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Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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 = 0.02 μ 2 + 0.696 μ + 1.539 , providing a reference for localizing the Z-R relationship in the South China Sea. [ABSTRACT FROM AUTHOR]

To improve precipitation estimation and forecasting, it is important to have a thorough understanding of the regional variability of raindrop size distribution (DSD) characteristics. This study aims to investigate the differences in DSDs between island and inland areas along the South China coastline, using observations from four disdrometers. The results of the study reveal that convective precipitation is the main contributor to summer rainfall in South China, with regional variations in DSD. The eastern stations (Shangchuan Island and Yangjiang) exhibit a higher concentration of large raindrops and a lower concentration of small raindrops compared to the western stations (Weizhou Island and Fangchenggang), which have more small raindrops. Additionally, the inland stations have a higher number of large raindrops (with a diameter greater than 3mm) compared to the island stations. The study also discusses the differences in environmental conditions that contribute to these variations. It is found that the warm cloud depth at the eastern (inland) stations is thicker than at the western (island) stations, indicating a longer pathway for raindrops to fall towards the ground and an enhanced collisional-coalescence process for raindrop growth. Furthermore, the shape-slope (μ -L) and radar reflectivity-rain rate (Z-R) relationships for the four stations also exhibit differences. Overall, this study provides a more comprehensive understanding of the regional variability of precipitation microphysics in South China. [ABSTRACT FROM AUTHOR]

This present investigation unveils a novel insight into the dominant airmass flows originating from both continental and maritime sources, shaping the characteristics of the raindrop size distribution (DSD), atmospheric instabilities, and cloud effective radius over Delhi (28.62°N, 77.17°E), the capital city of India. The investigation employs the Hybrid Single-Particle Lagrangian Integrated Trajectory model to analyze airmass back trajectories corresponding to days featuring rain events within the study area. An observable pattern emerges: days with rainfall driven by continental (maritime) activities experience higher (lower) atmospheric instabilities, which is validated by radiosonde measurements. This enhanced atmospheric instability leads to the prevalence of larger raindrops during continental-influenced rain events, in contrast to days marked by maritime airflow, as estimated from surface-based disdrometer measurements. Furthermore, observations from the INSAT 3D/3DR satellite indicate a tendency towards a smaller cloud effective radius associated with continental airmass inflow, as opposed to maritime inflow. Notably, a category of rain events characterized by ambiguous airmass back trajectories, referred to as mixed types, is identified. These mixed events showcase intermediary variations in the investigated atmospheric parameters, positioning them between the continental and maritime scenarios. The discernible differences in DSD characteristics between rain events influenced by continental and maritime activities yield fluctuations in the radar reflectivity-rain rate (Z-R) power law relations, which bear significance for the application of rain radar remote sensing. Hence, this investigation underscores the critical implication of incorporating airmass source information while characterizing rain features and related atmospheric parameters over a capital metropolis like Delhi. [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]

To study the microphysical characteristics of precipitation in different regions of Shandong province-the Yellow River Delta, inland region and coastal region, the raindrop spectrum data at three sites (Kenli, Pingyin, and Jiaonan) from 2017 to 2020 were used to compare the raindrop size distribution (DSD) characteristics of different rain rates (R) and rainfall types (stratiform and convective rainfall). The results are as follows. (1) In 6 rain rate levels at 3 sites, there were certain degree of differences in the DSD spectral, the spectra width of mean DSD, and the contribution of different precipitation levels to the total precipitation amount. (2) In stratiform precipitation, the average DSD characteristics of Pingyin and Jiaonan were comparable to each other, but differences were found in convective precipitation at these 3 sites. (3) The distribution of the normalized Gamma spectral intercept parameter (log10Nw) and the mass-weighted average diameter (Dm) showed that, log10Nw of Kenli station was the lowest during stratiform precipitation. While the comparison with other national and international stations demonstrated that the distribution of log10Nw versus Dm from Pingin agreed with the observations in Seoul, Korea. (4) The fitting curves of the shape parameter (μ) and slope parameter (λ) from the Gamma distribution exhibited localized characteristics. According to the trend of μ and λ with R at all 3 sites, R=10 mm·h-1 can be used as a criterion for precipitation classification. At all three sites, when R increased greater than 100 mm·h-1, the values of μ remained nearly constant towards 1~2, while the values of λ tend to 2. (5) The fitting results between the reflectivity factor (Z) and R (Z=ARb) at 3 sites indicated that, the typical Z-R fitting formula (Z=300R1.40) for weather radars overestimated the precipitation of Kenli and underestimated the precipitation of Jiaonan. The values of A and b in the Z-R fitting formula also exhibited differences between convective and stratiform precipitation.

Droplet size distribution (DSD) is an important parameter reflecting microphysical characteristics of rainfalls, research on vertical structures of DSD is helpful to understand rainfall evolution processes and improve radar quantitative precipitation estimations. Vertical structures of DSDs under different rain rates over different regions during the Meiyu period in 2020 have been investigated using the co-located two-dimensional video disdrometer (2DVD) and micro rain radar (MRR) at the stations of ZiGui, JingZhou and NanJing. It is found that, when raindrops fall, the number concentration of small raindrops and their contribution to rain rates decrease, while the number concentration of medium and large raindrops and their contribution to rain rates increase, which finally leads to that medium raindrops contribute most significantly to surface rain rates. Vertical structures of DSDs under different rain rates are different. For weak rainfall, there are narrow DSDs and the equilibrium of raindrop evaporation and coalescence causes small variation in DSD parameters. For heavy rainfall, there are significantly widened and increased DSDs, because obvious collision process increases the concentration of medium-large raindrops when raindrops falls and thus causes large variation in DSD parameters. Statistical analysis shows that DSDs present significant difference over different regions: the highest number concentration and the smallest raindrop diameter at ZiGui, medium number concentration and medium diameter at Jinzhou, the lowest number concentration and the largest diameter at Nanjing. Moreover, μ-Λ relationships present regional difference, and the fitted values of rain rates with local Z-R relationships over different regions agree well with the observations of 2DVD.

