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A ground-based microwave radiometer (MWR) provides continuous atmospheric profiles under various weather conditions. The change in total precipitable water vapor (PWV) and liquid water path (LWP) before rainfall events is particularly important for the improvement in the rainfall forecast. However, the analysis of the PWV and LWP before rainfall event on the plateau is especially worth exploring. In this study, the MWR installed at Xining, a city located over the northeastern Tibetan Plateau, was employed during September 2021 to August 2022. The results reveal that the MWR-retrieved temperature and vapor density demonstrate reliable accuracy, when compared with radiosonde observations; PWV and LWP values during the summer account for over 70% of the annual totals in the Xining area; both PWV and LWP at the initiating time of rainfall events are higher during summer, especially after sunset (during 20-00 local solar time); and notably, PWV and LWP anomalies are enhanced abruptly 8 and 28 min prior to the initiating time, respectively. Furthermore, the mean of LWP anomaly rises after the turning time (the moment rises abruptly) to the initiating time; as the intensity of rainfall events increases, the occurrence of the turning time is delayed, especially for PWV anomalies; while the occurrence of the turning time is similar for both convective cloud and stratiform cloud rainfall events, the PWV and LWP anomalies jump more the initiating time; as the intensity of rainfall events increases, the occurrence of the turning time is delayed, especially for PWV anomalies; while the occurrence of the turning time is similar for both convective cloud and stratiform cloud rainfall events, the PWV and LWP anomalies jump more dramatically after the turning time in convective cloud events. This study aims are to analyze the temporal characteristics of PWV and LWP, and assess their potential in predicting rainfall event. [ABSTRACT FROM AUTHOR]

Using multi-source observation data including automatic stations, radar, satellite, new detection equipment, and the Fifth Generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA-5) data, along with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) platform, an analysis was conducted on a rainstorm process that occurred in Nanjing on 15 June 2020, with the aim of providing reference for future urban flood control planning and heavy rainfall forecasting and early warning. The results showed that this rainstorm process was generated under the background of an eastward-moving northeast cold vortex and a southward retreat of the Western Pacific Subtropical High. Intense precipitation occurred near the region of large top brightness temperature (TBB) gradient values or the center of low TBB values on the northern side of the convective cloud cluster. During the heavy precipitation period, the differential propagation phase shift rate (KDP), differential reflectivity factor (ZDR), and zero-lag correlation coefficient (ρHV) detected by the S-band dual-polarization radar all increased significantly. The vertical structure of the wind field detected by the wind profile radar provided a good indication of changes in precipitation intensity, showing a strong correspondence between the timing of maximum precipitation and the intrusion of upper-level cold air. The abrupt increase in the integrated liquid water content observed by the microwave radiometer can serve as an important indicator of the onset of stronger precipitation. During the Meiyu season in Nanjing, convective precipitation was mainly composed of small to medium raindrops with diameters less than 3 mm, with falling velocities of raindrops mainly clustering between 2 and 6 m·s−1. The rainstorm process featured four water vapor transport channels: the mid-latitude westerly channel, the Indian Ocean channel, the South China Sea channel, and the Pacific Ocean channel. During heavy rainfall, the Pacific Ocean water vapor channel was the main channel at the middle and lower levels, while the South China Sea water vapor channel was the main channel at the upper level, both accounting for a trajectory proportion of 34.2%. [ABSTRACT FROM AUTHOR]

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]

Exploring the Venusian lower atmosphere is crucial for studying the atmospheric circulation, surface‐atmosphere interactions, and origin and evolution of the Venusian atmosphere and climate. In this study, we investigate the theoretical capabilities of a downward‐looking passive microwave sounder placed in low Venus orbit to measure the temperature, sulfur dioxide (SO2) and gaseous sulfuric acid (H2SO4(g)) profiles. A nonlinear iterative retrieval algorithm combining a radiative transfer (RT) model and an optimal‐estimation‐based inversion algorithm is established. With the RT model adapted to the Venusian atmosphere, simulations under different atmospheric conditions are performed to optimize the selection of frequency channels. The achievable altitude coverage, vertical resolution and corresponding expected precision of the temperature, SO2 and H2SO4(g) retrievals from the multi‐channel brightness temperature measurements are quantified via retrieval simulations. The temperature can be retrieved from the surface of Venus to an altitude of ∼61 km with a precision of 1–3.5 K and a vertical resolution of 6–15 km. A precision of 10%–35% is expected for SO2 in the ∼12–64 km altitude range and with a vertical resolution of 8–19 km. H2SO4(g) can be retrieved in the ∼36–54 km altitude range with a precision of 10%–30% and a vertical resolution of 6–13 km. Plain Language Summary: The hostile environment and thick sulfuric acid clouds make it difficult to measure the lower atmosphere of Venus. Although many attempts have been made, the existing measurements of the lower atmosphere have provided little information about the spatiotemporal distribution and variation in the temperature and sulfur compound profiles. This limits our understanding of the atmospheric circulation, surface‐atmosphere interactions, and origin and evolution of the Venusian atmosphere and climate. In this study, we investigate the theoretical capabilities of a passive microwave sounder placed in low Venus orbit to measure the temperature, sulfur dioxide (SO2) and gaseous sulfuric acid (H2SO4(g)) profiles. By model simulations, the frequency channel selection and performance simulation for the sounder are completed. Simulation results demonstrate that the sounder could provide the required high‐precision, high‐resolution and vertically resolved observations of the temperature, SO2 and H2SO4(g) in the Venusian lower atmosphere. Key Points: The ability of a microwave radiometer to measure the temperature, SO2 and H2SO4(g) profiles in the Venusian lower atmosphere is verifiedThe measurement channels of the radiometer are determined based on radiative transfer simulationsThe expected precision, vertical resolution and achievable altitude coverage are quantified via retrieval simulations [ABSTRACT FROM AUTHOR]

