Rain Cells Research Articles

On the physical mechanism causing strongly enhanced radar backscatter in C-Band SAR images of convective rain over the ocean

ABSTRACT Radar signatures of rain over the ocean have a complex structure since they receive contributions from surface scattering and volume scattering and attenuation by hydrometeors in the atmosphere. These contributions overlap and are often difficult to detangle. While most of the mechanisms contributing to radar signatures of rain over the ocean are well understood, there is one remaining issue that has been discussed controversially in the literature for a long time. It is the question what scattering mechanism causes the areas of strongly enhanced radar backscatter, also called ‘bright blobs’ or ‘bright patches’, which are frequently observed on spaceborne C-band SAR images acquired over tropical and subtropical oceans in the presence of convective rain. Recently, papers have been published in which it is hypothesized that they are caused by radar backscattering at hydrometeors in the melting layer (ML). Although many observational facts seem to support this hypothesis, there exists one strong argument against this hypothesis: It is the observation that the position of the ML radar signatures (bright blob) in the SAR image is not shifted in anti-range from the position, where the rain column hits the sea surface. This absence of a shift is observed when 1) comparing Sentinel-1 SAR images on which rain cells are visible with quasi-concurrently acquired weather radar images and 2) when inter-comparing of SAR images of rain cells acquired concurrently at different frequencies and polarizations. Based on these observations, we discard the hypothesis that the bright blobs are due to volume scattering at hydrometeors in the ML and hypothesize instead that they are due to scattering at splash products at the sea surface. This hypothesis is supported by radar backscattering measurements carried out in the laboratory and from a shore-based platform, which show that, at C-and X-band, strong rain can give rise to strong radar returns also at cross-polarization.

Potential of EOS-04 C-band Synthetic Aperture Radar in Identifying Oceanic Rain Cells

Potential of EOS-04 C-band Synthetic Aperture Radar in Identifying Oceanic Rain Cells

Seasonal variations in rain cells propagation over Central Africa and association with diurnal rainfall regimes

AbstractThree‐hourly data from two satellite rainfall estimates products, PERSIANN and TMPA, are analysed to document the seasonal patterns of diurnal rainfall distribution over the Congo Basin and neighbouring areas. PERSIANN data for 2001–2017, at a one‐hour time‐scale, are further used to identify rain cells (≥4 mm·h−1) in an attempt to explain the diurnal rainfall variations. Over land areas, an afternoon rainfall maximum is clearly shown, but over much of the region only a minor part of the rains (20%–30%) falls in the wettest 3‐h period. Substantial rains (often 50%–60%) occur in the evening and at night, as a progressively delayed peak from east to west, but a seasonal change is found in the meridional propagation of the peak diurnal rainfall, in a south‐westerly direction in January, and a north‐westerly direction in July. Rain cells have prominent genesis areas west of high terrain, but can develop over most regions, with a peak genesis time slightly ahead the diurnal phase of the rains. The size, mean lifetime and mean rainfall intensity of the rain cells are strongly related to each other and display a semi‐annual cycle not fully in phase with the seasonal cycle of the rains. The mean rain cell propagation speed (6.7 m·s−1) is much lower than in previous studies, which focused on mesoscale convective systems. Rain cells which have a longer lifetime move much faster, the mean speed of those lasting less than 6 h being half that of those lasting at least 24 h. Most (86%) of the mobile rain cells propagate westward, but the meridional component of their propagation shows an annual cycle (southward in austral summer, northward in boreal summer) which matches the mid‐tropospheric winds and explains the seasonal changes in the diurnal rainfall peak.

