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The L-band passive microwave radiometer on board the NASA Soil Moisture Active Passive (SMAP) satellite can measure brightness temperature to retrieve sea surface wind speed under tropical cyclone (TC) conditions without being affected by rainfall or signal saturation caused by high wind speeds. Based on this advantage, this paper used the SMAP wind products for parameterizing the key decay exponent α of the Rankine vortex model (a traditional parametric model of the TC wind field) and finally developed new Rankine models. The SMAP dataset included 67 TC cases. Through data statistics, we examined the relationship between α and the maximum wind speed Um, the relationship between α and the radius of maximum wind speed (Rm), and the relationship between Rm and the averaged radius of 17 m s−1 (R17). Results showed that the three relationships were both positive correlations, indicating that α can be parameterized in three empirical ways. The first way is to calculate solely with Um. The second way is to calculate solely with Rm. The third way is to calculate Um and Rm together. The three ways correspond to three new models: the SMAP Rankine Model-1 (SRM-1), the SMAP Rankine Model-2 (SRM-2), and the SMAP Rankine Model-3 (SRM-3). Finally, comparisons were made between the new models and three existing Rankine models, according to the model simulations and the Advanced Microwave Scanning Radiometer 2 measurements of 49 TC cases. Results showed that the SRM-3 performed best overall. Significance Statement: Ocean surface wind fields are important driving factors for numerical models to guide evolution forecasting and risk assessment of tropical cyclones. The purpose of this study is to utilize the SMAP products to modify the traditional Rankine vortex model for tropical cyclone surface wind field estimation. Rankine decay exponent was found between 0.3 and 0.9 and positively dependent upon maximum wind speed and its radius. Based on these characteristics, the proposed new Rankine models could calculate the decay exponent and further the TC surface wind field symmetrically with high accuracy, simply using maximum wind and its radius. This paper shows a practical use of SMAP measurements on TC wind field monitoring, analyzing, and modeling. [ABSTRACT FROM AUTHOR]

This study uses National Centers for Environmental Prediction (NCEP) Stage IV (Stage IV) precipitation data over the state of Alaska to assess and cross compare precipitation estimates from the most recent versions of multiple precipitation products, including satellite-based passive microwave (PMW) [Special Sensor Microwave Imager/Sounder (SSMIS)–F17, Microwave Humidity Sounder (MHS)–MetOp-B, MHS–NOAA-19, Advanced Microwave Scanning Radiometer 2 (AMSR2), Advanced Technology Microwave Sounder (ATMS), and Global Precipitation Measurement Microwave Imager (GMI) in V05 and V07], active microwave [AMW or radar; Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) in V06 and V07], combined active and passive microwave (DPRGMI in V06 and V07), infrared [Atmospheric Infrared Sounder (AIRS)], reanalysis [fifth major global reanalysis produced by ECMWF (ERA5) and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and satellite–gauge [Global Precipitation Climatology Project (GPCP) V1.3 and GPCP V3.2] products. PMW estimates are generally improved in V07 compared to V05 in terms of overall bias, pattern, and capturing precipitation extremes. DPR and DPRGMI show low skill in capturing different precipitation features. ERA5 and MERRA-2 show the highest agreement with Stage IV for all precipitation rate metrics. AIRS and GPCP capture the overall precipitation pattern and magnitude fairly well, performing better than the radar and comparable to the PMW V07 products, although the geographical maps suggest that they provide a relatively smoothed spatial distribution of mean precipitation rates. The outcomes of this study shed light on the performance of various precipitation products over Alaska (partly representing high-latitude regions) and can be useful to guide the development of multisensor products. [ABSTRACT FROM AUTHOR]

Real-Time RFI Detection and Flagging (RT-RDF) for microwave radiometers is a versatile new FPGA algorithm designed to detect and flag Radio-Frequency Interference (RFI) in microwave radiometers. This block utilizes computationally-efficient techniques to identify and analyze RF signals, allowing the system to take appropriate measures to mitigate interference and maintain reliable performance. With L-Band microwave radiometry as the main application, this RFI detection algorithm focuses on the Kurtogram and Spectrogram to detect non-Gaussian behavior. To gain further modularity, an FFT-based filter bank is used to divide the receiver's bandwidth into several sub-bands within the band of interest of the instrument, depending on the application. Multiple blanking strategies can then be applied in each band using the provided detection flags. The algorithm can be re-configured in the field, for example with dynamic integration times to support operation in different environments, or configurable thresholds to account for variable RFI environments. A validation and testing campaign has been performed on multiple scenarios with the ARIEL commercial microwave radiometer, and the results confirm the excellent performance of the system. [ABSTRACT FROM AUTHOR]

This study focuses on the characterization of aerosol hygroscopicity using remote sensing techniques. We employ a Mie–Raman–fluorescence lidar (Lille Lidar for Atmospheric Study, LILAS), developed at the ATOLL platform, Laboratoire d'Optique Atmosphérique, Lille, France, in combination with the RPG-HATPRO-G5 microwave radiometer to enable continuous aerosol and water vapor monitoring. We identify hygroscopic growth cases when an aerosol layer exhibits an increase in both aerosol backscattering coefficient and relative humidity. By examining the fluorescence backscattering coefficient, which remains unaffected by the presence of water vapor, the potential temperature, and the absolute humidity, we verify the homogeneity of the aerosol layer. Consequently, the change in the backscattering coefficient is solely attributed to water uptake. The Hänel theory is employed to describe the evolution of the backscattering coefficient with relative humidity and introduces a hygroscopic coefficient, γ , which depends on the aerosol type. The particularity of this method revolves around the use of the fluorescence which is employed to take into account and correct the aerosol concentration variations in the layer. Case studies conducted on 29 July and 9 March 2021 examine, respectively, an urban and a smoke aerosol layer. For the urban case, γ is estimated as 0.47 ± 0.03 at 532 nm; as for the smoke case, the estimation of γ is 0.5 ± 0.3. These values align with those reported in the literature for urban and smoke particles. Our findings highlight the efficiency of the Mie–Raman–fluorescence lidar and microwave radiometer synergy in characterizing aerosol hygroscopicity. The results contribute to advance our understanding of atmospheric processes, aerosol–cloud interactions, and climate modeling. [ABSTRACT FROM AUTHOR]

In the framework of the Multidisciplinary drifting Observatory for the Study of Arctic Climate Polarstern expedition, the Leibniz Institute for Tropospheric Research, Leipzig, Germany, operated the shipborne OCEANET-Atmosphere facility for cloud and aerosol observations throughout the whole year. OCEANET-Atmosphere comprises, amongst others, a multiwavelength Raman lidar, a microwave radiometer, and an optical disdrometer. A cloud radar was operated aboard Polarstern by the US Atmospheric Radiation Measurement program. These measurements were processed by applying the so-called Cloudnet methodology to derive cloud properties. To gain a comprehensive view of the clouds, lidar and cloud radar capabilities for low- and high-altitude observations were combined. Cloudnet offers a variety of products with a spatiotemporal resolution of 30 s and 30 m, such as the target classification, and liquid and ice microphysical properties. Additionally, a lidar-based low-level stratus retrieval was applied for cloud detection below the lowest range gate of the cloud radar. Based on the presented dataset, e.g., studies on cloud formation processes and their radiative impact, and model evaluation studies can be conducted. [ABSTRACT FROM AUTHOR]

The utilization of remote sensing soil moisture products in agricultural and hydrological studies is on the rise. Conducting a regional applicability analysis of these soil moisture products is essential as a preliminary step for their effective utilization. The triple collocation (TC) method enables the estimation of the standard deviation of errors in products when true soil moisture values are unavailable. It assesses data uncertainty and mitigates the influence of product errors on fusion, thereby enhancing product accuracy significantly. In this study, the TC uncertainty error analysis was employed to integrate Soil Moisture Active Passive (SMAP), the Advanced Microwave Scanning Radiometer 2 (AMSR-2), and the European Space Agency Climate Change Initiative (ESA CCI) active (ESA CCI A) and passive (ESA CCI P) products, with ground-based measurements serving as a reference. Traditional evaluation metrics, such as the correlation coefficient (R), bias, root mean square error (RMSE), and unin situed root mean square error (ubRMSE), were employed to evaluate the accuracy of the product. The findings indicate that SMAP and ESA CCI P products demonstrate strong spatiotemporal continuity within the research area and exhibit low uncertainty across various land types. The products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) exhibit a high level of temporal and spatial continuity; however, there is a requirement for enhancing their accuracy. The products of ESA CCI A exhibit notable spatiotemporal disjunction, contributing significantly to their elevated level of uncertainty. After fusion with TC analysis, the correlation coefficient (R = 0.7) of the TC-2 product derived from the fusion of SMAP, AMSR-2, and ESA CCI P products is significantly higher than the correlation coefficient of the TC-1 product (R = 0.65) obtained from the fusion of SMAP, AMSR-2, and ESA CCI A products at a 95% confidence level. The integration of data can efficiently mitigate the challenges associated with spatiotemporal gaps and inaccuracies in products, offering a dependable foundation for the subsequent utilization of remote sensing products. [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]

