Accurate sea level pressure (SLP) data are critical for the forecasting and monitoring of tropical cyclones (TCs). Previous studies have explored SLP retrieval for TCs using passive microwave observations in a single oxygen band (60 or 118 GHz). Leveraging the Fengyun-3 (FY-3) satellite’s capability to simultaneously observe radiation in both 60 and 118 GHz bands, this study proposes a neural network-based algorithm to retrieve TC SLP fields from combined observations of brightness temperature (TB) and warm TB anomalies in both frequency bands. Application studies were carried out using the joint observations from the Microwave Temperature Sounder-2 (MWTS-2) and Microwave Humidity Sounder-2 (MWHS-2) onboard the FY-3D satellite. Optimal frequency channels for the SLP retrieval were selected based on an information content analysis. SLP retrievals within the TC core regions (within a 2° radius from the centers) were compared with SLP data from the Global Data Assimilation System Final Analysis (GDAS/FNL). The results showed root mean square errors (RMSEs) of 2.78 hPa for tropical depressions and storms, 4.23 hPa for hurricanes or typhoons, and 6.15 hPa for major hurricanes or severe typhoons. Retrievals were also compared within situobservations, and the corresponding RMSEs were 3.82, 4.86, and 5.14 hPa, respectively. Furthermore, comparative experiments between the new algorithm and the previous algorithm that used only observations from a single oxygen band demonstrated that the new algorithm provides more comprehensive SLP information and achieves higher accuracy.

Ice sheet internal temperature profile plays a key role in the study of ice sheet thermodynamics and ice sheet mass balance. Wideband radiometric remote sensing measurement has been demonstrated to be an effective approach for estimating the ice sheet internal temperature profile. In this study, we make model analysis of the sensitivity of emission observation on different frequency channel to ice sheet internal temperature at different depth, aiming at assessing the value of each channel in deriving ice sheet internal temperature profile. As the emission contribution depth profile can give insight into the emission compositions and emission sensitivity to the ice sheet internal properties at different depth, this study analyses the impact on ice sheet emission contribution depth profile due to the uncertainties of ice sheet temperature profile based on Comprehensive Layer Emission Model (CLEM). The correlation of different frequency channel are further discussed. The analysis results give guidance to construct retrieval approach for better extraction and utilization of the information on ice sheet internal temperature profile providing by the wideband emission observation.

The hostile environment and thick sulfuric acid clouds make it difficult to measure the Venusian lower and middle atmosphere. The Venus Volcano Imaging and Climate Explorer (VOICE), which is an orbiting mission, has planned to exploring the thermal structure and composition of the Venusian lower and middle atmosphere by the Microwave Radiometric Sounder (MWRS). This study presents a forward- and inversion- model for the Venusian lower and middle atmosphere at microwave and submillimeter wavelengths, which is developed to simulate the nadir and limb sounding from the MWRS. Important properties of the model with respect to the Venusian atmosphere are its inclusion of molecular thermal movement and collisions induced spectral lines broadening as well as the effects of refraction on the signal propagation in the atmosphere. The model also has potential applications for definition studies for future downward-looking and limb-looking sensors dedicated to Venus exploration.

Reported radiometric measurements indicate that incoherent and coherent radiative transfer processes characterize the microwave emission features of layered irregular and inhomogeneous medium. In order to develop a forward emission model to be capable of considering the medium and boundary scattering and coherent boundary interaction that may exist in general layered medium, in this article, we present a comprehensive layer emission model (CLEM) based on the scattering operator formulation. We introduce wave-based coherent multiple reflection operators to account for coherent boundary interaction and first integrate them with the intensity-based multiple scattering processes, allowing the comprehensive description of rough boundary scattering, volume scattering, medium/boundary interaction, and coherent boundary interaction in the framework of CLEM. Simulations and analyses for ice- and snow-covered ground cases are conducted based on CLEM to evaluate the coherent boundary interaction and different impacting factors. Validation on CLEM is conducted with emission observations of snow-covered terrain in campaign Nordic Snow Radar Experiment (NoSREx) 2010–2013 during dry snow period and time-series emission observations of frozen soil during freezing and thawing processes. CLEM simulation results show a good agreement with measurements. For NoSREx, root-mean-square errors (RMSEs) at L- to Ka-band are below 5.5 K for both polarizations, which are of different levels of promotion compared with the incoherent model simulations, especially for horizontal polarization at L- and X-band. Application to frozen soil case illustrates the capability of CLEM to explain coherent oscillation feature with impact from incoherent scattering effect.

Combined use of active and passive remote sensing can provide complementary information to simultaneously characterize the internal structure and internal physical properties of ice sheet. Well constraints of layering structure and density variation are the precondition to retrieve ice sheet internal temperature profile using radiometric measurements. This paper makes further improvement to the combined active and passive remote sensing approach presented in our previous preliminary study, mainly in density variation profile characterization, emission modeling, and density variation estimation method. In the combined approach, ice-penetrating radar-echo profile is used to estimate the ice sheet density variation, correcting the input density parameter for ice sheet emission model when interpretating the ice sheet emission observations. Validation of the improved combined approach is conducted with SMOS data and radar profiles collected by CReSIS around several ice core sites in Greenland. Evaluation of availability and advantage of the improved combined approach is performed based on the results.

Microwave emission from layered inhomogeneous medium is affected by both incoherent scattering and coherent interaction within the layer. Reported emission observations of snow-covered surface and ice sheet indicate that effects of layer boundary interference are more prominent at low frequencies. This paper presents a comprehensive layer emission model (CLEM) for layered inhomogeneous medium that fully accounts for incoherent scattering and coherent boundary interactions within the layer. The CLEM model is based on scattering operator (matrix doubling) formulation, which provides the basic framework for integrating rough boundary scattering, volume scattering, and coherent multiple reflections at layer boundaries. Coherent boundary interactions are accounted for through novel integrated wave- and intensity-based layer scattering operators. Initial model validation using published Elbara-II data and SodRad data shows good agreement between CLEM predictions and measured results.

Wideband radiometric measurements can be used to retrieve the internal temperature of ice sheet [1]. However, emission from ice sheet is affected by many factors including layering structure, density profile and grain size distribution. These factors are not well constrained due to the lack of ice sheet characterization data, and thus severely limit the ability to retrieve ice internal temperature profile using radiometric measurements. This paper presents a combined active and passive remote sensing approach, in which ice-penetrating radar provides information that constraint the ice sheet parameters, to improve estimation of ice internal temperature profile using radiometric measurements. In this study, radar reflectivity measurements are used for estimating ice density variations, which are input parameters for the ice sheet emission model. Model analysis of emission from ice layer with internal temperature and structural profiles, and comparison with SMOS data are conducted to assess the viability of the combined approach.