Concurrence of temperature inversion (TI) and humidity inversion (HI) is a particular configuration of the atmospheric boundary layer with important implications for early warning of fog formation. With a microwave radiometer device deployed in a 2‐month winter campaign at a coastal island in Qingdao, China, we here examine the relationship between TI and HI, and investigate the underneath mechanisms. Cases of temperature inversion are further divided into surface‐based temperature inversion (SBTI) and elevated temperature inversion (ETI), which show different relationship with HI. SBTI typically occurs at night with its strength significantly and positively correlated with HI. ETI also shows a high degree of temporal overlap with HI, but its strength has no obvious relationship with HI. The main explanation for this phenomenon is that ETI may block the vertical diffusion of water vapor, resulting in the formation of HI. Plain Language Summary: Temperature inversion (TI) and humidity inversion (HI) often occur simultaneously. Knowledge of their concurrence is, however, still quite limited, due to the lack of continuous observations. Using a 2‐month winter observation data set, obtained with a microwave radiometer device deployed in Qingdao, China, a coastal city of the Yellow Sea, we carefully examined the correlation between TI and HI. TI is furthermore regrouped into surface‐based temperature inversion (SBTI) and elevated temperature inversion (ETI). Although SBTI and ETI both occur frequently, accompanied with HI, the underneath physical mechanism seems different. For SBTI, the bottom of HI absorbs the radiation emitted from the ground, increases the temperature difference between the land and atmosphere, and finally enhances SBTI. However, ETI plays the role of blocking the water vapor turbulent diffusion and contributes to the development of HI. Key Points: Both surface‐based temperature inversion (SBTI) and elevated temperature inversion (ETI) show concurrence with humidity inversion (HI)Theories of water vapor condensation and turbulent diffusion blockage may not be sufficient for the concurrence of SBTI and HIETI blocks the upward turbulent diffusion of water vapor, contributing to the formation of HI [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]

Stationary or mobile microwave radiometers (MRs) can measure atmospheric temperature, relative humidity, and water vapor density profiles with high spatio-temporal resolution, but cannot obtain the vertical variations of wind field. Based on a dataset of brightness temperatures (TBs) measured with a mobile MR over the Three-River-Source Region of the Tibetan Plateau from 18 to 30 July 2021, we develop a direct retrieval method for the wind profile (WP) based on the Long Short-Term Memory (LSTM) network technique, and obtain the reliable dynamic variation characteristics of the WP in the region. Furthermore, the ground-based radiative transfer model for TOVS (RTTOV-gb) was employed to validate the reliability of the TB observation, and we analyzed the impact of weather conditions, altitude, observational mode, and TB diurnal variation on the accuracy of the TB measurement and the retrieval of the WP. Results show that the TB from the mobile observation (MOTB) on clear and cloudy days are close to those of the simulated TB with the RTTOV-gb model, while TB measurements on rainy days are far larger than the modeled TBs. When compared with radiosonde observations, the WPs retrieved with the LSTM algorithm are better than the ERA5 reanalysis data, especially below 350 hPa, where the root mean square errors for both wind speed and wind direction are smaller than those of ERA5. The major factors influencing WP retrieval include the weather conditions, altitude, observational mode, and TB diurnal variation. Under clear-sky and cloudy conditions, the LSTM retrieval method can reproduce the spatio-temporal evolution of wind field and vertical wind shear characteristics. The findings of this study help to improve our understanding of meso-scale atmospheric dynamic structures, characteristics of vertical wind shear, atmospheric boundary layer turbulence, and enhance the assessment and forecasting accuracy of wind energy resources. [ABSTRACT FROM AUTHOR]

Thermodynamic and liquid water profiles can be retrieved by a ground-based microwave radiometer (MWR) in nearly all weather conditions, which is useful for detecting mesoscale phenomena. This paper reviews the advances in remote sensing of atmospheric profiles and cloud properties by MWR in central China. Comparative studies indicate that MWR retrieval accuracy is different under various skies, especially those that decay under precipitation. The off-zenith method is proven to be capable of reducing the impact of precipitation and snow on MWR retrieval accuracy. Application studies demonstrate that MWR retrievals are helpful for early warning of rainstorms, hailstorms, and thunderstorms. Moreover, MWR retrievals provide a way to study cloud properties. The temporal variations of cloud occurrence frequency (COF) and liquid water path (LWP) are different for low, middle, and high clouds, and the vertical distribution of COF is also different in autumn and other seasons. Note that MWR can infer valid retrievals over the eastern Tibetan Plateau due to the weak precipitation over there. Also, cloud properties over the eastern Tibetan Plateau present differences from those over central China, and this is related to the different characteristics of atmospheric water vapor between these two regions. To bring more benefits for mechanism study and early warning of severe weather and numerical weather prediction, the decayed accuracy of MWR zenith retrievals under precipitation should be resolved. And combining MWR with other instruments is necessary for MWR application in detecting multi-layer clouds and ice clouds. [ABSTRACT FROM AUTHOR]

In a joint effort, MeteoSwiss and Deutscher Wetterdienst (DWD) address the need for improving the initial state of the atmospheric boundary layer (ABL) by exploiting ground‐based profiling observations that aim to fill the existing observational gap in the ABL. We implemented brightness‐temperature observations from ground‐based microwave radiometers (MWRs) in our data assimilation systems using a local ensemble transform Kalman filter (LETKF) with Radiative Transfer for TIROS Operational Vertical Sounder, ground‐based (RTTOV‐gb) as a forward operator. We were able to obtain a positive impact on the brightness temperature first guess and analysis, as well as a slight impact on the ABL humidity, using two MWRs at MeteoSwiss. These results led to a subsequent operational implementation of the observing system at MeteoSwiss. Furthermore, we performed an extensive set of assimilation experiments at DWD to investigate further various aspects such as the vertical localisation of selected single channels. We obtained a positive impact on the 6‐hr forecast of ABL temperature and humidity by assimilating two channels employing a dynamical localisation based on the sensitivity functions of RTTOV‐gb but also with a static localisation in a single‐channel setup. Our experiments indicate the importance of vertical localisation when using more than one channel, although reliable improvements are challenging to obtain without a larger number of observations for both assimilation and verification. [ABSTRACT FROM AUTHOR]

