bluuebunny/crossref_metadata_2025 huggingface dataset:
Vertical velocity variance and its dependency on meteorological parameters in the convective boundary layer
Authors: Dewani Noviana , Sakradzija Mirjana , Schmidli Juerg , Schlemmer Linda
Published: June 2022

The variability of vertical profiles of the normalized vertical velocity variance was examined to characterize turbulence and to investigate its dependence on meteorological parameters in the convective boundary layer. The vertical profiles of the vertical velocity variance were calculated using the Doppler lidar dataset from the FESSTVaL measurement campaign collected during two consecutive summer periods. The measurements took place around the Lindenberg meteorological observatory (MOL-RAO), near Berlin, Germany, representative of a flat-terrain covered with crops and patches of forest. The variability of a daily average of the normalized variance was analyzed during a period of a well-developed boundary layer. We classified the dataset into three main categories based on the presence of clouds and extracting only the periods without precipitation: clear-sky, cloud-topped, and rainy days. The mean profile of the normalized variance of the vertical velocity systematically increases from the rainy, cloud-topped to clear-sky days categories. The magnitude of the mean profile during the clear-sky and cloud-topped days is similar to the reference profile of Lenschow et al. (1980), although the variability among the days is large. The dependence of this variability of the normalized variance on the meteorological parameters was examined. We found that the relative humidity, Bowen ratio, surface latent heat flux, and cloud fraction relate to the variability of the normalized variance. A closer look at the cloud fraction during the cloud-topped days shows an increment of the normalized variance with a decrease of humidity in the boundary layer similar to the increment from rainy to cloudy and clear-sky days.
Copernicus Climate Change Service (C3S) climate information for the energy sector
Authors: Cagnazzo Chiara , Buontempo Carlo , Almond Samuel , Zanacchi Marcus , Vermoote Stijn , Nicolas Julien , Vamborg Freja
Published: June 2022

The fraction of renewables in the energy mix has steadily increased over the last decade, but system integration remains a challenge due to their intrinsic volatility. The development, operation and maintenance of a renewable power system requires a clear understanding of the impact of current climatic conditions and their potential changes under climate change scenarios. For example, wind power generation will become one key technology in the global pathway toward the net-zero objectives of the European climate strategy and for EU energy independence, with expected demand to ramp up installations of both onshore and offshore production systems by 2050. Year 2021 was characterized by low annual average wind speeds in northwestern and central Europe, with implications for the wind power generation, as reported by the C3S European State of the Climate 2021.  The wind energy industry therefore needs relevant climate-derived information at different timescales, depending on the specific project and application. The Copernicus Climate Change Service (C3S) delivers data and information designed to address the needs of users to assess the impact of climate on energy operations, management and planning, as well as the needs of the community of energy modelers, needing user-friendly datasets for their assessment studies. Those users need datasets coherent at continental level, which make use of homogenized and long-term climate data sources, and that permit to link energy demand and production to climate variability and projected climate changes. Several applications built on C3S data have been further developed in downstream Services, demonstrating the relevance of the C3S data value chain to support the energy sector users.  
Spatial analysis of hourly surface air temperature - Entering the gate to spatiotemporal statistical modelling
Authors: Frey Louis , Frei Christoph
Published: June 2022

Most of the current climate grid datasets are bounded at a time resolution of one day. Particularly in the case of surface air temperature, with its pronounced sub-daily variation, this bound restricts the utility of datasets for environmental modelling. Processes with a non-linear temperature dependence, such as the melting of snow or the transpiration of plants, cannot be accurately represented with daily mean values only. Therefore, advancing into the sub-daily time resolution is highly desirable. But it is also challenging: In order to predict realistic temporal evolutions for unobserved locations, one needs to integrate observations from a whole period, not just one instant. This poster presents early results from a new project that aims at harnessing methods of spatiotemporal statistical modelling for the development of an hourly temperature grid dataset over Switzerland.

We propose and experiment with a dynamic spatiotemporal statistical model that is conceived in the framework of dynamical linear models. Conceptually, it is an extension of kriging with external drift, familiar in spatial climatology, but with time-varying and serially correlated trend coefficients. The coefficients represent characteristic temporal variations in the temperature field, such as a diurnal cycle with a gradually varying amplitude/phase, or the lifting/sinking of a temperature inversion, in response to observations. We apply the method to hourly temperature data from the operational station network in Switzerland and investigate its performance for selected weather episodes (incl. sunny summer days, a winter-time inversion period). Our results hint to more/less successful configurations of the model for hourly temperature gridding in complex terrain. Comparison against the sequential application of a purely spatial analysis of the data shows some added value of the spatiotemporal approach, i.e. a benefit of borrowing information both over space and time. We anticipate that this benefit will increase as we enhance the complexity of the model configuration later in the project.
A station-based evaluation of south foehn forecasting with COSMO-1
Authors: Tian Yue , Schmidli Juerg , Quimbayo-Duarte Julian
Published: June 2022

