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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]

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]

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. [ABSTRACT FROM AUTHOR]

The dual peak semidiurnal variation in surface rainfall rate over the tropics, simulated using a 3.5-km-mesh Nonhydrostatic Icosahedral Atmospheric Model (NICAM) for 26 - 31 December 2006, is analyzed and compared with data from the 17 year winter precipitation climatology of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Precipitation Radar (PR), and the same 6 day data of Global Satellite Mapping of Precipitation, as well as infrared data from geostationary satellites. We focus on land areas including southern Africa and the Amazon. Over these land areas, the NICAM simulation captures the primary peak in the afternoon and the secondary peak in the early morning, at similar times to those captured using TRMM data. In the PR observation, the primary peak of rainfall is mainly due to convective rain, whereas the secondary peak is due to stratiform rain. In the NICAM simulation, if a simple method is used for the classification of convective/stratiform rain, convective rain is dominant all day long, and the rainfall rate is generally higher than in the PR observation. Nevertheless, an analysis of deep convection (DC) areas indicates consistency between the observation and NICAM; the primary peak of rainfall rate occurs at the mature stage of the number of DC areas, whereas the secondary peak occurs when the mean size of DC areas is almost at its highest point. However, in the NICAM simulation, the relative magnitudes of the two peaks are not represented well, and the contribution of the stratiform rain is underestimated. The present study indicates that a high-resolution global nonhydrostatic model like NICAM has the potential to overcome the limitations of coarse-resolution general circulation models by reproducing the semidiurnal variation of DC, although there is room for improvement. [ABSTRACT FROM AUTHOR]

Agriculture is one of the main economic activities in the Peruvian Andes; rainwater alone irrigates more than 80% of the fields used for agriculture purposes. However, the cloud and rain generation mechanisms in the Andes still remain mostly unknown. In early 2014, the Instituto Geofísico del Perú (IGP) decided to intensify studies in the central Andes to better understand cloud microphysics; the Atmospheric Microphysics And Radiation Laboratory officially started operations in 2015 at IGP's Huancayo Observatory. In this work, a Ka-band cloud profiler [cloud and precipitation profiler (MIRA-35c)], a UHF wind profiler [Clear-Air and Rainfall Estimation (CLAIRE)], and a VHF wind profiler [Boundary Layer and Tropospheric Radar (BLTR)] are used to estimate rainfall rate at different conditions. The height dependence of the drop size diameter versus the terminal velocity, obtained by the radars, in the central Andes (3350m MSL) was evaluated. The estimates of rainfall rate are validated to ground measurements through a disdrometer [second-generation Particle, Size, and Velocity (PARSIVEL2)] and two rain gauges. The biases in the cumulative rainfall totals for the PARSIVEL2, MIRA-35c, and CLAIRE were 18%, 23%, and232%, respectively, and their respective absolute biases were 19%, 36%, and 63%. These results suggest that a real-time calibration of the radars, MIRA-35c and CLAIRE, is necessary for better estimation of precipitation at the ground. They also show that the correction of the raindrop terminal fall velocity, obtained by separating the vertical wind velocity (BLTR), used in the estimation the raindrop diameter is not sufficient, especially in convective conditions. [ABSTRACT FROM AUTHOR]

The accurate estimation of precipitation is an important objective for the Dual-frequency Precipitation Radar (DPR), which is located on board the Global Precipitation Measurement (GPM) satellite core observatory. In this study, a Bayesian correction (BC) approach is proposed to improve the DPR's instantaneous rainfall rate product. Ground dual-polarization radar (GR) observations are used as references, and a log-transformed Gaussian distribution is assumed as the instantaneous rainfall process. Additionally, a generalized regression model is adopted in the BC algorithm. Rainfall intensities such as light, moderate, and heavy rain and their variable influences on the model's performance are considered. The BC approach quantifies the predictive uncertainties associated with the Bayesiancorrected DPR (DPR_BC) rainfall rate estimates. To demonstrate the concepts developed in this study, data from the GPM overpasses of the Weather Service Surveillance Radar (WSR-88D), KHGX, in Houston, Texas, between April 2014 and June 2018 are used. Observation errors in the DPR instantaneous rainfall rate estimates are analyzed as a function of rainfall intensity. Moreover, the best-performing BC model is implemented in three GPM-overpass cases with heavy rainfall records across the southeastern United States. The results show that the DPR_BC rainfall rate estimates have superior skill scores and are in better agreement with the GR references than with the DPR estimates. This study demonstrates the potential of the proposed BC algorithm for enhancing the instantaneous rainfall rate product from spaceborne radar equipment. [ABSTRACT FROM AUTHOR]

Typhoon Bolaven caused significant damage with severe rainfall all over South Korea, including Cheju Island, which received more than 250 mm in 2 days in August 2012. It was regarded as the most powerful storm to strike the Korean Peninsula in nearly a decade. The rainfall-rate datasets were obtained from S-band radar operated by the Korea Meteorological Administration to be analyzed and compared with the mesoscale Cloud Resolving Storm Simulator (CReSS) model simulation. Multifractal analysis was conducted to understand the structure of the rainfall rate with height in the typhoon system. The radar rainfall data presented with strong intermittency across scales at lower altitudes (1 and 2 km) and a more homogeneous rainfall field at high altitude (5 km) with two parameters (fractal codimension and multifractality index). The statistical scaling moment function and maximal singularities show clear significant differences between radar and the CReSS model. [ABSTRACT FROM AUTHOR]

This contribution describes a realistic rain model that considers a cluster of axis-symmetric but flattened raindrops with a specified shape–size relation, a raindrop size distribution, and a distribution of orientation angle of the symmetry axis. The backscattering cross section and specific attenuation due to the rain medium are determined by the extended boundary condition method. The model is applied to data from two X-band radars to estimate reflectivity and rainfall rate, considering attenuation effects during the two-way propagation between the radar and each sampled rain volume. The results are then compared with corresponding ones from well-established attenuation-correction methods. One of these methods, which relies on unattenuated reflectivities measured by an S-band radar, has been carefully validated by a previous study. The reasonable agreement between the two methods indicates that the proposed model may be a suitable option to estimate reflectivity and rainfall rate fields based on X-band radar data, particularly when no such references are available. [ABSTRACT FROM AUTHOR]

Reliable rainfall rate complementary cumulative distributions are critical for the design of microwave communications systems operating above around 8 GHz. This Letter presents the results of the analysis of more than 5 years of 1-minute integrated rainfall accumulation data for 12 stations. This new dataset will prove useful considering the sharing analyses that must be executed for IMT services in the 24.25–27.5, 37–43.5, 45.5–47, 47.2–48.2 and 66–71 GHz spectrum bands. The resulting statistics can complement the entries in the database of ITU-R Study Group 3 for the region. [ABSTRACT FROM AUTHOR]

