bluuebunny/crossref_metadata_2025 huggingface dataset:
Future Changes in the Frequency and Duration of Extreme Winds in Japan — Focusing on the Risk of Low Wind Power Generation due to Strong Winds
Authors: Iwanaga Hiroyuki , Yamaguchi Kazuki
Published: July 2024

Average wind speeds are projected to decrease annually, mainly in the Northern Hemisphere, owing to the progression of global warming. Although the momentum for the spread of offshore wind power generation is growing in Japan, the average surface wind speeds in the region are also decreasing, and wind resources are expected to decline in the future. However, the risk of low wind power generation due to wind conditions exists not only when the wind is weak, but also when the wind is strong. This study investigated future changes in the frequency and duration of extremely low and high winds around mainland Japan.In this study, we used 2°C warming climate datasets (31 years × 12 members) and present climate datasets (31 years × 2 members) from the 5 km mesh downscaling data from the Database for Policy Decision-Making for Future Climate Change (d4PDF). The annual frequency of occurrence and maximum duration of weak (below cut-in wind speeds) and strong (above cut-out wind speeds) winds in the future (2°C warming) and present climates were compared in ensemble averages.Under the present climate, weak wind events were more pronounced over land in summer, with a frequency of up to 70% and a maximum duration of approximately 96 h, depending on the location. Under future climate conditions, the weak wind trend will intensify further, increasing the frequency by up to 2.5% and the maximum duration by up to approximately 24 h. Under the present climate, strong-wind events were less conspicuous than weak-wind events and occurred mainly over the ocean in both summer and winter, with a frequency of up to 1–2% and a maximum duration of up to 12 h. In the future, the frequency will increase by approximately 1%, and the maximum duration will increase by approximately 6 h. The maximum duration of strong winds during the 31-year analysis period was approximately 48 and 60 h in summer and winter, respectively, under the present climate and increased by up to 24 h each under the future climate. Factors contributing to these changes include an increase in the number of strong typhoons, a decrease in movement speed in summer, and a shift in the Japan Sea cold air mass convergence zone (JPCZ) with strong northwesterly winds to the north in winter.These findings suggest that under future climatic conditions, the probability of a long-term duration of extremely high winds will increase, potentially resulting in more severe low-generating events at offshore wind facilities than in the present climate.
A Numerical study for the effect of tidal parameterization scheme on the low visibility case at Incheon International Airport in South Korea
Authors: Min Jae-Sik , Kim Jung-Hoon
Published: July 2024

The Eastern part of the Yellow Sea near the Korean peninsula has a complicated coastline with numerous islands. And, semi-diurnal cycle of the sea level change is very drastic in this area, which results in the dynamic changes of physical and optical properties of the seawater and soil layers. Due to this tidal effects, the radiation, heat flux, moisture flux, and surface temperature near the surface can affect to land-sea breeze and sea fog in this area. The National Institute of Meteorological Research/Korean Meteorological Administration (NIMR/KMA) developed a tidal parameterization scheme implemented in the WRF model in conjunction with the Noah Land Surface Model (LSM). The primary role of tidal parameterization is to modify surface temperature by addressing the energy balance between the seawater and soil layers especially during the high tide. But, for the low tide period, there is no inflow of seawater, so that we use the water and soil temperatures from the previous time steps. Tidal information and bathymetry for each model grid are required to run the tidal parameterization. We used the tidal information from the DTU10, a global tide prediction model. For bathymetry, ETOPO2022 global data (15 arc-sec) from the National Centers for Environmental Information/National Oceanic and Atmospheric Administration (NCEI/NOAA) and bathymetry (150 m) from the Korea Hydrographic and Oceanographic Agency/Ministry of Oceans and Fisheries (KHOA/MOF) were used.In this study, the impact of tidal range on the event of low visibility due to the sea fog (November 16, 2019) occurred at the Incheon International Airport (ICN) in a wide tidal area was analyzed through high-resolution (~200 m) WRF experiments. In this case, a low visibility warning was issued from 5 AM to 10 AM due to the fog at ICN, and the simulated surface flux and fog event were compared between the control run with (TIDE) and without the tidal parameterization (CNTL). The tide prediction of DTU10 was relatively consistent, showing a correlation of over 0.9 compared to observations from the Incheon Tide Observatory. The TIDE experiment simulated more dense fog than that in the CNTL, and the timing of the generation and dissipation of the fog event was similar to the observation in the TIDE. To analyze the influence between tidal parameterization and the PBL scheme, we performed sensitivity tests on the YSU, SH, MYNN, and no PBL scheme (LES). Finally, the detailed physical processes near the surface will be discussed in the conference.
Climatology of dark doldrums and extremely high residual loads events and their future changes in Japan
Authors: Ohba Masamichi
Published: July 2024

