scholarly journals A statistical approach for rain intensity differentiation using Meteosat Second Generation–Spinning Enhanced Visible and InfraRed Imager observations

2014 ◽  
Vol 18 (7) ◽  
pp. 2559-2576 ◽  
Author(s):  
E. Ricciardelli ◽  
D. Cimini ◽  
F. Di Paola ◽  
F. Romano ◽  
M. Viggiano
Keyword(s):  
Second Generation ◽  
Rain Rate ◽  
Mediterranean Area ◽  
Skill Score ◽  
Probability Of Detection ◽  
Bias Score ◽  
Brightness Temperatures ◽  
Radar Network ◽  
Infrared Imager ◽  
The Mediterranean

Abstract. 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%).

scholarly journals A statistical approach for rain class evaluation using Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager observations

2013 ◽  
Vol 10 (11) ◽  
pp. 13671-13706
Author(s):  
E. Ricciardelli ◽  
D. Cimini ◽  
F. Di Paola ◽  
F. Romano ◽  
M. Viggiano
Keyword(s):  
Second Generation ◽  
Rain Rate ◽  
Heavy Rain ◽  
Mediterranean Area ◽  
Skill Score ◽  
Probability Of Detection ◽  
Bias Score ◽  
Weather Radars ◽  
Brightness Temperatures ◽  
Infrared Imager

Abstract. 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%.

Intercomparison of inversion algorithms to retrieve rain rate from SSM/I by using an extended validation set over the Mediterranean area

2004 ◽  
Vol 42 (10) ◽  
pp. 2226-2239 ◽  
Author(s):  
N. Pierdicca ◽  
L. Pulvirenti ◽  
F.S. Marzano ◽  
P. Ciotti ◽  
P. Basili ◽  
...  
Keyword(s):  
Rain Rate ◽  
Mediterranean Area ◽  
The Mediterranean ◽  
Validation Set

scholarly journals Multi-Index Rain Detection: A New Approach for Regional Rain Area Detection from Remotely Sensed Data

2014 ◽  
Vol 15 (6) ◽  
pp. 2314-2330 ◽  
Author(s):  
Shruti Upadhyaya ◽  
R. Ramsankaran
Keyword(s):  
Skill Score ◽  
Probability Of Detection ◽  
Validation Dataset ◽  
Remotely Sensed Data ◽  
Rain Area ◽  
Study Region ◽  
New Approach ◽  
Multi Index ◽  
Brightness Temperatures ◽  
Combined Indices

Abstract In this article, a new approach called Multi-Index Rain Detection (MIRD) is suggested for regional rain area detection and was tested for India using Kalpana-1 satellite data. The approach was developed based on the following hypothesis: better results should be obtained for combined indices than an individual index. Different combinations (scenarios) were developed by combining six commonly used rain detection indices using AND and OR logical connectives. For the study region, an optimal rain area detection scenario and optimal threshold values of the indices were found through a statistical multi-decision-making technique called the Technique for Order Preference by Similarity Ideal Solution (TOPSIS). The TOPSIS analysis was carried out based on independent categorical statistics like probability of detection, probability of no detection, and Heidke skill score. It is noteworthy that for the first time in literature, an attempt has been made (through sensitivity analysis) to understand the influence of the proportion of rain/no-rain pixels in the calibration/validation dataset on a few commonly used statistics. Thus, the obtained results have been used to identify the above-mentioned independent categorical statistics. Based on the results obtained and the validation carried out with different independent datasets, scenario 8 (TIRt &lt; 260 K and TIRt − WVt &lt; 19 K, where TIRt and WVt are the brightness temperatures from thermal IR and water vapor, respectively) is found to be an optimal rain detection index. The obtained results also indicate that the texture-based indices [standard deviation and mean of 5 × 5 pixels at time t (mean5)] did not perform well, perhaps because of the coarse resolution of Kalpana-1 data. It is also to be noted that scenario 8 performs much better than the Roca method used in the Indian National Satellite (INSAT) Multispectral Rainfall Algorithm (IMSRA) developed for India.

scholarly journals Evaluation of a Satellite Multispectral VIS–IR Daytime Statistical Rain-Rate Classifier and Comparison with Passive Microwave Rainfall Estimates

2009 ◽  
Vol 48 (2) ◽  
pp. 284-300 ◽  
Author(s):  
Davide Capacci ◽  
Federico Porcù
Keyword(s):  
Rain Rate ◽  
Skill Score ◽  
Passive Microwave ◽  
Statistical Parameters ◽  
Earth Observing System ◽  
Infrared Imager ◽  
Validation Procedure ◽  
Polar Orbiting Satellite ◽  
Infrared Radiances ◽  
Ann Classifier

