Microwave Imaging Radiometer Research Articles

Estimating Surface Nitrate Concentrations in the Coastal Areas of the Around Savu Sea and Southern Sumba Island Based on Remote Sensing Data

Nitrate is an essential nutrient in phytoplankton's photosynthesis process. In addition, phytoplankton uses nitrate for their growth and reproduction. Nitrate abundance on the coast will affect primary productivity and biogeochemical cycles. The availability of nitrate observation data, especially around the Savu Sea coast, is minimal. In this study, the estimation of nitrate in the coastal area of the southern part of Sumba Island and the eastern part of Savu Island by using the generalized additive model (GAM). Seventy-one nitrate observation data were used to build the GAM model, and remote sensing data were used as input data for nitrate estimation. Sea Surface Temperature (SST) and chlorophyll-a data were obtained from Aqua-MODIS. Sea Surface Salinity (SSS) and Sea Surface Windspeed (SSW) data were obtained from a Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) Soil Moisture-Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT), respectively. This study uses the Generalized Additive Model (GAM) approach to predict the distribution of nitrate concentrations and determine the main driving factors associated with nitrate. Based on the result, temperature is the dominant factor in nitrate estimation, while chlorophyll-a has a relatively small influence. The best model to predict nitrate distribution uses four parameters, namely SST, SSS, SSW, and chlorophyll-a. The validation results of the expected nitrate value obtained from the model with the observed nitrate value obtained results with the same value range of 0 - 0.35; the difference is the value of the distribution. From the comparison results, the R2 value is 0.357.

Impact of microwave observations on the estimation of Arctic sea surface temperatures

The frequent and persistent cloud cover in the Arctic limits the extent to which sea surface temperature (SST) can be retrieved from thermal infrared (IR) satellite sensors. Passive microwave (PMW) observations provide highly complementary information to IR, enabling measurements through non-precipitating clouds, although at a coarser spatial resolution. The differences in coverage, accuracy, footprint size, spatial resolution and error characteristics between IR and PMW SSTs require a systematic assessment of how to best combine IR and PMW SST retrievals. This is provided in this study on the basis of the ESA-CCI PMW SST climate data record (CDR) and an existing IR-based gap-free SST and sea ice surface temperature CDR covering the Arctic (>58°N), where cloud cover is a serious limitation to IR sensors. An important step towards a combined IR and PMW SST CDR is to correct for systematic biases in the PMW and IR SST datasets relative to each other. The PMW SSTs show reduced biases against in situ SSTs compared to the IR SSTs, but for consistency with time periods when no Arctic PMW SSTs were available, the PMW SSTs have been adjusted to the IR SSTs in this study. This is done using a dynamic bias correction to generate a consistent combined IR and PMW Arctic SST CDR for the period 2002–2017. Including PMW SSTs reduces the standard deviations from 0.54 °C, 0.55 °C and 0.47 °C to 0.47 °C, 0.54 °C and 0.41 °C against drifters, moorings and Argo floats, respectively. The improved performance is seen in almost all regions (including those already covered by IR observations), with the largest improvement in IR data sparse regions. The average theoretical uncertainty reduces by 0.08 °C, which is in good agreement with the observed improvement in the standard deviation against drifters. The results are very promising and expected to improve even further in the future with the launch of the Copernicus Imaging Microwave Radiometer (CIMR), which will enable PMW SST retrievals with lower uncertainties and much closer to coasts and sea ice (where the largest uncertainties arise) than what is possible with previous and current PMW radiometers.

