bluuebunny/crossref_metadata_2025 huggingface dataset:
Metrics of the Hadley circulation strength and associated circulation trends
Authors: Zaplotnik Žiga , Pikovnik Matic , Boljka Lina , Žagar Nedjeljka
Published: June 2022

This study compares trends of the Hadley cell (HC) strength using different metrics applied to the European Centre for Medium-range Weather Forecasts ERA5 and ERA-Interim reanalyses in the period 1979-2018. The HC strength is commonly evaluated by metrics derived from the mass-weighted zonal-mean stream function. Other metrics include the upper-tropospheric velocity potential, the vertical velocity in the mid-troposphere, and the water vapour transport in the lower troposphere. Seven known metrics of the HC strength are here complemented by a metric of the spatially-averaged HC strength, obtained by data-adaptive averaging of the stream function in the latitude-pressure ($\varphi$-$p$) plane, and by the total energy of zonal-mean unbalanced circulation in the normal-mode function decomposition. It is shown that metrics, which rely on single-point values in the $\varphi$-$p$ plane, produce unreliable 40-year trends of both the northern and southern HCs, especially in ERA-Interim; magnitudes and even the signs of the trends depend on the choice of the HC strength metric. The two new metrics alleviate the vertical and meridional inhomogeneities of the trends in the HC strength. In both reanalyses, there is a positive trend in the total energy of zonal-mean unbalanced circulation. The spatially-averaged HC strength metric also shows a positive trend in ERA5 in both hemispheres, while the trend in ERA-Interim is insignificant. The study calls for a unified metric of the HC strength, that allows easier comparison of different studies on the Hadley circulation changes. This would also allow a better estimation of the likelihood of the future changes in the global atmospheric circulation.
In-situ Observations at Sea, the Voluntary Observing Ship (VOS) Scheme
Authors: Kleta Henry
Published: June 2022

Since a few years, the cooperation between public and private sector is gaining importance to make more data available.

One example for this is the Voluntary Observing Ship (VOS) Scheme, which uses ships (commercial and private) to acquire and transmit many meteorological and oceanographic parameters. These variables provide essential input to weather warnings and forecasts at sea and at land for more than a century. Nowadays many commercial vessels are already equipped with meteorological and oceanographic sensors to provide the shipping company with data needed for the optimization of the operations of the ship. The VOS network relies on ships sailing all oceans and seas of the world. The ships are recruited by national meteorological services (NMSs) for taking and transmitting meteorological observations. The forerunner of the VOS scheme dates back as far as 1853, when seafaring nations organised the first formal international meteorological meeting to coordinate the weather observing at sea. The United Nations Decade of Ocean Science for Sustainable Development (2021-2030) aims at making and promoting all gathered data accessible and usable to the meteorological and scientific community as well as complementing the NMS operated VOS.

One key characteristic of the VOS network is that the ships and the observers onboard are volunteers, and not professionals. National meteorological services recruit vessels and the crew to a national VOS programme, equip them with calibrated instruments, train the observers, and provide means to code and transmit the weather observations from ship to shore, where the data provides essential input to numerical weather models, forecasts and warnings, as well as climatological applications. The reason for sailors, shipping companies and ship owners for joining the VOS scheme is the profound knowledge that mariners face many hazards while at sea: storms, rough seas, ice and icebergs, and only accurate warnings and forecasts can provide valuable information for safe routing. This important link between these observations and the chance to enhance the safety for life at sea led to the inclusion of weather observations as obligatory within the international convention for the Safety of Life at Sea (SOLAS).

Despite the increasing importance of the data from the VOS scheme, the number of participants has been decreasing in the past years. To compensate this loss, efforts are made to take and generate data from other sources, such as from collaborations with scientific and commercial projects and operational networks. One key task of these efforts is to present the requirements of the operational meteorological community to other groups.

The presentation will give an overview of the VOS network, other global ocean observing networks, show examples of public-private partnerships, and will give an outlook onto the next decade of meteorological and oceanographic observations at sea including 3rd party data.
Quantifying biases in open-path eddy covariance CO2 flux measurements caused by spectroscopic effects in broadband non-dispersive infrared gas analyzers
Authors: Bogoev Ivan
Published: June 2022

