bluuebunny/crossref_metadata_2025 huggingface dataset:
Study of the interaction of atmospheric wakes from several offshore wind farms as observed by Synthetic Aperture Radar (SAR) system
Authors: Djath Bughsin' , Schulz-Stellenfleth Johannes
Published: June 2022

Offshore wind turbines induce atmospheric wakes downstream of typically tens of kilometers length while converting wind energy into electrical power. In the recent years, wakes behind single wind farms have been extensively studied. The growing number of offshore wind farms (OWFs) in the German Bight increases the occurrence of interferences between close neighboring OWFs. Previous studies showed that the proximity of neighboring OWFs impacts the performance of downwind turbines and reduces their capacity factor. However, the interactions of wakes from different wind farms is not well understood, in particular concerning the resulting wind speed deficits, turbulence intensities and superimposed wake lengths.

The interaction of wakes from two OWFs generally leads to longer wakes. Also, it was observed frequently that  wakes resulting from 2 OWFs reach a third wind farm. Several conditions can entail interactions of wakes from several OWFs, such as the size of OWF (geometry and density of turbines), atmospheric stability and wind speed and directions. The study focuses on the investigation of the interactions of wakes from two and three OWFs using Synthetic Aperture Radar system (SAR), which is an interesting instrument to observe large spatial areas at resolution of 20 m.  The statistical analysis of a 5-year period of SAR data acquisitions by the  Sentinel-1A and Sentinel1-B satellites revealed that the occurrence of interactions of wakes between OWFs exceeds 75% of all the cases for which wakes were observed. The interaction between OWFs is clearly correlated with a certain wind direction range. Additionally, the geometry of OWFs plays a role in the two-dimensional structure of the wakes and the potential for impacting neighboring wind farms. Obviously, wind directions parallel to the alignment of several OWFs are likely to induce strong interactions of wakes.

SAR data are combined with stability information from atmospheric models and mast measurements to analyse the respective impacts on key parameters of superimposed wakes (e.g. deficit, wake length).  Results are compared with simplified empirical models, which make assumptions about the linearity of the wake superposition process.   
The Present and Future of Lagrangian Cloud Modeling: From the Centimeter to Kilometer Scale
Authors: Lim Jung Sub , Kainz Johannes , Hoffmann Fabian
Published: June 2022

Clouds are one of the most complex systems in the atmosphere, as the processes that constitute a cloud are of inherent multi-scale nature. Among the many challenges in cloud physics, representing the effects of turbulence on cloud microphysics is one of the most intriguing, with implications for precipitation development, cloud optical properties, and hence the role of clouds in the climate system. Lagrangian cloud models (LCMs) are a new approach for simulating cloud microphysics, which has been developed over the last decade. LCMs overcome the limitations of many previous cloud microphysical models, and enable new insights on important cloud microphysical questions, such as aerosol activation and regeneration, the role of giant aerosol particles in precipitation formation, and the effects of turbulent supersaturation fluctuations. In this presentation, we will introduce a new LCM approach for representing the effects of unresolved turbulence on cloud microphysics, focusing on the entrainment and mixing process. As the spatial scales of entrainment and mixing are usually smaller than the resolution of most dynamical models used for the simulation of clouds, unresolved mixing tends to be too fast, and the entire process may be misrepresented. Our new approach represents these unresolved scales by tracking air parcels, allowing for the explicit representation of entrainment and mixing on scales as small as a few centimeters, while entire clouds on scales as large as a few kilometers are covered simultaneously. Thus, we are able to represent different mixing scenarios such as homogeneous and inhomogeneous mixing naturally, applying only a minimum of assumptions. By highlighting how the mixing type changes during the lifecycle of a cloud, we outline how this scale-interactive approach can play a pivotal role in solving future problems in cloud physics.
Improving the weather and early-warning information dissemination - FMI open source mobile weather application
Authors: Eerikäinen Matti
Published: June 2022

The expert services of the Finnish Meteorological Institute (FMI) has been working over fifty years internationally in improving of the services provided by the different national hydro-meteorological services (NHMS’s) worldwide. The cornerstones of the cooperation with different institutes have been formed around improved disaster risk reduction and adaptation to climate change. So far, the assistance and transfer of know-how has reached more than 100 countries worldwide, mainly concentrating on the developing countries. The main objective of the projects has been to improve the quality, dissemination and effectiveness of the hydro-meteorological forecasts and warning services for the societies to first decrease number of fatalities related to natural disasters and to decrease the economical losses related to the same events.

