Search

Accurate and precise KDP estimates are essential for radar-based applications, especially in quantitative precipitation estimation and radar data quality control routines. The accuracy of these estimates largely depends on the post-processing of the radar's measured Φ DP , which aims to reduce noise and backscattering effects while preserving fine-scale precipitation features. In this study, we evaluate the performance of several publicly available KDP estimation methods implemented in open-source libraries such as PyArt and Wradlib, and the method used in the Vaisala weather radars. To benchmark these methods, we employ a polarimetric self-consistency approach that relates KDP to reflectivity and differential reflectivity in rain, providing a reference self-consistency KDP (K DPSC) for comparison. This approach allows for the construction of the reference KDP observations that can be used to assess the accuracy and robustness of the studied KDP estimation methods. We assess each method by quantifying uncertainties using C-band weather radar observations where the reflectivity values ranged between 20 and 50 dBZ. Using the proposed evaluation framework we could define optimized parameter settings for the methods that have user-configurable parameters. Most of such methods showed significant reduction in the estimation errors after the optimization with respect to the default settings. We have found significant differences in the performances of the studied methods, where the best performing methods showed smaller normalized biases in the high reflectivity values (i.e., ≥ 40 dBZ) and overall smaller normalized root mean squared errors across the range of reflectivity values. [ABSTRACT FROM AUTHOR]

Because of their high sensitivity to hydrometeors and their high vertical resolutions, space-borne radar observations are emerging as an undeniable asset for Numerical Weather Prediction (NWP) applications. The EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) NWP SAF (Satellite Application Facility) released an active sensor module within version 13 of the RTTOV (Radiative Transfer for TOVS) software with the goal of simulating both active and passive microwave instruments within a single framework using the same radiative transfer assumptions. This study provides an in-depth description of the radar simulator available within this software. In addition, this study proposes a revised version of the existing melting layer parametrization scheme of Bauer (2001) within the RTTOV-SCATT v13.1 model to provide a better fit to observations below the freezing level. Simulations are performed with and without melting layer schemes for the Dual precipitation radar (DPR) instrument onboard GPM using the ARPEGE (Action de Recherche Petite Echelle Grande Echelle) global NWP model running operationally at Météo-France for two different one-month periods (June, 2020 and January, 2021). Results for a case study over the Atlantic ocean show that the revised melting scheme produces more realistic simulations as compared to the default scheme both at Ku (13.5 GHz) and Ka (35.5 GHz) frequencies and these simulations are much closer to observations. A statistical assessment using more samples show significant improvement of the first-guess departure statistics with the revised scheme compared to the existing melting scheme. As a step further, this study showcases the use of melting layer simulations for the classification of precipitation (stratiform, convective and transition) using the Dual Frequency Ratio algorithm (DFR). The classification results also reveal a significant overestimation of the rain reflectivities in all hemispheres, which can either be due to a tendency of the ARPEGE model to produce a too large amount of convective precipitation, or to a mis-representation of the convective precipitation fraction within the forward operator. [ABSTRACT FROM AUTHOR]

Raindrop size distribution (DSD) plays a crucial role in enhancing the accuracy of radar quantitative precipitation estimates in the Tibetan Plateau (TP). However, there is a notable scarcity of long-term, high-resolution observations in this region. To address this issue, long-term observations from a two-dimensional video disdrometer (2DVD) were leveraged to refine the radar and satellite-based algorithms for quantifying precipitation in the hinterland of the TP. It was observed that weak precipitation (R<1, mm h−1) accounts for 86% of the total precipitation time, while small raindrops (D<2 mm) comprise 99% of the total raindrop count. Furthermore, the average spectral width of the DSD increases with increasing rain rate. The DSD characteristics of convective and stratiform precipitation were discussed across five different rain rates, revealing that convective precipitation in Yangbajain (YBJ) exhibits characteristics similar to maritime-like precipitation. The constrained relationships between the slope Λ and shape μ, Dm and Nw of gamma DSDs were derived. Additionally, we established a correlation between the equivalent diameter and drop axis ratio and found that raindrops on the TP attain a nearly spherical shape. Consequently, the application of the rainfall retrieval algorithms of the dual-frequency precipitation radar in the TP is improved based on the statistical results of the DSD. [ABSTRACT FROM AUTHOR]

In recent years, the application of compact and cost-effective deployable X-band polarimetric radars has gained in popularity, particularly in regions with complex terrain. The deployable radars generally use a radome constructed by joining multiple panels using metallic threads to facilitate easy transportation. As a part of the ESPOIRS project, Laboratoire de l'Atmosphεave;re et des Cyclones has acquired an X-band meteorological radar with four panel radome configuration. In this study, we investigated the effect of the radome on the measured polarimetric variables, particularly differential reflectivity and differential phase. Our observations reveal that the metallic threads connecting the radome panels introduce power loss at vertical polarization, leading to a positive bias in the differential reflectivity values. To address the spatial variability bias observed in differential reflectivity and differential phase, we have developed a novel algorithm based on the Discrete Fourier Transform. The algorithm's performance was tested during an intense heavy rainfall event caused by the Batsirai cyclone on Reunion Island. The comparative and joint histogram analysis demonstrates the algorithm's effectiveness in correcting the spatial bias in the polarimetric variables. [ABSTRACT FROM AUTHOR]

Concerns about perceived widespread declines in insect numbers have led to recognition of a requirement for long-term monitoring of insect biodiversity. Here we examine whether an existing, radar-based, insect monitoring system developed for research on insect migration could be adapted to this role. The radar detects individual larger (greater than 10 mg) insects flying at heights of 150–2550 m and estimates their size and mass. It operates automatically and almost continuously through both day and night. Accumulation of data over a 'half-month' (approx. 15 days) averages out weather effects and broadens the source area of the wind-borne observation sample. Insect counts are scaled or interpolated to compensate for missed observations; adjustment for variation of detectability with range and insect size is also possible. Size distributions for individual days and nights exhibit distinct peaks, representing different insect types, and Simpson and Shannon–Wiener indices of biodiversity are calculated from these. Half-month count, biomass and index statistics exhibit variations associated with the annual cycle and year to year changes that can be attributed to drought and periods of high rainfall. While species-based biodiversity measures cannot be provided, the radar's capacity to estimate insect biomass over a wide area indicates utility for tracking insect population sizes. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [ABSTRACT FROM AUTHOR]

Machine learning‐based approaches demonstrate a significant potential in radar quantitative precipitation estimation (QPE) applications. In contrast to conventional methods that depend on local raindrop size distributions, deep learning (DL) can establish an effective mapping from three‐dimensional radar observations to ground rain rates. However, the lack of transparency in DL models poses challenges toward understanding the underlying physical mechanisms that drive their outcomes. This study aims to develop a DL‐based QPE system and provide a physical explanation of radar precipitation estimation process. This research is designed by employing a deep neural network consisting of two modules. The first module is a quantitative precipitation estimation network that has the capability to learn precipitation patterns and spatial distribution from multidimensional polarimetric radar observations. The second module introduces a quantitative precipitation estimation shapley additive explanations method to quantify the influence of each radar observable on the model estimate across various precipitation intensities. Plain Language Summary: Ground radars can provide continuous spatial observations over large areas with high spatiotemporal resolutions, so they form the infrastructure for precipitation monitoring and observation in many countries. Recently, deep learning (DL) techniques have shown great potential for use in polarimetric radar‐based precipitation estimates. Nevertheless, the black‐box and turn‐key characteristics of DL models make it difficult for researchers to understand the model decision‐making process and cast doubt on the reliability of the model results. This study introduces a physically explainable polarization radar‐based quantitative precipitation estimation (QPE) system built on DL technology that can explain the causes of the precipitation estimates provided by deep learning models under different rainfall amounts. An experiment indicates that our model achieves better estimates than the conventional methods. Furthermore, the explainability methodology allows for visualization of the microphysical precipitation information. Being the initial attempt to apply explainability learning in the QPE domain, the explainability results may offer valuable guidance for rainfall estimation. Key Points: A polarimetric radar‐based rainfall estimation system is developed using deep neural networksThe deep learning‐based rainfall estimates generally outperform products derived from traditional parametric relationsThe proposed deep learning interpretation method can provide physical and statistical explanations of the model decision‐making process [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The IMERG data product is an optimal combination of precipitation estimates from the Global Precipitation Mission (GPM), making use of a variety of data types, primarily data from various spaceborne passive instruments. Previous versions of the IMERG product have been extensively validated by comparisons with gauge data and ground-based radars over land. However, IMERG rain rates, especially sub-daily, over open ocean are less validated due to the scarcity of comparison data, particularly with the relatively new Version 07. To address this issue, we consider IMERG V07 30-min data acquired in tropical cyclones over open ocean. We perform two tasks. The first is a straightforward comparison between IMERG precipitation rates and those retrieved from the GPM Dual-frequency Precipitation Radar (DPR). From this, we find that IMERG and DPR are close at low rain rates, while, at high rain rates, IMERG tends to be lower than DPR. The second task is the assessment of IMERG's ability to represent or detect structures commonly seen in tropical cyclones, including the annular structure and concentric eyewalls. For this, we operate on IMERG data with many machine learning algorithms and are able to achieve a 96% classification accuracy, indicating that IMERG does indeed contain TC structural information. [ABSTRACT FROM AUTHOR]