This study investigates the skill of the COSMO model (v5.7) at 1.1 km horizontal resolution in simulating the near-surface foehn properties and evolution for five south foehn events and a 5-year-long analysis dataset. A significant cold bias, as well as a moist bias, are found in the major northern foehn valleys, including the Rhine Valley, during foehn hours in all the chosen cases and the 5-year climatological statistics. The model biases in different foehn types are inspected over the 5-year period. Among deep foehn types, the cold bias is larger (smaller) and the moist bias is smaller for the moister (drier) foehn events. Several possible causes of the cold bias are examined with sensitivity experiments for the five foehn cases. The sensitivity experiments include changes to the parameterization of the land-atmosphere interface (i.e. adoption of a skin temperature, a change of the heat resistance in the laminar sublayer, and a new formulation of the bare soil evaporation), to the 1D turbulence parameterization (including horizontal shear production of turbulence as a first step towards 3D effects), and to the horizontal grid spacing (1.1 km versus 550 m). While several of the sensitivity experiments impact the 2-m temperature indifferently during both foehn- and non-foehn hours, only a change in the horizontal grid spacing has a significant impact on the 2-m temperature during foehn hours. The 550-m run shows also an improvement in the simulated foehn duration and northward foehn extent. Possible reasons for the improvements and the remaining bias will be discussed.
Provision of wind information for site tenders for offshore wind farms according to WindSeeG in the German North and Baltic Seas
Authors: Möller Thomas , Spangehl Thomas , Brast Maren , Andersson Axel , Tinz Birger
Published: June 2022

The construction of offshore wind farms in Germany's Exclusive Economic Zone (EEZ) is an important component for the successful implementation of the energy transition. In 2021 the Federal Network Agency started to launch yearly tenders for sites in the North Sea and Baltic Sea which are conducted in cooperation with the Federal Maritime and Hydrographic Agency (BSH) according to the Wind Energy Act (WindSeeG). The German Meteorological Service (DWD) supports the BSH in compiling the required wind information. This information is made publicly available by the BSH to bidders via the PINTA portal (https://pinta.bsh.de) as part of the tendering procedures.

Detailed information on the wind conditions on the sites is crucial for the bidders' bid calculations. This information is compiled in collaboration by the BSH and the DWD with the participation of external contractors. In detail, these are in-situ measurements on the research platforms in the North Sea and Baltic Sea, FINO1, FINO2 and FINO3 (https://www.fino-offshore.de/de/index.html). Furthermore, this comprises one-year LiDAR measurements are carried out by external contractors on behalf of the BSH at the sites to be tendered, as well as data and evaluation results of the COSMO-REA6 and ERA5 reanalyses are provided by the DWD. These data sets are the basis for the preparation of summarised overall reports on the wind conditions on the sites.

The reanalysis and measurement data provided allow a detailed investigation of the seasonal variability as well as an in-depth assessment of the current as well as the historical wind field on each site. The focus of the measurements is on the heights relevant for future wind turbine types, i.e. in the range of 40 to 200 metres. The evaluation of the reanalyses is carried out for the grid points closest to the areas as well as the surrounding grid points and is validated using the existing measurement data. Previous evaluations show a very good correlation, which gives the reanalyses a high significance to determine the wind conditions on the sites. In addition, information on interannual variability is required. Long-term time series of geostrophic wind derived from air pressure data from coastal stations enable an assessment of multi-decadal variations.
Reconstructing the Sunspot Number : challenges and impact
Authors: Lefevre Laure , Bhattacharya Shreya , Clette Frédéric
Published: June 2022

We will present the international effort that led to the first-ever revision of the Sunspot and Group Numbers (let us call them "the Sunspot Series"), a well-known series that had never been put into question since its creation by Rudolf Wolf in 1849. We will review the process and its challenges (ISSI review paper, https://www.issibern.ch/teams/sunspotnoser/). 

At this point in time, the number of Group Number series available is still based on different techniques that enable the stitching of datasets of various quality over wildly different periods, and although they present an undeniable improvement over the original Group Number from Hayt & Schatten (1998), the resulting series remain to be extensively tested in order to be clearly validated by the scientific community.

Since this first revision in 2015 (Solar Physics Topical Issue, 2016), the Sunspot Series have become living datasets that require constant monitoring since more source data are being recovered regularly (Arlt & Vaquero, 2020). With the team from ISSI, we are currently driving a large effort to gather raw data from all around the world. At the Royal Observatory of Belgium, within the WDC-SILSO (https://wwwbis.sidc.be/silso/) where the original Mittheilungen have been digitized (2017-2019) we also have 2 PhD students working on stitching historical and modern sunspot numbers and evaluating the quality of the reconstructed series through advanced statistical techniques.

