Passive Microwave Research Articles

The tasks for bathymetric investigations and cartographic support of the northern sea high latitude routes in the East Siberian Sea

The current state and issues of bathymetric investigations of the East Siberian Sea are examined in this study. The main sources of bathymetric data are characterized. The possibilities to solve these issues in order to improve the efficiency of the oncoming bathymetric investigation in the East Siberian Sea for supporting the urgent practical tasks of the Russian Federation concerning the Northern Sea high latitude routes are discussed. The importance of a new hydrographic investigation through the high latitude routes in the East Siberian Sea in order to provide the possibility of year-round navigation of the large-tonnage Arc7‑class icebreaking LNG carriers is especially emphasized. Compiling of the new coastal electronic navigation charts for the new Northern Sea high latitude routes is stated as one of the main priorities. The lack of coastal electronic navigation charts of the high latitude routes in the East Siberian Sea will present a challenge for the establishing of year-round navigation for high ice-capable Arc7‑class LNG carriers during the coming several years. The drafting of high latitude routes must also consider the seafloor gas venting in the East Siberian Sea. The satellite remote sensing data from passive microwave radiometers and optical sensors using «modis & amsr2» technology provided by the University of Bremen ensures the complementary observations of sea ice concentration in the eastern part of the East Siberian Sea. The re-analysis of data from microwave radiometers AMSR‑2 beginning from 2018 provides the conclusion about the autumn degradation of the Aion ice massif. The analysis of the sea-ice concentration of the Aion ice massif during autumn–winter of 2022, the most severe in the last 5 years, allows us to give a recommendation concerning the new high latitude route in the East Siberian Sea. This route passes along the northern coast of Vrangel island into the Chukchi Sea and it is more efficient for large-tonnage Arc7‑class icebreaking LNG carriers ensuring year-round navigation in the East Siberian Sea. It is stated that hydrographic investigations carried out on the sections of the Northern Sea Route must adhere to the requirements of the International Hydrographic Organization. The urgency of developing the state integrated bathymetric database for the Arctic Ocean including the Arctic Seas data along with the necessity of providing all Russian bathymetric data acquired in the Arctic region to the assigned national centre is discussed. The FKU 280 ZKP VMF is proposed as such national centre. The importance of coastal electronic navigation charts for the high latitude routes in the East Siberian Sea created on the basis of modern technology is emphasized.

Development of the SURface Fast Emissivity Model for Ocean (SURFEM‐Ocean) Based on the PARMIO Radiative Transfer Model

AbstractA Passive and Active Reference Microwave to Infrared Ocean (PARMIO) physical radiative transfer model has been selected by an international team of experts to be used as a community ocean surface emissivity model. A fast version of this physical community model is developed within the framework of European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Numerical Weather Prediction Satellite Application Facilities visiting scientist programme: the SURface Fast Emissivity Model for Ocean (SURFEM‐Ocean), for implementation in the Radiative Transfer code for TOVS (RTTOV) as an alternative/replacement of the well‐known FAST microwave Emissivity Model (FASTEM). First, the configuration of the PARMIO model is adapted to microwave frequencies from 500 MHz to 700 GHz, and issues observed in the preliminary studies with cold sea surface temperatures (SST) and high ocean wind speeds (OWS) are corrected. Second, an artificial neural network method is used to reproduce the results of the PARMIO model much faster. The computation of the analytical Jacobians is also implemented. Finally, SURFEM‐Ocean results are compared with satellite observations and other ocean emissivity models. SURFEM‐Ocean extends the capability of FASTEM. It computes emissivities from 500 MHz to 700 GHz, for all incidence angles, for the fourth Stokes polarizations, for OWS from 0 to 50 m/s, SST from −2 to 30°C, sea surface salinity from 0 to 40 psu, and for all wind directions.