Open-path eddy covariance systems, based on broad-band non-dispersive infrared (NDIR) gas analyzers, are widely used for CO2 and H2O flux measurements in remote locations around the world, because of their low power consumption, fast response, and reliable operation. Nevertheless, agreement between open- and closed-path CO2 fluxes has limited inter-site comparability, especially in cold or non-growing seasons and low-flux environments, where physiologically unreasonable CO2 uptake is often observed by the open-path systems. A possible explanation is sensor-surface heating from internal-electronics power dissipation and solar radiation, which causes unaccounted gas density changes in the optical path. Fast-response thermometers, co-located with the gas analyzer, have been used to correct these effects. However, the fragility of the thermometers has prevented the wide adoption of this approach. A challenge for the open-path sensor design is that in-situ air temperature affects not only the gas density but also the broadened half-width and intensity of the spectral absorption lines. We hypothesize that fast air temperature fluctuations in the optical path of the gas analyzer can change the amount of absorbed light and cause errors in the CO2 concentration measurement. Because of the natural covariance of sensible and CO2 fluxes, such errors are well correlated with the vertical wind and can potentially propagate into flux calculations. We used spectral-line parameters, obtained from the high-resolution transmission molecular spectroscopic database (HITRAN), to evaluate the temperature effects on the integrated absorption spectra of CO2-air-mixtures across the 4.2 to 4.3 μm infrared active region utilized by NDIR analyzers. Results show that air temperature strongly influences absorption, and if not properly corrected, potentially introduces biases in the CO2 concentration measurements. Strong lines exhibit Doppler broadening, where the line peak and width decline with increasing temperatures, causing underestimation of CO2 concentration. Weak lines exhibit the opposite behavior. Based on our simulations, optimizing the optical filter passband can balance these opposing effects and greatly reduce the temperature dependence. In practice, manufacturing tolerances, shifts in the center wavelength, and the temperature sensitivity of the optical filters prevent complete elimination of the temperature-line broadening. A 13-nanometer shift in the filter pass band can introduce a 0.008 mmol m-3 K-1 underestimation in the CO2 concentration, which is a 0.67 μmol m-2 s-1 systematic error in CO2 flux per 100 watts of sensible heat flux.
Relevant and robust climate information and climate change adaptation
Authors: Benestad Rasmus E.
Published: June 2022

In 2021, the world started taking climate change adaptation seriously. The Climate Adaptation Summit (CAS2021) was held as a global virtual event, hosted by the prime ministers of Britain and the Netherlands. There had been conferences on climate change adaptation before, as the 5th European Climate Change Adaptation Conference took place in May-June of the same year (ECCA2021), but not on such a high level. A remarkable observation made from both these events was the lack of visibility of the scientific community on regional climate modelling, such as the CORDEX project under the World Climate Research Programme (WCRP). Climate projections and downscaling of global climate results often provide the basis for climate change adaptation, but the process of downscaling is haunted by notorious obstacles and uncertainties. Hence, there is a strong need for inclusive debates about how to find the best answers. In other words, it is important to use the best available information in the right way. So the lack of a discussion about these concerns may be a result of failed communication efforts. Here I will present some thoughts about what we can learn from that and how we can improve the situation in the future.
The Our Way Model for Human Dispersal in Changing Paleoclimate
Authors: Shao Yaping , Klein Konstantin , Wegener Christian , Schmidt Isabell , Weniger Gerd-Christian
Published: June 2022

DNA studies on present-day humans and Anatomically Modern Humans (AMHs) point to our genetic origin in Africa around 300 ka BP. The debate on our ancestry is continuing, which hinges upon the history of human population networks and dispersal. We know in general that AMHs started to migrate out of Africa around 200 ka, arriving in the Middle East 90-65 ka, Asia 80-50 ka, Europe 60-40 ka and the Americas after 14 ka BP. But we also know that human dispersal is, rather than unidirectional, a highly complex process of advancement, retreat, abandonment and resettlement on different time scales. On a historical dimension, it implies that there are special windows of best or even one-time opportunities for human dispersal, provided by the constellation of climate conditions and human activities. Despite the achievements in reconstructing the AMH dispersal history, large knowledge gaps exist in understanding the underlying dynamics and many questions remain unanswered. One of the most essential is how climate influenced population dynamics which in turn affected human evolution, and what are the constellations for the historic momentum. 

Human dispersal is a manifestation of the human system. Like the Earth system, the human system is a complex system characterized by (1) a large degree of freedom, (2) a range of non-linearly processes competing and interacting on different scales, and (3) external forcing which are unsteady or stochastic. So far, the quantitative study on human dispersal has been bottlenecked by the critical lacks of knowledge of human-system dynamic processes, capacity for quantifying the climate-human relations, comprehensive Human System Models (HSMs), and the integration of HSMs and ESMs. The progress in Global Climate Models (GCMs) and ESMs and their applications to paleoclimate simulations signal that quantitative human-system and Earth-system coupled research is becoming feasible.

