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El presente proyecto muestra el desarrollo de un sistema IoT de medición, recolección y visualización de datos para el monitoreo remoto de ciclo-rutas. Este sistema está compuesto por un nodo de módulo de radar de alta frecuencia con antenas MIMO el cual se encargará de la recolección de datos para luego enviarlos a un servidor en la nube. Este servidor almacena las velocidades obtenidas en las ciclo-rutas y la relación señal a ruido respectivos (SNR) al nodo de medición, además proporciona la fecha y hora en la cual se tomaron los datos y permite el acceso a estos. La información recolectada puede ser accedida mediante un navegador de internet ya sea desde computador o teléfono móvil que permite la visualización y descarga de los datos obtenidos mediante una página web que sirve como herramienta para realizar un análisis del tránsito en la zona donde se encuentra el nodo. The present project shows the development of an IoT system of measurement, collection and visualization of data for the remote monitoring of cycle-routes. This system is composed of a high frequency radar module node with MIMO antennas which will be responsible for collecting data and then sending them to a cloud server. This server stores the speeds obtained in the cycle-paths and the respective signalto- noise ratio (SNR) to the measurement node, also provides the date and time in which the data were taken and allows access to these. The information collected can be accessed through an internet browser either from a computer or mobile phone that allows the visualization and download of the data obtained through a web page that serves as a tool to perform a traffic analysis in the area where the node is located Proyecto de grado (Ingeniero Electrónico y de Telecomunicaciones)-- Universidad Autónoma de Occidente, 2022 Pregrado Ingeniero(a) en Electrónica y Telecomunicaciones

An ability to accurately detect convective regions is essential for initializing models for short-term precipitation forecasts. Radar data are commonly used to detect convection, but radars that provide high-temporal-resolution data are mostly available over land, and the quality of the data tends to degrade over mountainous regions. On the other hand, geostationary satellite data are available nearly anywhere and in near-real time. Current operational geostationary satellites, the Geostationary Operational Environmental Satellite-16 (GOES-16) and Satellite-17, provide high-spatial- and high-temporal-resolution data but only of cloud top properties; 1 min data, however, allow us to observe convection from visible and infrared data even without vertical information of the convective system. Existing detection algorithms using visible and infrared data look for static features of convective clouds such as overshooting top or lumpy cloud top surface or cloud growth that occurs over periods of 30 min to an hour. This study represents a proof of concept that artificial intelligence (AI) is able, when given high-spatial- and high-temporal-resolution data from GOES-16, to learn physical properties of convective clouds and automate the detection process. A neural network model with convolutional layers is proposed to identify convection from the high-temporal resolution GOES-16 data. The model takes five temporal images from channel 2 (0.65 µm) and 14 (11.2 µm) as inputs and produces a map of convective regions. In order to provide products comparable to the radar products, it is trained against Multi-Radar Multi-Sensor (MRMS), which is a radar-based product that uses a rather sophisticated method to classify precipitation types. Two channels from GOES-16, each related to cloud optical depth (channel 2) and cloud top height (channel 14), are expected to best represent features of convective clouds: high reflectance, lumpy cloud top surface, and low cloud top temperature. The model has correctly learned those features of convective clouds and resulted in a reasonably low false alarm ratio (FAR) and high probability of detection (POD). However, FAR and POD can vary depending on the threshold, and a proper threshold needs to be chosen based on the purpose.

A method is proposed, which uses the digital image processing technique to identify cloud boundaries from scanning Ka-band (~35.29 GHz) radar imagery dataset. In this method, a cloud is considered as an uninterrupted region of radar echoes with radar reflectivity higher than -34 dBZ and area greater than 3 km2. The proposed algorithm involves 1) conversion of radar RGB image to grayscale by removing white background and noise, 2) identification of cloud boundaries by canny edge detection, and 3) estimation of cloud cross-section area (CCSA) and cloud top height (CTH) based on the pixel width. This method is effectively applied to Ka-band radar images collected over Mandhardev, a high altitude scanning station in the Western Ghats (WGs), India to derive CTH and CCSA. CTH distribution shows three peaks, one at about 2 km with others at about 7-8 km, and 12 km. The cloud occurrence shows an apparent diurnal variation with a maximum in the afternoon hours while a semidiurnal variation is observed in CCSA. The proposed method shows consistent statistics with Global Precipitation Measurement Ka-band radar observations and thus suitable to build robust cloud climatology over the WGs. Such cloud statistics are essential to validate the representation of clouds in weather and climate models.

[EN] Sentinel-1 interferometric time-series allow for the accurate retrieval of the target’s temporal decorrelation and, therefore, the inversion of land cover information and its temporal monitoring. This paper describes the development of an observation scenario for monitoring monthly deforestation over the Amazon rainforest, which relies on the use of radar for overcoming the physical limitations of optical sensors caused by the presence of cloud coverage. Specifically, we implement a classification scheme that exploits multi-temporal SAR features, such as backscatter, spatial textures, and interferometric parameters, to map forested areas. Distinct forest maps are generated for consecutive months and further processed to detect deforestation phenomena and map clear-cuts evolution. The obtained results are validated by selecting cloud-free Sentinel-2 multispectral data on the selected area and acquired during the same observation time., [ES] Las series temporales interferométricas Sentinel-1 permiten la estimación precisa de la decorrelación temporal de la muestra y, por tanto, la recuperación de información acerca de la cobertura terrestre y su seguimiento temporal. Este artículo describe el desarrollo de un escenario de observación para el seguimiento de la deforestación mensual sobre la selva amazónica, basándose en el uso de datos radar para superar las limitaciones físicas propias de los sensores ópticos debido a la presencia de cobertura nubosa. En particular, se ha implementado un esquema de clasificación que explota las características multitemporales SAR, como la retrodispersión, texturas espaciales y parámetros interferométricos, para identificar áreas de bosque. Distintos mapas de bosque para meses consecutivos fueron generados y procesados para detectar fenómenos de deforestación y su evolución. Los resultados obtenidos fueron validados a partir de imágenes Sentinel-2 libres de nubes adquiridas sobre la misma zona y hora de observación., The performed work has been partially financed through the HI-FIVE project, granted by the ESA Living Planet Fellowship 2018. The authors gratefully acknowledge the Sentinel-1 mission management, in the person of Luca Martino and Pierre Potin, for providing an additional acquisition scheduling over the state of Rondonia.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, 資料番号: PA2110044000