This study utilizes comprehensive observational data from a stratiform mixed-cloud precipitation event in Liupan Mountains, combined with ground-based millimeter-wave cloud radar (CR), micro rain radar (MRR), and microwave radiometer (MR) data, to study the evolution characteristics and conversion efficiency of precipitation particles in the ice–water mixed layer, melting layer, and below these layers during the formation and dissipation of precipitation. The results show the following: (1) When precipitation particles occupy more than 20% of cloud layers detected by cloud radar, the ice–water mixed cloud layer descends and evolves into a precipitating cloud. (2) During surface precipitation periods, the proportion of raindrops forming precipitation was equivalent to that of small-scale precipitation particles in the cloud layers. The proportion of precipitation particles in the cloud layers with temperatures below 0 °C averaged 25%. Ice-phase particles within the bright band (BB) melted, coalesced, and grew into larger precipitation particles, increasing their proportion to 55%. (3) After surface precipitation ended, the water content and precipitation rate of the cloud layer were 60% and 52% of those during the precipitation process, respectively. The proportion of small-scale precipitation particles in the cloud layers was approximately half of that during the precipitation period. A large number of evaporated small-scale precipitation particles floated in the air layer below the clouds, occupying less than 6.0% of the cloud layers. [ABSTRACT FROM AUTHOR]

Information on the spatial distribution of rainfall is required for many applications, including water and flood management. Weather radars can provide quantitative rainfall estimation over an area. Taking the example of an X-band radar installed at Mandhardev in the Western Ghats, we show that radar reflectivity data can have significant bias despite following standard calibration procedures. We report a technique to identify bias in the X-band radar reflectivity factor using collocated disdrometer observations and propose a methodology to correct it. Simultaneous data collected on 83 days during June–September of 2018 are used. Our results show that bias in the radar reflectivity factor reduced from –8.3 to –0.8 dB after correction. The estimated 83-day accumulated rainfall using uncorrected radar reflectivity data is 80% less compared to that of the disdrometer and the difference between the two reduces to 2% with correction. Bias in the estimated rainfall reduces from –30 to –0.7 mm day–1 and the RMSE reduces by 42%. There are days where daily rainfall from the two instruments agrees well with each other, while large differences exist on some days. We show that the sampling issue is one of the major sources of error, and its contribution depends on the nature/type of precipitating clouds, whereas uncertainties in the Z–R relationships account for about 15% difference. [ABSTRACT FROM AUTHOR]

Featured Application: The new algorithm proposed in this paper is mainly applied to radar data processing. By accurately distinguishing between targets and clutter, the clutter removal rate can be improved, and target trajectories can be quickly and accurately established. This algorithm can comprehensively utilize the advantages of classifiers and data characteristics, reducing the dependence on training samples. Therefore, it is also suitable for classification applications on small sample datasets. Accurately classifying targets and clutter plots is crucial in radar data processing. It is beneficial for filtering out a large amount of clutters and improving the track initiation speed and tracking accuracy of real targets. However, in practical applications, this problem becomes difficult due to complex electromagnetic environments such as cloud and rain clutter, sea clutter, and strong ground clutter. This has led to poor performance of some commonly used radar plot classification algorithms. In order to solve this problem and further improve classification accuracy, the radar plot classification algorithm based on evidence adaptive updating (RPC-EAU) is proposed in this paper. Firstly, the multi-dimensional recognition features of radar plots used for classification are established. Secondly, the construction and combination of mass functions based on feature sample distribution are designed. Then, a confidence network classifier containing an uncertain class was designed, and an iterative update strategy for it was provided. Finally, several experiments based on synthetic and real radar plots were presented. The results show that RPC-EAU can effectively improve the radar plot classification performance, achieving a classification accuracy of about 0.96 and a clutter removal rate of 0.95. Compared with some traditional radar pattern recognition algorithms, it can improve by 1 to 10 percentage points. The target loss rate of RPC-EAU is also the lowest, only about 0.02, which is about one third to one half of the comparison algorithms. In addition, RPC-EAU avoids clustering all radar points in each update, greatly saving the computational time. The proposed algorithm has the characteristics of high classification accuracy, low target loss rate, and less computational time. Therefore, it is suitable for radar data processing with high timeliness requirements and multiple radar plots. [ABSTRACT FROM AUTHOR]

Radar rainfall nowcasting has mostly been applied to relatively large (often rural) domains (e.g., river basins), although rainfall nowcasting in small urban areas is expected to be more challenging. Here, we selected 80 events with high rainfall intensities (at least one 1-km2 grid cell experiences precipitation >15 mm h−1 for 1-h events or 30 mm day−1 for 24-h events) in five urban areas (Maastricht, Eindhoven, The Hague, Amsterdam, and Groningen) in the Netherlands. We evaluated the performance of 9060 probabilistic nowcasts with 20 ensemble members by applying the short-term ensemble prediction system (STEPS) from Pysteps to every 10-min issue time for the selected events. We found that nowcast errors increased with decreasing (urban) areas especially when below 100 km2. In addition, at 30-min lead time, the underestimation of nowcasts was 38% larger and the discrimination ability was 11% lower for 1-h events than for 24-h events. A set of gridded correction factors for the Netherlands, CARROTS (Climatology-based Adjustments for Radar Rainfall in an Operational Setting) could adjust the bias in real-time QPE and nowcasts by 70%. Yet, nowcasts were still found to underestimate rainfall more than 50% above 40-min lead time relative to the reference, which indicates that this error originates from the nowcasting model itself. Also, CARROTS did not adjust the rainfall spatial distribution in urban areas much. In summary, radar-based nowcasting for urban areas (between 67 and 213 km2) in the Netherlands exhibits a short skillful lead time of about 20 min, which can only be used for last-minute warning and preparation. [ABSTRACT FROM AUTHOR]

The classification of movement in space is one of the key tasks in environmental science. Various geospatial data such as rainfall or other weather data, data on animal movement or landslide data require a quantitative analysis of the probable movement in space to obtain information on potential risks, ecological developments or changes in future. Usually, machine-learning tools are applied for this task, as these approaches are able to classify large amounts of data. Yet, machine-learning approaches also have some drawbacks, e.g. the often required large training sets and the fact that the algorithms are often hard to interpret. We propose a classification approach for spatial data based on ordinal patterns. Ordinal patterns have the advantage that they are easily applicable, even to small data sets, are robust in the presence of certain changes in the time series and deliver interpretative results. They therefore do not only offer an alternative to machine-learning in the case of small data sets but might also be used in pre-processing for a meaningful feature selection. In this work, we introduce the basic concept of multivariate ordinal patterns and the corresponding limit theorem. A simulation study based on bootstrap demonstrates the validity of the results. The approach is then applied to two real-life data sets, namely rainfall radar data and the movement of a leopard. Both applications emphasize the meaningfulness of the approach. Clearly, certain patterns related to the atmosphere and environment occur significantly often, indicating a strong dependence of the movement on the environment. [ABSTRACT FROM AUTHOR]