In this study, we applied an explainable machine learning technique based on the LightGBM method, a category of gradient boosting decision tree algorithm, to conduct a quantitative radar precipitation estimation and move to understand the underlying reasons for excellent estimations. By introducing 3D grid radar reflectivity data into the LightGBM algorithm, we constructed three LightGBM models, including 2D and 3D LightGBM models. Ten groups of experiments were carried out to compare the performances of the LightGBM models with traditional Z–R relationship methods. To further assess the performances of the LightGBM models, rainfall events with 11,483 total samples during August-September of 2022 were used for statistical analysis, and two heavy rainfall events were specifically chosen for the spatial distribution evaluation. The results from both the statistical analysis and spatial distribution demonstrate that the performance of the LightGBM 3D model with nine points is the best method for quantitative precipitation estimation in this study. Through analyzing the explainability of the LightGBM models from Shapley additive explanations (SHAP) regression values, it can be inferred that the superior performance of the LightGBM 3D model is mainly attributed to its consideration of the rain gauge station attributes, diurnal variation characteristics, and the influence of spatial offset. [ABSTRACT FROM AUTHOR]

Weather radar is a form of alternative indirect rainfall measurement for use in mitigating flash flood hazards. It is a challenging task to obtain accurate radar rainfall data without integration with automatic rain gauge networks. This paper investigated transformation equations to convert the calibrated daily Z–R relationship to the sub-hourly scale and proposed optional schemes for downscaling the daily bias adjustment factor into 15 min resolution scale to produce a high-resolution radar rainfall product for flash flood modelling. Radar reflectivity data from three radar stations in Thailand and their corresponding daily gauge rainfall data were used in the analysis. Two bias adjustment schemes (DMFB and DS_DMFB), accounting for the temporal variation, and one spatiotemporal scheme (SPTB_IDS) were used to generate three corresponding rainfall datasets for the unified river basin simulator (URBS) model to simulate flood hydrographs in the Tubma basin, Thailand. The results showed that combining the proposed 15-min Z–R scaling equation and the SPTB_IDS produced the most reliable radar rainfall amount leading to an increase in the accuracy of flood modelling with the lowest uncertainty. This indicated that the temporal downscaling solution together with spatial interpolation technique for sub-hourly radar rainfall assessment could benefit flash flood simulation in a data-scarce basin. HIGHLIGHTS Daily gauge rainfall cannot detect a variation of sub-hourly intense rainfall. This paper proposed alternative techniques to downscale the Z-R relationship and daily data and combine it with radar observations to synthesize 15-min radar rainfall data.; Results show the benefit of the downscaled Z-R relationship and the most complicated radar bias adjustment method substantially improved the accuracy of flood estimates and reduced uncertainties in the flash flood modelling.;

Based on the rain drop size distribution （DSD） observation data at different altitudes of Zongwulong mountain on the northeast edge of Qaidam Basin from July to September 2020， the contribution of different particle size raindrop microphysical parameters， particle size particle number concentration average spectrum， microphysical characteristic parameters and the relationship between reflectivity factor and rain rate（Z-R） under different rain rate were studied.The results show that the number density of raindrops is mainly contributed by small raindrops， while medium raindrops and heavy raindrops are the main contributions of reflectivity factor Z， rain rate R and liquid water content W.The contribution of heavy raindrops to Z， R and W is higher in Mountain Site with higher altitude.The spectral width of Mountain Site in light rain， moderate rain and heavy rain is wider， and the particle number concentration at the end of heavy rain drops is higher under different rain rate.The lifting of terrain can significantly promote the N（D）of larger raindrops.The mass weighted average diameter （DM） increases with the increase of altitude， and the raindrop diameter increases when R is higher.The generalized truncation parameter （log10Nw） will first increase and then decrease with the increase of rain rate.Smaller raindrops are the main source of rainfall.Rainfall in Qaidam Basin has its unique rain drop size distribution structure characteristics.The distribution of log10Nw and DM of Mountain Site is more discrete， and the log10Nw of Mountain Site is lower and DM is higher than that of Plain Site.If the unified precipitation estimation formula Z=300R1.4 were adopted， the rainfall will be overestimated， and the overestimation of rainfall at Mountain Site will be more serious.

The Gated Recurrent Unit (GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity (Z), radar echo-top height (ET) is also a good indicator of rainfall rate (R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship (Z=300R1.4), the optimal Z-R relationship (Z=79R1.68) and the GRU neural network with only Z as the independent input variable (GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar. To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_Z-ET is the best in the four methods for the quantitative precipitation estimation. [ABSTRACT FROM AUTHOR]

This study presents a year-long (January 2019–April 2020) analysis of the Z–R relationship and drop size distribution (DSD) scaling parameters for size, concentration, and shape of rain events over Santa Clara Valley, CA. External influences were analyzed based on synoptic variability and seasons. For the former, 850 hPa winds were separated into groups based on direction and magnitude. Results show that greater drop size, lower concentration, and larger shape parameters for spring, while winter and fall showed smaller drop sizes, higher concentrations, and smaller shape parameters. For synoptic variability, southeasterly-to-southwesterly flow was associated with larger drop sizes, larger concentrations, and smaller shape parameters relative to northwesterly flow. Differences in the DSD scaling parameter values and Z–R relationship were also observed between strong and weak low-level flow. The results of this study suggest that it is beneficial to derive specific microphysical relationships based on seasons and different synoptic events to improve radar rain rate retrieval algorithms using the Z–R relationship. [ABSTRACT FROM AUTHOR]

An alternative to the use of rain gauges as sources of precipitation data is provided by laser disdrometers, which inter alia allow for high-temporal-resolution measurement of the reflectivity (Z) and intensity (R) of precipitation. In the study detailed here, an OTT Parsivel1 laser disdrometer located at the Meteorological Station of Warsaw University of Life Sciences (SGGW) generated the 95,459 Z-R data pairs recorded across 1-min time intervals that were subject to further study. Included values for the reflectivity and instantaneous intensity of precipitation were found to be in the respective ranges of -9.998-67.898 dBZ and 0.004-153.519 mm h-1 (given that values for precipitation intensity below 0.004 mm h-1 were excluded from further consideration). The material obtained covered the months from April to October in the years 2012-2014 and 2019-2020 (30 months in total), which were selected for the study due to the completeness of data. [ABSTRACT FROM AUTHOR]

