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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]

• 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]

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]

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 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]

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]

Radio-frequency interference (RFI) is an increasing problem particularly for Earth observation using microwave radiometry. RFI has been observed, for example, at L-band by the European Space Agency’s (ESA’s) soil moisture and ocean salinity (SMOS) Earth Explorer and by National Aeronautics and Space Administration’s (NASA’s) soil moisture active passive (SMAP) and Aquarius missions, as well as at C-band by Advanced Microwave Scanning Radiometer (AMSR)-E and AMSR-2, and at 10.7 and 18.7 GHz by AMSR-E, AMSR-2, WindSat, and GPM Microwave Imager (GMI). Therefore, systems dedicated to interference detection and removal of contaminated measurements are nowadays a must in order to improve radiometric accuracy and reduce the loss of spatial coverage caused by interference. In this work, the feasibility of using the empirical mode decomposition (EMD) technique for RFI mitigation is explored. The EMD, also known as Hilbert–Huang transform (HHT), is an algorithm that decomposes the signal into intrinsic mode functions (IMFs). The achieved performance is analyzed, and the opportunities and caveats that this type of methods present are described. EMD is found to be a practical RFI mitigation method, albeit presenting some limitations and considerable complexity. Nevertheless, in some conditions, EMD exhibits a better performance than other commonly used methods (such as frequency binning). In particular, it has been found that EMD performs well for RFI affecting the $< 25\%$ lower part of the intermediate frequency (IF) bandwidth. [ABSTRACT FROM AUTHOR]

The refractive index structure constant ( C n 2 ) is a key parameter used in describing the influence of turbulence on laser transmissions in the atmosphere. Three different methods for estimating C n 2 were analyzed in detail. A new method that uses a combination of these methods for continuous C n 2 profiling with both high temporal and spatial resolution is proposed and demonstrated. Under the assumption of the Kolmogorov "2/3 law", the C n 2 profile can be calculated by using the wind field and turbulent kinetic energy dissipation rate (TKEDR) measured by coherent Doppler wind lidar (CDWL) and other meteorological parameters derived from a microwave radiometer (MWR). In a horizontal experiment, a comparison between the results from our new method and measurements made by a large aperture scintillometer (LAS) is conducted. The correlation coefficient, mean error, and standard deviation between them in a six-day observation are 0.8073, 8.18 × 10−16 m−2/3 and 1.27 × 10−15 m−2/3, respectively. In the vertical direction, the continuous profiling results of C n 2 and other turbulence parameters with high resolution in the atmospheric boundary layer (ABL) are retrieved. In addition, the limitation and uncertainty of this method under different circumstances were analyzed, which shows that the relative error of C n 2 estimation normally does not exceed 30% under the convective boundary layer (CBL). [ABSTRACT FROM AUTHOR]

A ground-based multichannel microwave radiometer (GMR) is commonly used to observe the atmospheric radiation brightness temperature (TB) in order to retrieve atmospheric temperature and humidity profiles. At present, GMRs are used only in meteorology and climate monitoring. However, theoretical analysis showed that GMRs can be also used to observe the solar radiation. Therefore, we tried to improve the antenna servo control system of a GMR so that it could track and observe the sun, and the results showed that the GMR could respond to the variation of solar radiation. A further question was: can a GMR observe the variation of the sun during a solar eclipse? Fortunately, two solar eclipse events were captured by the GMR on 26 December 2019 and 21 June 2020 in Xi'an, China. We used the upgraded GMR to observe the variation of solar radiation during the two solar eclipses. The observation and analysis results showed that (1) the GMR could accurately track the sun and respond to the variation of solar radiation during the solar eclipse. We analyzed the variation features of the solar radiation by combining the solar phase during the two solar eclipses. (2) We found that the GMR could respond to the variation of the solar radiation arising from the Earth–Sun distance, and we further propose a novel method to measure the eccentricity of earth orbit with the GMR by using the passive solar observation. The results show that the eccentricity measured was 0.0169, which agreed quite well with the value of 0.0167 in the literature. (3) The average variation percentages of both the Earth–Sun distance and the intensity of the incident solar radiation throughout the year were estimated to be 3.44% and 6.6%, respectively. According to these results, the solar observation techniques can broaden the field usage of GMR. [ABSTRACT FROM AUTHOR]

Hydrological variables such as surface water extent and soil moisture are continuously being monitored from satellites with good accuracy. However, orbital measurement of river stage and discharge remains a major challenge even today. Although altimeters provide valuable information on water level variations with a precision of few centimetres, they suffer from poor revisit time and limited gauging sites. Microwave radiometers that provide brightness temperature (BT) can be used to fill the gaps between altimeter passes with a revisit time of 1–2 days, although with limited accuracy. This paper presents an approach for estimation of river stage based on passive microwave radiometer using BT of river/measurement target (M) and upstream calibration target (C). We have used altimeter data from Jason-2/3 and BT data from Advanced Microwave Scanning Radiometer (AMSR2) at 36.5 GHz (descending orbit and horizontal polarization) for the period January 2013 to August 2020 to estimate river stage at 12 virtual gauging sites along Indus, Ganga, and Brahmaputra rivers. A new approach for selection of calibration targets is presented in this study which results in higher correlation between altimeter observed river stage and AMSR2 derived C/M ratio when compared to existing methods. Coefficient of determination (R2) for C/M ratio with water level for the period 2013–2017 ranged from 0.45 to 0.89. Model functions were generated between C/M ratio and altimeter observed water level for all the gauging sites. Estimated water levels were validated against altimeter and ground observed values for the period January 2018–August 2020 with RMSE ranging from 0.35 to 1.45 m, which is 7–15% of total stage variability. [ABSTRACT FROM AUTHOR]