In this study, we investigate the seasonal and diurnal variations in cloud occurrence frequency, as well as cloud vertical structure (CVS) characteristics under different seasons and precipitation intensities over the Guangzhou region in South China, based on the analysis of millimeter-wave cloud radar (MMCR) and ground automatic weather station rainfall observations from May 2019 to August 2021. The results showed that the occurrence frequency of clouds exhibits a bimodal distribution throughout the year, with peaks in March to June and October, reaching its highest occurrence in May at approximately 80% and its lowest from December to February at around 40%. Additionally, there are distinct diurnal variations in occurrence frequency, with the lowest rates occurring around 0005 LST, rapidly increasing after 0006 LST, and peaking during the afternoon to evening hours. Cloud top height (CTH) shows bimodal distributions during the pre-flood and post-flood seasons. The most frequently occurring range of CTH during the pre-flood season is below 3 km, accounting for approximately 43%, while during the post-flood season, it ranges from 11 to 14 km, constituting about 37%. For precipitation clouds, CTH can extend beyond 12 km, with the radar reflectivity decreasing gradually with increasing height. The highest frequencies of radar echoes are observed below 2 km and between 4 and 7 km, exhibiting clear diurnal variations, with echoes mainly below 2 km and between 4 to 6 km during the early morning, intensifying and shifting to higher altitudes during the day and reaching their maximum below 4 km during the afternoon to nighttime hours, while both the frequency and intensity increase in the height range of 4 to 12 km. Vertical profiles of radar reflectivity and cloud ice/liquid water content (IWC/LWC) exhibit similar trends under different precipitation intensities. The main differences are observed below 4 km, where both radar reflectivity and IWC/LWC generally increase with increasing precipitation intensity. These findings contribute to a better understanding of cloud characteristics in the South China region, enhance the accuracy of model simulations, and provide a scientific basis for accurate forecasting and warning of meteorological disasters. [ABSTRACT FROM AUTHOR]

Rainfall can reduce the radar signal through a radome. The effects of different water layers on the radome transmission are studied using quasi-optical measurement from 26 to 330 GHz including water permittivity characterization. The water is deposited as drops of different sizes or as a uniform layer while keeping a constant volume, highlighting the importance of the control of the total wet surface for radar applications. A compact model is proposed to predict the signal loss due to water on the radome depending on the rain flow. [ABSTRACT FROM AUTHOR]

Precipitation plays an important role in cloud and aerosol processes over the Southern Ocean (SO). The main objective of this study is to characterize SO precipitation properties associated with SO stratocumulus clouds. We use data from the Southern Ocean Clouds Radiation Aerosol Transport Experimental Study (SOCRATES), and leverage observations from airborne radar, lidar, and in situ probes. We find that for the cold‐topped clouds (cloud‐top‐temperature <0°C), the phase of precipitation with reflectivity >0 dBZ is predominantly ice, while reflectivity < −10 dBZ is predominantly liquid. Liquid‐phase precipitation properties are retrieved where radar and lidar are zenith‐pointing. Power‐law relationships between reflectivity (Z) and rain rate (R) are developed, and the derived Z–R relationships show vertical dependence and sensitivity to the presence of droplets with diameters between 10 and 40 μm. Using derived Z–R relationships, a reflectivity‐velocity (ZV) retrieval method, and a radar‐lidar retrieval method, we derive rain rate and other precipitation properties. The retrieved rain rate from all three methods shows good agreement with in‐situ aircraft estimates, with rain rates typically being quite light (<0.1 mm hr−1). We examine the vertical distribution of precipitation properties, and find that rain rate, precipitation number concentration, and precipitation liquid water all decrease as one gets closer to the surface, while precipitation size and distribution width increases. We also examine how cloud base rain rate (RCB) depends on cloud depth (H) and aerosol concentration (Na) for particles with a diameter greater than 70 nm, and find that RCB is proportional to H3.1Na−0.8 ${H}^{3.1}\,{N}_{a}^{-0.8}$. Plain Language Summary: Precipitation plays an important role over the Southern Ocean (SO), such as transferring water from the atmosphere to the ocean, and affecting clouds and aerosols (tiny airborne particles). This study aims to characterize SO precipitation properties using aircraft data that can count the number and size of cloud and precipitation droplets, as well as lidar and radar that measure light and microwaves respectively reflected by droplets. Using information from lidar, we can distinguish the precipitation phase, and we find that ice precipitation is more frequent when the amount of reflected energy measured by the radar (radar reflectivity) is larger than a certain threshold. We derived relationships between rain rate and radar reflectivity. We also find the precipitation properties inferred from radar and lidar data compare well with direct measurements from the aircraft, and the precipitation tends to be very light. We study how precipitation properties vary vertically, and find that as one gets closer to the surface, there is a decrease in precipitation droplet number and water, while there is an increase in the average size of droplets. We also find that rain rate depends on how thick the clouds are and the number of aerosols, consistent with theoretical expectations. Key Points: Liquid‐phase precipitation retrievals show good agreement with in situ observations and feature the prevalence of light rainReflectivity to rain rate relationships are developed, showing vertical dependence and sensitivity to the intermediate‐sized dropsThe below‐cloud precipitation phase with radar reflectivity >0 dBZ is mostly ice, while radar reflectivity <−10 dBZ is mostly liquid [ABSTRACT FROM AUTHOR]

Large satellite discrepancies and model biases in representing precipitation over the Southern Ocean (SO) are related directly to the region's limited surface observations of precipitation. To help address this knowledge gap, the study investigated the precipitation characteristics and rain rate retrievals over the remote SO using ship‐borne data of the Ocean Rainfall And Ice‐phase precipitation measurement Network disdrometer (OceanRAIN) and dual‐polarimetric C‐band radar (OceanPOL) aboard the Research Vessel (RV) Investigator in the Austral warm seasons of 2016–2018. Seven distinct synoptic types over the SO were analyzed based on their radar polarimetric signatures, surface precipitation phase, and rain microphysical properties. OceanRAIN observations revealed that the SO precipitation was dominated by drizzle and light rain, with small‐sized raindrops (diameter <1 mm) constituting up to 47% of total accumulation. Precipitation occurred most frequently over the warm sector of extratropical cyclones, while concentrations of large‐sized raindrops (diameter >3 mm) were prominent over synoptic types with colder and more convectively unstable environments. OceanPOL observations complement and extend the surface precipitation properties sampled by OceanRAIN, providing unique information to help characterize the variety of potential precipitation types and associated mechanisms under different synoptic conditions. Raindrop size distributions (DSD) measured with OceanRAIN over the SO were better characterized by analytical DSD forms with two‐shape parameters than single‐shape parameters currently implemented in satellite retrieval algorithms. This study also revised a rainfall retrieval algorithm for C‐band radars to reflect the large amount of small drops and provide improved radar rainfall estimates over the SO. Plain Language Summary: Precipitation is a major component of the hydrologic cycle in high‐latitude regions including the remote Southern Ocean (SO). However, large differences continue to exist among current precipitation products in the region, owing in part to the absence of high‐quality surface observational records suitable for evaluation across a range of temporal and spatial scales. This work uses two instruments aboard the RV Investigator over the Australian sector of the SO in the Austral warm seasons of 2016–2018: the OceanRAIN disdrometer and OceanPOL radar. We found variability in radar features and surface precipitation properties among seven distinct synoptic conditions over the SO. This work also discussed two important findings related to remote sensing retrievals of SO rain. First, we demonstrated why the rainfall retrieval assumptions in satellite algorithms may need to be refined to account for the unique rainfall properties in the SO. Second, we formulated a new set of equations suitable for shipborne C‐band radars in improving rain rate estimates over the region. This work leads toward more accurate, high‐resolution estimates of precipitation over the measurement‐sparse SO to better understand a range of climatological and meteorological processes in the region. Key Points: Different synoptic types across the Southern Ocean exhibit distinctive polarimetric signatures and surface precipitation propertiesSmall raindrops of less than one millimeter contribute up to 47% of total accumulation during the Austral warm seasons over the regionA new formulation for radar rainfall estimates that reflects the large numbers of small drops over the Southern Ocean is proposed [ABSTRACT FROM AUTHOR]