Mountainous terrains are typical over southeast China, with complex and diverse topography, large terrain undulations, rich geographic features, and meteorological variations. Previous studies show that ERA5 meteorological variables are generally accurate with respect to large plains or urban agglomerations, while their applicability to mountainous areas remains inconclusive. In this paper, using high-precision measurements probed by ground-based remote sensing instruments in May–July 2023 at a typical mountainous Shanghuang site in southeast China, the vertical accuracy of the ERA5 reanalysis datasets were comparatively evaluated. Our findings depict that the horizontal wind speeds of the ERA5 reanalysis data show a good performance compared to the Doppler lidar observations. In quantitative terms, ERA5 horizontal wind speeds are about 8% higher than the observed values below a height of 400 m, while above 400 m, an increasing negative bias is observed along as altitude increases. Differing from the horizontal wind speeds, there is a large discrepancy in the vertical wind speeds between the ERA5 and the observations, with a deviation of −150% to 40%. In terms of the thermal variables, the temperature extracted from ERA5 are consistent with the measurements in the low troposphere. Nevertheless, large systematic errors occur at 2000–3000 m, and the overall presentation shows that the errors gradually increase with the increase in altitude. Concerning the relative humidity, the general trend in ERA5 is similar to that observed by the microwave radiometer, but the relative errors from 500 to 2500 m range from 40% to 100%. This study also reveals that ERA5 is poorly representative and requires further improvements during extreme weather events such as rainstorms and typhoons. In particular, the horizontal wind speeds at the middle and lower levels deviate strongly from the observations. Given the importance of atmospheric thermodynamic stratifications in terms of both environmental and climatic issues, the results expand the application of the ERA5 reanalysis datasets in the mountainous areas of southeast China. More importantly, it provides credible reference data for the meteorological predictions and climate modelings in the southeast China mountainous region. [ABSTRACT FROM AUTHOR]

We present a comprehensive quality-controlled 15-month dataset of remote sensing observations of low-level mixed-phase clouds (LLMPCs) taken at the high Arctic site of Ny-Ålesund, Svalbard, Norway. LLMPCs occur frequently in the Arctic region and extensively affect the energy budget. However, our understanding of the ice microphysical processes taking place in these clouds is incomplete. The dual-wavelength and polarimetric Doppler cloud radar observations, which are the cornerstones of the dataset, provide valuable fingerprints of ice microphysical processes, and the high number of cases included allows for the compiling of robust statistics for process studies. The radar data are complemented with thermodynamic retrievals from a microwave radiometer, liquid base height from a ceilometer, and wind fields from large-eddy simulations. All data are quality controlled, especially the cloud radar data, which are accurately calibrated, matched, and corrected for gas and liquid-hydrometeor attenuation, ground clutter, and range folding. We finally present an analysis of the temperature dependence of Doppler, dual-wavelength, and polarimetric radar variables, to illustrate how the dataset can be used for cloud microphysical studies. The dataset has been published in and is freely available at: 10.5281/zenodo.7803064. [ABSTRACT FROM AUTHOR]

This paper presents a WR-2.2 band waveguide orthogonal mode transducer (OMT) for an ice cloud remote sensing radiometer. A holistic approach is proposed to the design and manufacturing. Given the difficulties of using computer numerically controlled (CNC) milling techniques to fabricate the OMT and its assembly, both the design and fabrication processes are optimized to reduce the possibility of error, which could affect OMT performance. In the design, the OMT utilizes a side-arm structure, with a compact 90° waveguide twist designed in the side-arm path to reduce insertion loss and achieve two symmetric and opposing output ports in the radiometer. Regarding the challenges in CNC milling technology for such high frequencies, a comprehensive sensitivity analysis is performed following the OMT design to identify the design parameters with high sensitivity. Then, an acceptable tolerance range for CNC milling is obtained. To control fabrication uncertainty and minimize tolerance, an effective fabrication refinement strategy is proposed for accessible CNC machining, reducing the range of fabrication errors to − 3 to 2 µm within 3 iterations. Comprehensive measurements and analyses are performed on the fabricated OMT as well as the one with an antenna and a receiver. The measured insertion losses of the H and V copolarization of the OMT are better than 1.4 dB and 2 dB, respectively. The isolation is better than 40 dB. The measured H and V cross-polarizations are better than 25 dB. They demonstrate the OMT with a good performance for radiometer system. [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]

This study focused on the total precipitable water (TPW) products of Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Global Change Observation Mission--Water (GCOM-W). The GCOM-W satellite has been flying in the Afternoon Constellation (A-train) orbit to synergize with other A-train satellites, such as Aqua. In this study, we compared two datasets of AMSR2 TPW from July 2012 to December 2020, independently produced by the Japan Aerospace Exploration Agency (JAXA) and Remote Sensing Systems (RSS). No significant differences were observed in the TPW anomaly trends between the two datasets. However, significant differences were observed in the absolute values of TPW in the northwest Pacific and northwest Atlantic Oceans during the boreal summer season. We investigated the meteorological conditions that caused these differences using reanalysis, in situ observation data, and visible and infrared data from the MODerate resolution Imaging Spectroradiometer (MODIS) on the Aqua. The results indicated that the lower atmosphere had an inversion layer with relative humidity close to 100 %, and very-low-altitude clouds (i.e., fog) were often distributed in the areas where the TPW differences between the JAXA and RSS are significant. The temperature profiles represented in the JAXA and RSS algorithms were approximated by a simple model. The influence of the inversion layer and fog on the JAXA and RSS TPW algorithms was also investigated using a radiative transfer model. Sensitivity experiments suggested that the inversion layer was associated with the underestimated TPW for the JAXA algorithm, whereas it was associated with the overestimated TPW for the RSS algorithm. [ABSTRACT FROM AUTHOR]

In remote sensing applications, clouds are generally characterized by two properties: cloud optical thickness (COT) and effective radius of water–ice particles (Reff), as well as additionally by geometric properties when specific information is available. Most of the current operational passive remote sensing algorithms use a mono-angular bispectral method to retrieve COT and Reff. They are based on pre-computed lookup tables while assuming a homogeneous plane-parallel cloud layer. In this work, we use the formalism of the optimal estimation method, applied to airborne near-infrared high-resolution multi-angular measurements, to retrieve COT and Reff as well as the corresponding uncertainties related to the measurement errors, the non-retrieved parameters, and the cloud model assumptions. The measurements used were acquired by the airborne radiometer OSIRIS (Observing System Including PolaRization in the Solar Infrared Spectrum), developed by the Laboratoire d'Optique Atmosphérique. It provides multi-angular measurements at a resolution of tens of meters, which is very suitable for refining our knowledge of cloud properties and their high spatial variability. OSIRIS is based on the POLDER (POlarization and Directionality of the Earth's Reflectances) concept as a prototype of the future 3MI (Multi-viewing Multi-channel Multi-polarization Imager) planned to be launched on the EUMETSAT-ESA MetOp-SG platform in 2024. The approach used allows the exploitation of all the angular information available for each pixel to overcome the radiance angular effects. More consistent cloud properties with lower uncertainty compared to operational mono-directional retrieval methods (traditional bispectral method) are then obtained. The framework of the optimal estimation method also provides the possibility to estimate uncertainties of different sources. Three types of errors were evaluated: (1) errors related to measurement uncertainties, which reach 6 % and 12 % for COT and Reff , respectively, (2) errors related to an incorrect estimation of the ancillary data that remain below 0.5 %, and (3) errors related to the simplified cloud physical model assuming independent pixel approximation. We show that not considering the in-cloud heterogeneous vertical profiles and the 3D radiative transfer effects leads to an average uncertainty of 5 % and 4 % for COT and 13 % and 9 % for Reff. [ABSTRACT FROM AUTHOR]

