bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiscale modeling of wind during an OCC event over an offshore wind park: Implication on wake and loads
Authors: Bakhoday-Paskyabi Mostafa , Bui Hai , Mohammadpour Penchah Mohammadreza , Krutova Maria , Ning Xu
Published: July 2023

Multiscale modeling is essential in atmospheric science as it allows for a better understanding of atmospheric processes at different scales, more accurate predictions of weather events, and specifically, it helps scientists and engineers in offshore wind to understand the complex flow physics involved in wind turbine operation. At the microscale, multiscale modeling for offshore wind applications may be based on Large Eddy Simulation of wind, which helps predict turbulent flow and mixing at small scales, and study the aerodynamic behavior and load characteristics of wind turbines. Simulations at small scales are further useful for optimizing the design of wind turbines for improved performance and efficient operations. At the mesoscale, multiscale modeling such as the Weather Research and Forecasting (WRF) provides capability to simulate a range of atmospheric phenomena from global climate patterns to regional weather episodes at coarse resolution. Since WRF at these coarse resolutions is not able to explicitly resolve the small-scale turbulence, the model-chain containing the WRF and LES combines the strengths of both models to resolve from several kilometers to several tens of centimeters.  
 
This study presents our recent development on offline nesting of the Weather Research and Forecasting (WRF) model with the Parallelized Large Eddy Simulation (PALM) model to improve simulation resolution in the area of Alpha Ventus offshore wind park, while maintaining reasonable computational costs. The WRF model simulates the entire domain covering the Southern North Sea, while the higher resolution PALM model is used to simulate the finest WRF nested domain within the area of interest. We use then the LES simulation results to investigate the wake dynamics behind the wind turbines and provide insights into the loads of downstream turbines. It is noted that we focus on the LES simulations of an Open Cellular Convection (OCC) event and the study of impacts of this transition event on the dynamics of wind, atmospheric turbulence in modulating wakes, loads, and in general flow patterns in the area of Alpha Ventus offshore wind park (OCCs can cause strong variations in wind speed and direction leading to significant modulation of the aerodynamic behavior of wind turbines and their power outputs).
RainRunner: A Deep Learning satellite rainfall retrieval model for West Africa
Authors: Estebanez Camarena Monica , Curzi Fabio , Taormina Riccardo , van de Giesen Nick , ten Veldhuis Marie-Claire
Published: July 2023

Food and economic safety in West Africa rely heavily on rainfed agriculture and are threatened by climate change and demographic growth. Accurate rainfall information is therefore crucial to ensure safety against these challenges. However, existing rainfall models fail to accurately represent the highly variable and sparsely monitored West African rainfall distribution. Satellite rainfall products show a poor correlation with ground-based rainfall measurements and literature suggests that atmospheric aerosols are partly to blame for this poor performance.
To address this challenge, we propose a Deep Learning (DL) model that utilizes satellite water vapor (WV) and Thermal Infrared (TIR) observations in conjunction with temporal information for satellite rainfall retrieval in West Africa. We leverage the TIR and WV channels of the Meteosat Second Generation satellite to develop a DL model for satellite rainfall detection. Our results indicate that incorporating WV data into the DL framework enables the detection of strong convective motions typically associated with heavy rainfall. This is particularly relevant in regions where convective rainfall is dominant, such as the tropics.
Furthermore, the WV data facilitate the identification of dry air masses advected from the nearby Sahara Desert, which often create discontinuities in precipitation events over our study area. The ability to detect such dry air masses is a significant advantage of our proposed DL model, as it aids to reduce false alarms and rainfall overestimation as compared to methods that rely only on TIR data.
Our DL model achieves a robust performance in rainfall binary classification, with fewer false alarms and lower rainfall overdetection (FBias < 2.0) than the state-of-the-art Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run. Our findings suggest that incorporating WV observations and temporal information in a DL framework efficiently complement TIR observations and enhances the accuracy of satellite rainfall retrieval in West Africa.
Communicating Weather on Digital Platforms - How AccuWeather Meteorologists Help People Make Better Decisions
Authors: Roys Tyler
Published: July 2023