In this study, the spherical particle model and ten nonspherical particle models describing the scattering properties of snow are evaluated for potential use in precipitation estimation from spaceborne dual-frequency precipitation radar. The single scattering properties of nonspherical snow particles are computed using discrete dipole approximation (DDA), while those of spherical particles are determined using Mie theory. The precipitation profiles from WRF output are then input to a forward radiative transfer model to simulate the radar reflectivity at Ka-band and Ku-band. The results are validated with Global Precipitation Mission Dual-Frequency Precipitation Radar measurements. Greater consistency between the simulated and observed reflectivity is obtained when using the sector- and dendrite-shape assumptions. For the case in this study, when using the spherical-shape assumption, radar underestimates the error of the cloud's top by about 300 m and underestimates the error of the cloud's area by about 15%. As snowflake shapes change with temperature, we use the range between −40 °C and −5 °C to define three temperature layers. The relationships between reflectivity (Z) and precipitation rate (R) are fitted separately for the three layers, resulting in  Z = 134.59 · R 1.184  (sector) and  Z = 127.35 · R 1.221  (dendrite) below −40 °C. [ABSTRACT FROM AUTHOR]

While heavy rainfall frequently takes place in southern China during summer monsoon seasons, quantitative precipitation forecast skills are relatively poor. Therefore, detailed knowledge about the raindrop size distribution (DSD) is useful in improving the quantitative precipitation estimation and forecast. Based on the data during 2018-2022 from 86 stations in a ground-based optical disdrometer measurement network, the characteristics of the DSD in Guangdong province are investigated in terms of the particle size distribution (N(D)), mass-weighted mean diameter (Dm) and other integral DSD parameters such as radar reflectivity (Z), rainfall rate (R) and liquid water content (LWC). In addition, the effects of geographical locations, weather systems (tropical cyclones, frontal systems and the summer monsoon) and precipitation types on DSD characteristics are also considered. The results are shown as follows. 1) Convective precipitation has a broader N(D) and larger mean particle diameter than the stratiform precipitation, and the DSD observations in Guangdong are consistent with the three-parameter gamma distribution. The relationships between the Z and R for stratiform and convective precipitation are also derived for the province, i.e., Z = 332.34 R1.32 and Z = 366.26 R1.42 which is distinctly different from that of the Next-generation Weather Radar (NEXRAD) Z-R relationship in United States. 2) In the rainy season (April-September), the Dm, R and LWC are larger than those in the dry season (October- March). Moreover the above parameters are larger, especially in mid-May, which is the onset of the South China Sea summer monsoon. 3) The spatial analysis of DSD shows that the coastal station observations indicate a smaller Dm and a larger normalized intercept parameter (log10Nw), suggestive of maritime-like rainfall. Dm is larger and log10Nw is smaller in the inland area, suggestive of continental-like rainfall. 4) Affected by such weather systems as the tropical cyclone, frontal system and summer monsoon, the DSD shows characteristics with distinct differences. Furthermore, frontal system rainfall tends to present a continental-like rainfall, tropical cyclone rainfall tends to have a maritime-like rainfall, and summer monsoon rainfall characteristic are between maritime- and continental-like cluster (raindrop concentration and diameter are higher than continental cluster and maritime cluster, respectively. [ABSTRACT FROM AUTHOR]

Flash floods are a major weather-related risk, as they cause more than 5000 fatalities annually, according to the World Meteorological Organization. Quantitative Precipitation Estimation is a method used to approximate the rainfall over locations where direct field observations are not available. It represents one of the most valuable information employed by meteorologists and hydrologists for issuing early warnings concerning flash floods. The current study is in line with the efforts to improve radar-based rainfall estimates through the use of machine learning techniques applied on radar data. With this aim, as a proof of concept, six machine learning models are evaluated to make estimations of the radar-based hourly accumulated rainfall using reflectivity data collected on the lowest radar elevation angles, and we employ a new data model for representing these radar data. The data were collected by a WSR-98D weather radar of the Romanian Meteorological Administration, located in the central region of Romania, during 30 non-consecutive days of the convective seasons, between 2016 and 2021. We obtained encouraging results using a stacked machine learning model. In terms of the Root Mean Squared Error evaluation metric, the results of the proposed stacked regressor are better than the radar estimated accumulated rainfall by about 33% and also outperform the baseline computed using the Z-R relationship by about 13%. [ABSTRACT FROM AUTHOR]

Raindrop size distributions collected by the DROPLET MEASUREMENT TECHNOLogies Precipitation imaging probe from 17 flights through 6 hurricanes during National Oceanic and Atmospheric Administration's hurricane field program in 2020 are used to study reflectivity (Z) and rainfall rate (RR) (R) relationship (i.e., Z‐R relationship). The results show that the Z‐R distribution is highly scattered and the scatter increases with RR and reflectivity up to 48 dBZ or 25 mm hr−1, after which it decreases rapidly. The range of the estimated RR from a power‐law Z‐R relationship can be as large as 50 mm hr−1 at reflectivity of 40 dBZ. The result from random forest regression model demonstrates that including the information of mass‐weighted‐diameter (Dm) along with radar reflectivity improves the estimated RR significantly. Plain Language Summary: One of the main applications of radar measurements is to estimate rainfall rate (RR) based on its power‐law relationships with radar reflectivity. Different relationships have been obtained for different weather scenarios. Hurricanes, in comparison with other severe weather systems, have a much longer life cycle and travel across much greater zonal and meridional extent. Therefore, in order to accurately estimate the RR for a specific time at a specific location in a hurricane, the power‐law relationship with constant coefficients needs to be improved. The uncertainties of the estimated RR using power‐law relationship are related to the raindrop sizes. The usage of a machine learning model (random forest regression) demonstrates that the uncertainties in the estimated rainfall are reduced significantly when both radar reflectivity and the drop sizes are taken into account. Key Points: The scatter in reflectivity (Z) ‐rainfall rate (R) distribution increases up to 48 dBZ or 25 mm hr−1, after which it decreases rapidlyThere are significant uncertainties associated with using a power law Z‐R relationship to estimate rainfall rate from radar reflectivityThe result from a machine learning model shows that rainfall estimation is improved if drop size information and reflectivity are both used [ABSTRACT FROM AUTHOR]

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In weather radar applications, the Z-R relationship is considered one of the most crucial factors for providing quality quantitative precipitation estimates. However, the relationship's parameters vary in time and space, making the derivation of an optimal relationship for a specific weather radar system challenging. This research focused on the analysis of the spatiotemporal variability of the parameters for a newly installed X-Band weather radar in Athens, Greece, by performing correlation and optimization analyses between high temporal resolution weather radar and rain gauge datasets. The correlation analysis was performed to assess the available datasets and provide the base of quality control. Multiple Z-R relationships were then derived for the following three optimization procedures; event-based relationships, station-based relationships, and a single area-based relationship. The results highlighted the region's spatial variability regarding the Z-R relationship and the correlation between the station location and its parameter values. Moreover, it was found that stations near the coast and the front end of precipitation systems featured parameter values typical of convective type events. Finally, a single Z-R relationship was determined under a calibration and validation scheme, Z = 321R1.53,, which was validated with good agreement. [ABSTRACT FROM AUTHOR]