The intrinsic small spatial scales and low-reflectivity structure of oceanic warm precipitating clouds suggest that millimeter spaceborne radars are best suited to providing quantitative estimates of cloud and rain liquid water paths (LWPs). This assertion is based on their smaller horizontal footprint; high sensitivities; and a wide dynamic range of path-integrated attenuations associated with warm-rain cells across the millimeter wavelength spectrum, with diverse spectral responses to rain and cloud partitioning. State-of-the-art single-frequency radar profiling algorithms of warm rain seem to be inadequate because of their dependence on uncertain assumptions about the rain–cloud partitioning and because of the rain microphysics. Here, high-resolution cloud-resolving model simulations for the Rain in Cumulus over the Ocean field study and a spaceborne forward radar simulator are exploited to assess the potential of existing and future spaceborne radar systems for quantitative warm-rain microphysical retrievals. Specifically, the detrimental effects of nonuniform beam filling on estimates of path-integrated attenuation (PIA), the added value of brightness temperature (TB) derived adopting radiometric radar modes, and the performances of multifrequency PIA and/or TB combinations when retrieving liquid water paths partitioned into cloud (c-LWPs) and rain (r-LWPs) are assessed. Results show that (1) Ka- and W-band TB values add useful constraints and are effective at lower LWPs than the same-frequency PIAs; (2) matched-beam combined TB values and PIAs from single-frequency or multifrequency radars can significantly narrow down uncertainties in retrieved cloud and rain liquid water paths; and (3) the configuration including PIAs, TB values and near-surface reflectivities for the Ka-band–W-band pairs in our synthetic retrieval can achieve an RMSE of better than 30 % for c-LWPs and r-LWPs exceeding 100 g m−2.

Acoustic interference of atmosphere has been an attractive research area because of its potential effect on environment, water resources, ecology, agriculture, and other areas. However, it is also a controversial topic because of the difficulty of quantitative assessment and high operating costs. In this study, a novel acoustic interference technology is proposed that uses strong low-frequency sound waves. There is no chemical pollution or dependence on airborne vehicles, and it can be remotely controlled at low cost. A complete equipment system for acoustic atmospheric interference technology is established, based on which a series of experimental studies on cloud and precipitation response under acoustic action are performed, mainly including the radar echo intensity, cloud microphysical characteristics and the spatial distribution of ground rainfall intensity. The trigger and periodic effect of the acoustic waves on the cloud are proposed to be the key responses of acoustic atmospheric interference. This study is important to further research on atmosphere interference technology based on low frequency strong sound waves.

Observational knowledge about polar cloud processes requires information about the hydrometeor phase structure of the clouds, preferentially at high resolutions. Therefore, there are various attempts to classify ground-based radar observations using different techniques. In this letter, we examine the potential of detecting air-volumes containing liquid water in polar clouds using the Ka-band zenith-pointing radar (KAZR). We utilized the measurements gathered at Barrow, Alaska, in 2015, to produce comprehensive statistics about the Doppler-radar moments and the Doppler spectra. We find that the cloud-top liquid-bearing cloud layers (LBCLs) can potentially be reliably detected at high percentages with the KAZR when the signal is above the radar noise floor. However, embedded LBCLs are significantly more challenging to detect and could potentially be reliably separated in bulk processing only in exceptional cases, which account for not more than a few tens of percent of these cloud layer occurrences.

Cloud-top height (CTH) and cloud-top temperature (CTT) retrieved from the Himawari-8 observations are evaluated using the active shipborne radar–lidar observations derived from the 31-day Clouds, Aerosols, Precipitation Radiation and Atmospheric Composition over the Southern Ocean (CAPRICORN) experiment in 2016 and 1-yr observations from the spaceborne Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud product over a large sector of the Southern Ocean. The results show that the Himawari-8 CTH (CTT) retrievals agree reasonably well with both the shipborne estimates, with a correlation coefficient of 0.837 (0.820), a mean bias error of 0.226 km (−2.526°C), and an RMSE of 1.684 km (10.069°C). In the comparison with CALIOP, the corresponding quantities are found to be 0.786 (0.480), −0.570 km (1.343°C), and 2.297 km (25.176°C). The Himawari-8 CTH (CTT) generally falls between the physical CTHs observed by CALIOP and the shipborne radar–lidar estimates. However, major systematic biases are also identified. These errors include (i) a low (warm) bias in CTH (CTT) for warm liquid cloud type, (ii) a cold bias in CTT for supercooled liquid water cloud type, (iii) a lack of CTH at ~3 km that does not have a corresponding gap in CTT, (iv) a tendency of misclassifying some low-/mid-top clouds as cirrus and overlap cloud types, and (v) a saturation of CTH (CTT) around 10 km (−40°C), particularly for cirrus and overlap cloud types. Various challenges that underpin these biases are also explored, including the potential of parallax bias, low-level inversion, and cloud heterogeneity.

With the CLC+ product suite as part of the Copernicus Land Monitoring Service (CLMS), the European Environment Agency (EEA) has initiated a true paradigm change in European land cover/land use monitoring, building on the 30-years-long rich legacy of the European CORINE Land Cover (CLC) flagship product. The CLC+ Backbone, as first component of the upcoming CLC+ era, will feature an object-oriented wall-to-wall high-resolution inventory of European land cover and its characteristics in unprecedented quality and detail, for the reference year 2018. It will comprise a pan-European combined “hardbone” and “soft-bone” segmentation of vector-based stable landscape objects and a raster-based classification of 11 EAGLE compliant land cover classes at 10m spatial resolution. To this end, a combination of image segmentation and Deep Learning approaches are implemented within a cloud-based infrastructure for a fully integrated analysis of optical/radar time series of Sentinel-1/-2 satellite imagery and auxiliary data. Vector and raster datasets will be fused into a fully attributed, 18 land cover class, vector product with 0.5 ha minimum mapping unit (MMU), additionally incorporating a multitude of further information layers derived from satellite data and various other Copernicus products.