After this review of previous efforts, we will focus more specifically on the reconstruction of the International Sunspot Number from raw sunspot data (Bhattacharya et al., 2021, 2022, FARSUN belgian project) end present the potential impact of these revisions on end users (e.g. F10.7, Clette, 2021). 
Using Satellite Information to Evaluate Cloud Forecast from WRF-Solar EPS 
Authors: Sengupta Manajit , Yang Jaemo , Xie Yu , Jimenez Pedro , Kim Ju-Hye
Published: June 2022

Cloud forecasting is an enormous challenge in numerical weather prediction (NWP) models because of the complex physical processes, high variability in spatial and temporal scales, and lack of observations for model evaluation. The capability to represent cloud fields in the NWP models directly impacts the accuracy in predicting surface solar irradiance. Verifying cloud forecasts from NWP models is essential to investigate the source of uncertainty and error stemming from predicting various types of clouds. However, it requires high-quality observations containing various information of clouds and in-depth analysis over a wide range of regions to assess the cloud fields. In this study, we produce day-ahead cloud forecast over the contiguous United States (CONUS) for 2018 using the Weather Research and Forecasting-Solar Ensemble Prediction System (WRF-Solar EPS) which is a state-of-the-art ensemble NWP model specialized for solar applications. The strengths and limitations of WRF-Solar EPS in reproducing cloud fields is diagnosed using satellite observations from the National Solar Radiation Database (NSRDB). The frequency of clouds and various cloud detection metrics including the probability of detection (POD), the false alarm rate (FAR), the hit rate (HR), Kuiper’s skill scores (KSS), and mismatched cloud frequency (MCF) are calculated to assess the performance of WRF-Solar EPS. In the first part of this study, we focus on monthly analysis using the detection metrics to account for seasonal performance of WRF-Solar EPS. In the second part, the MCF classified by cloud top height and cloud optical depth is analyzed to investigate the model’s capability to predict nine different types of clouds. The study exhibits that the WRF-Solar EPS has difficulty predicting optically thin clouds; overall MCFs show 46% (cumulus), 34% (stratocumulus), 19% (stratus), 33% (altocumulus), 23% (altostratus), 16% (nimbostratus), 27% (cirrus), 13% (cirrostratus), and 8% (deep convective) for the nine cloud types.
Parametrising nearest neighbor interaction in a convection scheme: an idealized squall line test case.
Authors: Goecke Tobias
Published: June 2022

 Most convection schemes in operational models for numerical weather prediction
 operate in a single column that is connected to other columns only via
 the large scale dynamics. In particular the triggering of convection is usually
 independent of the convective activity in the surrounding of the grid cell.
 This is not realistic since convective activity is able to trigger
 convection nearby [1,2].
 Here, the intention is to work toward an implementation of nearest neighbor
 interaction (NNI) within the framework of a parametrization of deep convection 
 within the global ICON model [3].
 The main research question is whether a convection scheme equipped with NNI
 is able to interact with the large scale dynamics in a constructive way.
 In particular the focus is laid on an idealized squall line case in which the 
 standard convection scheme fails. It is shown how a propagating squall line can be recovered
 either in the large scale dynamics, the pure NNI or the coupled dynamic.

Observations of tropical convection suggest continous phase

transition between precipitating and non-precipitating phases [4] similar

to what is observed in simple two-dimensional lattice models.

Thus, using simple two-dimensional presciptions to descibe horizontal correlations

in convection schemes might be suitable to introduce a scale-consistent behavior.

Percolation  models are good canditates to describe spatio-temporal

correlations, critical scaling or cloud size distributions [5].

This motivates the current work, which  investigates, how a given NNI might

interact with the dynamic of the host model and to establisch a proof of principle

that such a mechanism might be beneficial.

 

[1]: Tompkins (2001), JAS 58 (13), 1650.

[2]: Seifert & Heus (2013), ACP 13 (11), 5631.

[3]: Zängl et.al. (2015), QJRMS 141 (687), 563.

[4]: Peters & Neelin (2006), Nature Physics 2 (6), 393.

[5]: Windmiller (2017), PhD Thesis, LMU Munich, https://edoc.ub.uni-muenchen.de/21245/
Comparison of future rainfall projections over California: CMIP6 vs. CMIP5
Authors: Tarín-Carrasco Patricia , Petrova Desislava , Galí-Reniu Maria , Cvijanovic Ivana
Published: June 2022

Future projections of rainfall changes remain one of the most prominent challenges for climate models due to the high degree of uncertainties associated with them. This is especially true for regions featuring Mediterranean climate, where the uncertainties in projections of future rainfall changes in combination with increasing temperatures, pose a significant problem. One particular uncertainty hotspot is California, where models tend to disagree about the sign of future precipitation changes.

We analyse the simulations from the Coupled Model Intercomparison Projects Phase 5 (CMIP5) and 6 (CMIP6) and study their respective intermodel differences in projections of future rainfall changes over the state of California. We employ 30 models from CMIP5 and 30 models from CMIP6 initiatives and focus on the business-as-usual scenarios (SSP585 - RCP8.5). We consider the precipitation changes over the two time horizons  - mid-century (2040-2059) and the end of century (2080-2099).