As our understanding of the atmosphere evolves and the computing resources to model the atmosphere become more accurate, communicating the forecast to users remains as important as ever. Since the first radio and television weather forecasts, the role of the meteorologist has required effective communication for users to make better weather-impacted decisions. Now, as consumers glean weather forecasts and information from digital sources such as weather apps, websites, and social media outlets, meteorologists are called upon to create new ways to communicate the impacts and context of the weather forecast concisely and effectively. Over its 60-year history, AccuWeather has pioneered many advances in the field of weather communication, including the industry’s first 7-day local television forecast, AccuWeather RealFeel ® Temperature, and AccuWeather RealImpactTM Scale for Hurricanes. Meteorologists were crucial to the development of each of these innovations, and meteorologists remain at the heart of AccuWeather’s mission to save lives, protect property and help people, businesses and communities prosper – enabling better decisions. AccuWeather meteorologists are an integral part of an innovative forecast process that combines human expertise with state-of-the-science data ingestion, processing, and analysis methods. This process continuously evolves to ensure forecasts that are provided to a variety of users such as governments, businesses, and the general public are the most actionable, timely, and detailed. By providing forecasts that are the most accurate, localized, and impact-based, AccuWeather communicates weather information worldwide that helps people make decisions to improve their lives. The ways in which AccuWeather helps the public to understand the complexity of the forecast will be discussed in addition to unique role of the meteorologist at AccuWeather.
New climate service initiative under the Nordic Framework for Climate Services
Authors: Dyrrdal Anita Verpe , Paine Mark , Rosin Kai , Valiukas Donatas , Hygen Hans Olav , Ólafsdóttir Anna Hulda , Matthíasdóttir Theódóra , Wallin Pontus , Gaile Dace , Jokinen Pauli , Mändla Kaupo
Published: July 2023

We are excited to introduce a newly established initiative within the network Nordic Framework for Climate Services (NFCS), focusing on national climate services in the Nordic-Baltic countries.  
NFCS operates under NORDMET and has in the last few years cooperated within two activities related to new climate normals and heavy rainfall. With the completion of these activities, The Norwegian Meteorological institute (MET Norway) suggested revitalizing a previous activity focusing on climate services. In December 2022, the Danish Meteorological Institute (DMI) organized a workshop to establish a network and explore potential avenues for collaboration. The following countries were represented at the workshop and presented their respective national climate service: Norway, Latvia, Estonia, Iceland, Sweden, Finland, Lithuania and Denmark.
Climate change affects all countries and regions. Nordic countries (five) and Baltic countries (three) are located in the northern region of Europe, where the processes and impacts of climate change are quite similar.  Adequate provision of climate services can not only improve and strengthen knowledge about climate change, but also reduce the negative impacts of climate change. Communication, cooperation, exchange of knowledge and good practices among countries in the region can contribute to better knowledge about climate change and climate change impacts across the region. 
While the level of development of climate services varies across these countries, common concerns were also identified. Amongst common activities and concerns we can mention

Updating the knowledge base for national and local climate adaptation following the Sixth Assessment Report from IPCC
New sectors focusing on climate adaptation such as finance, insurance and health
Increasing need for sector specific climate indices
Data management and resources, code sharing and version control
Challenges related to outreach and communication. 

At EMS we will present and compare national climate services of these eight countries and highlight opportunities for future collaboration under very limited resources.
FAIRness wizard: FAIR Micromet Portal FMP2.
Authors: Lalic Branislava , Scriney Michael , Savic Stevan , Roantree Mark
Published: July 2023

The current state of weather-induced agricultural losses, water use for irrigation, the appearance of new invasive species and disease vectors (strongly depending on micrometeorological conditions), new environmental zoning of plant diseases and pests, deforestation, increased urbanization, rural-to-urban migration and increased urban energy consumption for cooling and heating impose scientific and societal demands for FAIR micrometeorological data.
 
It is important to remember the FAIR acronym for: Findability, Accessibility, Interoperability, and Reusability. This means that data and metadata should be easily discoverable by humans or machines, accessible under specific conditions or restrictions, conform to recognized formats and standards to be combined and exchanged, and licensed according to community norms, allowing users to know what kinds of reuse are permitted. While open data is the ultimate goal, it is important to have in mind that the FAIR concept implies open metadata only. Measurement results should be stored on a repository chosen by the data owner with a DOI and prefered licence, from closed to fully open with numerous options. More information can be found, for example at Open Data Commons (https://opendatacommons.org/licenses/).
 
Why?
The lack of FAIR data costs Europe a minimum of €10.2bn per year - approximately 78% of the Horizon 2020 budget per year (PwC EU Services, 2018). If data met the FAIR principle, it would improve data discovery and access, enable re-use, enhance understanding, especially across domains, reach as many people as possible, be cited more often, and open new routes to build cooperation.
How?
To support owners of micrometeorological data to make their data FAIR, the FAIR Micromet Portal (FMP2.0) was developed within the CA20108 COST Action. FMP2.0 is designed and built to guide owners through FAIR principles, in a step-by-step manner, to make large volumes of data FAIR compliant. More details about FMP2.0 and its functionalities can be found in Roantree et al. (2023).
Literature
PwC EU Services, 2018: The cost of not having FAIR research data. DOI 10.2777/02999
Roantree, M., Lalic, B., Savic, S., Milosevic, D., and Scriney, M., 2023: Constructing a Searchable Knowledge Repository for FAIR Climate Data, EGU General Assembly2023, Vienna, Austria, 24–28 Apr
2023, EGU23-7786. https://doi.org/10.5194/egusphere-egu23-7786, 2023.
Estimation of atmospheric parameters and solar irradiance based on a sky imaging system
Authors: Kazantzidis Andreas , Salamalikis Vasileios , Tzoumanikas Panagiotis , Logothetis Stavros-Andreas , Giannaklis Christos , Tsourounis Dimitrios , Economou George , Theocharatos Christos
Published: July 2023