Here, we present the Our Way Model (OWM) for human dispersal, a preliminary framework for HSM and ESM coupled modelling. In OWM, we attempt to account for the  biological, cultural, and climate dimensions of the human system, and to integrate human- and Earth-science concepts, model and data, and the macroscopic and microscopic views of human dispersal. The OWM provides a platform for knowledge integration, hypothesis testing and conceptual development. We also present the results of two OWM simulations. The first is a 25-kyr numerical reconstruction of the Aurignacian dispersion on the pan European scale, and the second is a simulation of the impact of climate change on the population dynamics on the Iberian Peninsula.
A storm-track connection between North American cold extremes and European wet/windy extremes
Authors: Riboldi Jacopo , Leeding Richard , Segalini Antonio , Messori Gabriele
Published: June 2022

The occurrence of cold spells over North America is statistically connected with an increased likelihood of extreme wind and precipitation events over Europe. This connection involves the state of the North Atlantic storm track in a nontrivial way. Cold spells over eastern Canada, for instance, are associated with windstorms over the United Kingdom in a configuration of positive North Atlantic Oscillation (NAO). However, cold spells over eastern Canada already tend to occur during the positive phase of the NAO, suggesting a chain of processes in which the internal dynamics of the storm track is responsible of both the cold spell and its European impact. Analogous considerations, although in the context of negative NAO, hold for cold spells hitting central United States and windstorms over the Iberian Peninsula.

Another key player in the dynamics of North American cold spells are Rossby wave trains propagating from the North Pacific towards North America and then the Atlantic Ocean. These low-frequency wave trains can impact the North Atlantic storm track directly, by reconfiguring the large-scale features defining locally the extratropical waveguide, or indirectly, by promoting anomalies in the genesis or propagation of extratropical cyclones.

We try to disentangle this complex interplay by analyzing systematically the state of the North Atlantic storm track before and after cold spells over several North American regions. Anomalies of wave activity flux, in the formulation by Takaya and Nakamura (2001), are computed to assess the relevance of Rossby wave trains for cold spells in each region. The main conclusion of this work is a reconsideration of the usual chain of processes connecting cold spells and wet/windy extremes over Europe: in some cases, the systematic connection between the former and the latter is likely mediated by the pre-existing state of the storm track and not simply due to the cold spells alone.
ICON NWP on GPUs
Authors: Jacob Marek , Alexeev Dmitry , Dietlicher Remo , Cherkas Victoria , Germann Elsa , Gessler Fabian , Hupp Daniel , Jocksch Andreas , Lapillonne Xavier , Müller Christoph , Osuna Carlos , Reinert Daniel , Sawyer William , Schättler Ulrich , Zängl Günther
Published: June 2022

Weather prediction centers are always looking for the best computational performance for their numerical weather prediction (NWP) model, given their financial budget. Over the last decades, most centers relied on computer systems with scalar x86 architectures. This, however, might not be the best choice for the mid-term future, as the development of CPUs with ever-increasing performance and memory bandwidth is slowing down.

Nowadays, hardware manufacturers advertise massively multiprocessing GPUs as one future pathway. Unfortunately, GPUs have their own programming paradigms, as they are still requiring a CPU as their driver and bring their own memory. This necessitates significant adaptions of existing codes. Porting a large and continuously developed community code, such as ICON, to emerging hardware architectures poses its own special challenges.

Many parts of the ICON framework have been made ready for GPU systems in a multi-institute effort over the past years. MeteoSwiss plans to use GPU ICON operationally for limited area forecasts in 2023. Current development activities also make ICON-GPU ready to support the enhanced feature set used operationally by the DWD (such as grid nesting and parametrizations for global simulations). It was decided to port ICON by introducing OpenACC compiler directives to the FORTRAN code. This iterative development model makes it possible to merge ported code back directly into the main code repository and to stay up-to-date with other developments like in model physics. A tolerance based testing suite was deployed to make sure that ported features remain functional also when non-GPU-related changes are introduced to already ported code sections.

We present the general porting strategy and the current state of the port. We discuss specific optimizations and the lessons learned while porting an actively developed code. Finally, we present the performance on current GPU and CPU machines and compare them to the currently operational setup on the DWD vector supercomputer.
Mechanistic Ocean-Atmosphere exchange of trace gases in Polar-WRF-Chem: Implications for Arctic tropospheric ozone
Authors: Barten Sjoerd , Ganzeveld Laurens , Steeneveld Gert-Jan , Krol Maarten
Published: June 2022

Dry deposition is an important removal mechanism for tropospheric ozone (O3). Its deposition to oceans is generally represented in atmospheric chemistry transport models using constant surface uptake resistances. However, observations show quite large spatiotemporal variability expressing differences in solubility, waterside turbulence and O3 reacting with iodide and dissolved organic matter. We hypothesize that for the Arctic O3 deposition is overestimated with consequences for background concentrations and lifetime of O3 also due to changes in long-range transport of O3 and its precursors. These are focal points of a project on observing and modelling of Arctic climate-active trace gas exchange as a contribution to the MOSAiC observational campaign with the icebreaker Polarstern being trapped in the Arctic sea-ice for ~1 year.