Constraining Plant Hydraulics With Microwave Radiometry in a Land Surface Model: Impacts of Temporal Resolution

AbstractVegetation water content (VWC) plays a key role in transpiration, plant mortality, and wildfire risk. Although land surface models now often contain plant hydraulics schemes, there are few direct VWC measurements to constrain these models at global scale. One proposed solution to this data gap is passive microwave remote sensing, which is sensitive to temporal changes in VWC. Here, we test that approach by using synthetic microwave observations to constrain VWC and surface soil moisture within the Climate Modeling Alliance Land model. We further investigate the possible utility of sub‐daily observations of VWC, which could be obtained through a satellite in geostationary orbit or combinations of multiple satellites. These high‐temporal‐resolution observations could allow for improved determination of ecosystem parameters, carbon and water fluxes, and subsurface hydraulics, relative to the currently available twice‐daily sun‐synchronous observational patterns. We find that incorporating observations at four different times in the diurnal cycle (such as could be available from two sun‐synchronous satellites) provides a significantly better constraint on water and carbon fluxes than twice‐daily observations do. For example, the root mean square error of projected evapotranspiration and gross primary productivity during drought periods was reduced by approximately 40%, when using four‐times‐daily relative to twice‐daily observations. Adding hourly observations of the entire diurnal cycle did not further improve the inferred parameters and fluxes. Our comparison of observational strategies may be informative in the design of future satellite missions to study plant hydraulics, as well as when using existing remotely sensed data to study vegetation water stress response.

Global Mapping of Microwave Emissivity and Emitting Depth in Arid Areas Using GMI Observations

AbstractOver arid areas, observations of brightness temperatures by passive microwave radiometers are affected by the variation of the emitting depth with wavelengths. When this variation is unaccounted for, it limits the assimilation of passive microwaves over deserts in Numerical Weather Prediction models and it causes large errors in passive microwave retrievals of land surface temperatures. The emitting depths, along with the corresponding emissivities, are estimated from 10 to 89 GHz, using the non‐Sun‐synchronous observations of the Global Precipitation Mission Microwave Imager to reconstruct the monthly diurnal cycle of brightness temperature. The soil temperature profile is modeled using a two‐term Fourier decomposition based on the ERA5 surface temperature. The combination of the observation and the modeled temperature allows for an estimation of the microwave effective emitting depth. The emitting depth is estimated to be up to 25 cm at 36 GHz, resulting in large differences between the surface temperature and the effective emitting temperature. The variation of emitting depth with frequency is parameterized, and a companion data set provides the necessary parameters to calculate the emitting depth for arid areas between 10 and 89 GHz, globally. The benefit of this parameterization is quantified, with an application to the modeling of observations from the Special Sensor Microwave Imager Sounder over arid areas.

Evaluation of GPM IMERG and error sources for tropical cyclone precipitation over eastern China

Long-term precipitation data with high temporal-spatial resolution and high precision is crucial for the monitoring of tropical cyclone (TC) precipitation and the understanding of its response to global climate change. The performance of the Final run of Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) V06B product to characterize the TC precipitation is evaluated during the TC season (May to September) from 2014 to 2019 against a dense network of gauges over eastern China, considering the impact of land cover types (LCTs), different techniques (morphing) and instruments, i.e., passive microwave (PMW) and infrared (IR) sensors. Overall, IMERG captures well the spatial pattern and temporal variations of mean TC rainfall rate (RR) over eastern China but shows underestimations of mean TC RR compared to gauge observations, with the mean error (ME) being −0.78 mm/h, which are mainly contributed by the underestimations of heavy TC RR (ME: −12.1 mm/h). Moreover, the IMERG performance shows dependence on the LCTs and IMERG components (i.e., morph, IR + morph, and PMW sensors). The overestimation/underestimation of light/heavy TC precipitation events is most obvious over water/evergreen broadleaf forests, with the bias being 1.2/0.5. Differently, IMERG even slightly underestimates light TC precipitation events over grasslands (bias = 0.88). Among four IMERG components, Microwave Humidity Sounder (MHS) performs the best in characterizing all TC precipitation events, followed by morph, Special Sensor Microwave Imager/Sounder (SSMIS), and finally IR + morph, indicating that IR algorithms might need to be further improved. The mean TC RR is slightly overestimated by MHS over most regions of eastern China with positive ME exceeding 2.8 mm/h, while is largely underestimated by IR + morph among four components with most MEs lower than −1.2 mm/h. This study may provide an observational reference for the continued development and future improvement of the IMERG product.

