Abstract
Total precipitable water (TPW), a column of water vapor content in the atmosphere, provides information on the spatial distribution of moisture. The high-resolution TPW, together with atmospheric stability indices such as convective available potential energy (CAPE), is an effective indicator of severe weather phenomena in the pre-convective atmospheric condition. With the advent of high performing imaging instrument onboard geostationary satellites such as Advanced Himawari Imager (AHI) onboard Himawari-8 of Japan and Advanced Meteorological Imager (AMI) onboard GeoKompsat-2A of Korea, it is expected that unprecedented spatiotemporal resolution data (e.g., AMI plans to provide 2 km resolution data at every 2 min over the northeast part of East Asia) will be provided. To derive TPW from such high-resolution data in a timely fashion, an efficient algorithm is highly required. Here, machine learning approaches—random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN)—are assessed for the TPW retrieved from AHI over the clear sky in Northeast Asia area. For the training dataset, the nine infrared brightness temperatures (BT) of AHI (BT8 to 16 centered at 6.2, 6.9, 7.3, 8.6, 9.6, 10.4, 11.2, 12.4, and 13.3 μ m , respectively), six dual channel differences and observation conditions such as time, latitude, longitude, and satellite zenith angle for two years (September 2016 to August 2018) are used. The corresponding TPW is prepared by integrating the water vapor profiles from InterimEuropean Centre for Medium-Range Weather Forecasts Re-Analysis data (ERA-Interim). The algorithm performances are assessed using the ERA-Interim and radiosonde observations (RAOB) as the reference data. The results show that the DNN model performs better than RF and XGB with a correlation coefficient of 0.96, a mean bias of 0.90 mm, and a root mean square error (RMSE) of 4.65 mm when compared to the ERA-Interim. Similarly, DNN results in a correlation coefficient of 0.95, a mean bias of 1.25 mm, and an RMSE of 5.03 mm when compared to RAOB. Contributing variables to retrieve the TPW in each model and the spatial and temporal analysis of the retrieved TPW are carefully examined and discussed.
Highlights
Water vapor, one of the most influential constituents of the atmosphere, is responsible for determining the amount of precipitation that a region can receive [1]
The XGB model shows the highest accuracy with the root mean square error (RMSE) of 2.46 mm (Figure 5b), followed by random forest (RF) with 2.63 mm (Figure 5a) while the deep neural network (DNN) model results in relatively less accurate performance with the RMSE of 2.69 mm (Figure 5c)
The mean biases are under the absolute value of 0.15 mm for all models (0.03 mm for RF, 0.00 mm for XGB, and 0.13 mm for DNN) and the
Summary
One of the most influential constituents of the atmosphere, is responsible for determining the amount of precipitation that a region can receive [1]. Total precipitable water (TPW) is a meteorological factor that shows the amount of water vapor contained in the column of air per unit area of the atmosphere in terms of the depth of liquid [2]. The amount of water vapor contained in the troposphere has significant implications for determining the strength and severity of a severe weather event [3]. The retrieved high temporal resolution TPW from GEO satellite sensor data can be utilized to monitor pre-convective environments and predict heavy rainfall, convective storms, and clouds that may cause serious damage to human life and infrastructure [6,7,8]. Lee et al [8] showed that the 10-min interval measurements from the AHI sensor successfully provided information about the pre-landfall environment for typhoon Nangka that occurred in 2015
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