Timely detection of flooding is paramount for saving lives as well as evaluating levels of damage. Floods generally occur under specific weather conditions, such as excessive precipitation, which makes the presence of clouds very likely. For this reason, radar-based sensors are most suitable for near-real-time flood mapping. The public dataset Sen1Floods11 recently released by the Cloud to Street is one example of ongoing beneficial initiatives to employ deep learning for flood detection with synthetic aperture radar. The present study used this dataset to improve flood detection using well-known segmentation architectures, such as SegNet and UNet, as networks. In addition, this study provided a deeper understanding of which set of polarized band combination is more suitable for distinguishing permanent water, as well as flooded areas from the SAR image. The overall performance of the models with various kinds of labels and a combination of bands to detect all surface water areas were also assessed. Finally, the trained models were tested on a completely different location at Kerala, India, during the 2018 flood for verifying their performance in the real-world situation of a flood event outside of the given test set in the dataset. The results prove that trained models can be used as off-the-shelf models to achieve an intersection over union (IoU) as high as 0.88 in comparison with optical images. The omission and commission error were less than 6%. However, the most important result is that the processing time for the whole satellite image was less than 1 min. This will help significantly for providing analysis and near-real-time flood mapping services to first responder organizations during flooding disasters.

Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-basedremote sensing instruments still pose observational challenges yet improvements are crucial since the existence of multi-layerliquid layers in mixed-phase cloud situations influences cloud radiative effects, cloud life time, and precipitation formationprocesses. Hydrometeor target classifications such as Cloudnet that require a lidar signal for the classification of liquid arelimited to the maximum height of lidar signal penetration and thus often lead to underestimations of liquid-containing cloudlayers. Here we evaluate the Cloudnet liquid detection against the approach of Luke et al. (2010) which extracts morphologicalfeatures in cloud-penetrating cloud radar Doppler spectra measurements in a artificial neural network (ANN) approach toclassify liquid beyond full lidar signal attenuation based on the simulation of the two lidar parameters particle backscattercoefficient and particle depolarization ratio. We show that the ANN of Luke et al. (2010) which was trained in Arctic conditionscan successfully be applied to observations in the mid-latitudes obtained during the seven-week long ACCEPT field experimentin Cabauw, the Netherlands, 2014. In a sensitivity study covering the whole duration of the ACCEPT campaign, different liquid-detectionthresholds for ANN-predicted lidar variables are applied and evaluated against the Cloudnet target classification.Independent validation of the liquid mask from the standard Cloudnet target classification against the ANN-based techniqueis realized by comparisons to observations of microwave radiometer liquid water path, ceilometer liquid-layer base altitude,and radiosonde relative humidity. Four conclusions were drawn from the investigation: First, it was found that the thresholdselection criteria of liquid-related lidar backscatter and depolarization alone control the liquid detection considerably. Second,nevertheless, all threshold values used in the ANN-framework were found to outperform the Cloudnet target classification fordeep or multi-layer cloud situations where the lidar signal is fully attenuated within low liquid layers and the cloud reflectivityin higher cloud layers was sufficiently high to be detectable by the cloud radar. Third, in convective situations for whichlidar data is available and for which the imprint of cloud microphysics on the radar Doppler spectrum is decreased, Cloudnetoutperforms the ANN retrieval. Fourth, in high-level clouds both approaches (Cloudnet and the ANN technique), are limited.

The article presents recent capabilities of active and passive earth observation sensors along with related processing image chains, for monitoring UNESCO World Heritage properties. Exceptional heritage sites and landscapes are found in dynamic environments, whereas both anthropogenic and natural changes are observed. The use of radar and optical satellite imageries can be used as a systematic observation tool for stakeholders, to map drastic or slowly driven landscape changes towards the better protection and management of these sites and their surrounding areas. The study presents the results from the analysis of the European Copernicus Sentinel-1 and Sentinel-2 satellite images over two broader areas in the Eastern Mediterranean basin that hold important UNESCO World Heritage properties. Initially, a recent strong earthquake of a 6.7 magnitude scale in the Aegean Sea is studied using radar Sentinel-1 images. These radar images were processed through the Hybrid Pluggable Processing Pipeline (HyP3) cloud platform for analyzing both significant changes of the VV (vertical transmit, vertical receive) and VH (vertical transmit, horizontal receive) backscattering signal as well as through an Interferometric Synthetic Aperture Radar (InSAR) analysis. Then, long-term changes in Cyprus during the last two decades are monitored by a Sentinel-2 image compared to the European Corine Land Use Land Cover data of 2000. These changes are mapped after a supervised classification process using the random forest (RF) classifier. The overall results demonstrate that the recent developments of the space sector in all its segments (resolution of the sensors, the capacity to storage in the cloud, processing advancements and open-access datasets and tools) can be beneficial for monitoring UNESCO World Heritage properties.

Prediction of local and steep weather change such as heavy rain and lightning strike are urgently needed for disaster prevention. In Japan, Automated Meteorological Data Acquisition System (AMeDAS) has been operational throughout the country, with a mesh size of 20 km. Lightning Location Positioning (LLP) system, Lightning Position and Tracking System (LPATS), and SAFIR (Surveillance et Alerte Foudre par Interferometrie Radioelectrique) have been also used for lightning detection. (MacGorman & Taylor, 1989; Thery, 2001; Hayashi, 2006; Matsudo et al, 2007; Hauf et al, 2007) All of these weather observation systems, however, are comprehensive and necessary to analyze the multipoint data for the weather forecast in a certain area. Heavy rain and lightning strike locally occur during a short time period. They are often broken out at low-altitude and mainly caused by ice-crystals in cloud. Such local and steep weather change should be observed and be distinguished at a point observation. Radar (Radio Detection and Ranging) is a solution to catch weather change, but it can only detect rain droplets and cannot detect cloud particles which are precursors of heavy rain. To predict the heavy rain and lightning strike, low-altitude atmosphere and cloud should be observed. Furthermore, the flow of ice-crystals should be monitored. Lidar (LIght Detection And Ranging) can observe the atmosphere and cloud. It is standalone system and can distinguish ice-crystals from water droplets. On the other hand, Lidar technique is unsuited for observation in the near range because the laser beam will be dangerous for human eye. To develop the disaster prediction system for local weather change, we apply the lidar technique. The system was designed with emphasis on the following characteristics: Stand-alone Compactness Eye-safety To realize such a system, in-line optics, or common optics for transmitter optics and receiver optics, was adopted. It is usual technique for microscope optics, while the pulsed energy on lidar is too high that no one could accomplish its development since the first proposal. (Measures, 1984) Micro Pulse Lidar (MPL) broke though the problem and built up the new generation in lidar field. (Spinhirne, 1993, 1994 & 2001; Grund & Sandberg, 1996; Lee et al, Source: Advances in Solid-State Lasers: Development and Applications, Book edited by: Mikhail Grishin, ISBN 978-953-7619-80-0, pp. 630, February 2010, INTECH, Croatia, downloaded from SCIYO.COM