Dependency of errors for four global reanalysis and satellite precipitation estimates on four crucial factors

Errors in the reanalysis and satellite precipitation products are closely related to four impacting factors, i.e., seasonality, topography, rainfall intensity, and climate type. Therefore, revealing the dependency of the errors of precipitation products on seasonality, topography, rainfall intensity, and climate type is important for data users and developers to improve the quality of precipitation products and enhance their applications in hydrometeorology. In this study, the links between errors and four impacting factors were used to investigate the dependency of errors in the ERA5 reanalysis and three satellite-only precipitation products (i.e., IMERG-Late, GSMaP-MVK, and PERSIANN-CCS) on the four impacting factors. Results from mainland China show that the errors of the four products depend on the four factors, whereas the dependency of different error indicators on the four factors differs significantly. In all seasons, ERA5 outperformed the three satellite-only precipitation products in most cases. In terms of topographic dependency, the probability of detection (POD) of ERA5 increases with topographic complexity but the opposite is true for the satellite-only precipitation products. Compared with purely infrared (IR) data, the fusion of passive microwave and IR data enhances the quality of precipitation estimates and reduces the topographical dependency of the errors. Regarding intensity-based performance, the performance of the four products in terms of the POD and normalized root-mean-squared error improves as the rain rate increases, whereas that of the three bias metrics (i.e., total bias, hit bias, and miss bias) deteriorates. In terms of the climatic dependency of the errors, the errors of the four products increase as the aridity increases in most cases. False bias contributes primarily to the total bias for the four products. More importantly, we found that the relationship between the false alarm ratio (FAR) and climate type cannot accurately characterize the dependency of false alarms based on climate type because of the interference of hit precipitation events. Based on this finding, the mean number of false alarms is recommended to replace the FAR to objectively investigate the false precipitation of various precipitation products.

Evaluation of Improvement Schemes for FY-3B Passive Microwave Soil-Moisture Estimates Retrieved Using the Land Parameter Retrieval Model

Satellite observations have provided global and regional soil-moisture estimates in the last four decades. However, the accuracy of these observations largely depends on reducing uncertainties in the retrieval algorithms. In this study, we address two challenges affecting the quality of soil-moisture estimates from a widely used soil-moisture-retrieval model, the land parameter retrieval model (LPRM). We studied two improvement schemes that were aimed at reducing uncertainties in open water signals (the LPRMv6_OWF) and vegetation signals (the LPRMv6_Veg), as well as a scheme to reduce their combined impacts (the LPRMv6_OWFVeg) on LPRM-retrieved soil moisture using the FengYun-3B (FY-3B) satellite observations. To assess the impacts of the improvement schemes, we utilized in situ soil moisture from the Jiangsu and Jiangxi provinces in China. We found that the retrievals (Rs) of the LPRMv6_Veg and the LPRMv6_OWFVeg were mainly in the range of 0.2 to 0.5 in Jiangsu and Jiangxi, with increases of 0.1 compared to those of the LPRMv6. The standard deviation (SD) of the LPRMv6_OWFVeg increased in Jiangsu, while the R of the LPRMv6_OWF increased in Jiangsu by 0.05–0.1 compared to that of the LPRMv6, but the SD tended to become worse. In Jiangxi, there was an increase of 0.1 in R. The results show that each of these algorithms improved the accuracy of soil-moisture inversion to some extent, compared to the original algorithm, with the LPRMv6_OWFVeg showing the greatest improvement, followed by the LPRMv6_Veg. The accuracy of both the LPRMv6_OWF and the LPRMv6_OWFVeg decreased to some extent when the open-water fraction (OWF) was greater than 0.2. Full areal extent analyses based on triple collocation showed significant improvements in correlations and minimized errors across different vegetation scenarios over the entire region of China in both the LPRMv6_OWF and the LPRMv6_Veg. However, reduced qualities were found in arid regions in northern China because of the nonlinear relationships between land-surface temperature, vegetation, and soil moisture in the LPRM. These results highlight important lessons for developing comprehensive improvement schemes for soil-moisture retrievals from passive microwave satellite observations.

Age-Related Changes in the Temperature of the Lumbar Spine Measured by Passive Microwave Radiometry (MWR)

A study was conducted to determine the age dependence of the temperature of the low back in the region of the five lumbar vertebrae by using passive microwave radiometry (MWR). The rationale for the study is that the infrared brightness on which the temperature measurement is based will be dependent upon blood circulation and thus on metabolic, vascular, and other regulatory factors. The brightness and infrared temperatures were determined in five zones above each of the medial, left, and right lateral projections of the vertebrae. A total of 115 healthy subjects were recruited, aged between 18 and 84 years. No significant differences in infrared temperature were detected. As predicted, brightness temperature increased until 25 years old and then gradually decreased. In subjects over 70 years of age, compared with those aged 60–70 years, there is a significant increase in brightness temperature at the level of 3–5 lumbar vertebrae by 0.3–0.7 °C. This is interpreted as indicating that individuals who have lived to an advanced age successfully maintain metabolic and regenerative processes. The benchmark data that has been obtained can be usefully employed in future studies of the aetiology of low back pain. In particular, the prospect exists for the technology to be used to provide a non-invasive biomarker to evaluate the effectiveness of antiaging therapies.