Abstract Exploring the Venusian lower atmosphere is crucial for studying the atmospheric circulation, surface‐atmosphere interactions, and origin and evolution of the Venusian atmosphere and climate. In this study, we investigate the theoretical capabilities of a downward‐looking passive microwave sounder placed in low Venus orbit to measure the temperature, sulfur dioxide (SO2) and gaseous sulfuric acid (H2SO4(g)) profiles. A nonlinear iterative retrieval algorithm combining a radiative transfer (RT) model and an optimal‐estimation‐based inversion algorithm is established. With the RT model adapted to the Venusian atmosphere, simulations under different atmospheric conditions are performed to optimize the selection of frequency channels. The achievable altitude coverage, vertical resolution and corresponding expected precision of the temperature, SO2 and H2SO4(g) retrievals from the multi‐channel brightness temperature measurements are quantified via retrieval simulations. The temperature can be retrieved from the surface of Venus to an altitude of ∼61 km with a precision of 1–3.5 K and a vertical resolution of 6–15 km. A precision of 10%–35% is expected for SO2 in the ∼12–64 km altitude range and with a vertical resolution of 8–19 km. H2SO4(g) can be retrieved in the ∼36–54 km altitude range with a precision of 10%–30% and a vertical resolution of 6–13 km.

Abstract Concurrence of temperature inversion (TI) and humidity inversion (HI) is a particular configuration of the atmospheric boundary layer with important implications for early warning of fog formation. With a microwave radiometer device deployed in a 2‐month winter campaign at a coastal island in Qingdao, China, we here examine the relationship between TI and HI, and investigate the underneath mechanisms. Cases of temperature inversion are further divided into surface‐based temperature inversion (SBTI) and elevated temperature inversion (ETI), which show different relationship with HI. SBTI typically occurs at night with its strength significantly and positively correlated with HI. ETI also shows a high degree of temporal overlap with HI, but its strength has no obvious relationship with HI. The main explanation for this phenomenon is that ETI may block the vertical diffusion of water vapor, resulting in the formation of HI.

A ground-based microwave radiometer (MWR) provides continuous atmospheric profiles under various weather conditions. The change in total precipitable water vapor (PWV) and liquid water path (LWP) before rainfall events is particularly important for the improvement in the rainfall forecast. However, the analysis of the PWV and LWP before rainfall event on the plateau is especially worth exploring. In this study, the MWR installed at Xining, a city located over the northeastern Tibetan Plateau, was employed during September 2021 to August 2022. The results reveal that the MWR-retrieved temperature and vapor density demonstrate reliable accuracy, when compared with radiosonde observations; PWV and LWP values during the summer account for over 70% of the annual totals in the Xining area; both PWV and LWP at the initiating time of rainfall events are higher during summer, especially after sunset (during 20-00 local solar time); and notably, PWV and LWP anomalies are enhanced abruptly 8 and 28 min prior to the initiating time, respectively. Furthermore, the mean of LWP anomaly rises after the turning time (the moment rises abruptly) to the initiating time; as the intensity of rainfall events increases, the occurrence of the turning time is delayed, especially for PWV anomalies; while the occurrence of the turning time is similar for both convective cloud and stratiform cloud rainfall events, the PWV and LWP anomalies jump more the initiating time; as the intensity of rainfall events increases, the occurrence of the turning time is delayed, especially for PWV anomalies; while the occurrence of the turning time is similar for both convective cloud and stratiform cloud rainfall events, the PWV and LWP anomalies jump more dramatically after the turning time in convective cloud events. This study aims are to analyze the temporal characteristics of PWV and LWP, and assess their potential in predicting rainfall event.

Using multi-source observation data including automatic stations, radar, satellite, new detection equipment, and the Fifth Generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA-5) data, along with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) platform, an analysis was conducted on a rainstorm process that occurred in Nanjing on 15 June 2020, with the aim of providing reference for future urban flood control planning and heavy rainfall forecasting and early warning. The results showed that this rainstorm process was generated under the background of an eastward-moving northeast cold vortex and a southward retreat of the Western Pacific Subtropical High. Intense precipitation occurred near the region of large top brightness temperature (TBB) gradient values or the center of low TBB values on the northern side of the convective cloud cluster. During the heavy precipitation period, the differential propagation phase shift rate (KDP), differential reflectivity factor (ZDR), and zero-lag correlation coefficient (ρHV) detected by the S-band dual-polarization radar all increased significantly. The vertical structure of the wind field detected by the wind profile radar provided a good indication of changes in precipitation intensity, showing a strong correspondence between the timing of maximum precipitation and the intrusion of upper-level cold air. The abrupt increase in the integrated liquid water content observed by the microwave radiometer can serve as an important indicator of the onset of stronger precipitation. During the Meiyu season in Nanjing, convective precipitation was mainly composed of small to medium raindrops with diameters less than 3 mm, with falling velocities of raindrops mainly clustering between 2 and 6 m·s−1. The rainstorm process featured four water vapor transport channels: the mid-latitude westerly channel, the Indian Ocean channel, the South China Sea channel, and the Pacific Ocean channel. During heavy rainfall, the Pacific Ocean water vapor channel was the main channel at the middle and lower levels, while the South China Sea water vapor channel was the main channel at the upper level, both accounting for a trajectory proportion of 34.2%.

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.

