Horizontal Grid Resolution Research Articles

Investigating the skills of HighResMIP in capturing historical and future mean precipitation shifts over Pakistan

AbstractClimatic change over the globe due to global warming affects the characteristics of climate variables that have critical implications on large fraction of population that depends on agriculture for livelihood like Pakistan. Consequently, this study examined how high horizontal grid resolution CMIP6 models simulate the observed precipitation variability during 1981–2014 and further explored the future changes during 2017–2050 under high emission scenario SSP5‐8.5 over Pakistan region. The performances of 12 (CMIP6) High Resolution Model Inter‐comparison Project version 1.0 (hereafter; HighResMIP) GCMs and their ensemble means in reproducing the observed climate were calculated at each station in the study domain and formed the basis for deriving HighResMIP ranking. Further, the study employed Shannon's Entropy and a modified version of Criteria Importance through Inter‐Criteria Correlation (D‐CRITIC) method to build an ensemble mean from the best performing models. Evaluation of HighResMIP GCMs performance revealed that most models showed mixed signals in the region, with fewer models such as HadGEM3‐GC31‐HH, HadGEM3‐GC31‐HM and HadGEM3‐GC31‐MM showing good agreement with the observed precipitation. Overall, HighResMIP multi‐model ensemble outperforms precipitation distribution over individual models. D‐CRITIC based ensemble mean implies higher increase in precipitation than entropy approach. Future changes depict an increase in mean annual in the northern region relative to the historical period. A pronounced increase of about 16%–18% in precipitation was noted in HadGEM3‐GC31‐HH and HiRAM‐SIT‐HR. Conversely, FGOAL‐f3‐H project noteworthy reduction (21%) in precipitation in the near future (2017–2050). The projected seasonal precipitation shows upsurge pattern of 5%–28% in pre‐monsoon season, whereas the reduction in monsoon precipitation is projected to be 29%–40%. The findings of this study can help in building future climate resilience and developing strategic policies in Pakistan.

Past and future of the Arctic sea ice in High-Resolution Model Intercomparison Project (HighResMIP) climate models

Abstract. We examine the past and projected changes in Arctic sea ice properties in six climate models participating in the High-Resolution Model Intercomparison Project (HighResMIP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Within HighResMIP, each of the experiments is run using a reference resolution configuration (consistent with typical CMIP6 runs) and using higher-resolution configurations. The role of horizontal grid resolution in both the atmosphere model component and the ocean model component in reproducing past and future changes in the Arctic sea ice cover is analysed. Model outputs from the coupled historical (hist-1950) and future (highres-future) runs are used to describe the multi-model, multi-resolution representation of the Arctic sea ice and to evaluate the systematic differences (if any) that resolution enhancement causes. Our results indicate that there is not a strong relationship between the representation of sea ice cover and the ocean/atmosphere grids; the impact of horizontal resolution depends rather on the sea ice characteristic examined and the model used. However, the refinement of the ocean grid has a more prominent effect compared to the refinement of the atmospheric one, with eddy-permitting ocean configurations generally providing more realistic representations of sea ice area and sea ice edges. All models project substantial sea ice shrinking: the Arctic loses nearly 95 % of sea ice volume from 1950 to 2050. The model selection based on historical performance potentially improves the accuracy of the model projections and predicts that the Arctic will turn ice-free as early as 2047. Along with the overall sea ice loss, changes in the spatial structure of the total sea ice and its partition in ice classes are noticed: the marginal ice zone (MIZ) will dominate the ice cover by 2050, suggesting a shift to a new sea ice regime much closer to the current Antarctic sea ice conditions. The MIZ-dominated Arctic might drive development and modification of model physics and parameterizations in the new generation of general circulation models (GCMs).

