High Rain Rates Research Articles

WindRAD Scatterometer Quality Control in Rain

Rain backscatter corrupts Ku-band scatterometer wind retrieval by mixing with the signatures of the σ∘ (backscatter measurements) on the sea surface. The measurements are sensitive to rain clouds due to the short wavelength, and the rain-contaminated measurements in a wind vector cell (WVC) deviate from the simulated measurements using the wind geophysical model function (GMF). Therefore, quality control (QC) is essential to guarantee the retrieved winds’ quality and consistency. The normalized maximum likelihood estimator (MLE) residual (Rn) is a QC indicator representing the distance between the σ∘ measurements and the wind GMF; it works locally for one WVC. JOSS is another QC indicator. It is the speed component of the observation cost function, which is sensitive to spatial inconsistencies in the wind field. RnJ is a combined indicator, and it takes both local information (Rn) and spatial consistency (JOSS) into account. This paper focuses on the QC for WindRAD, a dual-frequency (C and Ku band) rotating-fan-beam scatterometer. The Rn and RnJ have been established and thoroughly investigated for Ku-band-only and combined C–Ku wind retrieval. An additional 0.4% of WVCs are rejected with RnJ, as compared to Rn for both Ku-band-only and combined C–Ku wind retrievals. The number of accepted WVCs with high rain rates (>7 mm/h) is reduced by half, and the wind verification with respect to ECMWF winds is generally improved. The C-band measurements are little influenced by rain, so the Ku-based Rn is more effective for the combined C–Ku wind retrieval than the total Rn from both the C and Ku bands. The rejection rate of the combined C–Ku retrievals reduces by about half compared to the Ku-band-only retrieval, with similar wind verification statistics. Therefore, adding the C band into the retrieval suppresses the rain effect, and acceptable QC capabilities can be achieved with fewer rejected winds.

Microphysical Evolution of Heavy Rainfall During a Bow Echo Event in South China: Characteristics and the Mesovortex‐Related Impacts

AbstractA heavy rainfall (HR) event caused by a bow echo struck South China on 11 April 2019. Two extremely HR periods were identified within this event, and the second rainfall period led to severe flooding in Shenzhen city, resulting in 11 fatalities. The first rainfall period was dominated by warm‐rain processes, while the development of the second period was closely related to the intensification of ice‐phase processes. The contribution of raindrops from the melting process played a crucial role in the formation of extreme rainfall, which achieved a high rain rate (RR) exceeding 120 mm hr−1. The enhancement of the ice‐phase processes during the second rainfall period was found to be closely associated with the development of a low‐level mesoscale vortex (MV). Due to the complementary non‐linear dynamical accelerations induced by the MV, the vertical velocity within the convective system rapidly intensified, leading to a more upright and deeper convective organization. As a result, more water vapor and supercooled water were lifted above the freezing level, which increased the presence of ice‐phase particles with the potential to melt, subsequently contributing to the extreme high RR. This study investigates the microphysical characteristics of two periods of HR that occurred during and after the development of a MV within a bow echo event, and examines the key microphysical processes affected by the MV, which partially contributed to the second HR period.

Permeable friction course design with consideration of hydroplaning risk

This study investigates safety performance of permeable friction courses (PFCs) in terms of hydroplaning. An analytical model was derived to calculate water depth on PFC under static rainfalls. A tire–water–pavement interaction model was used to predict hydroplaning speeds. The results show that water film depth increases from innermost to outermost traffic lanes. PFC can mitigate hydroplaning risk under 0.5 cm/h rain rate. At rain rate of 1 cm/h, the impact of horizontal hydraulic conductivity and PRF thickness on hydroplaning speed is less than 10%, but becomes negligible at higher rain rates. On the other hand, the flow slope of PFC significantly affects hydroplaning speed by over 50%, while this effect decreases as rain rate increases. The surface macrotexture of PFC shows less than 5% impact on hydroplaning speed at all rain rates. An analysis framework with design example is proposed to incorporate hydroplaning speed in decision-making of roadway design with PFC.

Does Increasing Horizontal Resolution Improve the Simulation of Intense Tropical Rainfall in GFDL's AM4 Model?