The inversion data of the microwave radiometer atmospheric temperature profile were systematically analyzed with the temperature observation data of the microwave radiometer in coastal areas of Fujian. By comparing the microwave radiometer temperature inversion data with sounding and ERA5 temperatures, the ability of microwave radiometer to detect atmospheric temperature and the quality of the inversion data were analyzed, and the microwave radiometer inversion data were further applied to analyze the daily variation of atmospheric temperature in the boundary layer. The results show that the microwave radiometer temperature is in good agreement with the sounding temperature, with a correlation coefficient of 0.971, a mean deviation of 2.716 °C, and a root mean square error of 5.131 °C; the greater the intensity of rainfall, the more significant the effect of rainfall on the accuracy of the microwave radiometer in detecting atmospheric temperature. The correlation coefficients, mean deviations and root mean square errors of ERA5 and microwave radiometer are consistent from month to month; the temperature deviations between ERA5 and microwave radiometer in the middle atmosphere are smaller than those in the near-surface and upper atmosphere in each month; above 2 km in height, the temperature profiles of ERA5 and microwave radiometer are consistent. Above 2 km in altitude, the trend of ERA5 and microwave radiometer temperature profiles are basically the same. Below 3 km altitude, the daily variation of summer temperature is smaller than that of winter temperature at the same altitude. With the increase of altitude, the variation of atmospheric temperature in summer is large and that in winter is small [ABSTRACT FROM AUTHOR]

Based on Microwave Radiometer (MWR) observations in Wuhan over the course of 21 months, we compared the temperature and water vapor levels with those from radiosonde (RS) sounding data at 00:00 and 12:00 UTC, and then analyzed the seasonal and diurnal changes of temperature and water vapor levels from the MWR data. The MWR and RS mean temperatures and dew points are roughly consistent with each other below 2 km, whereas above 2 km, the MWR temperature is slightly lower than the RS temperature. The difference in their water vapor densities decreases quickly with height, and the bias of their relative humidities is generally in the range of −15% to 20%. The MWR observations show that in autumn, the surface temperature is 6.8 K lower during precipitation events than during non-precipitation events, indicating that precipitation in autumn is mainly caused by cold air from the north. The relative humidity during precipitation events exceeds 90% from the ground to 5 km, which is obviously larger than during non-precipitation events. During non-precipitation events, the seasonal mean water vapor density at 0–1.0 km shows an approximately linear increase with the mean temperature; however, their diurnal changes are opposite due to the effect of the boundary layer. At 4.5–5.5 km and 8.5–9.5 km, the mean temperature shows a synchronized diurnal evolution, with the maximum value prior to that at 0–1.0 km, indicating the strong influence of the air–land interaction on the temperature near the ground. Hence, this study is helpful for deepening our understanding of temperature and humidity variabilities over Wuhan. [ABSTRACT FROM AUTHOR]

The use of a ground-based multi-wavelength microwave radiometer (MWR) to produce atmospheric thermodynamic profiles of the troposphere up to 10 km utilizing brightness temperature derived from 35 different frequency channels for nowcasting severe convective weather is discussed in this study. In June 2014, MWR was implemented over Sriharikota (SHAR). The radiometer's measurements are first compared against temperature and humidity profiles of upper-air ascents to determine their accuracy. The goal of this research is to show the relationship between brightness temperature and thunderstorms that happened above SHAR. We found that combining the brightness temperature of the oxygen line in the atmosphere with the brightness temperature of the water vapour line provides far better antecedent signalling of convective occurrences over Sriharikota than the thermodynamic indicators which did not show any prominent results for thunderstorm nowcasting. [ABSTRACT FROM AUTHOR]

Microwave radiometers are passive remote sensing devices that are widely used in marine atmospheric observations. The accuracy of its inversion of temperature and humidity profiles is an important indicator of its performance. Back Propagation (BP) neural networks are widely used in the study of microwave radiometer inversion problems. However, the BP network which is carried by the radiometer inversion suffers from profile data collapse. To address this, this study introduced a residual network to improve the accuracy of water vapor vertical profiles. Aiming at the problem of large inversion temperature error due to the effect of turbulence on the light-travel phase induced by stationary fronts along the seashore in the subtropical monsoon climate region, we used historical data to establish the seasonal a priori mean profile and design a dead-zone residual adjustment model. The accuracy of the residual network and the deadband-adjusted residual network was verified using the meteorological records of the Taizhou region from 2013–2018, with the experimental data and BP hierarchical network as the comparison term. We found no data collapse in the temperature and humidity profile inversion results of the residual network. Relative to the initial BP hierarchical algorithm, where the error of water vapor in the range 6–10 km was reduced by 80%, the dead zone residual adjustment model in the inverse-temperature phenomenon reduced the sum of squares error by 21%, compared with the ordinary residual network inversion results. Our findings provide new insights into the accuracy improvement of radiometer remote sensing. [ABSTRACT FROM AUTHOR]

The calibration of the sea surface height (SSH) measured by satellite altimeters is essential to understand altimeter biases. Many factors affects the construction and maintenance of a permanent calibration site. In order to calibrate Chinese satellite altimetry missions, the feasibility of maintaining a calibration site based on the Qianliyan islet in Yellow Sea of China is taken into account. The related calibration facilities, such as the permanent tide gauge, GNSS reference station and meteorological station, were already operated by the Ministry of Natural Resources of China. The data could be fully used for satellite altimeter calibration with small fiscal expenditure. In addition, the location and marine environments of Qianliyan were discussed. Finally, we used the Jason-3 mission to check the possibility of calibration works. The result indicates that the brightness temperatures of three channels measured by microwave radiometer (MWR) and the derived wet tropospheric correction varies smoothly, which means the land contamination to MWR could be ignored. The high frequency waveforms at the Qianliyan site present no obvious difference from the normal waveforms received by satellite radar altimeter over the open ocean. In conclusion, the Qianliyan islet will not influence satellite altimetry observation. Following these analyses, a possible layout and mechanism of the Qianliyan calibration site are proposed.