Two approaches were designed to identify the echoes of clear air and precipitation when both coexist in the very-high-frequency (VHF) radar spectra: contour-based and peak-finding methods. The contour-based approach was used to model a 2D Doppler spectra to determine the locations of multiple spectral humps, and the peak-finding approach was used to find the spectral peaks on request. Grouping, sifting, and Gaussian fitting were performed further for such obtained contour centres and spectral peaks to yield Doppler velocities and spectral widths. In general, the two approaches resulted in corresponding outcomes and can be complementary to find the spectral peaks as fully as possible. The Doppler velocities retrieved from the two approaches were cooperatively used to develop an effective process of Doppler profiling for treating a great amount of radar data, which was validated with the radar data collected during a rainy and strongly convective atmosphere. As an application of Doppler profiling results, the hydrometeor parameters measured by a dual-polarized microwave radiometer were investigated jointly with radar observation, showing that a strong updraft air could bring the liquid water to a height above the melting layer and then the Bergeron effect and coalescence processes on formation of ice crystals and graupel particles occur accordingly. [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]

This work is aimed to solve technical difficulties for microwave radiometers in Ku band and above under indoor scenes to detect physical temperatures of targets with shelters. Firstly, a revised microwave radiometer in Ku-band is developed with a novel calibration scheme. Secondly, in order to the designed radiometer pocess a high temperature accuracy for targets with shelters, crucial technical difficulties are investigated: 1) The long-neglected impact on temperature sensitivity of the single channel radiometer by the frequency stability of LO is analyzed and a LO with high frequency stability and small volume is designed. 2) The calibration accuracy deterioration caused by the receiver's nonideality is analyzed, and a novel calibration algorithm is proposed. 3) the method of plane wave spectrum (PWS) is proposed to derive the planar electric field in near field of the horn antenna for temperature inversion. 4) A more complete microwave thermal radiation transmission model of clothed human body is proposed. Thirdly, several experiments are set up and show that the designed radiometer can track physical temperatures of targets sheltered by cotton cloth relatively accurately, so that methods proposed for indoor near field applications are confirmed effective. Noteworthily, it is the first time in the microwave passive sensing domain that our work systematically proposes the temperature inversion theory for physical temperatures of targets with shelters accurately in noncontact under indoor scenes, and the design scheme for the microwave radiometer optimized portable along with a high temperature sensitivity applied for microwave security and medical monitoring. [ABSTRACT FROM AUTHOR]

The current state of microwave radiometry for remote sensing of the Earth is considered. There are currently some 30 satellite microwave radiometers operating and supplying data in the world, while Russia has only one microwave radiometer (MTVZA-GYa) with a 65° sounding angle. We propose to create a constellation of small spacecraft for global meteorological observations on the basis of the MIRS microwave radiometer, which is being developed for the Konvergentsiya space experiment on the Russian Segment of the International Space Station. Optimal parameters for satellite data of the microwave space system are a spatial resolution of 10–12 km and a temporal resolution of 3–6 h for analyzing current atmospheric processes, improving the quality of weather forecast, and predicting emergency situations. This problem can be solved if there are 4 to 8 simultaneous satellites with onboard radiometers in a single orbit of the Earth. [ABSTRACT FROM AUTHOR]

Objectives: Atmospheric temperature and humidity are important parameters to describe the dynamic and thermal state of the atmosphere. We analyzed the diurnal and seasonal variations of temperature and humidity profiles, atmospheric boundary layer height (ABLH), and liquid water path (LWP) profiles measured by microwave radiometer in Wuhan.Methods: The detection results of radio sounding and RPG-HATPRO microwave radiometer were compared in the absence and presence of clouds. Then, the diurnal and seasonal variations of meteorological elements from 2011 to 2015 were analyzed to realize the thermodynamic characterization of the tropospheric atmosphere in Wuhan.Results: The temperature and humidity observation accuracy obtained by the microwave radiometer inversion would vary due to different weather conditions. When using products of microwave radiometer, the influence of cloud should be considered. ABLH began to increase in the morning, reached its highest value at 15:00, then decreased and remained stable throughout the night. The content of liquid water path is higher at night than that during the day, and higher in winter than in summer. Relative humidity (RH) rose with the increase of altitude at 0−1 km, and then decreased. Conclusions: The diurnal and seasonal variations of meteorological parameters in Wuhan are observed and analyzed, which provides reliable information for meteorological exploration, air pollution research, air quality monitoring, and weather forecast. [ABSTRACT FROM AUTHOR]