In radar observations of hydrometeors, the 0°C isotherm in the atmosphere (i.e., the freezing level) usually appears as a region of enhanced reflectivity. This region is known as the bright band (BB). In this study, observations over 12 months from a vertically pointing 35-GHz radar and a collocated disdrometer at the Natural Environment Research Council (NERC) Facility for Atmospheric and Radio Research (NFARR) are used to identify and compare microphysical differences between BB and non-brightband (NBB) periods. From these observations, the relationship between radar reflectivity Z and rainfall intensity R is found to be Z = 772R0.57 for BB periods and Z = 108R0.99 for NBB periods. Additionally, the brightband strength (BBS) was calculated using a novel method derived from the Michelson contrast equation in an attempt to explain the observed variability in BB precipitation. A series of Z–R relationships are computed with respect to BBS. The coefficients increase with increasing BBS from 227 to 926, while the exponents decrease with increasing BBS from 0.85 to 0.38. The results also indicate that NBB periods identified in the presence of a 0°C isotherm in other studies may be misclassified due to their inability to identify weak brightband periods. As such, it is hypothesized that NBB periods are solely due to warm rain processes. [ABSTRACT FROM AUTHOR]

Predicting precipitation rate and rainfall attenuation are key aspects in planning and dimensioning of wireless communication systems operating at frequencies above 10 GHz, such as satellite communication systems at Ku and Ka-bands. In this paper, contour maps of rainfall rate and rain attenuation are developed for the first time in Venezuela. It is based on 1-min rain rate statistics obtained from measurements carried out in Venezuela over a 30 years period with a higher integration time. The Rice-Holmberg model, refined Moupfouma-Martin model and Recommendation ITU-R P.837-7 for rain rate estimation were used, while Recommendation ITU-R P.618-13, Ramachandran- Kumar model, Yeo-Lee-Ong model and Rakshit-Adhikari-Maitra model were used for rain attenuation prediction in "Simón Bolívar" satellite links in Venezuela. The results of both types of maps represent a useful tool for preliminary planning of those links in the country, specifically, in Ku and Ka-bands. [ABSTRACT FROM AUTHOR]

Three global rainfall rate prediction methods are evaluated in their ability to estimate local precipitation statistics, which are the key to predict the impact of rain on the propagation of electromagnetic waves through the atmosphere. Specifically, the International Telecommunication Union-Radiocommunication Sector (ITU-R) P.837-6, model for rainfall statistics estimation (MORSE), and the ITU-R P.837-7 prediction methods are tested against long-term rainfall data collected in 19 sites in Ireland. The results indicate that the ITU-R P.837-7 prediction method delivers the best performance, and that both the ITU-R P.837-6 prediction method and MORSE exhibit a positive bias, likely due to the overestimation of the yearly rain amount in the maps used as input to such models. The results of the testing activity provide information on the accuracy of rainfall rate prediction methods at regional level, an important factor to consider given the direct link between the magnitude of rain-induced attenuation, and the operational frequency of wireless communication links. [ABSTRACT FROM AUTHOR]

One-minute rain rate exceeded for 0.01% of an average year (known as R0.01) is an important parameter, which is required in rain fade models for planning both satellite and terrestrial links, especially in the microwave and millimeter-wave bands. This paper presents the development of a new global model for estimating high-resolution R0.01. The model is developed using the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), ERA-Interim reanalyzed total precipitable water, and the cumulative distribution functions of the measured R0.01 via genetic algorithm. The newly proposed model performs better than the International Telecommunication Union Radio Sector (ITU-R) recommendation and widely used ITU-R P.837-6 model, particularly over the tropical regions, where ITU-R P.837-6 strongly underestimates R0.01. Overall, when compared to ITU-R P.837-6, the estimated R0.01 by the proposed model is improved by ~15%. Furthermore, the proposed model can provide R0.01 with a much higher spatial resolution (~5 km) as compared to ITU-R P.837-6 (~110 km). Another key advantage of the proposed model over ITU-R P.837-6 is that the inputs required for this model are readily available from meteorological database as well as from space-based observing system. This paper also investigated the long-term (1981–2016) trends in R0.01. The analysis revealed significant increasing trends in R0.01 over most parts of the globe, India and northern part of Southern America exhibited the strongest ($\sim 0.5\,\,\text {mm}\cdot \text {h}^{-1}\cdot $ year−1) increasing trends. It is anticipated that R0.01 obtained with the newly proposed model will have large implications in rain attenuation modeling for planning both terrestrial and earth–satellite microwave links. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite series R (GOES-R) will greatly expand the ability to observe the earth from geostationary orbit compared to the current-generation GOES, with more than 3 times as many spectral bands and a 75% reduction in footprint size. These enhanced capabilities are beneficial to rainfall rate estimation since they provide sensitivity to cloud-top properties such as phase and particle size that cannot be achieved using the limited channel selection of current GOES. The GOES-R rainfall rate algorithm, which is an infrared-based algorithm calibrated in real time against passive microwave rain rates, has been previously described in an algorithm theoretical basis document (ATBD); this paper describes modifications since the release of the ATBD, including a correction for evaporation of precipitation in dry regions and improved calibration updates. These improvements have been evaluated using a simplified version applicable to current-generation GOES to take advantage of the high-resolution ground validation data routinely available over the conterminous United States. Correcting for subcloud evaporation using relative humidity from a numerical model reduced false alarm rainfall by half and reduced the overall error by 35% for hourly accumulations validated against the National Centers for Environmental Prediction stage IV radar-gauge field; however, the number of missed events did increase slightly. Reducing the size of the calibration regions and increasing the training data requirements improved the consistency of the retrieved rates in time and space and reduced the overall error by an additional 4%. [ABSTRACT FROM AUTHOR]

The potential of rainfall-rate assignment using Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Instrument (SEVIRI) data is investigated. For this purpose, a new conceptual model for precipitation processes in connection with midlatitude cyclones is developed, based on the assumption that high rainfall rates are linked to a high optical thickness and a large effective particle radius, whereas low rainfall rates are linked to a low optical thickness and a small effective particle radius. Reflection values in the 0.56–0.71- μm (VIS0.6) and 1.5–1.78- μm (NIR1.6) channels, which provide information about the optical thickness and the effective radius, are considered in lieu of the optical and microphysical cloud properties. An analysis of the relationship between VIS0.6 and NIR1.6 reflection and the ground-based rainfall rate revealed a high correlation between the sensor signal and the rainfall rate. Based on these findings, a method for rainfall-rate assignment as a function of VIS0.6 and NIR1.6 reflection is proposed. The validation of the proposed technique showed encouraging results, especially for temporal resolutions of 6 and 12 h. This is a significant improvement compared to existing IR retrievals, which obtain comparable results for monthly resolution. The existing relationship between the VIS0.6 and NIR1.6 reflection values and the ground-based rainfall rate is corroborated with the new conceptual model. The good validation results indicate the high potential for rainfall retrieval in the midlatitudes with the high spatial and temporal resolution provided by MSG SEVIRI. [ABSTRACT FROM AUTHOR]