Integrating LEO and GEO Observations: Toward Optimal Summertime Satellite Precipitation Retrieval

Abstract Reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for understanding the Earth’s hydrological cycle. Precipitation estimation over land and coastal regions is necessary for addressing the high degree of spatial heterogeneity of water availability and demand, and for resolving the extremes that modulate and amplify hazards such as flooding and landslides. Advancements in computation power along with unique high spatiotemporal and spectral resolution data streams from passive meteorological sensors aboard geosynchronous Earth-orbiting (GEO) and low Earth-orbiting (LEO) satellites offer exciting opportunities to retrieve information about surface precipitation phenomena using data-driven machine learning techniques. In this study, the capabilities of U-Net–like architecture are investigated to map instantaneous, summertime surface precipitation intensity at the spatial resolution of 2 km. The calibrated brightness temperature products from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) radiometer are combined with multispectral images (visible, near-infrared, and infrared bands) from the Advanced Baseline Imager (ABI) aboard the GOES-R satellites as main inputs to the U-Net–like precipitation algorithm. Total precipitable water and 2-m temperature from the Global Forecast System (GFS) model are also used as auxiliary inputs to the model. The results show that the U-Net–like algorithm can capture fine-scale patterns and intensity of surface precipitation at high spatial resolution over stratiform and convective precipitation regimes. The evaluations reveal the potential of extracting relevant, high spatial features over complex surface types such as mountainous regions and coastlines. The algorithm allows users to interpret the inputs’ importance and can serve as a starting point for further exploration of precipitation systems within the field of hydrometeorology.

Soil Moisture and Sea Surface Salinity Derived from Satellite-Borne Sensors

AbstractThe monitoring of soil moisture and sea surface salinity over the Earth has been profoundly enhanced during the last thirteen years due to a new generation of satellite sensors. L-band radiometry is currently the only technology providing direct measurements of soil moisture, insensitive to surface roughness and distribution of elements in the soil, and the only technology the only technology for measuring that allows us to measure sea surface salinity from space. The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite missions resolve global and local variability with a spatial resolution of approximately 43 km, a swath width close to 1000 km, and a sampling time, for each mission, of at least twice every 3 days. These resolutions and samplings can be increased by either merging data from the two sensors, and with complementary information gathered from other passive or active sensors, or with in situ information at higher spatial resolution. Numerous scientific studies based on the use of this new type of measurement have led to a better understanding and constraint of the processes governing the variability of the water cycle, ocean circulation and the Earth's climate. The continuity of measurements, and the increased spatial and radiometric resolution is critical for fulfilling scientific needs. Future L-band radiometry missions currently being planned in Europe (the Copernicus Imaging Microwave Radiometer), and in China (the Ocean Salinity mission) should provide better constraints on auxiliary parameters by combining multiple frequencies, but they will not have improved spatial resolution beyond SMOS and SMAP. The temporal continuity with SMOS and SMAP will likely not be ensured. In parallel, new concepts are being developed to increase spatial resolution of both land and ocean parameters.

Retrieval of Arctic Sea Ice Motion from FY-3D/MWRI Brightness Temperature Data

Sea ice motion (SIM) has significant implications for sea–air interactions, thermohaline circulation, and the development of the Arctic passage. This research proposes an improved SIM retrieval method from Fengyun-3D’s (FY-3D) microwave radiometer imager’s (MWRI) brightness temperature (Tb) data based on the modified classical maximum cross-correlation (MCC) method and the multisource data merging method. This study utilized buoy data to establish the search area range, applied distinct thresholds across various Arctic regions, and merged the buoy data, reanalysis wind data, and SIM retrieved from FY-3D/MWRI Tb data. In 2019, for the final Arctic SIM results retrieved from the MWRI 89 GHz and 36.5 GHz Tb data, the root-mean-square error (RMSE) and the mean average error (MAE) in the east–west direction were 2.07 cm/s and 1.38 cm/s and those in the north–south direction were 1.96 cm/s and 1.15 cm/s, compared to the ice-tethered profiler (ITP) data. Compared with the daily average data of the National Snow and Ice Data Center (NSIDC), the RMSE and MAE of the SIM results obtained in this study were 0.74 cm/s and 0.93 cm/s in the east–west direction, and 0.56 cm/s and 0.72 cm/s in the north–south direction, respectively. The monthly average of the SIM retrieved from the MWRI Tb data in this research also showed a good agreement with the monthly average of the NSIDC SIM product. The comparison showed that the MWRI Tb data could be used to retrieve the Arctic SIM, and the Arctic SIM retrieval method presented in this paper was accurate and general.

