ALBERT

Description: On 6 February 2013 an Mw = 8.0 subduction earthquake occurred close to Santa Cruz Islands at the transition between the Solomon and the New Hebrides Trench. The ensuing tsunami caused significant inundation on the closest Nendo Island. The seismic source was studied with teleseismic broadband P-wave inversion optimized with tsunami forward modelling at DART buoys (Lay et al., 2013) and with inversion of teleseismic body and surface waves (Hayes et al., 2014a). The two studies also use different hypocentres and different planar fault models and found quite different slip models. In particular, Hayes et al. (2014a) argued for an aseismic slip patch SE from the hypocentre. We here develop a 3-D model of the fault surface from seismicity analysis and retrieve the tsunami source by inverting DART and tide-gauge data. Our tsunami source model features a main slip patch (peak value of ~ 11 m) SE of the hypocentre and reaching the trench. The rake direction is consistent with the progressively more oblique plate convergence towards the Solomon trench. The tsunami source partially overlaps the hypothesized aseismic slip area, which then might have slipped coseismically.

Description: We present the realization of a fault-source data set designed to become the starting point in regional-scale tsunami hazard studies. Our approach focuses on the parametric fault characterization in terms of geometry, kinematics, and assessment of activity rates, and includes a systematic classification in six justification levels of epistemic uncertainty related with the existence and behaviour of fault sources. We set up a case study in the central Mediterranean Sea, an area at the intersection of the European, African, and Aegean plates, characterized by a complex and debated tectonic structure and where several tsunamis occurred in the past. Using tsunami scenarios of maximum wave height due to crustal earthquakes (Mw=7) and subduction earthquakes (Mw=7 and Mw=8), we illustrate first-order consequences of critical choices in addressing the seismogenic and tsunamigenic potentials of fault sources. Although tsunamis generated by Mw=8 earthquakes predictably affect the entire basin, the impact of tsunamis generated by Mw=7 earthquakes on either crustal or subduction fault sources can still be strong at many locales. Such scenarios show how the relative location/orientation of faults with respect to target coastlines coupled with bathymetric features suggest avoiding the preselection of fault sources without addressing their possible impact onto hazard analysis results.

Description: On 6 February 2013 an Mw 8.0 subduction earthquake occurred close to Santa Cruz Islands at the transition between the Solomon and the New Hebrides Trench. The ensuing tsunami caused significant inundation on the closest Nendo Island. The seismic source was studied with teleseismic broadband P waves inversion optimized with tsunami forward modeling at DART buoys (Lay et al., 2013), and with inversion of teleseismic body and surface waves (Hayes et al., 2014). The two studies also use different hypocenters and different planar fault models, and found quite different slip models. In particular, Hayes et al. (2014) argued for an aseismic slip patch SE from the hypocenter. We here develop a 3-D model of the fault surface from seismicity analysis and retrieve the tsunami source by inverting DART and tide-gauge data. Our tsunami source model features a main slip patch (peak value of ~11 m) SE of the hypocentre, and reaching to the trench. The rake direction is consistent with the progressively more oblique plate convergence towards the Solomon trench. The tsunami source partially overlaps the hypothesized aseismic slip area, which then might have slipped coseismically.

Description: This paper describes a new multi-sensor approach for convective rain cell continuous monitoring based on rainfall derived from Passive Microwave (PM) remote sensing from the Low Earth Orbit (LEO) satellite coupled with Infrared (IR) remote sensing Brightness Temperature (TB) from the Geosynchronous (GEO) orbit satellite. The proposed technique, which we call Precipitation Evolving Technique (PET), propagates forward in time and space the last available rain-rate (RR) maps derived from Advanced Microwave Sounding Units (AMSU) and Microwave Humidity Sounder (MHS) observations by using IR TB maps of water vapor (6.2 μm) and thermal-IR (10.8 μm) channels from a Spinning Enhanced Visible and Infrared Imager (SEVIRI) radiometer. PET is based on two different modules, the first for morphing and tracking rain cells and the second for dynamic calibration IR-RR. The Morphing module uses two consecutive IR data to identify the motion vector to be applied to the rain field so as to propagate it in time and space, whilst the Calibration module computes the dynamic relationship between IR and RR in order to take into account genesis, extinction or size variation of rain cells. Finally, a combination of the Morphing and Calibration output provides a rainfall map at IR space and time scale, and the whole procedure is reiterated by using the last RR map output until a new MW-based rainfall is available. The PET results have been analyzed with respect to two different PM-RR retrieval algorithms for seven case studies referring to different rainfall convective events. The qualitative, dichotomous and continuous assessments show an overall ability of this technique to propagate rain field at least for 2–3 h propagation time.