Precipitation is a key driver of leading-edge erosion of wind turbine blades, which leads to a loss in annual energy production and high cost for repair of wind turbines. Precipitation type, drop size and their frequency are relevant parameters, but not easily available. Reflectivity-Rain Rate (Z‑R) relationships as well as annual sums of rainfall amount and rainfall kinetic energy potentially could be used to estimate leading-edge erosion. Although Z‑R relationships and amounts are known for several places, their spatial variation and dependence on weather types is unknown in the North Sea and Baltic Sea area. We analysed time series of multiple disdrometers located on the coast of the North Sea and Baltic Sea to characterize the variation and weather-type dependence of the Z‑R relationship, precipitation type, rainfall amount and kinetic energy.The Z‑R relationship as indication for the mean drop-size distribution showed small variations within different locations, but had a large variability for specific, but rare weather types. Only the precipitation types snow and hail showed some tendencies of weather-type dependence. Rainfall amount and rainfall kinetic energy were higher for stations in the eastern part of the North Sea compared to the western part and the Baltic Sea. Highest values were related to advection from the West. Overall, variations with location and weather type were found. These results will need to be considered in leading-edge erosion modelling and site assessment.

Based on the raindrop spectrum data observed in Xi'an, Shaanxi Province from 2013 to 2014, and combined with the observation data of the weather radar, the mean raindrop distribution, the microphysical characteristic quantity and Z-R relationship of 43 precipitation events of the mixed convective-stratiform clouds were statistically analyzed. The results show that the mean raindrop spectrum of the precipitation display a single peak, and the fitting of the Gamma distribution to the large precipitation particles is obviously better than that of the M-P distribution. The drop size corresponding to the peak value of raindrop number concentration and having the greatest contribution to rainfall intensity appeared at less than 1 mm in diameter in the raindrop spectrum. A Z-R relationship Z=168R1.43 has been established by the least square method for the mixed convective-stratiform clouds in Xi'an area. When the echo intensity calculated from raindrop spectrum data is less than (greater than) 30 dBz, the radar observation can overestimate the echo intensity. With echo intensity decreasing, the radar echo intensity is obviously overestimated (underestimated). A 5-grade revised scheme for the radar echo is proposed. The Z-R relationship Z=168R1.43 was used to estimate the rainfall during the precipitation events of the mixed convective-stratiform clouds in Xi'an area. The estimated amount of precipitation is closer to the actually measured rainfall. The relative error of the estimated rainfall was reduced from 51.3% to 25.4%.

Abstract Accurate precipitation estimation is significant since it matters to everyone on social and economic activities and is of great importance to monitor and forecast disasters. The traditional method utilizes an exponential relation between radar reflectivity factors and precipitation called Z-R relationship which has a low accuracy in precipitation estimation. With the rapid development of computing power in cloud computing, recent researches show that artificial intelligence is a promising approach, especially deep learning approaches in learning accurate patterns and appear well suited for the task of precipitation estimation, given an ample account of radar data. In this study, we introduce these approaches to the precipitation estimation, proposing two models based on the back propagation neural networks (BPNN) and convolutional neural networks (CNN) respectively, to compare with the traditional method in meteorological service systems. The results of the three approaches show that deep learning algorithms outperform the traditional method with 75.84% and 82.30% lower mean square errors respectively. Meanwhile, the proposed method with CNN achieves a better performance than that with BPNN for its ability to preserve the spatial information by maintaining the interconnection between pixels, which improves 26.75% compared to that with BPNN.

The relationships between the radar reflectivity factor for horizontal polarization (Zh) at the X-band and the liquid-equivalent snowfall rate (R) are presented for six hydrometeor classes of solid precipitation. These relationships were derived by comparing the values of Zh, R, and the hydrometeor class obtained by performing simultaneous observations in Niigata Prefecture, Japan. The relationships were assumed to be of the form Zh = B R1.67, where B denotes an experimentally determined coefficient. The values of R and the hydrometeor class were determined using a high-resolution precipitation gauge and an optical disdrometer, respectively, set up inside a windshield net. The average Zh value was determined for an analysis area of approximately 100 km2, located upwind of the ground-observation site. The prevailing hydrometeor class, its representative size, and the fall speed, along with the average Zh, R, and B values, were determined for 48 cases considered over three winters. The average B value for the "heavily rimed snow aggregate" was smaller than that for the "rimed snow aggregate". The largest B value was derived for a case of aggregates of unrimed dendritic particles (unrimed-D class). The case involving aggregates of unrimed low-temperature-type crystals (unrimed-C class) showed the smallest B value. For graupel cases, the average B value was roughly twice that of the rimed and heavily rimed snow aggregate classes and much smaller than that of the unrimed-D class. Snow aggregates demonstrated a stronger or weaker backscattering than graupel depending on the riming degree and types of constituent ice crystals in the X-band. [ABSTRACT FROM AUTHOR]

Monitoring and estimating rainfall for river basins in developing countries, such as Thailand, during severe storms are important and challenging studies due to the need for high spatiotemporal information of rainfall estimates. There is no end-software to produce rainfall estimate products for the basin because sophisticated systems and algorithms are needed to manipulate spatiotemporal data. Radar-based quantitative precipitation estimation (RQPE) provided by ground-based weather radar is considered to be suitable data for monitoring rainfall over a basin because its system can observe a rainfall event with high resolution of spatiotemporal scanning. This study presents the implementation of mosaicked RQPE from two stations over the Yom River basin in the northern part of Thailand. The study was performed using free and open source for a geographic information system (GIS) software with developing Python programming language in QGIS (PyQGIS) during the severe storm of Sontihn that occurred in July 2018. Using four-times-per-hour radar scanning, the RQPE mosaicking products were created over the basin by developing an algorithm in PyQGIS. The efficiency of the Z-R relationship was evaluated over the basin with multitemporal aggregation in the validation process between the mosaicked RQPE and rainfall observed at gauges. The Rosenfeld Z-R relationship produced the best performance based on validation statistics of the Marshall-Palmer and Summer Deep Tropical Convection Z-R relationship. Spatial mean field bias using the Rosenfeld Z-R relationship was greatly reduced during the storm evolution of Sontihn compared with the other two Z-R relationships. However, the RQPE based on instantaneous observations using the Marshall-Palmer Z-R relationship performed the best for rainfall estimation compared with the others. [ABSTRACT FROM AUTHOR]