To handle the complexity of the atmospheric boundary layer (ABL) and make accurate feature detection (top height, low‐level jets, inversions, etc.), a prior necessary step is to identify the type of boundary layer. This study proposes a new method to identify the boundary layer type through unsupervised classification and the synergistic use of ground‐based remote sensing. Unsupervised classification is used to lighten the human supervision. The new classification was applied to a 1‐day case study collected during wintertime in the Arve River valley near Chamonix–Mont‐Blanc during the Passy‐2015 field experiment. The ABL classification obtained from microwave radiometer and ceilometer observations (ground‐based remote sensors [GBReS]) combination is compared with high‐frequency radiosoundings (RS) data and the French convective scale AROME model outputs. Classifications from RS and GBReS broadly agree, demonstrating the good behavior of the method, AROME leading to different results at night. The difference of AROME is likely due to the different nature of the data (model fields are smoother and include forecasting errors). The results show the ability of unsupervised classification to segment relevant objects in the boundary layer and the benefit to use a combination of GBReS. [ABSTRACT FROM AUTHOR]

This study assesses the level-2 snowfall retrieval results from 11 passive microwave radiometers generated by the version 5 Goddard profiling algorithm (GPROF) relative to two spaceborne radars: CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Measurement (GPM) Ku-band Precipitation Radar (KuPR). These 11 radiometers include six conical scanning radiometers [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), its successor sensor AMSR2, GPM Microwave Imager (GMI), and three Special Sensor Microwave Imager/Sounders (SSMIS)] and five cross-track scanning radiometers [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHS)]. Results show that over ocean conical scanning radiometers have better detection and intensity estimation skills than cross-track sensors, likely due to the availability and usage of the low-frequency channels (e.g., 19 and 37 GHz). Over land, AMSR-E and AMSR2 have noticeably worse performance than other sensors, primarily due to the lack of higher than 89-GHz channels (e.g., 150, 166, and 183 GHz). Over both land and ocean, all 11 sensors severely underestimate the snowfall intensity, which propagates to the widely used level 3 precipitation product [i.e., Integrated Multi-satelliteE Retrievals for GPM (IMERG)]. These conclusions hold regardless of using either KuPR or CPR as the reference, though the statistical metrics vary quantitatively. The conclusions drawn from these comparisons apply solely to the GPROF version 5 algorithm. [ABSTRACT FROM AUTHOR]

The most common source of information about sea ice conditions is remote sensing data, especially images obtained from synthetic aperture radar (SAR) and passive microwave radiometers (PMR). Here we introduce an adaptive fusion scheme based on Graph Laplacians that allows us to retrieve the most relevant information from satellite images. In a first test case, we explore the potential of sea ice classification employing SAR and PMR separately and simultaneously, in order to evaluate the complementarity of both sensors and to assess the result of a combined use. Our test case illustrates the flexibility and efficiency of the proposed scheme and indicates an advantage of combining AMSR‐2 89 GHz and Sentinel‐1 data for sea ice mapping. Plain Language Summary: The Earth's land and ocean surface is monitored from space using different sensors mounted on various satellite platforms. Each type of sensor has its advantages and limitations. Combining data from different sensors can potentially solve ambiguities in information retrievals associated with the use of only a single sensor. Here, we apply a multi‐sensor fusion scheme that can be used for various data combinations in order to extract relevant information. The main goal of this work is to explore the potential of simultaneously applying two sensors for sea ice mapping and monitoring, namely synthetic aperture radar and passive microwave radiometers, in order to improve the separation of sea ice types. Key Points: We propose an adaptive scheme for the fusion of passive microwave radiometers and synthetic aperture radar data for sea ice classificationWe demonstrate the flexibility and efficiency of the proposed scheme on a test case by evaluating the performance of various data scenariosWe illustrate the advantages and limitations of applying the data of each sensor simultaneously and separately [ABSTRACT FROM AUTHOR]

The atmospheric boundary layer (ABL) defines the volume of air adjacent to the Earth's surface for the dilution of heat, moisture, and trace substances. Quantitative knowledge on the temporal and spatial variations in the heights of the ABL and its sub-layers is still scarce, despite their importance for a series of applications (including, for example, air quality, numerical weather prediction, greenhouse gas assessment, and renewable energy production). Thanks to recent advances in ground-based remote-sensing measurement technology and algorithm development, continuous profiling of the entire ABL vertical extent at high temporal and vertical resolution is increasingly possible. Dense measurement networks of autonomous ground-based remote-sensing instruments, such as microwave radiometers, radar wind profilers, Doppler wind lidars or automatic lidars and ceilometers are hence emerging across Europe and other parts of the world. This review summarises the capabilities and limitations of various instrument types for ABL monitoring and provides an overview on the vast number of retrieval methods developed for the detection of ABL sub-layer heights from different atmospheric quantities (temperature, humidity, wind, turbulence, aerosol). It is outlined how the diurnal evolution of the ABL can be monitored effectively with a combination of methods, pointing out where instrumental or methodological synergy are considered particularly promising. The review highlights the fact that harmonised data acquisition across carefully designed sensor networks as well as tailored data processing are key to obtaining high-quality products that are again essential to capture the spatial and temporal complexity of the lowest part of the atmosphere in which we live and breathe. [ABSTRACT FROM AUTHOR]

A better understanding of aerosol–cloud interaction processes is important to quantify the role of clouds and aerosols on the climate system. There have been significant efforts to explain the ways aerosols modulate cloud properties. However, from the observational point of view, it is indeed challenging to observe and/or verify some of these processes because no single instrument or platform has been proven to be sufficient. Discrimination between aerosol and cloud is vital for the quantification of aerosol–cloud interaction. With this motivation, a set of observational field campaigns named balloon-borne aerosol–cloud interaction studies (BACIS) is proposed and conducted using balloon-borne in situ measurements in addition to the ground-based (lidar; mesosphere, stratosphere and troposphere (MST) radar; lower atmospheric wind profiler; microwave radiometer; ceilometer) and space-borne (CALIPSO) remote sensing instruments from Gadanki (13.45 ∘ N, 79.2 ∘ E), India. So far, 15 campaigns have been conducted as a part of BACIS campaigns from 2017 to 2020. This paper presents the concept of the observational approach, lists the major objectives of the campaigns, describes the instruments deployed, and discusses results from selected campaigns. Balloon-borne measurements of aerosol and cloud backscatter ratio and cloud particle count are qualitatively assessed using the range-corrected data from simultaneous observations of ground-based and space-borne lidars. Aerosol and cloud vertical profiles obtained in multi-instrumental observations are found to reasonably agree. Apart from this, balloon-borne profiling is found to provide information on clouds missed by ground-based and/or space-borne lidar. A combination of the Compact Optical Backscatter AerosoL Detector (COBALD) and Cloud Particle Sensor (CPS) sonde is employed for the first time in this study to discriminate cloud and aerosol in an in situ profile. A threshold value of the COBALD colour index (CI) for ice clouds is found to be between 18 and 20, and CI values for coarse-mode aerosol particles range between 11 and 15. Using the data from balloon measurements, the relationship between cloud and aerosol is quantified for the liquid clouds. A statistically significant slope (aerosol–cloud interaction index) of 0.77 found between aerosol backscatter and cloud particle count reveals the role of aerosol in the cloud activation process. In a nutshell, the results presented here demonstrate the observational approach to quantifying aerosol–cloud interactions. [ABSTRACT FROM AUTHOR]

The O2-band channel configuration of existing microwave radiometers is not optimal for surface pressure retrieval, which limits the surface pressure retrieval accuracy. In this study, we present the results of theoretically what might be the optimal microwave channels for surface pressure retrieval. An improved iterative selection method is used to select the channels that contain the highest cumulative content of surface pressure information. The selected optimal channel set comprises 16 channels, among which 10 channels are centered at the 50–60 GHz oxygen absorption band and 6 channels are centered around the 118.75 GHz oxygen absorption line. Two representative spaceborne microwave radiometers are used for comparisons, the Advanced Technology Microwave Sounder (ATMS) on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite and the Microwave Humidity and Temperature Sounder (MWHTS) on board the Chinese Fengyun-3C (FY-3C) satellite. The results of information content analysis show that the optimal channel set contains more surface pressure information than that of the combination of SNPP/ATMS and FY-3C/MWHTS (SNPP/ATMS+FY-3C/MWHTS) channels. A representative dataset from the ERA5 data is input into the plane-parallel Microwave Radiative Transfer model to obtain the simulated brightness temperature observations of the selected optimal channels and the SNPP/ATMS+FY-3C/MWHTS channels. Using the simulated observations, retrieval experiments are performed. Experimental results show that retrieval accuracies of the optimal channel set are 1.09 and 1.64 hPa for clear-sky and cloudy conditions, respectively. The retrieval accuracies are 0.60 and 0.65 hPa better than that of the SNPP/ATMS+FY-3C/MWHTS channels for clear-sky and cloudy conditions, respectively. [ABSTRACT FROM AUTHOR]