Dual-polarization radar often detects columnar regions of enhanced differential reflectivity (ZDR) extending vertically above the environmental 0 °C level. Indicative of supercooled liquid drops and wet ice particles lofted by strong updrafts, these ZDR columns are increasingly understood to be of use in predicting extreme rainfall. With the aim of achieving practical application of ZDR column measurements, this paper focuses on the relationship between the height of ZDR columns and rainfall intensity near the ground. All the data on ZDR columns analyzed in this study was collected from weather radar stations in Japan. The height of each column and rainfall rates at low levels were analyzed using an automated algorithm. A regression analysis result reveals peak column height to be positively correlated with maximum rainfall rate near ground level, and that rainfall intensity on the ground is likely to exceed 50 mm h-1 when radar identifies a ZDR column. Furthermore, extreme rainfall with an intensity of 180 mm h-1 or more is likely associated with a column over 3 km tall from the 0 °C level. These findings suggest that surveillance of ZDR columns can contribute to the reliability of very short-range forecasts or nowcasts as well as assist with the issue of early warnings of extreme rainfall and flash floods. [ABSTRACT FROM AUTHOR]

Given that the Gaussianity of observation error distribution is the fundamental principle of some data assimilation and machine learning algorithms, the error structure of radar reflectivity becomes increasingly important with the development of high resolution forecasts and nowcasts of convective systems. This study examines the error distribution of radar reflectivity and discusses what give rise to the non-Gaussian error distribution by using 6 month observations minus backgrounds (OmBs) of composites of vertical maximum reflectivity (CVMRs) in mountainous and hilly areas. By following the symmetric error model in all-sky satellite radiance assimilation, we unveil the error structure of CVMRs as a function of symmetric rain rates, which is the average of observed and simulated rain rates. Unlike satellite radiance, the error structure of CVMRs shows a sharper slope in light precipitations than moderate precipitations. Thus, a three-piecewise fitting function is more suitable for CVMRs. The probability density functions of OmBs normalized by symmetric rain rates become more Gaussian in comparison with the probability density function normalized by the whole samples. Moreover, the possibility of using third-party predictor to construct the symmetric error model are also discussed in this study. The Gaussianity of OmBs can be further improved by using a more accurate precipitation observations. According to the Jensen-Shannon divergence, a more linear predictor, the logarithmic transformation of rain rate, can provide the most Gaussian error 20 distribution in comparison with other predictors. [ABSTRACT FROM AUTHOR]

Radar based microphysical retrievals of cloud and droplet properties are vital for informing model parameterisations of clouds and precipitation but these retrievals often do not capture the details of small droplets in light rain or drizzle. A state-of-the-art G-band radar is used here to demonstrate improvements to microphysical retrievals in a case study featuring light rain. Improvements are seen, as compared to W-band radar, in the retrieval of vertical wind speed, due to the location of Mie minima at smaller droplet sizes with the G-band radar. This, in turn, has an impact on the retrieval of the drop size distribution, allowing for better accuracy in the retrieval of the characteristic drop diameter and improvements in the retrieval of the number of concentration of small droplet sizes. The differential Doppler velocity between Ka- and G-bands shows increased dynamic range compared to the Ka-W pairing, particularly for instances presenting small characteristic drop diameters. The increased attenuation experienced at G-band enables improved retrievals of liquid water content and precipitation rate when paired with W-band or Ka-band as compared to the W-band and Ka- band pairing. This is particularly noticeable in periods of light rain where the W-band and Ka-band radars receive negligible attenuation while the attenuation at G-band is much greater. [ABSTRACT FROM AUTHOR]

River water-level prediction is crucial for mitigating flood damage caused by torrential rainfall. In this paper, we attempt to predict river water levels using a deep learning model based on radar rainfall data instead of data from upstream hydrological stations. A prediction model incorporating a two-dimensional convolutional neural network (2D-CNN) and long short-term memory (LSTM) is constructed to exploit geographical and temporal features of radar rainfall data, and a transfer learning method using a newly defined flow–distance matrix is presented. The results of our evaluation of the Oyodo River basin in Japan show that the presented transfer learning model using radar rainfall instead of upstream measurements has a good prediction accuracy in the case of torrential rain, with a Nash–Sutcliffe efficiency (NSE) value of 0.86 and a Kling–Gupta efficiency (KGE) of 0.83 for 6-h-ahead forecast for the top-four peak water-level height cases, which is comparable to the conventional model using upstream measurements (NSE = 0.84 and KGE = 0.83). It is also confirmed that the transfer learning model maintains its performance even when the amount of training data for the prediction site is reduced; values of NSE = 0.82 and KGE = 0.82 were achieved when reducing the training torrential-rain-period data from 12 to 3 periods (with 105 periods of data from other rivers for transfer learning). The results demonstrate that radar rainfall data and a few torrential rain measurements at the prediction location potentially enable us to predict river water levels even if hydrological stations have not been installed at the prediction location. [ABSTRACT FROM AUTHOR]

Rain cells are the most common units in the natural precipitation system. Enhancing the understanding of these rain cell characteristics can significantly improve the cognition of the precipitation system. Previous studies have mostly analyzed rain cells from a single radar data. In this study, we merged the precipitation parameters measured by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) with the muti-channel cloud-top radiance measured by the visible and infrared scanner (VIRS) and the muti-channel brightness temperature measured by the TRMM microwave imager (TMI). The rain cells were identified within the PR orbit, and the swath truncation effect was eliminated. We used two methods for rain cell identification: the minimum bounding rectangle (MBR) method and the best fit ellipse (BFE) method, and compared the differences between these two methods in describing the rain cell characteristics. The results indicate that both methods can better reflect the geometric characteristics of rain cells. Compared with the MBR method, the BFE method can obtain a smaller rain cell area, and the filling ratio is better. However, the MBR method can simplify the data storage volume. Consequently, we employed the MBR method to analyze the precipitation structure of two typical rain cell precipitation cases. The results show that the new rain cell dataset can be used for the analysis of rain cell precipitation parameters and visible/infrared and microwave signals, which provides valuable data for comprehensive studies on the rain cell structural characteristics and furthers the understanding of precipitation mechanisms. The data which were used in this paper are freely available at https://doi.org/10.5281/zenodo.8352587 (Wu et al., 2023). [ABSTRACT FROM AUTHOR]

In this study, the nowcast models provided by the Python pySTEPS library were evaluated using radar derived rain rate data and the satellite product Split-Window Difference (SWD) based on GOES-16 data, focusing on central Mexico. Initially, we obtained a characterization of the rainfall that occurred in the region using the radar rain rate and the SWD. Subsequently the nowcasts were evaluated using both variables. Two nowcast models were employed from pySTEPS: Extrapolation and S-PROG. The results indicate that average SWD is below 2.5 K, 90 min before the onset of rainfall events, and, on average, the SWD is 2 K during rainfall events. The results from both nowcast models were accurate and produced similar results. The nowcasts performed better when SWD data were used as input, having an average Probability of Detection (PoD) above 70% and a False Alarm Rate (FAR) reaching 40% for the 15-min prediction. The nowcasts were less accurate using the radar rain rate as input for the 15-min forecast, where the PoD was maximum 70% and FAR reaching 40%. However, these nowcasts were more reliable during well-organized precipitation events. In this work, it was determined that the nowcast models provided by pySTEPS can provide valuable rain forecasts using GOES-16 satellite and radar data for the central Mexico region. [ABSTRACT FROM AUTHOR]

Weather radar is a crucial tool in rainfall estimation, providing high-resolution estimates in both space and time. Despite this, radar rainfall estimates are subject to many error sources – including attenuation, ground clutter, beam blockage and the drop-size distribution – with the true rainfall field unknown. A flexible stochastic model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard weather radar processing methods, imposing path-integrated attenuation effects, a stochastic drop-size distribution field, along with sampling and random errors. This can provide realistic weather radar images, of which we know the true rainfall field, and the corrected 'best guess' rainfall field which would be obtained if they were observed in the real-world case. The structure of these errors is then investigated, with a focus on frequency and behaviour of 'rainfall shadows'. Half of simulated weather radar images have at least 3 % of significant rainfall rates shadowed and 25 % had at least 45 km2 containing rainfall shadows, resulting in underestimation of potential impacts of flooding. A model framework for investigating the behaviour of errors relating to the radar rainfall estimation process is demonstrated, with the flexible and efficient tool performing well at generating realistic weather radar images visually, for a large range of event types. [ABSTRACT FROM AUTHOR]

