Rain Rate Estimation Research Articles

Enhanced quantitative precipitation estimation through the opportunistic use of Ku TV-SAT links via a dual-channel procedure

Abstract. Earth–satellite microwave links such as TV-SAT can help for rainfall monitoring and could be a complement or an alternative to ground-based weather radars, rain gauges or Earth observation satellites. Rain-induced attenuation which is harmful for telecommunication is exploited here as an opportunistic way to estimate rain rate along the link path. This technique provides rain measurements at a fine temporal resolution (a few tens of seconds) and with a spatial resolution of a few kilometres, which is a good compromise for human activities such as civil security (watershed monitoring, flash flood), agriculture or transport. The advantages of this technique include the low cost of the equipment used, as well as the cost of on-site maintenance. However, the measured attenuation does not directly provide rain intensity, requiring the estimation of additional parameters. These include the contribution of natural radiation from the atmosphere. In this paper, we detail a theoretical framework allowing us to estimate rainfall from the measurements of a low-cost sensor operating simultaneously over two parts of the Ku frequency band. This framework is assessed in a densely instrumented area in the south of France, where very good results are obtained when compared to rain gauge measurements, in terms of both overall rain accumulation and rainfall rate distribution. Then we apply this dual-channel method in Côte d'Ivoire, in the metropolitan area of Abidjan, where such an approach is very promising. It is shown that this technique when compared to rain gauge measurements gives far better results than a naive single-channel approach neglecting the natural radiation of atmosphere but that significant errors remain in rainfall assessment, leading to a persistent underestimation of rain accumulation. Finally we discuss various effects that could lead to this remaining underestimation, opening the door for further studies.

A Statistical Approach to Retrieve Rainfall Intensity by Attenuation Measurements From Commercial Microwave Links

AbstractCommercial microwave link (CML) has become one of the most widely used opportunistic sensors for rainfall monitoring. The high density and coverage of CMLs enable us to monitor near‐ground rainfall in high spatial resolution. The empirical power‐law (PL) model proposed in 1978 is the leading approach in relating CML attenuation with rain rate. However, while giving good rain rate estimates for CMLs longer than 1 km, in short CMLs, large errors are observed when the PL approach is applied. In this paper, we studied a statistical approach to convert CML attenuation measurement to rainfall intensity. The proposed approach calculates the exceedance probability, p, of rain‐induced attenuation and derives the corresponding rainfall intensity based on the rain rate cumulative distribution curve. Data from two cities in two countries were used to validate the approach. The results of the proposed approach were compared with that of the PL model. Results show that the models derived using our approach outperform PL for short CMLs, while showing similar performance for long ones. Since in the next generation mobile networks short CMLs will become more and more dominant, our work provides a way to derive retrieval models for the future generation CML networks.

Improving Polarimetric Radar-Based Drop Size Distribution Retrieval and Rain Estimation Using a Deep Neural Network

Abstract Raindrop size distributions (DSD) and rain rate have been estimated from polarimetric radar data using different approaches with the accuracy depending on the errors both in the radar measurements and the estimation methods. Herein, a deep neural network (DNN) technique was utilized to improve the estimation of the DSD and rain rate by mitigating these errors. The performance of this approach was evaluated using measurements from a two-dimensional video disdrometer (2DVD) at the Kessler Atmospheric and Ecological Field Station in Oklahoma as ground truth with the results compared against conventional estimation methods for the period 2006–17. Physical parameters (mass-/volume-weighted diameter and liquid water content), rain rate, and polarimetric radar variables (including radar reflectivity and differential reflectivity) were obtained from the DSD data. Three methods—physics-based inversion, empirical formula, and DNN—were applied to two different temporal domains (instantaneous and rain-event average) with three diverse error assumptions (fitting, measurement, and model errors). The DSD retrievals and rain estimates from 18 cases were evaluated by calculating the bias and root-mean-squared error (RMSE). DNN produced the best performance for most cases, with up to a 5% reduction in RMSE when model errors existed. DSD and rain estimated from a nearby polarimetric radar using the empirical and DNN methods were well correlated with the disdrometer observations; the rain-rate estimate bias of the DNN was significantly reduced (3.3% in DNN vs 50.1% in empirical). These results suggest that DNN has advantages over the physics-based and empirical methods in retrieving rain microphysics from radar observations.