Under high variable renewable energy (VRE) penetration, the occurrence of low VRE production, such as dark doldrums (Dunkelflaute) and wind-solar drought, can threaten a secure and continuous energy supply owing to an imbalance between electricity demand and supply. In this study, historically reconstructed long-term VRE generation and electricity demand were used to investigate the relationship between extremely high residual load (demand minus VRE output) with dark doldrums, and weather/climate in Japan. The impact of changes in the VRE's installed capacity on this relationship was also investigated using three simple future target scenarios. The results showed that the increase in installed VRE capacity causes greater daily and weekly residual load variabilities and affects the seasonality of its peaks. To study the weather patterns associated with high residual load events, self-organizing maps were applied to atmospheric circulation fields derived from atmospheric reanalysis data. The high residual load was associated with enhanced cold surge-type weather patterns during winter at the current low VRE installation level. However, under future increased VRE penetration, the weather patterns leading to high residual load will change to cloudy-windless types typically caused by a southern coastal extratropical cyclone. There is also considerable interannual  variability in the frequency of  dark doldrums and high residual load events, that is strongly connected to climate variations in the tropical Indo-Pacific. However, this linkages are changed significantly with increasing VRE capacity. It is crucial to incorporate the dependence of climatic conditions into designing power systems to maintain the stability of a power system under future conditions.
Sensitivity of ocean vertical mixing schemes in the coupled Korean Integrated Model (KIM)
Authors: Lee Eunjeong , Han Yong-Jae , Koo Myung-Seo
Published: July 2024

The Korea Institute of Atmospheric Prediction Systems (KIAPS) aims to develop an integrated numerical prediction system that can handle various spatial and temporal scales. As the first step, efforts were made to couple the ocean-sea ice-wave-river routing models to the Korea Integrated Model (KIM), to improve predictability over an extended-range of time scales. The Nucleus for European Modelling of the Ocean (NEMO), Sea Ice modelling Integrated Initiative (SI3), and Wave Watch Ⅲ (WW3) were successfully coupled for the first stage (2020–2022). Subsequently, the refinements have been focused on ensuring the consistency of surface radiative/turbulent fluxes between the model components and optimizing the surface layer parameterization of each model component. Preliminary results showed that the initial coupled KIM is stable for long-range simulation over 20 years and could provide reasonable performance in medium-range forecast and seasonal simulation.The boundary condition for turbulent kinetic energy (TKE) can be expressed using the information of wave breaking calculated from the wave model to alleviate a systematic cold bias in summer regions. The reduction in TKE flux causes warming in regions with a shallow ocean mixed layer, which reduces the systematic cold bias but rather generates a warm bias in the summer hemisphere. Therefore, we attempt to address the bias by utilizing a parameterization of the Langmuir Circulation (LC) effects. LC effects can be carried out in a variety of ways, like including the contribution from the Stokes force in TKE or modifying a mixing length scale, as suggested in the previous studies. In this study, the effects of various LC parameterizations on ocean vertical mixing and sea surface temperature will be investigated and the optimal parameterization to the fully coupled model will be suggested.Acknowledgements. This work was carried out through the R&D project “Development of a Next-Generation Numerical Weather Prediction Model by the Korea Institute of Atmospheric Prediction Systems (KIAPS)”, funded by the Korea Meteorological Administration (KMA2020-02212).
Forecasting regional wireworm damage levels in potatoes based on soil temperatures
Authors: Hann Patrick , Trska Claus , Kamptner Anita , Eitzinger Josef , Wechselberger Katharina
Published: July 2024

Wireworms, the soil living larvae of several click-beetle species (Fam. Elateridae), are serious pests in various crops, e.g. vegetables, potatoes and maize, due to their feeding on underground plant parts. Controlling wireworm infestations in Europe has become increasingly challenging due to the lack of efficient insecticides and presumably the effects of reduced tillage and climate change. It is to be expected that rising temperatures will further enhance the population densities and spread of thermophilic species.In the frame of the ACRP-project “RIMPEST1”, the relationships between weather and wireworm damages in potatoes were investigated for an important potato production region in North-eastern Austria. An analysis was conducted using anonymised data on annual wireworm damages from 2002 to 2022, along with corresponding temperature and precipitation data. The resulting regression model calculates the damage level in potatoes in North-eastern Austria during late summer/autumn at the time of harvest, based on soil temperature sums from the previous months. The underlying significant positive correlation between soil temperatures and wireworm damages in the investigated region might be due to promoting effects of higher temperatures on the thermophilic species Agriotes ustulatus (Schäller) and A. brevis (Candeze).By utilizing the model, farmers and extension workers can calculate a first forecast of the damage risk prior to potato harvest, making it a decision support tool for determining the optimal harvesting date. Additionally, the model approach can estimate future trends of wireworm infestation in potatoes in the respective region based on representative climate scenarios. First results indicate a clear increase in the risk of severe wireworm damages in potatoes in the period from 2031 to 2060. To ensure optimal performance in the future, the model approach should be continuously validated and calibrated with new data from further years. [1] ACRP-13th Call Project RIMPEST (KR20AC0K17957) ("The effect of changing climate on potential risks from important insect pests on plant production in Austria and related adaptation options"). https://www.klimafonds.gv.at/report/acrp-13th-call-2020/https://rimpest.boku.ac.at
Rain-on-snow events in Romania: climatology and trend analysis using observation data (1961-2021)
Authors: Micu Dana , Dumitrescu Alexandru , Amihaesei Vlad-Alexandru , Cheval Sorin , Milian Narcisa
Published: July 2024