Description: This study exploits the Meteosat Second Generation (MSG)–Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations to evaluate the rain class at high spatial and temporal resolutions and, to this aim, proposes the Rain Class Evaluation from Infrared and Visible observation (RainCEIV) technique. RainCEIV is composed of two modules: a cloud classification algorithm which individuates and characterizes the cloudy pixels, and a supervised classifier that delineates the rainy areas according to the three rainfall intensity classes, the non-rainy (rain rate value 〈 0.5 mm h-1) class, the light-to-moderate rainy class (0.5 mm h−1 ≤ rain rate value 〈 4 mm h-1), and the heavy–to-very-heavy-rainy class (rain rate value ≥ 4 mm h-1). The second module considers as input the spectral and textural features of the infrared and visible SEVIRI observations for the cloudy pixels detected by the first module. It also takes the temporal differences of the brightness temperatures linked to the SEVIRI water vapour channels as indicative of the atmospheric instability strongly related to the occurrence of rainfall events. The rainfall rates used in the training phase are obtained through the Precipitation Estimation at Microwave frequencies, PEMW (an algorithm for rain rate retrievals based on Atmospheric Microwave Sounder Unit (AMSU)-B observations). RainCEIV's principal aim is that of supplying preliminary qualitative information on the rainy areas within the Mediterranean Basin where there is no radar network coverage. The results of RainCEIV have been validated against radar-derived rainfall measurements from the Italian Operational Weather Radar Network for some case studies limited to the Mediterranean area. The dichotomous assessment related to daytime (nighttime) validation shows that RainCEIV is able to detect rainy/non-rainy areas with an accuracy of about 97% (96%), and when all the rainy classes are considered, it shows a Heidke skill score of 67% (62%), a bias score of 1.36 (1.58), and a probability of detection of rainy areas of 81% (81%).

Description: Precipitation measurements are essential for short term hydrological and long term climate studies. Operational networks of rain gauges and weather radars provide fairly accurate rain rate measurements, but they leave large areas uncovered. Because of this, satellite remote sensing is a useful tool for the detection and characterization of the raining areas in regions where this information remains missing. This study exploits the Meteosat Second Generation – Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) observations to evaluate the rain class at high spatial and temporal resolutions. The Rain Class Evaluation from Infrared and Visible (RainCEIV) observations technique is proposed. The purpose of RainCEIV is to supply continuous monitoring of convective as well as of stratiform rainfall events. It applies a supervised classifier to the spectral and textural features of infrared and visible MSG-SEVIRI images to classify the cloudy pixels as non rainy, light to moderate rain, or heavy to very heavy rain. The technique considers in input also the water vapour channels brightness temperatures differences for the MSG-SEVIRI images acquired 15/30/45 min before the time of interest. The rainfall rates used in the training phase are obtained with the Precipitation Estimation at Microwave frequencies (PEMW), an algorithm for rain rate retrievals based on Atmospheric Microwave Sounder Unit (AMSU)-B observations. The results of RainCEIV have been validated against radar-derived rainfall measurements from the Italian Operational Weather Radar Network for some case studies limited to the Mediterranean area. The dichotomous assessment shows that RainCEIV is able to detect rainy areas with an accuracy of about 91%, a Heidke skill score of 56%, a Bias score of 1.16, and a Probability of Detection of rainy areas of 66%.

