Understanding the linkages between between climatic and surface properties that influence water uptake and loss by vegetation is essential for understanding the impact of drought on dryland regions. The Normalized Difference Vegetation Index (NDVI) is a common metric used to identify vegetation condition across LULC types. Here we employ empirical dynamic modeling (EDM) to forecast NDVI changes for savannas, grasslands, and croplands across East Africa at a dekadal (10-day) time scale using satellite-derived environmental forcing variables. The model relies on state space reconstruction with lagged coordinate embedding of multiple time series observations to recover the dynamic environmental system that links vegetation dynamics to environmental forcing. We apply convergent cross mapping based on Takens’ Theorem to detect the impact of landcover on directional causal interactions and time delays between driving (e.g. LST, rainfall) and response variables (NDVI). The model brings to light that certain regions are highly consistent in their trajectories and therefore easier to project while other regions are more dispersive and thus more difficult to determine anomalies. In terms of land cover, we are able to make projections with high accuracy for grasslands out to 6 months ahead while croplands and savannas show reduced forecast skill overall and prove less useful after 3 months. The use of historical seasonal NDVI patterns to diagnose the manner by which landcover and land use determine climate-land surface couplings provides a means for defining critical areas of inquiry related to the impacts of future change, particularly the expansion of agricultural areas. In addition, the EDM approach provides a robust means for creating short term vegetation forecasts across LULC types in East Africa. These predictions can assist relief organizations in advising drought management, declaring food security classifications and providing early response to famine.

Saturn’s moon Enceladus harbours a global subsurface ocean beneath its icy crust. Tidal dissipation within the moon’s core generates a substantial amount of heat which leads to ocean convection. Understanding this transport of mass and heat within the ocean is key to understand exchange processes between core, ocean and ice shell. Previous studies of ocean convection assume oceans of constant thickness and constant superadiabatic temperature gradients between the inner core and outer ice shell. However, observations indicate ocean thickness variations of up to ~20 km from equator to pole and heterogenous tidal dissipation within the core likely results in latitude-dependent temperature gradients. In this study, we analyse how heterogeneities affect circulation patterns and heat transfer in an Enceladan ocean using three-dimensional direct numerical simulations. We focus on a degree-2 meridional thickness profile with varying degree of thermal forcing to analyse the different rotating convection regimes and compare them with those of an ocean of constant thickness. Our results show that the non-uniform ocean gives rise to a latitudinally variable Rayleigh number, causing a potential decoupling of rotating convection regimes experienced locally within the ocean. As in the constant ocean thickness scenario, different regimes of convection exist, which depend on the relative influence of rotation. With increasing thermal forcing, convection moves from being restricted to equatorial regions to filling the whole fluid domain. Heat transport efficiency, as measured by the local Nusselt number, is different for the uniform and non-uniform cases and depends on the convection regime. Nusselt-Rayleigh relations are similar to those obtained for an ocean of uniform thickness, but the relevant Rayleigh number depends on the region of the domain that is convecting.

Abstract Introduction: Nowadays it is nebulous how much matter is delivered by the largest rivers into the Arctic ocean and how much sediments are eroded by bank and watershed erosion. Thus, these investigations were made for the two biggest watersheds of the Arctic Ocean (the Ob’ and Yenisei) and their components of the sediment budget estimated. Methods and results: 1.The fieldwork campaign allowed us to obtain characteristic models of the distribution of suspended and bed sediments in the estuaries of the Yenisei and Ob’ river 2.The method for estimating the instantaneous sediment yield is developed based on the author’s program for extrapolating ADCP measurement data (language R), water velocity and backscatter intensity. The average annual sediment runoff for the Ob’ is 63.5 Mt/year, for the Yenisei – 32.5 MT/year; 3.The watershed component of sediment runoff was estimated by the RUSLE, taking into account the trapping of sediments by reservoirs and lakes. For the Ob’ potential watershed is 85 MT/year, and 53.6 MT/year for the Yenisei 4.The Bank erosion was calculated based on GSWE, Arctic-DEM, and HYDROATLAS and GRWL databases for the downstream of rivers, which was 35.0 MT/year for the Ob and 21.9 MT/year for the Yenisei. 5.The total deposition of sediments was calculated as a difference between total sediment yield and total erosion in catchments. For the Ob’ total deposition was 56.5 MT/year, for the Yenisei is 43 MT/year. Discussion and conclusion The sediments deposited in the catchment area or in the bedforms can be eroded again by snowmelt and rainfall erosion in the catchment area or directly due to the erosion of the banks. The products of erosion gradually move to the end of the river, with the exception of the part of sediment trapped by reservoirs. For instance, for the Ob’, it is only 10% of the watershed erosion, and for the Yenisei, it is 17% of eroded soil on the catchment. According to the calculations, bank erosion for both downstream is less than the watershed component of sediment runoff. The fact that these components are comparable and it gives some progress in solving the still unexplored question of the role of the bank and watershed components in the origin of sediment runoff of large rivers. Acknowledgments This work powered by the grant №18-05-60219 of the Russian Foundation for Basic Research(RFBR)

