Raindrop Size Research Articles

Retrieving Estimates of the Storm‐Relative Wind Profile From the Vertical Variation of Hydrometeor Size Sorting Signatures

AbstractRaindrop fall speed increases with raindrop size. Because larger raindrops fall through a given layer of the atmosphere more quickly compared to smaller raindrops, they spend less time in the layer and, therefore, have less time to be acted on and advected by the storm‐relative winds. This results in hydrometeor size sorting which impacts the polarimetric radar variables such as differential reflectivity and specific differential phase . Previous work has shown a strong correlation between the mean storm‐relative wind in the sorting layer and the separation between regions of enhanced and at the bottom of the sorting layer. This study leverages this finding, along with a simple size sorting model, to construct radar‐derived estimates of the storm‐relative wind profile. The radar‐derived “pseudo‐hodographs” are well‐correlated with the magnitude and direction of the corresponding storm‐relative wind profiles. Further, these radar‐derived estimates of the storm‐relative winds are used to calculate estimates of shear and storm‐relative helicity, which also exhibit a strong correlation.

Cold-Frontal Rainbands over the United Kingdom: Sensitivity of Precipitation Processes to Microphysics Schemes in WRF Simulations

Abstract A narrow cold-frontal rainband (NCFR) and wide cold-frontal rainband (WCFR) over the United Kingdom on 24 January 2018 are selected to evaluate the sensitivity of cold-season precipitation to three bulk cloud microphysics schemes in the Weather Research and Forecasting (WRF) Model. The three simulations tend to overpredict the intensity and the areal coverage of NCFRs but underpredict those of WCFRs against the Met Office radar imagery. Nonetheless, the WRF double-moment 6-class (WDM6) simulation produces the most realistic NCFR and WCFR. More specifically, the intensity of the NCFR in the WDM6 simulation is overpredicted in the developing stage and underpredicted in the dissipating stage when compared to the Thompson and Morrison simulations. This phenomenon is mainly attributed to the rain mixing ratio at 1 km. The more intense and larger area of the WCFR in the WDM6 simulation is associated with more low-level snow, graupel, and rain than the other two simulations. In the WDM6 simulation, the larger low-level snow mixing ratios are due to the higher rates of aggregation between cloud ice and snow, whereas the larger rain mixing ratio is due to higher melting rates. Thus, differences in low-level ice-phase processes between microphysics schemes are large, strongly influencing the intensity of WCFRs, but the differences in warm-rain processes are smaller. Additionally, the small size of raindrops in the WDM6 simulation leads to wider and more intense WCFR as these raindrops are preferentially sent rearward relative to the cold front. Significance Statement Models are useful tools to predict weather but have errors in predicting different weather types. In the United Kingdom, precipitation is often predicted to be too strong at cold fronts and too weak behind the fronts compared to radar observations. Choosing different options for how precipitation is represented in one model produced different precipitation forecasts, with WDM6 being the best. One reason why WDM6 was the best was because it produced more and smaller raindrops, which would be transported behind the front, and it had higher melting rates at low levels, resulting in more precipitation behind the fronts than other options. Our results help identify why different options produce different forecasts, leading to possible future improvements in these options.

Evaluation of the Representation of Raindrop Self‐Collection and Breakup in Two‐Moment Bulk Models Using a Multifrequency Radar Retrieval

AbstractUsing multifrequency radar observations providing raindrop size distribution evolution with high spatial and temporal resolution, this study aims to assess the ability of different parameterizations of raindrop self‐collection and breakup processes applied in mesoscale models, to reproduce the statistics derived from observations. The stratiform zones of two types of precipitating systems are studied, a frontal situation that occurred over Finland in June 2014 and a squall line system observed over Oklahoma in June 2011. An analysis method for determining raindrop trajectories was used to obtain the temporal variation of the total raindrop concentration from the observations. The resulting raindrop concentration rate as a function of the mean volume diameter reveals significant differences with the parameterizations currently used in two‐moment bulk microphysics schemes. These results show that even if they produce variations in raindrop concentration of the same order of magnitude as the observations, the current parameterizations diverge from the median of the observations, resulting in an overestimation of either the self‐collection or the breakup process. From the median of radar observations, new parameterizations of the self‐collection and breakup processes and of rain self‐collection efficiency are developed and can be implemented in two‐moment bulk microphysics schemes.

