Limited Area Model Research Articles

Ocean-atmosphere-ice processes in the Ross Sea: A review

The Ross Sea has been the site of extensive investigations since the earliest days of polar exploration. The International Geophysical Year of 1957-58 enhanced research activities with the establishment of scientific stations and the collection of oceanographic observations in the area. While many features of its oceanography, ecology, physics, glaciology, geology, and biogeochemistry are known, recent advances provide new insights into its structure and function, as well as into its relationship to global climate. We present a comprehensive review of the advances of understanding the main processes occurring in the area, such as the formation of dense shelf water and the production of Antarctic Bottom Water (AABW), as well as the main drivers (at both large and local scales) of local dynamics and water mass variability. We also summarize the main modeling applications, which are still limited and need to be improved using high-resolution models and, locally, limited-area models to explain processes driven mainly by thermodynamics and water-mass transformations. The Ross Sea forms the most saline AABW due to the activity of two polynyas in the western sector. A salinity gradient occurs on the shelf, with fresh Low Salinity Shelf Waters concentrated in the eastern Ross Sea, which is influenced by the inflow of fresh water from the Amundsen and Bellingshausen Seas. This freshwater inflow was thought to be the cause of a multi-decadal freshening of the High Salinity Shelf Water, precursor to the AABW, although a rebound in salinity in the Ross Sea has been observed since 2014. The increase in salinity has also affected the production of AABW, with the respective rebound occurring almost simultaneously.

Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system

Abstract. The microwave radiances are key observations, especially over data-sparse regions, for operational data assimilation in numerical weather prediction (NWP). An often applied simplification is that these observations are used as point measurements; however, the satellite field of view may cover many grid points of high-resolution models. Therefore, we examine a solution in high-resolution data assimilation to better account for the spatial representation of the radiance observations. This solution is based on a footprint operator implemented and tested in the variational assimilation scheme of the AROME-Arctic (Application of Research to Operations at MEsoscale – Arctic) limited-area model. In this paper, the design and technical challenges of the microwave radiance footprint operator are presented. In particular, implementation strategies, the representation of satellite field-of-view ellipses, and the emissivity retrieval inside the footprint area are discussed. Furthermore, the simulated brightness temperatures and the sub-footprint variability are analysed in a case study, indicating particular areas where the use of the footprint operator is expected to provide significant added value. For radiances measured by the Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS) sensors, the standard deviation of the observation minus background (OmB) departures is computed over a short period in order to compare the statistics of the default and the implemented footprint observation operator. For all operationally used AMSU-A and MHS tropospheric channels, it is shown that the standard deviation of OmB departures is reduced when the footprint operator is applied. For AMSU-A radiances, the reduction is around 1 % for high-peaking channels and about 4 % for low-peaking channels. For MHS data, this reduction is somewhere between 1 %–2 % by the footprint observation operator.

Development of Multiscale EnKF within GSI and Its Applications to Multiple Convective Storm Cases with Radar Reflectivity Data Assimilation Using the FV3 Limited-Area Model

Abstract To improve the representation of all relevant scales in initial conditions for large-domain convection-allowing models, a new multiscale ensemble Kalman filter (MEnKF) algorithm is developed and implemented within the Gridpoint Statistical Interpolation analysis system (GSI) data assimilation framework coupled with the Finite-Volume Cubed-Sphere Dynamical Core (FV3) limited-area model. The algorithm utilizes ensemble background error covariances filtered to match the observations assimilated. This is realized in a sequential manner. 1) When assimilating coarse-resolution observations such as radiosondes, ensemble background perturbations are filtered to remove scales smaller than those the observations can represent, along with relatively large horizontal localization radii to ensure low-noise and balanced analysis increments. 2) The resulting ensemble analyses from the first step then serve as the background to assimilate denser observations such as radar data with smaller localization radii. Several passes can be taken to assimilate all observations. In this paper, vertically increasing horizontal filter scales are used when assimilating rawinsonde and surface observations together, while radar data are assimilated in the second step. The algorithm is evaluated through six convective storm cases during May 2021, with cycled assimilation of either conventional data only or with additional radar reflectivity followed by 24-h ensemble forecasts. Overall, positive impacts of the MEnKF on forecasts are obtained regardless of reflectivity data; its advantage over the single-scale EnKF is most significant in surface humidity and temperature forecasts up to at least 12 h. More accurate hourly precipitation forecasts with MEnKF can last up to 24 h for light rain. Furthermore, MEnKF forecasts higher ensemble probabilities for the observed hazardous events.