The rapid development of the radio communications system, especially in developed countries, has drawn the attention of telecommunication systems engineers to explore the frequency band above the Ku band. Because radio communication systems operating in the frequency band above the Ku band (10 GHz) suffer from rain attenuation during rainy conditions, prediction of rain attenuation using a 1-min rainfall rate distribution is indeed vital. However, a 1-min rainfall rate distribution is not widely available compared to rainfall rate distributions with longer integration times. Therefore, a suitable conversion method is required to predict 1-min rainfall rate distributions of distinct integration times. This paper presents several conversion methods such as Segal, Burgueno et al., Chebil and Rahman, Joo et al., EXCELL RSC and LG. The Segal method provides an overall Root Mean Square (RMS) error below 5% at different integration times and is suitable to be used in malaysia. [ABSTRACT FROM AUTHOR]

Abstract: The goal of this paper is to calculate new calibration matrices using Rapid Scan (RSS) Meteosat Second Generation (MSG) measurements and to evaluate their impact on precipitation estimates for a territory of the Czech Republic. The calibration matrices are the most important part of the Convective Rainfall Rate algorithm, which uses the IR 10.8μm, WV 6.2μm and VIS 0.6μm spectral SEVIRI (Spinning Enhanced Visible and Infrared Imager) channels of the MSG to assess satellite precipitation estimates (SPEs). The calibration matrices were calculated using Czech radar data from 21 summer days during which severe convection and heavy precipitation were observed. The resultant matrices were compared with those obtained using conventional 15min MSG scans. The comparison showed significant differences in the calibration matrices, which resulted in differences in the estimated precipitation. The application of RSS data significantly increased the rain rates and improved the structure of the matrices; however, the matrices were subjectively modified to increase the accuracy of the resulting SPEs. The calibration matrices were also calibrated by shifting the radar data forward 5, 10, 15 and 20min with respect to the MSG measurement, because some delay between the information obtained by the MSG and the radars was expected. The impact of the matrices was evaluated by verifying the SPEs with the radar-derived precipitation estimates merged with the rain gauge observations as the ground truth. The results showed that the calibration matrices that were based on the RSS data improved the categorical skill scores and reduced the mean error (ME), the mean absolute error (MAE), and root mean square error (RMSE) of SPEs. Subjectively, the estimated precipitation values are more realistic, the estimated maximum is closer to observations and convective cores are more pronounced and easily identifiable. The difference between the matrices calibrated by shifting the radar data in time was quite small, and the impact on the SPEs was not readily apparent. However, the time shifts of 15 and 20min slightly improved the categorical skill scores and reduced the ME, MAE and RMSE. [Copyright &y& Elsevier]

This study develops a Gaussian mixture rainfall-rate estimator (GMRE) for polarimetric radar-based rainfall-rate estimation, following a general framework based on the Gaussian mixture model and Bayes least squares estimation for weather radar-based parameter estimations. The advantages of GMRE are 1) it is a minimum variance unbiased estimator; 2) it is a general estimator applicable to different rain regimes in different regions; and 3) it is flexible and may incorporate/exclude different polarimetric radar variables as inputs. This paper also discusses training the GMRE and the sensitivity of performance to mixture number. A large radar and surface gauge observation dataset collected in central Oklahoma during the multiyear Joint Polarization Experiment (JPOLE) field campaign is used to evaluate the GMRE approach. Results indicate that the GMRE approach can outperform existing polarimetric rainfall techniques optimized for this JPOLE dataset in terms of bias and root-mean-square error. [ABSTRACT FROM AUTHOR]

A comprehensive system that is used to invert the geophysical products from microwave measurements has recently been developed. This system, known as the Microwave Integrated Retrieval System (MiRS), ensures that the final solution is consistent with the measurements and, when used as input to the forward operator, fits them to within the instrument noise levels. In the presence of precipitation, this variational algorithm retrieves a set of hydrometeor products consisting of cloud liquid water, ice water, and rain water content profiles. This paper presents the development and assessment of the MiRS rainfall rate that is derived based on a predetermined relationship of the rainfall with these hydrometeor products. Since this relationship relies on the geophysical products retrieved by the MiRS as inputs and not on sensor-dependent parameters, the technique is suitable for all microwave sensors to which the MiRS is applied. This precipitation technique has been designed to facilitate its transition from research to operations when applied to current and future satellite-based sensors. Currently, the MiRS rainfall rate technique has been implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) for the NOAA-18, NOAA-19, Metop-A Advanced Microwave Sounding Unit, and Microwave Humidity Sensor, as well as for the Defense Meteorological Satellite Program (DMSP)-F16 and DMSP-F18 Special Sensor Microwave Imager/Sounder microwave satellite sensors. For the validation of the MiRS rainfall rate technique, extensive comparisons with state-of-the-art precipitation products derived from rain gauge, ground-based radar, and satellite-based microwave observations are presented for different regions and seasons, and over land and ocean. The MiRS rainfall rate technique is shown to estimate precipitation, with a skill comparable to other satellite-based microwave precipitation algorithms, including the MSPPS, 3B40RT, and MWCOMB, while showing no discontinuities at coasts. This is a relevant result, considering that the MiRS is a system not merely designed to retrieve the rainfall rate but to consistently estimate a comprehensive set of atmospheric and surface parameters from microwave measurements. [ABSTRACT FROM AUTHOR]

Attenuation measured on terrestrial links can be used to estimate surface rainfall rates along a link path. There is current interest in the use of inverse methods to estimate rainfall over areas inferred from terrestrial links. A power-law relationship can be used to estimate rainfall rate from attenuation measured by a terrestrial link. However, the inverse method is simplified with a linear relationship between attenuation and rainfall rate when determining path averaged rainfall rates. This paper investigates power-law and linear relationships between rainfall rate and attenuation. It examines their goodness of fit for a range of frequencies and rainfall rates. Six years of disdrometer data from Chilbolton in the U.K. has been used to study both linear and power-law fits to attenuation and quantify the associated errors. [ABSTRACT FROM AUTHOR]

Radar measurements are useful for determining rainfall rates because of their ability to cover large areas. Unfortunately, estimating rainfall rates from radar reflectivity data alone is prone to errors resulting from variations in drop size distributions, precipitation types, and other physics that cannot be represented in a simple, one-dimensional Z- R relationship. However, improving estimates is possible by utilizing additional inputs, thereby increasing the dimensionality of the model. The main purpose of this study is to determine the value of surface observations for improving rainfall-rate estimation. This work carefully designed an artificial neural network to fit a model that would relate radar reflectivity, surface temperature, humidity, pressure, and wind to observed rainfall rates. Observations taken over 13 years from the Oklahoma Mesonet and the KTLX WSR-88D radar near Oklahoma City, Oklahoma, were used for the training dataset. While the artificial neural network underestimated rainfall rates for higher reflectivities, it did have an overall better performance than the best-fit Z- R relation. Most importantly, it is shown that the surface data contributed significant value to an unaugmented radar-based rainfall-rate estimation model. [ABSTRACT FROM AUTHOR]