Fire and smoke transport dynamics in a dead-end tunnel under heavy rainfall

Fire is one of the most common major accidents during tunnel construction and operation. This work conducted scale-model experiments to investigate the pool burning progress and flame characteristics in a dead-end tunnel with one end closed and the other open under heavy rainfall. The results show that, compared to opening both ends, the global burning time of fires is significantly decreased, and the quasi-stable burning phase is advanced under the combined effects of the closed end and heavy rainfall. The reason is that the increase in ceiling temperature, resulting from the accumulation of hot smoke, leads to an increase in radiative feedback of the fuel from the environment and an acceleration in the pool's burning rate. A coefficient (denoted by γ) is defined to evaluate the increase in the burning rate resulting from the dead-end. The enhancement ratio is found to be related to rainfall intensity, raindrop size, and pool size, and a correlation has been established based on the experimental results. The fire has an increased flame height and a reduced tilt angle under the impact of the dead-end. The changes in flame geometry parameters are mainly influenced by thermal buoyancy and the restriction of the dead-end. Prediction formulas for flame height and tilt angle under the combined effects of the dead-end and heavy rainfall have been proposed based on theoretical analysis. Under these combined effects, the tunnel space becomes eroded by dirty air.

Comparison of the Morrison and WDM6 Microphysics Schemes in the WRF Model for a Convective Precipitation Event in Guangdong, China, Through the Analysis of Polarimetric Radar Data

Numerical weather prediction (NWP) models are indispensable for studying severe convective weather events. Research demonstrates that the outcomes of convective precipitation simulations are profoundly influenced by the choice between single or double-moment schemes for ice precipitation particles and the categorization of rimed ice. The advancement of dual-polarization radar has enriched the comparative validation of these simulations. This study simulated a convective event in Guangdong, China, from May 7 to 8, 2017, employing two bulk microphysical schemes (Morrison and WDM6) in the WRF v4.2 model. Each scheme was divided into two versions: one representing rimed ice particles as graupel (Mor_G, WDM6_G) and the other as hail (Mor_H, WDM6_H). The simulation results indicated negligible differences between the rimed ice set as graupel or hail particles, for both schemes. However, the Morrison schemes (Mor_G, Mor_H) depicted a more accurate raindrop size distribution below the 0 °C height level. A further analysis suggested that disparities between the Morrison and WDM6 schemes could be attributed to the intercept parameter (N0) setting for snow and graupel/hail in WDM6 scheme. The prescribed snow and graupel/hail N0 of WDM6 scheme might influence the melting processes, leading to a higher number concentration but a reduced mass-weighted diameter of raindrops. Reducing the intercept parameter for snow and graupel/hail in the WDM6 scheme could potentially enhance the simulation of convective precipitation. Conversely, the increase in N0 might deteriorate the precipitation simulation performance of the WDM6_G scheme, whereas the WDM6_H scheme exhibits minimal sensitivity to such changes.

The Regional Characteristics of Raindrop Size Distribution by Airflow Conditions on Mountain Halla, a Single Bell-Shaped Mountainous Area in South Korea