Detection of Rain in Tropical Cyclones by Underwater Ambient Sound

Abstract Rain in tropical cyclones is studied using eight time series of underwater ambient sound at 40–50 kHz with wind speeds up to 45 m s−1 beneath three tropical cyclones. At tropical cyclone wind speeds, rain- and wind-generated sound levels are comparable, and therefore rain cannot be detected by sound level alone. A rain detection algorithm that is based on the variations of 5–30-kHz sound levels with periods longer than 20 s and shorter than 30 min is proposed. Faster fluctuations (<20 s) are primarily due to wave breaking, and slower ones (>30 min) are due to overall wind variations. Higher-frequency sound (>30 kHz) is strongly attenuated by bubble clouds. This approach is supported by observations that, for wind speeds < 40 m s−1, the variation in sound level is much larger than that expected from observed wind variations and is roughly comparable to that expected from rain variations. The hydrophone results are consistent with rain estimates by the Tropical Rainfall Measuring Mission (TRMM) satellite and with Stepped-Frequency Microwave Radiometer (SFMR) and radar estimates by surveillance flights. The observations indicate that the rain-generated sound fluctuations have broadband acoustic spectra centered around 10 kHz. Acoustically detected rain events usually last for a few minutes. The data used in this study are insufficient to produce useful estimation of rain rate from ambient sound because of limited quantity and accuracy of the validation data. The frequency dependence of sound variations suggests that quantitative rainfall algorithms from ambient sound may be developed using multiple sound frequencies. Significance Statement Rain is an indispensable process in forecasting the intensity and path of tropical cyclones. However, its role in the air–sea interaction is still poorly understood, and its parameterization in numerical models is still in development. In this work, we analyzed sound measurements made by hydrophones on board Lagrangian floats beneath tropical cyclones. We find that wind, rain, and breaking waves each have distinctive signatures in underwater ambient sound. We suggest that the air–sea dynamic processes in tropical cyclones can be explored by listening to ambient sound using hydrophones beneath the sea surface.

Assessing environmental conditions associated with spatially varying rainfall structure of North Atlantic tropical cyclones: An object‐based climatological analysis

AbstractThis study utilizes geographic information system‐based shape analyses with spatial regressions to examine the spatial variations in the relationships between North Atlantic TC rain field patterns and the environmental conditions. We measure the area, solidity, dispersion and closure of rain fields associated with North Atlantic tropical cyclones (TCs) during 1998–2014 from satellite‐based rain rate estimates. The potential contributing factors examined include storm intensity, translational speed, sea surface temperature, total precipitable water, upper‐tropospheric divergence, deep‐layer vertical wind shear and distance to land. Spatial metrics of rainfall, TC attributes and environmental conditions are aggregated onto a hexagon grid, and the average values are used in regression models. Spatial autoregressive regression and geographically weighted regression are applied to investigate the relationships between contributing factors and rainfall patterns. Storm intensity is positively correlated with area, solidity and closure and negatively correlated with dispersion. Higher moisture and larger upper‐level divergence contribute to a larger rainfall field with a more dispersed and less solid pattern, especially over the western Gulf and the western Caribbean. Wind shear and storm motion affect rain field patterns differently depending on the juxtaposition and relative magnitude of storm motion and wind shear vectors and thus exhibit more regional variations. This research further highlights the importance of incorporating a spatial component into the research on TC rainfall climatology. An improved understanding of basin‐wide and sub‐basin‐wide relationships between TC rain fields and contributing factors would benefit rainfall forecasting and hazard mitigation.