 

One of the most tangible results of the FMI projects in improving the service delivery of the NHMS’s in developing countries has been the international installations of the FMI-built weather forecast and early warning production system “SmartMet”. The license fee-free and partly open-source software is already installed in some 30 countries world-wide, and the number is constantly increasing. The SmartMet system has the capabilities to create and disseminate weather and early-warning products to multiple end-users and platforms such as newspapers or the Internet. To tackle the challenge of reaching the last mile and to further improve the dissemination of weather and early-warning information FMI has developed a universal mobile weather and early warning application. The application works everywhere, where SmartMet system is installed, and it is integrated seamlessly into the local SmartMet system. This means it also utilizes and displays local data and early-warnings. The SmartMet mobile phone application is based on the FMI’s own mobile application in Finland and therefore it will be constantly developed and updated. This improves the sustainability of the system and application also internationally. The application will be published for iOS and Android operating systems.
Development of assimilation schemes for the representation of dust in LAPS and WRF modeling systems based on MODIS and MSG satellite retrievals
Authors: Solomos Stavros , Spyrou Christos , Bartsotas Nikos , Katsafados Petros
Published: June 2022

Dust is the most abundant aerosol worldwide with multiple implications for radiative transfer, cloud processes and air quality. The dust emissions are usually represented in atmospheric models based on static dust source maps and surface wind properties. In operational dust forecasts, the models are commonly configured in warm start mode, meaning that each simulation cycle is initialized by the forecasted dust fields of the previous run. This technique inevitably inserts certain amounts of uncertainty due to the deviation of numerical solutions. In this study we present the development and evaluation of alternative methods for the initialization of dust emissions in the Georgia Tech Goddard Global Ozone Chemistry Aerosol Radiation and Transport of the Air Force Weather Agency (GOCART-AFWA) module of WRF model. First, we implement a time-varying dust source map based on the 16-day average Normalized Difference Vegetation (NDVI) from the MODIS-Terra instrument. Replacing the static dust source map with a dynamic satellite-based emissions map, allows a physically based representation of seasonal and annual variations of initial dust-source strength in the model. Second, we demonstrate the assimilation of dust Aerosol Optical Depth (AOD) satellite retrievals from the MSG/SEVIRI sensor in the Local Analysis and Prediction System (LAPS), for use in WRF model. A 3D-Var assimilation of satellite AOD retrievals from the MSG-SEVIRI instrument is performed in LAPS, similar to the standard assimilation of atmospheric variables. The forecasted WRF dust AOD is used as first-guess field for the generation of the assimilated AOD in LAPS. Finally, the LAPS AOD is used to initialize the WRF simulations, thus nudging the model towards the observational satellite values. Model runs with different configurations of the MODIS-NDVI and MSG-SEVIRI assimilation schemes are performed for the region of North Africa and the greater Mediterranean. First results verify the successful implementation of both assimilation parameterizations in WRF-Chem.  The improvements and deviations between the original and the newly developed schemes in GOCART-AFWA are discussed in comparison with the AERONET station measurements of AOD.