Rainfall-rate statistics are frequently derived on the basis of rain gauge recordings with effective integration times of 10 min or longer. The conversion of such data to equivalent statistics for an effective integration time of 1 min is very important to predict rainfall attenuation at a location accurately. Because of the limited availability of 1-min rainfall-rate data, conversion of available rainfall-rate distributions with longer integration times to the desired 1-min distributions is needed. Several methods for converting 60-min rainfall rates to 1-min rainfall rates are tested against measured rainfall rates from tropical, high-rainfall-rate regions. Segal’s method was found to perform the best overall when compared with other conversion methods. [ABSTRACT FROM AUTHOR]

Seasonal and spatial variation of the vertical gradient of rainfall rate was investigated using global precipitation data observed by the Precipitation Radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) satellite. The vertical gradient was rendered by features of downward decreasing (DD) or downward increasing (DI) rainfall rate in the lower part of the profile. The DD profiles dominated tropical interior landmasses such as Africa and the Brazilian Plateau in summer. The DI profiles were observed over land in winter and over ocean except for regions with very little rainfall. In addition, DI profiles appeared during the height of the wet season even over the tropical landmasses, such as the mature monsoon period over inland India and over the Amazon River basin. Individual precipitation systems were also investigated in terms of their areally averaged DD and DI characteristics mainly over India. Deep (shallow) profiles tended to be DD (DI) for all seasons except the premonsoon season. As the rain area increased, the vertical gradient of rainfall rate decreased (DD tendency). Embedded in the dominant DD signature for deep storms, deep but significant DI profiles were observed in every month. They characterized the precipitation in the premonsoon season. More than half of the mesoscale/ synoptic-scale systems (rain areas >104 km2) having the significant DD or DI regions had both of them as part of their slant cores. The vertical gradients for these systems had a similar trend for both their stratiform and convective parts. During the mature period of the southwest monsoon, the number of small systems that were DI and widespread systems with moderate vertical gradient increased. [ABSTRACT FROM AUTHOR]

Power law relationships of the form R = aA[SUPb] are derived, where R is the rainfall rate, and A is the 35-GHz specific attenuation A[SUBh] or specific differential attenuation ΔA = A[SUBh] - A[SUBv], where the subscripts h and v indicate horizontal and vertical polarizations. The effects of raindrop size distribution, canting, and oscillation on these relationships are evaluated quantitatively. The drop size distributions (DSDs) are obtained from ground-based disdrometer measurements from three different geographical locations around the world. The R-A[SUBh] relationship is negligibly affected by raindrop canting and oscillation. It is affected to some extent by DSD variations, with less than 15% fractional standard error (FSE) in the estimated rainfall rate R[SUBAh]. On the other hand, the R-Δ A relationship is most sensitive to raindrop oscillation, up to about 35% difference in RΔA compared to the no-oscillation case, and the effect of canting is about 9% for a standard deviation of 10° of the polar canting angle compared with no canting. The FSE due to variations in the DSD for R < 5 mm h[SUB-1] is greater than 30% and increases with decreasing R. However, for R > 20 mm h[SUB-1], the FSE in R[SUBΔA] is comparable (and even lower for R > 30 mm h[SUP-1] to that of R[SUBAh]. The exceptions to this are rainfall rates with DSDs dominated by smaller raindrops (diameters less than 2.4 mm). It is also emphasized that because oscillation and canting affect Δ A but not A[SUBh], they could be used in combination for determining the presence of drop oscillation and canting and for estimating an effective raindrop shape model (axial ratio versus size). [ABSTRACT FROM AUTHOR]

This paper presents MORSE (MOdel for Rainfall Statistics Estimation), a unified model for the prediction of spatial (P{S}(R)) and temporal (P{T}(R)) high-resolution rainfall rate statistics. Inputs to MORSE are the convective (M{c}) and total (M{t}) rain amounts cumulated in different time intervals, ranging from a few hours for the prediction of P{S}(R) to much longer intervals for the estimation of P{T}(R). Tests performed against P{T}(R)s on yearly (curves included in the DBSG3 database) and monthly (distributions derived from rain rate time series) basis provide very satisfactory results, which makes MORSE a reliable global model for the prediction of P{S}(R) on hourly basis and of P{T}(R) at any time scale (e.g., monthly, seasonal, yearly). [ABSTRACT FROM AUTHOR]

In 2012 the Météo France metropolitan operational radar network consists of 24 radars operating at C and S bands. In addition, a network of four X-band gap-filler radars is being deployed in the French Alps. The network combines polarimetric and nonpolarimetric radars. Consequently, the operational radar rainfall algorithm has been adapted to process both polarimetric and nonpolarimetric data. The polarimetric processing chain is available in two versions. In the first version, now operational, polarimetry is only used to correct for attenuation and filter out clear-air echoes. In the second version there is a more extensive use of polarimetry. In particular, the specific differential phase Kdp is used to estimate rainfall rate in intense rain. The performance of the three versions of radar rainfall algorithms (conventional, polarimetric V1, and polarimetric V2) at different frequency bands (S, C, and X) is evaluated by processing radar data of significant events offline and comparing hourly radar rainfall accumulations with hourly rain gauge data. The results clearly show a superior performance of the polarimetric products with respect to the nonpolarimetric ones at all frequency bands, but particularly at higher frequency. The second version of the polarimetric product, which makes a broader use of polarimetry, provides the best overall results. [ABSTRACT FROM AUTHOR]

Abstract: Recently, we proposed a novel remote sensing method for rainfall rate estimation in real time by means of tomographic processing applied to power attenuation measurements made across the microwave links defined by radiobase station networks for mobile communication systems. In this paper we analyze the results of a new tomographic algorithm that has been specifically developed for the typical topology of the urban radiobase station networks. Such algorithm has been tested on simulated specific attenuation (K) maps based on true polarimetric weather radar data. We considered 18, 23, and 38GHz for the carrier frequencies of the radiobase network and the weather data gathered by the POLAR 55C polarimetric radar located in Rome. [Copyright &y& Elsevier]

Tropical cyclones, also known as typhoons or hurricanes, are among the most devastating events in nature and often strike the western North Pacific region (including the Philippines, Taiwan, Japan, Korea, China, and others). This paper focuses on addressing the rainfall retrieval problem for quantitative precipitation forecast during tropical cyclones. In this study, Special Sensor Microwave Imager (SSM/I) data and Water Resources Agency (WRA) measurements of Taiwan were used to quantitatively estimate precipitation over the Tanshui River basin in northern Taiwan. Various retrievals for the rainfall rate over land are compared by five methods/techniques. They are the single-channel regression, multichannel linear regressions (MLR), scattering index over land approach (SIL), support vector regression (SVR), and the proposed SIL-SVR. This study collected 70 typhoons affecting the studied watershed over the past 12 years (1997-2008). The measurements of the SSM/I satellite comprise the brightness temperatures at 19.35, 22.23, 37.0, and 85.5 GHz. Overall, the results showed the approaches using the SVR and conjoined SVR and SIL performed better than regression and SIL methods according to their performances of the root-mean-square error (RMSE), bias ratio, and equitable threat score (ETS). [ABSTRACT FROM AUTHOR]