The use of microwave radiation data collected by lunar orbiters to infer the lunar brightness-temperature (TB) distribution is of great importance to lunar scientific exploration. The TB data acquired by China's Chang'E-2 passive microwave radiometer (MRM) is one of the basic materials for lunar temperature mapping. Core issues of lunar microwave TB mapping include data fitting at different latitudes, outlier removal, and integrated spatial interpolation considering influencing factors. We applied a third-order Fourier function to fit the lunar TB data, which effectively removes outliers considering the fitting characteristic and three-sigma rule (FCTSR). Because the lunar surface temperature is influenced by a variety of factors such as topography and albedo, we then built a multi-factor co-kriging interpolation method to perform lunar TB mapping accurately. The cross-validation shows that, in terms of the mean-absolute-error and root-mean-square-error, the multi-factor co-kriging interpolation improves the mapping accuracy by 30%–60% compared to inverse distance interpolation and ordinary kriging interpolation. The analysis shows that topography and albedo are the most important factors influencing the lunar TB, and the TB maps at 37 GHz reveal cold spots that can be considered as younger craters. It is concluded that the proposed data fitting, denoising and spatial interpolation methods significantly improve the lunar TB mapping. The results and scientific data can also provide a basic energy map for lunar roving path planning and subsequent lunar exploration. • TB outliers were effectively removed using the fitting characteristics and three-sigma rule. • A multi-factor co-kriging method was developed to produce accurate lunar TB maps. • The co-kriging improves accuracy by 30%–60% compared to IDW and ordinary kriging. • The TB maps at 37 GHz reveal cold spots that can be identified as younger craters. [ABSTRACT FROM AUTHOR]

NASA's Juno spacecraft has been monitoring Jupiter in 53‐day orbits since 2016. Its six‐frequency microwave radiometer (MWR) is designed to measure black body emission from Jupiter over a range of pressures from a few tenths of a bar to several kilobars in order to retrieve details of the planet's atmospheric composition, in particular, its ammonia and water abundances. A key step toward achieving this goal is the determination of the latitudinal dependence of the nadir brightness temperature and limb darkening of Jupiter's thermal emission through a deconvolution of the measured antenna temperatures. We present a formulation of the deconvolution as an optimal estimation problem. It is demonstrated that a quadratic expression is sufficient to model the angular dependence of the thermal emission for the data set used to perform the deconvolution. Validation of the model and results from a subset of orbits favorable for MWR measurements is presented over a range of latitudes that cover up to 60° from the equator. A heuristic algorithm to mitigate the effects of nonthermal emission is also described. Plain Language Summary: One of the instruments on the Juno spacecraft that is currently orbiting Jupiter every 53 days is the microwave radiometer (MWR). It has been sensing the atmosphere for the first time over a wide range of depths below the top‐most clouds, covering pressures from less than the Earth's surface pressure to several thousand times that value. This enables a deeper exploration than ever before of how winds distribute gases that can condense, such as water (as in the Earth's atmosphere) and ammonia (which forms Jupiter's highest level clouds). One challenge in understanding the MWR data is to convert each of its raw measurements into an estimate of the true brightness temperature of Jupiter as though it were observed in a perfect, narrow beam, a process known as a deconvolution. We determined that this correction for the angular dependence can be done reliably with a three‐term (quadratic) expression. The results of this approach have formed the basis of all of the analysis of MWR data to date, and we show some of the intriguing results from orbits that allowed for the best MWR observing geometry over latitudes that cover up to 60° from the equator. Key Points: A method to deconvolve Jupiter's thermal emission measured by the Juno microwave radiometer is presented and validatedDeconvolved nadir brightness temperatures and limb darkening results are presented for Juno observations between July 2016 and April 2018 [ABSTRACT FROM AUTHOR]

The Juno spacecraft provides unique close‐up views of Jupiter underneath the synchrotron radiation belts while circling Jupiter in its 53‐day orbits. The microwave radiometer (MWR) onboard measures Jupiter thermal radiation at wavelengths between 1.37 and 50 cm, penetrating the atmosphere to a pressure of a few hundred bars and greater. The mission provides the first measurements of Jupiter's deep atmosphere, down to ~250 bars in pressure, constraining the vertical distributions of its kinetic temperature and constituents. As a result, vertical structure models of Jupiter's atmosphere may now be tested by comparison with MWR data. Taking into account the MWR beam patterns and observation geometries, we test several published Jupiter atmospheric models against MWR data. Our residual analysis confirms Li et al.'s (2017, https://doi.org/10.1002/2017GL073159) result that ammonia depletion persists down to 50–60 bars where ground‐based Very Large Array was not able to observe. We also present an extension of the study that iteratively improves the input model and generates Jupiter brightness temperature maps which best match the MWR data. A feature of Juno's north‐to‐south scanning approach is that latitudinal structure is more easily obtained than longitudinal, and the creation of optimum two‐dimensional maps is addressed in this approach. Key Points: Residual analysis provides a direct method to compare any Jupiter atmosphere model with the MWR observationsThe iterative residual analysis process generates 2‐D Jupiter maps revealing the deep structures of upper atmosphere features [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to describe a new microwave radiometer designed for sensing natural mediums to solve various applied scientific problems. The research findings enable to make assertions about high efficiency of the described microwave radiometer being a part of mobile sensor systems with self-contained power supplies. Design/methodology/approach: A new microwave radiometer is based on the modification of the null method. Modification of the null method has been implemented by using two reference noise generators. The first reference noise generator is passive and its implementation is based on the matched load. A low-noise amplifier is used as the second reference noise generator. The use of the low-noise amplifier as the reference noise generator is based on the noise wave generation effect at its input whereby the waves form low-temperature noise. Findings: The use of the low-noise amplifier as the reference noise generator in the modified microwave radiometer has made it possible to simplify the device design at the system level while reducing the weight and power consumption and increasing sensitivity. Originality/value: The novelty of the modified radiometer lies in the modification of the null method and the removal of high-temperature reference noise generators based on avalanche transit-time diodes. Further, the novelty lies in the invariance of measurement results toward changes in the receiver's own noise and transmission factor while the design of the device has been simplified. [ABSTRACT FROM AUTHOR]