Seasonal snowpack plays a significant role in the climate system. Its characteristics are monitored as climate change indicators considering the snow importance from ecological, hydrological, and economic perspectives. Observation data provides evidence of declining depth, spatial extent and duration of the snow cover, in connection to changes in seasonal air temperature and precipitation, especially in the lowlands and mid-elevation mountain areas. However, these changes underpin the occurrence of compound hazards resulting from the joint actions of rain and snow (rain-on-snow events - ROS). Such hazards entail a wide range of physical and socioeconomic impacts related to the complex processes occurring on and within the snowpack, which influence the occurrence of floods, landslides and snow avalanches. In this context, this study aims to provide a baseline understanding of ROS  climatology and how it changes in the present climate conditions. Our work investigates the spatial and temporal changes in the rain-on-snow (ROS) events over the 1961-2021 period, based on 114 weather stations in Romania. This snow hazard is considered in the analysis as it is responsible for important socioeconomic impacts in different sectors (e.g., tourism, forestry, transport). The ROS event detection and trend analysis were conducted at annual and seasonal scales. The results indicate that ROS events occur mainly in winter months in the lowlands (below 1,000 m) and in late fall, winter and spring in the mountains (above 1,000 m). Overall, in the Romanian Carpathians, the highest ROS frequency is in the December-April interval, but at the highest elevation, such events occur throughout the entire year. The years of maximum ROS frequency ( >60/year) were 1962, 1966, 1981, and 2021 and were recorded in the mountain areas. Observation data indicate a decreasing frequency of ROS events, especially in the lowlands, due to the significant changes in the solid/liquid precipitation ratio (more frequent liquid precipitation than solid) and the decline in snow cover duration in response to seasonal warming. In the mountains, the observed change signal in ROS frequency is mixed, showing both decreases and increases. The high elevation areas (above 1,800-2,000 m) register the highest number of ROS events yearly (over 35-40), where the snow avalanche release areas are mostly located. AcknowledgementsThis research received funds from the project “Cross-sectoral framework for socio-economic resilience to climate change and extreme events in Europe (CROSSEU)” funded by the European Union Horizon Europe programme, under Grant agreement n°101081377.
Homogenized and gridded mean temperature data series in Hungary from the mid-19th century
Authors: Szentes Olivér , Lakatos Mónika , Pongrácz Rita
Published: July 2024

The longest period of meteorological measurements in Hungary is available for the temperature records. A substantial expansion of the number of data series used to homogenize temperatures is currently under way, extending back to the second half of the 19th century. The main motivation of the study is the fact that a long-term database based on high-quality measurements is essential to better understand the regional climate and its changes.To improve the understanding of climate and its changes requires temporally and spatially representative climate databases. However, measurement conditions change frequently: the relocation of stations, instrument changes, changes in measurement time, changes in environmental conditions can all cause inhomogeneities in the data series, and therefore homogenization is needed.For homogenization of data series, quality control and filling in the missing values the MASH (Multiple Analysis of Series for Homogenization) procedure (MASHv3.03 software) is used at the Climate Research Department of the HungaroMet Hungarian Meteorological Service. Nowadays, temperature is measured in many more places than, for example, 100-150 years ago, so the homogenization should consist of several steps (i.e. 3 or more MASH systems). For example, we currently use temperature data from 34 stations from 1901 and 55 stations from 1951. These station networks are further extended. Inhomogeneities are estimated using monthly data series. Monthly, seasonal and annual inhomogeneities are harmonized in all MASH systems. Then, we create temporally representative data series using the MASH homogenization procedure.However, weather stations are not evenly distributed, the station network consists of both densely and sparsely covered subregions. In order to estimate the values of meteorological variables at points where no measurements are available, a spatial interpolation method must be used. Our gridded climate datasets are generated using the MISH (Meteorological Interpolation based on Surface Homogenized Data Basis) method (MISHv1.03 software). The use of the MISH interpolation results in a spatially representative climate database.In this presentation, the new mean temperature station systems used for homogenization are described together with the most important verification statistics of the homogenization of mean temperature data series, and finally, the gridded spatial means (national averages for Hungary) are analyzed from the mid-19th century to the present. Acknowledgements:The research presented was carried out within the framework of the Széchenyi Plan Plus program with the support RRF 2.3.1 21 2022 00008 project.
Wind and solar PV generation ramping events from farm to national level: the case of Ireland
Authors: Maimo Far Aina , Sweeney Conor , Flynn Damian
Published: July 2024