Radar data can be of significant utility in investigating characteristics of rainfall events that cannot be studied with rain gauges alone. The recent establishment of a long‐term, quality‐controlled data set covering most of the radars on the Australian continent enables a deeper characterization of rainfall, including heavy rainfall events. This study develops a methodology to identify and characterize rainfall events from radar data and tests its utility by applying it to the regions surrounding the major cities of Brisbane, Sydney, and Melbourne. The event characteristics studied include rainfall accumulation and intensity, event duration and spatial extent, and the contribution convective areas make to the overall event rainfall. Rainfall events in Brisbane and Sydney are found to be more intense, more convective, and smaller in extent whilst producing larger rainfall accumulations than Melbourne rainfall events. Rainfall event duration and total accumulated rainfall are strongly positively correlated, as are the overall event intensity and the intensity of convective rainfall. The events that produce the largest rainfall accumulations exhibit significant differences from the events that produce the highest rainfall intensities. Overall, the study demonstrates that long‐term radar data sets in Australia provide an invaluable and rich source to study rainfall characteristics in a variety of regions at a high spatial and temporal resolution. Plain Language Summary: Weather radars produce a map of rainfall across a large area. Radars can observe characteristics of rain systems that are difficult to capture with rain gauge networks. A data set containing observations from weather radars in Australia can be used to study the characteristics of rain events. Such characteristics include how long an event lasts, how intense the rain is, how much rain falls overall, how large the event is, and how much of the rain comes from strong ascent (convection). This study develops a method of using radar to find rain events and assess their characteristics, and tests the method using Brisbane, Sydney, and Melbourne radar data. Rain events in Brisbane and Sydney have heavier rain and more rain coming from convective areas, and cover smaller areas, with more rain falling than in events in Melbourne. The longer a rain event is, the more rain is produced. An event having high rain intensity also means it produces intense rain from convective areas. Rain events that produce large rainfall amounts are distinct from those which have very intense rain. This study demonstrates that radar data sets in Australia are very useful for studying rain events. Key Points: Brisbane and Sydney rainfall events are more intense and convective than Melbourne rainfall events, which cover larger areasRainfall events with the highest accumulations and rainfall events with the highest intensities show significant differences in characterLong‐term radar data sets are useful for in‐depth analysis of rainfall and heavy rainfall event characteristics in Australia [ABSTRACT FROM AUTHOR]

The accurate prediction of high‐impact weather systems using cloud‐resolving models is still a challenge among researchers. This study evaluates the consistency of the combination of the three‐dimensional variational technique within the Gridpoint Statistical Interpolation assimilation system (GSI‐3DVAR) and nudging in the same modelling system on short‐range forecasts of three heavy rainfall events from the southwest monsoon season of 2021 over the Indian subcontinent. Three experiments have been conducted (i) control (CNTL): assimilated conventional and satellite observations; (ii) radar and lightning data assimilation (RLDA): assimilated radar reflectivity and lightning proxy reflectivity data along with all observations used in CNTL; and (iii) lightning data assimilation (LDA): same as RLDA but without the assimilation of radar data; particularly done to test the impact of assimilation of only lightning data. The model‐simulated rainfall is evaluated by using the Integrated Multi‐Satellite Retrievals (IMERG) for Global Precipitation Measurement (GPM IMERG) rainfall data. The intercomparison of LDA and RLDA for event 1 highlighted that both represent the convective regions reasonably better than CNTL, but RLDA outperforms LDA and thus further assimilation experiments are done with RLDA. RLDA provided reasonably accurate forecasts compared to CNTL, which is evident in the spatial distribution of rainfall and area‐averaged three‐hourly accumulated rainfall. Verification metrics for the three selected heavy rainfall events reveal that an optimal forecast performance (especially in the first six hours of free forecast) is obtained by the simulation with assimilation of radar and lightning data during the pre‐forecast period, through correcting the position and timing of convective centres. The probability of detection (POD) values are higher for light rainfall categories than for the heavy rain categories. POD values were higher in RLDA than CNTL throughout simulation for all three events. For all these three selected events, fractions skill scores (FSS) of RLDA are always better than CNTL with different neighbourhood sizes for different threshold values throughout the forecast period. [ABSTRACT FROM AUTHOR]

'Train effect' is defined as a cloud system in which several convective cells pass over the same place in a short time. Trains produce large amount of rainfall, frequently leading to flash floods, reported mainly over North America during spring and summer. Thirty train events were identified, using radar images, calibrated by rain-gauges, for four winters, all associated with Cyprus Lows (CL). The dynamic factors responsible for their formation in Israel were examined, utilizing the ECMWF Integrated Forecast System of 0.1° resolution. Seventeen out of the 30 events share common features. Each one was found at the cold sector in the southern periphery of a CL at its occluded stage, and located in the left flank of a maximum wind belt, where cyclonic shear vorticity exists. The trains cross the Israeli coast near 32.2°N, with a mean length of 45 km, last 1-3 hrs, and yield ~35 mm rainfall. The maximum wind belts right of the trains were found to delineate the limit of the precipitative region of the CLs. Unlike classical trains, activated by thermal or frontal forcing, the EM trains that develop in cold air-mass, can be entitled 'cold trains', rather than the classical 'warm trains'. [ABSTRACT FROM AUTHOR]

Quantitative precipitation estimates (QPE) can be further improved using estimation algorithms derived from localized raindrop size distribution (DSD) observations. In this study, DSD measurements from two disdrometer stations within Metro Manila during the Southwest monsoon (SWM) period were used to investigate the microphysical properties of rainfall and develop localized dual-polarimetric relations for different radar bands and rainfall types. Observations show that the DSD in Metro Manila is more distributed to larger diameters compared to Southern Luzon and neighboring countries and regions in the Western Pacific. This is reflected by the relatively higher mass-weighted mean diameter (Dm) and smaller shape (μ) and slope (Λ) parameters measured in the region. The average values of Dm and normalized intercept parameter (Nw) in convective rain samples also suggest that convective rains in Metro Manila are highly influenced by both continental and oceanic convective processes. Dual-polarimetric variables simulated using the T-matrix scattering method showed good agreement with disdrometer-derived reflectivity (ZH) values. The 0.5 dB and 0.3° km−1 thresholds for the differential reflectivity (ZDR) and specific differential phase (KDP) based on the blended algorithm of Cifelli (J Atmos Ocean Technol 28:352-364, 2011) and Thompson et al. (2017) are proven to be useful since the utility of the dual-polarimetric variables as rainfall estimators are shown to have dependencies on the radar band and rainfall type. Evaluation of the QPE products with respect to the C-band shows that R (KDP, ZDR) has the best performance among the dual-pol relations and statistically outperformed the conventional Marshall & Palmer relation [R(ZMP)]. The results show that dual-polarimetric variables such as ZDR and KDP can better represent the DSD properties compared to one-dimensional Z, hence providing more accurate QPE products than the conventional R(Z) relations. [ABSTRACT FROM AUTHOR]

During 6–7 May 2017, a record‐breaking nocturnal rainfall event occurred in Guangzhou, China, and it was a typical warm‐sector heavy rainfall event under weak synoptic forcing. A prior observational study by the authors revealed that warm‐rain microphysical processes were dominant and responsible for the record‐breaking precipitation. In this study, the double‐moment Morrison, Thompson and NSSL microphysics schemes in WRF are evaluated against polarimetric radar observations in their ability of reproducing observed microphysical characteristics. The Thompson scheme shows the greatest fidelity to the observed raindrop size distribution (RSD) median value, corresponding to the most amount of precipitation forecast. While the Morrison and NSSL simulations overestimate (underestimate) the raindrop size (number concentration), exhibiting continental‐type convective precipitation. The three experiments slightly overestimate differential reflectivity (ZDR), but significantly underestimate specific differential phase (KDP) and liquid water content by about 30%–50%, implying the undervaluation of number of medium‐sized raindrops. Examinations of the occurrence frequencies of ZDR, KDP, mass‐weighted diameter, and logarithmic normalized intercept parameter for rain suggest that all three schemes fail to reproduce the full variability of observed RSD for the extreme rainfall. The vertical variations of RSD parameters and the Kumjian‐Ryzhkov parameter space suggest that the collision–coalescence is the dominant warm‐rain microphysical process but the simulated process is too weak. This may be attributed to the misrepresented RSD near the melting layer, where the raindrops with lower number concentration and larger sizes cannot grow through the collision–coalescence process as actively. Plain Language Summary: Extreme rainfall is of great concern due to its potential to cause major disasters. Cloud microphysics has been considered one of the main sources of uncertainty in predicting rainfall at cloud‐resolving resolutions. This study investigates the performances of three commonly used schemes representing cloud precipitation processes in an extreme rainfall case that occurred in the warm and moist environment of South China. The Thompson scheme is found to perform better in simulating raindrop sizes and surface precipitation. None of the three schemes reproduces the full variation of observed raindrops, and all of them underestimate raindrop size growth through collision processes. These results suggest areas needing improvement in microphysics schemes for predicting extreme precipitation in sub‐tropical regions. Key Points: Thompson scheme better reproduces raindrop median size and number concentration than two other schemes in a Guangzhou extreme rain eventNone of the three schemes examined adequately predicts the full variability of raindrop size distribution important for extreme rainfallLow raindrop concentration and excessively large sizes in simulations jointly cause the too weak collision–coalescence processes [ABSTRACT FROM AUTHOR]