This research work focuses on the investigation of the Z-R relationship impact on weather radar rainfall estimates, through the correlation of a X-Band weather radar estimates and rain gauge measurements in the region of Attica, Greece. The methodology followed in this work is performed into two sections, where first, a framework is applied in order to access the raw rainscanner datasets and convert them into a compatible form for comparison, since the radar measurements consist of gridded reflectivity values of 2-min temporal scale, whereas the rain gauge measurements pertain to 10-min point precipitation measurements. Secondly, a statistical analysis is performed, regarding the spatial variability of the Z-R relationship for a single event. Specifically, four Z-R relationships are used and compared based on the correlation with the actual rain gauge measurements, for each station, and the results are then shown upon the study area. The results provide with important findings about the spatial distribution of the optimal Z-R relationship based upon the proximity of the stations to the weather radar location, the coastline and station elevation. [ABSTRACT FROM AUTHOR]

In weather radar applications, the Z-R relationship is considered one of the most crucial factors for providing quality quantitative precipitation estimates. However, the relationship’s parameters vary in time and space, making the derivation of an optimal relationship for a specific weather radar system challenging. This research focused on the analysis of the spatiotemporal variability of the parameters for a newly installed X-Band weather radar in Athens, Greece, by performing correlation and optimization analyses between high temporal resolution weather radar and rain gauge datasets. The correlation analysis was performed to assess the available datasets and provide the base of quality control. Multiple Z-R relationships were then derived for the following three optimization procedures; event-based relationships, station-based relationships, and a single area-based relationship. The results highlighted the region’s spatial variability regarding the Z-R relationship and the correlation between the station location and its parameter values. Moreover, it was found that stations near the coast and the front end of precipitation systems featured parameter values typical of convective type events. Finally, a single Z-R relationship was determined under a calibration and validation scheme, Z = 321R1.53,, which was validated with good agreement.

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Radar quantitative precipitation estimation (QPE) is a key and challenging task for many designs and applications with meteorological purposes. Since the Z-R relation between radar and rain has a number of parameters on different areas, and the rainfall varies with seasons, the traditional methods are incapable of achieving high spatial and temporal resolution and thus difficult to obtain a refined rainfall estimation. This paper proposes a radar quantitative precipitation estimation algorithm based on the spatiotemporal network model (ST-QPE), which designs a convolutional time-series network QPE-Net8 and a multi-scale feature fusion time-series network QPE-Net22 to address these limitations. We report on our investigation into contrast reversal experiments with radar echo and rainfall data collected by the Hunan Meteorological Observatory. Experimental results are verified and analyzed by using statistical and meteorological methods, and show that the ST-QPE model can inverse the rainfall information corresponding to the radar echo at a given moment, which provides practical guidance for accurate short-range precipitation nowcasting to prevent and mitigate disasters efficiently. [ABSTRACT FROM AUTHOR]

Accurate precipitation estimation is significant since it matters to everyone on social and economic activities and is of great importance to monitor and forecast disasters. The traditional method utilizes an exponential relation between radar reflectivity factors and precipitation called Z-R relationship which has a low accuracy in precipitation estimation. With the rapid development of computing power in cloud computing, recent researches show that artificial intelligence is a promising approach, especially deep learning approaches in learning accurate patterns and appear well suited for the task of precipitation estimation, given an ample account of radar data. In this study, we introduce these approaches to the precipitation estimation, proposing two models based on the back propagation neural networks (BPNN) and convolutional neural networks (CNN) respectively, to compare with the traditional method in meteorological service systems. The results of the three approaches show that deep learning algorithms outperform the traditional method with 75.84% and 82.30% lower mean square errors respectively. Meanwhile, the proposed method with CNN achieves a better performance than that with BPNN for its ability to preserve the spatial information by maintaining the interconnection between pixels, which improves 26.75% compared to that with BPNN. [ABSTRACT FROM AUTHOR]

Currently, Doppler weather radar in China is generally used for quantitative precipitation estimation (QPE) based on the Z-R relationship. However, the estimation error for mixed precipitation is very large. In order to improve the accuracy of radar QPE, we propose a dynamic radar QPE algorithm with a 6-min interval that uses the reflectivity data of Doppler radar Z9002 in the Shanghai Qingpu District and the precipitation data at automatic weather stations (AWSs) in East China. Considering the time dependence and abrupt changes of precipitation, the data during the previous 30-min period were selected as the training data. To reduce the complexity of radar QPE, we transformed the weather data into the wavelet domain by means of the stationary wavelet transform (SWT) in order to extract high and low-frequency reflectivity and precipitation information. Using the wavelet coefficients, we constructed a support vector machine (SVM) at all scales to estimate the wavelet coefficient of precipitation. Ultimately, via inverse wavelet transformation, we obtained the estimated rainfall. By comparing the results of the proposed method (SWT-SVM) with those of Z = 300 × R1.4, linear regression (LR), and SVM, we determined that the root mean square error (RMSE) of the SWT-SVM method was 0.54 mm per 6 min and the average Threat Score (TS) could exceed 40% with the exception of the downpour category, thus remaining at a high level. Generally speaking, the SWT-SVM method can effectively improve the accuracy of radar QPE and provide an auxiliary reference for actual meteorological operational forecasting. [ABSTRACT FROM AUTHOR]

A Ka-Band signal propagation study under tropical monsoon climate has been taken up jointly by ISRO/ONERA/CNES. The program has been aimed at finding the degree to which Ka-Band satellite signals get affected by rain. Dual polarized Ka Band beacon signals onboard GSAT-14 satellite, of frequencies 20.2 GHz and 30.5 GHz, have been used for the experiment. Data available by various instruments has been compared with ITU-R predicted values. Results for the years 2016 and 2017 at Umiam has shown underestimation of both Rainfall and Signal Attenuation by ITU-R predicted models. It has also been found that the Rainfall has been mostly concentrated in three months (June, July, August) and the maximum attenuation that Ka-Band signals have suffered during rain at Umiam has been around 30-40 dB. The actual Rainfall and Attenuation of both 20.2 GHz and 30.5 GHz signals have been compared with ITU-R predictions and has been found to be much above the predicted levels. A Study of the Z-R relationship for the rainfall and comparison with known relations has indicated that a new distribution is required for the region. [ABSTRACT FROM AUTHOR]