We present an advanced differential synthetic aperture radar (SAR) interferometry (DInSAR) processing chain, based on the Parallel Small BAseline Subset (P-SBAS) technique, for the efficient generation of deformation time series from Sentinel-1 (S-1) interferometric wide (IW) swath SAR data sets. We first discuss an effective solution for the generation of high-quality interferograms, which properly accounts for the peculiarities of the terrain observation with progressive scans (TOPS) acquisition mode used to collect S-1 IW SAR data. These data characteristics are also properly accounted within the developed processing chain, taking full advantage from the burst partitioning. Indeed, such data structure represents a key element in the proposed P-SBAS implementation of the S-1 IW processing chain, whose migration into a cloud computing (CC) environment is also envisaged. An extensive experimental analysis, which allows us to assess the quality of the obtained interferometric products, is presented. To do this, we apply the developed S-1 IW P-SBAS processing chain to the overall archive acquired from descending orbits during the March 2015-April 2017 time span over the whole Italian territory, consisting in 2740 S-1 slices. In particular, the quality of the final results is assessed through a large-scale comparison with the GPS measurements relevant to nearly 500 stations. The mean standard deviation value of the differences between the DInSAR and the GPS time series (projected in the radar line of sight) is less than 0.5 cm, thus confirming the effectiveness of the implemented solution. Finally, a discussion about the performance achieved by migrating the developed processing chain within the Amazon Web Services CC environment is addressed, highlighting that a two-year data set relevant to a standard S-1 IW slice can be reliably processed in about 30 h.The presented results demonstrate the capability of the implemented P-SBAS approach to efficiently and effectively process large S-1 IW data sets relevant to extended portions of the earth surface, paving the way to the systematic generation of advanced DInSAR products to monitor ground displacements at a very wide spatial scale.

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurements (ARM) facility has been at the forefront of millimeter-wavelength radar development and operations since the late 1990s. The operational performance of the ARM cloud radar network is very high; however, the calibration of the historical record is not well established. Here, a well-characterized spaceborne 94 GHz cloud profiling radar (CloudSat) is used to characterize the calibration of the ARM cloud radars. The calibration extends from 2007 to 2017 and includes both fixed and mobile deployments. Collectively, over 43 years of ARM profiling cloud radar observations are compared to CloudSat and the calibration offsets are reported as a function of time using a sliding window of 6 months. The study also provides the calibration offsets for each operating mode of the ARM cloud radars. Overall, significant calibration offsets are found that exceed the uncertainty of the technique (1–2 dB). The findings of this study are critical to past, ongoing, and planned studies of cloud and precipitation and should assist the DOE ARM to build a legacy decadal ground-based cloud radar dataset for global climate model validation.

A method is described to classify cloud mixtures of cloud top types, termed cloud scenes, using cloud type classification derived from the CloudSat radar (2B-CLDCLASS). The scale dependence of the cloud scenes is quantified. For spatial scales at 45 km (15 km), only 18 (10) out of 256 possible cloud scenes account for 90 % of all observations and contain one, two, or three cloud types. The number of possible cloud scenes is shown to depend on spatial scale with a maximum number of 210 out of 256 possible scenes at a scale of 105 km and fewer cloud scenes at smaller and larger scales. The cloud scenes are used to assess the characteristics of spatially collocated Atmospheric Infrared Sounder (AIRS) thermodynamic-phase and ice cloud property retrievals within scenes of varying cloud type complexity. The likelihood of ice and liquid-phase detection strongly depends on the CloudSat-identified cloud scene type collocated with the AIRS footprint. Cloud scenes primarily consisting of cirrus, nimbostratus, altostratus, and deep convection are dominated by ice-phase detection, while stratocumulus, cumulus, and altocumulus are dominated by liquid- and undetermined-phase detection. Ice cloud particle size and optical thickness are largest for cloud scenes containing deep convection and cumulus and are smallest for cirrus. Cloud scenes with multiple cloud types have small reductions in information content and slightly higher residuals of observed and modeled radiance compared to cloud scenes with single cloud types. These results will help advance the development of temperature, specific humidity, and cloud property retrievals from hyperspectral infrared sounders that include cloud microphysics in forward radiative transfer models.

A cloud clustering and classification algorithm is developed for a ground-based Ka-band radar system in the vertically pointing mode. Cloud profiles are grouped based on the combination of a time-height clustering method and the k-means clustering method. The cloud classification algorithm, developed using a fuzzy logic method, uses nine physical parameters to classify clouds into nine types: cirrostratus, cirrocumulus, altocumulus, altostratus, stratus, stratocumulus, nimbostratus, cumulus or cumulonimbus. The performance of the clustering and classification algorithm is presented by comparison with all-sky images taken from January to June 2014. Overall, 92% of the cloud profiles are clustered successfully and the agreement in classification between the radar system and the all-sky imager is 87%. The distribution of cloud types in Beijing from January 2014 to December 2017 is studied based on the clustering and classification algorithm. The statistics show that cirrostratus clouds have the highest occurrence frequency (24%) among the nine cloud types. High-level clouds have the maximum occurrence frequency and low-level clouds the minimum occurrence frequency.

The Japan Meteorological Agency (JMA) successfully launched the Himawari-8 (H-8) new-generation geostationary meteorological satellite with the Advanced Himawari Imager (AHI) sensor on October 7, 2014. The H-8/AHI level-2 (L2) operational cloud property products were released by the Japan Aerospace Exploration Agency during September 2016. The Voronoi light scattering model, which is a fractal ice particle habit, was utilized to develop the retrieval algorithm called “Comprehensive Analysis Program for Cloud Optical Measurement” (CAPCOM-INV)-ice for the AHI ice cloud product. In this paper, we describe the CAPCOM-INV-ice algorithm for ice cloud products from AHI data. To investigate its retrieval performance, retrieval results were compared with 2000 samples of the ice cloud optical thickness and effective particle radius values. Furthermore, AHI ice cloud products are evaluated by comparing them with the MODIS collection-6 (C6) products. As an experiment, cloud property retrievals from AHI measurements, with an observation interval time of 2.5 min and ground-based rainfall observation radar data (the latter of which is supplied by the JMA, with a 1-km grid mesh), are used to investigate the generation processes of deep convective (DC) cloud in the vicinity of the Kyushu island, Japan. It revealed that AHI measurements have the capability of monitoring the growth processes, including variation of the cloud properties and the precipitation in the DC cloud.