Revisiting Raindrop Axis Ratios Based on 3D Oblate Spheroidal Reconstruction: 2D Video Disdrometer Observations During Tropical Cyclone Passages

AbstractRaindrops are usually parameterized by oblate spheroids with axis ratios (ARs) determined from projected images. However, the adequacy of representing the 3‐Dimensional (3D) nature of raindrops with their 2‐Dimensional (2D) shadows remains an open question. This study reexamines raindrop AR parameterizations by applying a 3D oblate spheroidal reconstruction algorithm to 2D video disdrometer (2DVD) observations during the passages of 14 tropical cyclones. The results reveal an appreciable AR overestimation for large raindrops when retrieved from a single camera and especially for 2DVD raw products. This overestimation propagates into the simulated differential reflectivity (ZDR), resulting in an underestimation of ZDR by up to 0.5 dB at X‐band when large raindrops dominate the raindrop size distribution (RSD), which would introduce an underestimation of more than 20% for heavy rain rate (>60 mm hr−1) estimation by polarimetric radar. In precipitating systems with drastically changing RSDs, the importance of adopting more realistic ARs is warranted.

Evaluating Satellite Precipitation Estimates Over Oceans Using Passive Aquatic Listeners

AbstractPassive aquatic listeners (PALs) provide high‐quality, high‐resolution estimates of precipitation over ocean regions, but have been underutilized as a reference data set for the evaluation of satellite‐based precipitation estimates (SPEs). PALs are uniquely suited for this purpose due to their 5 km surface listening area when sampling at 1 km depth on drifting Argo Floats, providing rain rate estimates on a spatial scale similar to the native grid spacing of many SPEs. In this study we compare three SPE products (IMERG, CMORPH, and PDIR‐Now) to PAL measurements. Evaluations are performed over tropical, extratropical, and global oceans at SPE native spatiotemporal resolution and longer time scales. We find the SPEs to have rain rate frequency distributions similar to PAL, but with biases and varying performance characteristics that are dependent on region and time scale.

Listening to the raindrops from underwater for weather and climate

Jeffrey A. Nystuen is a pioneer in using the passive acoustic method to measure oceanic rainfall. Under Nystuen’s efforts, a self-contained passive acoustic recording device called the acoustic rain gauges were deployed all over the world, from a brackish pond in Florida, Carr inlet in Puget Sound, Crater Lake in Oregon, BioSphere II in Arizona, then to the South China Sea, the Pacific Ocean, the Taiwan strait, the Ionian Sea in Greece, the Aleutian Islands and ocean station Papa. This simple device records long-term spectra of sound pressure levels with minimum data screening, could detect the presence of oceanic rainfall, and provide estimates of rain rate and surface wind speed. Nystuen’s inspirational works over the past three decades set an example and path for future scientists and engineers to foster curiosity, pursue exploration and make discoveries.

Estimation of raindrop size distribution and rain rate with infrared surveillance camera in dark conditions

Abstract. This study estimated raindrop size distribution (DSD) and rainfall intensity with an infrared surveillance camera in dark conditions. Accordingly, rain streaks were extracted using a k-nearest-neighbor (KNN)-based algorithm. The rainfall intensity was estimated using DSD based on a physical optics analysis. The estimated DSD was verified using a disdrometer for the two rainfall events. The results are summarized as follows. First, a KNN-based algorithm can accurately recognize rain streaks from complex backgrounds captured by the camera. Second, the number concentration of raindrops obtained through closed-circuit television (CCTV) images had values between 100 and 1000 mm−1 m−3, and the root mean square error (RMSE) for the number concentration by CCTV and PARticle SIze and VELocity (PARSIVEL) was 72.3 and 131.6 mm−1 m−3 in the 0.5 to 1.5 mm section. Third, the maximum raindrop diameter and the number concentration of 1 mm or less produced similar results during the period with a high ratio of diameters of 3 mm or less. Finally, after comparing with the 15 min cumulative PARSIVEL rain rate, the mean absolute percent error (MAPE) was 49 % and 23 %, respectively. In addition, the differences according to rain rate are that the MAPE was 36 % at a rain rate of less than 2 mm h−1 and 80 % at a rate above 2 mm h−1. Also, when the rain rate was greater than 5 mm h−1, MAPE was 33 %. We confirmed the possibility of estimating an image-based DSD and rain rate obtained based on low-cost equipment during dark conditions.