Atmospheric icing, the accumulation of ice on surfaces, is a severe concern for the aviation industry. Deicing and icing prediction tools are necessary for pilots to ensure flight safety, and while there is established technology for large aircraft icing, more research is needed for smaller uncrewed aerial vehicles (UAVs). Here, we present measurements from 59 flights of a multirotor UAV into wintertime low stratus clouds of temperatures between − 3 and − 10 °C. The UAV is equipped with rotor heating to allow flights up to 10 min in icing conditions. Icing severity was quantified by using the rate of increase in battery current during icing, and was then compared with simultaneous, co-located measurements of liquid water content (LWC). LWC measurements were (a) calculated from cloud droplets measured with an in situ holographic imager on a tethered balloon system and (b) retrieved from remote sensing observations (microwave radiometer, ceilometer, cloud radar). We show that, for these environmental conditions, icing was strongly positively correlated to LWC over an LWC range of 0.02 to 0.5 g m−3, independent of temperature and mean droplet size, though droplets > 50 μm in diameter may contribute to increased icing severity. We also show that the LWC retrieved from remote sensing agrees well with the in situ measurements, indicating that remote sensing measurements can effectively be used to assess icing conditions. These are the first known measurements of multirotor UAV icing with co-located LWC measurements in natural clouds. • Icing severity quantified for a multirotor drone using the change in battery current. • Data presented from 59 flights of the drone into supercooled stratus clouds. • Icing severity was found to correlate strongly with supercooled liquid water content. [ABSTRACT FROM AUTHOR]

To further exploit atmospheric cloud water resources (CWRs), it is necessary to correctly evaluate the number of CWRs in an area. The CWRs are hydrometeors that have not participated in precipitation formation at the surface and are suspended in the atmosphere to be exploited and maximise possible precipitation in the atmosphere (Zhou et al., 2020). Three items are included in CWRs: the existing hydrometeors at a certain time, the influx of atmospheric hydrometeors along the boundaries of the study area, and the mass of hydrometeors converted from water vapour through condensation or desublimation, defined as condensate. Condensate constitutes the most important part of CWRs. At present, there is a lack of effective observation methods for atmospheric column condensate evaluation, and direct observation data of CWRs are thus insufficient. A detection method for atmospheric column condensate is proposed and presented. The formation of condensate is closely related to atmospheric meteorological parameters (e.g. temperature and vertical airflow velocity). The amount of atmospheric column condensate can be calculated by the saturated water vapour density and the ascending velocity at the cloud base and top. Active and passive remote sensing technologies are applied to detect the mass of atmospheric column condensate. Combining millimetre cloud radar, lidar and microwave radiometers can suitably observe the vertical velocity and temperature at the cloud boundary. The saturated vapour density can be derived from the temperature, and then, water vapour flux and the maximum possible condensate can be deduced. A detailed detection scheme and data calculation method are presented, and the presented method can realise the determination of atmospheric column condensate. A case of cloud layer change before precipitation is considered, and atmospheric column condensate is deduced and obtained. This is the first application, to our knowledge, of observations for atmospheric column condensate evaluation, which is significant for research on the hydrologic cycle and the assessment of CWRs. [ABSTRACT FROM AUTHOR]

Observing thermodynamic profiles within the planetary boundary layer is essential to understanding and predicting atmospheric phenomena because of the significant exchange of sensible and latent heat between the land and atmosphere within that layer. The Atmospheric Emitted Radiance Interferometer (AERI) is a ground-based infrared spectrometer used to obtain the vertical profiles of temperature and water vapor mixing ratio. Most AERIs are only capable of zenith views, although the Marine AERI (M-AERI) has a design that allows it to view various elevation angles. In this study, we quantify the improvement in the information content and accuracy of the retrieved profiles when nonzenith angles are included, as is common with microwave radiometer profilers. The impacts of the additional scan angles are quantified through both a synthetic study and with M-AERI observations from the ARM Cloud Aerosol Precipitation Experiment (ACAPEX) campaign. The simulation study shows that low elevation angles contain more information content for temperature whereas high elevation angles have more information content for water vapor. Outside of very humid environments, the addition of low elevation angles also results in lower root-mean-square errors when compared with high angles for both temperature and water vapor mixing ratio, although this is primarily a result of averaging multiple observations together to reduce instrument noise. Real-world results from the ACAPEX dataset indicate similar results as were found for the simulation study, although not all predicted benefits are realized because of the small sample size and observational uncertainties. [ABSTRACT FROM AUTHOR]

The impacts of hailstorms on human beings and structures and the associated high economic costs have raised significant interest in studying storm mechanisms and climatology, thus producing a substantial amount of literature in the field. To contribute to this effort, we have explored the hail frequency in the Mediterranean basin during the last two decades (1999–2021) on the basis of hail occurrences derived from the observations of the microwave radiometers on board satellites of the Global Precipitation Measurement Constellation (GPM-C) from 2014 (date of GPM Core Observatory launch) onwards and merging multiple other satellite platforms prior to 2014. According to the MWCC-H method, two hail event categories (hail and super hail) are identified, and their spatiotemporal distributions are evaluated to identify the hail development areas in the Mediterranean and the corresponding monthly climatology of hail occurrences. Our results show that the northern sectors of the domain (France, Alpine Region, Po Valley, and Central-Eastern Europe) tend to be hit by hailstorms from June to August, while the central sectors (from Spain to Turkey) are more affected as autumn approaches. The trend analysis shows that the mean number of hail events over the entire domain tends to substantially increase, showing a higher increment during 2010–2021 than during 1999–2010. This behavior was particularly enhanced over Southern Italy and the Balkans. Our findings point to the existence of "sub-hotspots", i.e., Mediterranean regions most susceptible to hail events and thus possibly more vulnerable to climate change effects. [ABSTRACT FROM AUTHOR]

Sea ice concentration (SIC) derived from 89 GHz data has up to four times finer spatial resolution compared to that from the widely used 19 and 37 GHz data. But it has lower accuracy due to the enhanced weather influences from water vapor, cloud liquid water (CLW), wind, and surface temperature. Here we improve a high‐resolution SIC algorithm, called the ASI algorithm, based on the difference between vertical and horizontal polarization 89 GHz data, by correcting the observed data for these weather influences through a radiative transfer model and geophysical data retrieved by an optimal estimation method. The improved algorithm denoted ASI3, is developed for the Arctic based on the weather‐corrected brightness temperatures and newly identified open water (80 K) and sea ice (14 K) tie‐points. The most important component of this correction is the inclusion of CLW, the largest weather influence contributor. ASI3 results are evaluated over pure surface sites of 0% and 100% SICs under various weather conditions, showing a much lower average standard deviation (1.1%) than ASI (16.2%). ASI3 reduces weather patterns over pack ice resulting in more homogeneous retrievals but biased toward lower values. Comparison to Landsat imagery under clear‐sky conditions shows that ASI3 results in better agreement with the Landsat SIC than ASI. The number of cases where real sea ice is falsely identified as open water is reduced by ASI3 due to its relaxed open‐water mask and wider water/ice dynamic range. Plain Language Summary: The recent decline of Arctic sea ice is a key manifestation of climate change. We use satellite microwave radiometers to monitor sea ice globally because they can observe under all weather conditions and also during the polar night. However, they only provide observations at a coarse spatial resolution of several to tens of kilometers. Sea ice concentration (SIC) describes what fraction of a known area is covered by sea ice. Radiometers that include the 89 GHz channels measure at finer spatial resolutions but are also affected more by atmospheric noise caused by water vapor and cloud liquid water (CLW), which causes errors in the derived SIC. Here, we present a new algorithm that corrects this weather‐induced noise and estimates SIC from the 89 GHz data of the AMSR‐E and AMSR2 satellite microwave radiometers. We estimate the atmospheric influence by modeling it, and then subtract it from the 89 GHz measurements. We particularly focus on correcting the influence from CLW, as it is the most important error source. Compared to the previous 89 GHz algorithm without weather correction, we find that the new algorithm provides SIC data with much lower error while maintaining the fine spatial resolution of 5 km. Key Points: An improved sea ice concentration retrieval algorithm based on weather‐corrected brightness temperatures at 89 GHz is developedWeather influences particularly from cloud liquid water are reduced, lowering the retrieval uncertainty by one order of magnitudeNoise in ice concentration caused by the atmosphere is much reduced over consolidated ice [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is critical in monitoring the time-lagged impacts of agrometeorological drought. In Australia and several south-west Pacific Small Island Developing States (SIDS), there are a limited number of in situ SM stations that can adequately assess soil-water availability in a near-real-time context. Satellite SM datasets provide a viable alternative for SM monitoring and agrometeorological drought provision in these regions. In this study, we investigated the performance of Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Operational Products System (SMOPS), SM from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) and SM from the Advanced Scatterometer (ASCAT) over Australia and south-west Pacific SIDS. Products were first evaluated in Australia, given the presence of several in-situ SM monitoring stations and a state-of-the-art hydrological model—the Australian Water Resources Assessment Landscape modelling system (AWRA-L). We further investigated the accuracy of SM satellite datasets in Australia and the south-west Pacific through Triple Collocation analysis with two other SM reference datasets—ERA5 reanalysis SM data and model data from the Global Land Data Assimilation System (GLDAS) dataset. All datasets have differing observation periods ranging from 1911-now, with a common period of observations between 2015–2021. Results demonstrated that ASCAT and SMOS were consistently superior in their performance. Analysis in the six south-west Pacific SIDS indicated reduced performance for all products, with ASCAT and SMOS still performing better than others for most SIDS with median R values ranging between 0.3–0.9. We conducted a case study of the 2015 El Niño and Positive Indian Ocean Dipole-induced drought in Papua New Guinea. It was shown that ASCAT is a valuable dataset indicative of agrometeorological drought for the nation, highlighting the value of using satellite SM products to provide early warning of drought in data-sparse regions in the south-west Pacific. [ABSTRACT FROM AUTHOR]