Abstract This study investigated the microphysical attributes of orographic precipitation based on the Froude number Fr. Orographic precipitation types were classified according to the background airflow movement condition, either around (Fr2 < 0.1, F1) or over (0.1 ≤ Fr2 < 1, F2) the mountain. Raindrop size distribution (DSD) was measured for 27 stratiform precipitation cases during a 2-yr changma (summer monsoon) season using two S-band single-pol weather radars and four Parsivel disdrometers installed along the major axis of Jeju Island—a single bell-shaped mountainous region (Mountain Halla) in South Korea. The present study revealed the orographic effect influenced by regional differences in precipitation characteristics during F1 condition, but these features were weakened during F2 condition. The leeward highlands exhibited the highest regional variation in precipitation characteristics: The smallest mass-weighted mean diameter Dm was 0.84 mm during F1, but it became similar to other regions during F2 (1.04 mm). The effects of orographic processes on precipitation and airflow conditions were apparent in the radar reflectivity Z–rainfall rate R relationship. Under F1 conditions, the radar-based estimated ground rainfall amount RA on the leeward highlands was significantly underestimated (−28.2%) by the Z–R relationship optimized for the windward lowlands, where precipitation is negligibly affected by orographic effects. However, this regional bias in RA estimation caused by orographic effects was reduced (−4.2%) under F2 conditions. Significance Statement This study aimed to identify geographical differences in precipitation over a mountainous area through quantitative methods. While previous research has investigated quantitative precipitation estimation (QPE) for different precipitation types (stratiform and convective), this study focused on QPE for orographic precipitation. Statistical analyses were performed on the microphysics of precipitation at various locations and altitudes within a single bell-shaped mountainous area using Parsivel disdrometers and weather radars. The microphysical characteristics of precipitation are influenced by the geographical features of mountains, and these relationships affected QPE over the mountainous area, resulting in regional biases that need to be addressed. In particular, the estimated rainfall amounts optimized for areas without orographic effects were underestimated by up to 28.2% in the leeward highlands.

Summer monsoon raindrop size distributions between polluted and non-polluted rainy days observed over Rourkela, India

Summer monsoon raindrop size distributions between polluted and non-polluted rainy days observed over Rourkela, India

An Investigation on Microphysical Characteristics of Early‐, Late‐, and Post‐Mei‐yu Season Rainfall Over Taiwan

AbstractOver Taiwan, Mei‐yu season (May and June), which is primarily linked to frontal systems, is the transition period between winter and summer. Using the Global Precipitation Measurement Mission dual‐frequency precipitation radar (GPM DPR), the current study examined the rain and microphysical characteristics of the Mei‐yu season in Taiwan. In order to examine the areal and intra‐seasonal aspects, May and June months are divided into three sub‐seasons: early‐Mei‐yu, late‐Mei‐yu, and post‐Mei‐yu. The three sub‐seasons exhibited differences in rainfall and raindrop size distributions, with abundance of large drops in the post‐Mei‐yu. Additionally, there were noticeable variations in the raindrop size distributions among the south, central, north, and eastern parts of Taiwan, with more large drops in central Taiwan. To comprehend the microphysical progressions causing the regional and intra‐seasonal fluctuations, CFADs (contoured frequency by altitude diagrams) of rain parameters are utilized. Compared to other two sub‐seasons, the early‐Mei‐yu season exhibited weaker convection. Dominance of stratiform precipitation in late‐Mei‐yu season, and convective precipitation in post‐Mei‐yu season resulted in higher rainfall amounts in these two sub‐seasons (more particularly in post‐Mei‐yu) than the early‐Mei‐yu season. Examination of warm rain microphysical processes (below 5 km height) among these sub‐seasons revealed that the early‐Mei‐yu season is dominated with break‐up process, late‐Mei‐yu season with breakup and coalescence balance, and post‐Mei‐yu with coalescence, size‐sorting and evaporation processes.

Raindrop Size Distribution Characteristics of the Precipitation Process of 2216 Typhoon “Noru” in the Xisha Region

