First results on a process-oriented rain area classification technique using Meteosat Second Generation SEVIRI nighttime data

Thies, Boris; Nauss, Thomas; Bendix, Jörg

doi:10.5194/adgeo-16-63-2008

Cited by 15 publications

(18 citation statements)

References 20 publications

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“…The same holds true for low BT 10.8 together with high Δ T WV6.2–IR10.8 values (Figure 3b). This corroborates the findings of Thies et al [2008c; B. Thies et al, submitted manuscript, 2007] that Δ T WV6.2–IR10.8 and Δ T WV7.3–IR12.1 increase with increasing cloud top height. At the same time, the higher the cloud reaches into the atmosphere the lower becomes BT 10.8 .…”

Section: Process Separation and Rainfall Intensity Differentiation Wisupporting

confidence: 89%

“…Concerning the two WV and two IR channels of SEVIRI, the studies of Thies et al [2008c; also Detection of high rain clouds using water vapor emission—Transition from Meteosat First (MVIRI) to Second Generation (SEVIRI), submitted to Advanced Space Research , 2007] revealed that the four possible WV‐IR combinations show different sensitivities to the cloud top height at different levels, which provide useful information for the detection and classification of convectively dominated precipitation areas. To incorporate the different sensitivities on cloud top height into the detection scheme, the two channel differences Δ T WV6.2–IR10.8 and Δ T WV7.3–IR12.1 have been chosen.…”

Section: Process Separation and Rainfall Intensity Differentiation Wimentioning

confidence: 99%

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Precipitation process and rainfall intensity differentiation using Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager data

2008

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[1] A new day and night technique for precipitation process separation and rainfall intensity differentiation using the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager is proposed. It relies on the conceptual design that convective clouds with higher rainfall intensities are characterized by a larger vertical extension and a higher cloud top. For advective-stratiform precipitation areas, it is assumed that areas with a higher cloud water path (CWP) and more ice particles in the upper parts are characterized by higher rainfall intensities. First, the rain area is separated into areas of convective and advective-stratiform precipitation processes. Next, both areas are divided into subareas of differing rainfall intensities. The classification of the convective area relies on information about the cloud top height gained from water vapor-IR differences and the IR cloud top temperature. The subdivision of the advective-stratiform area is based on information about the CWP and the particle phase in the upper parts. Suitable combinations of temperature differences (DT 3.9 -10.8 , DT 3.9 -7.3 , DT 8.7 -10.8 , DT 10.8 -12.1 ) are incorporated to infer information about the CWP during nighttime, while a visible and a near-IR channel are considered during the daytime. DT 8.7 -10.8 and DT 10.8 -12.1 are particularly included to supply information about the cloud phase. Intensity differentiation is realized by using pixel-based confidences for each subarea calculated as a function of the respective value combinations of the previously mentioned variables. For the calculation of the confidences, the value combinations are compared with ground-based radar data. The proposed technique is validated against ground-based radar data and shows an encouraging performance (Heidke skill score 0.07-0.2 for 15-min intervals).

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Section: Process Separation and Rainfall Intensity Differentiation Wisupporting

confidence: 89%

Section: Process Separation and Rainfall Intensity Differentiation Wimentioning

confidence: 99%

Precipitation process and rainfall intensity differentiation using Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager data

2008

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“…As these microphysical properties can be derived from band combinations between the water vapour bands and the middle and thermal infrared bands, the five most important combinations between these bands were included in the model data (cf. Kühnlein et al [31], Thies et al [32]). …”

Section: Preprocessing and Input Featuresmentioning

confidence: 99%

A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data

2018

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Fog has a substantial influence on various ecosystems and it impacts economy, traffic systems and human life in many ways. In order to be able to deal with the large number of influence factors, a spatially explicit high-resoluted data set of fog frequency distribution is needed. In this study, a hybrid approach for fog retrieval based on Meteosat Second Generation (MSG) data and ground truth data is presented. The method is based on a random forest (RF) machine learning model that is trained with cloud base altitude (CBA) observations from Meteorological Aviation Routine Weather Reports (METAR) as well as synoptic weather observations (SYNOP). Fog is assumed where the model predicts CBA values below a dynamically derived threshold above the terrain elevation. Cross validation results show good accordance with observation data with a mean absolute error of 298 m in CBA values and an average Heidke Skill Score of 0.58 for fog occurrence. Using this technique, a 10 year fog baseline climatology with a temporal resolution of 15 min was derived for Europe for the period from 2006 to 2015. Spatial and temporal variations in fog frequency are analyzed. Highest average fog occurrences are observed in mountainous regions with maxima in spring and summer. Plains and lowlands show less overall fog occurrence but strong positive anomalies in autumn and winter.

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“…The extinction coefficients for water in the atmospheric window band with longer wavelengths are larger than those with shorter wavelengths in all the solid, liquid, and gaseous states. Therefore, the cloud water path (CWP) can be described as the BT differences of two atmospheric window bands, called IR split-window (SW) technique (Inoue et al 1985;Lensky and Rosenfeld 2003;Thies et al 2008a). The original Himawari-8 standard data were provided by the Japan Meteorological Agency (JMA).…”

Section: Datamentioning

confidence: 99%

High Temporal Rainfall Estimations from Himawari-8 Multiband Observations Using the Random-Forest Machine-Learning Method

Hirose

Shige

Yamamoto

et al. 2019

Journal of the Meteorological Society of Japan

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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 resolution (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 statistical 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 (indicate that HRA has higher estimation accuracy for heavy rain from warm-type precipitating clouds than a conventional rain estimation method based on only one IR band.

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First results on a process-oriented rain area classification technique using Meteosat Second Generation SEVIRI nighttime data

Cited by 15 publications

References 20 publications

Precipitation process and rainfall intensity differentiation using Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager data

Precipitation process and rainfall intensity differentiation using Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager data

A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data

High Temporal Rainfall Estimations from Himawari-8 Multiband Observations Using the Random-Forest Machine-Learning Method

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