Calibration plays an important role in improving the accuracy of the microwave and millimeter-wave radiometric measurements. Several calibration techniques have been used in radiometers including external calibration targets, vicarious sources, and internal calibrators such as noise diodes or matched reference load. A new calibration technique based on deep learning has recently been developed to calibrate microwave and millimeter-wave radiometers. The deep-learning calibrator has been previously demonstrated on a computer noise-wave modeled Dicke-switching radiometer. This article applies the new deep-learning calibration technique for the calibration of the high-frequency airborne microwave and millimeter-wave radiometer (HAMMR) instrument. A deep-learning neural network model is built to calibrate the 2014 West Coast Flight Campaign antenna temperature measurements of the HAMMR. The deep-learning calibrator antenna temperature estimates are obtained from the radiometric measurements. The deep-learning calibration results are compared with the existing conventional calibration techniques used in HAMMR 2014 field campaign. The results have shown that the deep-learning calibrator is in agreement with the conventional calibration techniques. In this article, it is demonstrated that the deep-learning calibrator can be employed for calibrating the radiometers with high accuracy. [ABSTRACT FROM AUTHOR]

The growth of the daytime convective atmospheric boundary layer, which plays a pivotal role in the vertical mixing and dispersal of water vapour and pollutants, is modulated by cloud radiative effects. Assessment of this cloud effect is sparse in any geographical region and non-existent over tropical coastal regions. We investigate the effect of clouds on the diurnal evolution of boundary-layer height over tropical coastal location Thumba (8.5°N, 77°E) during onshore and offshore flow using multi-year (2010–2016) microwave radiometer profiler observations. The boundary-layer height during both cloudy and clear-sky periods increases rapidly from 0800 LT (local time = UTC + 5.1 h) to attain a daytime peak around noon (400–1500 m). The seasonal mean noontime boundary layer height during cloudy periods is lower than that during clear-sky periods by > 900 m (> 400 m) when offshore (onshore) flow prevails during winter and pre-monsoon seasons. The forenoon growth rate of the boundary-layer height during clear-sky offshore flow is rapid (> 380 m h−1) compared to that during cloudy offshore (160 m h−1), clear-sky onshore (160–250 m h−1), and cloudy onshore (> 100 m h−1) flow. Effects of shortwave cloud radiative forcing and soil temperature on the noontime boundary-layer height and their interdependencies are presented. These observations reveal the contrasting and significant effect of clouds on the growth of the daytime boundary layer during onshore and offshore flow, and the coupled effects of cloud radiative forcing and soil temperature on boundary-layer height over tropical coastal regions, which provide essential constraints for evaluating model simulations. [ABSTRACT FROM AUTHOR]

In this paper, a new high-temperature superconducting (HTS)-based microwave radiometer with an improved sensitivity is presented. The cryogenic receiver front end consists of an HTS filter and a cryogenic low noise amplifier (LNA). The cryogenic receiver front end shows an ultra-low noise figure and can suppress radio frequency interference (RFI) effectively. The proposed HTS filter works at a center frequency of 1.4135 GHz with a bandwidth of 25 MHz. The measured mid-band insertion loss, side band steepness, and out-of-band attenuation of the HTS filter are 0.14 dB, 35 dB/MHz, and 80 dB, respectively. The noise figure of the cryogenic LNA is about 0.27 dB at a temperature of 77 K. Compared with other total power radiometers, the proposed radiometer has a lower receiver noise temperature, which can improve the sensitivity with a short integration time of the satellite-based salinity meter. In addition, since the bandwidth of the salinity meter is fixed and the integration time of satellite-based equipment is limited, such a low receiver noise temperature can increase the flexibility of future satellite payload configuration program. [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]

Deep learning artificial neural network techniques can be applied for on-orbit calibration of microwave and millimeter-wave radiometer spaceborne instruments, including those for small satellites. The noise-wave model has been employed for noise characterization and validation of the proposed deep learning calibration technique for a synthetically generated Dicke-switching radiometer. The developed deep learning neural network radiometer calibrator produces high accuracy estimates of antenna temperatures from the measurements of radiometer output voltage and thermistor readings. Tests with noise-free and noisy samples of the developed model have shown that the proposed calibration method does not add any significant noise to the radiometer calibration. The performance of the proposed method does not degrade with increased nonlinearity for a radiometer, while nonlinearity is a challenging issue for conventional calibration techniques. The deep learning calibration model learns the radiometer noise characteristics from radiometer prelaunch measurements during thermal vacuum chamber testing. The neural network calibrator proposed in this paper has self-learning capability during the on-orbit operation of a radiometer that can be used to improve the performance of on-orbit calibration. The proposed technique is demonstrated by comparing the residual uncertainty of the deep learning calibration with the theoretical value. No numerical study is presented to compare the performance with conventional calibration techniques. The new method may be solely applied to calibrate the radiometer or applied along with conventional calibration techniques. [ABSTRACT FROM AUTHOR]