Cloud radars are unique instruments for observing cloud processes, but uncertainties in radar calibration have frequently limited data quality. Thus far, no single robust method exists for assessing the calibration of past cloud radar data sets. Here, we investigate whether observations of microphysical processes in liquid clouds such as the transition of cloud droplets to drizzle drops can be used to calibrate cloud radars. Specifically, we study the relationships between the radar reflectivity factor and three variables not affected by absolute radar calibration: the skewness of the radar Doppler spectrum (γ), the radar mean Doppler velocity (W), and the liquid water path (LWP). For each relation, we evaluate the potential for radar calibration. For γ and W, we use box model simulations to determine typical radar reflectivity values for reference points. We apply the new methods to observations at the Atmospheric Radiation Measurement (ARM) sites North Slope of Alaska (NSA) and Oliktok Point (OLI) in 2016 using two 35 GHz Ka-band ARM Zenith Radars (KAZR). For periods with a sufficient number of liquid cloud observations, we find that liquid cloud processes are robust enough for cloud radar calibration, with the LWP-based method performing best. We estimate that, in 2016, the radar reflectivity at NSA was about 1±1 dB too low but stable. For OLI, we identify serious problems with maintaining an accurate calibration including a sudden decrease of 5 to 7 dB in June 2016.

Cloud phase is one of the important parameters of weather and climate research and core elements of atmospheric cloud parametric inversion. The accuracy of its recognition directly influences the inversion precision of cloud optical thickness, spherical albedo, effective particle radius, ice/liquid water content and other parameters. In this paper, we combine with cloud phase products of spaceborne multi-angle polarimetric radiometer, polarized laser radar, and millimeter wave radar. Next, we put forward a Dynamic Spatial Optimal Fusion (DSOF) algorithm. In this algorithm we construct the spatial optimal fusion rules of Cloud Top Phase (CTP) for multi-source data. Finally, we realize the cloud phase spatial fusion using these rules. We used the typhoon “Lupit” as the research object and verified this method. The standard deviations are 4.67%, 7.18%, 40.14% and 36.51%, respectively, compared the results of fused CTP to that of CALIPSO, CloudSat, POLDER3 and MODIS. The fusion results are most close to the CTP of CALIPSO. The results show that this method can effectively achieve multi-source cloud phase inversion, and provide new technology for the development and data synergy of spaceborne multi-sensor satellite.

The radiances scattered or emitted by clouds demonstrate diverse features at different wavelengths due to different cloud physical structures. This paper presents a method (the smallest-radiance-distance method, SRaDM) of revealing the physical structures of clouds. The method is based on multi-spectral radiances measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua. The principle and methodology of SRaDM is deduced and provided in this paper. Correlation analysis based on data from MODIS and Cloud Profiling Radar (onboard CloudSat), collected from January 2007 to December 2010 over an ocean area (15°N–45°N, 145°E–165°E), led to selection of radiances at 13 wavebands of MODIS that demonstrated high sensitivity to cloud physical structures; radiances at the selected wavebands were subjected to SRaDM. The Standardized Euclidean distance is introduced to quantify the degree of changes in multi-spectral radiances (termed Drd) and in physical structures (termed Dst) between cloud profiles. Statistics based on numerous cloud profiles show that Drd decreases monotonically with a decrease in Dst, which implies that small Drd always accompanies small Dst. According to the law of Drd and Dst, the new method, SRaDM, for revealing physical structures of clouds from the collocation of cloud profiles of similar multi-spectral radiances, is presented. Then, two successful experiments are presented in which cloud physical structures are captured using multi-spectral radiances. SRaDM provides a way to obtain knowledge of the physical structures of clouds over relatively larger areas, and is a new approach to obtaining 3D cloud fields.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2018-12-14, 資料番号: PA1910015000

The increasing knowledge in the capabilities of satellite imagery to hazard applications is especially useful in emergency situations where timing and ability to cover large areas are of the essence. For optical imagery, cloud coverage can corrupt an image rendering it unusable for intended emergency analyses. This study proposes the use of Synthetic Aperture Radar (SAR) imagery for burn severity analysis for western United States sites, as an alternative to its optical based counterpart, differenced normalized burn ratio (dNBR). Unlike optical sensors, the radar sensor is an active sensor that is able to penetrate clouds and smoke, an attribute that is crucial in emergency situations where immediate burn severity data are needed to assess the vulnerability of fire affected areas to post-fire hazards. Using C5 decision tree algorithm we developed a SAR-based metric that attempts to classify burn severities of fire affected locations in the western USA. We then compared the performance of this developed metric to that obtained by the existing dNBR metric, to determine if there is any merit to its adoption as an alternative for the western USA landscape. The results showed the SAR approach to produce higher validation metrics in comparison to the dNBR. It had an overall accuracy and kappa of 60% and 0.35, respectively, in comparison to the 35% and 0.1 of the dNBR approach. This shows an improved ability to quickly obtain burn severity data and make better informed decisions in emergency situations.

Cloud top height (CTH) is an important parameter monitored in atmospheric observations, which has a significant impact on weather prediction, climate models, and flight services. CTH is typically obtained via three ways, namely, satellite, radiosonde, and ground-based radar, with their corresponding strengths and weaknesses. Traditionally, many studies have focused on independent comparison and analysis of CTHs retrieved from different observations. The researches on how to improve the reliability of the CTH by integrating multiple cloud measurements are rare in the literature despite the significance of this strategy to practical meteorological forecast and disaster prevention improvement. An integration technique of different CTHs retrieved from Fengyun 2 (FY-2) meteorological satellite, radiosonde, and ground-based millimeter wavelength cloud radar observations by using Bayesian decision theory is proposed in this study. A dataset is collected in Beijing, China for 12 months from June 2015 to May 2016 to validate the integration effect. Experimental results show that the integration observations improve the accuracy of single observations. Integration observations are more closely correlated with “true” CTH observations than the single observations. These all show the effectiveness of the proposed multiple source data integration strategy.