Polar Sea Ice Detection Using a Rotating Fan Beam Scatterometer

Scatterometers are dedicated to monitoring sea surface wind vectors, but they also provide valuable data for polar applications. As a new type of scatterometer, the rotating fan beam scatterometer delivers a higher diversity of incidence angles and more azimuth sampling. The paper takes the first rotating fan beam scatterometer, the China France Oceanography Satellite scatterometer (CSCAT), as an example to explore the effectiveness of this new type of scatterometer in polar sea ice detection. In this paper, a Bayesian method with consideration of geometric characteristics of CSCAT is developed for sea ice detection. The implementation of this method includes the definition of CSCAT backscatter space, an estimation of the sea ice Physical Model Function (GMF), a calculation of the sea ice backscatter distance to the sea ice GMF, a probability distribution function (PDF) estimation of the square distance to the GMF (sea ice GMF and wind GMF), and a calculation of the sea ice Bayesian posterior probability. This algorithm was used to generate a daily CSCAT polar sea ice mask during the CSCAT mission period (2019–2022) (by setting a 55% threshold on the Bayesian posterior probability). The sea ice masks were validated against passive microwaves by quantitatively comparing the sea ice edges and extents. The validation suggests that the CSCAT sea ice edge and extent show good agreement with the sea ice concentration distribution (i.e., sea ice concentration ≥ 15%) of the Advanced Microwave Scanning Radiometer 2 (AMSR2). The average Euclidean distance of the sea ice edges was basically less than 12.5 km, and the deviation of the sea ice extents was less than 0.3 × 106 km2.

Conversion of satellite passive microwave signals to land surface “skin” temperature for extremely dry deserts

Satellite passive microwave (PMW) observations are important data sources for obtaining land surface temperature (LST) especially for cloudy conditions. Considering the generally poorer spatial representativeness of PMW observations, fusion of PMW-based and thermal infrared-(TIR-) based observations have been considered as an important approach to generate all-weather LST with appropriate resolution. However, the existing penetration difference between PMW-based and TIR-based observations has been an obstacle against this objective. This is especially the case for desert regions, where the penetration depth of PMW observations is larger than under other conditions and the relationship between TIR-based and PMW-based signals are thus more complicated. In this regard, this study develops an easy and high-efficiency model suitable for transforming PMW-based subsurface LST to TIR-like skin surface temperature in four regions across the world characterized by typical arid climate. The developed model employs microwave-derived surface soil moisture (SSM) and vegetation transmissivity (VT) as major inputs to quantify that “resampling depth difference” between TIR-based and PMW-based observations. Results show that the model can significantly reduce that difference (RMSE) from >10 K to around 2–5 K in the typical desert regions. The developed model is beneficial to obtaining high-resolution all-weather LST for the vast desert regions across the world in subsequent studies.

Mapping snow depth distribution from 1980 to 2020 on the tibetan plateau using multi-source remote sensing data and downscaling techniques

Although passive microwave remote sensing has been widely used for snow depth (SD) retrieval, its coarse spatial resolution has led to large uncertainties in various applications, particularly in mountainous regions. Therefore, downscaling passive microwave data is an effective way to improve the accuracy of regional SD monitoring. In this study, we developed a SD downscaling algorithm based on multisource remote sensing data, generating a long-time series of daily 500 m resolution SD data from 1980 to 2020 over the Tibetan Plateau (TP). The downscaling SD product improved the spatial resolution and significantly reduced the overestimation of the SD for the TP. Based on this product, the spatial pattern and the spatiotemporal trend of the SD were analyzed by the Theil-Sen median method and the Mann–Kendall test. SD on the TP showed clear spatial heterogeneity by vertical zonal distribution, bimodal distribution features changing with longitude, and a thick SD distribution pattern at 30-32°N. The annual average SD, maximum SD, and accumulation of SD on the TP from 1980 to 2020 were 1.02 cm, 5.91 cm, and 39.45 cm, respectively, with trends of −0.06 cm/10a, −0.44 cm/10a, and 1.80 cm/10a (P > 0.05), respectively. SD on the TP increases with elevation above 5.5 km in mountainous areas and decreases in areas below 5.5 km.