Renewable energy sources (RES), such as wind and solar photovoltaic (PV), already account for a large share of today’s electricity systems. This share is set to grow significantly in the near future, due to ambitious emission reduction targets. A significant proportion of energy generation in the future, therefore, will be dependent on local weather conditions, which can change significantly over short time horizons. These sudden changes in renewable generation will need to be managed by electricity grid operators, who will need to ensure sufficient reserve capacity to maintain grid stability, particularly if an increase/decrease in renewable generation is coincident with a decrease/increase in electrical demand.RES-induced ramps are generally caused by changes in weather, which result in rapid, large changes in electricity generation, particularly as weather fronts sweep across a country with the associated winds and cloud coverage. Other events linked to large ramps are solar eclipses for PV and high-wind periods that can lead to wind farm shutdowns. RES ramps are defined as changes in generation, taking place over a number of hours, that exceed a given threshold. In this work, we explore ramping events over Ireland at farm and national scale. First, we explore ramps at individual farm level using data provided by EirGrid, the Irish transmission system operator. Next, we evaluate how ramping events at a national level compare, derived from the combination of all farms. The aim of this first part is to characterise the behaviour of current wind and PV ramps, by means of their duration, magnitude and timing, as well as their temporal and spatial patterns.We will then extend our analysis beyond the available historical generation data, by using models driven by ERA5 reanalysis data to generate hourly, farm-level wind and PV data from 1940 to today. We will quantify the skill of our modelled data, and use it for temporal and spatial analysis of RES ramping events, allowing us to capture a broader range of extremes and return periods, and better understand the seasonality and temporal cycles linked to ramps. Finally, anticipated changes in ramp patterns under the 2030 target of 80% RES in Ireland will be explored, considering the planned developments in capacity for the different technologies, located both onshore and offshore.
Use of Neural Networks for Statistical Downscaling of Long-term Air Temperature Forecasts for the Czech Republic
Authors: Kliegrová Stanislava , Metelka Ladislav , Solanská Jana , Štěpánek Petr
Published: July 2024

Dynamical forecasts use full three-dimensional climate models to simulate potential changes in the atmosphere and ocean over the next few months based on current conditions.  The ensembles of simulations provide probabilistic weather scenarios that indicate the likelihood of a given period being wetter, drier, warmer, or colder compared to the seasonal average. The added value of various downscaling approaches for seasonal forecasts is a topic of frequent debate. This work focuses on statistical downscaling, which is based on empirical relationships derived between a local observed predictand of interest (summer temperature in this case) and one or several suitable model predictors from global seasonal forecasting systems.Unlike tropical regions, seasonal predictability in Europe remains limited. This study analyses the seasonal forecast systems available in the Copernicus Climate Change Service (C3S) archive, which provide near-surface air temperature data at 1° to 1° spatial resolution. This study examines the statistical downscaling of summer air temperature forecasts for Central Europe from two weather forecast systems: the European Centre for Medium-Range Weather Forecasting (ECMWF) SEAS5.1 system and the Météo-France 8 (MF) system.The study analyses the period 1993-2016, which is the longest hindcast period common to all systems, and the domain of the Czech Republic in Central Europe (47-52°N, 11-20°E). To perform statistical downscaling using the neural network method in STATISTICA software, we tested air temperature and sea surface pressure from global forecast models as predictors. The reference data used in this study are gridded E-OBS observational air temperature data sets. The best neural networks found are tested on forecasts for the period 2020–2023.
High-resolution modelling of park cooling efficiency during summer in Paris
Authors: Havu Minttu , Nagel Tim , Wurtz Jean , Masson Valéry , Rivollet Margaux , Haeffelin Martial , Ribaud Jean-Francois , Kotthaus Simone , Dupont Jean-Charles , Lemonsu Aude
Published: July 2024

Heat waves pose a significant risk to human health, particularly pronounced in urban areas due to the urban heat island (UHI) effect. Understanding the varying impacts of heat waves across different neighbourhoods within cities is vital, as well as recognizing the potential of urban vegetation in mitigating temperatures. The Heat and Health in Cities (H2C) project aims to enhance urban climate services to support proactive measures and policies addressing extreme heat risks in cities, with a specific focus on the Paris region. Employing a multi-source observational approach alongside numerical models, the project seeks to provide a more comprehensive assessment of environmental conditions, ranging from regional to neighbourhood scales. The main aim of this study is to examine the efficiency of parks and urban forests in mitigating air temperatures during summer periods including heat waves. Using the Meso-NH model, this study conducts simulations of discrete events occurring in 2022 and 2023 within Paris and the Ile-de-France region. Meso-NH, a non-hydrostatic research atmospheric model, is driven by atmospheric boundary conditions from the French convective-scale operational Numerical Weather Prediction model AROME-France. Coupled with Meso-NH is the land surface model SURFace EXternalized (SURFEX), incorporating the Town Energy Balance (TEB) model for urban elements combined with a high-resolution surface database. Simulations are based on three nested domains with respective grid resolutions of 1200 m, 300 m (over Paris region), and 100 m (over the city of Paris). Model validation of 2-m air temperatures is performed using data from the PANAME (PAris region urbaN Atmospheric observations and models for Multidisciplinary rEsearch, https://paname.aeris-data.fr/) intensive measurement campaign. Previous works have established through the local observations the existence of weather types with contrasting UHI regimes resulting in distinct park cooling efficiencies. The aim of this study is to determine whether hectometric-scale modelling is able to replicate these observed results. The challenge is then to be able with modelling to study and better understand the microclimatic contrasts within the city, in particular the cooling effects generated by urban parks of various sizes and the factors contributing to their variability.
Mapping horizontal wind speed using a single Doppler Wind Lidar scanning horizontally: a test case over Paris
Authors: Toupoint Clement , Cespedes Jonnathan , Kotthaus Simone , Preissler Jana , Thobois Ludovic , Haeffelin Martial
Published: July 2024