The Role of Surface Potential Vorticity in the Vertical Structure of Mesoscale Eddies in Wind-Driven Ocean Circulations

Abstract The vertical structure of ocean eddies is generally surface-intensified, commonly attributed to the dominant baroclinic modes arising from the boundary conditions (BCs). Conventional BC considerations mostly focus on either flat- or rough-bottom conditions. The impact of surface buoyancy anomalies—often represented by surface potential vorticity (PV) anomalies—has not been fully explored. Here, we study the role of the surface PV in setting the vertical distribution of eddy kinetic energy (EKE) in an idealized adiabatic ocean model driven by wind stress. The simulated EKE profile in the extratropical ocean tends to peak at the surface and have an e-folding depth typically smaller than half of the ocean depth. This vertical structure can be reasonably represented by a single surface quasigeostrophic (SQG) mode at the energy-containing scale resulting from the large-scale PV structure. Due to isopycnal outcropping and interior PV homogenization, the surface meridional PV gradient is substantially stronger than the interior PV gradient, yielding surface-trapped baroclinically unstable modes with horizontal scales comparable to or smaller than the deformation radius. These surface-trapped eddies then grow in size both horizontally and vertically through an inverse energy cascade up to the energy-containing scale, which dominates the vertical distribution of EKE. As for smaller horizontal scales, the EKE distribution decays faster with depth. Guided by this interpretation, an SQG-based scale-aware parameterization of the EKE profile is proposed. Preliminary offline diagnosis of a high-resolution simulation shows the proposed scheme successfully reproducing the dependence of the vertical structure of EKE on the horizontal grid resolution.

Spatial Resolution Requirements for FDTD Modeling of Geoelectric Fields

AbstractTo ensure the robustness of both civilian and military infrastructure, it is important to protect electric power grids, smart grids, and other electrotechnologies from known and possibly as‐of‐yet unknown space weather hazards. Space weather can generate intense geoelectric fields at the surface of the Earth, as well as large voltage gradients across long distances of the Earth. These voltage gradients can lead to geomagnetically induced currents (GICs), which are known to produce hazards to electric power grids. The finite‐difference time‐domain (FDTD) method is a powerful and versatile method that has already been applied to the study of geoelectric fields. The advantages of FDTD over other methods are that it can account for more geometrical complexities and realistic time waveforms and that it directly solves for geoelectric fields. Snell's Law predicts that any electromagnetic waves incident on the ground should essentially propagate straight downwards into the low resistivity ground. For this reason, vertical FDTD grid resolutions of 1/3 of a skin depth were usually chosen, while the horizontal grid resolution was relaxed. We find, however, that there is another important consideration for choosing an FDTD grid resolution applied to real‐world scenarios: localized field variations due to currents generated by ground features. It turns out the grid resolution requirements are much stricter when taking this physics into account.

How Atmospheric Forcing Frequency, Horizontal and Vertical Grid Resolutions Impact Mesoscale Eddy Evolution in a Numerical Model

AbstractSeasonal evolution of both surface signature and subsurface structure of a Mediterranean mesoscale anticyclones is assessed using the Coastal and Regional Ocean Community high‐resolution numerical model with realistic background stratification and fluxes. In good agreement with remote‐sensing and in‐situ observations, our numerical simulations capture the seasonal cycle of the anomalies induced by the anticyclone, both in the sea surface temperature (SST) and in the mixed layer depth (MLD). The eddy signature on the SST shifts from warm‐core in winter to cold‐core in summer, while the MLD deepens significantly in the core of the anticyclone in late winter. Our sensitivity analysis shows that the eddy SST anomaly can be accurately reproduced only if the vertical resolution is high enough (∼4 m in near surface) and if the atmospheric forcing contains high‐frequency. In summer with this configuration, the vertical mixing parameterized by the k − ϵ closure scheme is three times higher inside the eddy than outside the eddy, and leads to an anticyclonic cold core SST anomaly. This differential mixing is explained by near‐inertial waves, triggered by the high‐frequency atmospheric forcing. Near‐inertial waves propagate more energy inside the eddy because of the lower effective Coriolis parameter in the anticyclone core. On the other hand, eddy MLD anomaly appears more sensitive to horizontal resolution, and requires SST retroaction on air‐sea fluxes. These results detail the need of high frequency forcing, high vertical and horizontal resolutions to accurately reproduce the evolution of a mesoscale eddy.

