Fengyun-4 (FY-4) is a series of Chinese operational geostationary meteorological satellites, providing crucial data for weather forecasting, climate prediction, and environmental monitoring. Advanced Geostationary Radiation Imagers (AGRI) onboard the FY-4A and FY-4B satellites play a key role in observing the Earth’s surface, oceans, and atmosphere. However, their calibration stability is still uncertain, which clearly limits corresponding downstream applications. This study evaluates the long-term radiometric stability of AGRI reflective solar bands (RSBs) using a general cloud target (CT) calibration method, covering the periods from March 2018 to December 2024 for FY-4A/AGRI and from June 2022 to December 2024 for FY-4B/AGRI. By utilizing MODIS cloud products as references for cloud properties, we simulate the top-of-atmosphere (TOA) reflectances of CTs through the Discrete Ordinates Radiative Transfer (DISORT) model and compare results with observed reflectances to infer the instrumental calibration stability. Our results indicate that the radiometric responses of AGRI exhibit significant degradation in visible bands, while showing relatively smaller degradation rates in near- and shortwave-infrared bands. Specifically, the annual degradation rates for band 1 (0.47 μm) of FY-4A/AGRI and FY-4B/AGRI are 4.3% and 8.8% respectively. Both AGRIs demonstrate comparable degradation rates of approximately 3.5% for band 2 (0.65 μm). In contrast, bands 3 (0.83 μm), 5 (1.61 μm), and 6 (2.25 μm) show annual degradation rates around −1.0%, despite they exhibit notable fluctuations. The operational calibration of FY-4B/AGRI is more accurate than that of FY-4A/AGRI and with smaller fluctuations. By fitting the time series of relative errors between simulated and current calibrated reflectances, we calculate daily recalibration coefficients and effectively recalibrated the long-term data, with a calibration accuracy within ±3%. This study demonstrates that the CT-based calibration method can successfully track the radiometric stability of AGRI and provide a robust calibration solution to ensure data stability and accuracy.

Cloud liquid water path (LWP) quantifies liquid water amount within the atmosphere and is closely related to water cycle, weather, and climate. Passive microwave (MW) observations are powerful tools for retrieving LWP. An empirical relationship between the LWPs and MW brightness temperatures (BTs) can be obtained for conventional retrievals, which consider only the influence of LWP on BTs. However, besides LWP, the cloud vertical extent [e.g., cloud top height (CTH)] can affect MW emission, absorption, and corresponding channel BTs, but it is ignored in conventional retrievals. This study investigates the influences of CTH on MW LWP retrievals, and a CTH-dependent algorithm is developed using CTHs from infrared retrievals. Synthetic radiative transfer simulations are performed to quantify CTH effects on MW channel BTs and to establish the CTH-dependent retrieval coefficients. We use the Advanced MW Scanning Radiometer 2 (AMSR2) observations. Cloud products from Moderate Resolution Imaging Spectroradiometer (MODIS) are collocated to provide the necessary CTH information. Thus, we develop an LWP retrieval algorithm by combining AMSR2 BTs with MODIS CTHs. The results indicate that incorporating CTH information into LWP retrievals enhances the consistency between MW and visible/infrared retrievals. Specifically, the CTH-dependent algorithm showed an improvement in the intraclass correlation coefficient (ICC) and a reduction in mean relative differences (MRDs) by approximately 4% (from 18% to 14%) compared to AMSR2 operational retrievals. The CTH-dependent results are slightly more consistent with the MODIS results than the CTH-independent ones, though it remains important to note that the CTH-dependent retrievals introduce less differences compared to their CTH-independent retrievals.