Accurate information on tropical cyclone position, intensity, and structure is critical for storm surge prediction. Atmospheric reanalysis datasets can provide gridded, full coverage, long-term and multi-parameter atmospheric fields for the research on the impact of tropical cyclones on the upper ocean, which effectively makes up for the uneven temporal and spatial distribution of satellite remote sensing and in situ data. However, the reanalysis data cannot accurately describe characteristic parameters of tropical cyclones, especially in high wind conditions. In this paper, the performance of the tropical cyclone representation in ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation) is investigated and analyzed with respect to IBTrACS (International Best Track Archive for Climate Stewardship) during the period 2018–2020. Comparisons demonstrate that ERA5 winds significantly underestimate the maximum wind speed during the tropical cyclones (>30 m/s) compared to those provided by IBTrACS. An effective wind reconstruction method is examined to enhance tropical cyclone intensity representation in reanalysis data in 94 cases of 31 tropical cyclones 2018–2020. The reconstructed wind speeds are in good agreement with the SFMR (Stepped Frequency Microwave Radiometer) measured data and SMAP (Soil Moisture Active Passive) L-band radiometer remotely sensed measurements. The proposed wind reconstruction method can effectively improve the accuracy of the tropical cyclone representation in ERA5, and will benefit from the establishment of remote sensing satellite retrieval model and the forcing fields of the ocean model. [ABSTRACT FROM AUTHOR]

During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, USA, active and passive ground-based remote sensing instruments were deployed to understand the response of the planetary boundary layer to heterogeneous land surface forcing. These instruments include radar wind profilers, microwave radiometers, atmospheric emitted radiance interferometers, ceilometers, high spectral resolution lidars, Doppler lidars, and collaborative lower-atmospheric mobile profiling systems that combine several of these instruments. In this study, these ground-based remote sensing instruments are used to estimate the height of the daytime planetary boundary layer, and their performance is compared against independent boundary layer depth estimates obtained from radiosondes launched as part of the field campaign. The impact of clouds (in particular boundary layer clouds) on boundary layer depth estimations is also investigated. We found that while all instruments are overall able to provide reasonable boundary layer depth estimates, each of them shows strengths and weaknesses under certain conditions. For example, radar wind profilers perform well during cloud-free conditions, and microwave radiometers and atmospheric emitted radiance interferometers have a very good agreement during all conditions but are limited by the smoothness of the retrieved thermodynamic profiles. The estimates from ceilometers and high spectral resolution lidars can be hindered by the presence of elevated aerosol layers or clouds, and the multi-instrument retrieval from the collaborative lower atmospheric mobile profiling systems can be constricted to a limited height range in low-aerosol conditions. [ABSTRACT FROM AUTHOR]

Lunar collapse pits may provide access to subsurface lava tubes of unknown extent. We present Diviner Lunar Radiometer measurements showing that the Mare Tranquillitatis and Mare Ingenii pits exhibit elevated thermal emission during the night, ∼100 K warmer than the surrounding surface. Using these data, along with computational thermophysical models, we characterize the thermal environment inside pits and potential caves. Near the equator, peak day‐time temperatures on regolith‐covered pit floors can potentially reach >420 K, whereas temperatures beyond the opening in permanent shadow would maintain a nearly constant temperature of ∼290 K, similar to that of a blackbody cavity in radiative equilibrium. Thermal IR measurements such as those of Diviner can readily detect pit thermal signatures but would be insensitive to the existence of caves they may host, as the latter would only induce a 0.1 K increase to night‐time temperatures of the overlying surface. Plain Language Summary: Since the discovery of pits on the Moon by JAXA's SELENE spacecraft in 2009, there has been interest in whether they provide access to caves that could be explored by rovers and astronauts. These features are likely created by the ceiling of a lava tube (or more generally, cave) collapsing. Using data from the Diviner instrument aboard the Lunar Reconnaissance Orbiter, which has been continuously measuring the temperature of the lunar surface for over 11 years, we thoroughly characterized the environment of one prominent pit. Located in Mare Tranquillitatis, the pit's thermal environment is more hospitable compared to anywhere else on the Moon, with temperatures varying minimally around a comfortable 17°C (or 63° F) wherever the Sun does not shine directly. If a cave extends from a pit such as this, it too would maintain this comfortable temperature throughout its length, varying by less than 1°C over an entire lunar day. Although we cannot be completely certain of a cave's existence through remote observations, such features would open the door for future exploration and habitation on the Moon: they could provide shelter from dramatic temperature variations present elsewhere on the lunar surface. Key Points: Lunar pits stay warmer than the surface during the night, with the floor maintaining temperatures >230 K according to computational modelsCaves stemming from lunar pits would behave like blackbody cavities at ∼290 K and have nearly invariable temperatures far from the openingLunar caves would provide a temperate, stable, and safe thermal environment for long term exploration and habitation of the Moon [ABSTRACT FROM AUTHOR]

This study proposes the use of a Spectral Linear Mixing Model (SLMM) on passive microwave data for mapping the concentration and area of young and/or first-year ice in the oceanic region located in the northwest of the Antarctic Peninsula. Sentinel-1A Synthetic Aperture Radar (SAR) data were used to estimate fraction images needed for subpixel analysis of passive microwave data acquired by the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS) in the 19H, 19 V, 37H, and 37 V channels and polarizations. Sea ice concentrations estimated by the SLMM approach had a difference of 1.4% (13.6% standard deviation, 15.3% root mean square error) when compared to sea ice concentrations mapped using Sentinel-1A SAR data. The evolution in the total area covered by young and/or first-year ice with concentrations ≥ 15% showed a negative linear trend for the studied region from 1979 to 2018. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated that wideband microwave radiometers provide significant potential for profiling important subsurface polar firn characteristics necessary to understand the dynamics of the cryosphere and predict future changes in ice and snow coverage. Different frequencies within the wide spectra of radiometers result in different electromagnetic propagation losses and thus reveal characteristics at different depths in ice and snow. This paper, expanding on those investigations, explores the utilization of the Global Precipitation Measurement (GPM) constellation as a single wideband (6.93 GHz–91.655 GHz) spaceborne radiometer, covering the entire microwave spectrum from C-band to W-band, to profile subsurface properties of the Antarctic firn. Results of the initial analyses over Concordia and Vostok Stations in Antarctica indicate that GPM brightness temperature measurements provide critical information regarding the subsurface temperatures and physical properties of the firn from the surface down to several meters of depth. Considering the high spatiotemporal coverage of polar-orbiting spaceborne radiometers, these results are promising for future continent-level thermal and physical characterization of the Antarctic firn. [ABSTRACT FROM AUTHOR]