Detailed observation of small scale perturbation in the atmospheric boundary layer during the first generated cumulus cloud are conducted. Our target is to study this small scale perturbation, especially related to the thermal activity at the first generated cumulus cloud. The observation is performed during the daytime on August 17, 2018, and September 03, 2018. Location is focused in the urban area of Kobe, Japan. High-resolution instruments such as Boundary Layer Radar, Doppler Lidar, and Time Lapse camera are used in this observation. Boundary Layer Radar, and Doppler Lidar are used for clear air observation. Meanwhile Time Lapse Camera are used for cloud existence observation. The atmospheric boundary layer structure is analyzed based on vertical velocity profile, variance, skewness, and estimated atmospheric boundary layer height. Wavelet are used to observe more of the period of the thermal activity. Furthermore, time correlation between vertical velocity time series from height 0.3 to 2 km and image pixel of generated cloud time series are also discussed in this study.

The development of ground-based cloud radars offers a new capability to continuously monitor fog structure. Retrievals of fog microphysics are key for future process studies, data assimilation, or model evaluation and can be performed using a variational method. Both the one-dimensional variational retrieval method (1D-Var) or direct 3D/4D-Var data assimilation techniques rely on the combination of cloud radar measurements and a background profile weighted by their corresponding uncertainties to obtain the optimal solution for the atmospheric state. In order to prepare for the use of ground-based cloud radar measurements for future applications based on variational approaches, the different sources of uncertainty due to instrumental, background, and forward operator errors need to be properly treated and accounted for. This paper aims at preparing 1D-Var retrievals by analysing the errors associated with a background profile and a forward operator during fog conditions. For this, the background was provided by a high-resolution numerical weather prediction model and the forward operator by a radar simulator. Firstly, an instrumental dataset was taken from the SIRTA observatory near Paris, France, for winter 2018–2019 during which 31 fog events were observed. Statistics were calculated comparing cloud radar observations to those simulated. It was found that the accuracy of simulations could be drastically improved by correcting for significant spatio-temporal background errors. This was achieved by implementing a most resembling profile method in which an optimal model background profile is selected from a domain and time window around the observation location and time. After selecting the background profiles with the best agreement with the observations, the standard deviation of innovations (observations–simulations) was found to decrease significantly. Moreover, innovation statistics were found to satisfy the conditions needed for future 1D-Var retrievals (un-biased and normally distributed).

In volcanology, remote sensing provides a global observation framework that is becoming increasingly valuable at multiple spatio-temporal scales. Satellite observations have improved monitoring for even the most remote volcanoes, provided a large-scale context to extrapolate field-based observations, and is now routinely used to characterize and monitor volcanic systems and their hazards. However, remote sensing remains underutilized to characterize volcanic impacts that result from the interaction of volcanic processes and societal assets. This chapter first provides a review of applications of remote sensing for impact assessment in volcanology, both in pre- and post-event contexts. Two case studies are used to illustrate the application of recent remote-sensing methods in impact assessments within the field of volcanology. First, the impact to the built environment caused by the 2014 eruption of Kelud volcano (Indonesia) is assessed using automated damage proxy maps produced from interferometric synthetic-aperture radar data. This automated approach is compared, validated and discussed against an impact assessment performed from the manual inspection of high-resolution satellite images. Second, Google Earth Engine is used to illustrate how simplified access to remote-sensing data offered by cloud-based platforms can provide a global and systematic framework to revisit the impacts of eruptions We illustrate the application of Google Earth Engine to various tasks, such as change detection and time series analyses, using the 2010 eruption of Merapi volcano (Indonesia). Very few volcanic impact assessments fully utilize remote sensing, but as such data become more easily available, stored and processed, there is the exciting potential for new methods and tools that can better capture, characterize and ultimately forecast volcanic impacts.

The establishment of a digital signal processing system for meteorological radar can realize in-depth research on the physical structure and evolution characteristics of changes of weather systems, and further accelerate the development of meteorology on the basis of deepening understanding. Meteorological radar analyzes and sorts out cloud parameters, cloud profile and precipitation profile through sounders, and effectively integrates them into a data set to predict the precipitation structure to better grasp the relationship between cloud and precipitation. This paper studies the composition and operation mode of the digital signal processing system for meteorological radar to provide reference for relevant practitioners.

Severe thunderstorm is the convective weather feature, originated from cumulonimbus cloud. It has devastating effect in the society. Accurate prediction with enough lead time is needed to make the people alert from such destructive event. In this study, analysis of grayscale cloud imageries and histograms has been used to nowcast severe thunderstorm.

In order to obtain the position of thunderstorm cloud in real time and make it possible to track the thunderstorm cloud motion, a method is proposed for tracking the moving path of thunderstorm cloud, with the aid of the three-dimensional atmospheric electric field apparatus (AEFA). According to the method of images, we establish a spatial model for tracking the moving path. Based on the model, we define the dynamic parameters of thunderstorm cloud position. Subsequently, to realize the moving path tracking of thunderstorm cloud, its coordinates are associated with the time points. Besides, we use the relationship between electric field component measurement error, horizontal angle, elevation angle, and the tracking accuracy to analyze the tracking performance. Finally, a fusion system combining an electric field measurement unit, electric field calibration unit, and permittivity measurement unit is designed to meet the actual needs. The results show that the method can accurately track the thunderstorm cloud moving path and has a better effect. In addition, the method can also be combined with a radar map, thus better predicting the development of the thunderstorm cloud.

Although the potential of mm-wave radar for cloud observation was demonstrated already in the 1950s, it took another 30 years before the technological development of microwave components and of digital signal processing allowed the realization of polarimetric Doppler radars, which became a crucial tool for cloud observations in various applications from climate modeling to weather services. This chapter provides an outline of cloud detection with radar, the historical development, contemporary cloud radar technology, and approaches for retrieving quantitative cloud parameters. The following refers to ground-based systems. The role of cloud radar in airborne applications and in upcoming space missions is addressed in Chap. 39 and Part D of this book.

Remote sensing for weather applications is gaining importance for accurate measurement of atmospheric parameters, especially in the higher microwave and millimeter-wave frequency ranges. In this work, a feasibility study on the design of a W-band FMCW radar has been carried out for cloud profiling application. Carrier frequency of 95 GHz has been chosen for the radar operation. Few practical examples of cloud radar have been investigated through literature survey and a design of FMCW-based cloud radar has been proposed. System-level simulation has been carried out in SystemVue and results have been produced.