Marine oil spill detection using improved polarimetric feature based on polarization SAR image

ABSTRACT Monitoring marine oil pollution holds both practical and scientific significance. Synthetic Aperture Radar (SAR) is an active microwave remote sensing technique capable of all-weather and all-day with fine spatial resolution. However, under low wind conditions, rain cells and young ice are examples of look-alikes affect the accuracy of oil spill detection. Polarimetric SAR assumes a crucial role in this context, as it can extract abundant polarimetric features by polarimetric target decomposition. Drawing inspiration from this advancement, an improved polarimetric feature named relative feature based on Cloude-Pottier target decomposition was proposed. The Jeffries-Matusita distance indicates the substantial potential of the relative feature in detecting oil spills. The improved polarimetric feature within U-Net, FCN-8s, and DeepLabv3+ResNet-18 for oil spill detection using polarization SAR images. Experiment results demonstrated that the relative feature has superior performance compared to other polarimetric features and obtained the highest accuracy and dice within U-Net compared to the other two models. These findings introduce promising concepts for achieving rapid and precise detection of oil spills in future applications.

Analysis of wave breaking on synthetic aperture radar at C-band during tropical cyclones

ABSTRACT The purpose of our work is to analyze the effect of wave breaking on dual-polarized (vertical-vertical (VV) and vertical-horizontal (VH)) synthetic aperture radar (SAR) image in the C-band during tropical cyclones (TCs) based on the machine learning method. In this study, more than 1300 Sentinel-1 (S-1) interferometric-wide (IW) and extra wide (EW) mode SAR images are collocated with wave simulations from the WAVEWATCH-III (WW3) model during 400 TCs. The validation of the significant wave height (SWH) simulated using the WW3 model against Jason-2 altimeter data. The winds for S-1 SAR images are reconstructed using wind retrievals in VV and VH polarization. The non-polarized (NP) contribution σ wb caused by wave breaking is assumed to be the result of the SAR-measured normalized radar cross-section (NRCS) σ 0 minus the Bragg resonant roughness σ br without the distortion of rain cells during TCs. The σ br is simulated by imputing wave spectra from the WW3 model into the theoretical backscattering model. It is found that the ratio (σ wb /σ 0) in VV polarization is related to the wind speed, the wind direction relative with the flight orientation, and radar incidence angle. Following this rationale, the Adaptive Boosting (AdaBoost) model was used for the estimation of NP contribution σ wb during TCs and are implemented for more than 300 dual-polarized S-1 images to validate the model. It is found that for the comparison between the sum of simulation NRCS and SAR observations, the root mean squared error (RMSE) is 1.95 dB and the coefficient (COR) is 0.86, which is better than a 2.83 dB RMSE and a 0.67 COR by empirical model. It is concluded that the AdaBoost model has a good performance on NP component simulation during TCs.

Comparison of the Minimum Bounding Rectangle and Minimum Circumscribed Ellipse of Rain Cells from TRMM

Comparison of the Minimum Bounding Rectangle and Minimum Circumscribed Ellipse of Rain Cells from TRMM

Detecting Cold Pool Family Trees in Convection Resolving Simulations

AbstractRecent observations and modeling increasingly reveal the key role of cold pools in organizing the convective cloud field. Several methods for detecting cold pools in simulations exist, but are usually based on buoyancy fields and fall short of reliably identifying the active gust front. The current cold pool (CP) detection and tracking algorithm (CoolDeTA), aims to identify cold pools and follow them in time, thereby distinguishing their active gust fronts and the “offspring” rain cells generated nearby. To accomplish these tasks, CoolDeTA utilizes a combination of thermodynamic and dynamical variables and examines the spatial and temporal relationships between cold pools and rain events. We demonstrate that CoolDeTA can reconstruct CP family trees. Using CoolDeTA we can contrast radiative convective equilibrium (RCE) and diurnal cycle CP dynamics, as well as cases with vertical wind shear and without. We show that the results obtained are consistent with a conceptual model where CP triggering of children rain cells follows a simple birth rate, proportional to a CP's gust front length. The proportionality factor depends on the ambient atmospheric stability and is lower for RCE, in line with marginal stability as traditionally ascribed to the moist adiabat. In the diurnal case, where ambient stability is lower, the birth rate thus becomes substantially higher, in line with periodic insolation forcing—resulting in essentially run‐away mesoscale excitations generated by a single parent rain cell and its CP.