Our results point at higher uncertainty in rainfall projections for the wet winter season December-January-February (DJF) than for the rest of the seasons. Comparing CMIP5 and CMIP6, we see that CMIP5 simulations show larger intermodel disagreement for the end-of-century period while CMIP6 show larger intermodel disagreement for the mid-century time horizon. For the new CMIP6 simulations, more than 70% of the models project that northern California is going to experience an increase in precipitation in the future, while the projections for southern California are more uncertain, with models being almost equally spread between those suggesting drier and those suggesting wetter conditions. This is an improvement compared to the CMIP5 simulations where 60% models suggested wetter conditions (and 40 % drier conditions) over northern California.

Finally, we analyse how the uncertainties in future model simulations of geopotential height, outgoing longwave ration (OLR) and ENSO changes relate to the described uncertainties in California’s rainfall in CMIP6 and CMIP5.
Turbulence Structure and Mixing in Strongly Stable Boundary-Layer Flows over Thermally Heterogeneous Surfaces 
Authors: Mironov Dmitrii , Sullivan Peter
Published: June 2022

Direct numerical simulations (DNS) at bulk Reynolds number Re=104 and bulk Richardson number Ri=0.25 are performed to analyze the structure and mixing intensity in strongly stable boundary-layer flows over thermally homogeneous and heterogeneous surfaces. An idealized plane Couette flow set-up is used as a proxy for real-world flows. The flow is driven by a fixed velocity at the upper surface, while the lower surface is at rest. The temperature at the horizontal upper and lower surfaces is either homogeneous or varies sinusoidally in the streamwise direction, while the horizontal-mean temperature is the same in the homogeneous and heterogeneous cases. 

The stratification is strong enough to quench turbulence over homogeneous surfaces, resulting in velocity and temperature profiles that vary linearly with height. However, turbulence survives over heterogeneous surfaces. Both the molecular diffusion and the turbulence contribute to the downward, i.e., the down-gradient, transfer of horizontal momentum. The total (diffusive plus turbulent) heat flux is directed downward. However, the turbulent contribution to the heat flux appears to be positive, i.e., up the gradient of the mean temperature. An analysis of the second-order velocity and temperature covariances and of the vertical-velocity and temperature skewness suggests that the counter-gradient heat transport is due to quasi-organized cell-like vortex motions generated by the surface thermal heterogeneity. These motions act to transfer heat upwards similar to quasi-organized cell-like structures that transfer heat upwards in atmospheric convective boundary layers. Thus, the flow over heterogeneous surface features local convective instabilities and upward eddy heat transport, although the overall stratification remains stable and the heat is transported downward in the mean. The DNS results are compared to the results from the large-eddy simulation study of weakly stable boundary layer (Mironov and Sullivan 2016, J. Atmos. Sci., 73, 449-464). The DNS findings corroborate the pivotal role of the temperature variance in setting the structure and transport properties of the stably-stratified flow over heterogeneous surfaces, and the importance of third-order transport of temperature variance.

The analysis is focused on the flow configuration where the crests of the surface temperature waves are normal to the mean flow. Work is underway to analyze the effect of the surface heterogeneity orientation (e.g., temperature-wave crests normal vs. parallel to the mean flow) on the flow structure and mixing intensity. 

Implications for modeling (parameterizing) strongly stable boundary layers in large-scale atmospheric models are discussed. 
Examining extreme sea levels for the support of nuclear power plant safety in Finland 
Authors: Leijala Ulpu , Johansson Milla M. , Jylhä Kirsti , Laine Marko , Rantanen Mika , Räihä Jani , Räty Olle , Särkkä Jani
Published: June 2022

Urbanized low-lying coastal areas become evidently increasingly vulnerable in the future climate, where higher mean sea level and more powerful storm surges are expected. Safe planning and operation particularly of critical coastal infrastructure, such as nuclear power plants, requires careful evaluation of external events due to extreme weather and sea level conditions. Finland is located in the northeast part of the Baltic Sea, where sea level variations are driven by short-lasting phenomena (storm surges, internal oscillations in the Baltic Sea, tides), long-term mean sea level change (global mean sea level, the post-glacial land uplift and the Baltic Sea water balance), and wind waves. In Finland, there are altogether five nuclear power plant units along the coast and approximately one third of electricity production in the country is grounded on nuclear energy.

The Finnish Meteorological Institute has studied weather, climate and sea level hazards potentially posing risks to nuclear power plants since 2007 in several research projects. In this presentation, we will introduce some preliminary results of studies on extreme sea levels on the Finnish coast, which are conducted in the PREDICT project (https://en.ilmatieteenlaitos.fi/predict). Their focus is on the short-term sea level variations, which might be several meters in the Baltic Sea, even if the tides in the region are mostly negligible.