This study characterizes different rainfall types using surface-based instruments (i.e. micro rain radar and laser precipitation monitor) installed at the Indian Institute of Technology Bhubaneswar Jatani, Odisha, India. A total of twelve rainfall cases including four from each season, i.e. pre-monsoon, monsoon and post-monsoon, are considered. The segregation of rainfall is carried out using radar reflectivity and rainfall intensity. In general, initial rainfall is dominantly convective and followed by a stratiform type. Two distinct maxima of radar reflectivity are noted at 3 and 5 km, suggesting the presence of high liquid water content and a melting band. The presence of liquid water content suggests occurrence of a warm rain process with shallow, intense convective cores. Results indicate a higher drop number density below 2 km with smaller size drops for convective rainfall and vice versa for the stratiform rainfall. Furthermore, Z–R relationships are computed for all the cases using a linear regression method, and the results suggest that the stratiform rainfall shows a higher slope parameter and lower intercept parameter as compared to convective rainfall. The distribution of drop number density shows a mono-modal and bimodal pattern for convective and stratiform rainfall, respectively. [ABSTRACT FROM AUTHOR]

Collisional drop breakup is one of the important processes during evolution of the rain drops and leads to modified shape of the drop size distribution (DSD), which is a primary parameter in rain retrieval in remote sensing techniques. Though several experimental and observational studies have been performed in the recent past on this aspect, the impact of the same in natural rain, particularly in orographic rain, is still poorly represented. The present study aims to examine and identify the rain conditions in which collisional drop breakup is prominent and is capable of modifying the shape of DSD in orographic rain over north-eastern India. The 2-min averaged DSDs from four hilly locations are studied in both stratiform and convective rain types separately using ground-based DSD measurements. Even though drop breakup in intense to moderate rain seemed to be in notable percentage (0.2% - 11.2%), the low intensity rain also showed a considerable break-up signature (2.5% - 9%), depending upon the locations. The overall results showed that collisional breakup was more prominent in convective rain (1%–11%) than in stratiform rain (4%–5%), however, breakup is also seen primarily during stratiform rain in two of the locations. The Z -R coefficients are found to be significantly different for the break-up and non-break up cases. For breakup cases, both convective and stratiform rainfall shows an increase in intercept parameter for all locations. For stratiform rain types, the coefficient a and b ranged from 307.6 to 321.4 (167.1–178.7) and 1.27–1.37 (1.31–1.34) for rain with break-up (without break-up), respectively. In the case of convective rain, the coefficients were found to be in the range of 377.5–654.7 (270.4–641.3) and 1.28–1.42 (1.14–1.31) with break-up (without break-up), respectively. The results suggest that collisional breakup is an important process in orographic rain over North-Eastern India and it has to be taken care of, while studying rainfall over these regions, particularly in conventional radar derived quantitative precipitation estimates (QPE). [Display omitted] • Collisional drop breakup in orographic rain has been explored in four tropical Indian locations. • Breakup occurs mainly in convective rain at two locations, while the other two show notable breakup in stratiform rain. • Occurrence of equilibrium DSD registered highest in low rain intensity for all locations. • The Z -R coefficient "A" was consistently higher across all locations and rain types in situations involving collisional drop breakup in comparison to scenarios with no breakup. [ABSTRACT FROM AUTHOR]

This study aims to explore the microphysical processes involved in stratiform precipitation, with in-situ observations carried out during Indian summer monsoon season of 2017, at two locations one each at the mid-altitude (Braemore; 400 m above MSL) and high-altitude (HACPO; Rajamallay; 1820 m above MSL) respectively, on the windward side of the Western Ghats (WG) mountains in peninsular India. The bright band (BB) events at both locations are identified using the observation from micro rain radar (MRR). The percentage of BB events are 27% higher and precipitation associated with BB events have higher consistency at high-altitude region. About 60% of observed BB peak height is found at 4.6 km in mid-altitude, whereas in high-altitude it varies from 4.4 to 4.6 km with percentage occurrences of 20% and 42% respectively. The BB bottom height variations in the range of 4.4 to 5.4 km contribute surface rain at mid-altitude in 0.1 < R ≤ 1 mmhr−1 rain class, however in high-altitude, all (0.1 ≤ R < 1 mmhr−1; 1 ≤ R < 2 mmhr−1; 2 ≤ R < 5 mmhr−1; 5 ≤ R < 10 mmhr−1) rain classes are influenced by bottom height variations. The drop size distribution (DSD) at the surface in mid-altitude shows a bimodal behaviour (dominant raindrops, 0.8 and 1.8 mm) in 0.1 ≤ R < 1 mmhr−1 rain category, while at high-altitude monomodal DSDs are observed irrespective of rain categories. The vertical profile of DSDs during BB events show that concave downward shape of DSDs are more prominent at mid-altitude which becomes concave upward at near surface levels that signifies collision and coalescence processes. The altitudinal variation of shape parameter indicates concave downward DSD in high-altitude throughout the layer and also less variation in slope (Λ) and intercept (N 0) values represents the collision, coalescence and breakup processes. The coefficient and exponent values in radar reflectivity-rain rate (Z -R) relations such as Z = 474.7R1.04 at mid-altitude and Z = 340.7R1.06 at high-altitude indicates the microphysical differences in rainfall at different elevations over WG. The results explain that internal dynamics between the shallow stable cloud layer and the melting layer present in the high-altitude creates seeder-feeder effects which accelerate the growth of small and moderate size raindrops that determines the rain DSD and enhance surface rain rates over WG. • Formation of Bright Band events are 27% higher in high altitude as compared to mid altitude in southern WGs. • Drop size distribution of surface rain (< 1 mmhr−1) in mid altitude shows a bimodal behavior during Bright Band events. • Size controlled microphysical processes are responsible for DSD evolution at mid altitude region in WGs. • Seeder-feeder mechanism plays an important role during stratiform precipitation at higher elevations over WGs. [ABSTRACT FROM AUTHOR]

Quantitative precipitation estimation and forecasting (QPE and QPF) are among the most challenging tasks in atmospheric sciences. In this work, QPE based on numerical modelling and data assimilation is investigated. Key components are the Weather Research and Forecasting (WRF) model in combination with its 3D variational assimilation scheme, applied on the convection-permitting scale with sophisticated model physics over central Europe. The system is operated in a 1-hour rapid update cycle and processes a large set of in situ observations, data from French radar systems, the European GPS network and satellite sensors. Additionally, a free forecast driven by the ECMWF operational analysis is included as a reference run representing current operational precipitation forecasting. The verification is done both qualitatively and quantitatively by comparisons of reflectivity, accumulated precipitation fields and derived verification scores for a complex synoptic situation that developed on 26 and 27 September 2012. The investigation shows that even the downscaling from ECMWF represents the synoptic situation reasonably well. However, significant improvements are seen in the results of the WRF QPE setup, especially when the French radar data are assimilated. The frontal structure is more defined and the timing of the frontal movement is improved compared with observations. Even mesoscale band-like precipitation structures on the rear side of the cold front are reproduced, as seen by radar. The improvement in performance is also confirmed by a quantitative comparison of the 24-hourly accumulated precipitation over Germany. The mean correlation of the model simulations with observations improved from 0.2 in the downscaling experiment and 0.29 in the assimilation experiment without radar data to 0.56 in the WRF QPE experiment including the assimilation of French radar data.