Characteristics of rain cells over the northwest Pacific warm pool

Characteristics of rain cells over the northwest Pacific warm pool

U‐Net Segmentation for the Detection of Convective Cold Pools From Cloud and Rainfall Fields

AbstractConvective cold pools (CPs) mediate interactions between convective rain cells and help organize thunderstorm clusters, in particular mesoscale convective systems and extreme rainfall events. Unfortunately, the observational detection of CPs on a large scale has been hampered by the lack of relevant near‐surface data. Unlike numerical studies, where fields, such as virtual temperature or wind, are available at high resolution and frequently used to detect CPs, observational studies mainly identify CPs based on surface time series, for example, from weather stations or research vessels—thus limiting studies to a regional scope. To expand to a global scope, we here develop and evaluate a methodology for CP detection that relies exclusively on data with (a) global availability and (b) high spatiotemporal resolution. We trained convolutional neural networks to segment CPs in high‐resolution cloud‐resolving simulation output by deliberately restricting ourselves to only cloud top temperature and rainfall fields. Apart from simulations, such data are readily available from geostationary satellites that fulfill both (a) and (b). The networks employ a U‐Net architecture, popular with image segmentation, where spatial correlations at various scales must be learned. Despite the restriction imposed, the trained networks systematically identify CP pixels. Looking ahead, our methodology aims to reliably detect CPs over tropical land from space‐borne sensors on a global scale. As it also provides information on the spatial extent and the relative positioning of CPs over time, our method may unveil the role of CPs in convective organization.

Deciphering the Signatures of Oceanic Convective Rain Cells Using Simultaneous Observations From C‐Band Synthetic Aperture Radar Onboard EOS‐04 Satellite and GPM Measurements

AbstractEarth Observation Satellite (EOS)‐04 launched on 14 February 2022, carries a C‐band Synthetic Aperture Radar (SAR) for Agriculture, Forestry, Hydrology, and Flood mapping applications. In this paper, we have used C‐band SAR images and near‐simultaneous observations from the global precipitation measurements (GPM) to study the signatures of multiple convective rain cells. The bright patches are found on C‐band SAR imagery, which depicts the information of hydrometeors such as graupels or hails in the melting layer. For the first time, unambiguously estimated the diameter of the convective core rain cells from the C‐band SAR backscattered signal and compared with near‐simultaneous observations from GPM‐GMI and Ku‐band radar to confirm our findings. Thus, the present study demonstrates the potential of C‐band SAR for identifying the signatures of convective rain cells.

Retrieval of Rain Rates for Tropical Cyclones From Sentinel-1 Synthetic Aperture Radar Images

The purpose of this study was to develop a method for retrieving the rain rate from C-band (∼5.3 GHz) synthetic aperture radar (SAR) images during tropical cyclones (TCs). Seven dual-polarized (vertical-vertical [VV] and vertical-horizontal [VH]) Sentinel-1 (S-1) SAR images were acquired in the interferometric-wide (IW) swath mode during the Satellite Hurricane Observation Campaign. These images were collocated with rain rates measured by the Stepped-Frequency Microwave Radiometers onboard National Oceanic and Atmospheric Administration aircraft. Wind speeds were retrieved from the VH-polarized SAR images using the geophysical model function (GMF) S1IW.NR. We determined the difference between the measured normalized radar cross section (NRCS) based on VV-polarized SAR and the predicted NRCS derived using the geophysical model function CMOD5.N forced with wind speeds retrieved from VH-polarized SAR images. Rain cells were identified as regions in the images where the NRCS difference was greater than 0.5 dB or smaller than −0.5 dB. We found that the difference in the NRCS decreased and the VH-polarized wind speed increased with increasing rain rate. Based on these findings, we developed an empirical function for S-1 SAR rain retrieval in a TC, naming it CRAIN_S1. The validation of the CRAIN_S1 results with Tropical Rainfall Measuring Mission data resulted in a root mean square error of 0.58 mm/hr and a correlation of 0.89. This study provides an alternate method for rain monitoring utilizing SAR data with a fine spatial resolution.

