The new Italian regional reanalysis SPHERA: benefits of the convection-permitting resolution in detecting severe-weather events

In recent years the interest towards the development of limited-area atmospheric reanalysis datasets has been growing more and more. Regional reanalyses in fact, as a consequence of the restricted domain that they cover, provide a data distribution displaced on a much finer grid compared to a coarser global dataset. This permits to better resolve those patterns related to rapid and high-impact weather events, first and foremost convection. Furthermore, with a finer horizontal resolution, a consistent increase in the level of detail in the description of the orography is also gained, that is a crucial point to achieve especially in a very complex territory such as Italy. This study presents the first application of the novel regional reanalysis dataset developed at ARPAE-SIMC: the High rEsolution ReAnalysis over Italy (SPHERA). SPHERA is a high-resolution convection-permitting reanalysis over the Italian domain and the surrounding seas covering 25 years, from 1995 to 2020, at hourly temporal frequency. SPHERA is based on the non-hydrostatic limited-area model COSMO, and produced by a dynamical downscaling of the global reanalysis ERA5, developed at ECMWF. A nudging data assimilation scheme is applied in order to steer the model outcomes towards the surface and upper-air observations. All the available conventional observations have been used.The added value of SPHERA in representing severe-weather and convective events is evident from its preliminar validation, which was performed on the multidecadal period against various datasets of surface observations, joined with the comparison against the global reanalysis ERA5. In fact, a clear advantage of SPHERA on its driver ERA5 is found for the detection of events with moderate to intense daily and sub-daily rainfalls, which are characterized by a strong seasonal and geographical component, that is further investigated. We report also the preliminary sensitivity analysis on the dimension of the box used to operate the upscaling for the validation of SPHERA, a process necessary to reduce the errors caused by geographical mismatches between observed and simulated events localizations, which are particularly frequent in case of strongly-localized and rapid processes. Furthermore, in order to give a quantitative evaluation of the performance of the new reanalysis in particular conditions, the results of the simulations for specific case studies involving the occurrence of severe-precipitation events in recent years was performed, focusing on events having different dynamical genesis, but interrelated by the important damages they caused. From this analysis, for which also a comparison with other regional reanalyses is performed, the advantage of SPHERA in representing the most intense rainfall occurrences, in terms of location, intensity and timing, clearly emerges.

Download Full-text

An Evaluation of Temperature-Based Agricultural Indices Over Korea From the High-Resolution WRF Simulation

Frontiers in Earth Science ◽

10.3389/feart.2021.656787 ◽

2021 ◽

Vol 9 ◽

Author(s):

Eun-Soon Im ◽

Subin Ha ◽

Liying Qiu ◽

Jina Hur ◽

Sera Jo ◽

...

Keyword(s):

Heat Stress ◽

High Resolution ◽

Dynamical Downscaling ◽

Initial Conditions ◽

Added Value ◽

Stress Index ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Hourly Temperature

This study evaluates the performance of dynamical downscaling of global prediction generated from the NOAA Climate Forecast System (CFSv2) at subseasonal time-scale against dense in-situ observational data in Korea. The Weather Research and Forecasting (WRF) double-nested modeling system customized over Korea is adopted to produce very high resolution simulation that presumably better resolves geographically diverse climate features. Two ensemble members of CFSv2 starting with different initial conditions are downscaled for the summer season (June-July-August) during past 10-year (2011–2020). The comparison of simulations from the nested domain (5 km resolution) of WRF and driving CFSv2 (0.5°) clearly demonstrates the manner in which dynamical downscaling can drastically improve daily mean temperature (Tmean) and daily maximum temperature (Tmax) in both quantitative and qualitative aspects. The downscaled temperature not only better resolves the regional variability strongly tied with topographical elevation, but also substantially lowers the systematic cold bias seen in CFSv2. The added value from the nested domain over CFSv2 is far more evident in Tmax than in Tmean, which indicates a skillful performance in capturing the extreme events. Accordingly, downscaled results show a reasonable performance in simulating the plant heat stress index that counts the number of days with Tmax above 30°C and extreme degree days that accumulate temperature exceeding 30°C using hourly temperature. The WRF simulations also show the potential to capture the variation of Tmean-based index that represents the accumulation of heat stress in reproductive growth for the mid-late maturing rice cultivars in Korea. As the likelihood of extreme hot temperatures is projected to increase in Korea, the modeling skill to predict the ago-meteorological indices measuring the effect of extreme heat on crop could have significant implications for agriculture management practice.