In this study, we utilized dual-polarization weather radar and disdrometer data to investigate the kinematic and microphysical characteristics of an extreme heavy rainfall event that occurred on 20 July 2021, in Zhengzhou. The results are as follows: FY-2G satellite images showed that extremely heavy rainfall mainly occurred during the merging period of medium- and small-scale convective cloud clusters. The merging of these cloud clusters enhanced the rainfall intensity. The refined three-dimensional wind field, as retrieved by the multi-Doppler radar, revealed a prominent mesoscale vortex and convergence structure at the extreme rainfall stage. This led to echo stagnation, resulting in localized extreme heavy rainfall. We explored the formation mechanism of the notable ZDR arc feature of dual-polarization variables during this phase. It was revealed that during the record-breaking hourly rainfall event in Zhengzhou (20 July 2021, 16:00–17:00 Beijing Time), the warm rain process dominated. Effective collision–coalescence processes, producing a high concentration of medium- to large-sized raindrops, significantly contributed to heavy rainfall at the surface. From an observational perspective, it was revealed that raindrops exhibited significant collision interactions during their descent. Moreover, a conceptual model for the kinematic and microphysical characteristics of this extreme rainfall event was established, aiming to provide technical support for monitoring and early warning of similar extreme rainfall events. [ABSTRACT FROM AUTHOR]

The recent development of satellite products for observing precipitation based on different technologies (microwaves, infrared, etc.) allows for near-real-time meteorological studies. The purpose of this article is to evaluate 11 satellite products (GHE, PDIR, IMERG-Early v6, IMERG-Late v6, CMORPH v0.x, CMORPH-RT v0.x, GSMaP-NRT v7, GSMaP-NRT-GC v7, GSMaP-NOW v7, GSMaP-NOW-GC v7, and DATABOURG) currently available and compare them to 2 ground-based radar networks (PANTHERE and OPERA) and the French rain-gauge network RADOME. Two case studies of intense precipitation over France (22 to 25 April 2022 and 24 to 29 June 2022) were selected. The radar estimations are closer to the RADOME observations than the satellite-based estimations, which tend to globally underestimate the precipitation amounts over the areas of interest while OPERA tends to strongly overestimate precipitation amounts during the June case study. The PANTHERE radar product and the carrier-to-noise product DATABOURG shows promising results. Near-real-time satellite products tend to have closer precipitation amounts to the reference dataset than satellite products with a shorter latency. The use of these datasets for nowcasting developments is plausible but further analyses must be conducted beforehand. [ABSTRACT FROM AUTHOR]

Radar reflectivity plays a crucial role in detecting heavy rainfall and is an important tool for meteorological analysis. However, the coverage of a single radar is limited, leading to the use of satellite data as a complementary source. Consequently, how to bridge the gap between radar and satellite data has become a growing research focus. In this paper, we present MAFormer, a novel model for reconstructing radar reflectivity using satellite data within the Transformer framework. MAFormer consists of two modules: the Axial Local Attention Module and the Mixup Global Attention Module, which extract both local saliency and global similarity. Quantitative and qualitative experiments demonstrate the effectiveness of our proposed method. Specifically, the MAFormer model exhibits notable advancements when compared to state-of-the-art deep learning techniques. It demonstrates an improvement ranging from 0.01 to 0.05 in terms of the Heidke skill score, indicating its superior performance. Additionally, MAFormer effectively mitigates false alarm rates by approximately 0.016 to 0.04, which further highlights its enhanced accuracy and reliability. [ABSTRACT FROM AUTHOR]

Exploiting their sensitivity to cloud droplets and small raindrops, Ka-band cloud radar observations are used to investigate weak stratiform precipitation over Wuhan during cooling on 16–17 February 2022. During cooling, the surface temperature drops by about 8 °C with the lowest value less than 0 °C because of the strong cold air from the north. The cold air lifts the warm and humid air transported by the southeasterly and southwesterly winds, causing thick stratiform clouds and persistent weak precipitation. The Ka-band radar captures the full process of stratiform cloud occurrence; light rain and then mixed rain and snow; and the characteristics of clouds and precipitation at each stage due to its fine sensitivity to small hydrometeors, whereas the reanalysis data alone cannot capture the transition to the mixed rain and snow regime, which can cause dangerous freezing rain or sleet on the ground. Hence, a detailed analysis of cooling and cold surges and their hazards to society, and their reproduction in numerical predictions, needs to use high-sensitivity radar data as much as possible. [ABSTRACT FROM AUTHOR]

Precipitation products play an important role in monitoring rainstorm processes. This study takes a rare historical event of extreme, heavy precipitation that occurred in Henan Province, China, in July 2021 as a research case. By analyzing the distribution of the spatial and temporal characteristics of precipitation errors, using a probability density function of the occurrence of precipitation and the daily variation pattern, we assess the capability of a radar precipitation estimation product (RADAR), satellite precipitation products (IMERG and GSMAP), a reanalysis product (ERA5) and a precipitation fusion product (the CMPAS) to monitor an extreme rainstorm in the Henan region. The CMPAS has the best fit with the gauge observations in terms of the precipitation area, precipitation maximum and the evolution of the whole process, with a low spatial variability of errors. However, the CMPAS slightly underestimated the precipitation extremum at the peak moment (06:00–08:00). The RADAR product was prone to a spurious overestimation of the originally small rainfall, especially during peak precipitation times, with deviations concentrated in the core precipitation area. The IMERG, GSMAP and ERA5 products have similar performances, all of which failed to effectively capture heavy precipitation in excess of 60 mm/h, with negative deviations in precipitation at mountainfront locations west of northern Henan Province. There is still a need for terrain-specific error revisions for areas with large topographic relief. By merging and processing precipitation data from multiple sources, the accuracy of the CMPAS is better than any single-source precipitation product. The CMPAS has the characteristic advantage of high spatial and temporal resolutions (0.01° × 0.01°/1 h), which play a positive role in precipitation dynamic monitoring, providing early warnings of heavy rainfall processes and hydrological application research. [ABSTRACT FROM AUTHOR]

The Indian summer monsoon trough (MT) is a dynamically active region of the quasi-stationary feature extending from north-western India towards the Bay of Bengal (BoB). The eastern rim of the MT (EMT) is modulated by the embedded synoptic-scale monsoon low-pressure systems (LPSs) in north BoB. We examine the spatiotemporal variability of the convective storm (CS) in the EMT region, which lies along the pathway of LPSs. A Lagrangian-based objective-tracking method, Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN), was injected into Kolkata S-band radar observations of nine wet seasons (June–September, 2009–2017). CSs frequently occur near the onshore areas and is linked to the formation and propagation of LPSs in the BoB. By examining the relationship of CS top heights with reflectivity lapse rate and fractional volume of 40 dBZ or more, three vertical categories of CS have been identified as shallow (SCS) below 5 km, medium depth (MDCS), and deep CS (DCS). The land–ocean contrast in the spatial distribution of MDCS and DCS and the contoured frequency by altitude diagram shows continental convective vigor. Although short-lived smaller CSs account for most storms in EMT, the bulk of the precipitation (75 % ) contributed from the infrequent largest storms. A weak (strong) linkage between the precipitation flux and CS top (area-time integral) gives a clue on the importance of CS voluminous organization rather than the linkage of heaviest rainfall to tallest CS. While most CSs propagate inland following the mass-weighted mean winds, the largest CS propagate offshore steered by the mid-level wind at 700 hPa. [ABSTRACT FROM AUTHOR]