Scanning Doppler Wind Lidars are used in a variety of applications, thanks to the versatility brought by their scanning head. Their principal output is the wind speed along the lidar beam, termed the radial wind speed. When used for vertical profiling, the horizontal wind speed and wind direction are obtained from a wind field reconstruction algorithm (DBS or VAD) applied to the radial wind speed along several high-elevation lines of sight.However, for other scanning strategies (i.e., with low elevation or horizontal scans), the use of such algorithms is not common, making the radial wind speed the sole output of the Doppler Wind Lidar. The radial wind speed is more difficult to interpret visually for a human user, harder to compare with numerical models, and requires more work to be used into advanced algorithms.Thus, we showcase the Volume Wind wind field reconstruction algorithm, capable of reconstructing the horizontal wind speed and wind direction from measurement points taken at the same elevation and varying azimuth.We present data taken from the PANAME2022 campaign, in which a Doppler Wind Lidar (WindCube Scan 400S) was set up on an 88m-high tower in Paris city. The lidar performs scans at 0° elevation above the urban area of Paris, measuring radial wind speed from within the Urban Boundary Layer.  Then, we create maps of horizontal wind speed and direction, spanning a large part of the Paris urban area, using the Volume Wind wind field reconstruction algorithm.This allows us to study the influence of the topography on the wind field at the height of the urban canopy. The effect of the bed of the Seine river is of particular interest, as it is thought to be an important ventilation corridor in periods of extreme heat. These results highlight the potential of remote sensors for studying the Urban Boundary Layer, and the added value of advanced processing algorithms. 
Evaluation and Attribution of Shortwave Feedbacks to ENSO in CMIP6 Models
Authors: Huang Junjie , Li Lijuan , He Yujun , Ran Haiyan , Liu Juan , Wang Bin , Feng Tao , Chang Youli , Liu Yimin
Published: July 2024

The shortwave (SW) feedback to El Niño–Southern Oscillation (ENSO) is one of the largest biases in climate models, as the feedback includes atmosphere–ocean interactions and cloud processes. In this study, the performance of SW feedbackover tropical Pacific in 19 models from the 6th Coupled Model Intercomparison Project (CMIP6) is evaluated against observations or reanalysis datasets, and the biases are attributed using the historical and Atmospheric Model Intercomparison Project (AMIP) runs and two coupled assimilation experiments. The results demonstrate that while superior to CMIP5 counterparts, most CMIP6 models still underestimate the strength of SW feedback to different degree. The underestimates of SW feedback arise mainly from the biased feedbacks to El Niño in the four models with relatively better skills, while from both underestimated negative feedbacks to El Niño and overestimated positive feedbacks to La Niña in other models, which reproduce better seasonal variations than corresponding CMIP5 models. Furthermore, the SW feedback bias is connected to weak convective/stratiform rainfall feedback, which is sensitive/insensitive to sea surface temperature (SST) biases during El Niño/La Niña. The total rainfall feedbacks and dynamical feedbacks are underestimated in the historical runs, more than in CMIP5, and shows close relationship with each other. Finally, several data assimilation experiments are conducted using FGOALS-g3, one of models in CMIP6 model ensemble, and the Dimension-Reduced Projection Four-Dimensional Variational (DRP-4DVar) data assimilation system to verify the causes and relationships between feedbacks and mean states, further conclusiona are made.
Enhancing precision in short-term precipitation interpolation with radar background: unraveling case studies through 10-minute radar data analysis
Authors: Bokros Kinga , Izsák Beatrix , Lakatos Mónika , Szentes Olivér
Published: July 2024