Download Full-text

Regional Dynamical Downscaling

Oxford Research Encyclopedia of Climate Science ◽

10.1093/acrefore/9780190228620.013.784 ◽

2020 ◽

Author(s):

Filippo Giorgi

Keyword(s):

Climate Change ◽

High Resolution ◽

Dynamical Downscaling ◽

Regional Climate ◽

Added Value ◽

Climate Information ◽

Atmospheric Models ◽

Future Directions ◽

Climate Change Research ◽

Climate Downscaling

Dynamical downscaling has been used for about 30 years to produce high-resolution climate information for studies of regional climate processes and for the production of climate information usable for vulnerability, impact assessment and adaptation studies. Three dynamical downscaling tools are available in the literature: high-resolution global atmospheric models (HIRGCMs), variable resolution global atmospheric models (VARGCMs), and regional climate models (RCMs). These techniques share their basic principles, but have different underlying assumptions, advantages and limitations. They have undergone a tremendous growth in the last decades, especially RCMs, to the point that they are considered fundamental tools in climate change research. Major intercomparison programs have been implemented over the years, culminating in the Coordinated Regional climate Downscaling EXperiment (CORDEX), an international program aimed at producing fine scale regional climate information based on multi-model and multi-technique approaches. These intercomparison projects have lead to an increasing understanding of fundamental issues in climate downscaling and in the potential of downscaling techniques to provide actionable climate change information. Yet some open issues remain, most notably that of the added value of downscaling, which are the focus of substantial current research. One of the primary future directions in dynamical downscaling is the development of fully coupled regional earth system models including multiple components, such as the atmosphere, the oceans, the biosphere and the chemosphere. Within this context, dynamical downscaling models offer optimal testbeds to incorporate the human component in a fully interactive way. Another main future research direction is the transition to models running at convection-permitting scales, order of 1–3 km, for climate applications. This is a major modeling step which will require substantial development in research and infrastructure, and will allow the description of local scale processes and phenomena within the climate change context. Especially in view of these future directions, climate downscaling will increasingly constitute a fundamental interface between the climate modeling and end-user communities in support of climate service activities.

Download Full-text

Downscaling of ECMWF Ensemble Forecasts for Cases of Severe Weather: Ensemble Statistics and Cluster Analysis

Monthly Weather Review ◽

10.1175/2008mwr2322.1 ◽

2008 ◽

Vol 136 (9) ◽

pp. 3323-3342 ◽

Cited By ~ 15

Author(s):

Čedo Branković ◽

Blaženka Matjačić ◽

Stjepan Ivatek-Šahdan ◽

Roberto Buizza

Keyword(s):

Clustering Algorithm ◽

Dynamical Downscaling ◽

Severe Weather ◽

Ensemble Prediction ◽

Small Scale ◽

Limited Area ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Orographic Forcing ◽

And Cluster Analysis

Abstract Dynamical downscaling has been applied to global ensemble forecasts to assess its impact for four cases of severe weather (precipitation and wind) over various parts of Croatia. It was performed with the Croatian 12.2-km version of the Aire Limitée Adaptation Dynamique Développement International (ALADIN) limited-area model, nested in the ECMWF TL255 (approximately 80 km) global ensemble prediction system (EPS). The 3-hourly EPS output was used to force the ALADIN model over the central European/northern Mediterranean domain. Results indicate that the identical clustering algorithm may yield differing results when applied to either global or to downscaled ensembles. It is argued that this is linked to the fact that a downscaled, higher-resolution ensemble resolves more explicitly small-scale features, in particular those strongly influenced by orographic forcing. This result has important implications in limited-area ensemble prediction, since it implies that downscaling may affect the interpretation or relevance of the global ensemble forecasts; that is, it may not always be feasible to make a selection (or a subset) of global lower-resolution ensemble members that might be representative of all possible higher-resolution evolution scenarios.

