A global weighted mean temperature model based on empirical orthogonal function analysis

A global empirical orthogonal function (EOF) model of the tropospheric weighted mean temperature called GEOFM_Tm was developed using high-precision Global Geodetic Observing System (GGOS) Atmosphere Tm data during the years 2008-2014. Due to the quick convergence of EOF decomposition, it is possible to use the first four EOF series, which consists base functions Uk and associated coefficients Pk, to represent 99.99% of the overall variance of the original data sets and its spatial-temporal variations. Results show that U1 displays a prominent latitude distribution profile with positive peaks located at low latitude region. U2 manifests an asymmetric pattern that positive values occurred over 30° in the Northern Hemisphere, and negative values were observed at other regions. U3 and U4 displayed significant anomalies in Tibet and North America, respectively. Annual variation is the major component of the first and second associated coefficients P1 and P2, whereas P3 and P4 mainly reflects both annual and semi-annual variation components. Furthermore, the performance of constructed GEOFM_Tm was validated by comparison with GTm_III and GTm_N with different kinds of data including GGOS Atmosphere Tm data in 2015 and radiosonde data from Integrated Global Radiosonde Archive (IGRA) in 2014. Generally speaking, GEOFM_Tm can achieve the same accuracy and reliability as GTm_III and GTm_N models in a global scale, even has improved in the Antarctic and Greenland regions. The MAE and RMS of GEOFM_Tm tend to be 2.49 K and 3.14 K with respect to GGOS Tm data, respectively; and 3.38 K and 4.23 K with respect to IGRA sounding data, respectively. In addition, those three models have higher precision at low latitude than middle and high latitude regions. The magnitude of Tm remains at the range of 220-300 K, presented a high correlation with geographic latitude. In the Northern Hemisphere, there was a significant enhancement at high latitude region reaching 270 K during summer. GEOFM_Tm is capable to represent the spatiotemporal variations of Tm, with the high accuracy and reliability in a global scale, therefore, will be of great significance to the real-time GNSS water vapor inversion and climate studies.