Can DSD Assumptions Explain the Differences in Satellite Estimates of Warm Rain?

Abstract Satellite-based oceanic precipitation estimates, particularly those derived from the Global Precipitation Measurement (GPM) satellite and CloudSat, suffer from significant disagreement over regions of the globe where warm rain processes are dominant. GPM estimates of average rain rate tend to be lower than CloudSat estimates, due in part to GPM being less sensitive to shallow and/or light precipitation. Using coincident observations between GPM and CloudSat, we find that the GPM_2BCMB product misses about two-thirds of total accumulated warm rain compared to the CloudSat 2C-RAIN-PROFILE product. This difference becomes much smaller when products are compared at 1000 m above the surface (mitigating surface clutter issues) and when forcing the frequency of rain from CloudSat to match the frequency from GPM (mitigating sensitivity issues). However, even then a gap of about 25% remains. Using an optimal estimation retrieval algorithm on the underlying data, we retrieve a similar result, but find that the remaining difference between the GPM and CloudSat retrieved rain rates can be almost entirely accounted for by inconsistent assumptions about the shape of the drop size distribution (DSD) that are made in the two retrievals. We conclude that DSD assumptions contribute significantly to the relative underestimation of warm rain by GPM compared to CloudSat. Because the choice of DSD model has such a large effect on retrieved rain rates, more work is needed to determine whether the DSD models assumed by either the GPM_2BCMB or 2C-RAIN-PROFILE algorithms are actually appropriate for warm rain.

Extracting 3-D Radar Features to Improve Quantitative Precipitation Estimation in Complex Terrain based on Deep Learning Neural Networks

Abstract This paper proposes a new quantitative precipitation estimation (QPE) technique to provide accurate rainfall estimates in complex terrain, where conventional QPE has limitations. The operational radar QPE in Taiwan is mainly based on the simplified relationship between radar reflectivity Z and rain rate R [R(Z) relation] and only utilizes the single-point lowest available echo to estimate rain rates, leading to low accuracy in complex terrain. Here, we conduct QPE using deep learning that extracts features from 3-D radar reflectivities to address the above issues. Convolutional neural networks (CNN) are used to analyze contoured frequency by altitude diagrams (CFADs) to generate the QPE. CNN models are trained on existing rain gauges in northern and eastern Taiwan with the three-year data during 2015–17 and validated and tested using 2018 data. The weights of heavy rains (≧10 mm h-1) are increased in the model loss calculation to handle the unbalanced rainfall data and improve accuracy. Results show that the CNN outperforms the R(Z) relation based on the 2018 rain-gauge data. Furthermore, this research proposes methods to conduct 2-D gridded QPE at every pixel by blending estimates from various trained CNN models. Verification based on independent rain gauges shows that the CNN QPE solves the underestimation of the R(Z) relation in mountainous areas. Case studies are presented to visualize the results, showing that the CNN QPE generates better small-scale rainfall features and more accurate precipitation information. This deep learning QPE technique may be helpful for the disaster prevention of small-scale flash floods in complex terrain.

Investigating the feasibility of using precipitation measurements from weather RaDAR to estimate potential recharge in regional aquifers: the Majella massif case study in Central Italy