The thermal structure of the Arctic sea ice is a critical indicator in the atmosphere–sea ice–ocean energy budget and, thus, for understanding Arctic warming and associated climate change. Therefore, understanding this thermal structure and its monitoring should be vital. However, it is challenging to obtain a 3-D view of the thermal structure of the sea ice (such as the temperature profile) through satellite measurements because of the lack of understanding of the nonlinear relationship between sea ice emission and measured radiance at the top of the atmosphere. In this study, a model was developed to estimate the temperature profile within the Arctic sea ice during winter using satellite-borne passive microwave measurements. An artificial neural network (ANN) technique based on deep learning was introduced, and the nonlinear relationship between satellite-measured brightness temperatures and buoy-measured sea ice temperature profiles was learned. The ANN model was mapped and verified using the tenfold cross-validation technique. The developed ANN model was able to restore the sea ice temperatures at all specified levels with correlation coefficients > 0.95, absolute biases < 0.1 K, and root mean square errors < 1.6 K. The retrieved temperature results well represent expected thermal structures, in addition to the snow–sea ice interface temperature similar to that in the published literature. Besides the data for validating climate model simulations, the results also promise applications for improving the sea ice growth model performance by tightly constraining the vertical thermal structure in the sea ice growth model.

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.

Radiative calibration of satellite spectral radiometers is essential for their downstream applications. The Medium Resolution Spectral Imager (MERSI-II) is a key instrument of the Chinese polar orbit Fengyun-3D (FY-3D) satellite. However, its calibration performance has not been sufficiently studied, which limits its broad application. This study revealed the feasibility of a cloud-target method for assessing the MERSI-II calibration performance in solar bands. The top-of-atmosphere (TOA) reflectances for six MERSI-II reflective solar bands (RSBs) were numerically simulated using a rigorous forward radiative transfer method and cloud properties from well-collocated and well-calibrated Moderate Resolution Imaging Spectroradiometer (MODIS) operational cloud products with strict constraints. Only ice cloud targets were examined in the collocation due to their better homogeneity. The excellent agreement between our simulated reflectance and the MODIS reflectance (relative differences (RDs) of over 90% are within a 5% uncertainty range in six bands) validates our models. The simulated results in MERSI-II bands 1–4 showed reasonable agreements with the MERSI-II operational reflectance, i.e., mean RDs$\sim $15% and$\sim $12% (in the three years), respectively. More importantly, we removed these seasonal and degradation biases to improve the current calibration accuracy to a stable value within 3%. Due to its robust performance, our cloud-target-based calibration method can be applied to future MERSI-II sensors to monitor solar band stability.

In the above article[1], a difference in the definitions of our simulated reflectance (with respect to instantaneous TOA radiance) and the operational MERSI-II L1 reflectance (with respect to solar constant) causes errors in their direct comparisons.

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 possible cause of radiative inconsistency between visible- and infrared (IR)-based optical properties is examined and corrected for. This study is motivated by significant IR modeling biases of around -7 K found in our previous study. In that study, the model simulation was conducted using cloud optical thickness (COT) and effective radius retrieved from the Moderate-Resolution Imaging Spectroradiometer, and cloud top and base heights measured by CloudSat. For single-layered and relatively optically thick clouds (COT >; 10 ), uncertainties in the COT and effective radius are shown to have a small contribution to the IR modeling biases; making an assumption that the clouds are vertically homogeneous seems to cause most of the IR modeling biases. By creating a cloud extinction profile from the CloudSat data, IR modeling biases are reduced to -2 K instead of - 7 K.

A series of validation statistics are presented from a operational global, minimum 3-hourly updating precipitation analysis. The technique was developed at the Naval Research Laboratory (NRL) for use in numerical weather prediction (NWP) model data assimilation and now casting operations. The technique is an adaptive statistical/hybrid technique which incorporates data from all current operational infrared-based geostationary satellites and from all (currently) available low-Earth orbiting microwave-based sensors, the Special Sensor Microwave Imager (SSMI), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Sounding Unit (AMSU-B). Knowledge of the rain estimation error statistics are needed in order to properly utilize precipitation observations in NWP variational assimilation techniques. Validation statistics are presented from comparisons with operationally supported raingauge networks in Australia and Korea in a two-dimensional fashion, where the spatial and temporal dimensions are each individually varied.