The presentation refers to DeepSky, a project aiming to develop an innovative and flexible on-site measurement system to fully address the needs of end-users of the meteorological, atmospheric and solar communities. DeepSky is based on the analysis of information retrieved by two head camera (an all-sky imager (ASI) and a thermal camera), radiative transfer in solar / thermal spectral bands and methods of computational optics and deep learning. The proposed system is able to reliably assess a range of geophysical variables (solar radiation, cloud cover percentage and type, aerosol optical properties, etc.).
For this scope, a bouquet of methodologies has been developed. Aerosol Optical Depth (AOD) is retrieved by the ratio of red to blue (RBR) color intensities, which are relevant to radiances at wavelengths 440 nm and 675 nm. A cloud type identification methodology is presented by exploiting the sky condition information: the proposed methodology uses a k-Nearest-Neighbor algorithm, considering as inputs specific information derived from the ASI such as color intensity, the cloud coverage, the saturated area around the Sun, the raindrop appearances and solar zenith angle. A methodology to retrieve precipitable water under cloud-free conditions using images from a thermal-infrared camera is also presented, by examining the relationship between PW and zenith-sky temperature.
Finally, this work is focused on modeling the global and diffuse horizontal irradiances (GHI and DHI) using deep learning techniques and ASI-derived information. The preliminary estimations underestimate GHI and DHI observations with systematic biases of –1.8 W m–2 and –0.5 W m–2, while the dispersion errors are 82.7 W m–2 and 39.8 W m–2, respectively. The correlation coefficient is high, approaching 0.95 and 0.85 for GHI and DHI.
Overall, a new on-site monitoring system is presented covering multiple needs in the areas of atmospheric physics and solar energy and providing parameters for which separate instruments would be needed.
A deep learning multimodal method for precipitation estimation
Authors: Moraux Arthur , Dewitte Steven , Munteanu Adrian
Published: July 2023

Deep-Learning (DL) is a sub-field of Machine-Learning (ML) whose popularity has grown exponentially during the last decade thanks to its numerous successes related to artificial intelligence, e.g. visual object recognition and detection, image generation, natural language processing or speech recognition. But, like other ML algorithms, DL can be applied to a broader variety of problems, as long as the necessary data is available to train the model. In this aspect, the field of meteorology satisfy the data requirement. Particularly, DL could improve the accuracy of precipitation estimation. Accurate precipitation estimation is a very important product of weather institutes for water supply monitoring, and as input and validation for Numerical Weather Prediction models and precipitation nowcasting. Currently, precipitation are mainly estimated using both radar and rain-gauge data, but an important limitation of this method is the limited coverage of these measurements. A possible solution to this problem could come from satellite radiometers observations. Unfortunately, estimating precipitation accurately from satellite radiometer data is challenging.
As a solution, we developed a DL method to merge rain gauge measurements with a ground-based radar composite and satellite radiometer imagery. The proposed convolutional neural network, composed of an encoder–decoder architecture, performs a multiscale analysis of the three input modalities to estimate simultaneously the rainfall probability and the precipitation rate. We used SEVIRI infrared channels, the OPERA radar composite and the measurements of automatic rain gauges. The training of our model and its performance evaluation are carried out on a dataset spanning 5 years from 2015 to 2019 and covering Belgium, the Netherlands, Germany and the North Sea. Our results for instantaneous precipitation detection, instantaneous precipitation rate estimation, and for daily rainfall accumulation estimation show that the best accuracy is obtained for the model combining all three modalities. We show that the combination of rain gauge measurements with radar data allows for a considerable increase in the accuracy of the precipitation estimation, and the addition of satellite imagery provides precipitation estimates where rain gauge and radar coverage are lacking. We also show that our multi-modal model significantly improves performance compared to the European radar composite product provided by OPERA and the quasi gauge-adjusted radar product RADOLAN provided by the DWD for precipitation rate estimation. Additionally, we carried a study of our method on the case of the extreme precipitation event of July 2021 that affected Belgium and Germany, which caused huge societal and economical damage.
An integrated meteorological forecasting system for emergency response
Authors: Hervo Maxime , Haefele Alexander , Bättig Philipp , Leuenberger Daniel , Merker Claire , Regenass Daniel , Kaufmann Pirmin , Arpagaus Marco
Published: July 2023

EMER-Met is the new meteorological forecasting system for the protection of the population in Switzerland. It provides the meteorological basis for coping with all types of emergencies, especially in case of nuclear and chemical accidents. EMER-Met consists of a dedicated upper air measurement network and a high-resolution numerical weather prediction model.
The measurement network is composed of state-of-the-art remote sensing instruments to measure accurate wind and temperature profiles in the boundary layer. At three sites, a radar wind profiler PCL1300, a Doppler lidar Windcube-200s and a microwave radiometer Hatpro-G5 are installed. The synergy between the radar wind profilers and the doppler lidars allowed to reach an availability of 99% in 2022. Compared to the radiosounding, the RMS of the vector difference was below 2m/s for both the lidar and the radar, reaching the "breakthrough" WMO/OSCAR requirement for NWP forecasting in the boundary layer. 

