LAUSR

Water vapor (WV) is the most variable greenhouse gas in the troposphere, therefore investigation of its spatio-temporal distribution and motion is of great importance in meteorology and climatology studies. Here, we suggest a new strategy for augmenting integrated water vapor (IWV) estimations using both remote sensing satellites and global positioning system (GPS) tropospheric path delays. The strategy is based first on the ability to estimate METEOSAT-10 7.3 µm WV pixel values by extracting the mathematical dependency between the IWV amount calculated from GPS zenith wet delays (ZWD) and the METEOSAT-10 data. We then use the surface temperature differences between ground station measurements and METEOSAT-10 10.8 µm infra-red (IR) channel to identify spatio-temporal cloud distribution structures. As a last stage, the identified cloud features are mapped into the GPS-IWV distribution map when preforming the interpolation between adjusted GPS station inside the network. The suggested approach improves the accuracy of estimated regional IWV maps, in comparison with radiosonde data, thus enables to obtain the total water amount at the atmosphere, both in the form of clouds and vapor. Mean and root mean square (RMS) difference between the GPS-IWV estimations, using the spatio-temporal clouds distribution, and radiosonde data are reduced from 1.77 and 2.81 kg/m2 to 0.74 and 2.04 kg/m2, respectively. Furthermore, by improving the accuracy of the estimated regional IWV maps distribution it is possible to increase the accuracy of regional Numerical Weather Prediction (NWP) platforms.