Polarimetric radars provide measurements that describe the shape and dimensions of hydrometeors and are unaffected by calibration, attenuation, and the presence of ice. These measurements can potentially lead to a more detailed description of hydrometeors and to an improvement in quantitative rainfall rate estimation. The authors present an algorithm that exploits polarimetric measures for rain-rate estimation and investigate its application in a real-time framework by using measurements from the C-band polarimetric radar at Monte Settepani in Savona, Italy. It is based on a flowchart decision tree that allows the use of the best rain-rate retrieval algorithm, depending on the value of polarimetric variables. The methodology was applied to a real-time framework for more than a year, and the results were presented for all the significant events observed during the test period. To evaluate the performance of the algorithm, a comparison was made with rain gauge observation from a dense regional network. The performances of the algorithm were compared with those obtained by standard operational Z–R formulations to evaluate the benefit of this approach for operational applications. [ABSTRACT FROM AUTHOR]

Polarization radar offers the promise of much more accurate rainfall-rate R estimates than are possible from radar reflectivity factor Z alone, not only by better characterization of the drop size distribution, but also by more reliable correction for attenuation and the identification of hail. However, practical attempts to implement retrieval algorithms have been hampered by the difficulty in coping with the inherent noise in the polarization parameters. In this paper, a variational retrieval scheme is described that overcomes these problems by employing a forward model for differential reflectivity Zdr and differential phase shift φdp and iteratively refining the coefficient a in the relationship Z = aRb such that the difference between the forward model and the measurements is minimized in a least squares sense. Two methods are used to ensure that a varies smoothly in both range and azimuth. In range, a is represented by a set of cubic-spline basis functions; in azimuth, the retrieval at one ray is used as a constraint on the next. The result of this smoothing is that the retrieval is tolerant of random errors in Zdr of up to 1 dB and in φdp of up to 5°. Correction for attenuation is achieved simply and effectively by including its effects in the forward model. If hail is present then the forward model is unable to match the observations of Zdr and φdp simultaneously. This enables a first pass of the retrieval to be used to identify the radar pixels that contain hail, followed by a second pass in which the fraction of the Z in those gates that is due to hail is retrieved, this time with the scheme being able to forward-model both Zdr and φdp accurately. The scheme is tested on S-band radar data from southern England in cases of rain, spherical hail, oblate hail, and mixtures of rain and hail. It is found to be robust and stable, even in the presence of differential phase shift on backscatter. [ABSTRACT FROM AUTHOR]

The duration of rainfall events as a function of precipitation rate is investigated in this paper. The experimental data used in this study were measured along a rain gauge network with 8 pluviographs located in the Amazon region. The most important point to be highlighted in this study is the evidence of a correlation between rain and fade events durations observed in a 12GHz satellite link. This result is quite important under the engineering point of view, once it can be used in the design of improvement techniques for low availability satellite systems. [ABSTRACT FROM AUTHOR]

Current attempts to measure short-term (<1 month) rainfall accumulations using spaceborne radiometers are characterized by large sampling errors associated with low observation frequencies for any single point on the globe (from two to eight measurements per day). This degrades the value of spaceborne rainfall retrievals for the monitoring of surface water and energy balance processes. Here a data assimilation system, based on the assimilation of surface L-band brightness temperature (T[sub B] ) observations via the ensemble Kalman filter (EnKF), is introduced to correct for the impact of poorly sampled rainfall on land surface model predictions of root-zone soil moisture and surface energy fluxes. The system is evaluated during the period from 1 April 1997 to 31 March 1998 over two sites within the U.S. Southern Great Plains. This evaluation includes both a data assimilation experiment, based on synthetically generated T[sub B] measurements, and the assimilation of real T[sub B] observations acquired during the 1997 Southern Great Plains Hydrology Experiment (SGP97). Results suggest that the EnKF-based assimilation system is capable of correcting a substantial fraction (>50%) of model error in root-zone (40 cm) soil moisture and latent heat flux predictions associated with the use of temporally sparse rainfall measurements as forcing data. Comparable gains in accuracy are demonstrated when actual T[sub B] measurements made during the SGP97 experiment are assimilated. [ABSTRACT FROM AUTHOR]

In this paper, a variational data assimilation approach is used to assimilate the rain rate (RR) data together with precipitable water (PW) measurements from the Experiment on Rapidly Intensifying Cyclones over the Atlantic (ERICA) (4–5 January 1989; IOP-4 cyclone). The PW and RR, which are assimilated into the Pennsylvania State University–NCAR Mesoscale Model version 5 (MM5), are computed from the Special Sensor Microwave/Imager (SSM/I) raw data—brightness temperatures—via a statistical regression method. The SSM/I-derived RR and PW at 0000 UTC and/or 0930 UTC are assimilated into the MM5. The data at 2200 UTC are used for verification of the prediction results. Numerical experiments are performed using the MM5. Two horizontal resolutions of 50 km and 25 km are used in the authors’ studies. Comparisons are made between the experiments with and without SSM/I-measured PW and RR observations. Results from these experiments showed the following. 1) The MM5 simulated a well-behaved but slightly less intense, position-shifted cyclogenesis episode based on the NCEP analysis enhanced with only radiosonde and surface observations through a Cressman-type objective analysis. 2) The satellite-derived PW and RR observations were assimilated successfully into the MM5 model by a variational method. The cost function that measures the distance between the model-predicted and the observed PW and RR decreased by about one order of magnitude. 3) Assimilation of PW and RR significantly improved the cyclone prediction, reflected mostly in the cyclone’s track, the associated frontal structure and the associated precipitation along the front. The model’s spinup problem during the simulation was greatly reduced after assimilating the PW and RR information into the model initial conditions. 4) Sensitivity experiments of RR assimilation indicated that the impact on the results of RR assimilation was less sensitive to errors in the... [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement mission is a major U.S.–Japan joint mission to understand the physics of the Earth's global precipitation as a key component of its weather, climate, and hydrological systems. The core satellite carries a dual-precipitation radar and an advanced microwave imager which provide measurements to retrieve the drop size distribution (DSD) and rain rates using a Combined Radar-Radiometer Algorithm (CORRA). Our objective is to validate key assumptions and parameterizations in CORRA and enable improved estimation of precipitation products, especially in the middle-to-higher latitudes in both hemispheres. The DSD parameters and statistical relationships between DSD parameters and radar measurements are a central part of the rainfall retrieval algorithm, which is complicated by regimes where DSD measurements are abysmally sparse (over the open ocean). In view of this, we have assembled optical disdrometer datasets gathered by research vessels, ground stations, and aircrafts to simulate radar observables and validate the scattering lookup tables used in CORRA. The joint use of all DSD datasets spans a large range of drop concentrations and characteristic drop diameters. The scaling normalization of DSDs defines an intercept parameter NW, which normalizes the concentrations, and a scaling diameter Dm, which compresses or stretches the diameter coordinate axis. A major finding of this study is that a single relationship between NW and Dm, on average, unifies all datasets included, from stratocumulus to heavier rainfall regimes. A comparison with the NW–Dm relation used as a constraint in versions 6 and 7 of CORRA highlights the scope for improvement of rainfall retrievals for small drops (Dm < 1 mm) and large drops (Dm > 2 mm). The normalized specific attenuation–reflectivity relationships used in the combined algorithm are also found to match well the equivalent relationships derived using DSDs from the three datasets, suggesting that the currently assumed lookup tables are not a major source of uncertainty in the combined algorithm rainfall estimates. [ABSTRACT FROM AUTHOR]