We have coupled the Coupled Ocean-Atmosphere Response Experiment Gas transfer algorithm (COAREG) to the mesoscale meteorology and atmospheric chemistry model Polar-WRF-Chem (PWRF-C). This includes a further development including a two-layer scheme for O3 deposition to oceans and coupling to recently updated ocean water composition databases. We have also reduced the deposition of O3 to sea ice based on a previous study of snow-ice O3 deposition.

In this study, we evaluate the performance of PWRF-C with hourly-averaged surface O3 measurements above 60 ºN and with O3 sondes. We show that the more mechanistic representation of O3 deposition over oceans and strongly reduced deposition over snow and ice results in improved simulated Arctic O3 mixing ratios. We found that it is important to nudge PWRF-C to the ECMWF ERA-Interim wind fields which secures a fair comparison of the model with measurements regarding their footprint. Our study indicates that representation of ocean and sea-ice O3 deposition in atmospheric chemistry models must be revised to improve the representation of Arctic O3 concentrations and chemistry.
Impact of atmospheric winter circulation on sea ice anomalies in the Arctic seas
Authors: Välisuo Ilona , Nygård Tiina , Uotila Petteri
Published: June 2022

Arctic sea ice is steadily retreating due to climate warming, but regional and seasonal variations in Arctic sea ice are important. This study aims in understanding how the winter atmospheric circulation affects the sea ice drift and how ice motion contributes to regional sea ice concentration and thickness anomalies. Sea ice conditions in late winter and spring are crucial for predicting summer sea ice. Understanding the mechanism that affect the spring sea ice concentration and thickness have potential to improve sea ice predictions year-round.  

Atmospheric pressure patterns affect the thermodynamic vertical structure of the atmosphere, heat and moisture transport to the Arctic, and radiative and turbulent fluxes at the surface (eg. Nygård et al 2021). Circulation types also control surface wind speed and direction, and are closely linked to ice drift speed (eg. Mallett et al 2021). Winter atmospheric circulation can precondition spring sea ice anomalies and summer melt by ice dynamics (eg. sea ice transport to lower latitudes where it is more vulnerable to melt) and thermodynamics (eg. positive surface energy balance anomalies prohibiting ice growth in winter leading to thinner ice in spring).

In this study we present a Self Organizing Maps (SOM) clustering of the winter (December-March) mean sea level pressure to detect the typical circulation patterns. The SOM-analysis covers the period from December 2000 to March 2021. The circulation patterns, or SOM nodes, are linked to atmospheric conditions (surface energy balance and wind speed) and sea ice conditions (concentration, drift speed and thickness). We use data from ERA5, ORAS5, and PIOMAS reanalyses, and Polar Pathfinder Sea Ice Motion and Cryosat2-SMOS ice thickness remote sensing products. We show how the circulation types are linked to near surface wind speed and direction, and consequent sea ice drift.  As a result, we analyze if the circulation patterns can be linked to sea ice anomalies thought sea ice dynamics or thermodynamics.
Behavioural decision-making Experiments in weather-driven Energy Systems
Authors: Möhrlen Corinna , Fleischhut Nadine , Escallon Antonio , Giebel Gregor
Published: June 2022

The number one target since the Ukraine war has been shaking our world in February 2022 and shown how much impact energy dependencies have on our life, is to increase the amount of renewable energy in an unseen effort and pace. Without the tools to predict and act upon the enormous amounts of variable generation to come, the electric grid will be difficult to keep stable. On top of the energy crisis, we start seeing impacts of the climate crisis with increasing temperatures that change weather systems, causing more extreme weather events that again have impact on the generation pattern from wind and solar driven generation units. The associated uncertainty of (1) strongly increasing penetration of renewable energy generation and (2) increasing amount of extreme weather events call for forecasting tools that take these uncertainties into account and help system operators, traders and balance responsible parties to be able to act upon uncertainties rather than being surprised by them. Mitigating balancing costs and increasing security of supply will have to be at the forefront of everybody’s top priorities.

The IEA Wind initiative “Probabilistic Forecasting Games and Experiments” is a collaboration with the Max-Planck Institute for Human Development’s WEXICOM project. The objectives of this initiative are (1) to empirically investigate the psychology behind adoption or refusal when dealing with uncertainty forecasts and (2) to use the empirical results to understand how weather and generation forecast providers have to present and communicate uncertainty forecasts to end-users for them to be able to exploit the benefits of the enhanced information in their decision process. Although we simplify our experiments, they are designed as realistic scenarios for many decision-makers in the industry. We are encouraged from the participant’s feedback that this type of gamification of a problem is received well as exemplary applications for the use and adoption of probabilistic forecasts into decision processes. The learning-by-doing strategy in a safe environment may not reproduce the entire context of a decision-making problem in a specific operational environment, but on the other hand it provides a platform to test different ways of introducing people with a complex topic, train and teach awareness for the challenges and benefits that come with the advanced technologies.