Rain gauge spatial sparsity and temporal discontinuity of data represent one of the major issues for reliable recharge estimations. In the past decades, the use of ground-based microwave weather RaDAR has dramatically improved quantitative rainfall estimation by providing spatially continuous estimates of rainfall over an area of more than 400 km2 every 10 minutes. Furthermore, weather RaDAR data have also proved relatively reliable in mountainous areas. These paramount features of RaDAR-derived precipitation data could improve the estimation of potential recharge of aquifers, which rely on geospatializations (e.g., Thiessen polygons) of rainfall data collected by a sparse rain gauge network which often shows lacking at high altitude (i.e., recharge areas), introducing additional uncertainty in the inflow volumes. Weather RaDAR rainfall estimation is also affected by various sources of error, which can be reduced by proper post-processing; however, uncertainties remain, especially for surface rain rate estimations. Despite the currently necessary complex numerical processing, the purpose of the study is to evaluate the use of the weather RaDAR data as an alternative or in addition to meteorological data. Based on the above considerations, the feasibility of using RaDAR-based precipitation data to estimate aquifer potential recharge and calculate a detailed water budget in the areas characterized by high elevations, such as the Majella massif in the central Apennines, has been evaluated. To address this objective, the water budget has been calculated in the 2017-2018 period using both RaDAR-based precipitation data and rain gauge data, as well as adopting different methods (i.e., Turc and Thornthwaite). Although intrinsically uncertain, the RaDAR-based precipitation data provided solid results, pointed out by comparing it with water budget obtained by rain gauge data, and especially with experimental literature data. This interdisciplinary work may pave the way for continuous monitoring of aquifer potential recharge at extremely high temporal and spatial resolution.

Accuracy Assessment of a Satellite-Based Rain Estimation Algorithm Using a Network of Meteorological Stations over Epirus Region, Greece

The study concerns the quantitative evaluation of a satellite-based rain rate (RR) estimation algorithm using measurements from a network of ground-based meteorological stations across the Epirus Region, Greece, an area that receives among the maximum precipitation amounts over the country. The utilized version of the rain estimation algorithm uses the Meteosat-11 Brightness Temperature in five spectral regions ranging from 6.0 to 12.0 μm (channels 5–7, 9 and 10) to estimate the rain intensity on a pixel basis, after discriminating the rain/non-rain pixels with a simple thresholding method. The rain recordings of the meteorological stations’ network were spatiotemporally correlated with the satellite-based rain estimations, leading to a dataset of 2586 pairs of matched values. A statistical analysis of these pairs of values was conducted, revealing a Mean Error (ME) of −0.13 mm/h and a correlation coefficient (CC) of 0.52. The optimal computed Probability of False Detection (POFD), Probability of Detection (POD), the False Alarm Ratio (FAR) and the bias score (BIAS) are equal to 0.32, 0.88, 0.12 and 0.94, respectively. The study of the extreme values of the RR (the highest 10%) also shows satisfactory results (i.e., ME of 1.92 mm/h and CC of 0.75). The evaluation statistics are promising for operationally using this algorithm for rain estimation on a real-time basis.

Characterizing Ice-Scattering Homogeneity in TRMM Microwave Imagers and Its Influence on Oceanic Rain-Rate Estimation Bias of TRMM Precipitation Radar

Precipitation homogeneity is one of the main factors that contribute to the difference in the rain-rate estimation from meteorological satellites. Using the Tropical Rainfall Measuring Mission (TRMM) products, this paper aims to characterize the homogeneity of ice-scattering signals from TRMM Microwave Imagers (TMIs) as related to rain-rate estimation bias with TRMM Precipitation Radar (PR). Statistical information about the polarization-corrected brightness temperature (PCT) from the TMI 85 GHz band is obtained over the global ocean in the tropics. The characteristics are the fraction of PCT below a given threshold, the minimum value, and the standard deviation that are calculated at a 0.25° × 0.25°grid level. The average values of rain-rate estimation from TRMM PR and TMI in the same grid position and time are then compared. This result indicates that the rain-rate estimation bias is influenced by the homogeneity and organization of precipitation systems. Using the statistical signature of ice-scattering signals at the grid level, an adjustment was implemented for TMI rain-rate estimation. The results could produce rain-rate estimations that conform more to PR, particularly for the inhomogeneous precipitation system mostly affected by stratiform rain. The characterization of ice-scattering signals as a proxy to the precipitation homogeneity, as presented in this research, could be implemented in order to improve the accuracy of satellite rain-rate estimation in the future.

Opportunistic Rain Rate Estimation from Measurements of Satellite Downlink Attenuation: A Survey.