In the first study, Bayesian hierarchical modelling is used to estimate return levels of annual sea level maximum on the Finnish coast and non-stationarity in the related sea level extremes. Our model setup enables sharing information on sea level extremes between the neighboring tide gauge stations. Additionally, it accounts temporal variations in the distribution parameters by incorporating climate indices such as North Atlantic oscillation (NAO) in the model. Preliminary results suggest that hierarchical approach reduces the range of uncertainty in the estimated parameters.

The second study tackles a question “What is the most severe flooding that could occur in the Baltic Sea coast in the present climate if the weather conditions are optimal?”. In this study, effects of low-pressure intensity, speed, direction and point of origin on the sea level extremes is examined by making simulations with numerical sea level model combined with the synthetic cyclones for the Finnish tide gauge locations. Tentative results indicate that the highest sea level extremes on the Finnish coast are caused by large and slowly propagating wind storms.
Atmospheric dynamics supporting the Alpine glaciers at the Last Glacial Maximum assessed with a high-resolution regional climate model
Authors: Del Gobbo Costanza , Colucci Renato R. , Monegato Giovanni , Žebre Manja , Giorgi Filippo
Published: June 2022

The Last Glacial Maximum (LGM) is a global event that occurred 26 to 21 ka BP and was characterized by lower temperatures (3 to 6 °C globally) and different precipitation regimes. The environment was marked by extended glaciers which released a large set of morphologies as the systems of well-preserved moraines in the alpine foreland that testify the advanced phases of the alpine glaciers during the LGM. Mountain glaciers, quickly responding to temperature and precipitation variations, are considered excellent indicators of climate change. Here we focus on the European Alps and attempt to unveil the physical processes that sustained the glacier extent during the LGM using a multiple regional climate model (RCM) nesting approach. Toward this goal, we completed and intercompared two high-resolution (12 km) RCM simulations, one for the steady-state LGM standard (21 ka BP) and one for pre-industrial (PI) conditions. We also calculated the LGM and PI environmental equilibrium line altitude (envELA) for the whole Alpine chain starting from physically-based summer temperature a total annual precipitation. Precipitation and temperature patterns show good consistency with proxy records and other RCM studies. In particular, our results present a predominance of convection over stratiform precipitation during summer, as well as increased southwesterly stratiform precipitation in the southern alpine region compared to the PI. Frequent summer snowfall, extending to low elevations, where caused by this precipitation pattern, along with lower temperatures, and led to a substantial drop in the ELA. Our model-based evaluation of the LGM ELA showed unprecedented consistency with the estimated LGM Alpine glacier reconstructions, further proving the great potential of this method for paleoclimate applications.
Multi-year trend errors in sub-seasonal reforecasts
Authors: Tietsche Steffen , Vitart Frederic , Mayer Michael , Balmaseda Magdalena
Published: June 2022

Multi-year trends in sub-seasonal reforecasts that are inconsistent with observed trends constitute a time- or state-dependent bias in either the physical forecast model or the forecast initialization data and methods, regardless of whether the observed trends are due to climate change or slow internal variability. These reforecast trend errors degrade the skill diagnosed from the reforecasts and point to deficiencies of the sub-seasonal forecasting system in representing a changing mean state. However, detection and quantification of these trend errors is non-trivial because of often high levels of sub-seasonal to interannual variability in combination with weak trends. Here, we propose methods to assess the robustness and importance of trends in observations, and detect when trends in sub-seasonal reforecasts are inconsistent with the observed trends. As a concrete example, we pick surface air temperature (SAT) trends in Northern Hemisphere winter, and assess consistency of trends in ECMWF 47R1 reforecasts at different lead times for the 20-year period 2000-2019 with the ERA5 reanalysis trend. We find that some regions - even for this relatively short period - exhibit positive SAT trends in ERA5 that are significantly different from zero, robust against small changes in the period and relatively large when compared to interannual variability. Among these regions are the Tropical Warm Pool and the Eurasian Arctic. In the latter region, the reforecasts clearly underrepresent the observed warming trend of about 2 K per decade at longer lead times: for lead times beyond three weeks, the reforecast trend is only about 1 K per decade, with a 95% confidence interval from 0.5 to 1.5 K per decade. We discuss potential reasons for this specific trend error, with the aim to provide guidance for future improvements in the physical forecast model and data assimilation methods.
Lessons Learned from the Tragedy during the 100 km Ultramarathon Race in Baiyin, Gansu Province on 22 May 2021
Authors: Zhang Qinghong , Ng Chan-Pang
Published: June 2022

