Temporal Variability Of Precipitation Research Articles

Prediction of evapotranspiration variance in the Budyko framework with the incorporation of soil storage and runoff

Water availability needs to be accurately assessed to understand and effectively manage hydrologic environments. However, the estimation of evapotranspiration (ET) is prone to errors due to the complex interactions that occur between the atmosphere, the Earth's surface, and vegetation cover. This paper proposes a novel approach for analyzing the sources of inaccuracy in estimating the annual ET using the Budyko framework (BF), particularly temporal variability in precipitation (P), potential evapotranspiration (EP), runoff (R), and the change in soil storage (ΔS). Error decomposition is employed to determine the individual contributions of P, R, EP, and ΔS to the ET error variance at 12 locations in the state of Illinois using a dataset covering a 22-year period. To the best of our knowledge, this study represents the first BF-based investigation that considers R in the error decomposition of the predicted ET variance. The ET error variance increases with the variance in the P and R in Illinois and decreases with the covariance between these two variables. In addition, when accounting for ΔS in the BF, the scenario in which ΔS affects the total available water (i.e., P) is reliable, with a low prediction error and a 13.87 % lower root mean square error compared with the scenario in which the effect of ΔS is negligible. We thus recommend the inclusion of ΔS and R as key variables in the BF to improve water budget estimations.

Harnessing the Radio Frequency Power Level of Cellular Terminals for Weather Parameter Sensing

In light of recent extreme weather events, it is imperative to explore innovative methodologies for promptly and accurately measuring various meteorological parameters. The high spatial and temporal variability in precipitation often surpasses the resolution capabilities of traditional rain gauge measurements and satellite estimation algorithms. Therefore, exploring alternative methods to capture this variability is crucial. Research on the correlation between signal attenuation and precipitation could offer valuable insights into these alternative approaches. This study investigates (a) the feasibility of the classification of precipitation rate using signal power measurements in cellular terminals and (b) the impact of atmospheric humidity as well as other meteorological parameters on the signal. Specifically, signal power data were collected remotely through a specialized Android application designed for this research. During the time of analysis, the power data were processed alongside meteorological parameters obtained from the meteorological station of the Physics Department at the University of Ioannina gathered over one semester. Having in mind the radio refractivity of the air as a fascinating concept affecting the way radio waves travel through the atmosphere, the processed results revealed a correlation with signal attenuation, while a correlation between the latter and absolute humidity was also observed. Moreover, a precipitation rate classification was attained with an overall accuracy exceeding 88%.

Spatiotemporal analysis of precipitation variability in an endorheic basin of Turkey with coordinated regional climate downscaling experiment data

Understanding how temporal precipitation variability will behave in the future is crucial. Change point detection and sub-trend analysis were done on monthly total precipitation data acquired from the Coordinated Regional Climate Downscaling Experiment project in this study. Sub-periods have been determined based on the natural structure of the data set, as opposed to standard sub-period determination approaches. For the period 1971–2100, six different time series were used under three global climate models (CNRM-CERFACS-CNRM-CM5, ICHEC-EC-EARTH, MPI-M-MPI-ESM-LR) and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5). The study area has been selected to be the Konya Endorheic Basin in Turkey, which has been significantly influenced by climate change and human impacts. For change point detection, the Pruned Exact Linear Time method (PELT) and Binary Segmentation (BS) were employed, for trend analysis, Mann Kendall (MK) and Şen-Innovative Trend Analysis (Şen-ITA), and for both change point detection and sub-trend analysis, the Onyutha Trend Test (OTT) was utilized. The findings revealed four change points and five sub-periods. Summer precipitation has reduced considerably at a 95% significance level. There is strong evidence that precipitation has decreased between 1981 and 2070. Precipitation decreases similarly in the middle basin for the 1981–2000 and 2041–2070 years.

Spatial and temporal variations of precipitation in Northwest China during 1973-2019

Spatial and temporal variations of precipitation in Northwest China during 1973-2019

Understanding temporal variability across trophic levels and spatial scales in freshwater ecosystems.