Download Full-text

Value of Real-Time Vegetation Fraction to Forecasts of Severe Convection in High-Resolution Models

Weather and Forecasting ◽

10.1175/2008waf2007097.1 ◽

2009 ◽

Vol 24 (1) ◽

pp. 187-210 ◽

Cited By ~ 16

Author(s):

Kenneth A. James ◽

David J. Stensrud ◽

Nusrat Yussouf

Keyword(s):

High Resolution ◽

Real Time ◽

Environmental Conditions ◽

Severe Weather ◽

Heat Fluxes ◽

Temperature Fields ◽

Synoptic Scale ◽

Low Level ◽

Vegetation Fraction ◽

Weather Events

Abstract Near-real-time values of vegetation fraction are incorporated into a 2-km nested version of the Advanced Research Weather Research and Forecasting (ARW) model and compared to forecasts from a control run that uses climatological values of vegetation fraction for eight severe weather events during 2004. It is hypothesized that an improved partitioning of surface sensible and latent heat fluxes occurs when incorporating near-real-time values of the vegetation fraction into models, which may result in improved forecasts of the low-level environmental conditions that support convection and perhaps even lead to improved explicit convective forecasts. Five of the severe weather events occur in association with weak synoptic-scale forcing, while three of the events occur in association with moderate or strong synoptic-scale forcing. Results show that using the near-real-time values of the vegetation fraction alters the values and structure of low-level temperature and dewpoint temperature fields compared to the forecasts using climatological vegetation fractions. The environmental forecasts that result from using the real-time vegetation fraction are more thermodynamically supportive of convection, including stronger and deeper frontogenetic circulations, and statistically significant improvements of most unstable CAPE forecasts compared to the control run. However, despite the improved environmental forecasts, the explicit convective forecasts using real-time vegetation fractions show little to no improvement over the control forecasts. The convective forecasts are generally poor under weak synoptic-scale forcing and generally good under strong synoptic-scale forcing. These results suggest that operational forecasters can best use high-resolution forecasts to help diagnose environmental conditions within an ingredients-based forecasting approach.

Download Full-text

A New Statistical–Dynamical Downscaling Procedure Based on EOF Analysis for Regional Time Series Generation

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-11-065.1 ◽

2013 ◽

Vol 52 (4) ◽

pp. 935-952 ◽

Cited By ~ 12

Author(s):

Yosvany Martinez ◽

Wei Yu ◽

Hai Lin

Keyword(s):

Time Series ◽

High Resolution ◽

Large Scale ◽

Dynamical Downscaling ◽

Resource Assessment ◽

Computational Effort ◽

Limited Area ◽

Eof Analysis ◽

Large Scale Data ◽

Wind Resource

AbstractA new statistical–dynamical downscaling procedure is developed and then applied to high-resolution (regional) time series generation and wind resource assessment. The statistical module of the new procedure uses empirical orthogonal function (EOF) analysis for the generation of large-scale atmospheric component patterns. The dominant atmospheric patterns (associated with the EOF modes explaining most of the statistical variance) are then dynamically downscaled or adjusted to high-resolution terrain and surface roughness by using the Global Environmental Multiscale–Limited Area Model (GEM-LAM). Regional time series are constructed using the model outputs. The new method is applied to the Gaspé region of Québec in Canada. The dataset used is the NCEP–NCAR reanalysis of wind, temperature, humidity, and geopotential height during the period 1958–2004. Regional time series of wind speed and temperature are constructed, and a numerical wind atlas of the Gaspé region is generated. The generated time series and the numerical wind atlas are compared with observations at different masts located in the Gaspé Peninsula and are also compared with a numerical wind atlas for the same region generated in Yu et al. The results suggest that the newly developed procedure can be useful to generate regional time series and reasonably accurate numerical wind atlases using large-scale data with much less computational effort than previous techniques.