Historical and real-time estimation of snow depth in Eurasia based on multiple passive microwave data

Current snow depth datasets demonstrate large discrepancies in the spatial pattern in Eurasia, and the lagging updates of datasets do not meet the operational requirements of the meteorological service department. This study developed a dynamic retrieval method for daily snow depth over Eurasia based on cross-sensor calibrated microwave brightness temperatures to enhance retrieval accuracy and meet the requirements of operational work. These brightness temperatures were detected by microwave radiometer imager carried on the FengYun 3 (FY-3) satellite and the special sensor microwave imager/sounder carried on the USA Defense Meteorological Satellite Program series satellites, which use the fewest sensors to provide the longest data and consequently introduce minimal errors during inter-sensor calibration. Firstly, inter-sensor calibration was conducted amongst brightness temperatures collected by the three sensors. A spatiotemporal dynamic relationship between snow depth and microwave brightness temperature gradient was then established, overcoming the large uncertainties induced by varying snow characteristics. This relationship can be utilised in FY-3 satellite data for operational service to obtain real-time snow depth. The generated daily snow depth dataset from 1988 to 2021 presents similar spatial patterns of snow depth to those observed in situ. Against in situ snow depth, the overall bias and root mean square error are −2.04 and 6.49 cm, respectively, facilitating considerable improvements in accuracy compared with the Advanced Microwave Scanning Radiometer 2 snow depth product, which adopts the static algorithm. Further analysis shows an overall decreasing trend from 1988 to 2021 for annual and monthly mean snow depths, demonstrating a noticeable reduction since around 2000. The reduction in monthly mean snow depth started earlier in shallow snow months than in deep snow months.

A 1D Bayesian inversion of microwave radiances using several radiative properties of solid hydrometeors

AbstractNumerical weather prediction centers increasingly make use of cloudy and rainy microwave radiances. Currently, the high microwave frequencies are simulated using simplified assumptions regarding the radiative properties of frozen hydrometeors. In particular, one single particle shape is often used for all precipitating frozen particles, all over the globe, and for all cloud types. In this paper, a multi‐SSP (single scattering properties) approach for 1D Bayesian inversions is examined. Two experiments were set up: (1) one with three SSPs and (2) one with the previous SSPs plus one which leads to very cold brightness temperature distributions. For that purpose, we used observations from the GPM Microwave Imager radiometer over 2 months period and forecasts from the Météo‐France convective scale AROME model. The results showed that mixtures of SSP are chosen by the inversion method for meteorological conditions with low scattering and that a single particle is chosen for those with high scattering to perform the inversions. Despite the fact that no specific weather scenes were found to be associated with a particular SSP the most efficient scattering particles can be favored for some of them.

An Antenna Pattern Correction Algorithm for Conical Scanning Spaceborne Radiometers: The CIMR Case

The rapid evolution of the effects observed in various areas of our planet related to climate change poses urgent questions about the knowledge of the state of the polar area and requires satellite acquisitions with fine spatial resolution and high accuracy to develop advanced products. The Copernicus Imaging Microwave Radiometer (CIMR) mission, based on a multifrequency microwave radiometer and designed to observe the ocean, sea ice, and Arctic environment, requires brightness temperature measurements with a total absolute uncertainty of 0.5 K and a spatial resolution of 5 km. This constraint demands very large reflectors with a gain value of tens of decibels. Mechanical constraints will be attained by using a mesh reflector, which guarantees the required resolution but with the drawback of a radiation pattern characterized by many grating lobes that contaminate the value of the brightness temperature associated with the boresight position. In this article, an antenna pattern correction (APC) is proposed to correct these effects. The algorithm takes advantage of an iterative formulation based on the Jacobi Method, providing a suitable correction that depends on the chosen spatial resolution. The APC algorithm was tested at both K- and Ka-bands with similar performance. Here, only the results from the latter are shown, as its antenna pattern is the most challenging among CIMR.