Recent years have witnessed a growing interest in techniques and systems for rainfall surveillance on regional scale, with increasingly stringent requirements in terms of the following: (i) accuracy of rainfall rate measurements, (ii) adequate density of sensors over the territory, (iii) space-time continuity and completeness of data and (iv) capability to elaborate rainfall maps in near real time. The devices deployed to monitor the precipitation fields are traditionally networks of rain gauges distributed throughout the territory, along with weather radars and satellite remote sensors operating in the optical or infrared band, none of which, however, are suitable for full compliance to all of the requirements cited above. More recently, a different approach to rain rate estimation techniques has been proposed and investigated, based on the measurement of the attenuation induced by rain on signals of pre-existing radio networks either in terrestrial links, e.g., the backhaul connections in cellular networks, or in satellite-to-earth links and, among the latter, notably those between geostationary broadcast satellites and domestic subscriber terminals in the Ku and Ka bands. Knowledge of the above rain-induced attenuation permits the retrieval of the corresponding rain intensity provided that a number of meteorological and geometric parameters are known and ultimately permits estimating the rain rate locally at the receiver site. In this survey paper, we specifically focus on such a type of “opportunistic” systems for rain field monitoring, which appear very promising in view of the wide diffusion over the territory of low-cost domestic terminals for the reception of satellite signals, prospectively allowing for a considerable geographical capillarity in the distribution of sensors, at least in more densely populated areas. The purpose of the paper is to present a broad albeit synthetic overview of the numerous issues inherent in the above rain monitoring approach, along with a number of solutions and algorithms proposed in the literature in recent years, and ultimately to provide an exhaustive account of the current state of the art. Initially, the main relevant aspects of the satellite link are reviewed, including those related to satellite dynamics, frequency bands, signal formats, propagation channel and radio link geometry, all of which have a role in rainfall rate estimation algorithms. We discuss the impact of all these factors on rain estimation accuracy while also highlighting the substantial differences inherent in this approach in comparison with traditional rain monitoring techniques. We also review the basic formulas relating rain rate intensity to a variation of the received signal level or of the signal-to-noise ratio. Furthermore, we present a comprehensive literature survey of the main research issues for the aforementioned scenario and provide a brief outline of the algorithms proposed for their solution, highlighting their points of strength and weakness. The paper includes an extensive list of bibliographic references from which the material presented herein was taken.

Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm

Satellite rainrate estimation is a great challenge, especially in mesoscale convective systems (MCSs), which is mainly due to the absence of a direct physical connection between observable cloud parameters and surface rainrate. The machine learning technique was employed in this study to estimate rainrate in the MCS domain via using cloud top temperature (CTT) derived from a geostationary satellite. Five kinds of machine learning models were investigated, i.e., polynomial regression, support vector machine, decision tree, random forest, and multilayer perceptron, and the precipitation of Climate Prediction Center morphing technique (CMORPH) was used as the reference. A total of 31 CTT related features were designed to be the potential inputs for training an algorithm, and they were all proved to have a positive contribution in modulating the algorithm. Random forest (RF) shows the best performance among the five kinds of models. By combining the classification and regression schemes of the RF model, an RF-based hybrid algorithm was proposed first to discriminate the rainy pixel and then estimate its rainrate. For the MCS samples considered in this study, such an algorithm generates the best estimation, and its accuracy is definitely higher than the operational precipitation product of FY-4A. These results demonstrate the promising feasibility of applying a machine learning technique to solve the satellite precipitation retrieval problem.