A tenet of ecology is that temporal variability in ecological structure and processes tends to decrease with increasing spatial scales (from locales to regions) and levels of biological organization (from populations to communities). However, patterns in temporal variability across trophic levels and the mechanisms that produce them remain poorly understood. Here we analyzed the abundance time series of spatially structured communities (i.e., metacommunities) spanning basal resources to top predators from 355 freshwater sites across three continents. Specifically, we used a hierarchical partitioning method to disentangle the propagation of temporal variability in abundance across spatial scales and trophic levels. We then used structural equation modeling to determine if the strength and direction of relationships between temporal variability, synchrony, biodiversity, and environmental and spatial settings depended on trophic level and spatial scale. We found that temporal variability in abundance decreased from producers to tertiary consumers but did so mainly at the local scale. Species population synchrony within sites increased with trophic level, whereas synchrony among communities decreased. At the local scale, temporal variability in precipitation and species diversity were associated with population variability (linear partial coefficient, β = 0.23) and population synchrony (β = -0.39) similarly across trophic levels, respectively. At the regional scale, community synchrony was not related to climatic or spatial predictors, but the strength of relationships between metacommunity variability and community synchrony decreased systematically from top predators (β = 0.73) to secondary consumers (β = 0.54), to primary consumers (β = 0.30) to producers (β = 0). Our results suggest that mobile predators may often stabilize metacommunities by buffering variability that originates at the base of food webs. This finding illustrates that the trophic structure of metacommunities, which integrates variation in organismal body size and its correlates, should be considered when investigating ecological stability in natural systems. More broadly, our work advances the notion that temporal stability is an emergent property of ecosystems that may be threatened in complex ways by biodiversity loss and habitat fragmentation.

Spatial and temporal variability of precipitation based on marginal and apportionment entropy disorder indices in Iran

Spatial and temporal variability of precipitation based on marginal and apportionment entropy disorder indices in Iran

Entropy based approach for precipitation monitoring network in Bihar, India

Study regionBihar State, located in India’s eastern region, displays significant spatial and temporal variation in rainfall during the Indian Summer Monsoon period with subsequent flooding problems. Study focusRecent severe flooding problems highlight the need for improved spatial precipitation monitoring to enable effective flood management and reduce water-related disasters. To address this challenge, we employed Shannon entropy theory to assess the spatial distribution of precipitation and identify critical areas for rain gauge network improvements. We used Principal of Maximum Entropy (POME) to compute entropy measures and Value of Monitoring (VOM) with Thiessen polygons, and Adjacent Station Groups (ASGs). New hydrological insights for the regionThe results showed that the Marginal Entropy (ME) values lie between 0.039 and 0.048. The maximum values of ME are in the northeast area of the study region, exhibiting larger complexity and variability in the environmental conditions typical for northeast Bihar. The VOM was in the range of − 1 to + 1 suggesting strategic placement of additional 12 rain gauge stations to improve the existing monitoring network. The new locations were in the south mountainous area, the east, and the northwest, enhancing network coverage and addressing spatial and temporal precipitation variability. These findings support the design of a more effective monitoring network and have significant implications in hydrological modelling, flood prediction, and water resources management.

UNDERSTANDING CLIMATE CHANGE IN ETHIOPIA: IMPACTS AND SOLUTIONS

Climate change poses a global challenge with profound impacts on diverse aspects of the environment and human society. Understanding these impacts is crucial for formulating effective mitigation strategies. As one of the most critical threats to sustainable development, climate change is anticipated to affect the environment, human health, food security, economic activities, natural resources and physical infrastructure. This review focuses on climate change, its consequences and mitigation efforts in Ethiopia. Ethiopia has encountered both climate variability and change, evident in an average annual temperature increase of about 1.3∘C since 1960, with a corresponding rise of 0.28∘C per decade. Spatial and temporal variations in precipitation have also intensified, highlighting the country’s exposure to climate fluctuations. To address these challenges, the Ethiopian government has implemented various policies, strategies and programs aimed at enhancing adaptive capacity and minimizing the impacts of climate variability and change. The Climate-Resilient Green Economy (CRGE) of Ethiopia centers on four pillars: agriculture, forestry, renewable energy and advanced technologies. These pillars form the foundation for Ethiopia’s pursuit of a sustainable green economy. In conclusion, this review delves into the examination of climate change, its repercussions and the ongoing efforts to mitigate its effects in Ethiopia.