The relationship between the radar reflectivity factor (Z) and the rainfall rate (R) is recalculated based on radar observations from 10 Doppler radars and hourly rainfall measurements at 6529 automatic weather stations over the Yangtze-Huaihe River basin. The data were collected by the National 973 Project from June to July 2013 for severe convective weather events. The Z-R relationship is combined with an empirical qr-R relationship to obtain a new Z-qr relationship, which is then used to correct the observational operator for radar reflectivity in the three-dimensional variational (3DVar) data assimilation system of the Weather Research and Forecasting (WRF) model to improve the analysis and prediction of severe convective weather over the Yangtze-Huaihe River basin. The performance of the corrected reflectivity operator used in the WRF 3DVar data assimilation system is tested with a heavy rain event that occurred over Jiangsu and Anhui provinces and the surrounding regions on 23 June 2013. It is noted that the observations for this event are not included in the calculation of the Z-R relationship. Three experiments are conducted with the WRF model and its 3DVar system, including a control run without the assimilation of reflectivity data and two assimilation experiments with the original and corrected reflectivity operators. The experimental results show that the assimilation of radar reflectivity data has a positive impact on the rainfall forecast within a few hours with either the original or corrected reflectivity operators, but the corrected reflectivity operator achieves a better performance on the rainfall forecast than the original operator. The corrected reflectivity operator extends the effective time of radar data assimilation for the prediction of strong reflectivity. The physical variables analyzed with the corrected reflectivity operator present more reasonable mesoscale structures than those obtained with the original reflectivity operator. This suggests that the new statistical Z-R relationship is more suitable for predicting severe convective weather over the Yangtze-Huaihe River basin than the Z-R relationships currently in use. [ABSTRACT FROM AUTHOR]

Tropical cyclones and meiyu-baiu fronts, as the two main synoptic systems over East Asia, bring heavy rain during summers, but their long-term and vertical raindrop size distribution (RSD) features over the midlatitude Japan Islands are limited. Radar-based quantitative precipitation estimation (QPE) techniques require RSD observations. In this study, five-year observations from Tokyo with a ground-based impact Joss-Waldvogel disdrometer (JWD) and a vertically pointing micro rain radar (MRR) with a vertical range of 0.2−6.0 km were used to study the vertical structures of RSD and QPE parameters. The results showed that the convective rain associated with tropical cyclones had a maritime nature, while the rain associated with the meiyu-baiu front had a continental nature. The rain associated with tropical cyclones had a relatively higher concentration of raindrops and a larger average raindrop diameter than the rain associated with the meiyu-baiu front. The Z−R (radar reflectivity-rain rate) relationships (Z = ARb) based on the JWD data for tropical cyclones, the meiyu-baiu front and total summer rainfall in Tokyo were Z = 189 R1.38, Z = 214 R1.35 and Z = 212 R1.33, respectively. When the Z−R relationships obtained in this study were used to replace the operational relationship of Z = 300 R1.4, the standard deviation of the rain rate was reduced from 5.50 mm/h (2.34 mm/h) to 2.34 mm/h (1.32 mm/h) for typhoon (meiyu-baiu front) rainfall, although the change for total summer rainfall was small. In addition, with increasing height below 4 km, the value of A and b decreased.

Mediterranean regions are prone to heavy rainfall, flash floods, and erosion issues. Drop size distribution (DSD) is a key element for studying these phenomena through the hydrological variables which can be derived from it (rainfall rates and totals, kinetic energy fluxes). This paper proposes a five-year (2012-2016) DSD climatology, summarized by scaling parameters for concentration, size, and shape. The DSD network is composed of two longitudinal transects of three OTT Parsivel optical disdrometers each, across the Mediterranean Cevennes-Vivarais region. The influence of several factors are analysed: location (distance from the sea, orographic environment), season, daily synoptic weather situation (derived from geopotential heights, at 700 and 1000 hPa), rainfall type (analysed from 5 min radar data), as well as some combinations of these factors. It was found and/or confirmed that the orographic environment, season, weather patterns associated with the exposure to low level atmospheric flows, and rainfall types influenced the microphysical processes, leading to rainfall, measured at the ground. Consequently, the DSD characteristics, as well as the relationships between the rainfall rate and reflectivity factor, are influenced by these factors. [ABSTRACT FROM AUTHOR]