This study investigates the refinement of short-term precipitation interpolation, focusing on regions prone to intense, localized thunderstorms like supercells. Traditional meteorological stations often miss these events due to their limited spatial coverage, leaving significant precipitation accumulations unrecorded, leading to incomplete representations and errors in interpolation. To mitigate these interpolation errors, auxiliary data sources such as satellite imagery, weather forecasts, and radar measurements are crucial for refining interpolation processes and enhancing our understanding of precipitation patterns. In our research we integrate radar background information into the MISH (Meteorological Interpolation based on Surface Homogenized Data) method as documented in the studies authored by Szentimrey and Bihari (2007, 2014).Using the MISH method, we processed 10-minute precipitation datasets with and without 10-minute radar-derived background information across the study area building on our prior research (Bokros et al., 2023). We examined how MISH handles radar anomalies, including errors, missing data, and spurious measurements from unintended reflections.Statistical techniques were employed to elucidate the extent to which the inclusion of radar-derived data enhanced the quality of interpolation. Furthermore, our investigation aimed to quantify the robustness of the relationship between interpolations conducted with radar-derived background information and those performed without such supplementary data.Integrating radar-derived background information into interpolation processes is essential for improving societal resilience, agricultural productivity, and hazard forecasting accuracy in areas susceptible to intense thunderstorms. This improvement can lead to better preparedness and mitigation strategies.The research was conducted within the framework of the Széchenyi Plan Plus program, with support from the RRF 2.3.1 21 2022 00008 project.
Estimating Long-Term Annual Energy Production of a Large Offshore Wind Farm from Short Large-Eddy Simulations: Methods and Validation with a 10-year LES Run
Authors: Postema Bernard , Verzijlbergh Remco , van Dorp Pim , Baas Peter , Jonker Harm
Published: July 2024

Atmospheric large-eddy simulation (LES), a computational fluid-dynamics technique that resolves turbulence in the atmospheric boundary layer, is increasingly used for wind resource assessment (WRA), by including wind turbine parametrizations and using external weather data as initial- and boundary conditions. The large computational costs of doing such a 'real-weather' LES, however, limits length of the simulation to < 1 year; whereas long-term, multi-year, mean power production values are of high interest to many parties in the wind energy sector. To address this need, this work presents several methods to estimate long-term mean power production/annual energy production and wind from a < 1 year LES run, by applying Bayes' theorem on short-term LES output and long-term ERA5 reanalysis data. A 10 year LES run of a hypothetical large offshore wind farm is performed in order to validate these 'long-term correction' methods, in three scenarios of increasing complexity. First, long-term correction of 365 consecutive days gives estimates of long-term mean power with a mean absolute error of 0.35 %, and 95th percentile of the absolute error within 0.8 % of the long-term mean, reducing the uncertainty by an order or magnitude. Second, in the scenario when the simulation period is not fixed, using several simple day selection techniques to select the simulation period can reduce the error further. Then, only around 200 days are needed to arrive at the same error values. The results indicate that long-term correction is insensitive to the particulars of the day selection methods, but that including a diverse set of days from different years and seasons is essential. Third, a method to also include wind observations in the long-term correction is presented and tested. This requires an additional 'free stream' LES run without active turbines, and gives estimates of long-term power and wind that are corrected for a potential LES bias. Although validation of this final approach is difficult in the employed modelling strategy, it gives valuable insights, and fits within the common WRA practice of combining models and observations.The presented techniques are based on physical arguments, computationally cheap, and simple to implement; and as such could be a useful extension to the diverse set of modelling, observational, and statistical techniques used in WRA.
Process-based classification of Mediterranean cyclones using potential vorticity
Authors: Givon Yonatan , Hess Or , Flaounas Emmanouil , Catto Jennifer L. , Sprenger Michael , Raveh-Rubin Shira
Published: July 2024

Cyclones handle extreme weather events across the Mediterranean and its neighboring continents, affecting the lives of hundreds of millions. Despite many studies addressing Mediterranean cyclones (MCs) in the last decades, their correct simulation and prediction remain a significant challenge to the present day. This may be attributed to the large variability between MCs, which differ greatly from each other in many aspects. Past classifications of MCs are primarily based on geographical and seasonal separations, yet recent advances and the appearance of “Medicanes” – devastating tropical-like Mediterranean cyclones - emphasize the need for a dynamical classification, focusing on the cyclone deepening mechanisms. A variety of processes alternately govern Mediterranean cyclones' evolution, including diabatic and adiabatic processes, topographic influences, surface temperature anomalies, and land-sea contrasts. Fortunately, each process bears a distinct signature on the potential vorticity (PV) field. Therefore, a PV perspective is called upon to distinguish among the driving mechanisms of the different “types” of Mediterranean cyclones. Here, a combined cyclone tracking algorithm is used to detect and track Mediterranean cyclones in ECMWF ERA5 from 1979-2020. Cyclone-centered, upper-level isentropic PV structures in the peak time of each cyclone track are classified using the Self Organizing Map (SOM). The SOM analysis reveals 9 classes of Mediterranean cyclones, with distinct cyclone characteristics, lifecycles, associated hazards, and long-term trends. Though classified by upper-level flow structures, each class shows different flow structures down to the surface. Unique synoptic, thermal, dynamical, seasonal, and geographical features indicate dominant processes in the evolution of each Mediterranean cyclone subset. Furthermore, the tropopause-surface coupling is explored and reveals the importance of topographically induced Rossby-wave breaking to the generation of the most intense Mediterranean cyclones. These results enhance our understanding of Mediterranean cyclones' predictability, by linking predictable large-scale Rossby wave formations and life cycles to under-predicted cyclonic variability and associated hazards.
Changing climate of Bangladesh
Authors: Rashid Md. Bazlur , Sultana Afruza , Hassan S M Quamrul , Kuya Elinah , Parding Kajsa , Hygen Hans Olav
Published: July 2024