The accuracy of temperature and relative humidity (RH) profiles retrieved by the ground-based microwave radiometer (MWR) is crucial for meteorological research. In this study, the four-year measurements of brightness temperature measured by the microwave radiometer from Huangpu meteorological station in Guangzhou, China, and the radiosonde data from the Qingyuan meteorological station (70 km northwest of Huangpu station) during the years from 2018 to 2021 are compared with the sonde data. To make a detailed comparison on the performance of machine learning models in retrieving the temperature and RH profiles, four machine learning algorithms, namely Deep Learning (DL), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are employed and verified. The results show that the DL model performs the best in temperature retrieval (with the root-mean-square error and the correlation coefficient of 2.36 and 0.98, respectively), while the RH of the four machine learning methods shows different excellence at different altitude levels. The integrated machine learning (ML) RH method is proposed here, in which a certain method with the minimum RMSE is selected from the four methods of DL, GBM, XGBoost and RF for a certain altitude level. Two cases on 29 January 2021 and on 10 February 2021 are used for illustration. The case on 29 January 2021 illustrates that the DL model is suitable for temperature retrieval and the ML model is suitable for RH retrieval in Guangzhou. The case on 10 February 2021 shows that the ML RH method reaches over 85% before precipitation, implying the application of the ML RH method in pre-precipitation warnings. [ABSTRACT FROM AUTHOR]

The purpose of our work is to investigate the performance of fusion wind from multiple remote-sensed data in forcing numeric wave models, and the experiment is described herein. In this study, 0.125° gridded wind fields at 12 h intervals were fused by using swath products from an advanced scatterometer (ASCAT) (a Haiyang-2B (HY-2B) scatterometer) and a spaceborne polarimetric microwave radiometer (WindSAT) during the period November 2019 to October 2020. The daily average wind speeds were compared with observations from National Data Buoy Center (NDBC) buoys from the National Oceanic and Atmospheric Administration (NOAA), yielding a 1.66 m/s root mean squared error (RMSE) with a 0.81 correlation (COR). This suggests that fusion wind was reliable for our work. The fusion winds were used for hindcasting sea surface waves by using two third-generation numeric wave models, denoted as WAVEWATCH-III (WW3) and Simulation Wave Nearshore (SWAN). The WW3-simulated waves in the North Pacific Ocean and the SWAN-simulated waves in the Gulf of Mexico were validated against the measurements from the NDBC buoys and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) for the period June−September 2020. The analysis of significant wave heights (SWHs) up to 9 m yielded a < 0.5 m RMSE with a > 0.8 COR for the WW3 and SWAN models. Therefore, it was believed that the accuracy of the simulation using the two numeric models was comparable with that forced by a numeric atmospheric model. An error analysis was systematically conducted by comparing the modeled WW3-simulated SWHs with the monthly average products from the HY-2B and a Jason-3 altimeter over global seas. The seasonal analysis showed that the differences in the SWHs (i.e., altimeter minus the WW3) were within ±1.5 m in March and June; however, the difference was quite significant in December. It was concluded that remote-sensed fusion wind can serve as a driving force for hindcasting waves using numeric wave models. [ABSTRACT FROM AUTHOR]

HY-2C is the third satellite in China's ocean dynamic environment satellite series, and carries a correction microwave radiometer (CMR) to correct the wet tropospheric path delay for the aligned radar altimeter. To effectively use the brightness temperatures (TB) of CMR to retrieve path delay, an on-orbit calibration effort is required. In this study, an antenna pattern correction (APC) method and a neural network method are used to perform an on-orbit calibration for CMR's antenna temperatures and a model based on the Whale Optimization Algorithm (WOA), Levenberg–Marquardt (LM) algorithm, and Back-Propagation neural network (WOA–LM–BP) has been proposed to retrieve the wet tropospheric correction (WTC) of CMR. The on-orbit calibration results, compared with the simulated brightness temperatures calculated by the radiative transfer model (RTM), have shown that compared with the APC method, the neural network method can almost eliminate the latitude variation, and the total bias and standard deviation of the on-orbit calibrated TB at all channels have obviously decreased. The retrieved WTC results also have shown that the retrieved WTC of CMR has a good agreement with the corresponding ones from the model-derived WTC and Jason-3. [ABSTRACT FROM AUTHOR]

Using ground-based remote sensing equipment (wind profile radar and microwave radiometer) data and ground automatic station data, a ground-based remote sensing sounding profile system (FAS) is constructed, which aims to make use of its advantages of high resolution, high accuracy, and low cost to make up for the lack of space–time density in existing conventional sounding layer information. The retrieval results of remote sensing sounding profiles in Beijing from May 2021 to September 2022 were tested and evaluated. The results show that the correlation coefficient between FAS and conventional sounding specific humidity is 0.89, the root–mean–square deviation is 1.53 g/kg, and the evolution trends of different data sources of convective available potential energy (CAPE) and vertical wind shear are synchronous. A case study was conducted to evaluate the effectiveness of 40 severe convective processes in the Beijing Plain area. The results show that, due to the minute-level time resolution of FAS, the retrieved convective parameters could track the evolution trend of the atmospheric state with high timeliness, dynamically describe the configuration of thermodynamic parameters, and indicate the time-varying local convective potential and instability level. Therefore, it has certain short-term forecasting significance for the occurrence time, intensity, and convection type. [ABSTRACT FROM AUTHOR]

The objective of this study is to cross-validate observations over precipitating systems by microwave radiometers on the temporal experiment for storms and tropical systems demonstration (TEMPEST-D) CubeSat mission and the global precipitation measurement microwave imager (GMI). The purpose of this article is twofold: first, to show consistency between TEMPEST-D and GMI observations, and second, to demonstrate the potential to enhance temporal sampling when TEMPEST-D and GMI observations are merged. Two cross-validation methodologies were employed. The first cross-validation methodology is to quantitatively compare TEMPEST-D and GMI brightness temperature (TB) observations over precipitation systems using a priori spatiotemporal constraints. The comparative analysis showed that the two instruments’ TB observations have similar probability distributions, with a mean absolute difference of 2.9 K. The second cross-validation methodology is to quantitatively compare TEMPEST-D and GMI TB observations over tropical cyclone systems. Three storm cases were analyzed in this comparative study. The analysis showed that the structure and intensity of the storms are similar in TEMPEST-D and GMI TB observations, and the overall average correlation coefficient (r) is 0.9. Combining TEMPEST-D and GMI TB observations over the hurricane systems increased the sampling frequency by a factor of 2.5, compared to using the GMI data alone.