AbstractWe examine tropical rainfall from the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4 (GFDL AM4) at three horizontal resolutions of 100 km, 50 km, and 25 km. The model produces more intense rainfall at finer resolutions, but a large discrepancy still exists between the simulated and the observed frequency distribution. We use a theoretical precipitation scaling diagnostic to examine the frequency distribution of the simulated rainfall. The scaling accurately produces the frequency distribution at moderate‐to‐high intensity (≥10 mm day−1). Intense tropical rainfall at finer resolutions is produced primarily from the increased contribution of resolved precipitation and enhanced updrafts. The model becomes more sensitive to the grid‐scale updrafts than local thermodynamics at high rain rates as the contribution from the resolved precipitation increases.

Evaluation of IMERG Data over Open Ocean Using Observations of Tropical Cyclones

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

Global Precipitation for the Year 2023 and How It Relates to Longer Term Variations and Trends

Global Precipitation for the Year 2023 and How It Relates to Longer Term Variations and Trends

A study of extreme rainfall events and urban flooding over Hyderabad, October 2020

The present study analyses and describes the evolution of the Mesoscale Convective Complex (MCC) and its atmospheric conditions during Extreme rainfall event in Hyderabad, on 13th October 2020. This extreme weather event was a mesoscale event embedded in a synoptic-scale system. During the second week of October 2020, a depression formed over the west-central Bay of Bengal (BoB) and travelled north-westwards through peninsular India, causing heavy rains in Andhra Pradesh and Telangana states of India on October 13-14. On October 13, many parts of Hyderabad and Cyberabad received more than 300 mm of rain within 24 hrs. Satellite imagery suggests that this mesoscale system constituted a unique set of structured convection those reported in MCC. This MCC has a cloud shield with a continuous low IR temperature of less than - 33 °C over an area of more than 100000 km2 and a cloud shield with a continuous low IR temperature of less than -54 °C over an area of more than 50000 km2 over Hyderabad with a life cycle of about 9 hours. This MCC featured multi cellular characteristics, showing that there was significant low-level moisture in its environment, as well as a mix of vigorous updrafts, implying significant rainfall rates over Hyderabad. The synoptic features suggest that with high precipitable water, the long axis of low-level moisture convergence at 0850 hPa and large horizontal vorticity at 0925 hPa were oriented parallel to the system's mean wind flow. In this case, a clusters of thunderstorms arose in the area of moisture convergence which prolonged the duration of extremely heavy rainfall. The high rain rate, relatively sluggish storm motion, and prolonged back-building over the same locations for several hours are likely to blame for the heavy rainfall accumulations that were observed. The hydrological conditions compounded the effects of the torrential rain, resulting in a natural hazard.

Modelling mass accumulation rates and 210Pb rain rates in the Skagerrak: lateral sediment transport dominates the sediment input

Sediment fluxes to the seafloor govern the fate of elements and compounds in the ocean and serve as a prerequisite for research on elemental cycling, benthic processes and sediment management strategies. To quantify these fluxes over seafloor areas, it is necessary to scale up sediment mass accumulation rates (MAR) obtained from multiple sample stations. Conventional methods for spatial upscaling involve averaging of data or spatial interpolation. However, these approaches may not be sufficiently precise to account for spatial variations of MAR, leading to poorly constrained regional sediment budgets. Here, we utilize a machine learning approach to scale up porosity and 210Pb data from 145 and 65 stations, respectively, in the Skagerrak. The models predict the spatial distributions by considering several predictor variables that are assumed to control porosity and 210Pb rain rates. The spatial distribution of MAR is based on the predicted porosity and existing sedimentation rate data. Our findings reveal highest MAR and 210Pb rain rates to occur in two parallel belt structures that align with the general circulation pattern in the Skagerrak. While high 210Pb rain rates occur in intermediate water depths, the belt of high MAR is situated closer to the coastlines due to lower porosities at shallow water depths. Based on the spatial distributions, we calculate a total MAR of 34.7 Mt yr-1 and a 210Pb rain rate of 4.7 · 1014 dpm yr-1. By comparing atmospheric to total 210Pb rain rates, we further estimate that 24% of the 210Pb originates from the local atmospheric input, with the remaining 76% being transported laterally into the Skagerrak. The updated MAR in the Skagerrak is combined with literature data on other major sediment sources and sinks to present a tentative sediment budget for the North Sea, which reveals an imbalance with sediment outputs exceeding the inputs. Substantial uncertainties in the revised Skagerrak MAR and the literature data might close this imbalance. However, we further hypothesize that previous estimates of suspended sediment inputs into the North Sea might have been underestimated, considering recently revised and elevated estimates on coastal erosion rates in the surrounding region of the North Sea.

