High Temporal Rainfall Estimations from Himawari-8 Multiband Observations Using the Random-Forest Machine-Learning Method
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JuneJournal 2019 of the Meteorological Society of Japan, 97(3),H. 689−710,HIROSE et2019. al. doi:10.2151/jmsj.2019-040 689 High Temporal Rainfall Estimations from Himawari-8 Multiband Observations Using the Random-Forest Machine-Learning Method Hitoshi HIROSE, Shoichi SHIGE, Munehisa K. YAMAMOTO Graduate School of Science, Kyoto University, Kyoto, Japan and Atsushi HIGUCHI Center for Environmental Remote Sensing, Chiba University, Chiba, Japan (Manuscript received 27 March 2018, in final form 26 Febuary 2019) Abstract We introduce a novel rainfall-estimating algorithm with a random-forest machine-learning method only from Infrared (IR) observations. As training data, we use nine-band brightness temperature (BT) observations, obtained from IR radiometers, on the third-generation geostationary meteorological satellite (GEO) Himawari-8 and precipitation radar observations from the Global Precipitation Measurement core observatory. The Himawari-8 Rainfall-estimating Algorithm (HRA) enables us to estimate the rain rate with high spatial and temporal resolu- tion (i.e., 0.04° every 10 min), covering the entire Himawari-8 observation area (i.e., 85°E – 155°W, 60°S – 60°N) based solely on satellite observations. We conducted a case analysis of the Kanto–Tohoku heavy rainfall event to compare HRA rainfall estimates with the near-real-time version of the Global Satellite Mapping of Precipitation (GSMaP_NRT), which combines global rainfall estimation products with microwave and IR BT observations obtained from satellites. In this case, HRA could estimate heavy rainfall from warm-type precipitating clouds. The GSMaP_NRT could not estimate heavy rainfall when microwave satellites were unavailable. Further, a sta- tistical analysis showed that the warm-type heavy rain seen in the Asian monsoon region occurred frequently when there were small BT differences between the 6.9-μm and 7.3-μm of water vapor (WV) bands (ΔT6.9 – 7.3). Himawari-8 is the first GEO to include the 6.9-μm band, which is sensitive to middle-to-upper tropospheric WV. An analysis of the WV multibands’ weighting functions revealed that ΔT6.9 – 7.3 became small when the WV amount in the middle-to-upper troposphere was small and there were optically thick clouds with the cloud top near the middle troposphere. Statistical analyses during boreal summer (August and September 2015 and July 2016) and boreal winter (December 2015 and January and February 2016) indicate that HRA has higher estima- tion accuracy for heavy rain from warm-type precipitating clouds than a conventional rain estimation method based on only one IR band. Keywords warm-type heavy rain; Himawri-8; GSMaP; GPM; machine-learning Citation Hirose, H., S. Shige, M. K. Yamamoto, and A. Higuchi, 2019: High temporal rainfall estimations from Himawari-8 multiband observations using the random-forest machine-learning method. J. Meteor. Soc. Japan, 97, 689–710, doi:10.2151/jmsj.2019-040. Corresponding author: Hitoshi Hirose, Center for Environ- mental Remote Sensing, Chiba University, 1-33 Yayoi, Inage, Chiba 263-8522, Japan E-mail: [email protected] J-stage Advance Published Date: 15 March 2019 ©The Author(s) 2019. This is an open access article published by the Meteorological Society of Japan under a Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0). 690 Journal of the Meteorological Society of Japan Vol. 97, No. 3 it is difficult to use liquid water emission over land, 1. Introduction the MWR algorithm estimates the rain rate mainly by Global rainfall observation datasets with high accu- scattering signatures from of ice crystals. Therefore, racy are important role in climatology and hydrology, the MWR algorithm also assumes that deeper and especially in terms of disaster countermeasures. colder clouds tend to cause heavier rain, like the IR al- Satellite observations are the most suitable means of gorithm (Spencer 1984; Ferraro et al. 2005). Hamada obtaining global observation data. Rainfall estimates et al. (2015) reported that the heaviest rain was caused from satellite signatures were first made using infrared by the clouds with lower echo top height (ETH), (IR) BT and visible (VIS) reflectance from cloud tops rather than those with the highest ETH, by using the observed via geostationary meteorological satellites Tropical Rainfall Measurement Mission (TRMM; (GEOs) (Barrett 1970). Observational studies in the Kummerow et al. 1998) precipitation radar (PR). United States created a classical model of heavy rain Sohn et al. (2013) reported that heavy rainfall over the from deep cumulonimbus clouds (Byers and Braham Korean peninsula was mainly caused by the clouds 1949). with ETH lower than 8 km. The rain rate of this type Based on this conceptual model, IR-radiometer- heavy rainfall was comparable to that of heavy rain based rainfall-estimating algorithms assume that from the deep convective cloud seen in Oklahoma in deeper and colder clouds tend to cause