Projections of the Adriatic wave conditions under climate changes

Assessing the impact of climate change on wave conditions, including average and extreme waves, is vital for numerous marine-related activities, industries, coastal vulnerability, and marine habitats. Previous research, primarily on a large scale, has investigated this topic, but its relevance for marginal basins like the Adriatic Sea is limited due to the low resolution of the wave models used and atmospheric forcing. To contribute to filling in the gap, here we implemented a high-resolution model (about 2 km) for the period 1992–2050. The future wave climate is simulated for the RCP8.5 emission scenario. This model, developed within the AdriaClim project, comprises, among others, a high-resolution atmospheric downscaling, a circulation Limited Area Model and a spectral wave model. A comparison of our simulation's results with Copernicus Marine Service wave reanalysis on the historical baseline, confirms its accuracy in reproducing both average wave parameters and 95th percentile values, as well as the seasonal cycle, showing the AdriaClim model's suitability as a source to predict future wave climates in the Adriatic Sea. The projected changes suggest a slight increase in average significant wave height and mean wave period, and a more significant decrease at the 95th percentile, with a relevant variability by location and season, partially aligning with previous studies. This study highlights the potential effect of local climate change in coastal areas and the importance of developing long-term simulation with a downscaled modeling system for regional areas.

DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin

Abstract. We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which successfully emulates the behaviour of a numerical surface ocean wave model (Simulating WAves Nearshore, SWAN) at a sparse set of locations, thus enabling numerically cheap large-ensemble prediction over synoptic to climate timescales. DELWAVE was trained on COSMO-CLM (Climate Limited-area Model) and SWAN input data during the period of 1971–1998, tested during 1998–2000, and cross-evaluated over the far-future climate time window of 2071–2100. It is constructed from a convolutional atmospheric encoder block, followed by a temporal collapse block and, finally, a regression block. DELWAVE reproduces SWAN model significant wave heights with a mean absolute error (MAE) of between 5 and 10 cm, mean wave directions with a MAE of 10–25°, and a mean wave period with a MAE of 0.2 s. DELWAVE is able to accurately emulate multi-modal mean wave direction distributions related to dominant wind regimes in the basin. We use wave power analysis from linearised wave theory to explain prediction errors in the long-period limit during southeasterly conditions. We present a storm analysis of DELWAVE, employing threshold-based metrics of precision and recall to show that DELWAVE reaches a very high score (both metrics over 95 %) of storm detection. SWAN and DELWAVE time series are compared to each other in the end-of-century scenario (2071–2100) and compared to the control conditions in the 1971–2000 period. Good agreement between DELWAVE and SWAN is found when considering climatological statistics, with a small (≤ 5 %), though systematic, underestimate of 99th-percentile values. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modelling is substantially weaker than the climate change signal.

A comprehensive study on changes in coastal hydrodynamics associated with cyclonic activity

A Mediterranean cyclone is a weather phenomenon capable of producing extremely severe conditions, including heavy rainfall and strong winds. Between March 24 and 26, 2023, a cyclone passed along the western Egyptian Mediterranean coast, spanning three days. This paper aims to investigate the cyclone's impact on wave characteristics, focusing particularly on simulating changes in the energy transported from wind to waves during its passage, which constitutes the core objective of this study. The research methodology involved collecting meteorological and hydrodynamic data over five days from March 23 to 27, 2023, utilizing databases of the Bologna Limited Area Model (BOLAM) and the General Bathymetric Chart of the Oceans (GEBCO). This data, combined with field data for model calibration and validation, was analyzed using the Simulating the WAves Nearshore (SWAN) model packaged within the Delft 3D hydrodynamical model, integrated with other data manipulation tools. (SWAN) demonstrated the ability to simulate energy transport during extreme weather events along the coastal area with high resolution, up to 500 m. The results indicate a significant increase in significant wave height, reaching up to 2.5 m, and disturbances in wind direction, with velocities exceeding 10 m per second. These conditions pose risks to the infrastructure in some cities along the study area and have severe impacts on coastal communities. A notable finding from the simulations is the excess energy transport, which reached up to 12,000 watts per meter over the sea surface during the cyclone. Furthermore, calibration and validation results affirm the (SWAN) model's capability to accurately study wave characteristics.