Influence of aircraft attitude on fully polarized radiometer and its correction

ABSTRACT As an important load of airborne/spaceborne passive microwave remote sensing, fully polarized microwave radiometer is widely used to the measurement of wind vector and salinity on sea surface. The retrieval of ocean surface parameters requires high accuracy data of radiometer. In recent years, the data correction scheme of fully polarized radiometer is common, but the attitude problem of radiometer is not paid enough attention. This paper establishes a full link transfer model for a fully polarized microwave radiometer, and focus on analysing the impact of aircraft attitude on various parameters. This paper aims to draw researchers’ attention to attitude control of fully polarized microwave radiometer. Through the model established in this paper, the influence of attitude on fully polarized microwave radiometers is clarified and corrected, then rapidly correction can be achieved. Through simulation model, the impact of different attitudes on data can be calculated in advance before flight tests, and can provide guidance for attitude control. Using the corrected data for wind vector analysis, the deviation of wind direction is within 10 ∘ , which also verifies the accuracy of the model in this paper. The model proposed in this paper can achieve fast and accurate correction of radiometer parameters and the error prediction caused by attitude, which provides a strong guarantee for the inversion of sea surface parameters.

Weekly mapping of surface water extent in the intertropical wetlands using spaceborne GNSS reflectometry

As of today, the uncertain distribution and dynamics of inundations in the tropical wetlands cause large uncertainties in the quantification of the world’s methane emissions. Long-term, global remote sensing observations currently rely on optical sensors, affected by cloud cover and vegetation, and on passive microwave imagery with a low spatial resolution. Global Navigation Satellite System Reflectometry (GNSS-R) L-band observations from space onboard the Cyclone GNSS (CYGNSS) mission can provide information about the dynamics of surface water at moderate to low spatial resolution and with a frequent revisit. In this article, we propose a methodology to compute the fraction of water contained in 0.1°pixels (∼10 km) using CYGNSS reflectivity and Above Ground Biomass (AGB) maps. We produced a 1-year time series of water fractions with a weekly time sampling for the full coverage of CYGNSS between ±38°of latitude, from August 2018 to July 2019. We evaluated the results against regional reference maps of surface water based on MODIS, JERS-1, and PALSAR, and against the Global Inundation Extent from Multi-Satellite (GIEMS) dataset. CYGNSS water fractions represent well the spatial distribution and the seasonality of inundation extent for most of the tropical wetlands, with a global RMSD of 15.0 % against GIEMS. We found a likely overestimation when the soil moisture (SM) exceeds 0.3-0.4 cm3/cm3, i.e. for saturated soils where GNSS-R reflections are coherent, as the SM was not parameterized in our retrieval model. Despite this, the results are consistent in the entire intertropical band including over densely vegetated areas. These weekly water fractions for 2018-2019 with a 0.1°pixel size are distributed to contribute to further comparisons and hydrological researches.

Remote Sensing-Based Simulation of Snow Grain Size and Spatial–Temporal Variation Characteristics of Northeast China from 2001 to 2019

The size of snow grains is an important parameter in cryosphere studies. It is the main parameter affecting snow albedo and can have a feedback effect on regional climate change, the water cycle and ecological security. Larger snow grains increase the likelihood of light absorption and are important for passive microwave remote sensing, snow physics and hydrological modelling. Snow models would benefit from more observations of surface grain size. This paper uses an asymptotic radiative transfer model (ART model) based on MOD09GA ground reflectance data. A simulation of snow grain size (SGS) in northeast China from 2001 to 2019 was carried out using a two-channel algorithm. We verified the accuracy of the inversion results by using ground-based observations to obtain stratified snow grain sizes at 48 collection sites in northeastern China. Furthermore, we analysed the spatial and temporal trends of snow grain size in Northeastern China. The results show that the ART model has good accuracy in inverting snow grain size, with an RMSD of 65 μm, which showed a non-significant increasing trend from 2001 to 2019 in northeast China. The annual average SGS distribution ranged from 430.83 to 452.38 μm in northeast China, 2001–2019. The mean value was 441.78 μm, with an annual increase of 0.26 μm/a, showing a non-significant increasing trend and a coefficient of variation of 0.014. The simulations show that there is also intermonth variation in SGS, with December having the largest snow grain size with a mean value of 453.92 μm, followed by January and February with 450.77 μm and 417.78 μm, respectively. The overall spatial distribution of SGS in the northeastern region shows the characteristics of being high in the north and low in the south, with values ranging from 380.248 μm to 497.141 μm. Overall, we clarified the size and distribution of snow grains over a long time series in the northeast. The results are key to an accurate evaluation of their effect on snow–ice albedo and their radiative forcing effect.