This study focuses on the comparative analysis and research of the raindrop size distribution (DSD) in the outer rainband and inner rainband of Typhoon “Noru” in 2022, using the OTT-Parsivel raindrop spectrometer deployed on Yongxing Island, Sansha City. The results indicate that precipitation intensity is lower when composed mainly of small and medium raindrops and increases with the presence of larger raindrops. Stronger precipitation is associated with a higher number of large raindrops. Due to the interaction of cold and warm air masses, the raindrop concentration is higher, and the raindrop diameters are larger compared to Typhoons “LEKIMA” and “RUMBIA”. The entire process predominantly consists of numerous small- to medium-sized raindrops, characteristic of a tropical typhoon. The precipitation in the inner and outer rainbands exhibits consistent types, characterized by a unimodal raindrop size distribution with a narrow spectral width, typical of stratiform-mixed cloud precipitation, where stratiform precipitation constitutes a significant portion. Strong echo reflectivity factors are often associated with higher raindrop number concentrations and larger particle sizes. The Z-R relationship of the precipitation shows a smaller coefficient but a consistent exponent compared to the standard. The calculated shape parameter slope relationship is Λ=0.02μ2+0.696μ+1.539, providing a reference for localizing the Z-R relationship in the South China Sea.

Contrasts in the raindrop size distributions of pre-monsoon polluted and non-polluted rainy days over Rourkela, India

Raindrop size distribution (RSD) is crucial for accurate quantitative precipitation estimation and forecasting and is fundamental to precipitation microphysics. This study examines the microphysical characteristics of RSD at Rourkela, Odisha, under polluted and non-polluted conditions. This investigation utilized pre-monsoon in-situ (disdrometer) and reanalysis (ERA5) data for 2018–2021, to identify the pollution impact on precipitation over Rourkela. The polluted rainfall day has been determined based on the air quality index (AQI) value provided by the Central Pollution Control Board (CPCB), Government of India, over the Rourkela region. Classifying the rainfall in polluted and non-polluted days into stratiform and convective types showed that convective rainfall had a higher raindrop concentration and higher mean diameter (Dm) in both polluted and non-polluted days. Convective rainfall has a lower normalized intercept parameter (log10Nw) in polluted rainfall days. The RSD empirical relations (Z-R, μ – λ, Dm-R, Nw-R) also showed a noteworthy difference between the polluted and non-polluted rainfall days. The results disclosed that non-polluted rainfall has higher concentration of small-diameter raindrops, whereas polluted day rain has higher concentrations of midsize and large-diameter raindrops.

Inter-seasonal variation of rainfall microphysics as observed over New Delhi, India

This study analyzes the raindrop size distribution (RSD) characteristics over New Delhi by dividing the year into three seasons: PreM (March–May), monsoon (June–September), and PostM (October–February). Data from a Joss-Waldvogel Disdrometer, installed at IITM New Delhi, Rajendra Nagar, was used for three years (2021–2023). The observed raindrop spectra were fitted with three-parameter Gamma functions to obtain the RSD. ERA-5 and satellite data were also employed to establish atmospheric and cloud properties for the three seasons. The RSD for the monsoon season shows the highest concentration of midsize (1–3 mm diameter) drops and the highest mean rain rate. PostM has the least concentration of midsize and large (diameter >3 mm) drops. General statistics of rain integral parameters reveal high variability in rain rate (R) and mass-weighted mean diameter (Dm) values during the monsoon season. The mu-lambda scatter plots show considerable differences among the three seasons, indicating slightly distinct rainfall mechanisms in the three seasons. Z-R relations of the form Z = aRb were derived, with the highest coefficient (a) values observed for the PreM precipitation. The exponent (b) is found to be greater than unity in all three seasons. Rainfall was stratified based on rain rate. RSD gets broader with increasing R. Large drops are not found appreciably in the spectrum for R < 20 mm/h. A notable disparity between convective and stratiform RSD is evident. The values of rain integral parameters show considerable differences between the convective and stratiform regimes. A higher fraction of large drops is found for the stratiform rainfall in the PreM season compared to the other two seasons. CAPE, water vapor, surface temperature, and surface winds were higher during PreM and monsoon months compared to PostM. The distribution of differential temperature (δT) indicates that clouds with significant depth are found in PreM and monsoon seasons but are often lacking during PostM.