Numerical Diffusion and Turbulent Mixing in Convective Self‐Aggregation

AbstractConvective Self‐Aggregation (CSA) is a common feature of idealized numerical simulations of the tropical atmosphere in Radiative‐Convective Equilibrium (RCE). However, at coarse grid resolution where deep convection is not fully resolved, the occurrence of this phenomenon is extremely sensitive to subgrid‐scale processes. This study examines the role of mixing and entrainment, provided by the turbulence model and the implicit numerical diffusion. The study compares the results of two models, WRF and SAM, by varying turbulence models, initial conditions, and horizontal spatial resolution. At a coarse grid resolution of 3 km, the removal of turbulent mixing prevents CSA in models with low numerical diffusivity but is preserved in models with high numerical diffusivity. When the horizontal grid resolution is refined to 1 km, CSA can only be achieved by increasing explicit turbulent mixing, even with a small amount of shallow clouds. Therefore, the sensitivity of CSA to horizontal grid resolution is not primarily caused by the decrease in shallow clouds. The analysis of the total water path spectrum suggests that the amplitude of initial humidity perturbations introduced by convection in the free troposphere is the key factor. This amplitude is regulated by turbulent mixing and diffusion at small scales. Prior to the onset of CSA, increased mixing makes updrafts more sensitive to the dryness of the free troposphere, which strengthens the moisture‐convection feedback. This leads to an increased distance between convective cores and a stronger humidity perturbation in the free troposphere, which can destabilize the RCE state.

Assessment of the national emission reduction strategies effects for Bulgaria (2020–2029 and after 2030) on surface FPRM and CPRM concentrations

Bulgaria has developed national emission reduction strategies for the period from 2020 to 2029 and the years after 2030, in accordance with EU Directive 2016/2284. Our fundamental aim in this study is to assess the effects of the strategy on the PM near surface concentrations in Bulgaria. All the simulation was done by the modeling system U.S. Environmental Protection Agency (US EPA) Models-3 for 2008 to 2014 period and with 9 km horizontal grid resolution for the selected region – Bulgaria. The meteorological background that was used is with 1°x1° resolution from the National Centers for Environmental Prediction (NCEP) Global Analysis Data. There are 5 emission scenarios structured: 2005 emissions (reference period), 2020–2029 emissions projected with existing measures (WEM) and with additional measures (WAM), projected after 2030 WEM and WAM emissions, as parallel calculations were performed with all of the scenarios. Making parallels between the concentrations, with different scenarios simulated, gives the possibility to evaluate the national emission reduction strategies’ effect.

Evaluating the performance of WRF in simulating winds and surface meteorology during a Southern California wildfire event

The intensity and frequency of wildfires in California (CA) have increased in recent years, causing significant damage to human health and property. In October 2007, a number of small fire events, collectively referred to as the Witch Creek Fire or Witch Fire started in Southern CA and intensified under strong Santa Ana winds. As a test of current mesoscale modeling capabilities, we use the Weather Research and Forecasting (WRF) model to simulate the 2007 wildfire event in terms of meteorological conditions. The main objectives of the present study are to investigate the impact of horizontal grid resolution and planetary boundary layer (PBL) scheme on the model simulation of meteorological conditions associated with a Mega fire. We evaluate the predictive capability of the WRF model to simulate key meteorological and fire-weather forecast parameters such as wind, moisture, and temperature. Results of this study suggest that more accurate predictions of temperature and wind speed relevant for better prediction of wildfire spread can be achieved by downscaling regional numerical weather prediction products to 1 km resolution. Furthermore, accurate prediction of near-surface conditions depends on the choice of the planetary boundary layer parameterization. The MYNN parameterization yields more accurate prediction as compared to the YSU parameterization. WRF simulations at 1 km resolution result in better predictions of temperature and wind speed than relative humidity during the 2007 Witch Fire. In summary, the MYNN PBL parameterization scheme with finer grid resolution simulations improves the prediction of near-surface meteorological conditions during a wildfire event.

Enhancing Air Quality Forecasts Across the Contiguous United States (CONUS) During Wildfires Using Analog-Based Post-Processing Methods