The global climate is changing, and Bangladesh is not spared. Bangladesh Meteorological Department (BMD) and Norwegian Meteorological Institute (MET Norway) have collaborated since 2011, and in 2016 we co published a report on the current status of the climate of Bangladesh called Climate of Bangladesh. The same cooperation has resulted in a new report that was published this year. The new report goes beyond the baseline of today's climate and explores detected changes in the Bangladeshi climate for the last 40 years through the weather observations in Bangladesh. In this report, a homogeneity test of the Bangladeshi observations is performed, and shows that data from about 1980 has an acceptable level of homogeneity. Based on this, trends have been calculated for maximum and minimum temperature, daily temperature range, precipitation amount and frequency, and sunshine hours and cloudiness. The major picture is that in Bangladesh all of these elements show clear signals of change consistent with the global pattern of climate change. This analysis also indicates regional differences with a more rapidly warming climate in the north and east of the country. More details of the effects of the changing climate on heatwaves and cold outbreaks have been analysed. This analysis shows a tendency to later onset of the heat, and, more worryingly, a tendency of increased heat waves in the monsoon season. A similar analysis of heavy to very heavy rainfall does not reveal any clear shift.The monsoon is very important for the agricultural sector, and has received special attention in the report. The dates for the onset of monsoon, as documented by meteorologists at BMD, is published, and a small analysis shows no significant change in onset day. The rainfall in the monsoon is changing, and a pattern of decreased rainfall in most of the country is emerging but not statistically significant.The report is co-published at the websites of BMD and MET Norway
SAREPTA - MET Norway's involvement in capacity building
Authors: Demissie Teferi Dejene , Kuya Elinah Khasandi , Hygen Hans Olav , Skjervagen Iselin Heimdal , Hole Lars Robert , Østvand Lene , Benestad Rasmus E.
Published: July 2024

MET Norway has been active on the scene of capacity building for a long term, with a more systematic approach since 2011 when the Foreign Ministry approved a cooperative project with Bangladesh Meteorological Department (BMD). This project had 4 work packages, each with a joint leader team in Norway and Bangladesh, thus ensuring BMD’s involvement in the governance of the project. This structure is kept as a core in the structure of the further engagement in other countries.Currently MET Norway is involved in projects in Bangladesh and Vietnam in Asia, and Ethiopia, Mozambique, Malawi, and  a new initiative in Tanzania, in Africa.  In addition, we have previously had engagements in Cuba, Myanmar, Sri Lanka, and India. The six countries where MET Norway currently is engaged show both common needs and a clear diversity in needs and capabilities, e.g. in the area of climate the engagement has covered everything from data rescue to downscaling of climate projections dependent on local needs and capabilities. This leads up to the main lesson learned from our engagement, the questions we need to ask repeatedly: “What is the need of your service to develop in the short and long term?” and “How can our cooperation aid your progress towards where you want your service to develop?”. And let the answers of these questions shape the engagement and training of personnel in the recipient countries.NORAD, the Norwegian Agency for Development Cooperation, asked MET Norway to coordinate all our projects into one major project. This coordination into one project, called SAREPTA, combined with the diverse needs in the recipient countries, resulted in a project containing a menu of possible work packages and sub projects for each country. These sub projects were built from the possible work packages reflecting the country's needs and available human and technical resources. For all work packages open source tools are used, and the resources are prioritized on training personnel in these tools, rather than on tools that require licenses. We believe that this approach is more efficient and better allows continuity in the long run.MET Norway is also engaged in SOFF and CORDEX, explicitly engaging in SOFF only in countries where MET Norway has activity through SAREPTA thus enabling coordination between the national efforts in SAREPTA and SOFF. The CORDEX Flagship Pilot Study (FPS) southeast Africa is coordinated by Mozambique’s national meteorological services, and aims to build a regional network of competence within climate research, and is supported by MET Norway. 
Investigating ground-level relative humidity and radar reflectivity using C-band weather radar observations in two contiguous irrigated and rainfed areas
Authors: Polls Francesc , Peinó Eric , Udina Mireia , Bech Joan
Published: July 2024