To explore the applicability of deep learning methods to radar rainfall nowcasting and flood forecasting, U-Net, Attention-Unet and TransAtt-Unet are used to carry out rainfall nowcasting. The nowcasted rainfall results are used as inputs to the HEC-HMS hydrological model for flood forecasting. The results show that with a 1-hour lead time, Attention-Unet has the best performance in nowcasting heavy rainfall with a short duration, and the relative errors in the simulated flood peak and runoff volume by the nowcasted rainfall of TransAtt-Unet are less than 20%. Each deep learning model has a good forecasting accuracy for rainfall and flood events with large magnitudes. The rainfall intensity, rainfall totals, flood peaks and runoff volumes are significantly underestimated with a 2-hour lead time, with U-Net achieving relatively good rainfall nowcasting. The 1-hour lead time radar rainfall nowcasting and flood forecasting based on deep learning can provide a scientific reference for watershed flood prevention and mitigation. [ABSTRACT FROM AUTHOR]

Tropical cyclones not only cause strong winds and heavy rainfall, but they can also facilitate the transport of birds and insects from tropical regions to areas along their paths. Before super Typhoon Lekima made landfall in 2019, an operational polarimetric radar in Wenzhou City observed biological scatterers in the typhoon's eye. These scatterers were likely birds and insects that were trapped in the calm center of the typhoon by strong winds and heavy rain. The polarimetric variables of these biological scatterers had specific characteristics: low reflectivity factor (ZH) values with a median of 7.5 dBZ, low cross‐correlation coefficient (ρhv) values with a median of 0.65, large differential reflectivity (ZDR) values with a median of 2.8 dB and a maximum of ∼7.8 dB, and widely‐distributed differential phase (ΦDP) values with 25th and 75th percentiles ranging from 1.8° to 36.0° and a median of 20.1°. When the edge of the eye reached the coastline, the birds and insects landed, with the biological scatterer signature changing from a circular shape to a band shape. We further compared Lekima with Atlantic Hurricane Irene which happened in 2011, and both storms had similar polarimetric characteristics contributed by both birds and insects. However, the biological echoes in Lekima better exhibited the characteristics of birds with a larger proportion of samples with ZDR lower than 5 dB as well as large ΦDP and low ρhv values. This finding could help to understand the role of typhoons in driving biological migration between oceans and/or islands and continents. Plain Language Summary: Tropical cyclones can facilitate the transport of tropical birds and insects to areas along their paths, in addition to causing strong winds and heavy rainfall. A polarimetric radar was used to observe biological scatterers, likely birds and insects, in the eye of super Typhoon Lekima before landfall near Wenzhou City in 2019. These organisms were probably brought into the typhoon's calm center by strong winds. The radar characteristics of the scatterers were low reflectivity factor, low correlation coefficient, large differential reflectivity, and widely distributed differential phase. As the typhoon approached the coastline, the shape of the biological scatterer signature changed from a circular shape to a band shape. After the landfall of the typhoon, the echoes within the eye disappeared, likely because the birds and insects may fly near or land on the ground for a rest. This study's findings can help us better understand how typhoons can transport biological matter between oceans and/or islands and continents, which can affect ecosystems and the spread of diseases between different regions. Key Points: Biological scatterers were observed by a polarimetric radar in the eye of Typhoon LekimaThe biological echo showed low ZH, low ρhv, and high ZDR values with a wide distribution of ΦDPThe biological echoes within the eye of Lekima originated from a larger proportion of bird scatterers compared to those in Irene (2011) [ABSTRACT FROM AUTHOR]

The BiLateral Operational Storm‐Scale Observation and Modeling (BLOSSOM) project was initiated to establish a long‐term supersite to improve understanding of cloud physical states and processes as well as to support satellite and climate model programs over the Wallops Flight Facility site via a bilateral approach of storm‐scale observations and process modeling. This study highlights a noble systematic validation framework of the BLOSSOM ensemble cloud‐process simulations through mixed‐phase, light‐rain, and deep‐convective precipitation cases. The framework consists of creating a domain‐shifted ensemble of large‐scale forcing data sets, and configuring and performing cloud‐process simulations with three different bulk microphysics schemes. Validation uses NASA S‐band dual‐POLarimetric radar observations in the form of statistical composites and skill scores via a polarimetric radar simulator and newly developed CfRad Data tool (CfRAD). While the simulations capture the overall structures of the reflectivity composites, polarimetric signals are still poorly simulated, mainly due to a lack of representation of ice microphysics diversity in shapes, orientation distributions, and their complex mixtures. Despite the limitation, this new ensemble‐based validation framework demonstrates that (a) no particular forcing or microphysics scheme outperforms the rest and (b) the skill scores of coarse‐ and fine‐resolution ensemble simulations with different domain‐shifted forcing and microphysics schemes are highly correlated with each other with no clear improvement. On the other hand, this suggests that coarse‐resolution ensemble simulations are relevant for selecting the best meteorological forcing and microphysics scheme before conducting computationally demanding large eddy simulations in support of aircraft and satellite instrument development as well as cloud‐precipitation‐convection parameterizations. Plain Language Summary: Cloud‐process simulations are state‐of‐art numerical simulations to represent realistic cloud and convection development through numerous microphysical processes. These simulations are largely affected by thermodynamic fields. This work generates slightly modified thermodynamic fields to create many different states of cloud process simulations, which are systematically validated by the weather radar available in NASA Wallops Flight Facility. Key Points: Systematic validation of ensemble cloud‐process simulations is established over the NASA Wallops Flight FacilitiesNASA polarimetric radar and simulators reveal strength and weakness of cloud‐process simulationsCoarse‐resolution cloud‐process simulations are sufficient to select best forcing and microphysics options [ABSTRACT FROM AUTHOR]

The National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Administration (NOAA) have a long and successful history of weather radar research. The NOAA ground-based radars—WSR-88D network—provide nationwide precipitation observations and estimates with advanced polarimetric capability. As a counterpart, the NASA–JAXA spaceborne radar—the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM DPR)—has global coverage and higher vertical resolution than ground-based radars. While significant advances from both NOAA's WSR-88D network and NASA–JAXA's spaceborne radar DPR have been made, no systematic comparisons between the WSR-88D network and the DPR have been done. This study for the first time generates nationwide comprehensive comparisons at 136 WSR-88D radar sites from 2014 to 2020. Systematic differences in reflectivity are found, with ground radar reflectivity on average 2.4 dB smaller than that of the DPR (DPR version 6). This research found the discrepancies between WSR-88D and DPR arise from different calibration standards, signal attenuation correction, and differences in the ground and spaceborne scattering volumes. The recently updated DPR version 7 product improves rain detection and attenuation corrections, effectively reducing the overall average WSR-88D and DPR reflectivity differences to 1.0 dB. The goal of this study is to examine the systematic differences of radar reflectivity between the NOAA WSR-88D network and the NASA–JAXA DPR and to draw attention to radar-application users in recognizing their differences. Further investigation into understanding and alleviating the systematic bias between the two platforms is needed. [ABSTRACT FROM AUTHOR]

In this study, we use GIS and other analytical platforms to analyze the landslide distribution pattern in the July 2018 heavy rain disasters in the southern part of Hiroshima Prefecture in Japan in conjunction with chronological XRAIN (eXtended Radar Information Network) radar-acquired localized rainfall data in order to better understand the relationship between rainfall characteristics and landslide probability. An analysis of event rainfall from the July 2018 disasters determines that landslide-inducing rainfall started from 8:30 AM on 5 July and continued until 7:30 AM on 7 July, accumulating to up to 368 mm in total precipitation, and that there were two intensity peaks, one around 7:30 PM on 6 July, and another one around 4:30 AM on 7 July. These two events are associated with particularly high landslide activity, which indicates that landslide activation is related to peak-intensity rainfall combined with accumulated continuous precipitation. The XRAIN data were also used together with landslide reports to calculate the intensity–duration (i.e., I-D) rainfall threshold for the area. The mean annual precipitation in the whole study area ranged between 2025 mm and 3030 mm, with an average value of about 2300 mm. The spatial distribution of rainfall throughout the sampled years indicates that rainfall is remarkably localized, with higher values concentrated on elevated areas. However, it was also observed that the maximum precipitation volumes are not so closely related to landslide occurrence, and the highest landslide activity was found in intermediate precipitation class zones instead. Correlating the localization patterns of event precipitation and mean annual precipitation using Pearson's correlation coefficient, we found an r value of 0.55, which is considered a moderate correlation between the two datasets (i.e., event precipitation and mean annual precipitation). [ABSTRACT FROM AUTHOR]