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.

Thermodynamic and liquid water profiles can be retrieved by a ground-based microwave radiometer (MWR) in nearly all weather conditions, which is useful for detecting mesoscale phenomena. This paper reviews the advances in remote sensing of atmospheric profiles and cloud properties by MWR in central China. Comparative studies indicate that MWR retrieval accuracy is different under various skies, especially those that decay under precipitation. The off-zenith method is proven to be capable of reducing the impact of precipitation and snow on MWR retrieval accuracy. Application studies demonstrate that MWR retrievals are helpful for early warning of rainstorms, hailstorms, and thunderstorms. Moreover, MWR retrievals provide a way to study cloud properties. The temporal variations of cloud occurrence frequency (COF) and liquid water path (LWP) are different for low, middle, and high clouds, and the vertical distribution of COF is also different in autumn and other seasons. Note that MWR can infer valid retrievals over the eastern Tibetan Plateau due to the weak precipitation over there. Also, cloud properties over the eastern Tibetan Plateau present differences from those over central China, and this is related to the different characteristics of atmospheric water vapor between these two regions. To bring more benefits for mechanism study and early warning of severe weather and numerical weather prediction, the decayed accuracy of MWR zenith retrievals under precipitation should be resolved. And combining MWR with other instruments is necessary for MWR application in detecting multi-layer clouds and ice clouds.

Stationary or mobile microwave radiometers (MRs) can measure atmospheric temperature, relative humidity, and water vapor density profiles with high spatio-temporal resolution, but cannot obtain the vertical variations of wind field. Based on a dataset of brightness temperatures (TBs) measured with a mobile MR over the Three-River-Source Region of the Tibetan Plateau from 18 to 30 July 2021, we develop a direct retrieval method for the wind profile (WP) based on the Long Short-Term Memory (LSTM) network technique, and obtain the reliable dynamic variation characteristics of the WP in the region. Furthermore, the ground-based radiative transfer model for TOVS (RTTOV-gb) was employed to validate the reliability of the TB observation, and we analyzed the impact of weather conditions, altitude, observational mode, and TB diurnal variation on the accuracy of the TB measurement and the retrieval of the WP. Results show that the TB from the mobile observation (MOTB) on clear and cloudy days are close to those of the simulated TB with the RTTOV-gb model, while TB measurements on rainy days are far larger than the modeled TBs. When compared with radiosonde observations, the WPs retrieved with the LSTM algorithm are better than the ERA5 reanalysis data, especially below 350 hPa, where the root mean square errors for both wind speed and wind direction are smaller than those of ERA5. The major factors influencing WP retrieval include the weather conditions, altitude, observational mode, and TB diurnal variation. Under clear-sky and cloudy conditions, the LSTM retrieval method can reproduce the spatio-temporal evolution of wind field and vertical wind shear characteristics. The findings of this study help to improve our understanding of meso-scale atmospheric dynamic structures, characteristics of vertical wind shear, atmospheric boundary layer turbulence, and enhance the assessment and forecasting accuracy of wind energy resources.

Currently, the spatial resolution of snowmelt detection based on low-frequency brightness temperature data (less than or equal to 36 GHz) from the Advanced Microwave Scanning Radiometer 2 (AMSR2) is low, which inhibits the determination of accurate snowmelt changes. The spatial resolution of the AMSR2 89 GHz data is at least twice that of other microwave channels, but liquid water in clouds seriously affects the propagation of the high-frequency microwave signal. In order to apply microwave radiometer AMSR-2 89 GHz data with high spatial resolution but easily disturbed by clouds and water vapor to Antarctic ice-sheet near-surface snowmelt detection, this paper builds a screening model and correction model for 89 GHz data with large interference based on the high reliability of AMSR-2 36 GHz data and the high spatial resolution of 89 GHz data and the internal relationship between the two data. The method proposed in this paper was compared with the cross-polarised gradient ratio (XPGR) algorithm and six automatic weather stations. The average detection accuracy of the proposed method and the XPGR algorithm is 97% and 95%, respectively. The experimental results show that the snowmelt detection method proposed in this paper can more accurately detect the snowmelt on the Antarctic Ice Sheet and Ice Shelves.

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Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Human body temperature is a fundamental physiological sign that reflects the state of physical health. It is important to achieve high-accuracy detection for non-contact human body temperature measurement. In this article, a Ka band (32 to 36 GHz) analog complex correlator using the integrated six-port chip is proposed, and a millimeter-wave thermometer system based on the designed correlator is completed for human body temperature measurement. The designed correlator utilizes the six-port technique to achieve large bandwidth and high sensitivity, and miniaturization of the correlator is achieved through an integrated six-port chip. By performing the single-frequency test and the broadband noise measurement on the correlator, we can determine that the dynamic range of input power of the correlator is −70 dBm to −35 dBm, and the correlation efficiency and equivalent bandwidth are 92.5% and 3.42 GHz, respectively. Moreover, the output of the correlator varies linearly with the input noise power, which reveals that the designed correlator is suitable for the field of human body temperature measurement. Then, a handheld thermometer system, with a size of 140 mm × 47 mm × 20 mm, is proposed using the designed correlator, and the measurement results show that the temperature sensitivity of the thermometer is less than 0.2 K. [ABSTRACT FROM AUTHOR]

With the Guiyang ground-based microwave radiometer, radiosonde and ground observation data at 08:00 and 20:00 from January 2017 to May 2019, this paper evaluates the seasonal variation and the standard deviation of bias of temperature profile retrieved from the microwave radiometer in clear sky. The results show that: (1) the average relative biases of the vertical profiles of temperature from spring to winter are as follows: -1.71-1.35℃, -1.69-0.78℃, -2.24-1.29℃ and -4.98-1.49℃, and the standard deviations of biases are as follows: 1.13-2.93℃, 0.43-1.86℃, 0.76-1.92℃, 0.62-3.48℃. The vertical changes and standard deviations of biases from spring to autumn are the same, while the changes and standard deviations of error in winter are larger than those in other seasons. (2) The samples of inversion and biases have the best correlation. Among the three expressions of inversion, the inversion thickness and the bias have an best correlation when the inversion thickness is 490-740 m. The largest correlation coefficient appears when the inversion thickness is 650 m. The inversion magnitude and the error have the best correlation when the inversion thickness is less than 490 m or more than 740 m and inversion threshold is taken as 1.2℃. (3) The correlation between the average height of inversion and bias is analyzed. It is found that the correlation between the two is poor. The height of bias can not be reflected by the height of inversion.