Download Full-text

Dynamical Global Downscaling of Global Reanalysis

Monthly Weather Review ◽

10.1175/2008mwr2281.1 ◽

2008 ◽

Vol 136 (8) ◽

pp. 2983-2998 ◽

Cited By ~ 61

Author(s):

Kei Yoshimura ◽

Masao Kanamitsu

Keyword(s):

High Resolution ◽

Wind Speed ◽

Surface Temperature ◽

Dynamical Downscaling ◽

Surface Wind ◽

Surface Wind Speed ◽

The United States ◽

Full Data ◽

Global Spectral Model ◽

Global Reanalysis

Abstract With the aim of producing higher-resolution global reanalysis datasets from coarse-resolution reanalysis, a global version of the dynamical downscaling using a global spectral model is developed. A variant of spectral nudging, the modified form of scale-selective bias correction developed for regional models is adopted. The method includes 1) nudging of temperature in addition to the zonal and meridional components of winds, 2) nudging to the perturbation field rather than to the perturbation tendency, and 3) no nudging and correction of the humidity. The downscaling experiment was performed using a T248L28 (about 50-km resolution) global model, driven by the so-called R-2 reanalysis (T62L28 resolution, or about 200-km resolution) during 2001. Evaluation with high-resolution observations showed that the monthly averaged global surface temperature and daily variation of precipitation were much improved. Over North America, surface wind speed and temperature are much better, and over Japan, the diurnal pattern of surface temperature is much improved, as are wind speed and precipitation, but not humidity. Three well-known synoptic/subsynoptic-scale weather patterns over the United States, Europe, and Antarctica were shown to become more realistic. This study suggests that the global downscaling is a viable and economical method for obtaining high-resolution reanalysis without rerunning a very expensive high-resolution full data assimilation.

Download Full-text

High-resolution ensemble precipitation simulations over a small domain with complex topography

10.5194/egusphere-egu2020-390 ◽

2020 ◽

Author(s):

Ioannis Sofokleous ◽

Adriana Bruggeman ◽

Corrado Camera ◽

George Zittis

Keyword(s):

High Resolution ◽

Dynamical Downscaling ◽

Eastern Mediterranean ◽

Added Value ◽

Computational Time ◽

Complex Topography ◽

Catchment Scale ◽

Simulated Precipitation ◽

Small Domain ◽

Model Setup

The reconstruction of detailed past weather and climate conditions, such as precipitation, is an essential part of hydrometeorological impact studies. Although this can be achieved through dynamical downscaling of reanalysis datasets, different model setup options can result in significantly different simulated fields. To select an efficient ensemble of the WRF atmospheric model for the simulation of precipitation at high resolution, suitable for hydrological studies at catchment scale, a series of simulation experiments is performed. The model experiments center on Cyprus, in the Eastern Mediterranean, a small domain with an area of 225&#215;145 km2 with complex topography. The simulations are made for the hydrologic year 2011-2012. Initial and boundary conditions are provided by the ERA5 reanalysis dataset. A stepwise approach is followed for the evaluation of monthly simulations for an ensemble comprised of 18 combinations of various model physics parameterizations. In the first step, the model ensemble is evaluated for three domain setups with different extends and nested downscaling steps, i.e. 19&#183;105 km2 with 12-, 4- and 1-km grids (12-4-1), 19&#183;105 km2 with 6- and 1-km grids (6-1a) and 7.28 &#183;105 km2 with 6- and 1-km grids (6-1b). The ensemble performance is then investigated for two initialization frequencies, 30 and 5 days, both with 6-hour spin-up. In the last step, the performance of the individual ensemble members is evaluated and the five best performing members are selected. A gridded precipitation dataset for the area over Cyprus is developed for the evaluation of the simulated precipitation. The statistical indicators used are bias, mean absolute error (MAE), Nash-Sutcliffe efficiency and Kling-Gupta efficiency. The four indicators are scaled and combined in a single composite metric score (CMS), ranging from 0 to 1.The best overall performance was achieved with the 12-4-1 domain setup. This setup resulted in the lowest bias of accumulated precipitation of the 18-member ensemble, i.e. 1%, compared to 8% for 6-1a and 10% for 6-1b, for the wet month of January. The 12-4-1 setup was also found to add value, in terms of computational time, to the least computationally demanding 6-1b setup by reducing the monthly bias by 47 mm per 1000 cpu hours. The statistical metrics for the ensemble with 5-day initialization exhibited very small variation from the metrics for the monthly initialization, with less than 4% difference in the MAE of the accumulated precipitation. The added value of the 5-day initialization, relative to the monthly initialization, was found to be negative for all four metrics in January and for two of the metrics in May. Despite the variable performance of individual ensemble members in different months, the combined metric showed that the overall highest (lowest) ranked members, with a CMS value of 0.63 (0.43), were those using the Ferrier and WRF-Double-Moment-6th-class (WRF-Single-Moment-6th-class) microphysical schemes. The proposed stepwise evaluation approach allows the identification of a reduced number of ensemble members, out of the initial ensemble, with a model setup that can simulate precipitation at high resolution and under different atmospheric conditions.