A new global C-band vegetation optical depth product from ASCAT: Description, evaluation, and inter-comparison

Active microwave measurements have the potential to estimate vegetation optical depth (VOD), an indicator related to vegetation water content and biomass. The Advanced SCATterometer (ASCAT) provides long-term C-band backscatter data at vertical-vertical (VV) polarization from 2007. So far, very few studies have considered retrieving VOD from this active sensor. This study presents a new publicly released global long-term and continuous (2007–2020) C-band VOD dataset retrieved from the ASCAT observations, named the ASCAT INRAE-BORDEAUX or ASCAT IB VOD product. The retrieval algorithm is based on the Water Cloud Model (WCM) including the Ulaby bare soil model. The algorithm takes advantage of a multi-temporal (MT) retrieval method relying on a cost function where constraints to the retrieved parameters are implemented and a reanalysis soil moisture (SM) dataset from ERA5-Land is used as an input. The performance of ASCAT IB VOD was evaluated by inter-comparing it with ASCAT Technische Universität Wien (TUW), the Advanced Microwave Scanning Radiometer 2 (AMSR2), and VOD Climate Archive (VODCA) VOD products (the last two products are estimated from passive microwave observations). Results showed that ASCAT IB VOD presented the highest spatial correlation with aboveground biomass (R ∼ 0.83) and with the Global Ecosystem Dynamics Investigation (GEDI) canopy height (R ∼ 0.84–0.85). In terms of temporal performance, ASCAT IB VOD had the highest correlation R values with leaf area index (LAI) and Normalized Difference Water Index (NDWI) in most parts of the globe from 2013 to 2018. This contrasts with AMSR2 VODs which correlated better with Normalized Difference Vegetation Index (NDVI). The new ASCAT-based VOD product on a global scale highlighted the potential benefit of combining active (namely ASCAT) and passive (namely AMSR2) VOD products for vegetation studies.

Correction for the Attenuation Due to Atmospheric Gas and Stratiform Clouds in Triple-Frequency Radar Observations of the Microphysical Properties of Snowfall

For triple-frequency radar, the attenuation attributed to atmospheric gases and stratiform clouds is diverse due to different snowfall microphysical properties, particularly in regions far from the radar. When using triple-frequency ground-based radar measurements, evaluating the attenuation of the three radars at different heights is common to derive attenuation-corrected effective reflectivity. Therefore, this study proposes a novel quality-controlled approach to identify radar attenuation due to gases and stratiform clouds that can be neglected due to varying snowfall microphysical properties and assess attenuation along the radar observation path. The key issue lies in the lack of information about vertical hydrometeor and cloud distribution. Therefore, European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data are employed. The Self-Similar-Rayleigh-Gans Approximation (SSRGA) for the nonspherical scattering model in the Passive and Active Microwave TRAnsfer model 2 (PAMTRA2) is compared and analyzed against other scattering models to obtain the optimal triple-frequency radar attenuation correction strategies for stratiform cloud meteorological conditions with varying snowfall microphysical properties. This methodology paves the way for understanding differential attenuation attributed to gas and stratiform clouds with snowfall microphysical properties. Simultaneously, the bin-by-bin approximation method is used to perform the attenuation correction. The two-way attenuation correction increased up to 4.71 dB for heights above 6 km, remaining minimal for regions with heights below 6 km. These values, attributable to gases and stratiform clouds’ two-way attenuation, are nonnegligible, especially at distances far from the W-band radar at heights above 6 km. Both values are relatively small for the X- and Ka-band radars and can be neglected for the varying snowfall microphysical properties. The attenuation correction of triple-frequency radar reflectivity is validated using the cross-calibration and dual-frequency reflectivity ratios. The results show that the method is valid and feasible.

