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 T_m 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 U_k and associated coefficients P_k, 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 T_m 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 T_m 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 T_m 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 T_m, 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.     

A new weighted mean temperature model in China

The Global Positioning System (GPS) has been applied in meteorology to monitor the change of Precipitable Water Vapor (PWV) in atmosphere, transformed from Zenith Wet Delay (ZWD). A key factor in converting the ZWD into the PWV is the weighted mean temperature ($T_m$), which has a direct impact on the accuracy of the transformation. A number of Bevis-type models, like $T_m-T_s$ and $T_m-T_s\text{,}{P}_s$ type models, have been developed by statistics approaches, and are not able to clearly depict the relationship between $T_m$ and the surface temperature, $T_s$. A new model for $T_m$, called weighted mean temperature norm model (abbreviated as norm model), is derived as a function of $T_s$, the lapse rate of temperature, δ, the tropopause height, $h_{\mathit{trop}}$, and the radiosonde station height, $h_s$. It is found that $T_m$ is better related to $T_s$ through an intermediate temperature. The small effects of lapse rate can be ignored and the tropopause height be obtained from an empirical model. Then the norm model is reduced to a simplified form, which causes fewer loss of accuracy and needs two inputs, $T_s$ and $h_s$. In site-specific fittings, the norm model performs much better, with RMS values reduced averagely by 0.45 K and the Mean of Absolute Differences (MAD) values by 0.2 K. The norm model is also found more appropriate than the linear models to fit $T_m$ in a large area, not only with the RMS value reduced from 4.3 K to 3.80 K, correlation coefficient $R^2$ increased from 0.84 to 0.88, and MAD decreased from 3.24 K to 2.90 K, but also with the distribution of simplified model values to be more reasonable. The RMS and MAD values of the differences between reference and computed PWVs are reduced by on average 16.3% and 14.27%, respectively, when using the new norm models instead of the linear model.     

A new global version of M(3000)F2 prediction model based on artificial neural networks

A new version of global empirical model for the ionospheric propagation factor, M(3000)F2 prediction is presented. Artificial neural network (ANN) technique was employed by considering the relevant geophysical input parameters which are known to influence the M(3000)F2 parameter. This new version is an update to the previous neural network based M(3000)F2 global model developed by Oyeyemi et al. (2007), and aims to address the inadequacy of the International Reference Ionosphere (IRI) M(3000)F2 model (the International Radio Consultative Committee (CCIR) M(3000)F2 model). The M(3000)F2 has been found to be relatively inaccurate in representing the diurnal structure of the low latitude region and the equatorial ionosphere. In particular, the existing hmF2 IRI model is unable to reproduce the sharp post-sunset drop in M(3000)F2 values, which correspond to a sharp post-sunset peak in the peak height of the F2 layer, hmF2. Data from 80 ionospheric stations globally, including a good number of stations in the low latitude region were considered for this work. M(3000)F2 hourly values from 1987 to 2008, spanning all periods of low and high solar activity were used for model development and verification process. The ability of the new model to predict the M(3000)F2 parameter especially in the low latitude and equatorial regions, which is known to be problematic for the existing IRI model is demonstrated.     

Effect of surface roughness on microwave brightness temperature from lunar surface: Numerical analysis with a hybrid method

A hybrid method, combining the radiative transfer theory and the method of moments (MoM), is proposed to study the potential effect of the lunar surface roughness on the microwave brightness temperature. The total upward emission reaching the lunar surface from below media is calculated by the radiative transfer theory, and then the brightness temperature is obtained by weighting the bidirectional transmission coefficients which is computed using the MoM. The method is validated by both flat and rough surface models with analytic solutions. With the hybrid method, brightness temperatures from simulated lunar model are calculated and compared to those from a flat layered model. The comparisons show that the effect of rough surface on brightness temperature cannot be ignored and also depends on many other factors, such as observation angle and polarizations. For vertical polarization, an optimal observation angle may exist to reduce the effect of surface roughness. These results indicate that the knowledge of lunar surface roughness is important in microwave remote sensing to the Moon and may probably provide a guide to lunar projects in future.     

Water surface temperature estimation from Landsat 7 ETM+ thermal infrared data using the generalized single-channel method: Case study of Embalse del Río Tercero (Córdoba,Argentina)

Monitoring of warm distribution in water is fundamental to understand the performance and functioning of reservoirs and lakes. Surface water temperature is a key parameter in the physics of aquatic systems processes since it is closely related to the energy fluxes through the water–atmosphere interface. Remote sensing applied to water quality studies in inland waterbodies is a powerful tool that can provide additional information difficult to achieve by other means. The combination of good real-time coverage, spatial resolution and free availability of data makes Landsat system a proper alternative. Many papers have developed algorithms to retrieve surface temperature (principally, land surface temperature) from at-sensor and surface emissivity data. The aim of this study is to apply the single-channel generalized method (SCGM) developed by Jiménez-Muñoz and Sobrino (2003) for the estimation of water surface temperature from Landsat 7 ETM+ thermal bands. We consider a constant water emissivity value (0.9885) and we compare the results with radiative transfer classic method (RTM).     

Distribution of chlorophyll and harmful algal blooms (HABs): A review on space based studies in the coastal environments of Chinese marginal seas

Monitoring of spatial and temporal distribution of chlorophyll (Chl-a) concentrations in the aquatic milieu is always challenging and often interesting. However, the recent advancements in satellite digital data play a significant role in providing outstanding results for the marine environmental investigations. The present paper is aimed to review ‘remote sensing research in Chinese seas’ within the period of 24 years from 1978 to 2002. Owing to generalized distributional pattern, the Chl-a concentrations are recognized high towards northern Chinese seas than the southern. Moreover, the coastal waters, estuaries, and upwelling zones always exhibit relatively high Chl-a concentrations compared with offshore waters. On the basis of marine Chl-a estimates obtained from satellite and other field measured environmental parameters, we have further discussed on the applications of satellite remote sensing in the fields of harmful algal blooms (HABs), primary production and physical oceanographic currents of the regional seas. Concerned with studies of HABs, satellite remote sensing proved more advantageous than any other conventional methods for large-scale applications. Probably, it may be the only source of authentic information responsible for the evaluation of new research methodologies to detect HABs. At present, studies using remote sensing methods are mostly confined to observe algal bloom occurrences, hence, it is essential to coordinate the mechanism of marine ecological and oceanographic dynamic processes of HABs using satellite remote sensing data with in situ measurements of marine environmental parameters. The satellite remote sensing on marine environment and HABs is believed to have a great improvement with popular application of technology.