Error Analysis of Rainfall Inversion Based on Commercial Microwave Links With A–R Relationship Considering the Rainfall Features

Rainfall inversion based on commercial microwave links (CMLs) has been extensively studied and experimented. However, as necessary part of inversion, the error caused by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A - R</i> relationship is lack of systematic evaluation. Based on the measured raindrop size distribution (RSD) data recorded by a disdrometer, the theoretical rain-induced attenuation and rainrate are calculated, and on this basis, the error between the inversed rainrate based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A - R</i> relationship from ITU-R recommendation and the real rainrate is compared under different rainfall types, rainfall classes and spatially heterogeneous rain cell. Firstly, the errors of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A - R</i> relationship under different rainfall classes show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A - R</i> relationship can effectively invert rainrate between 18 and 26 GHz for all classes, while the error increases significantly outside this frequency range, especially for heavy rainfall. In addition, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A - R</i> relationship performance of different rainfall types is evaluated. There is a significant difference in the unbiasedness of convective rainfall and stratiform rainfall, which are positive and negative bias at high frequency (greater than 50 GHz), respectively. Furthermore, aiming at the inhomogeneity of rainfall spatial distribution, the rainrate distribution and the corresponding path rain-induced attenuation are simulated based on an exponential model, and the influence of spatial location difference on the accuracy of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A - R</i> relationship is deeply analyzed. Finally, the error source and influence of localized <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A - R</i> relationship, integration time and link length are discussed.

Intelligent detection of rain cells with SAR imagery based on broad learning system

Intelligent detection of rain cells with SAR imagery based on broad learning system

Synthetic Rain Models and Optical Flow Algorithms for Improving the Resolution of Rain Attenuation Time Series Simulated From Numerical Weather Prediction

AbstractNew generation of satellite system communication is moving toward frequencies above 20 GHz and non‐geostationary satellites. In order to guaranty the quality of service, specific Fade Mitigation Techniques have to be applied, like Advanced Coding and Modulation (ACM). In order to accurately design ACM, time series of attenuation should be used. As there are no measurements available, the simulation of attenuation times series for non‐geostationary earth‐space links becomes critical. This paper presents a model consisting in a combination of a Numerical Weather Prediction model (Weather Research and Forecasting), a synthetic rain cell model MultiEXCELL and Optical Flow techniques. It gives an efficient method to generate more realistic rain attenuation time series. Long‐term CCDF statistics of simulated attenuation data are validated by comparison with the Alphasat measurement data over 2 years at Louvain‐la‐Neuve ground station.

Statistical characterization of rainfall fields based upon a 12-year high-resolution radar archive of Belgium

Spatial and temporal variability of rainfall input plays a critical role on the performance of the urban runoff simulations. For this reason, urban hydrological application demands accurate products of high-resolution and high-quality rainfall estimates, which use and understanding become critical for more robust models.In this study, the spatial and temporal characteristics of rainfall in Belgium are analyzed at fine scales. This is done by constructing a conceptual rain storm model using historical long-term radar data archive. This application involved the use of a state-of-the-art tracking algorithm, developed by Muñoz et al. (2018), to associate the spatial and temporal variations in rain storms and cells This algorithm enables better handling of high-resolution details and is capable of extracting rainfall objects/entities across various scales. Through tracking and conceptualizing these storm entities, spatial and temporal rainfall properties, such as geometric features, intensities, velocity, rainfall types or spatial patterns, can be extracted and analyzed. To accomplish this, twelve years of continuous high-resolution radar data are utilized in this work. The resulting analysis of the spatial and temporal characteristics of rainfall radar data is intended to set the ground for the future applications. More specifically, it can be used to refine and improve the existing long-term spatial rainfall generators, such as that the one implemented by Willems' in 2001.