Acknowledgment This work is supported by the Hellenic Foundation for Research and Innovation project MegDeth (HFRI no.703)
DWD-Crowdsourcing: User Reports available on Open Data
Authors: Spitzer Arne , Kempf Harald , Jerg Matthias , Blahak Ulrich
Published: June 2022

Since July 2020 the DWD WarnWetter-App comprises the Crowdsourcing-module “User Reports”. This module provides users the functionality to report observations about current weather conditions and severe weather to DWD and other users. The data is daily collected and available on DWD’s Open Data portal (https://opendata.dwd.de/weather/crowdsourcing/warnwetter/).

The user reports represent the current meteorological conditions at a certain place at a certain point of time. The Crowdsourcing-module provides 10 different meteorological categories (lightning, wind, hail, rain, wet icy conditions, snowfall, snow cover, cloudiness, fog, tornado), each of which contains specific characteristic levels and optionally additional attributes. In addition, the user has the option of setting the location und time of the event manually.

The benefit of the data is that meteorological information at ground level is collected at places where no weather station is located in the immediate vicinity. The dataset is able to complement the existing synoptic station network. In the future, the data could improve the evaluation of the current meteorological conditions and the warning management particularly during extreme weather events.

There is no sophisticated quality control for the user reports. Instead, the users are expected to estimate and report the weather conditions as accurate as possible. Badly inaccurate and false reports are detected by reference data and are excluded instantly. Additionally, in the app users have the opportunity to manually flag meteorologically doubtful reports. Other quality assurance methods are under development.

This contribution contains some numbers and statistics on previous user reports, shows some meteorologically interesting cases, and gives an insight into quality control.

 
Updated road weather forecast system using sky view factor and screening implemented on a motorway in Finland
Authors: Karsisto Virve , Horttanainen Matti
Published: June 2022

Spatially accurate road condition forecasts are important for road maintenance personnel to keep roads safe in changing weather conditions. Road surface temperature can vary significantly depending on the road surroundings. Buildings, trees, and terrain features can block solar radiation and cause the temperature to be lower than in open surroundings. On the other hand, the long wave radiation from the surrounding objects can make the road warmer on clear nights. Sky view factor and shadowing algorithm were added to the Finnish Meteorological Institute’s road weather model to take the surrounding features into account in road weather forecasts. Sky view factor means the portion of the sky that is visible at a certain point and shadowing algorithm determines whether the road point receives direct solar radiation at a certain time using local horizon angles. The local horizon angles and sky view factors were determined from the digital surface model (DSM) that was generated from National Land Survey of Finland’s laser scanning data. The data’s resolution is at average 0.5 points per square meter. The DSM differ from digital elevation model (DEM) as it includes vegetation and buildings. The DSM was generated in pieces to cover a 150 km long motorway from Helsinki to Turku in southern Finland. The sky view factors and local horizon angles were calculated for road points located every 50 m on both west and east leading carriageways. Some small-scale road structures like light and electricity poles and road signs caused problems as they appeared too bulky in the DSM. An overlay covering the carriageways was taken from a 2m resolution DEM and was added over the DSM to smooth the problematic features. As this was not enough to remove all the small-scale structures at the side of the road, the local horizon angles went through an algorithm that smoothed too large spikes and bulky features. The produced local horizon angles and sky view factors were given to the road weather model. The forecasts are generated once in an hour for all the selected road points on the motorway. Spatial differences in the forecasted surface temperatures can be seen especially in autumn and spring when the sun is low. At many places the northern carriageway leading to the west is warmer in these situations, as the trees or rock cuttings prevent the east leading southern carriageway from getting direct solar radiation. The forecast system is now in testing phase but has potential to improve spatial accuracy of road surface temperature forecasts.
Building storylines for wind resources: our experience with users
Authors: Lopez Ana , Lizcano Gil
Published: June 2022

Offering climate services to the wind energy industry is not without its challenges. Users are curious about the potential impacts of climate change on their projects. While estimates of wind resources are based on historical assessments, many questions arise. What if 20 years of past data is not enough to sample the internal variability of the site? Will the long term average wind speed change beyond the margins already taken into account in the project design? Will extreme speeds change in such a way that design parameters will no longer be valid? Very often, however, these users are reluctant to use information, specifically time series, that cannot be “correlated” with their onsite measurements.