Effects of extra-extreme precipitation variability on multi-year cumulative nitrous oxide emission in a semiarid grassland

High temporal precipitation variability, characterized by less frequent but larger-magnitude precipitation events, is increasing. However, how precipitation variability affects N2O emissions and the underlying mechanisms remain unclear, especially at multiple-year scales. We conducted a 3-year manipulative experiment in which the same long-term mean growing season precipitation total was repackaged into events of inversely varying magnitude and frequency to simulate four levels of precipitation variability (extra-extreme, extreme, medium, and normal) in a semiarid grassland. Cumulative N2O emissions was the smallest under the extra-extreme precipitation variability scenario (6 very large rainfall events), 26% less than emission under the other three treatments (10, 16, and 24 rainfall events) over the three growing seasons. However, this difference was almost entirely due to three sampling events in the third year. Plant community (biomass and biodiversity), soil abiotic properties (water, dissolved organic carbon and pH), soil microbial biomass (carbon, nitrogen and the ratio), and soil functional genes (archaeal and bacterial amoA, nirS, nirK, narG, and nosZ) explained 61% of the variation in N2O emissions in response to the precipitation variability. Structural equation modelling indicated that the precipitation variability had direct positive effects, and indirect negative effects via soil abiotic properties, on soil functional genes that ultimately had positive effects on N2O emissions. The reduction in N2O emission in the extra-extreme precipitation variability scenario in the third year was due to low levels of soil water content, soil pH, aboveground biomass, and microbial biomass carbon simultaneously. Our results suggest that at the multi-year timescale, semiarid grasslands may have negative feedbacks to future precipitation regimes with higher variability.

Characterizing CONUS-wide spatio-temporal changes in daily precipitation, flow, and variability of extremes

The hydrological cycle strongly relies on the spatial and temporal variability of precipitation, which has been considerably affected by climate change. Focusing on CONUS, this research extended the analysis by evaluating the variability of daily concentrations of various components of the hydrological cycle in terms of the Gini index and its potential effects on floods and droughts by integrating historical data with an existing database of extreme events. Differences were found in the geographical location of significant correlations among the hydrological variables investigated, implying that the impacts of variability are neither spatially nor temporally uniform. The work also showed the importance of selecting thresholds of precipitation and flow to define “extreme” events. We identified a precipitation deficit threshold at the 5th percentile and an extreme precipitation threshold at the 95th percentile as the most representative. Moreover, for flood, the 95th percentile and for hydrological drought, the 5th percentile were the most representative. Spatially the study showed that, the concentration of precipitation deficit and evapotranspiration had a major impact on the occurrence of hydrological hazards, such as droughts and floods.

Tillage system regulates the soil moisture tension, penetration resistance and temperature responses to the temporal variability of precipitation during the growing season

Tillage system regulates the soil moisture tension, penetration resistance and temperature responses to the temporal variability of precipitation during the growing season

Spatio-temporal variation of seed rain between urban and rural fragments of humid tropical forests

Introduction: The type of land use surrounding the remnants of tropical forest may generate changes in the characteristics of plant populations and communities. Consequently, there may be a significant reduction in processes of pollination and diasporas dispersion. Therefore, causing changes in some parameters of seed rain. Objective: To characterize and compare seed density, species richness, floristic composition, habit, dispersal syndrome, and successional category of seed rain between urban and rural fragments of Atlantic Forest, in the 2015 and 2016 weather seasons. Methods: The study areas were defined after mapping and quantification of urban and rural occupations around the remnants, based on satellite images. In each fragment, were installed 36 collectors of 0.25 m2. The material was collected monthly during two consecutive years. Results: Seed rain richness was higher in the urban fragment during the rainy season in the two years, whereas it was similar between the fragments in the dry season. The seed density in the rural fragment was higher than in the urban during the rainy season; did not vary in urban between years or between seasons; and it was higher in the rural fragment in the rainy season of one year. There was a difference in the floristic composition of the seed rain between the fragments along time. The variations in the functional attributes of habit, dispersal syndrome, and successional category, were explained by the variables fragment, season, and year. Conclusions: Differences in the characteristics of the seed rain between the fragments might reflect the spatial and temporal heterogeneity, due to the diverse uses of the soil and external pressures (anthropogenic actions) present in the surroundings of the forest fragments and temporal variation in precipitation.