A new implementation of FLEXPART with Enviro-HIRLAM meteorological input, and a case study during a heavy air pollution event

ABSTRACT We integrated Enviro-HIRLAM (Environment-High Resolution Limited Area Model) meteorological output into FLEXPART (FLEXible PARTicle dispersion model). A FLEXPART simulation requires meteorological input from a numerical weather prediction (NWP) model. The publicly available version of FLEXPART can utilize either ECMWF (European Centre for Medium-range Weather Forecasts) Integrated Forecast System (IFS) forecast or reanalysis NWP data, or NCEP (U.S. National Center for Environmental Prediction) Global Forecast System (GFS) forecast or reanalysis NWP data. The primary benefits of using Enviro-HIRLAM are that it runs at a higher resolution and accounts for aerosol effects in meteorological fields. We compared backward trajectories generated with FLEXPART using Enviro-HIRLAM (both with and without aerosol effects) to trajectories generated using NCEP GFS and ECMWF IFS meteorological inputs, for a case study of a heavy haze event which occurred in Beijing, China in November 2018. We found that results from FLEXPART were considerably different when using different meteorological inputs. When aerosol effects were included in the NWP, there was a small but noticeable difference in calculated trajectories. Moreover, when looking at potential emission sensitivity instead of simply expressing trajectories as lines, additional information, which may have been missed when looking only at trajectories as lines, can be inferred.

Spatial spin-up of precipitation in limited-area convection-permitting simulations over North America using the CRCM6/GEM5.0 model

Abstract. A fundamental issue associated with the dynamical downscaling technique using limited-area models is related to the presence of a “spatial spin-up” belt close to the lateral boundaries where small-scale features are only partially developed. Here, we introduce a method to identify the distance from the border that is affected by the spatial spin-up (i.e., the spatial spin-up distance) of the precipitation field in convection-permitting model (CPM) simulations. Using a domain over eastern North America, this new method is applied to several simulations that differ on the nesting approach (single or double nesting) and the 3-D variables used to drive the CPM simulation. Our findings highlight three key points. Firstly, when using a single nesting approach, the spin-up distance from lateral boundaries can extend up to 300 km (around 120 CPM grid points), varying across seasons, boundaries and driving variables. Secondly, the greatest spin-up distances occur in winter at the western and southern boundaries, likely due to strong atmospheric inflow during these seasons. Thirdly, employing a double nesting approach with a comprehensive set of microphysical variables to drive CPM simulations offers clear advantages. The computational gains from reducing spatial spin-up outweigh the costs associated with the more demanding intermediate simulation of the double nesting. These results have practical implications for optimizing CPM simulation configurations, encompassing domain selection and driving strategies.

A Wind Field Reconstruction from Numerical Weather Prediction Data Based on a Meteo Particle Model

In the present work, a methodology for wind field reconstruction based on the Meteo Particle model (MPM) from numerical weather prediction (NWP) data is presented. The development of specific wind forecast services is a challenging research topic, in particular for what concerns the availability of accurate local weather forecasts in highly populated areas. Currently, even if NWP limited area models (LAMs) are run at a spatial resolution of about 1 km, this level of information is not sufficient for many applications; for example, to support drone operation in urban contexts. The coupling of the MPM with the NWP limited area model COSMO has been implemented in such a way that the MPM reads the NWP output over a selected area and provides wind values for the generic point considered for the investigation. The numerical results obtained reveal the good behavior of the method in reproducing the general trend of the wind speed, as also confirmed by the power spectra analysis. The MPM is able to step over the intrinsic limitations of the NWP model in terms of the spatial and temporal resolution, even if the MPM inherits the bias that inevitably affects the COSMO output.

Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1

Abstract. Anthropogenic climate change is changing the Earth system processes that control the characteristics of natural hazards both globally and across Australia. Model projections of hazards under future climate change are necessary for effective adaptation. This paper presents BARPA-R (the Bureau of Meteorology Atmospheric Regional Projections for Australia), a regional climate model designed to downscale climate projections over the Australasian region with the purpose of investigating future hazards. BARPA-R, a limited-area model, has a 17 km horizontal grid spacing and makes use of the Met Office Unified Model (MetUM) atmospheric model and the Joint UK Land Environment Simulator (JULES) land surface model. To establish credibility and in compliance with the Coordinated Regional Climate Downscaling Experiment (CORDEX) experiment design, the BARPA-R framework has been used to downscale ERA5 reanalysis. Here, an assessment of this evaluation experiment is provided. Performance-based evaluation results are benchmarked against ERA5, with comparable performance between the free-running BARPA-R simulations and observationally constrained reanalysis interpreted as a good result. First, an examination of BARPA-R's representation of Australia's surface air temperature, precipitation, and 10 m winds finds good performance overall, with biases including a 1 ∘C cold bias in daily maximum temperatures, reduced diurnal temperature range, and wet biases up to 25 mm per month in inland Australia. Recent trends in daily maximum temperatures are consistent with observational products, while trends in minimum temperatures show overestimated warming and trends in precipitation show underestimated wetting in northern Australia. Precipitation and temperature teleconnections are effectively represented in BARPA-R when present in the driving boundary conditions, while 10 m winds are improved over ERA5 in six out of eight of the Australian regions considered. Secondly, the paper considers the representation of large-scale atmospheric circulation features and weather systems. While generally well represented, convection-related features such as tropical cyclones, the South Pacific Convergence Zone (SPCZ), the Northwest Cloudband, and the monsoon westerlies show more divergence from observations and internal interannual variability than mid-latitude phenomena such as the westerly jets and extratropical cyclones. Having simulated a realistic Australasian climate, the BARPA-R framework will be used to downscale two climate change scenarios from seven CMIP6 global climate models (GCMs).

Assimilating Aeolus Satellite Wind Data on a Regional Level: Application in a Mediterranean Cyclone Using the WRF Model

This study uses a limited area model to improve the understanding of assimilating Aeolus Level 2B wind profiles on a regional level under severe weather conditions. Aeolus wind profile measurements have offered new insights into weather analysis and applications. The assimilation of Aeolus Level 2B winds has enhanced the observed state of the atmosphere spatially and temporally in global modeling systems. This work is focused on the development and evolution of a Mediterranean tropical-like cyclone that occurred between 27–30 September 2018. Aeolus coverage had a good spatial and temporal alignment with the broader area and time periods during which the cyclone originated and developed, affording the opportunity to explore the direct influence of Aeolus satellite retrievals in model initialization processes. Using the WRF 3DVar modeling system, model results showcase the effects stemming from Aeolus data ingestion, with the main differences presenting after the first 24 h of simulation. Smaller or larger deviations in the runs with and without the Aeolus wind data assimilation are evident in most cyclonic characteristics, extending vertically up to the mid-troposphere. The absence of a consistent trend in cyclone intensification or weakening underlines the unique impact of the Aeolus dataset in each case.