Usually a single literature-suggested Z-R relationship, where Z the radar reflectivity factor and R the rain rate, is used for weather radar data interpretation. It is desirable to calculate a Z-R relationship by precipitation type to improve the accuracy of quantitative rainfall rate in case of coexistence of different precipitation types, such as, in the area of precipitation produced from a Mesoscale Convective System (MCS). In general, in the MCS trailing anvil, the stratiform precipitation does not fall as drizzle. Rather, the rainfall can assume significant intensity (~10 mm h-1) with showery character. For that reason, in this study, the precipitations were classified into convective and stratiform type, to produce optimum rainfall estimates. Therefore, Z-R relationships were developed for the Eastern Coast of Northeastern Brazil (NEB) using rainfall raindrop size distribution (DSD) data collected with a disdrometer RD-69, aiming their utilization to start the operation of a weather radar system. In this study, due to operation and maintenance facilities, the disdrometer was installed in the Campus of the Universidade Federal de Alagoas (The Federal University of Alagoas) in Maceió in 2001, 12 months before the complete installation of a new radar system. The DSD was stratified by rainfall rate classes. It is found that the DSD are clearly dependent on the parameters of the analytical distribution functions are, and show a marked monthly variability. The parameters of the frequency distributions are dependent on R. The forms of DSDs are similar but the amount of droplets in each one very strongly. This may be possible due to the short period of data collection or to the intraseasonal rainfall variability. The general relationship for the Eastern Coast of NEB was found to be Z = 176.5 R1.29, with correlation coefficient equal to 0.83. This equation is in accordance with the ones for stratiform rain reported in the literature. We found that the convective rain observed is produced by convective cells imbedded into stratiform cloud layers. However, when separating stratiform and convective rainfall we found that the linear coefficient of the Z-R relation is significantly smaller for convective rainfall than for stratiform one (65 and 167 respectively), but the exponential coefficient is higher for convective than for stratiform (1.69 and 1.26 respectively).A relação Z-R, onde Z é o fator reflectividade radar e R a taxa de chuva, utilizada para interpretação de dados de radar meteorológico é geralmente baseada nas relações sugeridas na literatura. É importante calcular uma relação Z-R para diferentes regimes de precipitação para a melhoria da exatidão dos quantitativos de taxa de chuva, principalmente em caso de coexistência de diferentes tipos de precipitação, como por exemplo, na área de precipitação produzida a partir de um Sistema Convectivo de Meso-escala (MCS). Em geral, no MCS, acoplados à bigorna, a precipitação estratiforme pode não ocorrer como chuvisco. Ao contrário, a precipitação pode assumir uma intensidade maior (~10 mm h-1) e, com características claramente flutuantes, apresentar pancadas de chuva. Por essa razão, neste estudo, as precipitações foram classificadas como convectivas e estratiformes, para se obter melhores estimativas de chuva. Portanto, relações Z-R foram estabelecidas para a Costa Leste do Nordeste do Brasil (NEB) utilizando dados de precipitação obtidos com um Disdrômetro RD-69, visando a operação de um novo sistema de radar meteorológico. Neste estudo, devido às facilidades de operação, manutenção e instalações, o Disdrômetro foi instalado no campus da Universidade Federal de Alagoas em Maceió em 2001, 12 meses antes de completar a instalação do sistema de radar. A Distribuição do tamanho de gotas (DSD) foi estratificada por classes de taxa chuvas. Verificou-se que as DSDs dependem claramente dos parâmetros das funções de distribuição analíticas e mostram uma variabilidade mensal acentuada. Os parâmetros da freqüência de distribuição são dependentes de R. As formas das DSDs são similares, mas a quantidade de gotas em cada uma varia consideravelmente. Isto possivelmente pode ser devido ao curto período de coleta de dados ou devido à variabilidade intra-sazonal. A relação Z-R geral para a Costa Leste do NEB é Z = 176,5 R1.29, com coeficiente de um correlação de 0,83. Esta equação está em conformidade com as equações encontradas para chuvas estratiformes descritas na literatura. Constata-se que as chuvas convectivas observadas são produzidas por células convectivas normalmente embutidas em camadas de nuvens estratiformes. No entanto, quando se separa chuvas estratiformes das convectivas, verifica-se que o coeficiente linear da relação Z-R é significativamente menor para chuvas convectivas do que para as estratiformes (65 e 167 respectivamente), mas, o coeficiente exponencial é maior para as convectivas do que para as chuvas estratiformes (1,69 e 1,26 respectivamente).

Quantitative precipitation estimation and forecasting (QPE and QPF) are among the most challenging tasks in atmospheric sciences. In this work, QPE based on numerical modelling and data assimilation is investigated. Key components are the Weather Research and Forecasting (WRF) model in combination with its 3D variational assimilation scheme, applied on the convection-permitting scale with sophisticated model physics over central Europe. The system is operated in a 1-hour rapid update cycle and processes a large set of in situ observations, data from French radar systems, the European GPS network and satellite sensors. Additionally, a free forecast driven by the ECMWF operational analysis is included as a reference run representing current operational precipitation forecasting. The verification is done both qualitatively and quantitatively by comparisons of reflectivity, accumulated precipitation fields and derived verification scores for a complex synoptic situation that developed on 26 and 27 September 2012. The investigation shows that even the downscaling from ECMWF represents the synoptic situation reasonably well. However, significant improvements are seen in the results of the WRF QPE setup, especially when the French radar data are assimilated. The frontal structure is more defined and the timing of the frontal movement is improved compared with observations. Even mesoscale band-like precipitation structures on the rear side of the cold front are reproduced, as seen by radar. The improvement in performance is also confirmed by a quantitative comparison of the 24-hourly accumulated precipitation over Germany. The mean correlation of the model simulations with observations improved from 0.2 in the downscaling experiment and 0.29 in the assimilation experiment without radar data to 0.56 in the WRF QPE experiment including the assimilation of French radar data. [ABSTRACT FROM AUTHOR]

A comparative analysis of the rain attenuation evaluated from beacon measurements and from a single polarization S-band Radar is performed. The beacon measurements are obtained for two slant satellite paths with different elevation angles in the Ku- and Ka-band. The single polarization S-band Radar reflectivity data is used to predict the attenuation along the satellite propagation paths. This is done by first converting the reflectivity into rain rate using the Z-R relations suitable for the tropical region and, afterwards, by evaluating the slant path attenuation through the integration of the specific rain attenuation derived from the point rainfall rate. An empirical calibration factor for the Radar reflectivity is provided. A comparison of the rain fade suffered from the two satellite paths is presented both on event basis and in terms of cumulative distribution functions. The empirical calibration factor and a single Z-R relation suitable for the tropical region are used for rain attenuation evaluation from single polarization Radar data. [ABSTRACT FROM PUBLISHER]

Abstract: A Local Area Weather Radar (LAWR) is an X-band weather radar developed to meet the needs of high resolution rainfall data for hydrological applications. The LAWR system and data processing methods are reviewed in the first part of this paper, while the second part of the paper focuses on calibration. The data processing for handling the partial beam filling issue was found to be essential to the calibration. LAWR uses a different calibration process compared to conventional weather radars, which use a power-law relationship between reflectivity and rainfall rate. Instead LAWR uses a linear relationship of reflectivity and rainfall rate as result of the log transformation carried out by the logarithmic receiver as opposed to the linear receiver of conventional weather radars. Based on rain gauge data for a five month period from a dense network of nine gauges within a 500×500m area and data from a nearby LAWR, the existing calibration method was tested and two new methods were developed. The three calibration methods were verified with three external gauges placed in different locations. It can be concluded that the LAWR calibration uncertainties can be reduced by 50% in two out of three cases when the calibration is based on a factorized 3 parameter linear model instead of a single parameter linear model. [Copyright &y& Elsevier]