SARAL carried onboard a radar altimeter that provides very precise measurements of the sea surface height (SSH). Like other altimetric missions, SARAL carries a passive microwave radiometer (PMR) for wet tropospheric correction to SSH. In the present study, new algorithms are developed for the retrieval of cloud liquid water (CLW) and total precipitable water vapor (TPW) over the global oceans from PMR measurements of the brightness temperatures. A radiative transfer and genetic algorithm based retrieval scheme is proposed for the estimation of CLW and TPW from SARAL PMR. The comparisons of CLW from PMR with independent measurements from GPM-GMI and SSMIS within and outside ±40° latitudes show correlation (R) of 0.86 and 0.83, bias of 0.7 and −3.61 mg/cm2, and root mean square error (RMSE) of 8.42 and 8.07 mg/cm2, respectively. Similarly, TPW from PMR with GPM-GMI and SSMIS show R of 0.99 and 0.98, bias of −0.04 and −0.03 g/cm2 and RMSE of 0.17 and 0.17 g/cm2, respectively. The retrieval accuracy of CLW and TPW from the new algorithms is compared with these parameters provided in the SARAL geophysical data records as finished products, which showed substantial improvement in the quality of the parameters from the new algorithm. [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]

Abstract: Accurate observations of liquid water content (LWC) in warm stratiform clouds are important for quantifying their radiative and hydrological effects and for studying aerosol‐cloud interactions. Retrieving LWC from radar reflectivity under drizzling or nondrizzling conditions has been investigated for several decades by the cloud remote sensing community. However, no physically plausible framework exists to address the biases introduced by drizzle on existing retrieval techniques. We present the modified Frisch retrieval (ModFrisch), which combines radar and microwave radiometer measurements to retrieve LWC in both nondrizzling and drizzling conditions. It is shown, using a 1‐D steady state microphysical model and a radar simulator, that the uncertainty of ModFrisch is up to four times smaller than the uncertainty of similar retrievals under drizzling conditions, enabling LWC profiling with an accuracy of 20%. The performance of the ModFrisch technique is evaluated using 1 year of observations. [ABSTRACT FROM AUTHOR]

ABSTRACT: Cloud microphysical variables are needed to evaluate and improve cloud parameterization and data assimilation in a numerical weather model. In order to obtain high spatiotemporal resolution data for the cloud, data from the Ka‐band cloud radar (KaCR), an instrument specialized for cloud observation, were used. In this study, the liquid water content (LWC) was estimated, and the reflectivity–liquid water content (Z–LWC) relationships were derived using the vertical profiles of radar reflectivity from the KaCR and the liquid water path (LWP) from a microwave radiometer. The data were collected at the Boseong National Center for Intensive Observation of Severe Weather in the Republic of Korea during an intensive observation period in 2014 (2014‐IOP, 16 June to 15 July 2014). First, the KaCR reflectivity was corrected using the linear depolarization ratio from the KaCR. The process also involved removing reflectivity profiles that have signal attenuation, compared with the cloud‐top heights retrieved from a satellite. The LWC profiles were calculated from the LWP by dividing using the weight of the KaCR reflectivity. Different constants were derived from the Z–LWC relationship according to each respective echo type. Therefore, the KaCR echoes were categorized into non‐precipitating clouds, precipitating clouds, and raindrops, using a micro rain radar and a ceilometer. The Z–LWC relationships were finally derived for each categorized echo. The results of this study suggest that the new method should be useful for retrieving cloud microphysical variables. These relationships allowed the estimation of 3D LWC in high resolution using a single KaCR platform. [ABSTRACT FROM AUTHOR]

• Excellent agreement of IWV from MWR and SP against GNSS. • Linear dependence of MBE and SD on IWV; different instrument- and site-wise. • Weaker dependence of MBE and SD with SZA for MWR-GNSS and SP-GNSS IWV differences. • Seasonal dependence of MBE and SD mainly modulated by dependence on IWV. • Importance of the use of metadata to filter out low-quality data. This work presents a comparison of integrated water vapor (IWV) data recorded from microwave radiometer (MWR) and sun-photometer (SP) using global navigation satellite system (GNSS) IWV as reference in five mid-latitude sites of Portugal and Spain (2003–2021). A very high correlation is obtained for both instruments (R2 between 0.94 and 0.98), although, while MWR shows a wet bias, SP exhibits a dry one. In addition, a dependence of mean bias error (MBE) and standard deviation (SD) on IWV has been observed, increasing (larger discrepancies) both indices as IWV increases. The solar zenith angle (SZA) dependence is also studied, finding slightly larger discrepancies for the MWR than for SP in comparison with GNSS for very high values of SZA. Finally, a marked seasonal dependence is observed for SP-GNSS differences, modulated by the IWV dependence explained above. In contrast, the seasonal dependence for MWR is quite weaker than it is for SP. Therefore, in spite of the excellent agreement found among the different instruments, it is recommended that: i) the dependence with IWV be studied and corrected to further increase the performance of the instruments, and ii) metadata be used to filter out situations in which the instrument cannot operate properly. [ABSTRACT FROM AUTHOR]

Microwave radiometer data is affected by many factors during the imaging process, including the antenna pattern, system noise, and the curvature of the Earth. Existing deconvolution methods such as Wiener filtering handle this degradation problem in the Fourier domain. However, under complex degradation conditions, the Wiener filtering results are not accurate. In this paper, a convolutional neural network (CNN) model is proposed to solve the degradation problem. The deconvolution procedure is defined as a regression problem in the spatial domain that can be solved with deep learning. For the real inverse process of microwave radiometer data, the CNN model has a more powerful reconstruction ability than Wiener filtering due to the multi-layer structure of the CNN, which enables the multiple feature transform of the data. Additionally, the complex degradation factor during the imaging process of a microwave radiometer can be solved with general framework-based learning. Experimental results demonstrated that the CNN model gains about 5 dB at the peak signal-to-noise ratio compared to the Wiener filtering deconvolution method, and can better distinguish the measured data. [ABSTRACT FROM AUTHOR]