Arctic sea-ice thickness and salinity retrievals are simulated to explore the performance of nadir-observing microwave radiometry operating with up to 16 frequency channels in the 0.5–2-GHz frequency range. A radiative transfer model is used to create lookup tables of the circularly polarized thermal emissions of first-year (FY) and multiyear (MY) sea ice, and the performance of two distinct retrieval methods is examined. The first method retrieves only sea-ice thicknesses, while the second retrieves both ice thickness and ice salinity. Retrieval errors are simulated for both FY and MY sea ice as a function of ice thickness, salinity, and temperature to investigate the impact of radiometric uncertainty, the frequency channels used, and any errors in ancillary information. To gain further insight into Arctic scale retrieval performance, a simulated brightness temperature dataset is produced for Arctic sea ice for the period October 2020–March 2021 using sea-ice thicknesses from the SMOS-CryoSat-2 algorithm. Compared to existing sea-ice thickness retrievals obtained from 1.4-GHz microwave radiometers, the results demonstrate that 0.5–2-GHz radiometry can achieve higher sensitivity to a sea-ice thickness within the range 0.5–1.5 m for FY sea ice and enable the retrieval of multiple sea-ice parameters (thickness and salinity) simultaneously. [ABSTRACT FROM AUTHOR]

The atmospheric boundary layer (ABL) height defines the volume of air within which heat, moisture and pollutants released at the Earth's surface are rapidly diluted. Despite the importance for air quality interpretation, numerical weather prediction, greenhouse gas assessment and renewable energy applications, amongst others, quantitative knowledge on the temporal and spatial variation in ABL height is still scarce. With continuous profiling of the entire ABL vertical extent at high temporal and vertical resolution now increasingly possible due to recent advances in ground-based remote sensingmeasurement technology and algorithm development, there are also dense measurement networks emerging across Europe and other parts of the world. To effectively monitor the spatial and temporal evolution of the ABL continuously at continent-scale, harmonised operations and data processing are key. Autonomous ground-based remote sensing instruments, such as microwave radiometers, radar wind profilers, Doppler wind lidars or automatic lidars and ceilometers, each offer different capabilities. The overarching objective of this review is to emphasize how these instruments are best exploited with informed network design, algorithm implementation, and data interpretation. A summary of the capability and limitations of each instrument type is provided together with a review of the vast number of retrieval methods developed for ABL height detection from different atmospheric quantities (temperature, humidity, wind, turbulence, aerosol). It is outlined how the diurnal evolution of the ABL can be monitored effectively with a combination of methods, highlighting where instrument or methodological synergy promise to be particularly valuable. To demonstrate the vast potential of increased ABL monitoring efforts, long-term observational studies are reviewed summarising our current understanding of ABL height variations. The review emphasizes that harmonised data acquisition and careful data processing are key to obtaining high-quality products, which are essential to capture the spatial and temporal complexity of the lowest part of the atmosphere in which we live and breathe. [ABSTRACT FROM AUTHOR]

Recent developments in passive microwave remote sensing have provided an effective tool for monitoring global soil moisture (SM) observations on a spatiotemporal basis, filling the gap of uneven in-situ measurement distribution. In this paper, four passive microwave SM products from three bands (L, C, and X) are evaluated using in-situ observations, over a dry–wet cycle agricultural (mostly paddy/wheat cycle crops) critical zone observatory (CZO) in the Central Ganga basin, India. The L-band and C/X-band information from Soil Moisture Active Passive (SMAP) Passive Enhanced Level 3 (SMAP-L3) and Advanced Microwave Scanning Radiometer 2 (AMSR2), respectively, was selected for the evaluation. The AMSR2 SM products used here were derived using the Land Parameter Retrieval Model (LPRM) algorithm. Spatially averaged observations from 20 in-situ distributed locations were initially calibrated with a single and continuous monitoring station to obtain long-term ground-based data. Furthermore, several statistical metrices along with the triple collocation (TC) error model were used to evaluate the overall accuracy and random error variance of the remote sensing products. The results indicated an overall superior performance of SMAP-L3 with a slight dry bias (−0.040 m3·m−3) and a correlation of 0.712 with in-situ observations. This also met the accuracy requirement (0.04 m3·m−3) during most seasons with a modest accuracy (0.059 m3·m−3) for the entire experimental period. Among the LPRM datasets, C1 and C2 products behaved similarly (R = 0.621) with a ubRMSE of 0.068 and 0.081, respectively. The X-band product showed a relatively poor performance compared to the other LPRM products. Seasonal performance analysis revealed a higher correlation for all the satellite SM products during monsoon season, indicating a strong seasonality of precipitation. The TC analysis indicated the lowest error variance (0.02 ± 0.003 m3·m−3) for the SMAP-L3. In the end, we introduced Spearman's rank correlation to assess the dynamic response of SM observations to climatic and vegetation parameters. [ABSTRACT FROM AUTHOR]

Mixing-layer-height (MLH) retrieval methods using backscattered lidar signals from a ceilometer (Jenoptik CHM -15k Nimbus) and temperature profiles from a microwave radiometer (MWR) and Humidity And Temperature PROfiler (HATPRO) radiometer physics GmbH (RPG) are compared in terms of their complementary capabilities and associated uncertainties. The extended Kalman filter (EKF) is used for MLH retrieval from backscattered lidar signals, and the parcel method is used for MLH retrieval from MWR-derived potential-temperature profiles. The two principal sources of uncertainty in ceilometer-based MLH estimates are: 1) incorrect layer attribution ($\sim $ hundreds of meters) and 2) noise-induced errors (about 50 m at $3\sigma $). MWR MLH uncertainties comprise: 1) the total uncertainty in the retrieved potential temperature profile and 2) ±0.5 K uncertainty in the surface temperature. Ceilometer- and MWR-based MLH estimates are, in turn, compared with reference to MLH estimates from radiosoundings. Twenty-one measurement days from the high definition clouds and precipitation for advancing climate prediction (HD(CP) 2) Observational Prototype Experiment (HOPE) campaign at Jülich, Germany, are considered. It is shown that the MWR can track the full mixed layer (ML) diurnal cycle (i.e., including morning and evening transitions) with height-increasing error bars. The ceilometer-EKF MLH estimates are much smaller errorbars than those from the MWR under the well-developed clear-sky ML, but the ceilometer-EKF is prone to ambiguous tracking some multilayer scenarios (e.g., the residual layer). We, therefore, introduce the synergistic MLH retrieval approach that combines both ceilometer and MWR estimates in order to optimize the benefits of both. [ABSTRACT FROM AUTHOR]

To further exploit atmospheres cloud-water resources (CWR), it is necessary to correctly evaluate the amount of CWR in an area. CWR are hydrometeors that have not participated in precipitation formation at the surface and are suspended in the atmosphere to be exploited to maximize possible precipitation in the atmosphere (Zhou, Y., et al. (2020)). CWR includes three items: the existing hydrometeors at a time, the influx of atmospheric hydrometeors along the boundaries of the study area, and the mass of hydrometeors converted from water vapor through condensation or desublimation, defined as condensate. Condensate is the most important part of CWR. At present, there is a lack of effective observation methods for atmospheric column condensates, so the direct observation data of CWR are insufficient. The method for detecting atmospheric column condensate in the atmosphere is proposed and presented. The formation of condensate is closely related to atmospheric meteorological parameters (e.g., temperature and vertical airflow velocity). For stratiform clouds, the amount of atmospheric column condensate can be calculated by the saturated water vapor density and the ascending velocity at the cloud base and top. Active and passive remote sensing technology are applied to detect the mass of atmospheric column condensate. Combining millimetre-wave radar (MWR), lidar and microwave radiometers can well observe the vertical velocity and temperature at the cloud boundary. The detailed detection scheme and data calculation method are presented, and the presented method can realize the deduction of atmospheric column condensate. A case of cloud layer change before precipitation was monitored, and atmospheric column condensate was deduced and obtained. This is the first application, to our knowledge, to operate observations of atmospheric column condensates, which is significant for research on the hydrologic cycle and the assessment of cloud water resources. [ABSTRACT FROM AUTHOR]