Effects of climate variability changes on runoff and erosion in the Western European Loess Belt region (NW, France)

Runoff and soil erosion are very pronounced in the Western European Loess Belt. In this study, the distributed physically-based model CLiDE is calibrated, validated, and applied to a catchment of this area (Dun, NW, France) to assess the hydro-sedimentary impacts of climate change scenarios. Despite considerable progress over the last decade in the study of runoff and soil erosion in the context of climate change, the effects of changes in the temporal variability of precipitation remain poorly understood, especially at the scale of a river basin. To examine these relationships more closely, we developed a stochastic weather generator to individually adjust the components that structure the temporal variability of rainfall. The climate scenarios considered represent projections to the year 2100 of the temporal variability of rainfall over NW Europe. The scenarios are based on historical daily rainfall records (1990–2012) and 4 exploratory assumptions: a 50 % decrease in the interannual rainfall regime (scenario 6yD), a 100 % increase in the interannual rainfall regime (scenario 6yI), a 50 % increase in the seasonal rainfall regime (scenario 1yI) and a 50 % increase in the synoptic rainfall regime (scenario 3dI). Simulated daily water and sediment discharges and erosion/deposition maps for each scenario are compared to those simulated for the situation without changes in rainfall. The time series were aggregated over different time intervals to allow for a multi-scale analysis of the differences. The results indicate that the model provides a satisfactory prediction of the catchment's water and sediment discharges, especially over the calibration period. Increased climate variability, whether on a synoptic (3dI), seasonal (1yI) or interannual (6yI) scale, leads to increased runoff and erosion. Increasing the synoptic rainfall variability (3dI) leads to the largest increase in mean annual runoff and erosion. Only the reduction of the interannual rainfall variability (6yD) provokes the decrease of these values.

Land-Use-Change-Induced Cooling and Precipitation Reduction in China: Insights from CMIP6 Models

In the 21st century, the effect of land use/land cover change (LULCC) on climate has become an area of active research. To explore the effects of LULCC on temperature and precipitation in China, we used outputs from the BCC-CSM2-MR, CESM2, IPSL-CM6A-LR, and UKESM1 models, which participated in the Land Use Model Intercomparison Project (LUMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Based on these models, we identified temporal variations in precipitation and near-surface air temperature (hereinafter temperature) with and without historical land use changes and their relation with LULCC in China during 1850–2014. We then determined the significant changing period (1972–2012) and revealed the relation between the spatial distribution of historical change in vegetation cover types, precipitation, and temperature. The results showed that annual historical precipitation decreased faster (132.23 mm/(1000 a) faster), while annual historical temperature increased slower (2.70 °C/(1000 a) slower) than that without LULCC during 1850–2014. LULCC not only influenced surface properties to change local precipitation and temperature distributions and mean values, but also affected other components through atmospheric circulations due to typical monsoon characteristics in China. The relative contribution of grassland change to precipitation variation was the largest, while relatively, cropland change contributed the most to temperature variation. Our study innovatively used new model outputs from LUMIP to analyze the impacts of LULCC on precipitation and temperature, which can help to guide and improve future land use management and predictions of precipitation and temperature.

Seasonal dependent suitability of physical parameterizations to simulate precipitation over the Himalayan headwater

The Himalayan ecosystem is fragile and needs robust management strategies for sustainability of natural resources such as water and vegetation. Therefore, reliable precipitation estimation becomes quite important from operational and regulation standpoints. It is crucial for numerous activities including policy/planning, agriculture, reservoir operations, disaster management, and others. In addition, reliable information on temporal variability of precipitation is also crucial for various applications such as agricultural and hydrological. The western Himalaya receives two distinct weather systems during summer and winter. Summer is responsible (largely) for rainfall and winter is for snowfall. Therefore, we hypothesize that there may not be a single set of parameterization schemes that can represent well both the weather systems. To investigate, we set up the WRF modeling system and performed six experiments with a combination of three microphysics (MP3, MP3, and WSM6) and two cumulus schemes (KF, and BMJ). It was found that the precipitation along the Himalayan foothills (near to basin terminal) is underestimated in four out of six experiments. Only experiments with BMJ cumulus scheme along with WSM6 and MP8 microphysics were able to show a considerable amount of precipitation along these foothills. It was noted that all six experiments showed high precipitation in the upstream region and over the mountain peaks and ridges in North-Western Himalaya. For DJF, each experiment was found to have large biases and none of them represented the observation with high confidence. However, the selection of observation reference data itself is a challenging task because of data paucity in this region. Therefore, the closest experiment to the most appropriate observation was selected as the reliable configuration (MP8_KF: MP8 microphysics and KF cumulus scheme) for DJF precipitation simulation. In this study we have, for the first time, reported the role of seasonal sensitivity for the climate scale simulations as we found that different schemes were suitable for different weather systems.