The data from the measurement network are assimilated into the operational 1-km ensemble numerical weather prediction (NWP) system. In the case of the microwave radiometers, we assimilate the brightness temperatures using an adapted version of the RTTOV observation operator. To ensure best impact on the NWP results, the data quality of the measurements is of high importance and is monitored closely on a daily and monthly basis against radiosondes and the NWP model itself. EMER-Met is operational since 2022 and to our best knowledge, it is the first time that the brightness temperatures measured by surface-based microwave radiometers are assimilated operationally. This presentation will focus on the upper air network performance and its impact on NWP. 

 
Wine, vacherin and volcanoes: impacts of the 17th century large volcanic eruptions
Authors: Bartlome Niklaus
Published: July 2023

It is today undisputed that the volcanic eruption of Tambora in 1815 caused a year without summer in Central and Western Europe, which led to crop failures, inflation, disease and famine. However, little attention has been paid to the teleconnections of the great volcanic eruptions in the 17th century on agriculture and society in early modern Switzerland.
Using a regional example, the present study attempts to examine the climatological and socio-economic effects of those eruptions and the coping strategies that were developed as a result. For several centuries, the Hôpital des Bourgeois de Fribourg meticulously recorded every year all their harvests of crop, wine and dairy products - such as the famous Vacherin cheese - and the number of cattle slaughtered. These primary or secondary climate proxies can be examined for potential climatological effects.
As a public institution, the Hôpital des Bourgeois de Fribourg is strongly linked to the state of Fribourg itself. In addition to these account books, the state archive possesses an extended corpus of sources related to the history of this institution. Essential archive material, such as the Ratsmanuale (protocols) and the Mandatenbücher (regulations), allows us to draw conclusions about the socio-economic impact of the climate crises of the 17th century and the responses of the state and this important social and economic institution. These findings can then be compared with modern climate reconstruction data from the ModE-RA project – presented in a newly developed tool – to better assess the interrelation between the two. In other words, we will see how volcanoes affected Fribourg's viticulture and their Vacherin production as a result of this highly interdisciplinary approach.
Interannual variability of the wintertime Asian-Bering-North American teleconnection linked to Eurasian snow cover and Maritime Continent sea surface temperature
Authors: Zhong Wogu , Wu Zhiwei
Published: July 2023

Recent studies propose that the Asian-Bering-North American (ABNA) teleconnection is a distinct atmospheric pattern that is related to Eurasian and North American winter climate besides the Pacific-North America (PNA) pattern, while its origin remains elusive. This study investigates the interannual variability of the ABNA during the past 42 winters (1979-2020) and the associated prior surface boundary forcings. The ABNA explains coherent surface air temperature changes in northern Asia, Eastern Siberia-Alaska, and eastern North America, even after removing the impacts of the PNA, the Arctic Oscillation, the North Atlantic Oscillation, and the North Pacific Oscillation. Surface boundary conditions linked to the ABNA could be traced back to a Eurasian snow cover dipole pattern (ESCDP) and a Maritime Continent sea surface temperature anomaly (MCSST) in November. The ESCDP leads to a displacement of the Arctic stratospheric polar vortex via troposphere-stratosphere coupling. The anomalous polar vortex propagates downwards in the following winter and generates the tropospheric ABNA pattern. The MCSST induces a diabatic heating anomaly, which is associated with a tropical western Pacific precipitation anomaly (TWPP) in winter. The TWPP excites a poleward Rossby wave train that propagates across the North Pacific and directly strengthens the ABNA. The above physical processes can be well reproduced by a linear baroclinic model (LBM). Based on the ESCDP and MCSST predictors, an empirical model is established and shows a promising prediction skill of the ABNA during the hindcast period. This can provide a useful strategy for seasonal prediction of winter climate in the Northern Hemisphere extratropics. 
AQUAS - A Data Quality Control tool at GeoSphere Austria
Authors: Filipović Niko
Published: July 2023