With our growing understanding of the risks of air pollution to human health, air quality forecasting has become a very important tool to enable decision-makers to take preventive and corrective measures for current and future policies. In addition, accurate predictions of air quality can help predict and mitigate the impacts of wildfires on human health, which have an increased risk due to anthropogenic climate change. However, errors in air quality forecasts limit their value in decision-making processes. Thus, increasing the accuracy of air quality forecasts is of significant importance. In this study, we have utilized the Community Multiscale Air Quality (CMAQ) modeling system with a 12 km horizontal grid resolution to generate daily 48-h fine particulate matter (PM2.5) forecasts for the Contiguous United States (CONUS) domain for June 1st through September 29th (major wildfire season) during 2015–2021. We conduct CMAQ offline simulations using meteorological inputs generated by the NOAA’s Unified Forecast System (UFS) numerical weather prediction model. We analyze the performance of the CMAQ PM2.5 and UFS meteorological forecasts over seven years of simulations for Environmental Protection Agency (EPA)-defined ten regions using the Air Quality System (AQS) ambient air pollution data from over a thousand monitoring sites across the CONUS. We have found that on average, the CMAQ model performs better in the eastern CONUS with the lowest Root Mean Square Error (RMSE) (10–30 μg/m3) while in the west, where wildfires are prevalent, the model has the highest RMSE of up to 120 μg/m3. To address these substantial model errors, we advance the field by applying Analog Ensemble (AnEn), a state-of-the-art statistical-dynamical method, to enhance PM2.5 forecast accuracy, with a particular focus on wildfire events, when producing an accurate forecast is highly challenging. We further refine this approach by incorporating a Carbon Monoxide-FIRE (CO-FIRE) tracer to precisely monitor fire smoke. In parallel to our objective and for the first time, we introduce two novel predictors, i.e., a CO−FIRE tracer in the model, and the maximum observed PM2.5 concentrations from the previous day (PM2.5−preday−max), and evaluate their potential significance in improving forecast accuracy, which aligns with the primary goal of our study. Despite the challenges of using AnEn for wildfires, we demonstrate that it has the potential to improve the CMAQ model forecast over the CONUS. We find that AnEn decreases the model RMSE by up to 25%, including additional 7% and 15% reduction by CO−FIRE and PM2.5−preday−max predictors, respectively, at different forecast lead times. In addition, the correlation between AnEn forecasts and observations is 20%–40% higher than that between CMAQ and observations. The Mean Bias Error (MBE) for AnEn forecasts is consistent and approximately −0.5 μg/m3 whereas CMAQ MBE varies between −1 and +1 μg/m3 between 0–48 forecast hours. AnEn significantly improves the PM2.5 forecast results during its highest episodes. During the initial days of wildfires, AnEn performs similarly to CMAQ. However, it soon catches up and decreases the error significantly.

Sensitivity of simulated wind power under diverse spatial scales and multiple terrains using the weather research and forecasting model

During the process of grid refinement and downscaling in the Weather Research and Forecasting (WRF) model, the horizontal grid resolution inevitably enters the realm of uncertainty known as the Terra Incognita. Within this region, conventional mesoscale Planetary Boundary Layer schemes fail to achieve the desired optimized outcomes following grid refinement. This study conducted a series of sensitivity analyses to explore the performance of the WRF model in simulating wind speed and direction at different spatial scales under various conditions. By comparing 10 min mean values for flat and complex terrains and then evaluating the performance of the model using statistical scoring. The results indicate that for wind resource assessment, the mesoscale boundary exceeds the height of the atmospheric boundary layer during the simulation period. For flat terrains, nesting is not recommended. Instead, nesting into Terra Incognita and microscale is preferred for complex terrains. The model demonstrates overall accuracy in wind direction estimation but tends to underestimate wind speed. Notably, the accuracy of simulations at 70 m above ground is higher than that at 10 m. The assessment of wind speed and direction exhibits distinct patterns across various wind speed ranges, wind direction, and atmospheric stabilities.

Modeling sensitivities of thermally and hydraulically driven ice stream surge cycling

Abstract. Modeling ice sheet instabilities is a numerical challenge of potentially high real-world relevance. Yet, differentiating between the impacts of model physics, numerical implementation choices, and numerical errors is not straightforward. Here, we use an idealized North American geometry and climate representation (similarly to the HEINO (Heinrich Event INtercOmparison) experiments – Calov et al., 2010) to examine the process and numerical sensitivity of ice stream surge cycling in ice flow models. Through sensitivity tests, we identify some numerical requirements for a more robust model configuration for such contexts. To partly address model-specific dependencies, we use both the Glacial Systems Model (GSM) and the Parallel Ice Sheet Model (PISM). We show that modeled surge characteristics are resolution dependent, though they converge (decreased differences between resolutions) at finer horizontal grid resolutions. Discrepancies between fine and coarse horizontal grid resolutions can be reduced by incorporating sliding at sub-freezing temperatures. The inclusion of basal hydrology increases the ice volume lost during surges, whereas the dampening of basal-temperature changes due to a bed thermal model leads to a decrease.