Download Full-text

Added value of high-resolution climate projections over South Korea on the scaling of precipitation with temperature

Environmental Research Letters ◽

10.1088/1748-9326/ac37d3 ◽

2021 ◽

Author(s):

Liying Qiu ◽

Eun-Soon Im

Keyword(s):

Global Warming ◽

High Resolution ◽

South Korea ◽

Spatial Pattern ◽

Climate Models ◽

Dynamical Downscaling ◽

Global Climate ◽

Global Climate Models ◽

Added Value ◽

Climate Projections

Abstract This study evaluates the resolution dependency of scaling precipitation with temperature from the perspective of the added value of high-resolution (5-km) dynamical downscaling using various kinds of long-term climate change projections over South Korea. Three CMIP5 Global Climate Models (GCMs) with different climate sensitivities, and one pseudo global warming (PGW) experiment, are downscaled by Weather Research and Forecasting (WRF) one-way double nested modeling system with convective parameterization for the reference (1976-2005) and future (2071-2100) periods under RCP8.5 scenario. A detailed comparison of the driving GCM/PGW, 20-km mother simulation, and 5-km nested simulation demonstrates improved representation of precipitation with increasing resolution not only in the spatial pattern and magnitude for both the mean and the extremes, but also in a more realistic representation of extreme precipitation’s sensitivities to temperature. According to the projected precipitation changes downscaled from both GCM ensemble and PGW, there will be intensified precipitation, particularly for the extremes, over South Korea under the warming, which is primarily contributed by CP increase that shows higher temperature sensitivity. This study also compares the extreme precipitation-temperature scaling relations within-epoch (apparent scaling) and between-epoch (climate scaling). It confirms that the magnitude and spatial pattern of the two scaling rates can be quite different, and the precipitation change over Korea under global warming is mainly controlled by thermodynamic factors.

Download Full-text

An evaluation of daily precipitation from atmospheric reanalyses over Australia

10.5194/hess-2018-607 ◽

2019 ◽

Cited By ~ 1

Author(s):

Suwash Chandra Acharya ◽

Rory Nathan ◽

Quan J. Wang ◽

Chun-Hsu Su ◽

Nathan Eizenberg

Keyword(s):