The station network of the Austrian Weather Service GeoSphere Austria comprises about 260 semi-automatic weather stations called TAWES. The observation data collected by TAWES are checked for plausibility and completeness in several steps. The main quality control is based on an automated tool called AQUAS (short for Austria Quality Service). The software was developed at ZAMG (now GeoSphere Austria) in Vienna as part of quality management in the area of real-time processing of near-surface observation data.The basis for quality control is formed by standard methods for checking meteorological and climatological data (e.g. plausibility check, temporal, spatial and internal consistency check, etc.), which are continuously improved and further developed within the framework of AQUAS. The individual system components are designed to test the incoming observation parameters in real time. For the greatest possible flexibility, each parameter can be processed autonomously, independently of the other measured variables of a weather station. In addition, data from other sources such as radar and satellite data as well as data from numerical weather prediction models and data from other measurement networks, like hydrological network or any other third-party network, can also be implemented in AQUAS.Some examples for the operational use of AQUAS and the current state of research on quality control procedures are presented. As an example, a method for real-time quality control of 10-minute wind speed data is shown, which detects doubtful wind gust peaks. Another example on the basiss of daily data shows a method using daily sums of global radiation and sunshine duration to detect instrument malfunctions.
Changes in drought occurrence due to climate change in Norway
Authors: Tveito Ole Einar , Gangstø Reidun
Published: July 2023

Due to climate change, droughts are becoming more frequent and severe in many parts of the world. The drought experienced in Norway during 2018 caused significant repercussions, particularly in the agricultural industry. Norway's rising temperatures and altered precipitation patterns are attributed to climate change. While annual precipitation has increased and precipitation events have become more intense, summer precipitation is not expected to increase everywhere in Norway, and evaporation will rise with temperatures. The impact of these changes on the risk of drought in different regions of Norway remains uncertain.
To address this uncertainty, we undertook an analysis of the frequency, extent, and duration of summer (April-September) drought in Norway, utilizing several meteorological drought indices such as the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) at timescales ranging from 3 to 12 months. We have developed monthly gridded datasets of SPI and SPEI based on gridded daily precipitation and potential evapotranspiration. The potential evapotranspiration is estimated by the Penman-Monteith formula. Using these datasets, we calculated trends and compared the last two official normal periods (1991-2020 and 1961-1990) to analyze historical changes. Furthermore, we compared future climate projections with the 1991-2020 period to identify potential future changes. 
Preliminary results did not indicate significant changes in occurrence of severe droughts in Norway. The two drought indices applied shows the same temporal variability, and the increase of total summer precipitation balances the increased potential evapotranspiration.  There is however a slight increase in the SPEI drought conditions compared to SPI. 
Our research has implications for Norway's agricultural sector and natural resource management. A better understanding of past and future drought changes can aid in the development of effective adaptation strategies.
New Capabilities in the National Solar Radiation Data Base
Authors: Sengupta Manajit , Habte Aron , Xie Yu , Buster Grant , Benton Brandon , Yang Jaemo
Published: July 2023

The National Solar Radiation Database (NSRDB) provides global solar resource data at a high temporal and spatial resolution. This data is primarily used in solar energy modeling and is updated on a regular basis. The NSRDB uses a physical approach to satellite-based solar modeling. The underlying Physical Solar Model (PSM) computes cloud-properties using satellite remote sensing and subsequently solar radiation using radiative transfer models. The retrieved cloud properties include cloud-mask, cloud-type, cloud optical depth and cloud droplet size. The radiative transfer models require additional input parameters such as aerosol optical properties (AOD), preciptable water vapor, surface albedo, temperature and pressure to accurately model solar radiation. While cloud properties are obtained directly from the geostationary satellites other inputs are obtained from additional source such as the National Aeronautical and Space Administration (NASA) Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA2), the Interactive Multisensor Snow and Ice Mapping System (IMS) model data from the U.S. National Ice Center and NASA’s polar orbiting satellites such as the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Aqua and Terra Platform.
 In 2022 the NSRDB was updated using the latest version of the underlying PSM. This update includes improved surface albedo and gap-filling of cloud properties. The inclusion of these updates reduced the uncertainty in the data compared to previous versions of the NSRDB. The Himawari and Meteosat Indian Ocean Data Coverage satellites were added to the Geostationary Operational Environmental Satellite and made our coverage global. While standard data from the GOES continues to be served at an hourly 4km x 4km resolution, full resolution data has also been made available to the user. The user is provided significant flexibility for downloading data depending on the amount of data required. Data can be downloaded using either the web-interface, an Application Programming Interface or directly from the cloud using Amazon Web Services. Services such as spectral data use on-demand computation and delivery.
Evaluation of the NSRDB was conducted for 18 stations and the the Mean Bias Error (MBE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were computed for both GHI and DNI. The evaluation was conducted for the 1998-2021 period. Generally, the MBE lies within plus or minus ±5% for GHI and ±20% for DNI. The RMSE is less than 30% for GHI and 35% for DNI. 
Significant new updates are planned for 2023 including the use of the new FARMS DNI model under cloudy sky situations. There are additional plans to partition the NSRDB data using cloud fraction when evaluating the accuracy of the NSRDB. This presentation will provide users with the latest information about the NSRDB as well as plans for future development and updates.
 