Future heat extremes and impacts in a convection-permitting climate ensemble over Germany

Abstract. Heat extremes and associated impacts are considered the most pressing issue for German regional governments with respect to climate adaptation. We explore the potential of a unique high-resolution, convection-permitting (2.8 m), multi-GCM (global climate model) ensemble with COSMO-CLM (Consortium for Small-scale Modeling Climate Limited-area Modelling) regional simulations (1971–2100) over Germany regarding heat extremes and related impacts. We find a systematically reduced cold bias especially in summer in the convection-permitting simulations compared to the driving simulations with a grid size of 7 km and parametrized convection. The projected increase in temperature and its variance favors the development of longer and hotter heat waves, especially in late summer and early autumn. In a 2 ∘C (3 ∘C) warmer world, a 26 % (100 %) increase in the heat wave magnitude index is anticipated. Human heat stress (universal thermal climate index (UTCI) > 32 ∘C) and region-specific parameters tailored to climate adaptation revealed a dependency on the major landscapes, resulting in significantly higher heat exposure in flat regions such as the Rhine Valley, accompanied by the strongest absolute increase. A nonlinear, exponential increase is anticipated for parameters characterizing strong heat stress (UTCI > 32 ∘C, tropical nights, very hot days). Providing region-specific and tailored climate information, we demonstrate the potential of convection-permitting simulations to facilitate improved impact studies and narrow the gap between climate modeling and stakeholder requirements for climate adaptation.

Evaluation of BOLAM Fine Grid Weather Forecasts with Emphasis on Hydrological Applications

The evaluation of weather forecast accuracy is of major interest in decision making in almost every sector of the economy and in civil protection. To this, a detailed assessment of Bologna Limited-Area Model (BOLAM) seven days fine grid 3 h predictions is made for precipitation, air temperature, relative humidity, and wind speed over a large lowland agricultural area of a Mediterranean-type climate, characterized by hot summers and rainy moderate winters (plain of Arta, NW Greece). Timeseries that cover a four-year period (2016–2019) from seven agro-meteorological stations located at the study area are used to run a range of contingency and accuracy measures as well as Taylor diagrams, and the results are thoroughly discussed. The overall results showed that the model failed to comply with the precipitation regime throughout the study area, while the results were mediocre for wind speed. Considering relative humidity, the results revealed acceptable performance and good correlation between the model output and the observed values, for the early days of forecast. Only in air temperature, the forecasts exhibited very good performance. Discussion is made on the ability of the model to predict major rainfall events and to estimate water budget components as rainfall and reference evapotranspiration. The need for skilled weather forecasts from improved versions of the examined model that may incorporate post-processing techniques to improve predictions or from other forecasting services is underlined.

Challenges in Numerical Weather Prediction of the 10 August 2020 Midwestern Derecho: Examples from the FV3-LAM

Abstract A severe derecho impacted the Midwestern United States on 10 August 2020, causing over $12 billion (U.S. dollars) in damage, and producing peak winds estimated at 63 m s−1, with the worst impacts in Iowa. The event was not forecast well by operational forecasters, nor even by operational and quasi-operational convection-allowing models. In the present study, nine simulations are performed using the Limited Area Model version of the Finite-Volume-Cubed-Sphere model (FV3-LAM) with three horizontal grid spacings and two physics suites. In addition, when a prototype of the Rapid Refresh Forecast System (RRFS) physics is used, sensitivity tests are performed to examine the impact of using the Grell–Freitas (GF) convective scheme. Several unusual results are obtained. With both the RRFS (not using GF) and Global Forecast System (GFS) physics suites, simulations using relatively coarse 13- and 25-km horizontal grid spacing do a much better job of showing an organized convective system in Iowa during the daylight hours of 10 August than the 3-km grid spacing runs. In addition, the RRFS run with 25-km grid spacing becomes much worse when the GF convective scheme is used. The 3-km RRFS run that does not use the GF scheme develops spurious nocturnal convection the night before the derecho, removing instability and preventing the derecho from being simulated at all. When GF is used, the spurious storms are removed and an excellent forecast is obtained with an intense bowing echo, exceptionally strong cold pool, and roughly 50 m s−1 surface wind gusts.

