Search

The microwave radiation imager (MWRI) onboard the Fengyun-3D satellite can provide valuable observation data in many fields such as meteorological research and weather forecasting. However, its coarse spatial resolution limits data application. Recently, image super-resolution (SR) methods based on deep learning have been introduced into the spatial resolution enhancement of radiometers and achieved better results than traditional methods. Most of them use the degradation model to generate dataset and build model based on convolutional neural network (CNN). However, the dataset generation method based on the degradation model may impair the information in the original brightness temperature (BT) data. Moreover, CNN-based SR methods often struggle to model long-distance dependencies, which may impact the spatial resolution enhancement of BT data. To address these issues, we propose a dataset generation method based on BT data matching and introduce a SR network model based on the Transformer structure. We refer to it as the SR transformer BT data matching method. Results indicate that the method significantly improves the spatial resolution of MWRI data over current methods and exhibits strong generalization for long-term data outside the training time range.

Higher spatial resolution can improve the application ability of spaceborne microwave radiometer data. To investigate the capability to enhance the spatial resolution of the radiometer measurements, we analyzed the effect of enhanced field of view (FOV) and sampling strategy on improving spatial resolution using the Backus–Gilbert (BG) method, with the scanning parameters of FengYun-3D Microwave Radiation Imager (MWRI) as an example. The results suggest that selecting an appropriate enhanced FOV and increasing the sampling overlap rate can yield better spatial resolution and effectively reduce the brightness temperature (BT) error that arises from resolution enhancement. As an example, the MWRI 18.7 GHz channel with a spatial resolution of 30 × 50 km is taken. When the spatial resolution of the enhanced FOV is set to 25 × 35 km, compared with other options, the average BT error caused by resolution enhancement is reduced to 0.38 K, and the spatial resolution is improved to 27.3 × 40.5 km. In addition, when the sampling overlap rate of cross-track and along-track directions is set to 91.4% × 92.4%, the highest in the experimental conditions, compared with the original sampling setting, the average BT error caused by resolution enhancement is reduced from 0.72 to 0.4 K, and the spatial resolution is improved to 22.8 × 35.7 km. These experimental findings may provide insights into designing future radiometers and applying the BG method.

Tropical cyclone (TC) vortex structure can be estimated from wind radii (34, 50, and 64 kt). Accurate wind radii are essential for assessing the impact range of TCs. Combining observations from active and passive microwave remote sensing instruments can provide long-time series data for monitoring changes in TC wind structure. Here, the evolution of the wind radii for Typhoon In-fa (2021), from its genesis to its first landfall, is evaluated using data from spaceborne microwave radiometers and synthetic aperture radars (SARs). Our results show that the retrieved wind radii are relatively close to those from the Joint Typhoon Warning Center (JTWC), the coastal automatic weather stations in China, and the advanced scatterometer. The time series for the wind radii show significant increasing trends during the lifecycle of In-fa and a massive vortex structure before In-fa made landfall. The average wind radii observed before landfall are all likely in the 90th percentile of the 2001–2021 JTWC historical wind radii records, indicating that the vortex structure is extraordinarily large before In-fa made landfall and represents a historical extreme. The extremely large vortex structure may be attributable to the interaction between In-fa and an extremely strong monsoon gyre. [Display omitted] • Wind radii for Typhoon In-fa (2021) are evaluated by multiple spaceborne microwave radiometers and SARs. • The retrieved wind radii are close to those from JTWC, AWSs in China, and ASCAT. • The significant increasing trends of wind radii and a massive vortex structure before landfall are revealed. [ABSTRACT FROM AUTHOR]

Overland rain retrieval using spaceborne microwave radiometer offers a myriad of complications as land presents itself as a radiometrically warm and highly variable background. Hence, land rainfall algorithms of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) have traditionally incorporated empirical relations of microwave brightness temperature (Tb) with rain rate, rather than relying on physically based radiative transfer modeling of rainfall (as implemented in the TMI ocean algorithm). In this paper, sensitivity analysis is conducted using the Spearman rank correlation coefficient as benchmark, to estimate the best combination of TMI low-frequency channels that are highly sensitive to the near surface rainfall rate from the TRMM Precipitation Radar (PR). Results indicate that the TMI channel combinations not only contain information about rainfall wherein liquid water drops are the dominant hydrometeors but also aid in surface noise reduction over a predominantly vegetative land surface background. Furthermore, the variations of rainfall signature in these channel combinations are not understood properly due to their inherent uncertainties and highly nonlinear relationship with rainfall. Copula theory is a powerful tool to characterize the dependence between complex hydrological variables as well as aid in uncertainty modeling by ensemble generation. Hence, this paper proposes a regional model using Archimedean copulas, to study the dependence of TMI channel combinations with respect to precipitation, over the land regions of Mahanadi basin, India, using version 7 orbital data from the passive and active sensors on board TRMM, namely, TMI and PR. Studies conducted for different rainfall regimes over the study area show the suitability of Clayton and Gumbel copulas for modeling convective and stratiform rainfall types for the majority of the intraseasonal months. Furthermore, large ensembles of TMI Tb (from the most sensitive TMI channel combination) were generated conditional on various quantiles (25th, 50th, 75th, and 95th) of the convective and the stratiform rainfall. Comparatively greater ambiguity was observed to model extreme values of the convective rain type. Finally, the efficiency of the proposed model was tested by comparing the results with traditionally employed linear and quadratic models. Results reveal the superior performance of the proposed copula-based technique. [ABSTRACT FROM PUBLISHER]