 
Delivering climate information needs for environmental decision-makers
Authors: Murphy Brad , Boulter Sarah , Burgess Tahnee , Clarke John , Hoffman David
Published: July 2023

The Australian Government's National Environmental Science Program (NESP) supports decision-makers by providing them with the best available information, based on world-class science. This helps Australian decision-makers to better understand, manage and conserve Australia’s environment. The Climate Systems Hub, one of four national environmental science research hubs, provides research to advance the understanding of Australia’s climate, its extremes and associated drivers, that will directly inform climate adaptation solutions for Australia.
Wide engagement with stakeholders has provided vital insight on how users want climate information delivered to enable widest possible uptake and increased impact. The climate information needs of users varies greatly with their level of technical expertise and intended applications. The most universally identified requirement is for a single location guiding users to the most appropriate, and sector-specific information, as well as to data at the appropriate level for their expertise and application. Calls for guidance, training, and support on how best to use information also emerged as pressing needs. 
To meet these user needs, the Climate Systems Hub is developing a gateway to deliver integrated training, guidance, and regional projection summaries. The gateway should help overcome barriers to finding and applying climate information in decision-making using the best available science and following good practice. This presentation will detail the key user needs identified through engagement activities, the recommendations drawn from these needs, a gap analysis, and review of existing climate data portals. We will also provide progress in co-designing integrated, tailored climate information and its delivery that fills a gap in the existing information landscape and meets the specific needs of NESP stakeholders.
Estimating location errors in precipitation forecasts with the Wasserstein and Attribution distances
Authors: Lledó Llorenç , Skok Gregor , Haiden Thomas
Published: July 2023

Due to the strongly fluctuating nature of precipitation both in space and time, there is a need for high-resolution forecasts in order to provide accurate information for user applications. However, as we transition to high-resolution forecasts, the predictability limits imposed by the physics of convective motions render traditional verification techniques less effective for measuring forecast quality. While high-resolution models might be able to realistically simulate convective motions and their associated precipitation, the exact location of the updrafts and the surface precipitation cannot be determined precisely. This poses a problem with classical point-to-point verification techniques such as root mean squared error (RMSE), because any displacement of the precipitation in the forecasts will result in a double penalty. There are three specific problems with RMSE when there are location errors: a) forecasts that have less variability than observations score better than misplaced but realistic forecasts in those cases, favouring unrealistic solutions; b) low-resolution forecasts can score better than more realistic high-resolution ones; and c) forecasts where an observed feature is misplaced but nearby receive the same score than forecasts that are misplaced and farther away.
Measuring the location error, i.e. the distance between precipitation spots in forecast and observation fields is an intuitive way to address the third issue. However, to measure the displacements, one needs to have an assignment between features in the forecasts and the observations. The Wasserstein distance, defined as the minimum displacement over all possible assignments, is a theoretical way forward. However, computing it is prohibitively expensive. Fortunately, there has been growing interest among the machine learning community in utilizing Wasserstein distances to circumvent too literal comparisons. As a result, new algorithms have been developed that can approximate Wasserstein distances and scale linearly with respect to the number of points to be assigned. In this presentation, we demonstrate the practical application of two fast approximate algorithms, namely the Flowtree and the Attribution distance methods, for measuring location errors. Both methods are very flexible, allowing the computation of location errors on gridded or unstructured datasets, even on the spheric geometry of the Earth. We showcase the utilization of these novel verification metrics in specific use cases with ECMWF forecasts to highlight their strengths and weaknesses.
Spatial temperature measurements at the land-air interface using Distributed Temperature Sensing
Authors: Vis Gijs , Hartogensis Oscar , ten Veldhuis Marie-Claire , Coenders Miriam
Published: July 2023