In remote sensing applications, clouds are generally characterized by two properties: cloud optical thickness (COT) and effective radius of water/ice particles (Reff). Most of the current operational passive remote sensing algorithms use a mono-angular bi-spectral method to retrieve COT and Reff. They are based on pre-computed lookup tables while assuming a homogeneous plane-parallel cloud layer and without considering measurement errors and the choice of ancillary data. We use the formalism of the optimal estimation method applied to near-infrared multi-angular measurements, to retrieve COT and Reff, and the corresponding uncertainties related to the measurement errors, the ancillary data, and the cloud model assumption. The used measurements were acquired by the airborne radiometer OSIRIS (Observing System Including PolaRization in the Solar Infrared Spectrum), developed by the Laboratoire d'Optique Atmosphérique. It provides multi-angular measurements at tens of meters resolution, very suitable for refining our knowledge of cloud properties and their high spatial variability. OSIRIS is based on the POLDER concept as a prototype of the future 3MI space instrument planned to be launched on the EUMETSAT-ESA MetOp-SG platform in 2023. The used approach allows the exploitation of all the angular information available for each pixel to overcome the radiance angular effects. More consistent cloud properties with lower uncertainty compared to operational mono-directional retrieval methods (MODIS-like methods) are then obtained. The framework of the optimal estimation method provides also the possibility to estimate uncertainties of different sources. Three types of errors were evaluated: (1) Errors related to measurement uncertainties, which reach 10 % for high values of COT and Reff, (2) errors related to an incorrect estimation of the ancillary data that remain below 0.5 %, (3) errors related to the simplified cloud physical model assuming homogeneous plane-parallel cloud and the independent pixel approximation. We show that not considering the in-cloud heterogeneous vertical profiles and the 3D radiative transfer effects lead to uncertainties on COT and Reff exceeding 10 %. [ABSTRACT FROM AUTHOR]

The noise equivalent delta temperature (NEDT) determines the radiometric resolution of a radiometer. Determining NEDT is indispensable for assessing the in-orbit radiometer performance and quantifying uncertainty propagation from radiance to climate data records. Agencies of EUMETSAT, UK MetOffice, and National Oceanic and Atmospheric Administration (NOAA) have developed their own algorithms for calculating and monitoring NEDT of in-orbit microwave radiometers. These algorithms are an essential means for monitoring NEDT and hardware health. While remarkable accomplishments have been made, there appears to be room for improvement. The investigation is needed for the NEDT underestimate found at channels like G-band that has pronounced $1/f$ noise. Also, it is necessary to explain the inconsistency in calculated NEDTs of different algorithms. We have reviewed the theoretical basis for determining NEDT and developed an improved algorithm of clear physics and mathematics. We have developed methods for handling error sources that can result in either negative or positive biases with an overall underestimate of NEDT. We have done comparison and validation with the prelaunch thermal vacuum chamber (TVAC) test, in-orbit data, and simulation. The new algorithm significantly improves the estimate of NEDT, including the G-band and advances the understanding of algorithm structures and physical foundations. It can accurately monitor in-orbit NEDT and facilitate quantifying the associated uncertainty propagation through science products. [ABSTRACT FROM AUTHOR]

This paper presents measurements using a dual-polarised radiometer operating at 93 GHz to detect ice or water on asphalt in laboratory conditions. The brightness temperatures of both H and V polarizations were measured for a dry surface, liquid water, and ice on asphalt at observation angles of 50° and 56°. The results presented in this paper demonstrate that the studied road conditions can be identified by the radiometer. The measurements are compared with a model and surface parameters, such as dielectric constant and roughness are fitted and compared to reference values. The experiments and results, described in this article, are the first steps towards the future installation of a polarimetric sensor on a moving vehicle for traffic safety. [ABSTRACT FROM AUTHOR]

During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, U.S.A., active and passive ground-based remote sensing instruments were deployed to understand the response of the planetary boundary layer to heterogeneous land surface forcing. These instruments include Radar Wind Profilers, Microwave Radiometers, Atmospheric Emitted Radiance Interferometers, Ceilometers, High Spectral Resolution Lidars, Doppler Lidars, and Collaborative Lower Atmospheric Modelling Profiling Systems that combine several of these instruments. In this study, these ground-based remote sensing instruments are used to estimate the height of the daytime planetary boundary layer, and their performance is compared against independent boundary-layer depth estimates obtained from radiosondes launched as part of the field campaign. The impact of clouds (in particular boundary layer clouds) on boundary-layer depth is also investigated. We found that while overall all instruments are able to provide reasonable boundary-layer depth estimates, each of them shows strengths and weaknesses under certain conditions. For example, Radar Wind Profilers perform well during cloud free conditions, and Microwave Radiometers and Atmospheric Emitted Radiance Interferometers have a very good agreement during all conditions, but are limited by the smoothness of the retrieved thermodynamic profiles. The estimates from Ceilometers and High Spectral Resolution Lidars can be hindered by the presence of elevated aerosol layers or clouds, and the multi-instrument retrieval from the Collaborative Lower Atmospheric Modelling Profiling Systems can be constricted to a limited height range in low aerosol conditions. [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]

The objective of this work is to evaluate GOES-R (Geostationary Operational Environmental Satellites-R series) data-based fog conditions which occurred during the C-FOG (Toward Improving Coastal Fog Prediction) field campaign. The C-FOG campaign was designed to advance understanding of fog formation, development, and dissipation over coastal environments to improve predictability. The project took place along coastlines and open water environments of eastern Canada (Nova Scotia, and the Island of Newfoundland) during August−October of 2018 where environmental conditions play an important role for late season fog formation. During the C-FOG field campaign, coastal instruments were mainly located at the Ferryland supersite, Newfoundland, with two main sites, and five satellite sites, as well as on the Research Vessel Hugh R. Sharp. Key in-situ measurement instruments included microphysical, meteorological, radiation, and aerosol sensors. A fog spectral probe was used for measuring droplet spectra from 1–50 µm at the Ferryland supersite. A laser precipitation monitor with 100 µm to 10 mm size range and an optical particle counter with 0.3–17 µm at 16 spectral channels provided information for fog and drizzle discrimination. Remote sensing platforms, e.g. profiling microwave radiometer, ceilometer, microwave rain radar, lidar, meteorological towers, tethered balloons, and GOES-R products for fog coverage, and droplet size and liquid water path) were used to evaluate fog over horizontal and vertical dimensions. Results suggest that effective radius, phase, liquid water path, and liquid water content values obtained from GOES-R and the profiling microwave radiometer are comparable to ground-based in-situ observations. It is concluded that integration of observations and nowcasting products may help improve short-term local fog predictions. [ABSTRACT FROM AUTHOR]

Vertical profiles of atmospheric temperature, moisture, wind, and aerosols are essential information for weather monitoring and prediction. Their availability, however, is limited in space and time because of the significant resources required to observe them. To fill this gap, the New York State Mesonet (NYSM) Profiler Network has been deployed as a national testbed to facilitate the research, development, and evaluation of ground-based profiling technologies and applications. The testbed comprises 17 profiler stations across the state, forming a long-term regional observational network. Each profiler station comprises a ground-based Doppler lidar, a microwave radiometer (MWR), and an environmental Sky Imager--Radiometer (eSIR). Thermodynamic profiles (temperature and humidity) from the MWR, wind and aerosol profiles from the Doppler lidar, and solar radiance and optical depth parameters from the eSIR are collected, processed, disseminated, and archived every 10 min. This paper introduces the NYSM Profiler Network and reviews the network design and siting, instrumentation, network operations and maintenance, data and products, and some example applications that highlight the benefits of the network. Some sample applications include improved situational awareness and monitoring of the sea--land breeze, long-range wildfire smoke transport, air quality (PM2.5 and aerosol optical depth) and boundary layer height. Ground-based profiling systems promise a path forward for filling a critical gap in the U.S. observing system with the potential to improve analysis and prediction for many weather-sensitive sectors, such as aviation, ground transportation, health, and wind energy. [ABSTRACT FROM AUTHOR]

Previous studies showed that conical scanning radiometers greatly outperform cross-track scanning radiometers for precipitation retrieval over ocean. This study demonstrates a novel approach to improve precipitation rates at the cross-track scanning radiometers' observation time by propagating the conical scanning radiometers' retrievals to the cross-track scanning radiometers' observation time. The improved precipitation rate is a weighted average of original cross-track radiometers' retrievals and retrievals propagated from a conical scanning radiometer. The cross-track scanning radiometers include the Advanced Technology Microwave Sounder (ATMS) on board the SNPP satellite and four Microwave Humidity Sounders (MHSs). The conical scanning radiometers include the Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs), while the precipitation retrievals from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) are taken as the reference. Results show that the morphed precipitation rates agree much better with the reference. The degree of improvement depends on several factors, including the propagated precipitation source, the time interval between the cross-track scanning radiometer and the conical scanning radiometer, the precipitation type (convective versus stratiform), the precipitation events' size, and the geolocation. The study has potential to greatly improve high-impact weather systems monitoring (e.g., hurricanes) and multisatellite precipitation products. It may also enhance the usefulness of future satellite missions with cross-track scanning radiometers on board. [ABSTRACT FROM AUTHOR]