Retrieving the Motion of Beaufort Sea Ice Using Brightness Temperature Data from FY-3D Microwave Radiometer Imager.

Sea ice is an important marine phenomenon in the Arctic region, and it is of great importance to study the motion of Arctic sea ice in the present day when its melting is accelerated by global warming. This study proposes a method to retrieve the motion of sea ice based on the maximum cross-correlation (MCC) and the successive correction method (SCM). The proposed method can apply different scales of search ranges to template matching according to the location of sea ice in the Arctic area. In addition, the data assimilation method can assign different weights to different data. We used 36.5 GHz and 89 GHz brightness temperature (Tb) data from the microwave radiometer imager (MWRI) aboard the Fengyun-3D (FY-3D) satellite, for the first time in the literature, to retrieve the sea ice motion in the Beaufort Sea from January to April 2019. The retrieved sea ice motion results were in good agreement with those obtained from the motion of the buoys. Compared with the data from the buoys, the root mean-squared error (RMSE) of the sea ice motion retrieved from FY-3D/MWRI Tb data was 1.1418 cm/s in the zonal direction and 1.0481 cm/s in the meridional direction, and the mean absolute error (MAE) between them was 0.7166 cm/s in the zonal direction and 0.6777 cm/s in the meridional direction. The RMSE between the sea ice motion obtained from the National Snow and Ice Data Center (NSIDC) and the motion of the buoys was 0.9515 cm/s in the zonal direction and 0.67003 cm/s in the meridional direction, and the MAE between them was 0.6576 cm/s in the zonal direction and 0.4922 cm/s in the meridional direction. The RMSE of daily average velocity from the FY-3D/MWRI results and NSIDC data product was 2.2726 cm/s in zonal and 1.9270 cm/s in meridional, and the MAE was 1.5103 cm/s in zonal and 1.1071 cm/s in zonal. The density of the merged data was higher than that obtained from a single polarization or frequency in this paper. The results indicate that FY-3D/MWRI Tb data can retrieve the sea ice motion successfully.

Validation of IMERG Oceanic Precipitation over Kwajalein

The integrated Multi-satellitE Retrievals for GPM (IMERG) Version V05B and V06B precipitation products from the Global Precipitation Measurement (GPM) mission are validated against ground-based observations from the Kwajalein Polarimetric S-band Weather Radar (KPOL) deployed at Kwajalein Atoll in the central Pacific Ocean. Such a validation is particularly important as comprehensive surface measurements over the oceans are practically infeasible, which hampers the identification of possible errors, and improvement of future versions of IMERG and other satellite-based retrieval algorithms. The V05B and V06B IMERG products are validated at their native 0.1°, 30 min resolution from 2014 to 2018 based on both volumetric and categorical metrics. This validation study indicates that precipitation rates from both IMERG V05B and V06B are underestimated with respect to radar surface estimates, but the underestimation is much reduced from V05B to V06B. IMERG V06B outperforms V05B with reduced systematic bias and improved precipitation detectability. The IMERG performance is further traced back to its individual sensors and morphing-based algorithms. The overall underestimation in V05B is mainly driven by the negative relative biases from morphing-based algorithms which are largely corrected in V06B. Imagers perform generally better than sounders because of the usage of low-frequency channels in imagers which can better detect emission signals by the hydrometeors. Among imagers, the GPM Microwave Imager (GMI) and Advanced Microwave Scanning Radiometer Version 2 (AMSR2) are the best, followed by Special Sensor Microwave Imager/Sounder (SSMIS). Among sounders, the Microwave Humidity Sounder (MHS) is the best, followed by Advanced Technology Microwave Sounder (ATMS) and the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) for V06B. Among all categories, morph-only and IR + morph only perform better than SAPHIR. SAPHIR shows the worst performance among all categories, likely due to its limited channel selection. It is envisaged that these results will improve our understanding of IMERG performance over oceans and aid in the improvement of future versions of IMERG.