Using high resolution climate models to explore future changes in post-tropical cyclone precipitation

One of the most costly effects of climate change will be its impact on extreme weather events, including tropical cyclones (TCs). Understanding these changes is of growing importance, and high resolution global climate models are providing potential for such studies, specifically for TCs. Beyond the difficulties associated with TC behavior in a warming climate, the extratropical transition (ET) of TCs into post-tropical cyclones (PTCs) creates another challenge when understanding these events and any potential future changes. PTCs can produce excessive rainfall despite losing their original tropical characteristics. The present study examines the representation of PTCs and their precipitation in three high resolution (25–50 km) climate models: CNRM, MRI, and HadGEM. All three of these models agree on a simulated decrease in TC and PTC events in the future warming scenario, yet they lack consistency in simulated regional patterns of these changes, which is further evident in regional changes in PTC-related precipitation. The models also struggle with their represented intensity evolution of storms during and after the ET process. Despite these limitations in simulating intensity and regional characteristics, the models all simulate a shift toward more frequent rain rates above 10 mm h−1 in PTCs. These high rain rates become 4%–12% more likely in the warmer climate scenario, resulting in a 5%–12% increase in accumulated rainfall from these rates.

Radar Signal Behavior in Maritime Environments: Falling Rain Effects

Precision modeling of radar signal behavior in maritime environments holds paramount importance in ensuring the robust functionality of maritime radar systems. This work delves into the intricate dynamics of radar signal propagation in maritime environments, with a particular focus on the effects of falling rain. A theoretical model encompassing raindrop scattering, gaseous absorption, and ocean surface backscattering was developed and validated. Key findings reveal that rain significantly alters radar backscattering, with a noticeable decrease in signal strength under higher rainrates. Additionally, gaseous absorption, particularly at elevated frequencies and humidity levels, emerged as a critical factor. The study also highlights the complex interplay between wind-induced ocean surface roughness and radar signal behavior. We think these insights are pivotal for enhancing maritime radar system accuracy, particularly in adverse weather conditions, and paving the way for future research in refining environmental impact models on radar signals.

Water isotopic characterisation of the cloud–circulation coupling in the North Atlantic trades – Part 1: A process-oriented evaluation of COSMOiso simulations with EUREC4A observations

Abstract. Naturally available, stable, and heavy water molecules such as HDO and H218O have a lower saturation vapour pressure than the most abundant light water molecule H216O; therefore, these heavy water molecules preferentially condense and rain out during cloud formation. Stable water isotope observations thus have the potential to provide information on cloud processes in the trade-wind region, in particular when combined with high-resolution model simulations. In order to evaluate this potential, nested COSMOiso (isotope-enabled Consortium for Small Scale Modelling; Steppeler et al., 2003; Pfahl et al., 2012) simulations with explicit convection and horizontal grid spacings of 10, 5, and 1 km were carried out in this study over the tropical Atlantic for the time period of the EUREC4A (Elucidating the role of clouds-circulation coupling in climate; Stevens et al., 2021) field experiment. The comparison to airborne in situ and remote sensing observations shows that the three simulations are able to distinguish between different mesoscale cloud organisation patterns as well as between periods with comparatively high and low rain rates. Cloud fraction and liquid water content show a better agreement with aircraft observations with higher spatial resolution, because they show strong spatial variations on the scale of a few kilometres. A low-level cold-dry bias, including too depleted vapour in the subcloud and cloud layer and too enriched vapour in the free troposphere, is found in all three simulations. Furthermore, the simulated secondary isotope variable d-excess in vapour is overestimated compared to observations. Special attention is given to the cloud base level, which is the formation altitude of shallow cumulus clouds. The temporal variability of the simulated isotope variables at cloud base agrees reasonably well with observations, with correlations of the flight-to-flight data as high as 0.7 for δ2H and d-excess. A close examination of isotopic characteristics under precipitating clouds, non-precipitating clouds, clear-sky and dry-warm patches at the altitude of cloud base shows that these different environments are represented faithfully in the model with similar frequencies of occurrence, isotope signals, and specific-humidity anomalies as found in the observations. Furthermore, it is shown that the δ2H of cloud base vapour at the hourly timescale is mainly controlled by mesoscale transport and not by local microphysical processes, while the d-excess is mainly controlled by large-scale drivers. Overall, this evaluation of COSMOiso, including the isotopic characterisation of different cloud base environments, suggests that the simulations can be used for investigating the role of atmospheric circulations on different scales for controlling the formation of shallow cumulus clouds in the trade-wind region, as will be done in part 2 of this study.