Uncertainty Calibration of Passive Microwave Brightness Temperatures Predicted by Bayesian Deep Learning Models

Abstract Visible and infrared radiance products of geostationary orbiting platforms provide virtually continuous observations of Earth. In contrast, low-Earth orbiters observe passive microwave (PMW) radiances at any location much less frequently. Prior literature demonstrates the ability of a machine learning (ML) approach to build a link between these two complementary radiance spectra by predicting PMW observations using infrared and visible products collected from geostationary instruments, which could potentially deliver a highly desirable synthetic PMW product with nearly continuous spatiotemporal coverage. However, current ML models lack the ability to provide a measure of uncertainty of such a product, significantly limiting its applications. In this work, Bayesian deep learning is employed to generate synthetic Global Precipitation Measurement (GPM) Microwave Imager (GMI) data from Advanced Baseline Imager (ABI) observations with attached uncertainties over the ocean. The study first uses deterministic residual networks (ResNets) to generate synthetic GMI brightness temperatures with as little mean absolute error as 1.72 K at the ABI spatiotemporal resolution. Then, for the same task, we use three Bayesian ResNet models to produce a comparable amount of error while providing previously unavailable predictive variance (i.e., uncertainty) for each synthetic data point. We find that the Flipout configuration provides the most robust calibration between uncertainty and error across GMI frequencies, and then demonstrate how this additional information is useful for discarding high-error synthetic data points prior to use by downstream applications.

The co-located arrangement of ECEI and MIR microwave imaging diagnostics on EAST tokamak

The co-located configuration of Passive Electron Cyclotron Emission Imaging (ECEI) and active Microwave Imaging Reflectometry (MIR) diagnostics systems has been successfully implemented on the EAST fusion reactor. These advanced 2D microwave diagnostic tools offer 10–20 mm poloidal spatial resolution through large aperture quasi optics. This arrangement not only optimizes the utilization of limited observation windows but also facilitates concurrent measurements of plasma density and electron temperature at specific toroidal positions. This co-located setup holds great promise for enhancing research into electron heat transport and other pivotal plasma phenomena.

The synergies of SMAP enhanced and MODIS products in a random forest regression for estimating 1 km soil moisture over Africa using Google Earth Engine

ABSTRACT Due to the coarse scale of soil moisture products retrieved from passive microwave observations (SMPMW), several downscaling methods have been developed to enable regional scale applications. However, it can be challenging for users to access final data products and algorithms, as well as managing different data sources and formats, various data processing methods, and the complexity of the workflows from raw data to information products. Here, the Google Earth Engine (GEE), which as of late offers SMPMW, is used to implement a workflow for retrieving 1 km SM at a depth of 0–5 cm using MODIS optical/thermal measurements, the SMPMW coarse scale product, and a random forest regression. The proposed method was implemented on the African continent to estimate weekly SM maps. The results of this study were evaluated against in-situ measurements of three validation networks. Overall, in comparison to the original SMPMW product, which was limited by a spatial resolution of only 9 km, this method is able to estimate SM at 1 km spatial resolution with acceptable accuracy (an average correlation coefficient of 0.64 and a ubRMSD of 0.069 m3/m3). The results show that the proposed method in GEE provides a precise estimation of SM with a higher spatial resolution across the entire continent.

Experimental demonstration of passive microwave pulse amplification via temporal Talbot effect

The temporal Talbot effect is a passive phenomenon that occurs when a periodic signal propagates through a dispersive medium with a quadratic phase response that modulates the output pulse repetition rate based on the input period. As previously proposed, this effect enables innovative applications such as passive amplification. However, its observation in the microwave regime has been impractical due to the requirement for controlled propagation through a highly dispersive waveguide. To overcome this challenge, we employed an ultra-wide band linearly chirped Bragg grating within a standard microwave X-Band waveguide. By utilizing backwards Talbot array illuminators aided by particle swarm optimization, we achieved passive amplification with a gain of 3.45 dB and 4.03 dB for gaussian and raised cosine pulses, respectively. Furthermore, we numerically verified that with higher quality substrates this gain can be theoretically increased to over 8 dB. Our work paves the way for numerous applications of the Talbot effect in the microwave regime, such as temporal cloaking, sub-noise microwave signal detection, microwave pulse shaping, and microwave noise reduction.