Space weather monitoring and predictions largely rely on ground magnetic measurements and geomagnetic indices such as the Disturbance Storm Time index (Dst or SYM-H), Auroral Electrojet Index (AL) or the Polar Cap Index (PCI) all constructed using the individual station data. The global MHD simulations such as the Space Weather Modeling Framework (SWMF) can give predictions of these indices, driven by solar wind observations obtained at L1 giving roughly one hour lead time. The accuracy of these predictions especially during geomagnetic storms is a key metric for the model performance, and critical to operational space weather forecasts. In this presentation, we perform the largest statistical study of global simulation results using a database of 140 storms with minimum Dst below -50 nT during the years from 2010 to 2020. We compare SWMF results with indices derived from the SuperMAG network, which with its denser station network provides a more accurate representation of the true level of activity in the ring current and in the auroral electrojets. We show that the SWMF generally gives good results for the SYM-H index, whereas the AL index is typically underestimated by the model with the model predicting lower than observed ionospheric activity. We also examine the Cross Polar Cap Potential (CPCP) and compare it with a model derived using the PCI (Ridley et al., 2004) as well as with results obtained from the SuperDARN network. We show that the Ridley et al. CPCP model is much closer to the SWMF values. The results are used to discuss factors governing energy dissipation in magnetosphere - ionosphere system as well as possibilities to improve on the operational space weather forecasts.

Seismic hazard due to fluid invasion in hydraulic fracturing, wastewater disposal, and enhanced geothermal systems have become a concern for industry and nearby residents. Some of the challenges associated with the fluid-induced seismic hazard are the estimation of the spatial effects of these industry operations as well as the presence or absence of aftershock triggering. In some cases (e.g. Geysers, California, Hoadley gas field of Alberta), aftershocks triggering do occur, while in other cases (e.g. Soultz‐sous‐Forêts, France), this is not the case. First, to address the spatial effects, using several previously published high-resolution well-log data, we first show that there is a tendency that porosity within the basement resembles fractional Gaussian noise (fGn), while above the basement it resembles fractional Brownian motion (fBm). Based on this observation, we introduce a novel conceptual model of the fluid-induced seismicity in disordered porous media by integrating the notion of fluid diffusion and invasion percolation with spatially correlated permeability and porosity. We find that our model does not only capture the observed variations in frequency-magnitude distribution of seismic events but it also exhibits a much slower decay in seismic activity at large distances for fBm compared to fGn. Second, to address the presence of aftershock triggering, we also introduce nonlinear viscoelastic effects in our model to augment the failure mechanics. This allows us to test whether the presence or absence of aftershocks is coupled to the validity of a time scale separation between fluid dynamics and nonlinear viscoelastic response, for example. Our findings can be directly incorporated in the seismic hazard assessment related to fluid injections.

Growin is a module that calculates the flux-tube-integrated linear Rayleigh-Taylor instability (grow)th rate of (i)o(n)ospheric irregularities is often used in the study of equatorial spread F(ESF). The Rayleigh-Taylor instability is often the prevailing explanation for the growth of plasma irregularities in the bottomside into plumes of depleted plasma, therefore a computation of its growth rate is valuable. This package includes many common approximations used in the computation of the Rayleigh-Taylor growth rate. Although not the focus of this discussion, Kelvin-Helmholtz instabilitiesfrom neutral wind shear below the bottomside F region are another source of plasma instability in the bottomside F region intended to be included in this package eventually.