The Study of Air Pressure System and Extreme Rain in The Southern Java Island

Extreme rain can trigger other natural phenomena that can interfere with human activities. Rain phenomenon extremes are common in tropical cyclones. This study aims to characterize the occurrence of extreme rain events concerning the air pressure system on Java Island. This study uses rainfall data from GSMAP MVK Product and Sea Level Pressure (SLP) from NCEP/NCAR Reanalysis. The identification of extreme rain is carried out with pixel detection of the GSMAP precipitation data from 2015 to 2019. The most extreme rain events were then ranked, and ten events with the highest rain rate were selected. The SLP data at the selected extreme rain event was analyzed and plotted as a map. The principal result shows that in the 10 highest extreme rain events, 4 of them occurred due to the influence of low-pressure systems and tropical cyclones. The rest are associated with other climatic phenomena. In conclusion, this study shows that extreme rainfall tends to be associated with low-pressure anomalies over the Indian ocean. However, not all of the low-pressure anomalies were intensified as a tropical cyclone. Therefore, extreme rainfall in Southern Java Island can be developed due to small air pressure differences.

A study of rain drop size distributions and associated rain microphysical processes over a subtropical station in the Northeast India

A study of rain Drop Size Distributions (DSDs) and associated microphysical processes is carried out during the premonsoon and monsoon seasons at Kohima (25.67° N, 94.08° E) in the Patkai hill range of the Northeast India. The study is carried out with the help of Micro Rain Radar (24.1 GHz). The rainfall is classified in three categories namely, Bright Band (BB), No Bright Band Low rain rate (R) i.e. 0.10 mm h−1< R ≤ 10 mm h−1 (NBBL) and No Bright Band High rain rate i.e. 10 mm h−1 <R ≤ 20 mm h−1 (NBBH). During the premonsoon and monsoon, for R ≤ 20 mm h−1, rainfall occurrence time constitute around 96% and 98% respectively, whereas rainfall accumulation constitute around 51%. and 72% respectively. The BB height is found to be lower during the premonsoon (∼2.2 km from surface level) compared to monsoon (∼3.6 km from surface level) season. The observational results suggest that, during the premonsoon as well monsoon seasons, BB rain is predominantly associated with collision–coalescence–breakup process with varying proportion, whereas NBBL and NBBH rain are predominantly associated with collision–coalescence process with varying proportion. In each season, collision-coalescence process is most dominant for NBBH type of rain. Seasonally, collision-coalescence process is found to be stronger during the premonsoon season. For the falling rain drops, there is a transition in the microphysical process from number controlled (collision-coalescence-breakup) to size-controlled (collision-coalescence) process with varying proportion. For each season, for R ≤ 10 mm h−1, larger rain drops are found for BB type of rain compared to NBBL rain. Overall, relatively larger raindrops are found to be associated with NBBH rain. Seasonally, larger drops are found during the premonsoon compared to the monsoon season. The rain DSDs and associated microphysical processes during the premonsoon are found to be associated with stronger convection compared to the monsoon season.