In this study we introduce four Distributed Temperature Sensing (DTS) set-ups deployed at an irrigated alfalfa field site (La Cendrosa) as part of the LIAISE field campaign during 15-30 July 2021 in the north-east of Spain.  The DTS technique relies on the temperature dependence of Raman backscattering of light in a fibre optic cable. DTS provides spatially distributed temperature data over time. The type of profile (horizontal vs vertical), spatial- and temporal resolution and distance covered depend on the measurement configuration. To observe land-atmosphere interactions, we installed four set-ups in the LIAISE campaign, each of which focus on a different aspect.
We deployed a 1.6 mm diameter fibre covering a total distance of 600 m, which measured temperature at 5 s and 25.4 cm resolutions. In this continuous cable we included three DTS set-ups: 1) a surface layer profile between 1.6 - 40 m with a 25.4 cm vertical resolution, 2) a canopy profile averaged horizontally over 2.5 m with cables at 8 vertical levels between 0 - 1 m height inside of the rapidly-growing alfalfa and 3) a soil profile between -0.5 - 0 m with a vertical resolution of 1.25 cm; the enhanced spatial resolution was obtained by winding the cable onto a coil that was installed in the soil. An overview of these measurements is given, with an emphasis on factors limiting accuracy and on potential use in further research.
A fourth set-up was a so-called “turbulence harp”, which used a thinner and faster optical cable (0.5 mm) over a horizontal path of 70 m, measuring at four heights between 0.40 m and 2.05 m. Measurements were made at 1 Hz and 12.7 cm resolutions and a turbulent temperature spectrum was resolved up to 0.15 Hz. From this set-up, turbulence intensity was expressed in terms of the structure parameter of temperature (CT2). We estimated CT2 both from the temperature time series along the DTS cables, as well as, and this is a novel approach, from the spatial temperature series over the DTS cable. The spatially determined structure parameter correlated with a sonic anemometer CT2 estimate, with a correlation coefficient of 0.88.
This work outlines the potential for using DTS in land-atmosphere interaction campaigns and provides a first step towards using DTS in capturing turbulent information along a spatial temperature series. 
ARCLIM: new bioclimatic dataset for terrestrial Arctic
Authors: Aalto Juha , Rantanen Mika , Kämäräinen Matti , Niittynen Pekka , Phoenix Gareth , Lenoir Jonathan , Maclean Ilya , Luoto Miska
Published: July 2023

Over the four decades, the Arctic has warmed three to four times faster than globally. In addition to the long-term trend in average temperatures, extreme weather events are becoming increasingly frequent causing severe disturbances to the Arctic terrestrial ecosystems. Many existing climate datasets primarily concentrate on seasonal precipitation and temperature at coarse spatial (10-100 km) and temporal (30-year average climatologies) resolutions forming the basis of current understanding of how Arctic terrestrial ecosystems will respond to climate change. Therefore, the conventional datasets likely neglect many ecologically significant aspects of the Arctic climate relevant for biological or biogeochemical processes. For instance, snow cover duration, rain-on-snow events, or extreme wind events are known to be important variables for Arctic ecology that may not be adequately represented by the more widely used climate statistics. Today, atmospheric reanalyses provide temporally and spatially comprehensive and consistent evolution on climate variables without being limited by the challenges arising from the uneven coverage of in-situ observations. 
In this presentation, we introduce a new dataset of bioclimatic indices particularly relevant for investigating the changes of Arctic terrestrial ecosystems. The dataset, called ARCLIM, consists of several climate and event-type indices for the northern high-latitude land areas. The indices are calculated from the hourly ERA5-Land reanalysis data for 1950-2021 in a spatial grid of 0.1 degree (~9 km) resolution. We provide the indices in three subsets: (1) the annual values during 1950-2021; (2) the average conditions for the 1991-2020 climatology; and (3) temporal trends over 1951-2021. The 72-year time series of various climate and event-type indices draws a comprehensive picture of the Arctic bioclimate variability. We anticipate that the ARCLIM dataset opens new research opportunities aiming to better understand the impacts of climate change in Arctic terrestrial ecosystems.
How are observed point-rainfall climatologies represented by global models with different spatial resolutions? A comparative study.
Authors: Pillosu Fatima , Hewson Tim , Prudhomme Christel , Cloke Hannah
Published: July 2023

To establish climatologies that facilitate the contextualization of extreme, high-impact weather events in relation to historical occurrences or to comprehend the influence of climate change on their intensity and frequency, an extensive collection of observations extending as far back as possible is essential. Yet, these observations exhibit inaccuracies and uneven distributions in space and time. These attributes may lead to a distorted representation of past weather and climate, particularly for variables like rainfall, which can exhibit substantial variations in space and time. 

Reanalyses and reforecasts fill the gaps in the observational records. Existing literature has demonstrated that both reanalysis and reforecast datasets offer a more accurate representation of past weather and climate, owing to their global completeness and temporal consistency. Nonetheless, reanalyses and reforecasts may not adequately depict localized and/or rare events as effectively as observational climatologies might do (provided sufficient observations are available) due to the coarse spatial resolutions of both modelled datasets. This misrepresentation is particularly pertinent for discontinuous variables, such as precipitation. 
In this presentation, we will examine the representation of point-rainfall climatologies by four distinct global model datasets: ERA5_EDA (reanalysis, 62km), ERA5 (reanalysis, 31km), ECMWF reforecasts (reforecasts, 18 km), and ERA5_ePoint (reanalysis, point-scale on a 31km grid), and compare with observed point climatologies from raingauge sites. Furthermore, we will discuss the implications of this study on future calculations of reference climatologies for localized extreme precipitation events.
Post-processing ensemble forecasts using Ensemble Model Output Statistics (EMOS)
Authors: Aleksovska Ivana
Published: July 2023