During COVID-19 pandemic in 2020, many cities and areas were shutdown to control the virus spread. The shutdown introduced reduced emissions from vehicles and power plants. In this study, we studied the impact on pollution by analyzing Suomi NPP satellite Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol optical depth (AOD); AOD is a proxy for particulate pollution in the atmosphere. The investigation is performed over several areas and cities around the globe, i.e. China, India, Europe, the United States, New York city, Los Angeles, etc. In general, reductions in AOD compared to the previous years are found in these areas but with some differences. In China, where the pollution in general is high than the other areas, the reduction in AOD is the most obvious. However, in Europe and the United States, the reduction in AOD is less obvious. In India, the effect is in between. Comparing reductions in S5P Tropospheric Ozone Monitoring Instrument (TROPOMI) nitrogen dioxide (NO2) and AOD, we found that they sometimes co-vary and sometimes do not. The possible reason is that NO2 and aerosols do not have the same life time and therefore may not always co-exist at the same time and place. NO2 has a shorter life time and therefore tends to be observed close to the source region. Because of longer life time of aerosols, the aerosols from smoke, dust, and pollution can be transported with long distance and interfere with those from local sources. To tease out the AOD reductions from lockdown compared to business as usual (BAU), AOD data were analyzed using NO2 as a filter. Using this approach we found marginal reductions to particulate pollution in some regions to reductions of up to 20% in other regions. Analysis of particulate pollution in 2020 compared to BAU globally and regionally will be presented.

The Hawaiian Island chain in the middle of the Pacific Ocean is a well-studied example of hotspot volcanism caused by an underlying upwelling mantle plume. However, the thermal and compositional nature of the plume is still uncertain. The depth and amplitude of seismic discontinuities can show how the plume effects phase transitions in mantle minerals, providing insights into the plume’s thermo-chemical properties. This study utilises >5000 high quality receiver functions from Hawaiian island stations to detect P-to-s converted phases. These receiver functions are stacked in a variety of ways in order to image seismic discontinuities between 200 to 800 km depth. In the mantle transition zone, we find that to the southwest of the Big Island the 660 discontinuity is split. This is inferred to represent the position of the hot plume at depth, with the upper discontinuity caused by an olivine phase transition and the lower by a garnet phase transition. In the upper mantle, the so-called X-discontinuity, which has an enigmatic origin, is found across the region at depths varying between 290 to 350 km. To the east of the Big Island the X-discontinuity lies around 336 km and is particularly strong in amplitude, to such an extent that the discontinuity around 410 km disappears. Synthetic modelling reveals that such observations can be explained by a silica phase transition from coesite to stishovite. This suggests there is widespread ponding of silica-saturated material (such as eclogite, which is silica-rich relative to pyrolite) spreading out from the plume to the east, a hypothesis which is consistent with dynamical models. We suggest that this seemingly thermochemical plume could be sampling recycled basalt, now in the form of eclogite, from lower in the mantle. Therefore these results support the presence of a significant garnet and eclogite component within the Hawaiian mantle plume. We will briefly highlight further work comparing Hawaii with other hotspot locations around the world to consider whether this is also occurring in other plumes and what heterogeneous plumes may imply about the recycling of material in the mantle.

Interannual changes in spectral irradiance at UV wavelengths is one of sources of solar-driven decadal variability in the atmosphere and in particular in the stratosphere as most of these changes happen at UV wavelengths. In last recent years, it has been suggested that energetic electron precipitation (EEP) also contributes not only to alter the chemical composition of the middle atmosphere but also its dynamical variability through changes in stratospheric ozone. In this study we investigate the effects of the two forcings and their interactions. We employ 35-year long integrations of the atmospheric version of the Whole Atmosphere Community Climate Model version 5 with different idealized and stationary solar and geomagnetic forcing terms. Four experiments are carried out combining high (H) and low (L) solar radiative forcing and high (7) and low (3) EEP associated with geomagnetic activity: H7 (with high radiative forcing and high EEP), H3 (high/low), L7 (low/high), and L3 (low/low). We find that the extension, the seasonality, and the significance of the effects of these two forcing terms on the atmospheric variables like temperature, NOx, and ozone are dependent on the joint effects of both forcing terms. The effect of the impact on NOx, ozone and temperature was measured, on constant pressure levels, by means of a Monte Carlo test. Under minimum solar conditions EEP affects mainly the NOx field on constant pressure levels, even though vertical cross sections show statistically significant effects on temperature and ozone in the southern hemisphere during austral winter and the following spring. Under solar maximum condition the effect of EEP becomes important. Ozone depletion (augmentation) in the upper levels of the stratosphere due to more (less) NOx, produced by EEP, allows UV radiation to produce more (less) ozone in the lower levels of the stratosphere. Solar irradiance hence plays an important role in modulating the impact of geomagnetic activity on climate variables.