heavier rain the United States of America, although the ETH was (Richards and Arkin 1981). However, the estimation obviously low. They referred to low-level clouds, accuracy of IR-radiometer-based techniques is low which are associated with heavy rainfall, as warm- for optically thin clouds, such as cloud anvils, due to type clouds, and we use “warm-type” to indicate the the weak linkage between the cloud-top temperature same meaning in this study. Significant underestima- (CTT) and precipitation (Adler et al. 1993). For more tion of rainfall occurred in the coastal mountains in accurate and precise precipitation estimates, research- the Asian monsoon region when using the GSMaP ers have attempted to use microwave radiometer MWR algorithm (Aonashi et al. 2009; Shige et al. (MWR) signatures, which observe microwave emis- 2009). Shige et al. (2013) reported that such heavy sions from liquid hydrometeors and scattering from rainfall, with low precipitation-top heights, occurs due ice particles. Compared to IR information obtained to orographically forced upward motion over coastal only from the cloud top, MWR algorithm can estimate mountains where large amounts of WV constantly the vertical structure inside the precipitating cloud converge. An orographic–nonorographic rainfall clas- (Arkin and Ardanuy 1989; Smith et al. 1998). sification scheme to identify orographic rainfall with However, there are gaps in the overpasses of MWR low precipitation-top heights has been incorporated satellites observation networks by low-Earth-orbiting into the GSMaP MWR algorithm (Shige et al. 2013; satellites when more frequent rainfall outputs are re- Taniguchi et al. 2013; Shige et al. 2015; Yamamoto quired. The Global Satellite Mapping of Precipitation and Shige 2015; Yamamoto et al. 2017). Using this (GSMaP) uses two consecutive IR GEO observations scheme, the rainfall estimate accuracy is improved at 1 h intervals to calculate the cloud-moving vector over almost all Asian regions; however, the rainfall (Ushio et al. 2009). Using the moving vector, the rate complemented by the IR algorithms in GSMaP rainfall observed by an MWR satellite is propagated still depends only on CTT information. The number along with the moving vector to interpolate gaps in of observation bands on the past GEOs has been very the MWR observation coverage. In addition to the limited, as shown in Table 1, and GSMaP uses only cloud-moving vector, GSMaP uses a Kalman filter one IR band, 10.8 μm. to update the rainfall intensity correspond to the IR As the performance of GEOs has improved in TB after propagation along with the moving vector recent years, studies of rainfall estimates by GEOs (GSMaP_MVK; Ushio et al. 2009). By combining have progressed. Upadhyaya and Ramsankaran (2016) MWR and IR, the global rainfall can be estimated proposed the multispectral rainfall estimation algo- from satellite observations at high frequency. rithm using the Indian National Satellite System The matching algorithms of MWR and IR have (INSAT). They improved the estimate accuracy by succeeded, to a certain extent, where deep convection incorporating topographical information into the em- or large organized convection dominates, such as over pirical formula between the GEO-observed BT and the the tropical ocean or over continental North America. rain rate in all the climatic regions in India. However, The MWRs provide the liquid water emission of the INSAT radiometer has only three spectral bands, 8 lower frequency bands over the ocean. However, since km spatial resolution of IR and WV bands and 30 min June 2019 H. HIROSE et al. 691 temporal resolution. to-upper troposphere using three WV bands. Using The problem of spatial resolution and the number of the Himawari-8 observational data, we can expect to GEO observation bands was solved after the Meteosat obtain more detailed precipitation related information Second Generation (MSG; Aminou 2002) satellite was to analyze “warm-type” rain in the Asian monsoon launched in December 2005. The Spinning Enhanced region, as mention in Sohn et al. (2013). Visible and Infrared Imager (SEVIRI) sensor on MSG To investigate how the estimate accuracy of warm- operates in 12 spectral bands: 3 VIS bands with a spa- type heavy rain improves when using the multiband IR tial resolution of 1 km and 8 IR and WV bands with of Himawari-8, we created high-frequency precipita- a spatial resolution of 3 km. Roebeling and Holleman tion data by applying the RF machine-learning method (2009) developed a rainfall-estimating algorithm to the multiband IR observations of Himawari-8. This based on an empirical relation between the cloud makes it possible to estimate rainfall over the entire physical properties estimated from MSG SEVIRI and observation range of Himawari-8 (85°E – 155°W) the rainfall observed via weather radar in Europe. using the Global Precipitation Measurement (GPM; Bergès et al. (2010) built a neural network based on Hou et al. 2014) satellite equipped with the Dual- training data created from simultaneous observations frequency Precipitation Radar (DPR; Kojima et al.