Retrieval of tropospheric instability from METEOSAT second generation data

被引：0

作者：

Fuhrhop, R ^{[1
]}

Simmer, C ^{[1
]}

Thiemann, C ^{[1
]}

Kerkmann, J ^{[1
]}

机构：

[1] Inst Marine Sci, D-24105 Kiel, Germany

来源：

IGARSS '98 - 1998 INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, PROCEEDINGS VOLS 1-5: SENSING AND MANAGING THE ENVIRONMENT | 1998年

关键词：

D O I：

10.1109/IGARSS.1998.699560

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

This study investigates the performance of neural network algorithms to sense tropospheric instability with the future METEOSAT Second Genertation satellite. Three algorithm approaches are developed with 'EuropaModell' data: (i) Global algorithms (ii) monthly algorithms, and (iii) so called 'on-line' algorithms which use recently observed data for training. ALI three ap preaches retrieve the Surface K-Index, a modified Surface K-Index and the precipitable water content with high performance, where the best performance is achieved with the 'on-Iine' algorithms. Investigations using 'Lokal Modell' data of a limited sample size show a better representation of the tropospheric instability and an increased retrieval performance, especially for other instability indices.

引用

页码：716 / 718

页数：3