A Multiplicative-Exponential function to correct precipitation for distributed hydrological modeling in Poorly-gauged basins

Given the greater spatial and temporal variability in precipitation and the absence of rain gauges at mountainous regions, it is challenging to accurately estimate precipitation, consequently impeding the predictive capability of hydrological models. Here, we developed a nonlinear precipitation correction method in the SWAT (Soil and Water Assessment Tool) model to correct gridded (remote sensing or reanalysis) precipitation data for better distributed hydrological modeling. The precipitation correction module is featured by a multiplicative-exponential equation with two parameters, a multiplicative coefficient (PCP_COE) and an exponent (PCP_EXP). We proposed three configurations to determine PCP_COE and PCP_EXP, i.e., curve fitting between gridded and gauge precipitation data pairs (Configuration C1) or using gridded precipitation from multi-grids within an elevation range of the gauge (Configuration C2), and the calibration of the SWAT model against observed streamflow (Configuration C3). We applied the proposed method to a typical poorly-gauged alpine inland basin, the Xiangride River Basin of Northwest China, with only one rain gauge and streamflow station. Results show that Configuration C1 outperformed C2 and C3 by significantly improving streamflow modeling in both model calibration and validation, particularly when the GPM (Global Precipitation Measurement) precipitation datasets were used. We also argue that Configuration C3 is promising in ungauged basins with only streamflow available, since this configuration does not require gauge precipitation. This study puts forward a feasible method to derive more accurate spatiotemporal precipitation estimate based on gridded precipitation products, hence improving distributed hydrological modeling in poorly-gauged and ungauged basins.

Long-term spatiotemporal variability of precipitation and its linkages with atmospheric teleconnections in the Yellow River Basin, China

AbstractPrecipitation is widely considered as a crucial index toward the apprehension of global climate change. Hence, it becomes imperative to explore spatiotemporal patterns and the interlinked factors of precipitation in the basin. In the study, the spatial and temporal variability of precipitation and the individual or integrated effects of various atmospheric teleconnections on precipitation variations are explored in the Yellow River Basin. The total precipitation showed a slightly declining tendency during 1950–2019 and the dependence relationship of precipitation gradient on latitude and longitude is different in various seasons and elevations. The spatiotemporal variability of precipitation is more sensitive to the latitude gradient. For each 1-degree increase in longitude and latitude, the average annual precipitation increases/decreases by 10.73 and 57.24 mm, respectively. Moreover, the precipitation spatiotemporal patterns could be interpreted by four empirical orthogonal functions (EOFs) modes about 71.9% of precipitation variations. The strength of the linkages between various circulation factors and precipitation varied at different time scales. The integrated effects of multiple factors should be taken into consideration in explaining precipitation variability at all time scales. It is expected that the study can be helpful for understanding the internal mechanism of the hydrological cycle in the YRB.

A multiple-step scheme for the improvement of satellite precipitation products over the Tibetan Plateau from multisource information

Precipitation data with high accuracy and spatial resolution characteristics play significant roles in the regional hydrological and eco-environmental system applications. Thus, satellite-based precipitation products (SPPs) with high spatial resolution and high accuracy must be developed. This study proposed a multiple-step scheme to improve the global precipitation measurement (GPM) at daily and monthly timescales over the Tibetan Plateau (TP) from 2014 to 2017. First, combined with the geographically weighted regression (GWR) method using geographic and topographic factors, the gamma-distribution mapping and local intensity scaling (GDM-LOCI) method is applied to effectively merge the observed data attributes and the spatial representation of SPPs at the daily scale by correcting precipitation volumes and frequencies. Second, the areal merged precipitation, normalized difference vegetation index (NDVI), and reanalyzed atmospheric data are used to improve the spatial resolution of monthly GPM with a random forest (RF) model that uses the 17 land cover types to establish the local downscaling model windows. The results show that daily merged precipitation can better reflect the spatial and temporal variability of precipitation than can satellite estimates, and the correlation coefficient (R), and critical success index (CSI) increased by 0.12 and 0.26, respectively. In the merged downscaling model, the merged precipitation factor can weaken the negative effect of the other auxiliary predictors due to its spatial autocorrelation in precipitation estimation. Most importantly, by using the land cover types to establish local model windows for the downscaling model, not only the spatial resolution of the GPM product is downscaled to 1 km, but also the spatial structure of the downscaled products is enhanced, with less deviation and a higher spatial correlation. The R, the root mean square error (RMSE) and the relative bias (BIAS) were 0.89, 50.19 mm and 0.57, respectively. This study presents a promising scheme for generating high-quality precipitation data for regional hydrometeorological research in data-scarce regions.