Two new parameters (W6H and W6V) were defined that represent brightness temperature increments for different low-frequency channels due to ocean wind. We developed a new wind speed retrieval model inside hurricanes based on W6H and W6V using brightness temperature data from AMSR-E. The AMSR-E observations of 12 category 3-5 hurricanes from 2003 to 2011 and corresponding data from the H*wind analysis system were used to develop and validate the AMSR-E wind speed retrieval model. The results show that the mean bias and the overall root-mean-square (RMS) difference of the AMSR-E retrieved wind speeds with respect to H*wind (HRD Real-time HurricaneWind Analysis System) analysis data were -0.01 m/s and 2.66 m/s, respectively. One case study showed that W6H and W6V were less sensitive to rain than the observed AMSR-E C-band and X-band brightness temperature data. The AMSR-E retrieval model was further validated by comparing the retrieved wind speeds against stepped-frequency microwave radiometer (SFMR) measurements. The comparison showed an RMS difference of 3.41 m/s and a mean bias of 0.49 m/s. [ABSTRACT FROM AUTHOR]

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2–3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m 3 m −3 , 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m 3 m −3 , 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones. [ABSTRACT FROM AUTHOR]

We have developed algorithms and procedures for calculating daily sea ice thickness (SIT) and open water-sea ice (OWSI) charts, based on the Moderate Resolution Imaging Spectroradiometer (MODIS), ice surface temperature (IST) (night-time only), and reflectance (R) swath data, respectively. The resolution of the SIT chart is 1 km and that of the OWSI chart is 250 m. The charts are targeted to be used in development and validation of sea ice products from microwave sensor data. We improve the original MODIS cloud masks for the IST and R data, with a focus on identifying larger cloud-free areas in the data. The SIT estimation from the MODIS IST swath data follows previous studies. The daily SIT chart is composed from available swath charts by assigning daily median SIT to a pixel. The OWSI classification is simply conducted by a fixed threshold for the MODIS band 1 R. This was based on manually selected R data for various ice types in late winter, early melt, and advanced melt conditions. The composition procedures for the daily SIT and OWSI charts somewhat compensates for errors due to the undetected clouds. The SIT and OWSI charts were compared against manual ice charts by Arctic and Antarctic Research Institute in Russia and by Norwegian Meteorological Institute, respectively, and on average, a good relationship between the charts was found. Pixel-wise comparison of the SIT and OWSI charts showed very good agreement in open water vs. sea ice classification, which gives further confidence on the reliability of our algorithms. We also demonstrate usage of the MODIS OWSI and SIT charts for validation of sea ice concentration charts based on the SENTINEL-1 SAR and AMSR2 radiometer data and two different algorithms. [ABSTRACT FROM AUTHOR]

Automatic nowcasting of convective initiation and thunderstorms has potential applications in several sectors including aviation planning and disaster management. In this paper, random forest based machine learning algorithm is tested for nowcasting of convective rain with a ground based radiometer. Brightness temperatures measured at 14 frequencies (7 frequencies in 22–31 GHz band and 7 frequencies in 51–58 GHz bands) are utilized as the inputs of the model. The lower frequency band is associated to the water vapor absorption whereas the upper frequency band relates to the oxygen absorption and hence, provide information on the temperature and humidity of the atmosphere. Synthetic minority over-sampling technique is used to balance the data set and 10-fold cross validation is used to assess the performance of the model. Results indicate that random forest algorithm with fixed alarm generation time of 30 min and 60 min performs quite well (probability of detection of all types of weather condition ∼90%) with low false alarms. It is, however, also observed that reducing the alarm generation time improves the threat score significantly and also decreases false alarms. The proposed model is found to be very sensitive to the boundary layer instability as indicated by the variable importance measure. The study shows the suitability of a random forest algorithm for nowcasting application utilizing a large number of input parameters from diverse sources and can be utilized in other forecasting problems. [ABSTRACT FROM AUTHOR]

The TROpospheric WAter RAdiometer (TROWARA) is a ground-based microwave radiometer with an additional infrared channel observing atmospheric water parameters in Bern, Switzerland. TROWARA measures with nearly all-weather capability during day- and nighttime with a high temporal resolution (about 10 s). Using the almost complete data set from 2004 to 2016, we derive and discuss the diurnal cycles in cloud fraction (CF), integrated liquid water (ILW) and integrated water vapour (IWV) for different seasons and the annual mean. The amplitude of the mean diurnal cycle in IWV is 0.41 kg/m². The sub-daily minimum of IWV is at 10:00 LT while the maximum of IWV occurs at 19:00 LT. The relative amplitudes of the diurnal cycle in ILW are up to 25% in October, November and January, which is possibly related to a breaking up of the cloud layer at 10:00 LT. The minimum of ILW occurs at 12:00 LT, which is due to cloud solar absorption. In case of cloud fraction of liquid water clouds, maximal values of +10% are reached at 07:00 LT and then a decrease starts towards the minimum of -10%, which is reached at 16:00 LT in autumn. This breakup of cloud layers in the late morning and early afternoon hours seems to be typical for the weather in Bern in autumn. Finally, the diurnal cycle in rain fraction is analysed, which shows an increase of a few percent in the late afternoon hours during summer. [ABSTRACT FROM AUTHOR]