Aquatic vegetation alters the hydrodynamics of natural waters, such as rivers, lakes, and estuaries. Plants can generate turbulence that propagates throughout the entire water column, which affects gas transfer mechanisms at both air-water and water-sediment interfaces, driving changes of dissolved oxygen (DO), an important indicator of water quality. We conducted a series of laboratory experiments with rigid cylinder arrays to mimic vegetation using a staggered configuration in a recirculating race-track flume. Walnut shells were chosen as the sediment substrate, which interacts with DO in water. 2D planar Particle Image Velocimetry was used to characterize the flow field under various submergence ratios, highlighting the effect of vegetation on turbulence quantities. Gas transfer rates were determined by measuring the DO concentration during the re-aeration process based on the methodology proposed by the American Society of Civil Engineers. Our data provide new insight on Air-Water-Vegetation-Sediment interactions in streams as a function of submergence ratio, array density, and flow turbulence. A modified surface renewal model using turbulence production as an indicator of gas transfer efficiency is used to predict surface gas transfer rates. A delayed time of re-aeration between the bulk and the near-bed region was observed and varies with flow velocities and submergence ratios, which controls the oxygen flux from water to sediment. Future studies are required to investigate the cause of the delayed time to incorporate sediment oxygen demand in a substrate-to-surface transfer model.

Worldwide, the fisheries sector is growing with improved technologies to meet the ever-growing demand for seafood. However, with climate change and increasing marine pollution, there are also significant concerns about declining biodiversity and its impact on income and livelihoods for small-scale fishers. There is a significant shift from traditional fishing methods to mechanization and aquaculture in India. However, the new alternatives have come with many downsides which often go unnoticed. Mechanized fishing contributes to GHGs emissions, destruction of the seabed, overexploitation, and disrupting the ocean food chain. Likewise, coastal aquaculture emits GHGs during feed production, discharges organic and inorganic wastes, involves LULC changes and destroys ecologically sensitive areas (ESAs), leading to loss of various ecosystem services that increase environmental vulnerability and leads to higher ecological footprint. The impacts of climate change on the fishing sector and coastal areas are well established. However, there is a need to understand the impact of changes in fishing techniques on coastal zones and climate change. In this paper, the changes in the fishing sector are evaluated, followed by the development of a framework to assess the impacts of those changes on coastal zones and climate change. The changes in commercial fishing techniques are assessed by determining the dominant fishing techniques prevailing across India, along with reasons for a shift from traditional methods. A Life-Cycle Cost Assessment (LCCA) of the dominant techniques, aquaculture and mechanized fishing, helped to verify the need for such a shift. Two case study areas are selected for sample data collection for computing LCCA of aquaculture and mechanized fishing - Worli in Mumbai and Bhimavaram in Andhra Pradesh for studying mechanized fishing and aquaculture respectively. LCCA of the dominant techniques against traditional techniques showed that economic viability is one of the major reasons for the shift, which was later confirmed during the interviews with fishermen. These methods though profitable have various impacts on coastal ecosystems and emit GHGs making them unsustainable. The paper develops a framework to categorize the impacts of these changes on coastal zones and climate change and suggests appropriate methods to compute those impacts.

The variability in summer North Pacific subtropical high (NPSH) has a significant impact on the monsoon and typhoon over the East Asia and the drought over the California. However, the future projection of the NPSH through the state-of-art climate models, particularly those pertaining to the tropical precipitation uncertainty remain unclear. In this research, we explore the connection between tropical precipitation uncertainty and the NPSH uncertainty through both fully coupled global circulation models from CMIP5 and a dry-core atmospheric primitive equation model. We have found that the biased condensational heating released through the tropic precipitation interacts with the NPSH through the Rossby wave train and the Kelvin wave response. The source of the tropical precipitation uncertainty can be potentially responsible for the uncertainty of the NPSH under the anthropogenic forced climate change.

