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The Ku band microwave scatterometer (SCA) and scanning microwave radiometer (SMR) onboard HaiYang-2B (HY-2B) can simultaneously supply active and passive microwave observations over the polar region. In this paper, a polar ice water discrimination model and Arctic sea-ice-type classification model based on the support vector machine (SVM) method were established and used to produce a daily sea ice extent dataset from 2019 to 2021 with data from SCA and SMR. First, suitable scattering and radiation parameters are chosen as input data for the discriminant model. Then, the sea ice extent was obtained based on the monthly ice water discrimination model, and finally, the ice over the Arctic was classified into multiyear ice (MYI) and first-year ice (FYI). The 3-year ice extent and MYI extent products were consistent with the similar results of the National Snow and Ice Data Center (NSIDC) and Ocean and Sea Ice Satellite Application Facility (OSISAF). Using the OSISAF similar product as validation data, the overall accuracies (OAs) of ice/water discrimination and FYI/MYI discrimination are 99% and 97%, respectively. Compared with the high spatial resolution classification results of the Moderate Resolution Imaging Spectroradiometer (MODIS) and SAR, the OAs of ice/water discrimination and FYI/MYI discrimination are 96% and 86%, respectively. In conclusion, the SAC and SMR of HY-2B have been verified for monitoring polar sea ice, and the sea ice extent and sea-ice-type products are promising for integration into long-term sea ice records. [ABSTRACT FROM AUTHOR]

• Validation of tropospheric wet delay for 1.2 GHz signal retrieved with MPM93 and microwave radiometer profiles compared to GNSS tropospheric wet delay. • Quantitative analysis of tropospheric delay differences for millimeter and submillimeter wave signals (1–300 GHz) using microwave radiometer profiles and MPM93. • Such quantitative analysis is valuable when GNSS-retrieved tropospheric delay is used to correct other high-frequency microwave measurements. Tropospheric delay is a significant source of error in various microwave measurement technologies. As the tropospheric delay differences associated with the signal frequency can be neglected for signals near the L band, the Global Navigation Satellite System (GNSS) is frequently used to assist other microwave measurements on tropospheric delay correction for its high precision and all-weather capabilities. However, when the signal frequency differences are significant, further analysis of tropospheric delay related to signal frequency is required for higher accuracy in long-distance propagation. This study quantitatively analyzed the tropospheric delay differences from L-band to millimeter band (1–300 GHz) affected by dry air, water vapor, and liquid water, based on Microwave Radiometer (MWR) measurement profiles and the Millimeter-wave Propagation Model93 (MPM93) at Shanghai. Relative to the GNSS L-band signal of 1.2 GHz, the RMS of tropospheric delay differences over 1–300 GHz varies from 0.033 mm to 9.436 mm, with the maximum tropospheric delay differences up to 18.03 mm at 300 GHz. Tropospheric delay differences strongly correlate with surface temperature and surface pressure, except for some of the atmosphere absorption bands. These quantitative analyses are helpful for the comprehensive application of GNSS to assist other microwave measurement techniques. [ABSTRACT FROM AUTHOR]

Significant knowledge gaps exist in our understanding of urban boundary layer processes, particularly the hygrothermal state. The earth system community has successfully used microwave radiometers for several decades. However, the applicability in complex urban environments has never been adequately tested. Here, observations from a microwave radiometer are compared to radiosonde readings in a densely urbanized site in Houston, Texas. The site was influenced by both an urban heat island and the sea breeze phenomenon. The analysis showed significant disagreement between the virtual potential temperature predicted by the microwave radiometer and the radiosonde for all periods within the boundary layer. However, the values were reasonably comparable above the boundary layer. The microwave radiometer incorrectly predicted an inversion layer instead of a mixed layer during convective periods. The microwave radiometer measurements deviated from the radiosonde measurements throughout the lower troposphere for the relative humidity. [ABSTRACT FROM AUTHOR]