Twenty-one runners died of hypothermia during the 100 km Ultramarathon Mountain race in Baiyin, Gansu Province on 22 May 2021. The hypothermia was caused by a combination of low temperatures, precipitation, and high winds associated with a typical large-scale cold front passing by the race site that morning. Based on historical hourly records of 13 meteorological surface stations over the past six years, temperature (3.0°C) and apparent temperature (−5.1°C) at 1200 LST as well as gust wind speed (11.2 m s−1) at 1100 LST on the day of the tragedy were found to be within the top or bottom 5thpercentile for the month of May. The precipitation was only moderate at this time, but when temperature lower than 3.0°C, gust wind speed greater than 11.2 m s−1, and precipitation greater than 0.1 mm for any adjacent three hours were combined together, 1200 LST 22 May fell within the top 0.1% of cases. The European Centre for Medium-range Weather Forecasting model produced reasonably good forecasts of the low temperature and high wind one day and seven days before the event, respectfully. Based on this study, lessons that can be learned from this tragedy are summarized from an academic perspective: Hazard and impact forecasts of high-impact weather events should be developed to increase the value of weather forecasts. Probability forecasts should be issued by government weather agencies and communicated well to the public. And more importantly, knowledge of how to evaluate the impact of weather should be delivered to the public in the future.How to increase the value of weather forecasts to provide better service to users and to prevent weather-related disasters is a great challenge. From observation to decision making, there are several “mountains and valleys” to overcome. Collaborations among scientists in different areas, governments, social media, and citizens are needed for successful hazard prevention.
Delivering the Met Office Research & Innovation Strategy through UK Science Partnerships
Authors: Pankiewicz George , Payne Verity
Published: June 2022

As our ability to better understand and predict future weather and climate develops, so too do the expectations of our customers and stakeholders. The Met Office’s Research and Innovation Strategy 2020-2030 (R&I strategy) has identified a number of Research and Innovation themes to help with these evolving demands, and is structured into three core activities: pioneering research, foundational capability and science to services. The Met Office doesn’t have all the skills or resources needed to deliver the R&I strategy, so together with research organisations across the UK, Science Partnerships are needed to undertake joint research aimed at delivering the strategy.

These Science Partnerships aren’t new. A close collaboration with the University of Reading started in the 1980s, and in the late 2000s, a Joint Weather and Climate Research Programme had been established with the Natural Environment Research Council (NERC) Centres, shortly followed by the growth of the Met Office Academic Partnership, which now includes the Universities of Reading, Exeter, Leeds, Oxford, Bristol and University College London.

However, as the customer and stakeholder demands and the R&I strategy change, the Partnerships need to evolve. The R&I strategy can be considered a blueprint needed to meet this challenge, and now includes themes such as fusing simulation with data sciences; technology architecture and innovation; and hazard to decision making, all of which involve disciplines outside the scope of traditional weather and climate services.

Indeed, the last 2 years has seen a new Joint Centre for Excellence in Environmental Intelligence established with the University of Exeter to help fill gaps around data science. The Met Office is currently in discussion with NERC Centres around a new UK National Climate Science Partnership, to co-ordinate the national capability in climate science needed to inform climate solutions across society, to help build resilience and adapt to the challenges of the coming decades.

This contribution to the session will provide further detail of the Met Office’s R&I strategy, an overview of the Met Office UK Science Partnerships, the practicalities of developing successful Science Partnerships, and lessons learned. In depth examples can provided where research has translated into operations, such as the GungHo dynamical core developed with our Academic Partners, NERC and the Science and Technology Facilities Council (STFC) as part of the Met Office’s Next Generation Modelling Systems, and how the UK Earth System Modelling project (jointly funded with NERC) contributed to the 6th Climate Model Intercomparison Project, CMIP6.
Co-design of sectoral climate services based on seasonal prediction information in theMediterranean
Authors: Rodríguez-Guisado Esteban , Rodríguez-Camino Ernesto , Sánchez-García Eroteida , Bacciu Valentina , Chiarle Marta , Costa-Saura Jose , Garrido Maria Nieves , Lledo Llorenç , Navascués Beatriz , Paranuncio Roberta , Terzago Silvia , Mereu Valentina , Nigreli Guido , Santini Monia , Soret Albert , von Hardenberg Jost
Published: June 2022

We present in this contribution the varied experiences gathered in the co-design of a
sectoral climate services collection, developed in the frame of the MEDSCOPE project.
This colletion of climate services have in common the application of seasonal predictions for the Mediterranean
geographical and climatic region. The project was originally conceived to develop and
evaluate climate services for various sectors with high societal impact in the region:
renewable energy, hydrology, and agriculture and forestry. The project also served as
the scientific branch of the WMO led Mediterranean Climate Outlook Forum (MedCOF)
that had as objective -among others- partnership strengthening on climate services
between providers and users within the Mediterranean region. The diversity of the
MEDSCOPE experiences in co-designing shows the wide range of involvement and
engagement of users in this process across the Mediterranean region, which benefits
from an existing solid and organized community of climate services providers and
users. A common issue among the services described here -and also among other
prototypes developed in the project- was related with the communication of forecasts
uncertainty and skill for efficiently informing decision-making in practice. Another
challenge concerns the demonstration of the economic, social, and environmental
value of predictions provided by these MEDSCOPE prototypes.
Precipitation characteristics and related boundary-layer processes during LIAISE 2021
Authors: Udina Mireia , Bech Joan , Peinó Eric , Polls Francesc , Balagué Marta
Published: June 2022

In the context of the “Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment” (LIAISE), the WISE-PreP project was designed to study precipitation processes aiming to characterize possible differences in precipitation induced by surface characteristics (irrigated vs non-irrigated areas). Instrumentation deployed during the 2021 campaign included three sites equipped each with a vertical radar Doppler Micro Rain Radar (MRR) and a laser disdrometer (PARSIVEL), covering both irrigated and non-irrigated sites. Time series of vertical precipitation profiles are recorded to study microphysical processes trough the evolution of raindrop size distributions and related variables including precipitation intensity or convective vs stratiform rainfall regimes.