There are still many open questions that we need to answer, such as how decisions depend on the structure of the decision context, the communication and graphical or textual presentation, and many more. In this context, we will present the results of our second experiment, which is also dealing with the decision-making in extreme wind cases that can lead to power shutdown of wind turbines, and hope with this to inspire a vivid discussion on the topic in the session.
The European Weather Cloud (EWC) – Collaboration Platform for Meteorological Application Development and Data Processing
Authors: Tervo Roope , Schulz Joerg , Saalmueller Joachim , Abellan Xavier , Modigliani Umberto , Baousis Vasileios
Published: June 2022

The European Weather Cloud (EWC) is set to be the cloud-based collaboration platform for meteorological application development and operations in Europe and enables the digital transformation of the European Meteorological Infrastructure.

It consists of cloud infrastructure established by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and the European Centre for Medium-Range Weather Forecasts (ECMWF) but is also open to federation partners with relevant data or infrastructure assets. All resources in EWC are specifically designed to have fast access to the EUMETSAT and ECMWF data holdings.

Major data accessible by the EWC users is the sum of all online data products available at ECMWF and EUMETSAT, accessible in a seamless manner across the boundaries of their respective cloud infrastructures. Services to access the data include the basic data access services supported by related functions, such as display, reformat, etc., as per the applicable data and service policies and already available at different levels at EUMETSAT and ECMWF. The data offering will be augmented over time based on user needs in line with the aspiration of the EWC driven by meteorological applications development. Where relevant, data federation agreements will be sought with federation partners for third-party data access in line with the basic architecture of the system.

From a technological viewpoint, EUMETSAT and ECMWF seek to offer services that carry the highest benefits from cloud technology by exploiting the full potential of hosting a given processing function, a project, or a service close to a large variety of readily available data. The hosted services are augmented with the Software Marketplace, which allows EWC users to easily share and use meteorological applications and algorithms.

The EWC is available for EUMETSAT and ECWMF Member States activities. Resources will also be allocated to research initiatives via specific EUMETSAT Research and Development calls and ECMWF Special Projects, experiments, or investigations of a scientific or technical nature, undertaken by one or more EUMETSAT or ECMWF Member States, likely to be of interest to the general scientific community.

EWC has reached the end of the pilot phase at the end of 2021 and is currently in 'operational ramp-up'. During the pilot phase, the EWC hosted over 40 different types of use cases containing, for example, data processing, application development, training, and experimenting with cloud technologies. The target is to reach a fully operative state by the end of September 2022. After launching the operational service, EWC looks forward to developing the service towards Platform as a Service (PaaS) based on user feedback.
A Modeling Study on the Extreme Rainfall Event along the Northern Coast of Taiwan on 2 June 2017
Authors: Wang Chung-Chieh , Yeh Ting-Yu , Li Ming-Siang
Published: June 2022

In the present study, two experiments at 1-km grid size are compared on the extreme-rainfall event (up to 635 mm in 12 h) along the northern coast of Taiwan on 2 June 2017. The first is a forecast (F1) that was driven by the best forecast member in a previous study at 3-km grid size (360 mm) and produced a peak amount of 541 mm in 24 h along the northern shore (618 mm at 15 km offshore), and the second is a simulation (S1), driven by a 3-km simulation (S3), that produced a maximum of 393 mm over land. The frontal moving speed are both slow in the two experiments, and the front spends slightly more time moving across the area in S1 (15 h) than in F1 (12 h). So, under similar conditions in most of the important factors previously identified, the present comparison allows us to examine and isolate the reasons for the main differences between the peak rainfall amounts.

The results show that in S1, multiple rainbands moved slowly across northern Taiwan to produce the rainfall, with a more widespread heavy rainfall area, but the peak amount was lower due to the trainsient nature of these rainbands (however slow they are). In F1, on the contrary, linked to the presence of a low-pressure disturbance along the front to the north-northwest of Taiwan, the low-level westerlies was enhanced to produce a persistent convergence zone with the cold northeasterly air behind the front, right across the northern coast of Taiwan without movement. Thus, one rainband remained at more or less the same location over several hours, and produced the localized peak rainfall reaching 541 mm on land. The results indicate that it is possible to produce a higher peak amount from km-scale models with a finer grid size, but the predictability of such extreme amount might be relatively low and requires further study.
Improving ASI-based solar radiation nowcasting by using automatic cloud type recognition methods
Authors: López-Cuesta Miguel , Jiménez-Garrote Antonio , Aler-Mur Ricardo , Galván-León Inés , Tovar-Pescador Joaquín , Pozo-Vazquez David
Published: June 2022