Numerical weather prediction models have systematic errors and biases, which can be reduced through statistical correction methods. The most intuitive approach to correcting weather forecasts is to establish a statistical relationship between the forecast and the corresponding observations. Once the relationship is established, it can be used to correct future forecasts. These approaches, also known as statistical adaptation, are used on a daily basis to improve the quality of operational forecasts. Many methods have been proposed in the literature to calibrate ensemble forecasts, and these can be divided into two main groups: parametric (that has a hypothesis on the underlying distribution) and non-parametric methods. One of the most widespread and used methods of the parametric group is the so-called Ensemble Model Output Statistics (EMOS).
We show the performance of EMOS for the post-processing of probabilistic temperature forecasts at 2m. This method assumes that the underlying distribution is Gaussian (in the case of 2m T), and the parameters to be estimated are therefore the mean and the standard deviation. First results were obtained using the ECMWF operational forecast ensemble against SYNOP observations. Further studies were carried out to investigate the reliability of the estimated parameters using not only the raw ensemble data, but also the ECMWF deterministic operational forecast HRES and the control member. The results showed an improvement, especially for the shorter lead-times. The improvement was measured using weather scores for forecast verification and performance: bias, CRPS (Continuous Ranked Probability Score) and CRPSS (Continuous Ranked Probability Skill Score). These post-processed forecasts will serve as a reference for future studies.
Evaluating the Impact of Urbanization on Thermal Comfort in Turin: A Numerical Simulation Study
Authors: Pauly Lippin , Ferrero Enrico , Canonico Massimo
Published: July 2023

Heatwaves are increasing in both intensity and frequency every year. In urban areas, the urban heat island (UHI) effect exacerbates this impact, with significant consequences for city dwellers and urban ecosystem functions. This work evaluated the impact of urbanization on thermal comfort in Turin, a city in the North-West region of Italy bordered by the Alps to the west and hills up to 600m high to the east. Two numerical simulations were performed: one representing a real-case urban scenario and another idealized simulation where all urban surfaces were replaced with dense forest. The real-case scenario used weather research and forecast models (WRF) with a multilayer urban canopy model (MLUCM) and was simulated over a heatwave (HW) period in June 2019. High-resolution urban land use/land cover data was taken from local climate zone (LCZ) maps provided by the World Urban Database and Access Portal Tools (WUDAPT) repository. The simulation was validated with data from ARPA meteorological stations located in the region. The lower root mean squared error of air temperature and higher index of agreement showed that the simulation was in good agreement with observational data. Results from both simulations were used to study the effect of urbanization on UHI phenomena by comparing the real case with the ideal simulation where no urban area was present. The study revealed that urbanization had the greatest impact on UHI during nighttime, with a maximum deviation of 5°C, while its effect was less during mid-day, with a maximum deviation of 1.2°C. Furthermore, an average temperature increase of 3°C was found in the city center due to urbanization compared to the idealized simulation.
KeywordsUrban heat island (UHI), WRF/MLUCM, heatwave (HW), Urban Microclimate
High-speed Video Observations for Precipitation Microphysics: Case of Raindrop Collisions
Authors: Testik Firat , Saha Rupayan
Published: July 2023

This study utilizes an optical-type disdrometer, called High-speed Optical Disdrometer (HOD), that we recently developed for precipitation microphysics observations and investigates raindrop collisions through HOD’s high-speed video observations during rainfall events.  HOD’s innovative technology enables capturing high-resolution sequential images of the same hydrometeor multiple times as it passes through the measuring volume.  Hydrometeor characteristics are then accurately measured via digital processing of the recorded images.  HOD offers unique observational capabilities such as, for the case of raindrops as hydrometeors, observations of raindrop oscillations and collisions as well as high-accuracy measurements of relevant characteristics.  This study focuses mostly on raindrop collisions observed during rainfall events using HOD.  Raindrop collision rates and outcomes are important quantities for applications such as raindrop size distribution (DSD) modeling in hydrological and meteorological models.  Direct field observations of these quantities have not been available due to the technological limitations of the existing disdrometer technologies, and HOD’s measurement capabilities provided us the opportunity to investigate these quantities through field observations.  Rainfall events considered in this study were observed during a 3-year long field campaign conducted at our outdoor rainfall laboratory located on the West campus of the University of Texas at San Antonio, Texas, USA.  This field campaign provided a dataset on collision observations that extended the small number of raindrop collision observations that we had previously reported as the first-time raindrop collision observations to visually demonstrate the presence of raindrop collisions in rainfall events.  We will provide an overview of this high-resolution disdrometer technology and present our observations on raindrop collisions with discussions on our findings and their applications.  This material is based upon work supported by the National Science Foundation under Grants No. AGS-1741250.