A model predictive control for attitude stabilization and spin control of a spacecraft with a flexible rotating payload

Many Earth Observation missions, implementing space-based microwave sensing techniques for collecting surface information, employ spinning sensors to cover large swaths of terrestrial areas, thus improving the rate at which global maps of those measured data are generated. These spacecraft (as Soil Moisture Active Passive (SMAP) developed by NASA or Copernicus Imaging Microwave Radiometer (CIMR) currently under development by Thales Alenia Space) consist of a main non-spun platform and a rotating part composed of an antenna boom, a deployable reflector and a rotation mechanism. As the reflector is designated to rotate about the nadir axis producing conically scanned antenna beams with precise surface incidence angle, the payload pointing accuracy needs to be addressed at both spin subsystem and platform level. In this work, a representative model of the dynamic behaviour of SMAP satellite is developed as a study case to design the proposed control strategies; in particular, a SMAP-like payload structural model is built using FEM commercial codes. The spacecraft is equipped with a Reaction Wheels Assembly (RWA) to accomplish both momentum compensation for the spun element and three-axis attitude control and a motor for the spin mechanism. The objective of the study is to develop the spacecraft control architecture in the frame of Model Predictive Control (MPC) theory. MPC refers to a class of algorithms in which the control action is obtained by computing an open-loop optimal sequence of control moves over a predefined time horizon; moreover, the ability to set constraints on process inputs and outputs directly in the problem formulation allows to account for actuators’ limits. In the study two operative phases of the satellite are addressed: the Spin-up, in which the 6-meter diameter antenna is spun-up to the operative condition of 14.6 RPM, and the Science Phase, in which precise nadir pointing and stability of the flexible system must be kept for acquiring high-resolution measurements. To this purpose, control–structure interaction between attitude/spin control system and flexible dynamics, as well as system’s imbalances, are carefully addressed by the proposed control architecture. The nonlinear in-orbit dynamics of the flexible spacecraft is then used to evaluate the performance of the MPC controller in terms of pointing accuracy and robustness to uncertainties.

Year-round sea ice and snow characterization from combined passive and active microwave observations and radiative transfer modeling

Satellite microwave observations from 1.4 to 36 GHz already showed sensitivity to several geophysical parameters of sea ice such as Sea Ice Concentration (SIC), Sea Ice Thickness (SIT) or snow depth. The main goal of this article is to provide a realistic and comprehensive characterization of the sea ice and its snow cover that explains the microwave observations during a whole year using a radiative transfer model. For this purpose, we construct a unique dataset of passive microwave observations, to mimic the future Copernicus Imaging Microwave Radiometer (CIMR), along with the active microwave scatterometer data (ASCAT). CIMR database is used to classify sea ice microwave signatures in their spectral dimension with a machine learning technique while ASCAT data are used to help interpret the results of the classification. Classification results are then interpreted with a state-of-art sea ice and Snow Microwave Radiative Transfer model (SMRT) for all highlighted signatures and all seasons. Results make it possible to identify the specific behaviors from the observation co-variabilities for SIC, SIT, and snow structure. Our analysis underlined the role of the depth hoar over multi-year ice, for the interpretation of scattering signals in winter. Scattering signals that appear in late summer are explained by the presence of superimposed ice. This characterization will benefit from future advances in SMRT development, as well as the improved observations of future satellite missions.