The northeastern China cold vortex (NCCV) is the main weather system affecting Northeast China. Based on the precipitation products from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement core observatory (GPM) satellite, the precipitation structures and microphysical properties for different rain types in 6432 NCCVs from 2014 to 2019 were studied using dynamic composite analysis. Our results show that the precipitation in NCCVs is dominated by stratiform precipitation. Regions with high stratiform and convective precipitation frequency have a comma shape. The growth mechanism of precipitation particles changes at ~4 km in altitude, the lower particles grow through collision (more pronounced in convective precipitation), and the upper hydrometeors grow through the Bergeron process. Additionally, the precipitation structures and microphysical properties exhibit great regional variations in NCCVs. The rainfall for all rain types is the strongest in the southeast region within an NCCV, mainly characterized by higher near-surface droplet concentration, while precipitation events occur more frequently in the southeast region for all rain types. There are active rimming growth processes above the melting layer for convective precipitation in the western region of an NCCV. In the southeast region of an NCCV, the collision growth of droplets in both types of precipitation is the most obvious. [ABSTRACT FROM AUTHOR]

This study assimilates for the first time polarimetric C-band radar observations from the German meteorological service (DWD) into DWD's convective-scale model ICON-D2 using DWD's ensemble-based KENDA assimilation framework. We compare the assimilation of conventional observations (CNV) with the additional assimilation of radar reflectivity Z (CNV+Z), with the additional assimilation of liquid or ice water content (LWC or IWC) estimates below or above the melting layer instead of Z where available (CNV+LWC/Z or CNV+IWC/Z, respectively). Hourly quantitative precipitation forecasts (QPF) are evaluated for two stratiform and one convective rainfall event in the summers of 2017 and 2021. With optimized data assimilation settings (e.g., observation errors), the assimilation of LWC mostly improves first guess QPF compared to the assimilation of Z alone (CNV+Z), while the assimilation of IWC does not, especially for convective cases, probably because of the lower quality of the IWC retrieval in these situations. Improvements are, however, notable for stratiform rainfall in 2021, for which the IWC estimator profits from better specific differential phase estimates due to a higher radial radar resolution compared to the other cases. The assimilation of all radar data sets together (CNV+LWC+IWC+Z) yields the best first guesses. All assimilation configurations with radar information consistently improve deterministic nine-hour QPF compared to the assimilation of only conventional data (CNV). Forecasts based on the assimilation of LWC and IWC retrievals on average slightly improve FSS and FBI compared to the assimilation of Z alone (CNV+Z), especially when LWC is assimilated for the 2017 convective case and when IWC is assimilated for the high-resolution 2021 stratiform case. However, IWC assimilation again degrades forecast FSS for the convective cases. Forecasts initiated using all radar data sets together (CNV+LWC+IWC+Z) yield the best FSS. The development of IWC retrievals more adequat for convection constitutes one next step to further improve the exploitation of ice microphysical retrievals for radar data assimilation. [ABSTRACT FROM AUTHOR]

Inundation dynamics are the primary control on greenhouse gas emissions from peatlands. Situated in the central Congo Basin, the Cuvette Centrale is the largest tropical peatland complex. However, our knowledge of the spatial and temporal variations in its water levels is limited. By addressing this gap, we can quantify the relationship between the Cuvette Centrale's water levels and greenhouse gas emissions, and further provide a baseline from which deviations caused by climate or land-use change can be observed, and their impacts understood. We present here a novel approach that combines satellite-derived rainfall, evapotranspiration and L-band Synthetic Aperture Radar (SAR) data to estimate spatial and temporal changes in water level across a sub-region of the Cuvette Centrale. Our key outputs are a map showing the spatial distribution of rainfed and flood-prone locations and a daily, 100 m resolution map of peatland water levels. This map is validated using satellite altimetry data and in situ water table data from water loggers. We determine that 50% of peatlands within our study area are largely rainfed, and a further 22.5% are somewhat rainfed, receiving hydrological input mostly from rainfall (directly and via surface/sub-surface inputs in sloped areas). The remaining 27.5% of peatlands are mainly situated in riverine floodplain areas to the east of the Congo River and between the Ubangui and Congo rivers. The mean amplitude of the water level across our study area and over a 20-month period is 22.8 ± 10.1 cm to 1 standard deviation. Maximum temporal variations in water levels occur in the riverine floodplain areas and in the inter-fluvial region between the Ubangui and Congo rivers. Our results show that spatial and temporal changes in water levels can be successfully mapped over tropical peatlands using the pattern of net water input (rainfall minus evapotranspiration, not accounting for run-off) and L-band SAR data. [ABSTRACT FROM AUTHOR]

This paper presents the results of the research on radar rainfall estimate errors with the support vector regression (SVR) method using the observed rain gauge data. The paper depicts the methodological base of the algorithm that covers additive and multiplicative corrections and the practical implementations considering the locations of gauge measurements. The preliminary results show that the SVR has a location-oriented performance. The multiplicative and additive correction factors show decreasing and unstable trends respectively, as the distance from the radar location increase. Another particular outcome is that the SVR shows better results for almost all stations in decreasing the error in maximum rainfall amounts measured with weather radar. [ABSTRACT FROM AUTHOR]

Remote sensing plays an increasingly important role in climate research, and as remote sensing observation files span longer periods of time, we are receiving more and more data on climate parameters such as clouds. Four decades ago, it was proposed that the area of planar shapes change with their perimeter following the power law. The exponent of this relation for the areas of clouds (typical and noctilucent) and rain determined by satellites and radar is 1.35 close to 4/3 of the classical turbulence theory of 1941. In this paper, we propose a new relationship between the area and the perimeter of rain and clouds using remote sensing observations. This relationship leads to the development of a new model for the detection of extreme natural phenomena related to rain and clouds. [ABSTRACT FROM AUTHOR]

In order to improve the understanding of the microphysical characteristics of raindrop size distribution (DSD) under different rainfall rates (R) classes, and broaden the knowledge of the impact of radar wavelengths and R classes on the QPE of dual-polarization radars in the Tianshan Mountains, a typical arid area in China, we investigated the microphysical characteristics of DSD across R classes and dual-polarimetric radar QPE relationships across radar wavelengths and R classes, based on the DSD data from a PARSIVEL2 disdrometer at Zhaosu in the Tianshan Mountains during the summers of 2020 and 2021. As the R class increased, the DSD became wider and flatter. The mean value of the mass-weighted mean diameters (Dm) increased, while the mean value of logarithm normalized intercept parameters (log10 Nw) decreased after increasing from C1 to C3, as the R class increased. The largest contributions to R and the radar reflectivity factor from large raindrops (diameter > 3 mm) accounted for approximately 50% and 97%, respectively, while 84% of the total raindrops were small raindrops (diameter < 1 mm). Dual-polarization radars—horizontal polarization reflectivity (Zh), differential reflectivity (Zdr), and specific differential phase (Kdp)—were retrieved based on the DSD data using the T-matrix scattering method. The DSD-based polarimetric radar QPE relations of a single-parameter (R(Zh), R(Kdp)), and double-parameters (R(Zh,Zdr), R(Kdp,Zdr)) on the S-, C-, and X-bands were derived and evaluated. Overall, the performance of the R(Kdp) (R(Kdp,Zdr)) scheme was better than that of R(Zh) (R(Zh,Zdr)) for the QPE in the three bands. Furthermore, we have for the first time confirmed and quantified the performance differences in the QPE relationship of dual-polarization radars under different schemes, radar wavelengths, and R classes in typical arid areas of China. Therefore, selecting an appropriate dual-polarization radar band and QPE scheme for different R classes is necessary to improve the QPE ability compared with an independent scheme under all R classes. [ABSTRACT FROM AUTHOR]