Dam construction is on the rise in developing nations. Monitoring of these dams is necessary to understand downstream hydrologic impacts and for better planning and management of water resources. Satellite observations and advancements in information technology now present a unique opportunity to overcome the traditional limitations of reservoir monitoring. In this study, a global reservoir monitoring framework is developed as an online tool for near-realtime monitoring and impact analysis of existing and planned reservoirs based on publicly available and global satellite observations. The framework uses a mass balance approach to monitor 1598 reservoirs in South America, Africa, and South-East Asia region. The framework simulated streamflow was validated in 18 river basins and the storage change was validated against in-situ data of 77 reservoirs. The framework was able to capture reservoir state realistically for more than 75% of the reservoirs. The tool can now be used to study existing and planned reservoirs for hydrologic impact and operating pattern for short and long term decision making and policy analysis.

Long Short-Term Memory (LSTM) networks are a deep learning technology to exploit long-term dependencies in the input-output relationship, which has been observed in the response of groundwater dynamics to atmospheric and land surface processes. We introduced an indirect method based on LSTM networks to estimate monthly water table depth anomalies (wtd_a) across Europe from monthly precipitation anomalies (pr_a). The network has further been optimized by including supplementary hydrometeorological variables, which are routinely measured and available at large scales. The data were obtained from daily integrated hydraulic simulation results over Europe from 1996 to 2016, with a spatial resolution of 0.11° (Furusho-Percot et al., 2019), and separated into a training set, a validation set and a test set at individual pixels. We compared test performances of the LSTM networks locally at selected pixels in eight PRUDENCE regions with random combinations of monthly pr_a, evapotranspiration anomaly, and soil moisture anomaly (θ_a) as input variables. The optimal combination of input variables was pr_a and θ_a, and the networks with this combination achieved average test R^2 between 47.88% and 91.62% in areas with simulated wtd ≤ 3 m. Moreover, we found that introducing θ_a improved the ability of the trained networks to handle new data, indicating the substantial contribution of θ_a to explain groundwater state variation. Therefore, including information about θ_a is beneficial, for instance in the estimation of groundwater drought, and the proposed optimized method may be transferred to a real-time monitoring of groundwater drought at the continental scale using remotely sensed soil moisture observations. Furusho-Percot, C., Goergen, K., Hartick, C., Kulkarni, K., Keune, J. and Kollet, S.: Pan-European groundwater to atmosphere terrestrial systems climatology from a physically consistent simulation, Sci. data, 6(1), 320, doi:10.1038/s41597-019-0328-7, 2019.

Environmental factors influencing phytoplankton assemblage dynamics in freshwater ecosystems are an area of ongoing research in the Great Lakes, particularly Lake Erie where harmful algal blooms have impacted the system. Due to impacts on aquatic life and public health, studies worldwide have investigated environmental thresholds influencing the emergence and abundance of phytoplankton species. These thresholds are useful in the evaluation of ecosystem health and the implementation of conservation strategies. However, how these thresholds influence the phytoplankton assemblage shifts over time and space have yet to be explored in Lake Erie, USA. Our goal was to investigate the thresholds of environmental variables responsible for major phytoplankton group dynamics (cyanobacteria (CY), cryptophyta (CP), diatoms (DI), and chlorophyta (GA)) in the western basin of Lake Erie. Using phytoplankton group concentrations determined by a Fluoroprobe (bbe) and water quality data collected between spring-fall from 2015 to 2019, we explored the most significant variables driving changes in phytoplankton concentration at 4 monitoring locations. We applied a multi-method approach, starting with principal component analysis (PCA), Redundancy Analysis (RDA), Regression Trees, and ending with a change point analysis. This approach was successful in the detection of major environmental variables and their thresholds responsible for the emergence and dominance of each phytoplankton group. Results revealed that CY concentrations are primarily correlated to turbidity conditions while DI are more likely to dominate when dissolved oxygen concentrations are high. The presence of CP was mostly related to lower temperatures compared to CY. Lastly, N:SRP ratio was a strong predictor of GA. These environmental variables were relevant predictors of both seasonal and spatial dynamics. Our results reveal critical thresholds in environmental conditions that shape the predominance of each phytoplankton group in the western Lake Erie and emphasize how the spatial component of these conditions can affect phytoplankton assemblage dynamics. These findings may serve as a guide to modelers and decision-makers.