Sub-estimation of weather radar quantitative precipitation estimates (QPE) is often attributed to the classical mechanism of precipitation evaporation below the cloud layer. The aim of this study is to investigate the potential impact of evaporation on radar reflectivity profiles using data from co-located automatic weather stations (AWS), which furnish ground-level measurements of air temperature, pressure, and relative humidity.  The study is based on a six-year observational dataset from an area characterized by intense agricultural activity and divided into two sub-areas: an irrigated area and a rainfed area, separated by an artificial channel. The research is carried out over The Land Surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) domain, in the eastern Ebro valley in Catalonia (NE Spain) using C-band weather radar observations and AWS data from the Meteorological Service of Catalonia.Although the analysis revealed clear differences in average ground-level temperature and humidity between the irrigated and non-irrigated areas in dry days during the warm season, no clear differences were found on average precipitation frequency, intensity, amount and convective fraction between the two sub-areas.  A more detailed study, specifically focusing on reflectivity profiles occurring during the first 30 minutes of rain following a 24-hour dry period, was conducted to examine cases prone to rainfall evaporation. The results indicated that after a 30-minute period of the rainfall onset, ground-level AWS temperature and relative humidity of both irrigated and rainfed areas -which were different before rainfall- tended to converge indicating that during rainfall ground level conditions are quickly homogenized. Finally, for this 30 first minutes and specific conditions, radar reflectivity observations at 1 km height did exhibit a statistically significant correlation with ground-level relative humidity for convective cases, irrespective of the sub area (irrigated or rainfed) considered. These results contribute to enhance our understanding of possible evaporation effects on weather radar QPE and may serve as a basis for the future development of an evaporation correction method. This study was supported by projects RTI2018-098693-B-C32 and PID2021-124253OB-I00. 
On the ubiquitous unstable relation among climate modes and their local effects
Authors: Gallego David , Alvarez-Castro Carmen , Barriopedro David , García-Herrera Ricardo , Peña-Ortiz Cristina
Published: July 2024

Meteorological data recorded in logbooks form the sailing era has received increased attention in the last decades. The daily and even sub-daily data kept by seamen, allows extending back in time the time series of wind and other relevant variables along the historically busiest naval routes. Different projects have integrated these data in the ICOADS open database, and the availability of this massive data collection in its raw format has inspired the development of a new class of climatic indices based on the wind direction variability known as “directional indices”. They have the advantage that wind direction is the only variable needed, and little or no metadata are required to convert historical observations to current standards. This minimizes the possibility of appearance of data-induced inhomogeneities and non-climatic biases in the resulting series. On these bases, during the last decade, our team has worked in the development of a suite of instrumental circulation indices for remote areas and for periods of time significantly longer than those attained by any other methodology based uniquely on in-situ meteorological observations.Directional indices have been applied to the construction of NAO-like records, to the evaluation of the intensity of several monsoons, to the examination of the variability of local wind systems and even to the analysis of the long-term variability of upwelling favorable winds for several Eastern Boundary Upwelling Systems. The common factor of all these investigations is the lack of stationarity in the relation of the structures analyzed and the classical modes of global climate variability such as El Niño / Southern Oscillation, the Atlantic Multidecadal Oscillation, both Annular Modes, etc. Our combined results provide direct instrumental evidence of the lack of stationarity between global variability modes and their associated local impacts at worldwide scale, challenging the implicit assumption of stationarity that underlies the use of proxies for climate reconstruction.
Identification and Risk Characteristics of Agricultural Drought Disaster Events Based on the Copula Function in Northeast China
Authors: zhang shujie
Published: July 2024

Accurate feature identification of drought disaster events is required for proper risk management in agriculture. This study improved the crop water deficit index (CWDI) by including the daily meteorological, crop development stage, soil moisture content, and yield data for 1981–2020 in northeastern China. Two drought characteristic variables (drought duration and intensity) were extracted using the theory of runs to produce the improved crop water deficit index (CWDIwp). Thresholds for the bivariate indicators were also determined for agricultural drought events of varying severity. A joint distribution model for drought variables was constructed based on five types of Archimedean copulas. The joint probability and the joint recurrence period for agricultural drought events were analyzed for drought events with varying intensities in northeast China. The results suggest that the CWDIwp can reliably identify the onset, duration, and intensity of drought events over the study area and can be used to monitor agricultural drought events. The conditional probability of drought intensity (duration) decreased as the drought duration (intensity) threshold increased, whereas the drought recurrence period increased as the threshold for drought duration and intensity rose. In the period (1981–2020), drought intensity in the three Northeastern provinces showed an increasing trend in the order Jilin Province > Liaoning Province > Heilongjiang Province. The spatial distribution of the joint probability and the joint recurrence period was obvious, and the joint probability showed a decreasing distribution trend from west to east. The distribution trend for the joint probability was opposite to that of the joint recurrence period. Furthermore, the areas with high drought probability values corresponded to the areas with low values for the recurrence period, indicating that the drought occurrence probability was higher, and the recurrence period value was lower in the drought-prone areas. The high-risk drought areas (60–87%) were in western Liaoning and western Jilin, with a recurrence period of 1–3 years, whereas the low-risk areas ( moderate drought > severe drought > extreme drought. The order for the recurrence period was light drought < moderate drought < severe drought < extreme drought. The results provide technical support for disaster prevention and mitigation in drought risk management.