The main meteorological variables at the surface are analysed during LIAISE 2021 campaign and compared with A reference period (2010-2019) for irrigated and non-irrigated areas to characterize local differences either related with dynamics or land-use influences, and their influence in the boundary layer development and precipitation initiation.

First results show higher accumulated precipitation in the non-irrigated area (eastern area) than those in irrigated area (western area) in summer 2021, a feature also observed in summers for the reference period. Maximum and minimum daily temperatures are higher in irrigated areas than in non-irrigated areas. Both results are consistent with current climatology based on monthly precipitation and temperature that indicate the existence of a zonal gradient that increases semi-arid conditions (drier and warmer) from the east to the west. Sea-breeze regime also influences the two type areas, with different time arrival and a slight shift in wind components.

Funding for this research was provided by “Analysis of Precipitation Processes in the Eastern Ebro Subbasin” (WISE-PreP, RTI2018-098693-B-C32) and the Water Research Institute (IdRA) of the University of Barcelona.
Predicting the direct and indirect effects of atmospheric aerosol on photovoltaic power generation
Authors: Seifert Axel , Förstner Jochen , Porz Nikolas , Hoshyaripour Ali , Filipitsch Florian , Wagner Annette , Doppler Lionel , Vogel Heike , Bachmann Vanessa , Rohde Anika , Hanisch Thomas
Published: June 2022

For an efficient integration of photovoltaic (PV) energy into the power grids, more accurate forecasts of the expected PV-power production are needed. However, most operational numerical weather prediction models rely on an aerosol climatology and ignore the spatio-temporal variability of the atmospheric aerosol. For specific weather conditions like during mineral dust outbreaks or major wildfire events, the negligence of prognostic aerosol often leads to significant deficiencies in the operational forecasts, however.

At Deutscher Wetter­dienst (DWD) and Karlsruhe Insti­tute of Technology (KIT) the project “PermaStrom” aims at the operational prediction of various natural aerosol species to improve radiation forecasts. Emission, transport and deposition of mineral dust, black carbon from vegetation fires, and sea salt are thus explicitly simulated in the ICON-ART model system. In the model, direct aerosol effects on radiation are considered using state-of-the-art optical properties. Microphysical effects of aerosol acting as cloud condensation nuclei (CCN) and ice nucleating particles (INPs) are investigated in a high-resolution regional model with the long-term goal to improve the parameterization of aerosol-cloud effects in global models.

Aerosol-cloud-radiation effects are studied in a regional ICON-ART model with 2 km grid spacing with an aerosol-aware two-moment bulk microphysics scheme. In addition, first steps are made towards a global ensemble system for aerosol forecasts using ICON-ART. This will allow to quantify the uncertainty of the forecasts. A multi-fidelity ensemble, which combines ICON-ART and ICON simulations to optimally sample the aerosol- and flow-dependent variability, is used to keep the computational processing manageable. The ICON-ART simulations are validated with aerosol and radiation measurements at surface stations as well as cloud, aerosol and radiation products from satellites and ceilometers.

We will give an overview of the ICON-ART configuration of the pre-operational real-time global aerosol prediction system at DWD. This includes aspects like mineral dust, sea salt, and wildfire emissions. For the latter, a machine learning emulator of the plume rise model is currently being developed.
Completion of gaps in 10-min air temperature series from automatic weather stations in Belgium
Authors: Journée Michel , Loucheur Benoît , Absil Pierre-Antoine , Bertrand Cedric
Published: June 2022

Air temperature is historically monitored in Belgium by a network of climatological stations. Most of these stations are till nowadays mainly manual: they rely on a volunteer observer who records every morning the daily extreme air temperatures by reading liquid-in-glass thermometers (including mercury thermometers) placed in Stevenson screens. Since 2020, the UNEP Minamata Convention on Mercury bans all production, import and export of observing instruments containing mercury. In the recent years, various alternatives to mercury thermometers have therefore been considered and tested by the Royal Meteorological Institute of Belgium (RMI). Parallel measurements have been performed with several synoptic weather stations in Belgium. Based on these comparisons as well as on practical arguments (e.g., the price as well as the ease of installation, maintenance and operation), a compact low-cost automatic weather station was selected by RMI to transition away from the conventional manual climatological stations. In 2022, almost 50 of these compact stations have already been installed and around 50 further ones are planned in the coming months. These stations provide 10-min observations in real-time through wireless data transmission. 