The Mixed Layer Salinity Budget in the Northern South China Sea: A Modeling Study

The seasonal variation in mixed layer salinity (MLS) plays a crucial role in global ocean circulation and hydrological cycle. The salinity budget of the mixed layer is important to understand the mechanism of the variation, but in the South China Sea (SCS), the details in the budget are missing due to insufficient observations. Here, we employed an eddy-resolving (horizontal grid resolution ~10 km) SCS circulation model to quantify the key physical processes in the seasonal cycling of MLS in the northern South China Sea (NSCS). Built on the success of the realistic numerical simulation for 2008–2018, the model reproduced the primary features of the observed seasonal MLS, wherein fresher waters are present in the region during the summer monsoon and salty waters appear along the slope during the winter monsoon. According to the salinity budget that was calculated during model execution, the term for air–sea freshwater flux and meridional advection represent the primary freshwater input in the summer and winter, respectively, while vertical processes including vertical mixing and entrainment form the major balancing terms in the budget. In different regions of the NSCS, vertical mixing can play a dominant role in the vertical processes, but the associated seasonality is different for regions of strong internal wave influence and regions of strong horizontal advection influence. In the winter, the intrusion and spreading of western Pacific water over the NSCS could modify the MLS structure and cause larger vertical entrainment than mixing in regions where the effect of mixing decreases with the slackening of the seasonal internal wave activities. Overall, the analysis of the ML salinity budget reveals that vertical mixing, together with vertical entrainment, is vital to maintaining the seasonal variation in MLS of the NSCS.

Evaluation of PM Chemical Composition in Thessaloniki, Greece Based on Air Quality Simulations

The average PM10 daily levels over the urban area of Thessaloniki, Greece, usually exceed the air quality limits and therefore the improved PM chemical composition and air quality modeling results that will facilitate the design of the most appropriate mitigation measures (e.g., limitations in wood combustion for heating purposes) are essential. The air quality modeling system WRF-CAMx was applied over a 2 × 2 km2 horizontal resolution grid covering the greater area of Thessaloniki for the year 2015, when Greece was still confronting the consequences of the financial crisis. The output hourly surface concentrations of twelve PM species at three sites of different environmental type characterization in the city of Thessaloniki were temporally and spatially analyzed. Carbonaceous aerosols (organic and elemental) are the major contributor to total PM10 levels during winter representing a 35–40% share. During summer, mineral aerosols (excluding dust) distribute by up to 48% to total PM10 levels, being the major contributor attributed to road traffic. PM species, during winter, increase in the morning and in the afternoon mainly due to road transport and residential heating, respectively, in addition with the unfavorable meteorological conditions. An underestimation of the primary organic carbon aerosol levels during winter is identified. The application of the modeling system using a different speciation profile for the fine particles emissions from residential heating based on observational data instead of the CAMS emissions profile revealed an improvement in the simulated OC/EC values for which a 50% increase was identified compared to the base run.

Development and Application of an Infragravity Wave (InWave) Driver to Simulate Nearshore Processes

AbstractInfragravity waves are key components of the hydro‐sedimentary processes in coastal areas, especially during extreme storms. Accurate modeling of coastal erosion and breaching requires consideration of the effects of infragravity waves. Here, we present InWave, a new infragravity wave driver of the Coupled Ocean‐Atmopshere‐Waves‐Sediment Transport (COAWST) modeling system. InWave computes the spatial and temporal variation of wave energy at the wave group scale and the associated incoming bound infragravity wave. Wave group‐varying forces drive free infragravity wave growth and propagation within the hydrodynamic model of the coupled modeling system, which is the Regional Ocean Modeling System (ROMS) in this work. Since ROMS is a three‐dimensional model, this coupling allows for the combined formation of undertow currents and infragravity waves. We verified the coupled InWave‐ROMS with one idealized test case, one laboratory experiment, and one field experiment. The coupled modeling system correctly reproduced the propagation of gravity wave energy with acceptable numerical dissipation. It also captured the transfer of energy from the gravity band to the infragravity band, and within the different infragravity bands in the surf zone, the measured three‐dimensional flow structure, and dune morphological evolution satisfactorily. The idealized case demonstrated that the infragravity wave variance depends on the directional resolution and horizontal grid resolution, which are known challenges with the approach taken here. The addition of InWave to COAWST enables novel investigation of nearshore hydro‐sedimentary dynamics driven by infragravity waves using the strengths of the other modeling components, namely the three‐dimensional nature of ROMS and the sediment transport routines.

