It has been reported that brightness temperatures (TBs) measured by Advanced Microwave Scanning Radiometer 2 (AMSR2) show discrepancies compared to those measured by other microwave (MW) radiometers. To address this issue, the interconversion coefficients between AMSR2 and AMSR-E were developed by Japan Aerospace Exploration Agency (JAXA) and have been applied in many studies. Despite this, this study shows that the single differences (SDs) remain in AMSR2 TBs even after applying the JAXA-provided coefficients. In this study, a set of bias correction coefficients for AMSR2 is fit for each$20^{\circ } \times 20^{\circ }$grid box to eliminate these residual SDs over the calm global ocean. The root-mean-square differences (RMSDs) between the observed and simulated TBs are significantly reduced after applying the bias correction scheme developed in this study by at least 64.40% at 7.3-GHz v-pol and at most 86.42% at 10.65-GHz v-pol during descending orbits. The estimation of two-scale roughness after the bias correction agrees with the physically expected values, demonstrating that the bias correction of AMSR2 TBs from this study can improve the accuracy of oceanic parameter estimation.

Expansion of the Arctic melting season with an earlier melt onset date (MOD) is a well-known indicator of Arctic warming. Since 1979, the pan-Arctic MOD distributions usually have been estimated using passive satellite microwave radiometer observations. However, there is a poor agreement in MOD between previous MOD detection algorithms based on passive microwave measurements, raising doubts regarding the accuracy of their MOD products. Thus, this study developed a new MOD algorithm, namely TBmax algorithm, to improve the estimation accuracy of continuous melt onset. The TBmax algorithm utilizes the microwave radiation characteristics of sea ice, and the daily brightness temperature time series shows their maximum brightness temperature on MOD. By using Advanced Microwave Scanning Radiometer 2 brightness temperature data, the pan-Arctic MOD distributions estimated from 2013 to 2021 using the TBmax algorithm successfully reproduced a feature of sea ice melting that mainly during May or June over the Arctic, including the late melting tendency of ice at high latitudes and multiyear ice. Validation with independent dataset (ice mass balance buoy data) suggested that the TBmax MODs showed superior performance compared to other previous algorithms (biases of 0.1 days vs. -2.7 and 13.9 days). As MOD can provide information about surface emissivity and the energy budget of the sea ice, the improved MOD may contribute to a more precise analysis of Arctic environment change and enhanced estimation of sea ice parameters.

Microwave sounder observations are essential for numerical weather prediction (NWP) systems, but utilizing channels sensitive to surface over sea ice has been challenging due to difficulties in estimating the sea ice surface radiance. This study presents a pre-processing method to assimilate near-surface microwave sounding observations over winter sea ice, including an estimation of a real-time surface emissivity from satellite radiance and a bias correction scheme to minimize the radiance discrepancy between observation and model simulation. Our results show that the radiance simulated using dynamic emissivity exhibits a much better agreement with the measured one, although a significant negative bias of about 0.61 to 1.18 K remains over the winter sea ice. Thus a new bias correction procedure, based on the regression relationships between the residual bias and potential bias sources such as the surface temperature and surface emissivity, is added. When it is applied, the remaind bias were successfully estimated. Moreover, the sea ice observations from all temperature sounding channels have been better utilized in the Korean Integrated Model. The additional information on the polar regions has increased the analysis increment and reduced the ensemble spread. In addition, a neutral to slightly positive impact in temperature analysis errors in layers sensitive to surface radiance encourages further utilization of microwave sounder data over sea ice.

Information on snow depth on sea ice and bulk sea ice density is required to convert CryoSat-2 radar freeboard (hf) into sea ice thickness (SIT). It is difficult to obtain their information on an Arctic basin scale; therefore, most CryoSat-2 SIT products largely rely on the distributions of snow depth and bulk sea ice density derived from parameterizations, which are based on sea ice type and climatological values. Several observational studies have found that the distributions of parameterized variables are inaccurate compared to the actual distributions. This study aims to develop a new type of retrieval algorithm for snow depth, SIT and bulk density, and ice freeboard in the Arctic winter by synergizing active CryoSat-2 with passive microwave and infrared measurements. Two parameterizations for the snow-ice thickness ratio and bulk sea ice density were combined with the hydrostatic balance and radar wave speed correction equations. Consequently, solutions for the four target variables were obtained and applied to different CryoSat-2hf, derived from empirical and waveform-fitting retracker algorithms. The retrieved thickness-related parameters based onhffrom the lognormal waveform-fitting retracker algorithm showed good agreement with the airborne snow depth, total freeboard, and mooring ice draft measurements. The retrieved multiyear sea ice bulk density was significantly higher than the value of 882 kg m-3, which was used in the previous density parameterization, showing a higher agreement with values from in-situ measurements. The spatial and interannual variabilities of SIT increased when the results from this study were compared with those based on previous parameterizations.