The wet tropospheric correction (WTC) is a required correction to satellite altimetry measurements, mainly due to the atmospheric water vapor delay. On-board microwave radiometers (MWR) provide information for WTC estimation but fail in coastal zones and inland waters. In view to recover the WTC in these areas, the Global Navigation Satellite System (GNSS)-derived Path Delay Plus (GPD+) method, developed by the University of Porto, uses Zenith Tropospheric Delays from GNSS global and regional networks’ stations combined with other sources of information, providing a WTC solution for all along-track altimeter points. To densify the existing dataset used by GPD+, it is necessary to add new GNSS stations, mainly in the southern hemisphere, in regions such as South America, Africa and Oceania. This work aims to exploit the SIRGAS-CON data and its potential for densification of the GPD+ input dataset in Latin America and to improve GPD+ performance. The results for the three analyzed satellites (Sentinel-3A, Sentinel-3B and CryoSat-2) show that, when compared with the WTC from GNSS and radiosondes, the densified GPD+ WTC leads to a reduction in the RMS of the WTC differences with respect to the non-densified GPD+ solution, up to 2 mm for the whole region and up to 5 mm in some locations.

The water vapour column density or vertically integrated water vapour (IWV) ranges from about 8 mm in winter to about 25 mm in summer in Bern, Switzerland. However, there can be day episodes when IWV drops to 2 mm or even less so that the atmosphere is extremely dry. We selected an event in February 2021 when the tropospheric water radiometer TROWARA measured a mean IWV value of about 1.5 ± 0.2 mm for a time interval of about one day in Bern. The ECMWF reanalysis ERA5 indicated a slightly higher IWV value of about 2.2 ± 0.4 mm where the uncertainty is the standard deviation of IWV during the time of IWV depression. The ERA5 profiles of relative humidity and specific humidity during this episode are reduced by 50% and more compared to the monthly mean profiles. On a global map, it can be seen that Bern is within a mesoscale dry region on that day with descending wind. Back trajectory analysis gives the result that the dry air masses in Bern came from the North and the trajectories are descending in altitude so that dry air from the mid troposphere came into the lower troposphere. These descending air masses from the North explain the minimum of IWV observed in Bern on 13–14 February 2021. The surface climate in Switzerland was dominated by a cold wave at that time. At the same time, severe cold waves occurred in Greece and Northern America. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The internal heat flow related to the Moon’s composition, interior structure, and evolution history is not well-constrained and understood on a global scale. Up to now, only two in situ heat flow experiments, Apollo 15 and 17 were deployed nearly 50 years ago. The measured high values of heat flow might be influenced by lateral heat at highland/mare boundaries and enhanced by heat production from radioactive elements enriched unit, and may also be disturbed by astronauts’ activities. In this study, we proposed a new method to retrieve heat flows at two permanently shadowed craters, Haworth and Shoemaker of the Moon’s south pole, from Chang’E-2 microwave radiometer data and Diviner observations. Our results show that the average heat flow is 4.9 ± 0.2 mW/m2. This provides a constraint for the bulk concentration of Thorium within the lunar south polar crust 656 ± 54 ppb, which helps us understand the Moon’s thermal evolution and differentiation.

Based on Microwave Radiometer (MWR) observations in Wuhan over the course of 21 months, we compared the temperature and water vapor levels with those from radiosonde (RS) sounding data at 00:00 and 12:00 UTC, and then analyzed the seasonal and diurnal changes of temperature and water vapor levels from the MWR data. The MWR and RS mean temperatures and dew points are roughly consistent with each other below 2 km, whereas above 2 km, the MWR temperature is slightly lower than the RS temperature. The difference in their water vapor densities decreases quickly with height, and the bias of their relative humidities is generally in the range of −15% to 20%. The MWR observations show that in autumn, the surface temperature is 6.8 K lower during precipitation events than during non-precipitation events, indicating that precipitation in autumn is mainly caused by cold air from the north. The relative humidity during precipitation events exceeds 90% from the ground to 5 km, which is obviously larger than during non-precipitation events. During non-precipitation events, the seasonal mean water vapor density at 0–1.0 km shows an approximately linear increase with the mean temperature; however, their diurnal changes are opposite due to the effect of the boundary layer. At 4.5–5.5 km and 8.5–9.5 km, the mean temperature shows a synchronized diurnal evolution, with the maximum value prior to that at 0–1.0 km, indicating the strong influence of the air–land interaction on the temperature near the ground. Hence, this study is helpful for deepening our understanding of temperature and humidity variabilities over Wuhan.