In the last years there is an increasing interest for developing enhanced solar nowcasting methods, fostered by the massive deployment of small-scale / residential PV systems. Reference nowcasting methods are based on the use of All-Sky-Imagers (ASI). Although ASI-based nowcasting methods has received a notable attention in the last decade, their reliability is relatively low, because the processes involved in deriving the nowcasts are prone to many uncertainties. In this work, we propose and evaluate new methods aimed at increasing this reliability. Notably, we propose the use of an automatic cloud-type recognition system in the nowcasting procedure, allowing the use of cloud-specific transmittance values for the different cloud types. The study was carried out at a location of southern Spain, using a total of 1901 samples representing all the cloud types. Each sample is composed by one-minute time resolution ASI images, GHI and DNI measurements as well as cloud base height values derived from a ceilometer. Up to 30 minutes ahead one-minute time resolution forecasts were obtained and benchmarked against reference methods that not uses cloud-specific transmittances.

In the first part of the study, a statistical analysis was conducted to determine the GHI and DNI transmittance of 11 different cloud types; to this end, a Gaussian Mixture Model (GMM) was used. For some of the cloud types, the cloud base height was also used as a parameter of the model. In a second part of the study, these cloud-specific transmittances were incorporated in the nowcasting procedure using an operational automatic cloud type recognition method. This new nowcasting method was evaluated as an operational forecasting procedure.

Results of the first study reveals, firstly, that DNI total transmittances are almost negligible for cumuliform clouds. In contrast, stratiform clouds show a wide range of transmittances. Notably, while stratus and altostratus clouds behaves as cumuliform clouds, transmittances for cirrostratus and cirrus are, respectively, 30% and 80%, approximately. For the GHI, the total transmittance values ranges from 80% (cirrus) to 30% (stratus).

Results from the second study show that the proposed method provide slightly enhanced GHI nowcasts associated with cirrus, altocumulus and cirrocumulus clouds skies. For these cloud-types skies, the reduction achieved in the rRMSE values ranges from 2% to 6%. On the other hand, the proposed method provide a clear superior performance for the DNI nowcasting, with an overall reduction of rRMSE values of around 10% for the entire dataset. The reduction in rRMSE values also show a dependence of the cloud types, ranging from 6% (cirrostratus/cirrocumulus) to 20% (cirrus clouds).
Providing AI- and ML-ready data
Authors: Tervo Roope , Grant Mike
Published: June 2022

Artificial Intelligence (AI) and Machine Learning (ML)-applications have become a huge hype. What does it mean to serve data for AI and ML? EUMETSAT climate reprocessing data records try to meet following guidelines as far as possible.

In ML applications data is typically combined from several sources. Training ML model needs normally a long history of data. Typical environmental ML applications employ 1-5 years of historical data while for example impact forecasts require often at least 10 years of history to contain enough extreme weather samples. ML applications are often trained with history data but applied to near-real-time (NRT) data. Thus, corresponding NRT data should be always available.

The historical data series should obviously be as harmonised as possible. However, the harmonisation doesn’t need to be perfect. Small changes in the data are not necessary affecting the performance of ML model too much. The changes in the underlying data should well documented.

Data quality is also very important aspect as ML models are just as good as underlying data. Thus, quality flags should be always available and provided in a way that they can be used to filter out bad samples. While reasonable assumption for default is to provide only good quality data, also other samples should be available as sometimes more lower quality data yields better results than less higher quality data. Whenever possible, users should be provided with option to access the raw data as well since it may open avenues to new ways to apply ML models or pre-process.

Data access should be obviously as fast as possible and all data should always be served from online data storage. As datasets are almost always combined with each other, data formats should be as well-known and supported as possible, even that would mean loss of metadata. Typically, it’s better to provide metadata beside the actual data and keep the data as consist as possible.

Some of the ML methods, such as Random Forests (RF) are more often used for supervised learning to specific points while i.e. neural networks (NN) are used for images and gridded fields, tensors. Serving data for point-based applications greatly benefits from API capable to provide best representative samples for any given point so that it’s easy to be combined with labels. Serving data for grid-based applications, however, benefit of relatively raw interfaces, such as S3, with wide client support.  Critical requirements for the interface and the data model is to enable sub-setting and slicing.

Finally, providing well-known and documented reference datasets with ready labels would be highly beneficial for ML developers. Such general domain datasets, such as the Iris Dataset already exist. Meteorological community should publish such datasets along with ready methods in common libraries to load the dataset easily.
A fine-scale-preserving bias correction approach for global solar irradiance simulated with a numerical weather prediction model over a high-resolution spatial and temporal grid
Authors: Ruiz Javier , Jiménez-Garrote Antonio , Ruiz-Arias José Antonio
Published: June 2022

The feasibility of national and supra-national low carbon power systems (LCPSs) is challenged under high penetration rates of renewables because the inherent variability of renewable sources increases the system’s operation and transmission costs. Optimizing the operation of the systems to make them efficient and profitable requires adapting their design to the regional solar and wind patterns.