Comparison of ozone formation attribution techniques in the northeastern United States

Abstract. The Integrated Source Apportionment Method (ISAM) has been revised in the Community Multiscale Air Quality (CMAQ) model. This work updates ISAM to maximize its flexibility, particularly for ozone (O3) modeling, by providing multiple attribution options, including products inheriting attribution fully from nitrogen oxide reactants, fully from volatile organic compound (VOC) reactants, equally from all reactants, or dynamically from NOx or VOC reactants based on the indicator gross production ratio of hydrogen peroxide (H2O2) to nitric acid (HNO3). The updated ISAM has been incorporated into the most recent publicly accessible versions of CMAQ (v5.3.2 and beyond). This study's primary objective is to document these ISAM updates and demonstrate their impacts on source apportionment results for O3 and its precursors. Additionally, the ISAM results are compared with the Ozone Source Apportionment Technology (OSAT) in the Comprehensive Air-quality Model with Extensions (CAMx) and the brute-force method (BF). All comparisons are performed for a 4 km horizontal grid resolution application over the northeastern US for a selected 2 d summer case study (9 and 10 August 2018). General similarities among ISAM, OSAT, and BF results add credibility to the new ISAM algorithms. However, some discrepancies in magnitude or relative proportions among tracked sources illustrate the distinct features of each approach, while others may be related to differences in model formulation of chemical and physical processes. Despite these differences, OSAT and ISAM still provide useful apportionment data by identifying the geographical and temporal contributions of O3 and its precursors. Both OSAT and ISAM attribute the majority of O3 and NOx contributions to boundary, mobile, and biogenic sources, whereas the top three contributors to VOCs are found to be biogenic, boundary, and area sources.

A coupled turbine-interaction wind farm parameterization in the Weather Research and Forecasting model

The wind farm parameterization in the mesoscale numerical weather prediction (NWP) model is a valuable approach to exploring the performance and atmospheric impact of the large-scale wind farm. However, the sub-grid interactions between turbines are not resolved in the current parameterization method. In this study, we propose an improved wind farm parameterization by coupling a novel sub-grid wind turbine interference model. It is incorporated in the state-of-the-art Weather Research and Forecasting (WRF) model. To verify the model, the coupled model is applied in a real offshore wind farm in China for comparing the model performances with the original parameterization. The findings demonstrate that the proposed approach improves the wind simulation and has minimal sensitivity to horizontal grid resolution, hence strengthening the model’s robustness. Additionally, it is discovered that the sub-grid interaction reduces the turbine-produced turbulence and shortens the wake length by more than 30% compared to the original parameterization. Importantly, the power output prediction of turbine cells is modified, approximately resulting in a 1 % reduction of the entire wind farm relative to the original model. The effects of sub-grid turbine interaction are effectively captured in the proposed model and it is recommended for further wind farm simulations.

Simulations of Severe Convective Systems Using 1- versus 3-km Grid Spacing

Abstract Herein, 14 severe quasi-linear convective systems (QLCS) covering a wide range of geographical locations and environmental conditions are simulated for both 1- and 3-km horizontal grid resolutions, to further clarify their comparative capabilities in representing convective system features associated with severe weather production. Emphasis is placed on validating the simulated reflectivity structures, cold pool strength, mesoscale vortex characteristics, and surface wind strength. As to the overall reflectivity characteristics, the basic leading-line trailing stratiform structure was often better defined at 1 versus 3 km, but both resolutions were capable of producing bow echo and line echo wave pattern type features. Cold pool characteristics for both the 1- and 3-km simulations were also well replicated for the differing environments, with the 1-km cold pools slightly colder and often a bit larger. Both resolutions captured the larger mesoscale vortices, such as line-end or bookend vortices, but smaller, leading-line mesoscale updraft vortices, that often promote QLCS tornadogenesis, were largely absent in the 3-km simulations. Finally, while maximum surface winds were only marginally well predicted for both resolutions, the simulations were able to reasonably differentiate the relative contributions of the cold pool versus mesoscale vortices. The present results suggest that while many QLCS characteristics can be reasonably represented at a grid scale of 3 km, some of the more detailed structures, such as overall reflectivity characteristics and the smaller leading-line mesoscale vortices would likely benefit from the finer 1-km grid spacing. Significance Statement High-resolution model forecasts using 3-km grid spacing have proven to offer significant forecast guidance enhancements for severe convective weather. However, it is unclear whether additional enhancements can be obtained by decreasing grid spacings further to 1 km. Herein, we compare forecasts of severe quasi-linear convective systems (QLCS) simulated using 1- versus 3-km grids to document the potential value added of such increases in grid resolutions. It is shown that some significant improvements can be obtained in the representation of many QLCS features, especially as regards reflectivity structure and in the development of small, leading-line mesoscale vortices that can contribute to both severe surface wind and tornado production.