The rain and snow event in cold wave in Nanjing at the end of March 2020 is analyzed using conventional observation data and new detection data such as microwave radiometer, wind profile radar and laser precipitation spectrometer. The results show that: (1) The weather systems that caused this cold wave are Ural high pressure ridge, upper cold vortex, ground strong cold high pressure and ground cold front. The water vapor provided by the southwest airflow in front of the southern trough and the continuous decrease of temperature in the middle and low levels caused by the cold air below 850 hPa are the key factors to produce changes in precipitation phase. (2) The microwave radiometer data showed that the lower level temperature drops first, followed by the ground temperature. There is a warm layer near the ground. The height of 0 ℃ layer changed obviously during the precipitation phase transformation, and it reached minimum in snowfall period. The water vaper during this event is mainly distributed at 3 to 4 km. The liquid water and the vaper density reached maximum in sleet period and reached minimum in snowfall period. (3) The vertical wind from wind profile radar reflected that when the cold air moves southward, the cold lower layer became thicker, the warm and humid air raised and the temperature decreased, and that leaded to the precipitation phase transformation. There was no heavy snow because the southwest jet was not established stably. (4) The laser precipitation spectrometer observed that the snowfall particle's diameter is bigger and falling velocity is slower, while the rainfall particle's diameter is smaller and falling velocity is faster. It can be used to monitor and forecast changes in precipitation phase. From the period of rainfall, sleet and snowfall, the patterns experienced a process from double spectrum to undulation and multimodal spectrum. Furthermore, both spectrum width and number concentration are significantly in increasing trend.

A brief description of the method of ground-based microwave radiometry for retrieving the characteristics of atmospheric moisture content is given. The results are reported of experimental studies of the integrated water vapor and cloud liquid water content using ground-based microwave radiometers, in particular, during the development of severe weather events associated with the development of convective clouds and thunderstorms. The capabilities are discussed of microwave radiometry in improving the diagnosis of the synoptic and mesoscale structure of atmospheric fronts. The prospects are considered for using radiophysical facilities of the Mozhaisky Military Space Academy Geophysical Observatory for experimental studies of the atmosphere, clouds, and precipitation in the Leningrad region, which are aimed at improving methods for forecasting severe weather events associated with the development of convective clouds, thunderstorms, and precipitation. [ABSTRACT FROM AUTHOR]

Spaceborne microwave sensors, such as microwave scatterometers, radar altimeters, and microwave radiometers, can observe the sea surface day and night and are not affected by clouds. Except for the synthetic aperture radar, their spatial resolution is in the order of 10 km. While visible and infrared radiometers provide high-resolution images of chlorophyll a concentration and sea surface temperature, microwave sensors focus on global observations of physical parameters of the sea surface. To achieve accurate observation, it is indispensable to develop algorithms that are based on the physical processes at the air-sea boundary, evaluate observation accuracy, comprehend the error characteristics, and provide feedback for algorithm improvement. In this paper, I would like to summarize my research on ocean observations using three different types of microwave sensors and their applications as well as describe future prospects. [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 article, we propose an accurate and fast method that is based on the genetic algorithm and the Back-Propagation neural network (GABP) to improve the accuracy and speed of retrieving atmospheric parameters (temperature, humidity, liquid water content, and so on). Firstly, the genetic algorithm is applied to improve and optimize the connection weights and thresholds of the BP neural network according to situ data measured by an sixteen-channel ground-based radiometer and a radiosonde, and then obtain a more proper range of those weights and thresholds. Secondly, the BP neural network is trained once again to get an ideal BP neural network model in a relatively short time. Finally, the model (or GABP), in which a genetic algorithm (GA) is combined with the BP neural network algorithm, is tested by the situ measurements of the sixteen-channel microwave radiometer. The tested results by GABP are not only in a good agreement with those of other known models, and but also has better accuracy and faster convergence speed than those reported algorithms for retrieving atmospheric parameters. It can make a conclusion that GABP is a new algorithm which is capable of quickly retrieving accurately atmospheric parameters.

Deep core temperature is the most basic and important information about health conditions. Non-contact infrared thermometers are widely used for human body temperature measurement. However, infrared thermometers are not suitable for deep core temperature measurement. To overcome this limitation, measuring deep core temperature with a microwave radiometer is gaining attention. In this study, we propose a dual-band on-body near-field antenna for measuring deep core temperature with a microwave radiometer application. The proposed antenna has a compact size of 20 mm $\times30$ mm $\times1.52$ mm. The negative (−) radiator is located at the top of the substrate and the positive (+) radiator is located at the bottom of the substrate. The dual-band is proposed for high temperature resolution and is achieved by a meander slot on the radiator. The antenna is fabricated on a high dielectric substrate and the stubs are proposed to reduce the size of the proposed antenna so that it can be used on various human body parts.

To reveal the spatial-temporal characteristics of atmospheric pollution during dust events on the North Slope of the Tianshan Mountains (NSTM), this study conducted a joint detection experiment from April to June from 2019 to 2021 at Shihezi, using satellite remote sensing, a microwave radiometer, meteorological sensors, and environmental monitors. Using long-term detection data from the aforementioned equipment, this study analyzed the characteristics of meteorological elements, Aerosol Optical Depth (AOD), PM, and gaseous pollutants. The main findings are as follows: The dust particles from the two severe pollution dust events on April 9, 2020, and May 1, 2021, on the NSTM originated from the Gurbantünggüt Desert and were transported along the northwest direction, leading to a significant increase in AOD (averaging 1.36 g/m2) and dust column mass concentration (averaging 1.58 g/m2). During the two dust storms, the PM 10 concentration peaks reached 2536.5 μg/m³ and 1804.5 μg/m³, respectively. The average relative humidity (RH) was less than 30%, the average wind speed was more than 6 m/s, and the average visibility (V) was less than 1000 m. Moreover, during the dust events from April to June, the temperature and relative humidity were higher in June, but the wind speed was lower. The vertical thermodynamic interactions of the dust storms were stronger than those of the blowing and floating dust storms. Finally, the relationship between PM 10 and V was fitted using the following equation: V = 11326.8166 e − 0.0015 P M 10 .This study provides scientific support for the prediction of dust storms on the NSTM. • The particles from the Gurbantünggüt Desert were transported along the northwest direction, leading the NSTM dust storms. • The temporal-spatial characteristics of meteorological elements, the Aerosol Optical Depth and PM were analyzed in the NSTM. • During in the dust events, the relationship between the PM 10 and V (visibility) was constructed as V = 11326.8166 e − 0.0015 PM 10 . [ABSTRACT FROM AUTHOR]