A two-scale ocean surface emissivity model tuned to WindSat and Special Sensor Microwave/Imager (SSM/I) polarimetric brightness temperatures for general passive microwave applications is detailed. The model provides a full Stokes vector emissivity calculation at arbitrary microwave frequencies and observation angles for wind speeds of up to 15 m/s. During model development, it was found that the untuned two-scale model generally produced plausible azimuthal behavior in the ocean surface emissivity vector; however, large discrepancies between the untuned model and WindSat and SSM/I observations were observed, in particular for the zeroth-azimuthal-harmonic coefficients. These discrepancies can be ascribed to inaccuracies in contemporary ocean foam coverage and emissivity models. Accordingly, foam influences were treated using machine-tunable correction parameters incorporated as a means of improving and extending the physically based two-scale model. In addition, a hydrodynamic modulation function and the lower cutoff wavenumber for small-scale perturbation integration were treated as empirically tunable. Model tuning was performed by minimizing the$\chi ^{2}$metric over all available wind bins, channel frequencies, polarizations, and azimuthal harmonics. The result is an approximately eightfold reduction in$\chi ^{2}$from its initial untuned model value, indicating that machine tuning can considerably reduce model errors inherent in the two-scale model to levels acceptable for oceanic passive microwave remote sensing applications. The tuned model is independently validated against NASA Global Precipitation Measurement Microwave Instrument (GMI) measurements. The tuned model and GMI observed emissivities, when scaled to the surface temperature, are in agreement to within ±0.3 K root-mean-square (rms) error, thus suggesting good applicability of the model over a wide range of microwave frequencies and wind speeds.

In this study, a method for estimating two-scale roughness influences on the ocean surface emissivity is developed by solving a simplified two-scale ocean emissivity model equation. In this model, scatterings by small-scale roughness are described by the Kirchhoff approximation. For large-scale roughness, the mean local incidence angle (LIA) is introduced to describe slanted surface slope deviation from flat surface. This study focuses on the ocean state under low/moderate wind conditions in order to preclude foam and anisotropic influences within the model. Consequently, a unique pair of two-scale roughness parameters are estimated from the equation using observed ocean emissivities from AMSR2-measured radiances. The results show that the estimated small-scale roughness at 6.925 and 10.65 GHz is linearly correlated with the 10-m height wind speed$U_{10}$. As the frequency reaches 36.5 GHz, however, the scatters between small-scale roughness and$U_{10}$are increased, which suggests that the Kirchhoff bistatic scattering function is not fully suitable to describe the small-scale roughness at this frequency. The linear relationships between mean LIA and$U_{10}$are found with high correlation coefficients. In addition, the estimated mean LIA corresponds well with associated roughness calculated from both observed and modeled ocean wave height spectra. This evidence demonstrates that the proposed large-scale roughness parameterization is physically meaningful and, therefore, the mean LIA has a physical basis in large-scale roughness. In addition, the strong correlations between the roughness parameters and$U_{10}$demonstrate the possibility to estimate$U_{10}$from the AMSR2 data using intermediate parameters that are physically based on ocean surface characteristics.

Retrievals of sea ice thickness from passive microwave measurements have been limited to thin ice because microwaves penetrate at most the upper 50 cm of sea ice. To overcome such a limitation, a method of retrieving Arctic basin-scale ice thickness is developed. The physical background of this method is that the scattering optical thickness at microwave frequencies within the freeboard layer is linearly proportional to the physical thickness of the ice freeboard. In this study, we relate the optical thickness estimated from the Advanced Microwave Scanning Radiometer 2 (AMSR2) with ice freeboard estimated from the CryoSat-2 (CS2) by employing a piecewise linear fit. The results show a strong linear relationship between the AMSR2-estimated and CS2-measured ice freeboards with a correlation coefficient of 0.85 and bias and RMSE of 0.0001 and 0.04 m, respectively; this evidence suggests that the method can provide Arctic basin-scale ice freeboard with a comparable accuracy level of CS2. The method is also applied to estimate ice freeboard for the periods of the Scanning Multichannel Microwave Radiometer (SMMR) (1978-1987) and AMSR-E (2002-2011). It is shown that the area-averaged ice freeboard has decreased significantly with the linear trends of 1.5 cm/decade. In addition, there seems to be a change of ice freeboard distributions over the Arctic. Furthermore, the algorithm is extended to the ice thickness retrieval by using the hydrostatic balance equation, showing that operational basin-scale ice thickness retrieval will be possible from satellite passive microwave measurements if a realistic snow depth on sea ice is employed.

A full-Stokes vector model for microwave ocean surface emissivity based on two-scale theory and incorporating a wide range of published results is being developed for broadband microwave satellite data assimilation. The model is based on the six different modules such as ocean surface permittivity, Fresnel emission, omnidirectional wave height spectrum, small-scale perturbation, large-scale correction, and foam effect modules. The results showed that the calculated Full-stokes emissivities range in theoretically expected values and logically understandable variation with respect to incidence and azimuth angle. This model will be used for understanding of the emissivity uncertainties due to inputs of permittivity, ocean height distribution, and foam influences and for assimilation of fully-polarimetric satellite microwave radiances.

We propose an image-based visual servoing algorithm of a wheeled mobile robot utilizing a ceiling mounted camera. The visual navigation of a mobile robot is defined as an unconstrained optimization problem to minimize the image difference between the goal position and the position of a mobile robot in the image plane. Full New ton's method is applied for unconstrained optimization. We also propose a method to find the Hessian term of the image error using a secant approximation method. The performance of visual servoing was evaluated using the simulation.

This paper suggests a robustness bound of modeling errors in computed torque linearization for control of a manipulator in a contact task. Modelling errors due to sensing and estimation errors can cause instability of the overall control. A stability condition for the modelling errors is derived from the error dynamics using the operator theory and the small gain theorem. The result is a single equation, which gives an allowable bound in a combined form of the inertia modelling error and Jacobian estimation error. It shows that the Jacobian estimation errors degrade the stability when the force feedback is used in the computed torque linearization. Especially for control of a manipulator in a stiff environment, the Jacobian should be estimated accurately. The Coriolis and nonlinear terms are not as critical factors as inertia and Jacobian estimation. The bound can be used in controller design, which is insensitive to the modelling errors.