Raindrop size distribution (DSD) retrieval from polarimetric radar observations using neural networks

As a key component of rainfall estimation, the understanding of raindrop size distribution (DSD) is a long-standing goal in meteorology and hydrology. Given that weather radar can observe the precipitation microphysics over large spatial and temporal scales, it has been broadly applied in DSD estimation. Traditional polynomial regression algorithms that correlate DSD parameters and radar signatures are still widely applied due to their simple structure and acceptable accuracy. This study proposes a new DSD retrieval model using dual-polarization radar observations based on long short-term memory (LSTM) network techniques. Three schemes able to retrieve the parameters of a normalized gamma DSD (LSTM-D0, LSTM-Nw, and LSTM-μ) are proposed with different combinations of polarimetric radar measurement inputs. All LSTM estimators exhibit better performance than the polynomial regression method. The Nash-Sutcliffe efficiency coefficient for estimates of drop median diameter (D0) and intercept parameter (Nw) increases from 0.93 and 0.70 to 0.95 and 0.93 respectively at Chilbolton station. Poor estimates of the shape parameter (μ) using the polynomial regression estimator complicate real applications, whereas the remarkable improvement of LSTM model estimation facilitates practical applications. The temporal and spatial predictability is then estimated to investigate long-term estimator performance for various radars, or at least for all radar pixels of a single radar. The predictability, measured by the Nash coefficient, increases temporally by 0.08, 0.31, and 0.39 and spatially by 0.03, 0.19, and 0.23 for the parameters D0, log10Nw, and μ respectively. This study contributes to improving quantitative precipitation estimates from radar polarimetry, enabling a better understanding of precipitation microphysics.

Combined effects of fine and coarse marine aerosol on vertical raindrop size distribution

Climate models commonly overestimate warm rain frequency and underestimate its intensity over the ocean, primarily due to insufficient representation of the aerosol effects. This pertains to both fine aerosols (FA) and coarse sea spray aerosols (CSA), where the latter is mostly absent in the models. Here, our observations show that adding CSA enhances vertical warm rain structure, in contrast to the effect of FA. The magnitude of the effect of CSA is larger than the opposite effect of the FA. For rain with top heights of 2–3 km, the raindrop size, concentration, and rain rate can be increased by factors of 1.03, 1.47, and 1.60, respectively. These CSA-induced changes are larger for thicker clouds, reaching a maximum by factors of 1.12, 1.85, and 2.21, respectively. Therefore, the combined FA and CSA effects should be incorporated into climate models for accurately simulated precipitation microphysical processes.

Raindrop size distribution characteristics of pre-monsoon precipitation observed over eastern India

The knowledge of raindrop size distribution (DSD) is crucial for understanding the microphysical processes involved with the precipitation. Different empirical relationships established with DSD parameters, like radar reflectivity– rainfall rate (Z–R) relationships and shape–slope (μ–Ʌ) relationships, can progress the rainfall estimation algorithms and cloud modeling simulations. In the present study, long-term (2018–2021) measurements of a Laser Precipitation Monitor (LPM) disdrometer installed at the National Institute of Technology, Rourkela, India is used to investigate the DSD characteristics of pre-monsoon (March–May) rainfall. Along with the disdrometer data, auxiliary parameters like convective available potential energy (CAPE), total column water vapor (TCWV), vertical profiles of temperature and relative humidity from reanalysis data sets of ECMWF (European Centre for Medium-Range Weather Forecasts) fifth-generation reanalysis (ERA5) are also used in this study. Based on standardized rainfall anomaly, the pre-monsoon precipitation days are classified into strong, moderate, and weak rainy days, and they contributed to 58.69%, 32.7%, and 8.61% of total rainfall, respectively. The average DSD indicated noteworthy variations among strong, moderate, and weak rainy days with maximum (minimum) concentration of raindrops in strong (weak) rainy days. The mean value of rain rate (R), normalized intercept parameter (Nw), and mass-weighted mean diameter (Dm) is maximum during days of strong rainfall. Strong rainy days showed high-value CAPE, TCWV and vertical profile of relative humidity. The majority of R is contributed by moderate-sized raindrops with a significant difference in the Z–R and μ–Λ relationships among three types of rainy days.