Effective Path Length for Estimating Rain Attenuation Over an Earth‐Space Path Using Features of Stratiform and Convective Precipitation at a Tropical Location

AbstractThe effective path length (LE) for the rain attenuation estimation over the Earth‐space path depends on rain cell size, rain rate, and rain height which in turn are determined by the types of rain. The present study examines the variation of LE based on the experimental measurements of Ku‐band attenuation and rain rate at a tropical location Kolkata (22°34′N, 88°22′E) in comparison with the ITU‐R. The study also utilizes measurements from a 24‐GHz micro rain Doppler radar to differentiate the precipitation types as stratiform and convective rain on the basis of the presence and absence of a melting layer. Power‐law models of LE in terms of rain rate are proposed for convective and stratiform types of rain. The investigation reveals the physical phenomena behind the variability of LE at low and high rain rates which are associated with stratiform and convective rain respectively. The proposed models provide a significantly better agreement with the experimental data compared to the ITU‐R model and also account for distinguishing features of two types of rain, thus giving a physical basis of the proposed model which is not reflected in the ITU‐R formulations.

Dark Spot Detection from SAR Images Based on Superpixel Deeper Graph Convolutional Network

Synthetic Aperture Radar (SAR) is the primary equipment used to detect oil slicks on the ocean’s surface. On SAR images, oil spill regions, as well as other places impacted by atmospheric and oceanic phenomena such as rain cells, upwellings, and internal waves, appear as dark spots. Dark spot detection is typically the initial stage in the identification of oil spills. Because the identified dark spots are oil slick candidates, the quality of dark spot segmentation will eventually impact the accuracy of oil slick identification. Although certain sophisticated deep learning approaches employing pixels as primary processing units work well in remote sensing image semantic segmentation, finding some dark patches with weak boundaries and small regions from noisy SAR images remains a significant difficulty. In light of the foregoing, this paper proposes a dark spot detection method based on superpixels and deeper graph convolutional networks (SGDCNs), with superpixels serving as processing units. The contours of dark spots can be better detected after superpixel segmentation, and the noise in the SAR image can also be smoothed. Furthermore, features derived from superpixel regions are more robust than those derived from fixed pixel neighborhoods. Using the support vector machine recursive feature elimination (SVM-RFE) feature selection algorithm, we obtain an excellent subset of superpixel features for segmentation to reduce the learning task difficulty. After that, the SAR images are transformed into graphs with superpixels as nodes, which are fed into the deeper graph convolutional neural network for node classification. SGDCN leverages a differentiable aggregation function to aggregate the node and neighbor features to form more advanced features. To validate our method, we manually annotated six typical large-scale SAR images covering the Baltic Sea and constructed a dark spot detection dataset. The experimental results demonstrate that our proposed SGDCN is robust and effective compared with several competitive baselines. This dataset has been made publicly available along with this paper.

Impact of Multi-Thresholds and Vector Correction for Tracking Precipitating Systems over the Amazon Basin

Different algorithms for forecasting and tracking meteorological systems have been developed over the years. Many of them are used to study cloud propagation, precipitation and lightning for nowcasting. Therefore, it is necessary to define carefully the parameters (e.g., intensity thresholds and minimum size) that impact tracking of these variables. In order to represent the physical aspects of rain propagation over the Amazon region, several methods of correction and displacement detection were studied. Different parameters were used to validate the methods based on the extrapolated rain cell. A probability detection of 78.4% and 68.6% was achieved for 20 dBZ thresholds during the wet and dry season, respectively. However, the POD decreases for higher reflectivity thresholds. The results for corrections by Inner Nuclei showed that embedded convection can dictate the propagation of rain cells. Split and merge corrections performed well; however, they applied only to a few cases. Corrections performed better for precipitating systems with larger areas and longer duration. The correction methods showed similar skills for both seasons. Which shows that they are able to monitor rain cells throughout the year. The automated combination of different methods for the 20 dBZ threshold proved to be the best choice for tracking rainfall in the Amazon region.