Estimation of rain parameters for microwave backscattering model using PSO

The intention of the geophysical modelling of rain is to provide a better explanation for the effect of rainfall to the microwave backscattering and thus to interpret radar measurements. However, in the model, physical characteristic of raindrops should be estimated primarily and accurately by considering observation system, measurements and suitable rain rate retrieval algorithms to calculate backscattering coefficients from rainfall. In this study, a geophysical microwave backscattering model of rain type precipitation over sea surface is constructed by using Particle Swarm Optimization (PSO) algorithm in the multilayered Vector Radiative Transfer (VRT) model to estimate vertical profile of rain by using GPM DPR data. Rain column is partition into sublayers and for each sublayer, physical properties of raindrops such as drop radius, water volume fraction or layer thickness are estimated by using PSO to provide the best fit with measurements by searching within certain limits defined by rain rate. Backscattering coefficients from entire rain is provided by the solution of VRT equations via Matrix Doubling Method to consider multilayer effect. Results show that, vertical profile of rain parameters can be estimated accurately for moderate /high rain rates (up to 11–12 mm/h) by using presented model.

Multi‐Year Analysis of Rain‐Snow Levels at Marquette, Michigan

AbstractThis study uses observations from a ground‐based instrument suite to investigate the rain‐snow level (RSL) in stratiform rainfall from January 2014 to April 2020 in the Upper Great Lakes Region. The height above the surface where ice melts to rain, the rain‐snow level, influences microphysical assumptions in remote sensing precipitation retrievals and the ability of space‐based radar to discriminate surface precipitation phase because of ground clutter. The instrument suite is installed at the Marquette, MI (MQT) National Weather Service station adjacent to Lake Superior. Rain events and the RSLs are studied through a ground‐based vertically profiling radar (Micro Rain Radar), a custom NASA‐developed video disdrometer (Precipitation Imaging Package), and reanalysis products from ECMWF ERA5 and NASA MERRA‐2. Distinct macro and microphysical characteristics are observed in precipitation events with shallow RSLs (&lt;1.8 km above ground level [AGL]) and intermediate RSLs (&gt;1.8 km AGL). Intermediate RSLs correspond to rain events with relatively higher rain rates and a higher concentration of small drops in the drop size distributions (DSDs). Shallow RSL DSDs contain relatively higher concentrations of large drops with lower fall speeds suggesting that partially melted snowflakes may be reaching the surface. Reflectivity‐rain‐rate relationships are also impacted by microphysical differences associated with RSL regimes. Radar‐detected RSLs agree with reanalysis‐derived melt levels‐especially at wet‐bulb isotherms of 0.5°C and 1°C. Seasonal differences such as shallow RSLs in winter, fall, and spring have subsequent implications for satellite detectability.

The Response of Ocean Skin Temperature to Rain: Observations and Implications for Parameterization of Rain‐Induced Fluxes

AbstractRainfall alters the physical and chemical properties of the surface ocean, and its effect on ocean skin temperature and surface heat fluxes is poorly represented in many air‐sea interaction models. We present radiometric observations of ocean skin temperature, near‐surface (5 cm) temperature from a towed thermistor, and bulk atmospheric and oceanic variables, for 69 rain events observed over the course of 4 months in the Indian Ocean as part of the DYNAMO project. We test a state‐of‐the‐art prognostic model developed by Bellenger et al. (2017, https://doi.org/10.1002/2016JC012429) to predict ocean skin temperature in the presence of rain, and demonstrate a physically motivated modification to the model that improves its performance with increasing rain rate. We characterize the vertical skin‐bulk temperature gradient induced by rain and find that it levels off at high rain rates, suggestive of a transition in skin‐layer physics that has been previously hypothesized in the literature. We also quantify the small bias that will be present in turbulent sensible heat fluxes parameterized from ocean temperature measurements made at typical “bulk” depths during a rain event. Finally, a wind threshold is observed above which the surface ocean remains well‐mixed during a rain event; however, the skin temperature is observed to decrease at all wind speeds in the presence of rain.