In this study, the enhancement of the spatial resolution of microwave radiometer measurements is addressed by contrasting the accuracy of a gradient-like antenna pattern deconvolution method with its accelerated versions. The latter are methods that allow reaching a given accuracy with a reduced number of iterations. The analysis points out that accelerated methods result in improved performance when dealing with spot-like discontinuities; while they perform in a similar way to the canonical gradient method in case of large discontinuities. A key application of such techniques is the research on global warming and climate change, which has recently gained critical importance in many scientific fields, mainly due to the huge societal and economic impact of such topics over the entire planet. In this context, the availability of reliable long time series of remotely sensed Earth data is of paramount importance to identify and study climate trends. Such data can be obtained by large-scale sensors, with the obvious drawback of a poor spatial resolution that strongly limits their applicability in regional studies. Iterative gradient techniques allow obtaining super-resolution gridded passive microwave products that can be used in long time series of consistently calibrated brightness temperature maps in support of climate studies. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel approach to enhance the spatial resolution of single-pass microwave data collected by mesoscale sensors. The proposed rationale is based on an $L^{p}$ -minimization approach with a variable $p$ exponent. The algorithm automatically adapts the $p$ exponent to the region of the image to be reconstructed. This approach allows taking benefit of the advantages of both the regularization in Hilbert ($p = 2$) and Banach ($1< p< 2$) spaces. Experiments are undertaken considering the microwave radiometer and refer to both actual and simulated data collected by the special sensor microwave imager (SSM/I). Results demonstrate the benefits of the proposed method in reconstructing abrupt discontinuities and smooth gradients with respect to conventional approaches in Hilbert or Banach spaces. [ABSTRACT FROM AUTHOR]

The brightness temperature data acquired during 2012–2015 from the scanning microwave radiometer (SMR), onboard the first Chinese ocean dynamic environment satellite—Haiyang-2A, were matched up with the WindSat Polarimetric Radiometer (WindSat) $0.25^{\circ }\,\,\times0.25^{\circ }$ gridded daily sea surface temperature (SST) data. Then, the artificial neural network (ANN) ensemble (ANNE) method implementing the Crogging technique was used to build the SMR SST retrieval algorithm. Different from a regular ANN, an ANNE combines the outputs of its ANN members to generate an algorithm. The developed ANNE algorithm for SMR SST was validated based on the SMR/WindSat data pairs that were not used in the tuning of the algorithm. The SST comparison shows the root mean square (rms) of 1.16 °C for the ANNE algorithm. We further validate the SMR SST products using the in situ measurements from the National Oceanic and Atmospheric Administration iQuam System. The rms of the ANNE algorithm in comparison with the global iQuam SSTs is 1.46 °C. All validations showed that ANNEs were more accurate than the other statistically based SST retrieval algorithms for SMR, and generally had much smaller uncertainties than regular ANNs. [ABSTRACT FROM AUTHOR]

This article deals with the tropospheric water vapour distribution at Niamey (Niger) observed with a high-temporal-resolution (14 s) microwave radiometric profiler. Data were collected during the whole year 2006 in the framework of the African Monsoon Multidisciplinary Analysis (AMMA) campain. Two seasonal periods are considered: the dry season, when the northeasterly Harmattan is flowing at low tropospheric level, and the wet season, associated with the southwesterly monsoon circulation. The fine vertical structure of temperature, convective air stability and water vapour for each seasonal period is described in detail and differences are emphasized. Typical temporal series and monthly averaged diurnal cycles are presented. It is shown that a diurnal cycle of water vapour is present throughout the year, including the dry season. The diurnal cycle of water vapour is controlled mainly by the nocturnal low-level jet (NLLJ). During the dry season, the diurnal cycle of water vapour is organized into two layers: a lower layer (LL) from the surface up to 0.6-1.4 km above ground level (agl) and an upper layer (UL) from 1.4 up to 5-6 km agl. The water vapour distributions in the LL and UL are anticorrelated, with a half-day temporal shift. As a result, the vertically integrated water vapour (IWV), which displays a quasi-sinusoidal diurnal cycle when computed separately for the LL and UL, appears almost flat for the total tropospheric height, due to the half-day period shift. This structure of two layers is not observed during the wet season. Probability density functions (pdfs) of water vapour content are presented. In dry conditions, the pdfs are well fitted by a log-normal distribution, while the Weibull distribution fits the pdfs for wet conditions better. [ABSTRACT FROM AUTHOR]

The microwave radiometer TROWARA measures integrated water vapour ( IWV) and integrated cloud liquid water ( ILW) at Bern since 1994 with a time resolution of 7 s. In this study, we compare TROWARA measurements with a simulation of summer 2012 in Switzerland performed with the Weather Research and Forecasting ( WRF) model. It is found that the WRF model agrees very well with TROWARA's IWV variations with a mean bias of only 0.7 mm. The ILW distribution of the WRF model, although similar in shape to TROWARA's distribution, overestimates the fraction of clear sky periods (83% compared to 60%). [ABSTRACT FROM AUTHOR]