Publication Type:

Journal Article

Source:

Asia-Pacific Journal of Atmospheric Sciences (APJAS), Springer, p.1–10 (2018)

URL:

http://link.springer.com/article/10.1007/s13143-018-0084-1

Keywords:

Ice Water Path, Megha-Tropiques, neural network, SAPHIR

Abstract:

This study derives the ice water path of the atmospheric column from the microwave sounder SAPHIR onboard Megha-Tropiques. SAPHIR (Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie) is a cross-track, multichannel microwave humidity sounder with six channels ranging from 183.3 ± 0.2 to 183.3 ± 11 GHz near the 183.31 GHz water vapor absorption line. It measures the earth emitted radiation at these six frequencies. In this paper, Concurrent and collocated observations of Channel 183.31 ± 6.6 GHz, and 183.3 ± 11 GHz from SAPHIR and IWP (Ice water Path) from CloudSat have been used in the development the algorithm. A total of five sets of neural network model, each for 10° of incidence angle of SAPHIR have been developed. The model shows a correlation of 0.83 and RMSE of 195 g/m2 with an independent test dataset. The validation of the algorithm has been done by comparing the retrieval with various satellite derived IWP products such as CloudSat, GMI (Global precipitation measuring mission Microwave Imager) and MSPPS (Microwave Surface and Precipitation Products System). The instantaneous comparisons of IWP over a cyclonic storm ROANU demonstrate a good agreement between NN (Neural Network) derived IWP and CloudSat. A probability distribution of IWP indicates consistency between SAPHIR and CloudSat. A comparison of zonal mean between all the IWP products shows that SAPHIR performs better than GMI, and MSPPS.

Cite this Research Publication

D. N. Piyush, Jyotirmayee Satapathy, and Srinivasan, J., “Remote Sensing of Ice Water Path for Mega-Tropiques-SAPHIR”, Asia-Pacific Journal of Atmospheric Sciences (APJAS), pp. 1–10, 2018.