Performance of the WRF Model for the Forecasting of the V-Shaped Storm Recorded on 11–12 November 2019 in the Eastern Sicily

During the autumn season, Sicily is often affected by severe weather events, such as self-healing storms called V-shaped storms. These phenomena cause significant total rainfall quantities in short time intervals in localized spatial areas. In this framework, this study analyzes the meteorological event recorded on 11–12 November 2019 in Sicily (southern Italy), using the Weather Research and Forecasting (WRF) model with a horizontal spatial grid resolution of 3 km. It is important to note that, in this event, the most significant rainfall accumulations were recorded in eastern Sicily. In particular, the weather station of Linguaglossa North Etna (Catania) recorded a total rainfall of 293.6 mm. The precipitation forecasting provided by the WRF model simulation has been compared with the data recorded by the meteorological stations located in Sicily. In addition, a further simulation was carried out using the Four-Dimensional Data Assimilation (FDDA) technique to improve the model capability in the event reproduction. In this regard, in order to evaluate which approach provides the best performance (with or without FDDA), the Root Mean Square Error (RMSE) and dichotomous indexes (Accuracy, Threat Score, BIAS, Probability of Detection, and False Alarm Rate) were calculated.

Spatial Distribution of Groundwater Level over Lowlands of Morang District of Eastern-Nepal

A better understanding of spatial distribution of groundwater level is necessary for the development of groundwater development strategies and sustainable use of available resources. The present study evaluates the spatial distribution of groundwater level over the Morang Administrative District of Eastern Nepal and the groundwater accessibility over the area. The study was realized by performing an extensive field survey of groundwater level during the post-monsoon season over 126 sites. The study area of 1224.80 km2 was gridded at 3 km x 3 km horizontal grid resolution and at least one survey was ensured for each grid point. Downscaled spatial distribution of groundwater level was achieved by interpolating the observed data using the Inverse Distance Weighting (IDW) with different weighting parameters available with the geostatistical module of ArcGIS. The performances of the interpolation methods were evaluated based on the cross-validation of results characterized by the statistical parameters RMSE, R2 and MAE and optimal power (α) for weighting function. The IDW (with α=4) appears to perform well for the study area with Coefficient of Determination (R2) and Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values of 0.336, 4.750 and 2.967, respectively. The spatial distribution of groundwater level over the low-lands of Morang District mapped with the IDW interpolation method, revealed that the ground water level is maximum in south-western part of the district. The depth to groundwater lies between the ranges of 4.5 to 10 meters covering almost 67.47% of the total study area.

Post Monsoon Season Spatial Distribution of Groundwater Depth over the Low-Lands of Sunsari District of Eastern Nepal

The knowledge and an accurate representation of the spatial distribution of groundwater depth of an area are very important to realize sustainable use of groundwater resource, protection of ecosystem and for the development of adaptation policies in the changing climate. The present study has, thus, developed a groundwater depth distribution map of southern low-lands of the Sunsari District at the horizontal grid resolution of 3 km × 3 km. The groundwater depths were surveyed at predefined 82 numbers of geo-referenced grid points scattered over the region during the post monsoon season of the year 2019. Referencing the sampled depths, spatial distribution of the groundwater depth over the area has been predicted using the Ordinary Kriging Method. Three widely used Semivariogram modules such as Spherical, Exponential, and Gaussian were implemented. The Gaussian model of Semivariogram outperformed the other methods with R2 of 0.336, RMSE of 2.591, and MAE of 1.946. The predicted spatial distribution of groundwater depth reasonably well correlated with the observed distribution. The spatial distribution of groundwater level suggests that 12.36% the total area, particularly, over the western, southwestern and northern parts hold groundwater at much shallower depth (< 5 m). Over 51.59 % of the total area in the southeastern and northeastern parts, the groundwater level remained below 8 m from the ground and the rest of the total area holds the groundwater deeper than 8 m.