An ability to accurately detect convective regions is essential for initializing models for short term precipitation forecasts. Radar data are commonly used to detect convection, but radars that provide high temporal resolution data are mostly available over land and the quality of the data tends to degrade over mountainous regions. On the other hand, geostationary satellite data are available nearly anywhere and in near-real time. Current operational geostationary satellites, the Geostationary Operational Environmental Satellite-16 (GOES-16) and -17 provide high spatial and temporal resolution data, but only of cloud top properties. One-minute data, however, allow us to observe convection from visible and infrared data even without vertical information of the convective system. Existing detection algorithms using visible and infrared data look for static features of convective clouds such as overshooting top or lumpy cloud top surface, or cloud growth that occurs over periods of 30 minutes to an hour. This study represents a proof-of-concept that Artificial Intelligence (AI) is able, when given high spatial and temporal resolution data from GOES-16, to learn physical properties of convective clouds and automate the detection process. A neural network model with convolutional layers is proposed to identify convection from the high-temporal resolution GOES-16 data. The model takes five temporal images from channel 2 (0.65 μm) and 14 (11.2 μm) as inputs and produces a map of convective regions. In order to provide products comparable to the radar products, it is trained against Multi-Radar Multi-Sensor (MRMS), which is a radar-based product that uses rather sophisticated method to classify precipitation types. Two channels from GOES-16, each related to cloud optical depth (channel 2) and cloud top height (channel 14), are expected to best represent features of convective clouds: high reflectance, lumpy cloud top surface, and low cloud top temperature. The model has correctly learned those features of convective clouds, and resulted reasonably low false alarm ratio (FAR) and high probability of detection (POD). However, FAR and POD can vary depending on the threshold, and a proper threshold needs to be chosen based on the purpose.

Cloud mask products are essential for the retrieval of aerosol and cloud properties as well as in understanding the impacts of clouds on the Earth's radiation budget and global climate change. This paper evaluates the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask products through comparison with four years of ground-based radar data over Beijing. Different data collocation schemes are applied to analyze and thoroughly understand the differences between the MODIS and the radar. Comparison results show that the agreement of cloud mask between the MODIS and radar is 84%. There are 11% comparisons of which the MODIS is cloudy while radar is clear because of their different field of view. It is found that the missed thin clouds by MODIS account for 5% of the disagreement and discrepancies are mostly due to thin clouds.

This study relies on ground-based observations to quantify the limitations of current spaceborne radars when it comes to capturing the coverage and vertical structure of Warm Marine Boundary Layer (WMBL) clouds. Its finding that neither the CloudSat-Cloud Profiling Radar (CPR) nor the EarthCARE-CPR will be able to accurately characterize cloud cover, cloud fraction and the location of a large fraction of WMBL clouds further encourages the development of concepts for new spaceborne radar systems targeting WMBL clouds. Given the properties of WMBL clouds extracted from ground-based radar measurements collected in the eastern northern Atlantic, we recommend that the next generation of space-borne radars targeting WMBL science shall operate interlaced pulse modes including both a highly sensitive long-pulse and a less sensitive but clutter limiting short-pulse mode. The findings of this study are particularly relevant for shaping the Aerosol and Cloud, Convection and Precipitation (ACCP) mission currently under investigation at the National Aeronautics and Space Administration (NASA).

The influence of the energy potential of the meteorological radar on the results of measuring the cloud thickness and its water content profile is considered

Mechatronic systems for remote monitoring of such dangerous natural phenomena as thunderstorms and hail clouds are based on radar research methods. The features of using the millimeter wavelength range for studying the microstructure of thunderstorm and hail clouds are shown. A mathematical model of the scattering of millimeter-wave electromagnetic waves by various cloud particles has been developed.

The ability to separate cloud and drizzle returns in active remote sensing observations is important for understanding the microphysics of clouds and precipitation. Yet, robust separations remain challenging in radar remote sensing. Prior methods for cloud and drizzle separation for radar observations use the properties of the Doppler spectra such as skewness. However, these methods have challenges when the drizzle becomes dominant in the observation volume. This paper presents a parametric time domain method (PTDM) that separates cloud and drizzle using the Doppler spectra measurements without assuming any prior properties of cloud and drizzle. The advantage of PTDM is that it can estimate the signal properties in the time domain and can obtain the cloud and drizzle estimates simultaneously. Based on our radar signal simulations, the uncertainty in estimated power and velocity from PTDM are within 2 dB and 0.02 m s−1, respectively. We have also evaluated the PTDM algorithm using observations from the Atmospheric Radiation Measurement (ARM) Program W-band cloud radar in the Clouds, Aerosols, and Precipitation in the Marine Boundary Layer (CAP-MBL) campaign at the Azores in 2009–10. Two cases corresponding to light and moderate drizzling conditions are considered for the study. The statistics of the estimates obtained show that the PTDM method performs well in separating the cloud and drizzle returns. Finally, the estimated cloud and drizzle reflectivity from PTDM were used to retrieve their corresponding microphysical properties, showing that the retrieved liquid water path agrees to 25 g m−2 with the benchmark microwave method.

Spaceborne radars offer a unique three‐dimensional view of the atmospheric components of the Earth's hydrological cycle. Existing and planned spaceborne radar missions provide cloud and precipitation information over the oceans and land difficult to access in remote areas. A careful look into their measurement capabilities indicates considerable gaps that hinder our ability to detect and probe key cloud and precipitation processes. The international community is currently debating how the next generation of spaceborne radars shall enhance current capabilities and address remaining gaps. Part of the discussion is focused on how to best take advantage of recent advancements in radar and space platform technologies while addressing outstanding limitations. First, the observing capabilities and measurement highlights of existing and planned spaceborne radar missions including TRMM, CloudSat, GPM, RainCube, and EarthCARE are reviewed. Then, the limitations of current spaceborne observing systems, with respect to observations of low‐level clouds, midlatitude and high‐latitude precipitation, and convective motions, are thoroughly analyzed. Finally, the review proposes potential solutions and future research avenues to be explored. Promising paths forward include collecting observations across a gamut of frequency bands tailored to specific scientific objectives, collecting observations using mixtures of pulse lengths to overcome trade‐offs in sensitivity and resolution, and flying constellations of miniaturized radars to capture rapidly evolving weather phenomena. This work aims to increase the awareness about existing limitations and gaps in spaceborne radar measurements and to increase the level of engagement of the international community in the discussions for the next generation of spaceborne radar systems., Key Points Current spaceborne radars offer a unique three‐dimensional view of the atmospheric component of the Earth's hydrological cycleCurrent spaceborne radar have limitations with respect to observations of low‐level clouds, midlatitude/high‐latitude precipitation, and convectionRecent advances in radar technology will enable filling current science gaps