Large‐scale blending in an hourly 4D‐Var framework for a numerical weather prediction model

AbstractThe Met Office limited‐area model (LAM) data assimilation (DA) system is based on an hourly 4D‐Var method with lateral boundary conditions (LBCs) provided by the Met Office global model (GM). This set‐up was introduced operationally in summer 2017. The work presented here improved the representation of large‐scale dynamics in the LAM DA, which is now integrated on an extended domain and therefore also needs to represent larger scales than were present in 2017. To improve the representation of the large‐scale motions in the convective‐scale system, we choose to take advantage of the better estimation of these scales from the host model. The method presented here is called large‐scale blending (LSB), a spectral nudging solution which is applied to the Met Office LAM (UKV). Spectral nudging allows the LAM evolution to be constrained by a host model with a better representation of the larger scales while preserving the smaller scales predicted by the LAM. In this article, we investigate different options for the implementation of LSB, such as cut‐off wavelength and update frequency. The LAM forecast is typically improved for various variables over a forecast range from about to by the introduction of LSB. We also see a reduction in the internal gravity‐wave activity generated when new lateral boundary conditions are introduced to the LAM from the latest GM forecast. This research proves the benefits of a better representation of large‐scale motions for LAM forecasts.

Implementation and Testing of Radar Data Assimilation Capabilities Within the Joint Effort for Data Assimilation Integration Framework With Ensemble Transformation Kalman Filter Coupled With FV3‐LAM Model

AbstractCapabilities to directly assimilate radar data are implemented within the local ensemble transform Kalman filter (LETKF) and the gain‐form LETKF (LGETKF) algorithms of the Joint Effort for Data assimilation Integration (JEDI) system. The capabilities are evaluated for the analysis and forecast of a severe convection case of 20 May 2019 in the Southern Great Plains using the limited area model version of the FV3 dynamical core (FV3‐LAM) from a recent release for Short‐Range Weather Application (SRW App). The LETKF and LGETKF implementations are shown to produce analyses and short‐range forecasts comparable to those using the ensemble square‐root Kalman Filter (EnSRF) within the Gridpoint Statistical Interpolation (GSI) framework used by current NCEP operational models. In addition, LGETKF retaining only 60% variances for model‐space vertical localization performs similarly to LGETKF retaining 99% of variance and LETKF using observation error‐based vertical localization. JEDI LETKF shows better parallel scalability than LGETKF and GSI EnSRF.

Exploratory analysis of citizen observations of hourly precipitation over Scandinavia

Abstract. We present a comparison between Netatmo hourly precipitation amounts and observations of the same quantity from weather stations managed by national meteorological services, the latter used as reference values. The empirical distributions of the crowdsourced observations in the surroundings of reference stations are used to assess accuracy and precision of crowdsourced data. We found that reference values are typically within the distribution of the crowdsourced data. However, as the amount of precipitation increases, the spread of the crowdsourced distribution increases and the reference values are more and more frequently found towards the right tail of the distribution. These results indicate that accuracy and precision of crowdsourced data change as precipitation increases. We have studied the sensitivity of our results to the size of the neighbourhood chosen around the reference stations and we show that by aggregating the values over those neighbourhoods, crowdsourced data can be trusted in determining precipitation occurrence. We have assessed the variability of precipitation within small neighbourhoods (of radius 1, 3 and 5 km) and we provide estimates on the basis of the precipitation amounts. Our study quantifies the variability of hourly precipitation over small regions, of the size of the so-called “unresolved spatial scales” in limited area models, based on three years of data collected at several places in Scandinavia.

Valid Time Shifting for an Experimental RRFS Convection-Allowing EnVar Data Assimilation and Forecast System: Description and Systematic Evaluation in Real Time

Abstract This study describes a real-time implementation of valid time shifting (VTS) within the Gridpoint Statistical Interpolation–based ensemble-variational (EnVar) data assimilation system, developed at the Multi-Scale Data Assimilation and Predictability Laboratory. This system, featuring data assimilation of mesoscale conventional observations and storm-scale radar reflectivity observations and interfaced with the next-generation Finite Volume Cubed Sphere Dynamical Core limited-area model (FV3-LAM), was run in real-time during the 2021 Hazardous Weather Testbed Spring Forecast Experiment. The VTS method efficiently increases ensemble size by incorporating ensemble forecast output before and after the central analysis. Two configurations were examined to systematically evaluate VTS: a baseline 36-member system with hourly data assimilation (NOVTS), and an experiment testing VTS for the radar analysis step. Verification across 22 cases shows statistically significant benefits of VTS to increase ensemble spread and better fit first guesses to observations. Control member forecasts launched at 0000 UTC have consistently higher skill, lower bias, and higher reliability in VTS than in NOVTS throughout the 18-h forecast evaluation period, especially from severe cases often featuring upscale growth into mesoscale convective systems. Verification of updraft helicity-based ensemble surrogate severe probabilistic forecasts against observed storm reports shows higher skill of VTS when verifying on finer scales, with benefits to constraining higher probabilities over report locations and reducing probabilities over no-report locations. This study is a first step toward the next-generation Rapid Refresh Forecast System (RRFS), demonstrating the feasibility of such a real-time system and the potential benefits of VTS implementation.