Revealing historical observations and future projections of precipitation over Northwest China based on dynamic downscaled CMIP6 simulations

The warming climate driven by global change has great potential in altering regional and global hydrologic cycles, thus leading to considerable changes in spatial variability and temporal pattern of precipitation. Northwest China (NW) has witnessed a significant wetting trend over the past decades, while the persistence of this wetting trend and potential changes in precipitation under future climate impacts remains elusive. In this study, long-term meteorological observations were used to probe historical variations of precipitation from 1951 to 2020, and the WRF model was employed as a regional climate model to examine future precipitation patterns over NW. Two 9-year downscaled WRF simulations were conducted comprising of historical (WRF-HIST; 2012–2020) and future climate change scenarios (WRF-SSP585; 2047–2055) using bias-corrected global climate model outputs from Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared with ground observations, the WRF model exhibited strong capability in capturing the spatial pattern and temporal variations of precipitation across the NW. Intense precipitation was mainly found in stations located at northern NW and southeastern NW. Summertime precipitation substantially contributed to annual precipitation over the study region. Future precipitation projections suggest significant decreases of precipitation across the southern and eastern NW, with a stronger reduction magnitude in summer. Further, extreme precipitation events were projected to decrease in spring and summer, suggesting that the NW may become drier and the wetting trend may shift to another pattern in the 2050s under the SSP585 climate scenario. Overall, this study reveals historical and future potential changes in precipitation over NW through a high-resolution, dynamically downscaled dataset from WRF modeling, which in turn will help inform regional mitigation and adaption on potential impacts of future climate change on NW.

Evaluation of WRF Simulation of Deep Convection in the U.S. Southern Great Plains

Abstract The Southern Great Plains (SGP) region exhibits a relatively high frequency of periods with extremely high rainfall rates (RR) and hail. Seven months of 2017 are simulated using the Weather Research and Forecasting (WRF) Model applied at convection-permitting resolution with the Milbrandt–Yau microphysics scheme. Simulation fidelity is evaluated, particularly during intense convective events, using data from ASOS stations, dual-polarization radar, and gridded datasets and observations at the DOE Atmospheric Radiation Measurement site. The spatial gradients and temporal variability of precipitation and the cumulative density functions for both RR and wind speeds exhibit fidelity. Odds ratios > 1 indicate that WRF is also skillful in simulating high composite reflectivity (cREF, used as a measure of widespread convection) and RR > 5 mm h−1 over the domain. Detailed analyses of the 10 days with highest spatial coverage of cREF > 30 dBZ show spatially similar reflectivity fields and high RR in both radar data and WRF simulations. However, during periods of high reflectivity, WRF exhibits a positive bias in terms of very high RR (>25 mm h−1) and hail occurrence, and during the summer and transition months, maximum hail size is underestimated. For some renewable energy applications, fidelity is required with respect to the joint probabilities of wind speed and RR and/or hail. While partial fidelity is achieved for the marginal probabilities, performance during events of critical importance to these energy applications is currently not sufficient. Further research into optimal WRF configurations in support of potential damage quantification for these applications is warranted. Significance Statement Heavy rainfall and hail during convective events are challenging for numerical models to simulate in both space and time. For some applications, such as to estimate damage to wind turbine blades and solar panels, fidelity is also required with respect to hail size and joint probabilities of wind speed and hydrometeor type and rainfall rates (RR). This demands fidelity that is seldom evaluated. We show that, although this simulation exhibits fidelity for the marginal probabilities of wind speed, RR, and hail occurrence, the joint probabilities of these properties and the simulation of maximum size of hail are, as yet, not sufficient to characterize potential damage to these renewable energy industries.