The MET4LOWCAR Spanish project aims at demonstrating the benefits of a synergistic design of the generation and transmission power system that accounts for the regional climatic patterns of both solar and wind renewable resources, using the Spanish territory as a testbed. To that aim, a 30-yr weather integration is being performed with the Weather Research and Forecasting (WRF) model over the Iberian Peninsula and the Balearic Islands throughout a 5-km spatial grid and a 10-min temporal grid. The ultimate goal is to compound a climatic data base of solar- and wind-related variables to simulate the performance of a high-penetration LCPS in multiple scenarios, from long runs to extreme weather events.

Here, we present an original debiasing approach of the WRF global horizontal solar irradiance (GHI) that guarantees a reliable and more realistic representation of the solar power generation in the LCPS. First, the GHI simulated by WRF is compared against radiometric observations from the Spanish National Radiometric Network to demonstrate that it is indeed affected by a seasonal bias, related to a misrepresentation of convective clouds in the WRF model. Then, the spatial and temporal GHI grid is corrected cell by cell and day by day using the monthly GHI SARAH satellite product as a reference. The method is purposedly designed to interfere only at monthly scale (thus using monthly GHI SARAH as a reference) in order to preserve the original fine-scale spatial and temporal structure of GHI. However, it propagates the debiasing to daily steps using a tailored interpolation approach that prevents any spikes and data artifacts in the bounds between consecutive months.

The method will be described in detail, and some preliminary results will be shown at least for the period 2010–2020, using ground observations and satellite-based GHI data as a reference to assess the debiasing performance.
Influence of offshore wind farms on the latent heat flux in the marine boundary layer
Authors: Platis Andreas , Büchau Yann , Bange Jens
Published: June 2022

Unique airborne in-situ measurements were evaluated to investigate the influence of offshore wind farms on the latent heat flux in the marine boundary layer. 21 of the total 42 measurement flights carried out in the framework of the WIPAFF project over the German Bight in the year 2016 and 2017 enabled such an evaluation under different atmospheric conditions. The airborne data was collected with the research aircraft Dornier 128 belonging to the Technische Universität Braunschweig, Germany. The aircraft is equipped among others with high-resolution sensors for water vapor, three-dimensional  wind vector, temperature and pressure. The measurements of 15 flights showed a significant increase of the vertical upward latent heat flux over the offshore wind farm clusters Amrumbank West, Nordsee Ost, Meerwind Süd/Ost or the wind farm cluster Godewind. For thermally stable conditions, all nine measurement flights except one showed an enhanced latent heat flux downstream of the wind farms at hub height, with an increase of up to +70 W m-2compared to the undisturbed flow. For flights during unstable thermal conditions, 8 out of 13 cases showed an increase, with the largest difference with respect to undisturbed flow of +400 W m-2 above the wind farm. The results also suggest that not only the thermal stratification but also the moisture gradient plays a decisive role in whether the influence of the wind farm is visible in the latent heat flux. 
Changes in the maximum daily and multi-day precipitation totals in Slovakia in the current course of the 21st century.
Authors: Faško Pavel , Markovič Ladislav
Published: June 2022

Analysis of annual precipitation totals in Slovakia in the last decades of the 20th century and during the 21st century shows a certain, although not very significant, upward trend. Paradoxically, the incidence and severity of the periods with lack of precipitation is also increasing. In this context, it is appropriate to analyze precipitation events resulting in heavy precipitation, whereas one of the consequences of their occurrence is an increase in high daily, resp. multi-day precipitation totals. This paper presents analysis of new record values of the maximum daily, resp. multi-day precipitation totals in Slovakia recorded in the period 2001-2021 with focus on the comparison with previous record values from the period 1951-2000 on more than 450 precipitation stations. The analysis was performed on annual and seasonal time series ​​of maximum daily and multi-day total precipitation totals. The paper presents results of the analysis for those months in which the changes were most significant. Remarkable is, in particular, the increase in the values ​​of the analyzed characteristics of precipitation during the whole winter season (XII – II). Similar development was also detected in other seasons, but was limited to individual months only. The increase in the winter precipitation totals is associated with an increase in air temperature. Warmer atmosphere results not only in the proportional change in the total amount of the solid, liquid and mixed precipitation but modifies character of the winter precipitation. Changes in in the maximum daily and multi-day precipitation totals were more pronounced in the more continental climate of eastern Slovakia.
Merging independent networks of commercial microwave links from the Czech Republic and Germany to generate transboundary rainfall fields
Authors: Blettner Nico , Fencl Martin , Špačková Anna , Bareš Vojtěch , Chwala Christian , Kunstmann Harald
Published: June 2022

Commercial microwave links (CMLs) have become established opportunistic rainfall sensors used to estimate rainfall fields of various temporal resolutions and spatial scales. A major potential of CMLs lies in their great abundance even in sparsely gauged regions. Moreover, they provide close-to-ground measurements and can be particularly valuable in mountainous terrain where the reliability of weather radars is limited. These advantages theoretically allow for valuable rainfall products of continental scale in near real-time availability.