The convective rainfall rate from cloud physical properties (CRPh) algorithm for Meteosat second-generation satellites is a day-only precipitation algorithm developed at the Spanish Meteorological Agency (AEMET) for EUMETSAT' Satellite Application Facility in support of nowcasting and very short-range forecasting (NWC SAF). It is therefore mainly intended to provide input for monitoring and near-real-time forecasts for a few hours. This letter critically discusses the theoretical basis of the algorithm with special emphasis on the empirical values and assumptions in the microphysics of precipitation, and compares the qualitative performances of the CRPh with its antecessor, the convective rainfall rate algorithm (CRR), using an object-based method applied to a case-study. The analyses show that AEMET's CRPh is physically consistent and outperforms the CRR. The applicability of the algorithm for nowcasting and the challenges of improving the product to an all-day algorithm are also presented. [ABSTRACT FROM AUTHOR]

Eight microwave links operating at frequencies ranging from 6 to 8 GHz and with path lengths ranging from 5.7 to 37.4 km traversing the city of Seoul, Korea are used to detect rainfall and estimate path-averaged rainfall rates. Rainfall detection using rain-induced attenuation (dB) was validated by rain detectors installed at automatic weather stations, and the results confirmed that microwave links can be used to detect rainfall with an accuracy ≥80%. The power-law R-k relationships between rain-induced specific attenuation, k (dB km−1), and the rainfall rate, R (mm h−1), were established and cross-validated by estimating the path-averaged rainfall rate. The mean bias of the path-averaged rainfall rate, as compared to the rainfall rate from ground rain gauges, was between −3 and 1 mm h−1. The improved accuracy of rainfall detection led to the improved accuracy of the path-averaged rainfall rate. Hence, it was confirmed that microwave links, used for broadcasting and media communications, can identify rainy or dry periods (rain spells or dry spells) in a way comparable to rain detectors and provide high time-resolution rainfall rates in real time. [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. [ABSTRACT FROM AUTHOR]

We made an effort to inspect the raindrop size distribution (RSD) characteristics of Indian Ocean and Pacific Ocean tropical cyclones (TCs) using ground-based disdrometer measurements from observational sites in India and Taiwan. Five TCs (2010 - 2013) from the Indian Ocean and six TCs (2014 - 2016) from the Pacific Ocean were measured using particle size and velocity disdrometers installed in south India and south Taiwan, respectively. Significant differences between the RSDs of Indian Ocean and Pacific Ocean TCs are noticed. For example, a higher number of small drops is observed in Indian Ocean TCs, whereas Pacific Ocean TCs have more mid-size and large drops. RSDs of Pacific Ocean TCs have higher mass-weighted mean diameter and lower normalized intercept parameter than Indian Ocean TCs. RSD values quantified based on rainfall rate and precipitation types also showed similar characteristics between Indian Ocean and Pacific Ocean TCs. The radar reflectivity and rainfall rate (Z-R) relations and shape and slope (μ-Λ) relations of both oceanic (Indian and Pacific) TCs are found to be distinctly different. Possible causes for the dissimilarities in RSD features between Indian Ocean and Pacific Ocean TCs are due to relative differences in water vapor availability and convective activity between TCs in these two oceanic basins. [ABSTRACT FROM AUTHOR]

During the pre-summer rainy season, heavy rainfall occurs frequently in South China. Based on polarimetric radar observations, the microphysical characteristics and processes of convective features associated with extreme rainfall rates (ERCFs) are examined. In the regions with high ERCF occurrence frequency, sub-regional differences are found in the lightning flash rate (LFR) distributions. In the region with higher LFRs, the ERCFs have larger volumes of high reflectivity factor above the freezing level, corresponding to more active riming processes. In addition, these ERCFs are more organized and display larger spatial coverage, which may be related to the stronger low-level wind shear and higher terrain in the region. In the region with lower LFRs, the ERCFs have lower echo tops and lower-echo centroids. However, no clear differences of the most unstable convective available potential energy (MUCAPE) exist in the ERCFs in the regions with different LFR characteristics. Regardless of the LFRs, raindrop collisional coalescence is the main process for the growth of raindrops in the ERCFs. In the ERCFs within the region with lower LFRs, the main mechanism for the rapid increase of liquid water content with decreasing altitude below 4 km is through the warm-rain processes converting cloud drops to raindrops. However, in those with higher LFRs, the liquid water content generally decreases with decreasing altitude. [ABSTRACT FROM AUTHOR]

The characteristics of the raindrop size distribution (DSD) in summer in the western (Nilek) and central (Urumqi) regions in the Tianshan Mountains of China were studied based on three years of second-generation Particle Size Velocity (Parsivel2) disdrometer data. The FengYun-2G satellite remote sensing data and the ERA5 reanalysis product were used to reveal the possible thermo-dynamical and microphysical processes that caused the dissimilarities in DSD between Urumqi and Nilek. The DSD in Nilek is significantly different from that in Urumqi. The concentration of mid- and large-size drops is higher in Nilek than in Urumqi. The DSD characteristics for six rain rate classes and two rain types (convective and stratiform) are studied. It is found that the raindrops in Nilek have overall higher mass-weighted mean diameters (Dm) and lower logarithm of normalized intercept parameters (log10 Nw) than those in Urumqi, which is true for different rain rates and rain types. Convective clusters in Urumqi are similar to maritime clusters, whereas those in Nilek are more similar to continental clusters, according to a classification standard of convective clusters. The radar reflectivity, rain rate relations, and the shape and slope relations for rainfall in Urumqi and Nilek are also different. The DSD variability in the two regions may be attributed to differences in convective intensity that are closely related to the specific terrain of the Tianshan Mountains. [ABSTRACT FROM AUTHOR]

