2025-07-08 パシフィック・ノースウェスト国立研究所(PNNL)
＜関連情報＞
- https://www.pnnl.gov/publications/characterizing-giant-storm-precipitation-central-amazon
- https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024JD041004
メソスケール対流システム追跡アルゴリズムを用いたアマゾン中央部における雨季降水量の特徴づけ Characterizing Wet Season Precipitation in the Central Amazon Using a Mesoscale Convective System Tracking Algorithm
Sheng-Lun Tai, Zhe Feng, James Marquis, Jerome Fast
Journal of Geophysical Research: Atmospheres Published: 03 October 2024
DOI:https://doi.org/10.1029/2024JD041004
Abstract
To comprehensively characterize convective precipitation in the central Amazon region, we utilize the Python FLEXible object TRacKeR (PyFLEXTRKR) to track mesoscale convective systems (MCSs) observed through satellite measurements and simulated by the Weather Research and Forecasting model at a convection-permitting resolution. This study spans a 2-month period during the wet seasons of 2014 and 2015. We observe a strong correlation between the MCS track density and accumulated precipitation in the Amazon basin. Key factors contributing to precipitation, such as MCS properties (number, size, rainfall intensity, and movement), are thoroughly examined. Our analysis reveals that while the overall model produces fewer MCSs with smaller mean sizes compared to observations, it tends to overpredict total precipitation due to excessive rainfall intensity for heavy rainfall events (≥10 mm hr−1). These biases in simulated MCS properties could vary with the constraints on the convective background environment. Moreover, while the wet bias from heavy (convective) rainfall outweighs the dry bias in light (stratiform) rainfall, the latter can be crucial, particularly when MCS cloud cover is significantly underestimated. A case study for 1 April 2014 highlights the influence of environmental conditions on the MCS lifecycle and identifies an unrealistic model representation in both stratiform and convective precipitation features.
Key Points
- Simulated and observed mesoscale convective system (MCS) clouds and precipitation are tracked during the 2014/15 wet seasons in the central Amazon
- Excessive heavy rain intensity (≥10 mm hr−1) of simulated MCS lead to an overprediction of precipitation
- Dry bias in stratiform rainfall can also drive MCS precipitation bias when stratiform cloud cover is substantially underpredicted
Plain Language Summary
We tracked large-size rain storms called mesoscale convective systems (MCSs) in the central Amazon during the wet seasons of 2014 and 2015. Data generated from the MCS tracking helps us understand how rainfall is produced as a function of the number of storms, as well as their size, rain intensity, and motion, and how those can be better simulated by weather and climate models. We found that the model produces fewer and smaller MCSs than in reality, but the total rainfall amount is often overestimated. This is because simulated MCSs produce unrealistically intense heavy rainfall events. On the other hand, light rainfall events are mostly underrepresented by the model. Thus, the model error in total precipitation is determined by how these two counterbalance each other. Our analysis also suggests model representation of the background environment can be critical for simulating realistic MCS properties.