Microphysical properties of low level liquid clouds at the Naqu site over the Tibetan Plateau (TP) are characterized using empirical regression algorithms based on ground-based millimeter cloud radar (MMCR) boundary mode observations in July 6-31, 2014. Monthly averaged temperature profiles measured over the Naqu site by radiosondes at Beijing local time 8:00 and 20:00 and diurnal variation of microwave radiometer (MWR) temperature profiles indicate a 0 °C layer above 1.2 km. Only clouds below 1.2 km are examined in this study and they are assumed as pure liquid in phase. The parameters used in the regression equations have been scaled based on MWR liquid water path when there are only low non-precipitating liquid clouds and MWR measurements are available. Statistically, the characteristic properties of liquid clouds show a single mode distribution for cloud droplet effective radius (re) with most frequent values around 5-7 μm, and for cloud liquid water content (LWC) with most frequent values below 0.2 g/m3. The diurnal distribution shows weak variation with slightly low values in the morning and evening time; and the vertical distribution shows increasing cloud droplet re and LWC with height. Especially, cloud droplet re increases from approximately 4-6 μm to 8-12 μm with height. The monthly mean cloud droplet re and LWC are approximately 5.7 μm and 0.07 g/m3 in July 2014. The liquid cloud properties characterized here have been shown comparable to those obtained from MODIS satellite observations, which has an average value of 5.1 μm for the same observation period. [ABSTRACT FROM AUTHOR]

We present the results of the development of high sensitivity microwave radiometer designed for observation of the atmospheric carbon monoxide (CO) emission lines at 115 GHz. The receiver of this radiometer has the double-sideband noise temperature of 250 K at a temperature of 10°C. To date, this is the best noise performance for uncooled Schottky diode mixer receiver systems. The designed radiometer was tested during the 2014-2015 period at observations of the carbon monoxide emission lines over Kharkiv, Ukraine (50° N, 36.3° E). These tests have shown the reliability of the receiver system, which allows us in the future to use designed radiometer for continuous monitoring of carbon monoxide. The first observations of the atmospheric carbon monoxide spectral lines over Kharkiv have confirmed seasonal changes in the CO abundance and gave us reasons to assume the spread of the influence of the polar vortex on the state of the atmosphere up to the latitude of 50° N where our measurement system is located. [ABSTRACT FROM AUTHOR]

A one degree beamwidth, multi-frequency (20 to 30 and 89GHz), dual-polarization radiometer with full azimuth and elevation scanning capabilities was built with the purpose of improving the detection of in-flight icing hazards to aircraft in the near airport environment. This goal was achieved by collocating the radiometer with Colorado State University's CHILL polarized Doppler radar and leveraging the similar beamwidth and volume scan regiments of the two instruments. The collocated instruments allowed for the liquid water path and water vapor measurements derived from the radiometer to be merged with the radar moment fields to determine microphysical and water phase characteristics aloft. The radiometer was field tested at Colorado State University's CHILL radar site near Greeley, Colorado during the summer of 2009. Instrument design, calibration, and initial field testing results are discussed in this paper. [ABSTRACT FROM AUTHOR]

Due to its large space-time variability, the wet tropospheric correction (WTC) is still considered a significant error source in satellite altimetry. This paper presents the GNSS (Global Navigation Satellite Systems) derived Path Delay Plus (GPD+), the most recent algorithm developed at the University of Porto to retrieve improved WTC for radar altimeter missions. The GPD+ are WTC estimated by space-time objective analysis, by combining all available observations in the vicinity of the point: valid measurements from the on-board microwave radiometer (MWR), from GNSS coastal and island stations and from scanning imaging MWR on board various remote sensing missions. The GPD+ corrections are available both for missions which do not possess an on-board microwave radiometer such as CryoSat-2 (CS-2) and for all missions which carry this sensor, by addressing the various error sources inherent to the MWR-derived WTC. To ensure long-term stability of the corrections, the large set of radiometers used in this study have been calibrated with respect to the Special Sensor Microwave Imager (SSM/I) and the SSM/I Sounder (SSM/IS). The application of the algorithm to CS-2 and Geosat Follow-on (GFO), as representative altimetric missions without and with a MWR aboard the respective spacecraft, is described. Results show that, for both missions, the newWTC significantly reduces the sea level anomaly (SLA) variance with respect to the model-based corrections. For GFO, the new WTC also leads to a large reduction in SLA variance with respect to the MWR-derived WTC, recovering a large number of observations in the coastal and polar regions and full sets of tracks and several cycles when MWR measurements are missing or invalid. Overall, the algorithm allows the recovery of a significant number of measurements, ensuring the continuity and consistency of the correction in the open-ocean/coastal transition zone and at high latitudes. [ABSTRACT FROM AUTHOR]

The Global Change Observation Mission 1st-Water (GCOM-W1) satellite, which was launched by the Japan Aerospace Exploration Agency in May 2012, contains an Advanced Microwave Scanning Radiometer-2 (AMSR2). The AMSR2 provides multifrequency measurements of microwave energy (brightness temperature) emitted by the Earth's surface and atmosphere. However, each frequency field of view (FOV) differs in size because of its hardware design. For the retrieval of more accurate geophysical parameters from multifrequency brightness temperatures, the brightness temperatures should be modified to be the same as measured in the same FOV. The Backus–Gilbert (BG) method is one of the antenna pattern matching techniques used for this modification. We applied the BG method to the AMSR2 data to define a new data set of modified brightness temperatures, a level 1R (L1R) product that is freely and widely available. We optimized the implementation of the BG method to obtain the L1R product, with smoothing factors dynamically determined for all modified brightness temperatures. This paper describes the implementation method, including the criterion used to determine the smoothing factors and presents a quality assessment of the L1R product. [ABSTRACT FROM PUBLISHER]