However, in practice, limitations to such large scale CML-based products exist due to legal and administrative burdens which hinder data exchange and lead to processing algorithms that are customized to specific data sets. Not being dedicated rainfall sensors, CMLs require careful processing and filtering algorithms to retrieve rain rates from raw signal data. These algorithms have so far been developed by individual research groups for rather homogeneous data sets stemming from single acquisition systems. Up to now rainfall products have not been based on two independent national CML data sets.

In this study we merge data sets of 3900 CMLs in Germany and 2500 CMLs in the Czech Republic that are obtained from different network operators and have distinct lengths and frequencies distributions. We develop and adjust universal processing algorithms, and calculate transboundary rainfall fields. Our focus lies on producing and analyzing maps for the mountainous border region where radar observations particularly suffer from the large measuring height above ground. We analyze a period of one month in summer 2021 which contains several rainfall events. For evaluation we compare our results with German and Czech national radar and rain gauge observations.

We find that independent CML data sets can be merged successfully. We are able to produce coherent transboundary rainfall maps, which is an important step towards rainfall products at continental scale. This study demonstrates the interoperability using independent CML data sets and identifies limitations of current custom-made preprocessing algorithms.
How is the extratropical circulation affected by reduced Atlantic and Pacific land-sea thermal contrast?
Authors: Portal Alice , Pasquero Claudia , D'Andrea Fabio , Davini Paolo , Hamouda Mostafa , Rivière Gwendal
Published: June 2022

Long-term projections of the future climate display a robust reduction of winter land-sea thermal contrast in the Northern Hemisphere (NH), caused by a faster warming of the cold continents compared to the warm oceans. The reduction is expected to be strong in the extratropics, a region where the thermal contrast is relevant for maintaining the strong baroclinicity near the western coasts of the continents and for shaping the NH jets and large-scale stationary waves.

In this work idealised perpetual-winter experiments characterised by a reduced land-sea thermal contrast are compared to control simulations featuring a thermal contrast similar to that observed in present-day climate. We use an intermediate-complexity AGCM with prescribed sea-surface and land temperatures. Warm temperature anomalies in East Asia and/or North America set a reduced thermal contrast in the whole NH or in individual NH sectors. We find that the Pacific-sector land-sea thermal contrast is by far more important than the Atlantic one for the large-scale mid-latitude circulation, as it impacts strongly the jet streams and the stationary planetary waves. While the local effects are coherent with the changes in baroclinicity brought by the surface forcing, the remote effects seem to be mediated by the response of the thermal and orographic components of the stationary waves. Based on the idealised-modelling results it is possible to hypothesise how the projected change in winter land-sea thermal contrast influences climate scenarios for the end of the XXI century. This factor has been rarely considered as a possible source of dynamical changes for the mid-latitude winter season.
Generative machine learning methods for multivariate ensemble post-processing
Authors: Lerch Sebastian , Chen Jieyu
Published: June 2022

Ensemble weather predictions typically show systematic errors that need to be corrected via post-processing. While much research interest has been focused on univariate approaches, many practical applications such as energy forecasting, hydrological applications and air traffic management require accurate modeling of spatial, temporal, and inter-variable dependencies. Over the past years, a variety of two-step approaches where ensemble predictions are first post-processed separately in each margin and multivariate dependencies are restored via copula functions in a second step has been proposed to address this need [1]. However, these approaches share common limitations in that incorporating additional predictor variables beyond forecasts of the variable of interest is not possible in a straightforward manner in specifying the copula functions that govern the multivariate dependence structure, which makes it challenging to draw from substantial benefits that have recently been demonstrated in the context of univariate post-processing [2].

To address this challenge, we propose a novel data-driven one-step approach to multivariate ensemble post-processing based on conditional generative machine learning  which allows for obtaining multivariate probabilistic forecasts directly as output of a generative neural network while incorporating additional exogenous variables as predictors. In case studies on multivariate probabilistic forecasts of surface temperature and wind speed at observation stations in Germany, our conditional generative models show state-of-the-art forecast performance and advantages over benchmark approaches, for example by allowing for generating an arbitrary number of samples from the multivariate  forecast distributions. 

References
[1]  Lerch, S. et al. (2020) Simulation-based comparison of multivariate ensemble post-processing methods. Nonlinear Processes in Geophysics, 27: 349–371
[2] Rasp, S. and Lerch, S. (2018) Neural networks for post-processing ensemble weather forecasts. Monthly Weather Review, 146(11): 3885–3900