Real-Time Parameter Estimation of a Dual-Pol Radar Rain Rate Estimator Using the Extended Kalman Filter

The extended Kalman filter is an extended version of the Kalman filter for a non-linear problem. This study applies this extended Kalman filter to the real-time estimation of the parameters of the dual-pol radar rain rate estimator. The estimated parameters are also compared with those based on the least squares method. As an application example, this study considers four storm events observed by the Beaslesan radar in Korea. The findings derived include, first, that the parameters of the radar rain rate estimator obtained by the extended Kalman filter are totally different from those by the least squares method. In fact, the parameters obtained by the extended Kalman filter are found to be more reasonable, and are similar to those reported in previous studies. Second, the estimated rain rates based on the parameters obtained by the extended Kalman filter are found to be similar to those observed on the ground. Even though the parameters estimated by applying the least squares method are quite different from previous studies as well as those based on the extended Kalman filter, the resulting radar rain rate is found to be quite similar to that based on the extended Kalman filter. In conclusion, the extended Kalman filter can be a reliable method for real-time estimation of the parameters of the dual-pol radar rain rate estimator. The resulting rain rate is also found to be of sufficiently high quality to be applicable for other purposes, such as various flood warning systems.

Physical Retrieval of Rain Rate from Ground-Based Microwave Radiometry

Because of its clear physical meaning, physical methods are more often used for space-borne microwave radiometers to retrieve the rain rate, but they are rarely used for ground-based microwave radiometers that are very sensitive to rainfall. In this article, an opacity physical retrieval method is implemented to retrieve the rain rate (denoted as Opa-RR) using ground-based microwave radiometer data (21.4 and 31.5 GHz) of the tropospheric water radiometer (TROWARA) at Bern, Switzerland from 2005 to 2019. The Opa-RR firstly establishes a direct connection between the rain rate and the enhanced atmospheric opacity during rain, then iteratively adjusts the rain effective temperature to determine the rain opacity, based on the radiative transfer equation, and finally estimates the rain rate. These estimations are compared with the available simultaneous rain rate derived from rain gauge data and reanalysis data (ERA5). The results and the intercomparison demonstrate that during moderate rains and at the 31 GHz channel, the Opa-RR method was close to the actual situation and capable of the rain rate estimation. In addition, the Opa-RR method can well derive the changes in cumulative rain over time (day, month, and year), and the monthly rain rate estimation is superior, with the rain gauge validated R2 and the root-mean-square error value of 0.77 and 22.46 mm/month, respectively. Compared with ERA5, Opa-RR at 31GHz achieves a competitive performance.

Characterizing underwater noise during rain at the northeast Pacific continental margin.

Large scale studies of underwater noise during rain are important for assessing the ocean environment and enabling remote sensing of rain rates over the open ocean. In this study, approximately 3.5 yrs of acoustical and meteorological data recorded at the northeast Pacific continental margin are evaluated. The acoustic data are recorded at a sampling rate of 64 kHz and depths of 81 and 581 m at the continental shelf and slope, respectively. Rain rates and wind speeds are provided by surface buoys located in the vicinity of each hydrophone. Average power spectra have been computed for different rain rates and wind speeds, and linear and nonlinear regression have been performed. The main findings are (1) the linear regression slopes highly depends on the frequency range, rain rate, wind speed, and measurement depth; (2) noise levels during rain between 200 Hz and 10 kHz significantly increase with increasing wind speed; and (3) the highest correlation between the spectral level and rain rate occurs at 13 kHz, thus, coinciding with the spectral peak due to small raindrops. The results of this study indicate that previously proposed algorithms for estimating rain rates from acoustic data are not universally applicable but rather have to be adapted for different locations.

Rainfall rate estimation over India using global precipitation measurement’s microwave imager datasets and different variants of fuzzy information system

Effective rain rate estimation using satellite-based measurement is imperative for many hydro-meteorological applications. With the recent advancement in satellite products and retrieving algorithms, rain rate estimations are continuously improving. This study provides a comparative performance appraisal of three hybrid machine learning algorithms namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS) and Hybrid Fuzzy Inference System (HYFIS) for rain rate estimation using the Global Precipitation Measurement (GPM)’s Microwave Imager (GMI) and ground-based Disdrometer data. The in situ sampling was conducted at four different locations (both land and ocean) across the Indian region and different statistical metrics were used to evaluate the performances of these models. The results showed that HYFIS algorithm has provided better rain rate estimation than ANFIS and DENFIS. The study endorses these neuro-fuzzy models for generating accurate precipitation products and can be considered as an alternative for future satellite retrieval algorithms.