temporal resolution of source data
%A Li, Lintao
%A Li, Yanping
%A Li, Zhenhua
%J Climate Dynamics, Volume 55, Issue 9-10
%D 2020
%V 55
%N 9-10
%I Springer Science and Business Media LLC
%F Li-2020-Object-based
%X Object-based algorithm provides additional spatiotemporal information of precipitation, besides traditional aspects such as amount and intensity. Using the Method for Object-based Diagnostic Evaluation with Time Dimension (MODE-TD, or MTD), precipitation features in western Canada have been analyzed comprehensively based on the Canadian Precipitation Analysis, North American Regional Reanalysis, Multi-Source Weighted-Ensemble Precipitation, and a convection-permitting climate model. We found light precipitation occurs frequently in the interior valleys of western Canada while moderate to heavy precipitation is rare there. The size of maritime precipitation system near the coast is similar to the continental precipitation system on the Prairies for moderate to heavy precipitation while light precipitation on the Prairies is larger in size than that occurs near the coast. For temporal features, moderate to heavy precipitation lasts longer than light precipitation over the Pacific coast, and precipitation systems on the Prairies generally move faster than the coastal precipitation. For annual cycle, the west coast has more precipitation events in cold seasons while more precipitation events are identified in warm seasons on the Prairies due to vigorous convection activities. Using two control experiments, the way how the spatiotemporal resolution of source data influences the MTD results has been examined. Overall, the spatial resolution of source data has little influence on MTD results. However, MTD driven by dataset with coarse temporal resolution tend to identify precipitation systems with relatively large size and slow propagation speed. This kind of precipitation systems normally have short track length and relatively long lifetime. For a typical precipitation system (0.7 ∼ 2 \times 104 km2 in size) in western Canada, the maximum propagation speed that can be identified by 6-h data is approximately 25 km h−1, 33 km h−1 for 3-h, and 100 km h−1 for hourly dataset. Since the propagation speed of precipitation systems in North America is basically between 0 and 80 km h−1, we argue that precipitation features can be identified properly by MTD only when dataset with hourly or higher temporal resolution is used.
%R 10.1007/s00382-020-05388-y
%U https://gwf-uwaterloo.github.io/gwf-publications/G20-13001
%U https://doi.org/10.1007/s00382-020-05388-y
%P 2421-2437
Markdown (Informal)
[Object-based tracking of precipitation systems in western Canada: the importance of temporal resolution of source data](https://gwf-uwaterloo.github.io/gwf-publications/G20-13001) (Li et al., GWF 2020)
ACL
- Lintao Li, Yanping Li, and Zhenhua Li. 2020. Object-based tracking of precipitation systems in western Canada: the importance of temporal resolution of source data. Climate Dynamics, Volume 55, Issue 9-10, 55(9-10):2421–2437.
