IR‐based satellite and radar rainfall estimates of convective storms over northern Italy

Amorati, Roberta; Alberoni, P. P.; Levizzani, Vincenzo; Nanni, Sandro

doi:10.1017/s1350482700001328

Cited by 29 publications

(13 citation statements)

References 30 publications

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“…The moist convection appears in a variety of horizontal scales ranging from 1-10 km as individual convective clouds to ∼ 1000 km as mesoscale convective systems (Houze, 2004;Roberts and Lean, 2008;Miyamoto et al, 2013) with heavy rainfall and thunderstorm events (Amorati et al, 2000;Sieglaff et al, 2011;Zuidema, 2003;Haile et al, 2010;Hane et al, 2002;Vondou et al, 2010). The convective events in north-eastern Asia often occur during the summer season accompanied by many meteorological hazards such as lightning, floods, and strong winds (Kim and Lee, 2006;Wang et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Lee

Han

et al. 2017

Atmos. Meas. Tech.

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Abstract. The detection of convective initiation (CI) is very important because convective clouds bring heavy rainfall and thunderstorms that typically cause severe socio-economic damage. In this study, deterministic and probabilistic CI detection models based on decision trees (DT), random forest (RF), and logistic regression (LR) were developed using Himawari-8 Advanced Himawari Imager (AHI) data obtained from June to August 2016 over the Korean Peninsula. A total of 12 interest fields that contain brightness temperature, spectral differences of the brightness temperatures, and their time trends were used to develop CI detection models. While, in our study, the interest field of 11.2 µm T b was considered the most crucial for detecting CI in the deterministic models and the probabilistic RF model, the trispectral difference, i.e. (8.6-11.2 µm)-(11.2-12.4 µm), was determined to be the most important one in the LR model. The performance of the four models varied by CI case and validation data. Nonetheless, the DT model typically showed higher probability of detection (POD), while the RF model produced higher overall accuracy (OA) and critical success index (CSI) and lower false alarm rate (FAR) than the other models. The CI detection of the mean lead times by the four models were in the range of 20-40 min, which implies that convective clouds can be detected 30 min in advance, before precipitation intensity exceeds 35 dBZ over the Korean Peninsula in summer using the Himawari-8 AHI data.

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Section: Introductionmentioning

confidence: 99%

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Lee

Han

et al. 2017

Atmos. Meas. Tech.

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“…Convective clouds are developed by various weather systems such as meso-scale convective systems, migrating frontal cyclones, and large-scale monsoonal fronts, which often result in heavy rainfall and thunderstorm events [1][2][3][4][5]. Various meteorological hazards, such as lightning, hail, gusty winds, and floods, are closely related to heavy rainfall and thunderstorms accompanied by convective clouds [3,[6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Detection of Convective Initiation Using Meteorological Imager Onboard Communication, Ocean, and Meteorological Satellite Based on Machine Learning Approaches

Han

Lee

et al. 2015

Remote Sensing

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As convective clouds in Northeast Asia are accompanied by various hazards related with heavy rainfall and thunderstorms, it is very important to detect convective initiation (CI) in the region in order to mitigate damage by such hazards. In this study, a novel approach for CI detection using images from Meteorological Imager (MI), a payload of the Communication, Ocean, and Meteorological Satellite (COMS), was developed by improving the criteria of the interest fields of Rapidly Developing Cumulus Areas (RDCA) derivation algorithm, an official CI detection algorithm for Multi-functional Transport SATellite-2 (MTSAT-2), based on three machine learning approaches-decision trees (DT), random forest (RF), and support vector machines (SVM). CI was defined as clouds within a 16 × 16 km window with the first detection of lightning occurrence at the center. A total of nine interest fields derived from visible, water vapor, and two thermal infrared images of MI obtained 15-75 min before the lightning occurrence were used as input variables for CI OPEN ACCESSRemote Sens. 2015, 7 9185 detection. RF produced slightly higher performance (probability of detection (POD) of 75.5% and false alarm rate (FAR) of 46.2%) than DT (POD of 70.7% and FAR of 46.6%) for detection of CI caused by migrating frontal cyclones and unstable atmosphere. SVM resulted in relatively poor performance with very high FAR ~83.3%. The averaged lead times of CI detection based on the DT and RF models were 36.8 and 37.7 min, respectively. This implies that CI over Northeast Asia can be forecasted ~30-45 min in advance using COMS MI data.

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“…A correlation between water vapour structure above these storms and rainfall is to be more closely investigated. Amorati et al (2000) have found a qualitative correspondence between the presence of such positive temperature differences T WV -T IR and rainfall amount above deep convective storms in northern Italy.…”

Section: Multispectral Identification Of Rain Cloudsmentioning

confidence: 75%

“…The NOAA-NESDIS technique (Scofield & Oliver, 1977;Scofield, 1987) involves direct interaction by a meteorologist and is currently used for operational nowcasting of heavy rainfall and flash floods (Scofield & Naimeng, 1994;Robinson & Scofield, 1994;Vicente & Scofield, 1996). Reasonable performances of this type of method are obtained for deep convective storms while contradictory results arise from their application to stratiform systems or weak convection (Amorati et al, 2000). An application of the NAW to deep convection is shown in Figure 1: while the technique correctly identifies rain clouds, the apportionment of rain between stratiform (light) and convective (heavy) does not exactly match the radar rainfall pattern.…”

Section: Levizzani Et Almentioning

confidence: 99%

Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation

Levizzani

Schmetz

Lutz

et al. 2001

Meteorological Applications

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The use of meteorological satellites for rainfall estimation and monitoring was introduced as a way of augmenting conventional ground-based rainfall data for hydrological models and weather forecasting. Today the primary scope of satellite rainfall monitoring is to provide information on rainfall occurrence, amount and distribution over the globe for a number of applications such as meteorology at all scales, climatology, hydrology and environmental sciences. The uneven distribution of raingauges and weather radars and the lack of rainfall data over the oceans have always been a concern and until now the rainfall, as a prominent branch of the global hydrological cycle, is not well understood. In this sense the problem is not different from the determination of wind, pressure, temperature and humidity fields although precipitation is by far the most variable in space and time. Furthermore, unlike many other atmospheric phenomena, precipitation (or the lack of it) has a direct impact on human life (e.g. flash floods, Barrett & Michell, 1991). Therefore, satellite monitoring is used to address the key questions of spatial and temporal coverage, which cannot be achieved by other observing systems.Among the challenges that face the science and technology of satellite remote sensing the quantitative determination of rainfall from the variety of precipitating systems is one of the most difficult and is largely unsolved. Barrett & Martin (1981) and Kidder & Vonder Haar (1995) give excellent reviews of the methods available. Petty (1995) has examined the status of satellite rainfall estimation over land. A recent review by Levizzani (1998a) has covered results and future perspectives from the geostationary orbit. The perspecMeteorol. Appl. 8, 23-41 (2001) Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation

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IR‐based satellite and radar rainfall estimates of convective storms over northern Italy

Abstract: Convective precipitation events in northern Italy during 1996 and

Cited by 29 publications

References 30 publications

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Detection of Convective Initiation Using Meteorological Imager Onboard Communication, Ocean, and Meteorological Satellite Based on Machine Learning Approaches

Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation

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