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The advent of new telecommunication systems with large bandwidths like 5G/6G and satellites operating at higher frequency bands such as Ku, Ka-band, and even Q/V bands, has brought to the forefront the issue of rain attenuation, particularly in equatorial and tropical regions. Many existing models developed to address these inaccuracies in the rainfall rate prediction rely on a single rain gauge measurement, which still can lead to inaccuracy when the model is generalized for a larger area. This research, therefore, is of utmost importance as it aims to develop an area-based rainfall rate model using multiple rain gauges spread across many locations in a specific area. The area of focus for this research is the Klang Valley area, a crucial economic territory that includes the capital city, Kuala Lumpur, in Malaysia. Five rain gauges distributed in Klang Valley were chosen to measure the rainfall rate. The rainfall rate model was then developed based on the data from these five rain gauges. The results indicate that each rain gauge's rainfall rate at R0.01% exceedance level varied greatly from 102 mm/hr to 138 mm/hr and exceeded Malaysia's recommended ITU-R at 100 mm/hr. The new model, presented herein, accounts for the variation of rainfall rate across a larger area, which can provide accurate modeling of specific attenuation and rain attenuation in the equatorial regions, thereby enhancing the reliability of communication systems. ABSTRAK: Kemunculan sistem telekomunikasi jalur lebar baru seperti 5G/6G dan satelit yang beroperasi pada jalur frekuensi tinggi seperti Ku, Ka-band, dan jalur Q/V, menyebabkan penurunan ketara amplitud gelombang akibat hujan, terutamanya di kawasan khatulistiwa dan tropika. Kebanyakan model sedia ada yang dibangunkan bagi menangani ketidak tepatan ramalan kadar hujan hanya berdasarkan pengukuran dari satu tolok hujan, ini menyebabkan ketidaktepatan model itu apabila digeneralisasikan bagi kawasan lebih luas. Oleh itu, kajian ini adalah amat penting kerana ia bertujuan bagi membangunkan model kadar hujan berasaskan kawasan menggunakan beberapa tolok hujan yang tersebar di pelbagai lokasi pada satu kawasan tertentu. Kawasan tumpuan kajian ini adalah kawasan Lembah Klang, sebuah wilayah ekonomi penting yang melingkupi ibu negara, Kuala Lumpur, Malaysia. Lima tolok hujan yang tersebar di Lembah Klang dipilih bagi mengukur kadar hujan. Model kadar hujan kemudiannya dibangunkan berdasarkan data dari kelima-lima tolok hujan ini. Dapatan menunjukkan kadar hujan R0.01% bagi lima tolok hujan adalah antara 102 mm/jam hingga 138 mm/jam dan melebihi kadar cadangan ITU-R Malaysia iaitu pada 100 mm/jam. Model baru yang mengambil kira variasi kadar hujan pada suatu kawasan lebih luas menggunakan banyak tolok hujan, di mana dapat memberikan pemodelan amplitud gelombang yang lebih tepat bagi hujan di kawasan khatulistiwa, dengan itu meningkatkan kebolehpercayaan sistem komunikasi.

Based on data from the National Disaster Management Agency (Ind.: Badan Nasional Penanggulangan Bencana – BNPB), throughout 2022, more than 91% of disaster events were hydrometeorological disasters, with floods at 43% and landslides at 17%. One of the factors for floods and landslides is high rainfall intensity. Automatic rain gauge (ARG) is a rainfall observation instrument that can accurately measure rainfall at observation points. However, it has problems such as communication systems that cause delays in data transmission, low instrument density, and inability to cover a wide spatial area, which can affect the accuracy of rainfall information. Weather radar is a remote sensing instrument that can estimate rainfall spatially so that weather radar observations can reach areas of the region that do not have ARG. However, before being used as rainfall information, estimation rainfall needs to be evaluated or calibrated. Evaluation of rainfall estimation on weather radar to ARG in Banten at a 30– 120 km distance range, shows a coefficient of determination above 0.8. Based on the studies that have been conducted, increase of root mean square error (RMSE) is due to influence of radar observation range and observation distance on ARG. Adjustment of rainfall estimation improves the accuracy of rainfall estimation. Adjusting rainfall estimation can reduce RMSE by 50%.

Characteristics of rainfall rate useful in the estimation of attenuation due to rain are presented. Rain data collected at Ogbomoso between January–October, 2009 were used in the analysis. Result shows that power law relationship exists between the equiprobable rain rates of two different integration times. The value of conversion factor CE and CR obtained for Ogbomoso are 0.28(60) and 0.64(90) respectively. Our result then shows that different conversion factor is required for different location even within the same climatic region.