Backscattering Statistics of Labeled Sentinel-1 Wave Mode Imagettes for Ten Geophysical Phenomena

Synthetic aperture radar (SAR) is a sensor that is proven to have great potential in observing atmospheric and oceanic phenomena at high-spatial resolutions (∼10 m). The statistics of SAR backscattering that describe the image characteristics are essential to help interpret the properties of the geophysical processes. In this study, we took advantage of a hand-labeled database of ten commonly observed geophysical processes created based on the Sentinel-1 wave mode vignettes to document the SAR backscattering statistics. The probability density function (PDF), normalized variance, skewness, and kurtosis were investigated among the ten labeled categories. We found that the NRCS PDFs differ between types, implying the influences of these large-scale features on the radar backscattering distribution. The statistical parameters exhibited distinct variations among classes at the two incidence angles of 23.5∘ and 36.5∘. In particular, the normalized variance of low wind area at 23.5∘ exceeded other phenomena by an order of magnitude. This lays the basis for directly identifying the SAR images of low wind areas in terms of this parameter. Sea ice and rain cells at 36.5∘ span within a similar range of kurtosis values, much higher than the other groups. While sea ice could be excluded using a latitude threshold, the rain cells are readily detected. The global percentage map of directly identified rain cells is consistent with the deep-learning results but with higher efficiency. The influence of these large-scale atmospheric and oceanic features on radar backscattering statistics must be considered in the future wave retrieval algorithm for improved accuracy.

Wind Field Retrieval with Rain Correction from Dual-Polarized Sentinel-1 SAR Imagery Collected during Tropical Cyclones

The purpose of this study is to include rain effects in wind field retrieval from C-band synthetic aperture radar (SAR) imagery collected under tropical cyclone conditions. An effective and operationally attractive approach to detect rain cells in SAR imagery is proposed and verified using four Sentinel-1 (S-1) SAR images collected in dual-polarized (vertical-vertical (VV) and vertical-horizontal (VH)) interferometric-wide swath imaging mode during the Satellite Hurricane Observation Campaign. SAR images were collocated with ancillary observations that include sea surface wind and rain rate from the Stepped-Frequency Microwave Radiometer (SFMR) on board of the National Oceanic and Atmospheric Administration aircraft. The winds are inverted from VV- and VH-polarized S-1 image using the CMOD5.N and S1IW.NR geophysical model functions (GMFs), respectively. Location and radius of cyclone’s eye, together with the TC central pressure, are calculated from the VV-polarized SAR-derived wind and a parametric model. A cost function is proposed that consists of the difference between the measured VV-polarized SAR normalized radar cross section (NRCS) and the NRCS predicted using CMOD5.N forced with the wind speed retrieved by the VH-polarized SAR images using S1IW.NR GMF and the wind direction retrieved from the patterns visible in the SAR image. This cost function is related to the SFMR rain rate. Experimental results show that the difference between measured and predicted NRCS values range from 0.5 dB to 5 dB within a distance of 100 km from the cyclone’s eye, while the difference increases spanning from 3 dB to 6 dB for distances larger than 100 km. Following this rationale, first the rain bands are extracted from SAR imagery and, then, the composite wind fields are reconstructed by replacing: (1) dual-polarized SAR-derived winds over the rain-free regions; (2) winds simulated using the radial-vortex model over the rain-affected regions. The validation of the composite wind speed against SFMR winds yields a &lt;2 m s−1 and &gt;0.7 correlation (COR) at all flow directions up to retrieval speeds of 70 m s−1. This result outperforms the winds estimated using the VH-polarized S1IW.NR GMF, which call for high error accuracy, such as about 4 m s−1 with a 0.45 COR ranged from 330° to 360°.