Dual-frequency precipitation radar (DPR) on the Core GPM satellite provides spaceborne three-dimensional observations of precipitation fields and surface rainfall rate with quasi-global coverage. The present study evaluates the behavior of liquid precipitation intensity, radar reflectivity factor (ZKu and ZKa) and drop size distribution (DSD) parameters (weighted mean diameter Dm and intercept parameter Nw) of the GPM DPR-derived products, version 07, from 2014 to 2023. Observations from seven Parsivel disdrometers located in different topographic zones in the Western Mediterranean are taken as ground references. Four matching techniques between satellite estimates and ground level observations were tested, and the best results were found for the so-called optimal comparison approach. Overall, GPM DPR products captured the variability of the observed DSD well at different rainfall intensities. However, overestimation of the mean Dm and underestimation of the mean Nw were observed, being much more sensitive to errors in drop diameters larger than 1.5 mm. Moreover, the lowest errors were found for radar reflectivity factor and Dm, and the highest for Nw and rainfall rate. In addition, the GPM DPR convective and stratiform classification was tested, and a substantial overestimation of stratiform cases compared to disdrometer observations were found. [ABSTRACT FROM AUTHOR]

The microphysical processes of cloud and precipitation in the Tianshan Mountains still remain unclear due to the complex terrain and the lack of ground-based observational data. In this study, the characteristics of summer raindrop size distribution (RSD) in the western and central Tianshan Mountains of China were investigated in 2020 and 2021. According to the average raindrop spectra, the RSD characteristics exhibit significant differences that the concentration of medium and large particles is relatively higher at Zhaosu than that at Tianchi. Weak precipitation prevails in the Tianshan Mountains most of the time, yet moderate rainfall and heavy rainfall have a great impact on the accumulated rainfall amount. For each rainfall rate class, raindrops of diameter less than 0.75mm have a lower concentration and raindrops of diameter greater than 3 mm have a higher concentration at Zhaosu as compared to those at Tianchi. Compared with Tianchi, Zhaosu has a larger mass-weighted mean diameter (Dm) and a lower normalized intercept parameter (log10Nw). Compared with the classical convective spectrum, the RSDs at both Zhaosu and Tianchi are similar to the continental convective cluster. The relationships between the shape and slope (μ–Λ), and radar reflectivity and rainfall rate (Z– R) exhibit a significant difference in the western and central Tianshan Mountains of China. [ABSTRACT FROM AUTHOR]

From January 2017 to December 2021, Office National d’Etudes et de Recherches Aérospatiales (ONERA) and Centre National d’Etudes Spatiales (CNES) operated a beacon receiver able to record the 20.1995 GHz beacon signal of the Amazonas-3 satellite at the Guiana Space Centre in Kourou, French Guiana. A rain gauge was also deployed on site to collect rainfall rate measurements concurrently to beacon data. All years of the experiment, except 2019, had excellent data availability, hence giving four full years of data (2017, 2018, 2020, and 2021). First, the climatic characteristics of French Guiana are shown. Second, the propagation experiment and the data processing methodology are described. Third, the results of the statistical analysis are highlighted and commented. Comparisons are performed with the prediction methods of Recommendation ITU-R P.837-7 (rainfall rate), Recommendation ITU-R P.618-13 (rain attenuation), Recommendation ITU-R P.678-3 (variability of propagation phenomena), and Recommendation ITU-R P.1623-1 (fade dynamics). [ABSTRACT FROM AUTHOR]

The diurnal variation characteristics of raindrop size distribution (RSD) in the Ili River Valley are investigated in this study, using the RSD data from May to September during 2020-2021 collected by a Parsivel2 disdrometer in Zhaosu. Significant diurnal variations (02–07, 08–13, 14–19, and 20-01 local standard time (LST)) of precipitation and RSD in Zhaosu are revealed during the rainy seasons. Precipitation mainly occurs in the late afternoon and early evening. A higher concentration of small raindrops is observed in the morning, whereas more mid-size and large raindrops are observed in the afternoon. The RSD exhibits diurnal differences between different rainfall rate classes; the diurnal difference of RSD is more pronounced in the case of high rainfall rates. Stratiform precipitation can occur at any time of the day, yet convective precipitation mainly occurs during the late afternoon and early evening. The RSD of stratiform rainfall shows a similar distribution over the four time periods. For convective rainfall, the concentration of small raindrops is the highest (lowest) over 02–07 (14–19) LST, while the highest (lowest) concentration of medium and large drops is observed over 14–19 (02–07) LST. Convective rain in the Ili River Valley over 14–19 LST can be characterized as the continental convective cluster, while in the rest time of the day, it is neither in the maritime cluster nor in the continental cluster. The empirical relationships between the radar reflectivity factor and rainfall rate (Z-R) for stratiform and convective rain types are also derived. The purpose of this study is to advance our understanding of precipitation microphysics in arid mountainous region. [ABSTRACT FROM AUTHOR]

The low air pressure and density over the Tibetan Plateau may have an impact on the microphysical features of rainfall. Using a two-dimensional video disdrometer (2DVD), a Micro Rain Radar (MRR), and a microwave radiometer (MWR), the features of the raindrop size distribution (DSD) on the southeastern Tibetan Plateau (SETP) are explored and compared with those in low-altitude regions. The falling speed of raindrops on the SETP is higher than that in low-altitude areas. Under different rainfall-rate categories, the number concentration and the maximum diameter of raindrops on the SETP are smaller than those in low-altitude regions. The convective rainfall on the SETP is more maritime-like because the South Asian summer monsoon brings water vapor from the ocean here. For stratiform and convective rainfall, the SETP has more small-sized raindrops than low-altitude locations. The mass-weighted mean diameters (Dm) on the SETP are the smallest among six sites. The generalized intercept parameter (Nw) of stratiform rainfall is balanced at a low rainfall rate, while that of convective rainfall is balanced at a high rainfall rate. Furthermore, for a given μ (the shape parameter of gamma distribution) value, the λ (the slope parameter of gamma distribution) value on the SETP is the highest of the six sites. Significance Statement: For the occurrence and progression of rainfall, microphysical processes (for instance, collision, fragmentation, coalescence, and evaporation) that take place when rainfall particles descend are crucial. A key factor in the microphysical features of rainfall that varies with rainfall rates and types is the raindrop size distribution (DSD). The southeastern Tibetan Plateau (SETP)'s unique terrain ensures that there is enough moisture for rain to fall there, but little is known about the microphysical aspects of this type of precipitation. There has not been enough research done on how the high altitude affects the microphysical features of rainfall. The microphysical features of rainfall in this area must thus be studied. [ABSTRACT FROM AUTHOR]

The demand for accurate rainfall rate maps is growing ever more. This paper proposes a novel algorithm to estimate the rainfall rate map from the attenuation measurements coming from both broadcast satellite links (BSLs) and commercial microwave links (CMLs). The approach we pursue is based on an iterative procedure which extends the well-known GMZ algorithm to fuse the attenuation data coming from different links in a three-dimensional scenario, while also accounting for the virga phenomenon as a rain vertical attenuation model. We experimentally prove the convergence of the procedures, showing how the estimation error decreases for every iteration. The numerical results show that adding the BSL links to a pre-existent CML network boosts the accuracy performance of the estimated rainfall map, improving up to 50% the correlation metrics. Moreover, our algorithm is shown to be robust to errors concerning the virga parametrization, proving the possibility of obtaining good estimation performance without the need for precise and real-time estimation of the virga parameters. [ABSTRACT FROM AUTHOR]

