High-Resolution Fengyun-4 Satellite Measurements of Dynamical Tropopause Structure and Variability

Yi-Xuan Shou,Shaowen Shou,Feng Lu

doi:10.3390/rs12101600

Abstract

The dynamical tropopause is the interface between the stratosphere and the troposphere, whose variation gives indication of weather and climate changes. In the past, the dynamical tropopause height determination mainly depends on analysis and diagnose methods. While, due to the high computational cost, it is difficult to obtain tropopause structures with high spatiotemporal resolution in real time by these methods. To solve this problem, the statistical method is used to establish the dynamical tropopause pressure retrieval model based on Fengyun-4A geostationary meteorological satellite observations. Four regression schemes including random forest (RF) regression are evaluated. By comparison with GEOS-5 (the Goddard Earth Observing System Model of version 5) and ERA-Interim (European Center for Medium-Range Weather Forecasts Reanalysis-Interim) reanalysis, it is found that among the four schemes, the RF-based retrieval model is most accurate and reliable (RMSEs (root mean square errors) are 25.99 hPa and 43.05 hPa, respectively, as compared to GEOS-5 and ERA-Interim reanalysis). A series of sensitivity experiments are performed to investigate the contributions of the predictors in the RF-based model. Results suggest that 6.25 μm channel information representing the distributions of the potential vorticity and water vapor in upper troposphere has the greatest contribution, while 10.8 and 12 μm channels information have relatively weak influences. Therefore, a simplified model without involving a brightness temperature of 10.8 and 12 μm can be adopted to improve the calculation efficiency.

Highlights

The tropopause is like a “two-way valve” separating the stratosphere and the troposphere, controlling the exchange of mass, water vapor, and chemical species between the two parts of atmosphere [1,2]
The inversion model based on the random forest (RF) method has good performance and strong robustness, its computational efficiency is relatively poor among the four models, especially when having many predictors
The inversion models of Fengyun-4A dynamical tropopause pressure are established by using linear regression, K-nearest neighbor (KNN), gradient boosted decision trees (GBDT), and RF methods, respectively

Summary

Introduction

The tropopause is like a “two-way valve” separating the stratosphere and the troposphere, controlling the exchange of mass, water vapor, and chemical species between the two parts of atmosphere [1,2]. As a key area for studying weather, climate, and atmospheric composition, the tropopause has attracted the attention of a large number of scientists over the past few decades [2,3,4,5,6,7]. Scientists observed significant differences in temperature distributions in the atmosphere on both sides of the tropopause, i.e., temperature generally decreases in the troposphere and increases in the stratosphere with altitude. By using this feature, WMO (World Meteorological Organization) proposed a method to determine the tropopause height with lapse rate of temperature in 1957 [8]. Because only temperature profiles are needed for calculation, much of the observations, e.g., radiosonde [9,10], radar [11] and satellite observations [12], as well as numerical model data [13,14]

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Conclusion