Daily Precipitation ◽

Transition Probabilities ◽

Reanalysis Dataset ◽

Atmospheric Processes ◽

Precipitation Estimates ◽

Spatio Temporal ◽

Regional Reanalysis ◽

Global Reanalysis ◽

Better Than

Abstract. An accurate representation of spatio-temporal characteristics of precipitation fields is fundamental for many hydro-meteorological analyses but is often limited by the paucity of gauges. Reanalysis models provide systematic methods of representing atmospheric processes to produce datasets of spatio-temporal precipitation estimates. The precipitation from the reanalysis datasets should, however, be evaluated thoroughly before use because it is inferred from physical parameterization. In this paper, we evaluated the precipitation dataset from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) and compared it against (a) gauged point observations, (b) an interpolated gridded dataset based on gauged point observations (AWAP), and (c) a global reanalysis dataset (ERA-Interim). We utilized a range of evaluation metrics such as continuous metrics (correlation, bias, variability, modified Kling-Gupta efficiency), categorical metrics, and other statistics (wet day frequency, transition probabilities and quantiles) to ascertain the quality of the dataset. BARRA, in comparison with ERA-Interim, shows a better representation of rainfall of larger magnitude at both point and grid scale of 5 km. BARRA also consistently reproduces the distribution of wet days and transition probabilities. The performance of BARRA varies spatially, with better performance in the temperate zone than in the arid and tropical zones. A point-to-grid evaluation based on correlation, bias and modified Kling-Gupta efficiency (KGE') indicates that ERA-Interim performs on par or better than BARRA. However, on a spatial scale, BARRA outperforms AWAP in terms of KGE' score and the components of the KGE' score. Our evaluation illustrates that BARRA, with richer spatial variations in climatology of daily precipitation, provides an improved representation of precipitation compared with the coarser ERA-Interim. It is a useful complement to existing precipitation datasets for Australia, especially in sparsely gauged regions.

Download Full-text

Evaluating current convection-permitting ensembles for past high-impact weather events in Italy: results from the SPITCAPE Special Project

10.5194/egusphere-egu2020-18941 ◽

2020 ◽

Author(s):

Valerio Capecchi ◽

Bernardo Gozzini

Keyword(s):

Dynamical Downscaling ◽

Horizontal Resolution ◽

High Impact ◽

Ensemble Prediction ◽

Limited Area ◽

Ensemble Forecasts ◽

Area Of Interest ◽

Weather Events ◽

Special Project ◽

High Impact Weather

The main goal of the ECMWF Special Project SPITCAPE is to understand the information content of the current ensemble systems both at global and meso scales in re-forecasting past high-impact weather events. In particular one of the main questions addressed in the project is: what is the added value of running a high-resolution (namely convection-permitting) ensembles for high-impact weather events with respect to global ones? Running operational Ensemble Prediction Systems (EPS) at the convection-permitting (CP) scale is currently on the agenda at a number of European weather forecasting services and research centres: UK Met Office, M&#233;t&#233;o France and DWD to mention a few. Moreover, in the framework of the activities of the forthcoming ItaliaMeteo agency, it is foreseen the development of a regional EPS at CP scale for the Italian domain. Recently, it has been demonstrated that the baseline approach of dynamical downscaling using CP models nested in a global ensemble with a coarser horizontal resolution (e.g. 20 km) provides valuable information. Since the introduction of the IFS model cycle 41r2 in March 2016, the horizontal resolution of the ECMWF ensemble forecasts (ENS) is about 18 km and it is planned to be further increased up to 10 km in the next future (after the installation of the new supercomputer in the Bologna data center). Thus, these higher-resolution global ENS data allow us to estimate the technical feasibility and value of the simple dynamical downscaling method to initialise limited-area and CP models (the WRF-ARW, MESO-NH and MOLOCH models in the present case) directly nested in the new ECMWF global ensemble. We applied this pragmatic approach in re-forecasting two high-impact weather events occurred in Italy in recent years (the Cinque Terre flooding occurred in October 2011 and the flash flood of Genoa in November 2011) with the ENS global forecasts and the data produced with the WRF-ARW, MESO-NH and MOLOCH models. The skills of the forecasts in the short-range are evaluated in terms of Probability of Precipitation exceeding predefined rainfall thresholds. In the medium-range we report and discuss the forecast uncertainty (i.e. ensemble spread) of ENS at different starting dates. Besides the fact that both global and regional model data under-estimate rainfall maxima in the area of interest, results demonstrate that CP ensemble forecasts provide better predictions regarding the occurrence of extreme precipitations and the area most likely affected. The comparison among results obtained with regional models contribute to the debate regarding the reliability of these models and their strengths and weaknesses with respect to: (I) the accuracy of the results for the two events considered, (II) the integration with ECMWF products, (III) the ease of implementation and (IV) the computational costs in view of a potential use for operational forecasting activities.

Download Full-text