The utilization of polarimetric weather radars for optimizing cloud models is a next frontier of research. It is widely understood that inadequacies in microphysical parameterization schemes in numerical weather prediction (NWP) models is a primary cause of forecast uncertainties. Due to its ability to distinguish between hydrometeors with different microphysical habits and to identify “polarimetric fingerprints” of various microphysical processes, polarimetric radar emerges as a primary source of needed information. There are two approaches to leverage this information for NWP models: (1) radar microphysical and thermodynamic retrievals and (2) forward radar operators for converting the model outputs into the fields of polarimetric radar variables. In this paper, we will provide an overview of both. Polarimetric measurements can be combined with cloud models of varying complexity, including ones with bulk and spectral bin microphysics, as well as simplified Lagrangian models focused on a particular microphysical process. Combining polarimetric measurements with cloud modeling can reveal the impact of important microphysical agents such as aerosols or supercooled cloud water invisible to the radar on cloud and precipitation formation. Some pertinent results obtained from models with spectral bin microphysics, including the Hebrew University cloud model (HUCM) and 1D models of melting hail and snow coupled with the NSSL forward radar operator, are illustrated in the paper.

This paper presents a method to retrieve three-dimensional cumulus cloud macro- and microphysics measured by remote sensing instruments on the German research aircraft HALO. This is achieved by combining our hyper-spectral pushbroom spectrometer specMACS with active and passive remote sensing instruments, such as a lidar, a microwave radiometer, a radar and dropsondes. Two-dimensional cloud information such as cloud size, optical thickness, effective radius and thermodynamic phase are retrieved by specMACS with established remote sensing methods. Information of the other active and passive remote sensing instruments with a smaller field-of-view are mapped to the wider specMACS swath following Barker et al. (2011). The combination of specMACS with passive and active remote sensing quantities, for example, the Cloud Top Height from lidar measurements, allows new possibilities: three-dimensional cloud macrophysics can be reconstructed. Applying a sub-adiabatic microphysical model constrained with measurements allows to extend the measured quantities to a three-dimensional representation of microphysics. A consistency check by means of a three-dimensional radiative transfer simulation of the specMACS observations of these derived three-dimensional cloud fields shows good agreement.

This article presents a new Cloud Radar calibration methodology using solid reference reflectors mounted on masts, developed during two field experiments held in 2018 and 2019 at the SIRTA atmospheric observatory, located in Palaiseau, France, in the framework of the ACTRIS-2 research and innovation program. The experimental setup includes 10 cm and 20 cm triangular trihedral targets installed at the top of 10 m and 20 m masts, respectively. The 10 cm target is mounted on a pan-tilt motor at the top of the 10 m mast to precisely align its boresight with the radar beam. Sources of calibration bias and uncertainty are identified and quantified. Specifically, this work assesses the impact of receiver compression, incomplete antenna overlap, temperature variations inside the radar, clutter and experimental setup misalignment. Setup misalignment is a source of bias previously undocumented in the literature, that can have an impact on the order of tenths of dB in calibration retrievals of W band Radars. A detailed analysis enabled the design of a calibration methodology which can reach a cloud radar calibration uncertainty of 0.3 dB based on the equipment used in the experiment. Among different sources of uncertainty, the two largest terms are due to signal-to-clutter ratio and radar-to-target alignment. The analysis revealed that our 20 m mast setup with an approximate alignment approach is preferred to the 10 m mast setup with the motor-driven alignment system. The calibration uncertainty associated with signal-to-clutter ratio of the former is ten times smaller than for the latter. Cloud radar calibration results are found to be repeatable when comparing results from a total of 18 independent tests. Once calibrated the cloud radar provides valid reflectivity values when sampling mid-tropospheric clouds. Thus we conclude that the method is repeatable and robust, and that the uncertainties are precisely characterized. The method can be implemented under different configurations as long as the proposed principles are respected. It could be extended to reference reflectors held by other lifting devices such as tethered balloons or unmanned aerial vehicles.

The 29th International Laser Radar Conference (ILRC 29); International audience; Particle attenuated backscatter and depolarization ratio at 532 nm from the WALES (Water Vapor Lidar Experiment in Space) instrument are used in combination with the radar reflectivity from the 35 GHz MIRA35 cloud radar in order to perform an aerosol-cloud target classification on the lidar/radar collocated observations carried out with the High Altitude and LOng-range research aircraft (HALO), in high temporal and spatial resolution.The methodology is applied to the measurements conducted during the North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX) in fall 2016. Here, we present one case study to show the feasibility and information context of the aerosol-cloud discrimination, which can serve as a good complement to the Cloud target categorization applied already in the measurements. Our results demonstrate that the developed mask is capable to identify complex stratifications with different aerosol and cloud types and even aerosol layers of low signal.

The aim of this paper is the estimation of precipitation in northern Algeria using satellite data. To do this, a multi-threshold method based on the exploitation of the clouds optical and microphysical properties is developed. Depending on the availability of the MSG / SEVIRI (M eteosat Second Generation/Spinning Enhanced Visible and Infrared Imaging) satellite channels, the database is divided into daytime data and nighttime data. In the learning phase, daytime and nighttime two-dimensional thresholds are determined from comparisons between the MSG / SEVIRI satellite data of rainy season 2010/2011 and the corresponding data from the Sétif meteorological radar. Using linear regression, an empirical relationship between the SEVIRI spectral data and the precipitation intensities from the radar is determined.To estimate rainfall, the determined empirical relationship is ap plied to the validation dataset collected during rainy season 2011/2012. To evaluate the method, a comparison of the estimation results with reference radar data is performed. The results show that these estimations are in good correlation with those measured by radar.

Weather radar generally used volume scan data discrete summation method when calculating VIL (vertically integrated liquid water), usually this method will produce a large error, in this paper uses a method based on meteorological radar multi-source data fusion to calculate VIL, uses a joint observation data of X-band rain radar and Ka-band solid state cloud radar to calculate the VIL products, and comparative analysis using GPM (Global Precipitation Measurement) precipitation data. Synchronous observation combined with radar advantages in different frequency bands can refine the analysis of cloud structure. This method extracts the synchronous headspace vertical detection data of Ka and X-band radars, and reduces the process of discrete summation using volume scan data, which improves the accuracy of liquid water content in the cloud.