Description: The Precipitation Estimation at Microwave Frequencies (PEMW) algorithm was developed at the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (IMAA-CNR) for inferring surface rain intensity (sri) from satellite passive microwave observations in the range from 89 to 190 GHz. The operational version of PEMW (OPEMW) has been running continuously at IMAA-CNR for two years. The OPEMW sri estimates, together with other precipitation products, are used as input to an operational hydrological model for flood alert forecast. This paper presents the validation of OPEMW against simultaneous ground-based observations from a network of 20 weather radar systems and a network of more than 3000 rain gauges distributed over the Italian Peninsula and main islands. The validation effort uses a data set covering one year (July 2011–June 2012). The effort evaluates dichotomous and continuous scores for the assessment of rain detection and quantitative estimate, respectively, investigating both spatial and temporal features. The analysis demonstrates 98% accuracy in correctly identifying rainy and non-rainy areas; it also quantifies the increased ability (with respect to random chance) to detect rainy and non-rainy areas (0.42–0.45 Heidke skill score) or rainy areas only (0.27–0.29 equitable threat score). Performances are better than average during summer, fall, and spring, while worse than average in the winter season. The spatial–temporal analysis does not show seasonal dependence except over the Alps and northern Apennines during winter. A binned analysis in the 0–15 mm h−1 range suggests that OPEMW tends to slightly overestimate sri values below 6–7 mm h−1 and underestimate sri above those values. With respect to rain gauges (weather radars), the correlation coefficient is larger than 0.8 (0.9). The monthly mean difference and standard deviation remain within ±1 and 2 mm h−1 with respect to rain gauges (respectively −2–0 and 4 mm h−1 with respect to weather radars).

Description: In the last years satellite remote sensing applications in hydrology have considerably progressed. A new multi-temporal satellite data-analysis approach has been recently suggested in order to estimate space-time changes of geophysical parameters possibly related to the increase of environmental and hydro-geological hazards. Such an approach has been already used both for flooded area mapping (using AVHRR data) and for soil wetness index estimation (using AMSU data). In this work, a preliminary sensitivity analysis of the proposed Soil Wetness Variation Index (SWVI) is made in the case of low intensity meteorological events by the comparison with hydrological (precipitation) data. This analysis, as a first step of a more complex work in progress, is targeted to a first evaluation of the reliability of the SWVI in describing soil response to precipitations of different duration and intensity.

Description: The Precipitation Estimation at Microwave Frequencies (PEMW) algorithm was developed at the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (IMAA-CNR) for inferring surface rain intensity (sri) from satellite passive microwave observations in the range from 89 to 190 GHz. The operational version of PEMW (OPEMW) has been running continuously at IMAA-CNR for two years, producing sri estimates feeding an operational hydrological model for forecasting flood alerts. This paper presents the validation of OPEMW against simultaneous ground-based observations obtained by a network of 20 weather radars and a network of more than 3000 rain gauges distributed over the Italian peninsula and main islands. The validation effort uses a data set spanning a one-year period (July 2011–June 2012). The effort evaluates dichotomous and continuous scores for the assessment of rain detection and quantitative estimate, respectively, investigating both spatial and temporal features. The analysis demonstrates 98% accuracy in correctly identifying rainy and non-rainy areas, and it quantifies the increased ability (with respect to random chance) to detect rainy and non-rainy areas (0.42–0.45 Heidke skill score) or rainy areas only (0.27–0.29 equitable threat score). Performances are better than average during summer, fall, and spring, while worse than average in the winter season. The spatial-temporal analysis does not show seasonal dependence except for larger mean absolute difference over the Alps and northern Apennines during winter, attributable to residual effect of snow cover. A binned analysis in the 0–15 mm h−1 range suggests that OPEMW tends to slightly overestimate sri values below 6–7 mm h−1, and to underestimate sri above those values. Depending upon the ground reference (either rain gauges or weather radars), the mean difference is 0.8–2.8 mm h−1, with a standard deviation within 2.6–3.1 mm h−1 and correlation coefficient within 0.8–0.9. The monthly mean difference was shown to remain within ±1 mm h−1 with respect to rain gauges and within −2 mm h−1 with respect to weather radars, with 2–4 mm h−1 standard deviation.