Abstract A daytime surface rain-rate classifier, based on artificial neural networks (ANNs), is proposed for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat-8 geostationary satellite. It is developed over the British Isles and surrounding waters, where the Met Office radar network provided the “ground precipitation truth” for training and validation. The algorithm classifies rain rate in five classes at 15 min and 5 km of time and spatial resolution, and is applied on daytime hours in a summer and winter database. A further ANN application is restricted to hours between 1200 and 1400 UTC for which the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) on board the Aqua polar-orbiting satellite scans the U.K. area: ANN-classifier algorithms for the SEVIRI and AMSR-E data have been developed and the results have been compared. A reliable validation procedure is adopted to quantify the performance in view of the operational application of the daytime classifier and to investigate the relative skills of passive microwave and visible–infrared radiances in sensing precipitation if processed with equivalent algorithms. The key statistical parameters used are the equitable threat score (ETS) and the bias for rain–no rain classes and the Heidke skill score (HSS) for rain-rate classes. The SEVIRI daytime classifier shows, for mean seasonal conditions, the best performance in summer, with ETS = 47% and HSS = 22%, and in winter ETS = 36% and HSS = 17% were found. The comparison between AMSR-E and SEVIRI noon classifiers reveals a similar overall skill: in detecting rain areas, SEVIRI is slightly better than AMSR-E, while the opposite is true for rain-rate classification.

scholarly journals RAINBOW: An Operational Oriented Combined IR-Algorithm

2020 ◽  
Vol 12 (15) ◽  
pp. 2444
Author(s):  
Leo Pio D’Adderio ◽  
Silvia Puca ◽  
Gianfranco Vulpiani ◽  
Marco Petracca ◽  
Paolo Sanò ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Probability Of Detection ◽  
Precipitation Rate ◽  
Precipitation Pattern ◽  
European Organization ◽  
Surface Precipitation ◽  
Radar Network ◽  
Infrared Imager ◽  
Precipitation Estimates ◽  
Rain Gauge Network

In this paper, precipitation estimates derived from the Italian ground radar network (IT GR) are used in conjunction with Spinning Enhanced Visible and InfraRed Imager (SEVIRI) measurements to develop an operational oriented algorithm (RAdar INfrared Blending algorithm for Operational Weather monitoring (RAINBOW)) able to provide precipitation pattern and intensity. The algorithm evaluates surface precipitation over five geographical boxes (in which the study area is divided). It is composed of two main modules that exploit a second-degree polynomial relationship between the SEVIRI brightness temperature at 10.8 µm TB10.8 and the precipitation rate estimates from IT GR. These relationships are applied to each acquisition of SEVIRI in order to provide a surface precipitation map. The results, based on a number of case studies, show good performance of RAINBOW when it is compared with ground reference (precipitation rate map from interpolated rain gauge measurements), with high Probability of Detection (POD) and low False Alarm Ratio (FAR) values, especially for light to moderate precipitation range. At the same time, the mean error (ME) values are about 0 mmh−1, while root mean square error (RMSE) is about 2 mmh−1, highlighting a limited variability of the RAINBOW estimations. The precipitation retrievals from RAINBOW have been also compared with the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) official microwave (MW)/infrared (IR) combined product (P-IN-SEVIRI). RAINBOW shows better performances than P-IN-SEVIRI, in terms of both detection and estimates of precipitation fields when they are compared to the ground reference. RAINBOW has been designed as an operational product, to provide complementary information to that of the national radar network where the IT GR coverage is absent, or the quality (expressed in terms of Quality Index (QI)) of the RAINBOW estimates is low. The aim of RAINBOW is to complement the radar and rain gauge network supporting the operational precipitation monitoring.

Spatial Distribution of Pre-Warm Front Rainfall in the Mediterranean Area

1988 ◽  
Vol 19 (1) ◽  
pp. 53-64 ◽  
Author(s):  
C. Corradini ◽  
F. Melone
Keyword(s):  
Mediterranean Area ◽  
Compact Structure ◽  
Large Variability ◽  
Air Mass ◽  
Orographic Effects ◽  
Warm Front ◽  
Average Rainfall ◽  
Convective Motions ◽  
Rainfall Type ◽  
The Mediterranean

Evidence is given of the distribution of pre-warm front rainfall at the meso-γ scale, together with a discussion of the main mechanisms producing this variability. An inland region in the Mediterranean area is considered. The selected rainfall type is commonly considered the most regular inasmuch as it is usually unaffected by extended convective motions. Despite this, within a storm a large variability in space was observed. For 90% of measurements, the typical deviations from the area-average total depth ranged from - 40 to 60 % and the storm ensemble-average rainfall rate over an hilly zone was 60 % greater than that in a contiguous low-land zone generally placed upwind. This variability is largely explained in terms of forced uplift of air mass over an envelope type orography. For a few storms smaller orographic effects were found in locations influenced by an orography with higher slopes and elevations. This feature is ascribed to the compact structure of these mountains which probably determines a deflection of air mass in the boundary layer. The importance of this type of analysis in the hydrological practice is also emphasized.

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