A realistic approach for gathering high-resolution observations of the rainfall rate, R, in the vertical plane is to use data from vertically pointing Doppler radars. After accounting for the vertical air velocity and attenuation, it is possible to determine the fine, spatially resolved drop size spectra and to calculate R for further statistical analyses. The first such results in a vertical plane are reported here. Specifically, we present results using MRR-Pro Doppler radar observations at resolutions of ten meters in height over the lowest 1.28 km, as well as ten seconds in time, over four sets of observations using two different radars at different locations. Both the correlation functions and power spectra are useful for translating observations and numerical model outputs of R from one scale down to other scales that may be more appropriate for particular applications, such as flood warnings and soil erosion, for example. However, it was found in all cases that, while locally applicable radial power spectra could be calculated, because of statistical heterogeneity most of the power spectra lost all generality, and proper correlation functions could not be computed in general except for one 17-min interval. Nevertheless, these results are still useful since they can be combined to develop catalogs of power spectra over different meteorological conditions and in different climatological settings and locations. Furthermore, even with the limitations of these data, this approach is being used to gain a deeper understanding of rainfall to be reported in a forthcoming paper.

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]

Raindrop size distribution （RSD） is fundamental in understanding the precipitation microphysics and quantitative precipitation estimation， especially in complex terrain with complicated rainfall mechanism and high spatial and temporal variability.In this paper， the RSD characteristics of different rainfall rates and rain types in the mountainous and plain areas of the Ili River Valley during the summer of 2020 -2022 are investigated using Parsivel2 disdrometer.The results show that the precipitation in the mountainous and plain areas is mainly composed of small raindrops， and medium raindrops are the largest contribution to the rainfall rate R.Precipitation in the mountainous and plain areas mainly occurs during the late afternoon and early evening， and the concentration of large and medium-sized raindrops is higher in the mountainous areas in all time periods.The spectral width and the concentration of drops with diameters increase with increasing rainfall rate.The number concentration of medium and large particles in the mountainous areas is obviously higher than that in the plain areas during light rain and heavy rain.For the same rainfall rate and rain type， precipitation in the mountainous areas has higher mass-weighted mean diameter （Dm） and lower normalized intercept parameter （log10Nw）.In addition， the convective rain spectrum in the mountainous and plain areas of the Ili River Valley tend to be “continental-like convective cluster”.It is found that there are also significant differences in the μ-Λ relationship and Z-R relationship of precipitation between the two areas， and the empirical relationship of Z =300R1.40 clearly overestimates precipitation.The results reveal the microphysical characteristics of precipitation under different terrain conditions in the Ili River Valley， and provide a reliable factual basis for the subsequent retrieval of precipitation using radar data.

This article addresses the opportunistic use of satellite downlink attenuation measurements for the site-specific estimation of rainfall rate. In particular, this article proposes a regression model characterized by the introduction of correction elements extracted from the frequency-domain analysis of the attenuation time series, and the use of a simple linear regression function with a small modification, which allows to using a negative intercept with the goal of minimizing the maximum error (large underestimation/overestimation). The performance, assessed using attenuation measurements of a satellite downlink in Q/V band shows that the proposed model allows to estimate the rainfall rate with low average error and low maximum error with respect to a more standard ITU model, so as to offer more reliable estimations. [ABSTRACT FROM AUTHOR]

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

Using the sequential intensity filtering technique (abbreviation is the SIFT method), two parameters (rainfall rate R and median diameter Dm) were selected to analyze the drop spectrum data of rainfall in Shenyang from 2020-2021, the results show that: 1. The SIFT method reduces distribution fluctuations and sampling errors by comparing standard deviations SD, mean relative error AFE, standard deviation SDFE of the rainfall rate R, mean diameter Dm, raindrops concentration Nt, radar reflectance factor Z of the measured spectrum with the mean spectrum obtained by the SIFT method. 2. By analyzing the difference between the average spectrum and the SIFT method, the SIFT method has the smallest error of fitting, which shows that the SIFT method can reduce the influence of the sampling error caused by natural rainfall changes on the droplet spectrum fitting, improve the accuracy of the inversion droplet spectrum distribution characteristics. [ABSTRACT FROM AUTHOR]

The accurate prediction of heavy precipitation in convective environments is crucial because such events, often occurring in Italy during the summer and fall seasons, can be a threat for people and properties. In this paper, we analyse the impact of satellite-derived surface-rainfall-rate data assimilation on the Weather Research and Forecasting (WRF) model’s precipitation prediction, considering 15 days in summer 2022 and 17 days in fall 2022, where moderate to intense precipitation was observed over Italy. A 3DVar realised at CNR-ISAC (National Research Council of Italy, Institute of Atmospheric Sciences and Climate) is used to assimilate two different satellite-derived rain rate products, both exploiting geostationary (GEO), infrared (IR), and low-Earth-orbit (LEO) microwave (MW) measurements: One is based on an artificial neural network (NN), and the other one is the operational P-IN-SEVIRI-PMW product (H60), delivered in near-real time by the EUMETSAT HSAF (Satellite Application Facility in Support of Operational Hydrology and Water Management). The forecast is verified in two periods: the hours from 1 to 4 (1–4 h phase) and the hours from 3 to 6 (3–6 h phase) after the assimilation. The results show that the rain rate assimilation improves the precipitation forecast in both seasons and for both forecast phases, even if the improvement in the 3–6 h phase is found mainly in summer. The assimilation of H60 produces a high number of false alarms, which has a negative impact on the forecast, especially for intense events (30 mm/3 h). The assimilation of the NN rain rate gives more balanced predictions, improving the control forecast without significantly increasing false alarms.