The accuracy of integrated precipitable water vapor (IPWV) estimates by using scanning microwave radiometers, Global Positioning System receivers, and radiosondes is assessed by analyzing the measurements from four experimental campaigns that have been carried on in various environments since 1999. The first two campaigns were conducted in Italy, in Cagliari in 1999, and in Perugia and Elba Island in 2000-2002, by the Remote Sensing groups of the University of Perugia, L’Aquila and by the Fondazione Ugo Bordoni. The last two field experiments were conducted by the US Department of Energy’s Atmospheric Radiation Measurement program (ARM) in Oklahoma, USA, in 2003, and Alaska, USA, in 2004. Our results have shown that the IPWV accuracy is on the order of 2 mm, and reduces to 1 mm or less during clear skies and with the availability of co-located instruments. Our studies have also indicated the potential of using a neural network with respect to a polynomial regression for IPWV retrievals during rainfall.
Experimental campaigns for IPWV estimates in the atmosphere: comparisons between dual-channel microwave radiometers, Global Positioning Systems and radiosondes
BASILI, Patrizia;BONAFONI, Stefania;
2006
Abstract
The accuracy of integrated precipitable water vapor (IPWV) estimates by using scanning microwave radiometers, Global Positioning System receivers, and radiosondes is assessed by analyzing the measurements from four experimental campaigns that have been carried on in various environments since 1999. The first two campaigns were conducted in Italy, in Cagliari in 1999, and in Perugia and Elba Island in 2000-2002, by the Remote Sensing groups of the University of Perugia, L’Aquila and by the Fondazione Ugo Bordoni. The last two field experiments were conducted by the US Department of Energy’s Atmospheric Radiation Measurement program (ARM) in Oklahoma, USA, in 2003, and Alaska, USA, in 2004. Our results have shown that the IPWV accuracy is on the order of 2 mm, and reduces to 1 mm or less during clear skies and with the availability of co-located instruments. Our studies have also indicated the potential of using a neural network with respect to a polynomial regression for IPWV retrievals during rainfall.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