Ground weather observations are scarce in many parts of the globe, hampering effective climate monitoring and disaster management. In the Amazon basin, this occurs due to its remoteness and the challenging measurement of rainfall within the forest. Innovative rainfall estimation methods are thus requested to fill this gap. Here we present an approach to estimate rainfall based on sound measurements. We identified the best frequency range to estimate rainfall occurrence and intensity, trained classification and regression models with sound and rain gauge data collected in the Central Amazon during 9 months. By training a random forest classifier/regression model based on power spectrum values it was possible to identify and satisfactorily estimate hourly rainfall rates in two vegetation environments distinct from the training site, located 30 km from it. The proposed method is a promising approach for future weather monitoring in remote tropical areas. Plain Language Summary: Understanding and predicting rainfall is a complex task, especially in areas where the availability of data from surface stations is limited, a common feature in many developing regions with insufficient rain gauge coverage. Recently, new opportunistic methods of rainfall measurement have emerged. Among them, is the use of the relationship between rainfall intensity and the sound produced by droplets hitting a surface. Sound recorders offer a low‐cost solution and could provide an interesting means to increase spatial coverage of rainfall measurements, but also to fill information gaps under dense forests where conventional devices do not work. Our study developed a new technique and applied it to the Central Amazon region, by training a supervised machine learning model applied to sound recordings obtained in a tropical rainforest. To our knowledge, for the first time, such techniques are validated in locations far from the calibration site. We showed that reasonable results can be obtained for sites with distinct vegetation types and up to 30 km of distance from where the training data was acquired. Our findings demonstrate a strong capability for estimating hourly rainfall rates. Key Points: Rainfall intensity estimated from sound measurements in the Amazon rainforest, tipping bucket rain gauge, and machine learning modelsThe best model successfully detects rainfall in 88% of the cases, with R2 > 0.87 for hourly rainfall rates on the training siteModel validated over two sites in the Amazon, 97% accuracy identifying rainfall events, R2 of 0.69 and 0.93 for hourly rainfall rate [ABSTRACT FROM AUTHOR]

This study investigates the characteristics of the raindrop size distribution (DSD) for five typhoons that made landfall or passed by Zhejiang on the eastern coast of China, from 2019 to 2022. Additionally, it examines the raindrop shape–slope (µ-Λ) relationship, as well as the local Z-R relationship for these typhoons. The DSD datasets were collected by the DSG1 disdrometer located in Ningbo, Zhejiang Province. Based on rainfall rate (R), the DSD can be categorized into convective and stratiform rainfall types. Some rainfall parameters can also be derived from the DSDs to further analyze the specific characteristics of rainfall. The histograms of the generalized intercept parameter (log10Nw) exhibit negative skewness in both convective and stratiform rainfall, whereas the histograms of the mass-weighted mean diameter (Dm) of raindrops display positive skewness. During typhoon periods on the eastern coast of China, the DSD characteristic was composed of a lower number concentration of small and midsize raindrops (3.42 for log10Nw, 1.43 mm for Dm in the whole dataset) as compared to Jiangsu in eastern China, Tokyo, in Japan, Miryang, in South Korea, and Thiruvananthapuram in south India, respectively. At the same time, the scatter plots of Dm and log10Nw indicate that the convective rain during typhoon periods exhibits characteristics that are intermediate between "maritime-like" and "continental-like" clusters. Additionally, the raindrop spectra of convective rainfall and midsize raindrops in stratiform rainfall are well-represented by a three-parameter gamma distribution. The µ-Λ relation in this region is similar to Taiwan and Fujian, located along the southeastern coast of China. The Z-R relationship for eastern coastal China during typhoons based on filtered disdrometer data is Z = 175.04R1.53. These results could offer deeper insights into the microphysical characteristics of different rainfall types along the eastern coast of China and potentially improve the accuracy of precipitation estimates from weather radar observations. [ABSTRACT FROM AUTHOR]

Accurate prediction of rain attenuation is critical for the design of terrestrial line‐of‐sight link systems above 5 GHz, particularly for millimetre‐wave frequency. Anomalous behaviour in the adjustment factor in existing rain attenuation prediction models is significantly greater than 1 at short distances and has been reported. To solve this problem and improve the accuracy, a rain attenuation prediction model for terrestrial line‐of‐sight links is proposed. The total rain attenuation described in the model accounts for the contributions of wet antenna attenuation and path rain attenuation. In addition, a rainfall rate adjustment factor is proposed. The proposed model has a root‐mean‐square error of 21.54% in prediction, which is an improvement compared to other existing models. Because the rainfall rate adjustment factor used in the model approaches 1 at short distances, the model can be applied to rain attenuation prediction for short‐distance links. The results will assist designers to design link margins more accurately. [ABSTRACT FROM AUTHOR]

Raindrop size distribution (DSD) over the complex terrain of Guangdong Province, southern China, was studied using six disdrometers operated by the Guangdong Meteorology Service during the period 1 March 2018 to 30 August 2022 (~5 years). To analyze the long-term DSD characteristics over complex topography in southern China, three stations on the windward side, Haifeng, Enping and Qingyuan, and three stations on the leeward side, Meixian, Luoding and Xuwen, were utilized. The median mass-weighted diameter (Dm) value was higher on the windward than on the leeward side, and the windward-side stations also showed greater Dm variability. With regard to the median generalized intercept (log10Nw) value, the log10Nw values decreased from coastal to mountainous areas. Although there were some differences in Dm, log10Nw and liquid water content (LWC) frequency between the six stations, there were still some similarities, with the Dm, log10Nw and LWC frequency all showing a single-peak curve. In addition, the diurnal variation of the mean log10Nw had a negative relationship with Dm diurnal variation although the inverse relationship was not particularly evident at the Haifeng site. The diurnal mean rainfall rate also peaked in the afternoon and exceeded the maximum at night which indicated that strong land heating in the daytime significantly influenced the local DSD variation. What is more, the number concentration of drops, N(D), showed an exponential shape which decreased monotonically for all rainfall rate types at the six observation sites, and an increase in diameter caused by increases in the rainfall rate was also noticeable. As the rainfall rate increased, the N(D) for sites on the windward side (i.e., Haifeng, Enping and Qingyuan) were higher than for the sites on the leeward side (i.e., Meixian, Luoding and Xuwen), and the difference between them also became distinct. The abovementioned DSD characteristic differences also showed appreciable variability in convective precipitation between stations on the leeward side (i.e., Meixian, Luoding and Xuwen) and those on the windward side (Haifeng and Enping, but not Qingyuan). This study enhances the precision of numerical weather forecast models in predicting precipitation and verifies the accuracy of measuring precipitation through remote sensing instruments, including weather radars located on the ground. [ABSTRACT FROM AUTHOR]