Raindrop size distributions of summer monsoon rainfall observed over Eastern India

The present study aims to quantify the raindrop size distributions (RSDs) of Indian summer monsoon (ISM) rainfall over a tropical eastern Indian station, Rourkela using five years (2018–2021) of Thies disdrometer measurements. The RSD measurements of ISM rainfall at Rourkela are segregated into active and break spells using the standardized rainfall anomalies over the study area. The RSDs characteristics of active and break spells are analyzed and their empirical relations, which are appropriate for precipitation estimation algorithms, are also reported. A significant annual and monthly variations in RSDs is noticed between the active and break spells of the ISM rainfall over the study region. The probability distribution function (PDF) RSDs parameters (rainfall rate, liquid water content, mass-weighted mean diameter, and normalized intercept parameter) demonstrated clear distinctions between active and break spells. The active spells showed higher rainfall amounts, large mass-weighted mean diameter, and smaller normalized intercept parameters over the break spells. A further segregation of active and break spells into stratiform and convective types revealed more large size drops in convective (stratiform) precipitations of active (break) spells. Occurrence of higher rainfall amounts and large size raindrops in the active spells over the break spells can be attributed to the presence of abundant moisture and convective activity in active spells. Furthermore, the radar reflectivity-rainfall rate (Z–R), shape and slope (μ–Λ), and mass-weighted mean diameter-rainfall rate (Dm-R) relations established for the active and break spells could aid in improving the precipitation estimations of over the study region.

Top-Down Phenomenon Observed in the Outer Rainbands of Typhoon Maysak (2020): Insights from the Comparative Analysis of Cloud and Precipitation Microphysics with a Typical Precipitation Event in Northeast China

Abstract Cloud and precipitation microphysics in tropical cyclones (TCs) moving toward Northeast China may exhibit significant distinctions compared with the typical precipitation of this region. In this study, observations from ground-based particle size velocity (Parsivel) disdrometers in Liaoning Province, China, and cloud property data from the Himawari geostationary satellite were utilized to analyze the raindrop size distribution (DSD) characteristics and cloud vertical evolution associated with the outer rainbands of Typhoon Maysak (2020). A comparative analysis was conducted with a typical precipitation event in Northeast China induced by a cold vortex (cold-core low). Our findings reveal distinctive DSD characteristics related to the TC, where medium-sized raindrops dominate, with a smaller diameter but higher concentration in the TC case compared to the typical cold-vortex-induced precipitation case in Northeast China. Convective precipitation falls between maritime-like and continental-like patterns, leaning more toward continental convection. This varies significantly with TCs in Southeast China but is similar to that observed in coastal-front-like rainbands, suggesting extratropical influence. A detailed analysis of the vertical profile of cloud droplets shows a unique “top-down” phenomenon during the extratropical transition process of the TC, where the development of lower-level clouds follows that of upper-level clouds, inconsistent with previous studies and the case for comparison. Further investigation indicates the significant role of the intrusion of dry and cold air from upper levels and the presence of high humidity in low levels in driving this phenomenon. Our results will provide novel insights into cloud and precipitation microphysics associated with TCs in midlatitude regions.

Land-sea contrast of vertical structure of precipitation over Sumatra revealed by GPM DPR observations