An Enhanced Resolution Brightness Temperature Product for Future Conical Scanning Microwave Radiometers

An enhanced spatial resolution brightness temperature product is proposed for future conical scan microwave radiometers. The technique is developed for Copernicus Imaging Microwave Radiometer (CIMR) measurements that are simulated using the CIMR antenna pattern at the L-band and the measurement geometry proposed in the Phase A study led by Airbus. An inverse antenna pattern reconstruction method is proposed. Reconstructions are obtained using two CIMR configurations, namely, using measurements collected at L-band by the forward (FWD) scans only, and combining forward and backward (FWD+BWD) scans. Two spatial grids are adopted, namely, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3 \mathrm {\,\,km} \times 3 \mathrm {\,\,km}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$36 \mathrm {\,\,km} \times 36 \mathrm {\,\,km}$ </tex-math></inline-formula> . Simulation results, referred to synthetic and realistic reference brightness fields, demonstrate the soundness of the proposed scheme that provides brightness temperature fields reconstructed at a spatial resolution up to ~1.9 times finer than the measured field when using the FWD+BWD combination.

Preliminary Studies on CIMR Antenna Pattern Brightness Temperature Compensation

Spaceborne microwave radiometry provides an essential contribution to monitoring the Earth with varying spatial resolution both related to the reflector dimension and the frequency of operation. The ESA's Copernicus imaging microwave radiometer (CIMR) mission aims at collecting the geophysical observables at a spatial resolution ranging from 60 km in L band to 4 km in Ka band. This goal can be achieved by equipping CIMR with a large unfurlable mesh reflector antenna. A limitation of the antenna design is that the antenna pattern includes grating lobes that contaminate the scene measurement with contributions originated far from the nominal footprint. This effect introduces inaccuracies in brightness temperature measurements, particularly when facing radiometric discontinuities, e.g., near the coastlines and sea ice edges, which can be greater than the mission required maximum of 0.5 K. The aim of this article is to assess a technique which will be able to correct the effects of antenna pattern and obtain reliable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T_B</i> measurements. The analyzed simple technique is based on a regularized deconvolution of the antenna pattern to reconstruct the actual brightness temperatures. The technique was tested over a synthetic scenario that mimics both steep and smooth variations in spatial and thermal domains.

Ocean and Sea Ice Retrievals From an End‐To‐End Simulation of the Copernicus Imaging Microwave Radiometer (CIMR) 1.4–36.5 GHz Measurements

AbstractThe Copernicus Imaging Microwave Radiometer (CIMR) is currently being implemented by the European Space Agency (ESA) as a Copernicus Expansion Mission primarily designed to observe the Polar Regions in support of the Integrated European Policy for the Arctic. It is a conically scanning microwave radiometer with polarized channels centered at 1.414, 6.925, 10.65, 18.7, and 36.5 GHz and channel NEΔT between 0.2 and 0.7 K. A large rotating deployable mesh reflector will provide real‐aperture resolutions ranging from 60 (1.4 GHz) to 5 km (36.5 GHz). To evaluate CIMR retrieval performance, a simplified end‐to‐end simulation of the mission has been carried out. The simulation includes important processes and input parameters, such as test geophysical datasets, forward models, an instrument simulator, and retrieval algorithms to derive the key mission geophysical products. The forward modeling is tested by producing Brightness Temperatures (TBs) from 4 global scenes. A comparison with current observations of the open ocean and sea ice at similar frequencies confirmed the realism of the simulations. The produced top‐of‐atmosphere TBs are converted to Antenna brightness Temperatures (TAs), taking into account the instrument design, and are then inverted to retrieve Sea Ice Concentration (SIC), Sea Surface Temperature (SST), and Sea Surface Salinity (SSS). Evaluating the retrieval performance showed that the simulated CIMR instrument can provide SST, SSS, and SIC measurements with precisions and spatial resolutions conforming with the mission requirements. The evaluation also highlighted the challenges of observing the Arctic environment and put in perspective CIMR capabilities compared with current instruments.

