Daily Rainfall Patterns During Storm “Daniel” Based on Different Satellite Data
Extreme rainfall from a long-lived weather system called storm “Daniel” occurred from 4th to 11th September 2023 over the central and eastern Mediterranean, leading to many devastating flood events mainly in central Greece and the western coastal parts of Libya. This study analyzes the daily rainfal...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/15/11/1277 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850149506799108096 |
|---|---|
| author | Stavros Kolios Niki Papavasileiou |
| author_facet | Stavros Kolios Niki Papavasileiou |
| author_sort | Stavros Kolios |
| collection | DOAJ |
| description | Extreme rainfall from a long-lived weather system called storm “Daniel” occurred from 4th to 11th September 2023 over the central and eastern Mediterranean, leading to many devastating flood events mainly in central Greece and the western coastal parts of Libya. This study analyzes the daily rainfall amounts over all the affected geographical areas during storm “Daniel” by comparing three different satellite-based rainfall data products. Two of them are strictly related to Meteosat multispectral imagery, while the other one is based on the Global Precipitation Measurement (GPM) satellite mission. The satellite datasets depict extreme daily rainfall (up to 450 mm) for consecutive days in the same areas, with the spatial distribution of such rainfall amounts covering thousands of square kilometers almost during the whole period that the storm lasted. Moreover, the spatial extent of the heavy rainfall patterns was calculated on a daily basis. The convective nature of the rainfall, which was also recorded, characterizes the extremity of this weather system. Finally, the intercomparison of the datasets used highlights the satisfactory efficiency of the examined satellite datasets in capturing similar rainfall amounts in the same areas (daily mean error of 15 mm, mean absolute error of up to 35 mm and correlation coefficient ranging from 0.6 to 0.9 in most of the examined cases). This finding confirms the realistic detection and monitoring of the different satellite-based rainfall products, which should be used for early warning and decision-making regarding potential flood events. |
| format | Article |
| id | doaj-art-0e9aa4afb6514536a6fb35b7ab61c8c2 |
| institution | OA Journals |
| issn | 2073-4433 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-0e9aa4afb6514536a6fb35b7ab61c8c22025-08-20T02:26:54ZengMDPI AGAtmosphere2073-44332024-10-011511127710.3390/atmos15111277Daily Rainfall Patterns During Storm “Daniel” Based on Different Satellite DataStavros Kolios0Niki Papavasileiou1Department of Aerospace Science and Technology, Faculty of Sciences, National Kapodistrian University of Athens (NKUA), 34400 Psachna, GreeceDepartment of Aerospace Science and Technology, Faculty of Sciences, National Kapodistrian University of Athens (NKUA), 34400 Psachna, GreeceExtreme rainfall from a long-lived weather system called storm “Daniel” occurred from 4th to 11th September 2023 over the central and eastern Mediterranean, leading to many devastating flood events mainly in central Greece and the western coastal parts of Libya. This study analyzes the daily rainfall amounts over all the affected geographical areas during storm “Daniel” by comparing three different satellite-based rainfall data products. Two of them are strictly related to Meteosat multispectral imagery, while the other one is based on the Global Precipitation Measurement (GPM) satellite mission. The satellite datasets depict extreme daily rainfall (up to 450 mm) for consecutive days in the same areas, with the spatial distribution of such rainfall amounts covering thousands of square kilometers almost during the whole period that the storm lasted. Moreover, the spatial extent of the heavy rainfall patterns was calculated on a daily basis. The convective nature of the rainfall, which was also recorded, characterizes the extremity of this weather system. Finally, the intercomparison of the datasets used highlights the satisfactory efficiency of the examined satellite datasets in capturing similar rainfall amounts in the same areas (daily mean error of 15 mm, mean absolute error of up to 35 mm and correlation coefficient ranging from 0.6 to 0.9 in most of the examined cases). This finding confirms the realistic detection and monitoring of the different satellite-based rainfall products, which should be used for early warning and decision-making regarding potential flood events.https://www.mdpi.com/2073-4433/15/11/1277Meteosathydrology satellite application facilitiesstorm Danielsatellite-based rainfall dataGPM |
| spellingShingle | Stavros Kolios Niki Papavasileiou Daily Rainfall Patterns During Storm “Daniel” Based on Different Satellite Data Atmosphere Meteosat hydrology satellite application facilities storm Daniel satellite-based rainfall data GPM |
| title | Daily Rainfall Patterns During Storm “Daniel” Based on Different Satellite Data |
| title_full | Daily Rainfall Patterns During Storm “Daniel” Based on Different Satellite Data |
| title_fullStr | Daily Rainfall Patterns During Storm “Daniel” Based on Different Satellite Data |
| title_full_unstemmed | Daily Rainfall Patterns During Storm “Daniel” Based on Different Satellite Data |
| title_short | Daily Rainfall Patterns During Storm “Daniel” Based on Different Satellite Data |
| title_sort | daily rainfall patterns during storm daniel based on different satellite data |
| topic | Meteosat hydrology satellite application facilities storm Daniel satellite-based rainfall data GPM |
| url | https://www.mdpi.com/2073-4433/15/11/1277 |
| work_keys_str_mv | AT stavroskolios dailyrainfallpatternsduringstormdanielbasedondifferentsatellitedata AT nikipapavasileiou dailyrainfallpatternsduringstormdanielbasedondifferentsatellitedata |