This study examines the land-sea contrast in the vertical structure of precipitation over Sumatra and its surrounding ocean using eight years (2014–2021) of dual-frequency precipitation radar (DPR) data from the Global Precipitation Measurement (GPM) mission. The study area is segmented into Ocean, Coast I (west coast), Land I (west of Barisan Mountains), Land II (east of Barisan Mountains), and Coast II (east coast). The effective reflectivity factor (Ze), rainfall rate (R), and two raindrop size distribution (DSD) parameters, namely the mass-weighted mean diameter (Dm) and the normalized intercept (Nw), were analyzed. Stratiform and deep convective rainfall were the most frequent types in Coast I, followed by Ocean, Land I, Land II, and Coast II. In contrast, shallow convective rainfall was the most common type in Ocean, followed by Coast I, Land I, Land II, and Coast II. The average rain top height (RTH) was found to be higher in Land II and Coast II than in Ocean, Coast I, and Land I, in accordance with the surface rainfall intensity pattern previously reported by other studies. Furthermore, the data on heavy ice precipitation demonstrated an increase from the ocean to the coast and land, with a significant number of instances observed in Land I, Land II, and Coast II, in addition to Coast I. This contrast in heavy ice precipitation and RTH influences DSD. The land-sea contrast of DSD is more pronounced for deep convective rains. Deep convective exhibited a lower frequency of large raindrops over the ocean than over the coast and land, as reflected in the Dm profile. Conversely, raindrop concentration, particularly small drops, was higher at sea. The percentage of Dm > 2 mm at sea was approximately 2%, rising to 4–6% in Land II and Coast II. The land-sea contrast in the vertical structure of precipitation over Sumatra exhibited apparent diurnal variation. Larger Dm and smaller Nw were observed over Land I and Coast I, particularly in the afternoon and evening, correlating with peak rainfall. This pattern aligns with heavy ice precipitation profiles, vertical relative humidity (RH), and wind profiles. This study highlights the complexities and potential discrepancies between vertical precipitation profiles and surface precipitation data, underscoring the nuanced nature of land-sea precipitation migration.

AIMHNet: An Attribute-Insensitive Multiscale Hourglass Network for Rain Streak and Raindrop Removal

CNN-based methods have made great progress in single-image rain removal. Most recent methods improve performance by increasing the depth of the network. To fully extract local and global features while reducing inference time, we propose a top-to-down attribute-insensitive multiscale hourglass network for rain streak and raindrop removal. For the rain removal task, we expect that the constructed network can accurately identify the various attributes of the rain information characteristics of the small target. Considering the difference in the size, shape, direction and density of rain streak and raindrop, inspired by the performance of hourglass architecture to capture multiscale features in human pose estimation, we introduce an attribute-insensitive hourglass module to recognize the attributes of rain streak and raindrop in a unified framework. This feature extraction module could capture the characteristics of rain streak and raindrop with different attributes. This stacked hourglass blocks down-sample features and then up-samples them back to the original resolution based on discrete wavelet transform and inverse discrete wavelet transform. We perform extensive experiments on five synthetic and real-world de-raining datasets to validate the effectiveness of our proposed network on rain streak and raindrop removal. The qualitative and quantitative results show that our method is suitable for removing rain streak and raindrop in a unified framework. We present the results of generalization and ablation study for key components, we also report the accuracy of semantic segmentation after preprocessing with all rain removal methods. Our source code will be available on the GitHub: https://github.com/Ruini94/AIMHNet .

Improving Explainability of Deep Learning for Polarimetric Radar Rainfall Estimation

AbstractMachine 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.

Dependence of rain attenuation on rain rate and drop size distribution for Ka-band satellite communication over India

The Raindrop size distribution (DSD) is the most effective parameter to calculate rain-induced attenuation, which can be severe at Ka-band and higher frequencies. The authors have investigated the impact of rain rate and DSD on rain-induced specific attenuation over a tropical location Kolkata, India. DSD data from disdrometer has been used to calculate the specific rain attenuation with the help of Mie scattering theorem. Low to medium drop diameters (up to 3 mm) are the largest contributors to the various classes of rain rate and attenuation. Double peaks of drop sizes are significant for medium rain rate. The contribution of smaller drop diameters decrease, whereas the contribution of higher drop diameters increase with increasing rain attenuation. At lower rain attenuation, (up to 1.5 dB), complementary cumulative distribution function (CCDF) shows higher value for stratiform rain, with respect to convective rain. The higher attenuation occurrences are mostly observed during 12–18 Indian standard time (IST), i.e., in the afternoon. The results will be helpful for Ka-band (GSAT-14) and higher frequency satellite communication channel modeling over India.