In practice, the communication of these compact stations is however not perfectly robust as data are regularly missing due to frequent down-time of usually short duration. As these gaps in the time series can be problematic for end-users applications, a procedure to automatically provide estimations for all missing data is needed. In the complete data processing chain (i.e., from the sensor to the end-user), this automatic data completion step is performed just after the real-time data quality control (i.e., quality tests that directly eliminate obvious errors). It is followed by a final data validation (i.e., detailed but delayed and partly manual verification of the data to ensure the quality of the climate archives). Observations and estimations are labeled by distinct quality flags in the climate archives.

This contribution provides an overview of the recent developments regarding the automatic completion of gaps in 10-min air temperature series. Various approaches including inverse distance weighted interpolation (IDW), principal component analysis (PCA), linear interpolation and linear regression have been compared in order to select the method to be implemented in the operational data processing chain.

 
The double-degree MSc Programme in Environmental Meteorology  jointly offered by the University of Trento (Italy) and the University of Innsbruck (Austria)
Authors: Zardi Dino
Published: June 2022

The presentation outlines the main features of the double-degree MSc programme in Environmental Meteorology jointly offered by the University of Trento (Italy) and the University of Innsbruck (Austria) since the academic year 2018/19,  and results achieved so far.

The programme welcomes candidates with a solid background in mathematics, theoretical and applied physics, and chemistry.
All lectures are taught in English. The regular duration of the programme is two tears arranged in four terms, scheduled alternately at the two Universities, so as students can get the most out of the specific expertise available on each side.

The curriculum aims at preparing graduates not only with a sound basis in meteorology (including weather analysis and forecasting, atmospheric boundary layer processes,  numerical prediction models), but also interdisciplinary contents connected to a number of environmental applications, such as hydrology, glaciology, agricultural and forest meteorology, renewable energy resources, environmental chemistry, air pollution monitoring, modelling and management.

Students are also stimulated to become familiar with different complementary approaches, including theoretical analysis, numerical modelling, and experimental techniques, specifically oriented to environmental measurements.

Besides class lectures and laboratory exercises, students are offered weekly seminars from visiting speakers, visits to weather stations, meteorological offices, and environmental agencies, as well as opportunities to attend major events related to meteorology.

Special emphasis is devoted to the master thesis project. Students concentrate on their thesis especially in the final semester, and they can work on that at either Univesity, or, upon specific projects and agreements, at partner institutions, such as universities, research bodies or meterological offices, anviroenmental agenices, and so on.  

The preparation offered through this program is meant to prepare professionals enabled not only to work on typical jobs connected to meteorology and climatology, but also to support a variety of environmental applications, such as the assessment of available energy form renewable resources, air quality management, water resource management, applied climatology, agricultural and forest meteorology.
Effect of blue and green features on thermal exposure and thermal sensation in Czech cities
Authors: Lehnert Michal , Geletič Jan , Jurek Martin , Brabec Marek
Published: June 2022

Climate change becomes more evident even in the generally mild climate of Central Europe. Analyses based on long-term temperature observations have confirmed growing numbers of hot days and nights and an increasing frequency of heat waves. In response to this risk, municipalities seek and invest into climate adaptation measures. However, the implemented measures are often inadequate or inefficient. In this study, we focus on the analyses of the effect of blue and green features to reduce heat stress in open public spaces in city centres; our research is conducted in collaboration with local representatives of four Czech cities of Brno, Olomouc, Ostrava and Pilsen. We selected representative locations in each of the four city centres as field measurement sites for air temperature, humidity, wind velocity and globe temperature during hot days. Based on our measurements the UTCI values in five-minute steps were calculated. At the same time, we conducted extensive questionnaire surveys of thermal comfort perceived by passers-by in each of the investigated locations. Our results show that in studied cities, trees within open areas of the city centre lead to a decrease in UTCI by 5–8°C during daytime in tree shade compared to sunlit paved areas. Irrigated and regularly cut lawns in open areas of the city centres cause a decrease of 0–1°C compared to sunlit paved areas. Small features of blue infrastructure (fountains, misting systems etc.) in open areas of the city centre lead to changes in UTCI in the range of −2.5 to +1.0°C, depending on the time of day and on the character of the water feature. Sprinkling the open areas (city squares) with water from tank trucks during heat waves lowers the UTCI by 1–3°C, yet this effect lasts only around 20 minutes (depending on air temperature and air flow). Influence of small water sprinklers and misting systems on the microclimate in distances exceeding 0.5 m from the given water feature was inconclusive. Finally, the results suggest a highly complex relationship between biometeorological indices and thermal sensation vote (TSV) in urban environments − open grassy areas exhibit a lower probability of higher TSV, on the other hand, the probability of higher TSV is higher under trees and near sprayed water-mist.