Across spatial and temporal scales, sea-ice motion has implications for ship navigation, the sea-ice thickness distribution, sea-ice export to lower latitudes and re-circulation in the polar seas, among others. Satellite remote sensing is an effective way to monitor sea-ice drift globally and daily, especially using the wide swaths of passive microwave missions. Since the late 1990s, many algorithms and products have been developed for this task. Here, we investigate how processing sea-ice drift vectors from the intersection of individual swaths of the Advanced Microwave Scanning Radiometer 2 (AMSR2) mission compares to today's status quo (processing from daily averaged maps of brightness temperature). We document that the "swath-to-swath" (S2S) approach results in many more (2 orders of magnitude) sea-ice drift vectors than the "daily map" (DM) approach. These S2S vectors also validate better when compared to trajectories of on-ice drifters. For example, the RMSE of the 24 h winter Arctic sea-ice drift is 0.9 km for S2S vectors and 1.3 km for DM vectors from the 36.5 GHz imagery of AMSR2. Through a series of experiments with actual AMSR2 data and simulated Copernicus Imaging Microwave Radiometer (CIMR) data, we study the impact that geolocation uncertainty and imaging resolution have on the accuracy of the sea-ice drift vectors. We conclude by recommending that a swath-to-swath approach is adopted for the future operational Level-2 sea-ice drift product of the CIMR mission. We outline some potential next steps towards further improving the algorithms and making the user community ready to fully take advantage of such a product. [ABSTRACT FROM AUTHOR]

A cloud particle sensor (CPS) sonde is an observing system attached with a radiosonde sensor to observe the vertical structure of cloud properties. The signals obtained from CPS sondes are related to the phase, size, and number of cloud particles. The system offers economic advantages including human resource and simple operation costs compared with aircraft measurements and land-/satellite-based remote sensing. However, the observed information should be appropriately corrected because of several uncertainties. Here we made field experiments in the Arctic region by launching approximately 40 CPS sondes between 2018 and 2020. Using these data sets, a better practical correction method was proposed to exclude unreliable data, estimate the effective cloud water droplet radius, and determine a correction factor for the total cloud particle count. We apply this method to data obtained in October 2019 over the Arctic Ocean and March 2020 at Ny-Ålesund, Svalbard, Norway, to compare with a particle counter aboard a tethered balloon and liquid water content retrieved by a microwave radiometer. The estimated total particle count and liquid water content from the CPS sondes generally agree with those data. Although further development and validation of CPS sondes based on dedicated laboratory experiments would be required, the practical correction approach proposed here would offer better advantages in retrieving quantitative information on the vertical distribution of cloud microphysics under the condition of a lower number concentration. [ABSTRACT FROM AUTHOR]

This paper describes a new Passive Microwave Empirical Cold Surface Classification Algorithm (PESCA) developed for snow-cover detection and characterization by using passive microwave satellite measurements. The main goal of PESCA is to support the retrieval of falling snow, since several studies have highlighted the influence of snow-cover radiative properties on the falling-snow passive microwave signature. The developed method is based on the exploitation of the lower-frequency channels (<90 GHz), common to most microwave radiometers. The method applied to the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the cross-track-scanning Advanced Technology Microwave Sounder (ATMS) is described in this paper. PESCA is based on a decision tree developed using an empirical method and verified using the AutoSnow product built from satellite measurements. The algorithm performance appears to be robust both for sensors in dry conditions (total precipitable water < 10 mm) and for mean surface elevation < 2500 m, independent of the cloud cover. The algorithm shows very good performance for cold temperatures (2-m temperature below 270 K) with a rapid decrease of the detection capabilities between 270 and 280 K, where 280 K is assumed as the maximum temperature limit for PESCA (overall detection statistics: probability of detection is 0.98 for ATMS and 0.92 for GMI, false alarm ratio is 0.01 for ATMS and 0.08 for GMI, and Heidke skill score is 0.72 for ATMS and 0.69 for GMI). Some inconsistencies found between the snow categories identified with the two radiometers are related to their different viewing geometries, spatial resolution, and temporal sampling. The spectral signatures of the different snow classes also appear to be different at high frequency (>90 GHz), indicating potential impact for snowfall retrieval. This method can be applied to other conically scanning and cross-track-scanning radiometers, including the future operational EUMETSAT Polar System Second Generation (EPS-SG) mission microwave radiometers. [ABSTRACT FROM AUTHOR]

NASA Precipitation Measurement Mission observations are used to evaluate the diurnal cycle of precipitation from three CMIP6 models (NCAR-CESM2, CNRM-CM6.1, CNRM-ESM2.1) and the ERA5 reanalysis. NASA's global-gridded IMERG product, which combines spaceborne microwave radiometer, infrared sensor, and ground-based gauge measurements, provides high-spatiotemporal-resolution (0.1° and half-hourly) estimates that are suitable for evaluating the diurnal cycle in models, as determined against the ground-based radar network over the conterminous United States. IMERG estimates are coarsened to the spatial and hourly resolution of the state-of-the-art CMIP6 and ERA5 products, and their diurnal cycles are compared across multiple decades of June–August in the 60°N–60°S domain (IMERG and ERA5: 2000–19; NCAR and CNRM: 1979–2008). Low-precipitation regions (and weak-amplitude regions when analyzing the diurnal phase) are excluded from analyses so as to assess only robust diurnal signals. Observations identify greater diurnal amplitudes over land (26%–134% of the precipitation mean; 5th–95th percentile) than over ocean (14%–66%). ERA5, NCAR, and CNRM underestimate amplitudes over ocean, and ERA5 overestimates over land. IMERG observes a distinct diurnal cycle only in certain regions, with precipitation peaking broadly between 1400 and 2100 LST over land (2100–0600 LST over mountainous and varying-terrain regions) and 0000 and 1200 LST over ocean. The simulated diurnal cycle is unrealistically early when compared with observations, particularly over land (NCAR-CESM2 AMIP: −1 h; ERA5: −2 h; CNRM-CM6.1 AMIP: −4 h on average) with nocturnal maxima not well represented over mountainous regions. Furthermore, ERA5's representation of the diurnal cycle is too simplified, with less interannual variability in the time of maximum relative to observations over many regions. SIGNIFICANCE STATEMENT: Identifying and addressing climate model errors in representing the diurnal cycle of precipitation are critical to improving their accuracy. This study provides an update on the diurnal cycle performance of state-of-the-art climate models and reanalysis against state-of-the-art satellite observations. The models and reanalysis have varying biases in diurnal amplitude over land, where amplitudes are stronger, and they underestimate amplitudes over ocean. They also simulate precipitation over land to peak too early in the day, from −1 to −4 h on average depending on the model. Nocturnal maxima in mountainous regions are not well simulated, although the reanalysis outperforms the models in this case. Future work can use these findings to improve realism in the next generation of climate models. [ABSTRACT FROM AUTHOR]

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Many indices are used to monitor drought events. However, different indices have different data requirements and applications. Hence, evaluating their applicability will help to characterize drought events and refine the development of effective drought indices. We constructed different drought indices based on multisource remote sensing data and comprehensively evaluated and compared their applicability for drought monitoring throughout China. The characteristics of drought events in 2009 and 2011 were compared using various drought indices. The different time scales of the Palmer Drought Severity Index (PDSI) and the Standardized Precipitation Index (SPI) were used to evaluate remote sensing drought indices in different regions. Single drought indices, including the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, the Precipitation Condition Index (PCI) derived from Tropical Rainfall Measurement Mission (TRMM) data, and the TCI and Soil Moisture Condition Index (SMCI) derived from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) data, as well as combined drought indices, including the Microwave Integrated Drought Index (MIDI), Optimized Vegetation Drought Index (OVDI), Optimized Meteorological Drought Index (OMDI), Scale Drought Conditions Index (SDCI), and Synthesized Drought Index (SDI), were analyzed and compared to evaluate their applicability. The results showed that different drought indices have specific characteristics under different land use types in China. The VCI and TCI can better monitor long-term drought conditions, but they have a weak correlation with the in situ drought index in forestland and grassland areas. The correlation of SPI-1 with the PCI is higher than that with other single indices, which indicates that the PCI is a good short-term drought index. The SMCI has a better correlation with the short-term in situ drought index, but it is not conducive to drought monitoring in areas such as densely forested land and grassland. The correlations of the in situ drought index with the combined drought indices (the MIDI, OVDI, OMDI, SDCI, and SDI) are better than those with the single drought indices. [ABSTRACT FROM AUTHOR]