Fog Detection Based on Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager High Resolution Visible Channel

Nilo, Saverio T.; Romano, Filomena; Čermák, Jan; Cimini, Domenico; Ricciardelli, Elisabetta; Cersosimo, Angela; Paola, Francesco Di; Gallucci, Donatello; Gentile, Sabrina; Geraldi, Edoardo; Larosa, Salvatore; Ripepi, Ermann; Viggiano, Mariassunta

doi:10.3390/rs10040541

Cited by 20 publications

(21 citation statements)

References 20 publications

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“…It should be noted that the algorithm presented here does not differentiate between ground fog and low-level clouds. The discrimination of low-level liquid-water and higherlevel ice clouds with satellite observations is comparatively easy, especially in the Namib region (Olivier, 1995). In this study, high clouds are identified based on the brightness temperature at 10.8 µm as a proxy for cloud-top temperature, and the difference in 8.7 and 12.0 or 13.4 µm brightness temperatures is an indication of ice clouds (Strabala et al, 1994).…”

Section: Algorithm Designmentioning

confidence: 93%

First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib

Andersen

Čermák

2018

Atmos. Meas. Tech.

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Abstract. Fog and low clouds (FLCs) are a typical feature along the southwestern African coast, especially in the central Namib, where fog constitutes a valuable resource of water for many ecosystems. In this study, a novel algorithm is presented to detect FLCs over land from geostationary satellite data using only infrared observations. The algorithm is the first of its kind as it is stationary in time and thus able to reveal a detailed view of the diurnal and spatial patterns of FLCs in the Namib region. A validation against net radiation measurements from a station network in the central Namib reveals a high overall accuracy with a probability of detection of 94 %, a false-alarm rate of 12 % and an overall correctness of classification of 97 %. The average timing and persistence of FLCs seem to depend on the distance to the coast, suggesting that the region is dominated by advection-driven FLCs. While the algorithm is applied to study Namib-region fog and low clouds, it is designed to be transferable to other regions and can be used to retrieve long-term data sets.

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Section: Algorithm Designmentioning

confidence: 93%

First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib

Andersen

Čermák

2018

Atmos. Meas. Tech.

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“…A detailed overview of C-MACSP [15,41,42], RainCEIV [40], and PET [15,39] is proposed in [43]. All of the three remote sensing techniques exploit the high temporal resolution (15 min) of the MSG-SEVIRI [44] to provide a continuous monitoring of the extreme events.…”

Section: Methodsmentioning

confidence: 99%

Analysis of Livorno Heavy Rainfall Event: Examples of Satellite-Based Observation Techniques in Support of Numerical Weather Prediction

et al. 2018

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This study investigates the value of satellite-based observational algorithms in supporting numerical weather prediction (NWP) for improving the alert and monitoring of extreme rainfall events. To this aim, the analysis of the very intense precipitation that affected the city of Livorno on 9 and 10 September 2017 is performed by applying three remote sensing techniques based on satellite observations at infrared/visible and microwave frequencies and by using maps of accumulated rainfall from the weather research and forecasting (WRF) model. The satellite-based observational algorithms are the precipitation evolving technique (PET), the rain class evaluation from infrared and visible observations (RainCEIV) technique and the cloud classification mask coupling of statistical and physics methods (C-MACSP). Moreover, the rain rates estimated by the Italian Weather Radar Network are also considered to get a quantitative evaluation of RainCEIV and PET performance. The statistical assessment shows good skills for both the algorithms (for PET: bias = 1.03, POD = 0.76, FAR = 0.26; for RainCEIV: bias = 1.33, POD = 0.77, FAR = 0.41). In addition, a qualitative comparison among the three technique outputs, rain rate radar maps, and WRF accumulated rainfall maps is also carried out in order to highlight the advantages of the different techniques in providing real-time monitoring, as well as quantitative characterization of rainy areas, especially when rain rate measurements from Weather Radar Network and/or from rain gauges are not available.

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“…From the data listed in Table 2, three datasets were derived (clear, cloudy and partially cloudy) by using cloud detection AMESIS modules for low (VIS) [31,49] and high spatial (HRV) [53] resolution. In the validation phase, we did not find features that could be related to geographic/altitude characteristics of ground-based stations.…”

Section: Resultsmentioning

confidence: 99%

Improvement in Surface Solar Irradiance Estimation Using HRV/MSG Data

et al. 2018

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The Advanced Model for the Estimation of Surface Solar Irradiance (AMESIS) was developed at the Institute of Methodologies for Environmental Analysis of the National ResearchCouncil of Italy (IMAA-CNR) to derive surface solar irradiance from SEVIRI radiometer on board the MSG geostationary satellite. The operational version of AMESIS has been running continuously at IMAA-CNR over all of Italy since 2017 in support to the monitoring of photovoltaic plants. The AMESIS operative model provides two different estimations of the surface solar irradiance: one is obtained considering only the low-resolution channels (SSI_VIS), while the other also takes into account the high-resolution HRV channel (SSI_HRV). This paper shows the difference between these two products against simultaneous ground-based observations from a network of 63 pyranometers for different sky conditions (clear, overcast and partially cloudy). Comparable statistical scores have been obtained for both AMESIS products in clear and cloud situation. In terms of bias and correlation coefficient over partially cloudy sky, better performances are found for SSI_HRV (0.34 W/m 2 and 0.995, respectively) than SSI_VIS (−33.69 W/m 2 and 0.862) at the expense of the greater run-time necessary to process HRV data channel.

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Fog Detection Based on Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager High Resolution Visible Channel

Cited by 20 publications

References 20 publications

First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib

First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib

Analysis of Livorno Heavy Rainfall Event: Examples of Satellite-Based Observation Techniques in Support of Numerical Weather Prediction

Improvement in Surface Solar Irradiance Estimation Using HRV/MSG Data

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