Effective Path Length for Estimating Rain Attenuation Over an Earth‐Space Path Using Features of Stratiform and Convective Precipitation at a Tropical Location

AbstractThe effective path length (LE) for the rain attenuation estimation over the Earth‐space path depends on rain cell size, rain rate, and rain height which in turn are determined by the types of rain. The present study examines the variation of LE based on the experimental measurements of Ku‐band attenuation and rain rate at a tropical location Kolkata (22°34′N, 88°22′E) in comparison with the ITU‐R. The study also utilizes measurements from a 24‐GHz micro rain Doppler radar to differentiate the precipitation types as stratiform and convective rain on the basis of the presence and absence of a melting layer. Power‐law models of LE in terms of rain rate are proposed for convective and stratiform types of rain. The investigation reveals the physical phenomena behind the variability of LE at low and high rain rates which are associated with stratiform and convective rain respectively. The proposed models provide a significantly better agreement with the experimental data compared to the ITU‐R model and also account for distinguishing features of two types of rain, thus giving a physical basis of the proposed model which is not reflected in the ITU‐R formulations.

Wind, Rain, and the Closed to Open Cell Transition in Subtropical Marine Stratocumulus

AbstractLagrangian transitions in mesoscale cellular convective (MCC) clouds beginning as closed cell MCC and transitioning to open cells or disorganized but cellular MCC are explored on timescales from 12 to 72 hr. Potential drivers of MCC transitions are shown to act on multiple timescales. Closed‐to‐open MCC cloud transitions are preceded by strong surface winds and large moisture fluxes at lead times of up to 72 hr; and by high cloud water content, reduced cloud drop concentrations, and intense rain rates at lead times of 12–36 hr. The relationship between intense rain and the formation of open cells is consistent with a cold pool convergence mechanism. A Lagrangian analysis shows that anomalously strong surface winds are associated with higher rain rates as well as subsequent increases in rain rates through modifications to moisture flux and content in the boundary layer. The closed‐open MCC transition contrasts with the closed‐to‐disorganized transition which at long lead times is associated with warm sea surface temperature and at diurnal‐scale lead times is associated with variables related to cloud top entrainment drying such as a deepening boundary layer, weakening subsidence and inversion strength, and a drier free troposphere. A conceptual model is proposed where excess boundary layer moisture associated with wind breaks up stratocumulus through the closed‐to‐open transition, while excess dry‐air entrainment at cloud top breaks up stratocumulus through the closed‐to‐disorganized transition.

Applying Predictive Modeling to Enhancing Solar Energy

Though analysis has been provided on how factors like temperature or humidity impact panel efficiency, there has not been as much research conducted on how the various environmental conditions all relate with each other to affect solar energy output real-time. Having a model that correlates several environmental predictor variables known to impact solar energy can help determine what adjustments need to be made to optimize solar panel performance. In this project, prediction models like artificial neural networks (ANN), multiple linear regression (MLR), elastic, ridge, and lasso were used for relating environmental variables like high temperature, outside humidity, or rain rate to the total solar energy output produced. Data containing thirty-three different weather measurements and their respective solar energy outputs was obtained from the UK Power Networks and will be used as the principal dataset for the models. To check linear model assumptions like normality of residuals or heteroscedasticity for models like MLR, several functions provided from a model performance package in R verified whether the assumptions for the model were met. Predictive model selection was based on cross validation with k = 10 and Mean Squared Error (MSE). Neural networks were the highest performing model, but lasso and elastic net were the most interpretable models in terms of how conditions affected energy output.

Communication Systems Performance at mm and THz as a Function of a Rain Rate Probability Density Function Model.

6G is already being planned and will employ much higher frequencies, leading to a revolutionary era in communication between people as well as things. It is well known that weather, especially rain, can cause increased attenuation of signal transmission for higher frequencies. The standard methods for evaluating the effect of rain on symbol error rate are based on long-term averaging. These methods are inaccurate, which results in an inefficient system design. This is critical regarding bandwidth scarcity and energy consumption and requires a more significant margin of effort to cope with the imprecision. Recently, we have developed a new and more precise method for calculating communication system performance in case of rain, using the probability density function of rain rate. For high rain rate (above 10 mm/h), for a typical set of parameters, our method shows the symbol error rate in this range to be higher by orders of magnitude than that found by ITU standard methods. Our model also indicates that sensing and measuring the rain rate probability is important in order to provide the required bit error rate to the users. This will enable the design of more efficient systems, enabling design of an adaptive system that will adjust itself to rain conditions in such a way that performance will be improved. To the best knowledge of the authors, this novel analysis is unique. It can constitute a more efficient performance metric for the new era of 6G communication and prevent disruption due to incorrect system design.