Dual-Band Piecewise-Linear Profile Feed-Horns for the Copernicus Imaging Microwave Radiometer Mission

This letter presents the design strategy and the experimental results on the Ku/Ka dual-band feed-horn developed within the Phase A/B1 study of the Copernicus imaging microwave radiometer, which is one of the new six expansion missions of the European Copernicus flagship program on Earth observation operating in five frequency channels. The radiometer requirements translate into a dense focal plane composed by compact and lightweight dual-band feed horns. The optimum illumination was derived in terms of Bloch waves and the applicability of piecewise-linear profile feed-horns for its synthesis in the case of two highly separated Ku--/Ka--frequency bands (1:1.95) was investigated. The presented feed-horn design was validated up to a technological readiness level of 6. The reported performances at antenna level for the complete Ku-/Ka-band feed cluster demonstrate the feasibility of dual-band smooth-wall feed-horns focal-planes meeting all the Earth observation requirements in terms of footprints, beam- and wide-beam efficiencies, and cross-polarization.

Cloud Seeding Evidenced by Coherent Doppler Wind Lidar

Evaluation of the cloud seeding effect is a challenge due to lack of directly physical observational evidence. In this study, an approach for directly observing the cloud seeding effect is proposed using a 1548 nm coherent Doppler wind lidar (CDWL). Normalized skewness was employed to identify the components of the reflectivity spectrum. The spectrum detection capability of a CDWL was verified by a 24.23-GHz Micro Rain Radar (MRR) in Hefei, China (117°15′ E, 31°50′ N), and different types of lidar spectra were detected and separated, including aerosol, turbulence, cloud droplet, and precipitation. Spectrum analysis was applied as a field experiment performed in Inner Mongolia, China (112°39′ E, 42°21′ N ) to support the cloud seeding operation for the 70th anniversary of China’s national day. The CDWL can monitor the cloud motion and provide windshear and turbulence information ensuring operation safety. The cloud-precipitation process is detected by the CDWL, microwave radiometer (MWR) and Advanced Geosynchronous Radiation Imager (AGRI) in FY4A satellites. In particular, the spectrum width and skewness of seeded cloud show a two-layer structure, which reflects cloud component changes, and it is possibly related to cloud seeding effects. Multi-component spectra are separated into four clusters, which are well distinguished by spectrum width and vertical velocity. In general, our findings provide new evidence that the reflectivity spectrum of CDWL has potential for assessing cloud seeding effects.

Towards a swath-to-swath sea-ice drift product for the Copernicus Imaging Microwave Radiometer mission

Abstract. 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.

GPM‐DPR Observations on TGFs Producing Storms

AbstractUnique spaceborne measurements of the three‐dimensional structure of convective clouds producing terrestrial gamma ray flashes (TGFs) were performed using both active and passive microwave sensors on board the Global Precipitation Measurement (GPM)‐Core Observatory satellite, finding coherent features for nine TGF‐producing storms. The delineation of cloud structure using the radar reflectivity factor shows convective cells with significant vertical development and thick layers with high ice content. Compared to other cumulonimbus clouds in the tropics, the TGFs counterparts have higher reflectivity values above 3 and 8 km altitude showing in all cases a cumulonimbus tower and the TGFs locations are very close, or coincident, to these high Z columns, where reflectivity exceeds 50 dBz. Using the GPM Microwave Imager radiometer, most thunderstorms show a very strong depression of polarization corrected temperature (PCT) at channel 89 GHz, indicating a strong scattering signal by ice in the upper cloud layers. At channel 166 GHZ, the difference between vertical and horizontal brightness temperature signal always returns positive values, from 0.2 up to 13.7 K indicating a complex structure with randomly/vertically oriented ice particles. The PCT was used to characterize the analyzed storms in terms of hydrometeor types, confirming in 7/9 cases a high likelihood of hail/graupel presence. To perform analysis on the TGFs parent flashes, radio atmospherics data from the Earth Networks Total Lightning Network lightning network were used. Waveform data indicate that all cases are intra‐cloud events and TGFs typically take place during the peak of flash rate production. Finally, the analysis of the most intense event is shown.

Band Sampling Beam Formation Principle and Two-Element Band Sampling 1-D Imaging Microwave Radiometer

Band Sampling Beam Formation Principle and Two-Element Band Sampling 1-D Imaging Microwave Radiometer