WindSat has been providing accurate ocean vector winds (OVWs) for over a decade. WindSat makes polarimetric brightness temperature measurements of Earth looking forwards and backwards. However, because the overlap of these two swaths is relatively narrow, the benefit of two-look polarimetric (2LP) retrieval accuracy has not been utilized. This paper derives OVW from WindSat 2LP measurements using a radiative transfer model and maximum-likelihood estimation. The purpose of this paper is a comparison of WindSat 2LP wind direction accuracy with WindSat one-look, QuikSCAT, and Advanced Scatterometer (ASCAT) wind directions. Retrievals are compared with anemometer measurements on collocated moored buoys. Statistics are examined for both the first-ranked wind direction ambiguity and the selected ambiguity after median filtering initialized with a numerical weather prediction wind field. For winds above 8 m/s, WindSat 2LP retrievals have the most accurate first-ranked direction compared with all other sensors. For winds of 6–9 m/s, the standard deviation relative to buoys is 17°, and for 9–20 m/s, it is less than 10°. One-look standard deviations are nearly twice as large. At low winds, QuikSCAT provides the most accurate wind directions, for first-ranked and selected ambiguity. Thus, scatterometer and radiometer OVW measurements provide complementary capabilities. The accuracy of 2LP OVW is particularly relevant now that new internal calibration technology allows for a 360° conical scan of earth observations. Moreover, new low-cost designs would make it possible to affordably deploy a constellation of OVW sensors capable of providing accurate winds under a wide range of conditions, described herein. [ABSTRACT FROM PUBLISHER]

A Raman Lidar (RL) system is developed to measure the water vapor mixing ratio (WVMR) and aerosol optical property in Wuhan with high temporal-spatial resolution during rainless nights. The principle of the self-developed lidar system and data processing method are discussed. WVMR profiles of a representative case retrieved by RL, Radiosonde (RS), and microwave radiometer (MR) are in good agreement. The relationship of WVMR and aerosol optical depth (AOD) indicates that water vapor dramatically reduces with the decline of the AOD. Moreover, the mean relative difference of mean WVMRs at low-troposphere obtained by RL and RS (MR) is about 5.17% (9.47%) during the analyzed year. The agreement certifies that the self-developed RL system can stably provide accurate and high temporal-spatial resolution data for the fundamental physical studies on water vapor. Furthermore, the maximum AOD from 0.5 km to 3 km is 0.41 at night in spring, which indicates that the air quality in Wuhan is heavily influenced by aerosols that are transported by air mass from the north during this time. Moreover, abundant rainfall led to relatively low AOD in summer (0.22), which demonstrates that water vapor is crucial for air purification. [ABSTRACT FROM AUTHOR]

The wet tropospheric correction (WTC) is still a significant error source in most altimetric products. For studies such as sea level change, as those performed in the scope of the ESA Sea Level Climate Change Initiative (SL_cci) project, the use of uniform and consistent WTC datasets are of major importance. For this purpose, a set of improved WTC, using the Global Navigation Satellite System (GNSS) derived Path Delay (GPD) algorithm, was envisaged for the main six altimetric missions: the so-called reference missions (TOPEX/Poseidon, Jason-1 and Jason-2) and the three ESA missions (ERS-1, ERS-2 and Envisat). The GPD methodology is based on the combination of wet path delays derived from zenith total delays calculated at a network of coastal GNSS stations and valid microwave radiometer (MWR) measurements at altimeter nearby points. At each altimeter point with an invalid MWR value, the WTC is estimated from the set of observations, along with the associated mapping error, using a linear space-time objective analysis technique that takes into account the spatial and temporal variability of the WTC field and the accuracy of each data set used. In the absence of observations, tropospheric delays from the European Centre for Medium-range Weather Forecasts (ECMWF) ReAnalysis (ERA) Interim model are adopted. Originally designed to improve the WTC in the coastal zone, the GPD evolved to include the global ocean, correcting for land and ice contamination in the MWR footprint, or spurious measurements due to e.g. instrument malfunction. This paper presents an overview of the GPD implementation for the afore-mentioned six altimetric missions. The GPD products have been validated by comparison with the WTC adopted as the reference correction by the Archiving, Validation, and Interpretation of Satellite Data in Oceanography (AVISO): the so-called composite correction (Comp) for all missions except Jason-2, for which the version D of the Geophysical Data Records (GDR-D) Advanced Microwave Radiometer (AMR) WTC is adopted. Various sea level anomaly (SLA) statistical analyses have been performed and are summarised in this paper: differences in SLA variance calculated along satellite tracks and at crossovers; SLA variance difference function of distance from the coast or function of latitude. Results show that the GPD WTC evidence a very significant improvement with respect to the Comp correction, particularly at polar and coastal regions, for all ESA and TOPEX/Poseidon missions. For the last, the impact is particularly significant in the second part of the mission, since detected anomalies present in the TOPEX Microwave Radiometer products are corrected by the algorithm. For Jason-1 and Jason-2, some improvements are observed in the coastal regions, although globally not very significant, particularly for Jason-2. This is attributed to the good performance of the WTC present in the most recent Jason-1 and Jason-2 products. The GPD WTC constitutes a coherent dataset of global and continuous corrections, for most missions a major improvement with respect to the baseline MWR and the Comp wet tropospheric corrections. [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]