Proper assessment of the economic risk from hazards is an important prerequisite toward enhancing resilience and is often overlooked or underestimated in importance. This research describes a method of assessing risk due to extreme cold temperature, hail, lightning, and tornado in 2050, utilizing projections from well-respected model output, with Louisiana as a case study. Our approach improves upon previous hazard risk assessments by considering the magnitude of the exposed population in weighing the property loss. This makes the approach her preferable over previous risk assessments. Furthermore, the present research uses current model projections to estimate changes in future conditions of the hazard presence. Finally, our use of downscaled data to the census-block circumvents the complications of examining hazards at the county level, particularly in cases for which population is unevenly distributed in the county. Results suggest that extreme cold temperature and tornado are by far the costliest of the four hazards in terms of property loss, although tornado loss is inherently difficult to project due to the unpredictable nature of individual tornado paths. Both extreme temperatures and hail are projected to decrease in loss as temperatures warm, especially in the New Orleans area, where population may decrease. The lightning hazard, while small and likely underestimated due to assignment of lightning damage to the phenomena in which it is embedded, is projected to increase, both on an absolute and per capita basis. Our results can assist environmental planners in protecting life and property, while also promoting hazard resilience and environmental, economic, and social sustainability.

Sustainable agriculture and forestry is an important topic under climate change. This is a potential route for increasing long-term soil and biomass carbon storage, soil water retention capacity and for reducing water and wind erosion risks. This study uses the Styrian Raab and Enns catchment regions in Southeastern Austria as showcase regions for exploring sustainable whole-system options for climate change adaptation and mitigation under increased hot-dry conditions in agriculture and forestry. Based on dense data of the WegenerNet observing network and further hydrometeorological data, combined with hydrological modelling (WaSiM), the current hydrological disturbance potential in the Southeastern Austria focus regions is assessed. Furthermore, downscaled IPCC climate change scenarios are used for future projections and the results are evaluated for increasing heat and drought risks. This work provides the hydrological context for modelling the soil water and carbon storage enhancement options that farming, forestry and land-use practices might apply. A first key study aspect in this context is the sustainable potential of bioenergy crops. Using the local-scale WegenerNet data combined with Harmonized World Soil Database (HWSD) soil data, potential yields for bioenergy from lignocellulosic biomass (forest and Miscanthus, willow and poplar) is modelled using MiscanFor, SalixFor, PopFor and DAYCENT models for representative local areas in the showcase regions. Using DAYCENT biogeochemical modelling with different agricultural, forest management, and land use practices under climate change, sustainable system options under different future climate change scenarios are developed. These results will be used in turn to develop whole-system options, namely to jointly achieve increase of soil carbon and robustness of soil water retention capacity, increase of soil quality, reduction of soil erosion and degradation, reduced compaction, stabilisation of slopes, sustainability and resilience in the soil as well as the agricultural and forest production system. Sustainable whole-system options for farmers and forest managers are the major study focus and will supply data for local estimates in the chosen representative areas in the showcase regions as well as for upscaling to country level.

Although the intensity and frequency of lightning and rainfall are higher over the continents and during the daytime, the place with the most lightning in the world is Lake Maracaibo, a tropical lake in Venezuela with nighttime thunderstorms. Lake Maracaibo is a very large and warm lake surrounded by Andes mountains, the perfect scenario for convergent windflow (mountain–valley, lake, and sea breezes) over the lake nearly year-round, contributing to nocturnal thunderstorm development over 290 days per year on average. Several other tropical lakes with similar physical features (e.g., large area and elevated topography nearby), also exhibit deep nocturnal convection driven by locally forced convergent flow. We use the 16 years of precipitation and lightning activity observed by the Tropical Rainfall Measuring Mission (TRMM) to study the relationship between the physical characteristics (size, shape, local topography and water temperature) of tropical lakes and the precipitation and lightning activity of their thunderstorms. We found that nearby elevated topography and the size of the lake are important for deepening the convection and for increasing the electrical activity, having local driven breeze circulations as one of the consequences. Our results also suggest a nearly linear relationship between the lake area and lightning activity over the lakes. Each water body presented different characteristics, however, on average they show more intense thunderstorms in the summer months and nighttime period (e.g., higher reflectivity values deeper in the cloud, lower minimum brightness temperature, higher convective precipitation volume).