A Global–Regional-Unified Atmospheric Dynamical Core on the Yin–Yang Grid

Abstract A global–regional-unified nonhydrostatic dynamical core was constructed on the Yin–Yang grid using a semi-implicit semi-Lagrangian solver. Arbitrary coordinate rotation was possible for both global and limited-area models with a multilevel nesting capability. Significant flexibility is available when configuring the model’s horizontal mesh using coordinate rotation. The performance of the dynamical core was assessed using a series of numerical tests in Cartesian and spherical coordinates. The results illustrate the reasonable ability of the piecewise rational method to manage sharp gradient advection and the capability of the dry dynamical core to simulate fine structures in target systems in the Cartesian and spherical configurations. An average convergence rate of 2.43 was confirmed for the dry dynamical core in the balanced flow test. The nested grid improved the fine structure simulation of the baroclinic wave development without affecting wave propagation. The propagating speed of the vortex remained unchanged in the nested grid in the colliding modons test, although the vorticity amplitude decayed more slowly than that in the coarse grid. Proper development of topographic waves was achieved for both large- and small-scale mountains with a clear damping effect in association with the off-center semi-implicit average. A deepened trough in the nesting region, in comparison with that in the parent grid, was simulated in a topographic Rossby wave test. The implementation of coordinate rotation and grid nesting improved the numerical performance when managing atmospheric dynamical problems at relatively low cost.

Just What Is “Good”? Musings on Hail Forecast Verification through Evaluation of FV3-HAILCAST Hail Forecasts

Abstract Hail forecasts produced by the CAM-HAILCAST pseudo-Lagrangian hail size forecasting model were evaluated during the 2019, 2020, and 2021 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments (SFEs). As part of this evaluation, HWT SFE participants were polled about their definition of a “good” hail forecast. Participants were presented with two different verification methods conducted over three different spatiotemporal scales, and were then asked to subjectively evaluate the hail forecast as well as the different verification methods themselves. Results recommended use of multiple verification methods tailored to the type of forecast expected by the end-user interpreting and applying the forecast. The hail forecasts evaluated during this period included an implementation of CAM-HAILCAST in the Limited Area Model of the Unified Forecast System with the Finite Volume 3 (FV3) dynamical core. Evaluation of FV3-HAILCAST over both 1- and 24-h periods found continued improvement from 2019 to 2021. The improvement was largely a result of wide intervariability among FV3 ensemble members with different microphysics parameterizations in 2019 lessening significantly during 2020 and 2021. Overprediction throughout the diurnal cycle also lessened by 2021. A combination of both upscaling neighborhood verification and an object-based technique that only retained matched convective objects was necessary to understand the improvement, agreeing with the HWT SFE participants’ recommendations for multiple verification methods. Significance Statement “Good” forecasts of hail can be determined in multiple ways and must depend on both the performance of the guidance and the perspective of the end-user. This work looks at different verification strategies to capture the performance of the CAM-HAILCAST hail forecasting model across three years of the Spring Forecasting Experiment (SFE) in different parent models. Verification strategies were informed by SFE participant input via a survey. Skill variability among models decreased in SFE 2021 relative to prior SFEs. The FV3 model in 2021, compared to 2019, provided improved forecasts of both convective distribution and 38-mm (1.5 in.) hail size, as well as less overforecasting of convection from 1900 to 2300 UTC.