In this presentation we describe our journey as a climate services SME, in search of tools and approaches that we could use not only to provide robust information to these users, but also information that they could trust and eventually incorporate in their decision making. 

In particular, we focus on a use case building storylines for a concrete wind development target area. These storylines are built as  plausible future conditions that are consistent with the current climate projections, and at the same time maximally sample their uncertainty. Our storylines provide a rationale not just to estimate the potential impacts of climate change on wind resources, but also to understand the mechanisms driving these changes and the robustness of the climate model projections. Additionally, being conditioned on for instance, different levels of global warming, they provide the wind resource scenarios that can be fed into climate risks analysis now being required by new sustainability regulations, such as the ‘EU taxonomy for sustainable activities’.  

Faced with the challenge of providing uncertain climate change information to users strongly tied to observation-driven reconstructions of climate conditions, we believe that these storylines offer an opportunity to facilitate the adoption of climate change information by a range of users, from the technical experts doing the energy assessment, to the management in charge of strategic decisions.
Ensemble forecast of an index of the Madden Julian Oscillation using a stochastic weather generator based on analogs of  Z500
Authors: Krouma Meriem , Yiou Pascal , Silini Riccardo
Published: June 2022

The Madden-Julian Oscillation (MJO) is one of the main sources of sub-seasonal atmospheric predictability in the Tropical region. The MJO affects precipitation over highly populated areas, especially around Southern India. Therefore, predicting its phase and intensity is a scientific challenge of high societal impact.

Indices of the MJO can be derived from the first principal components of wind speed and outgoing longwave radiation (OLR) in the Tropics (RMM1 and RMM2 indices). The amplitude and phase of the MJO are derived from those indices. Our challenge is to forecast these two indices on a sub-seasonal timescale. This study aims to provide an ensemble forecast of MJO indices from analogs of the atmospheric circulation, computed from the geopotential at 500 hPa (Z500) by using a stochastic weather generator (SWG). The SWG is based on the random sampling of circulation analogs, which is a simple form of machine learning simulation.

We generate an ensemble of 100 members for the MJO amplitude and the RMMs for sub-seasonal lead times (from 2 to 4 weeks). Then we evaluate the skill of the ensemble forecast and the ensemble mean using respectively probabilistic (CRPSS) and deterministic skill scores (correlation and RMSE). We found that a reasonable forecast could be achieved for 40-day lead times for the different seasons. We compare our SWG approach with other forecasts of the MJO mainly with the ECMWF forecast and machine learning forecast. We found that the SWG has reliable forecast skills compared to other forecasts in particular for lead times up to 20 days.
Observing Cloud-Driven Surface Irradiance Patterns
Authors: Mol Wouter , Heusinkveld Bert , Hartogensis Oscar , van Heerwaarden Chiel
Published: June 2022

Clouds cast shadows and locally enhance solar irradiance through absorbing and scattering sunlight, resulting in fast and large solar irradiance fluctuations on the surface.  
The resulting spatiotemporal variability poses a challenge for solar energy production amidst increasing need for reliable renewable energy. It furthermore influences biological processes and the exchange of water and energy. Yet, no numerical weather prediction model is able to reproduce the observed local properties of irradiance, due to the complexity of radiative transfer and its dependence on accurately resolved cloud fields. Improving the radiative transfer models, whether it involves running full Monte Carlo raytracing in academic setups or simplified paramerizations,  ultimately requires observations for validation. However, dense spatial observation of irradiance on the scale of cloud shadows are rare. Even single 1D time series are rarely available at high enough temporal resolution to capture irradiance variability.

In ongoing work, we provide those missing observations using a dense network of our custom, low-cost radiometers that we deployed at two field campaigns in summer 2021, FESSTVaL (Germany) and LIAISE (Spain). I will present our gathering and analyses of these new and detailed observations of surface irradiance to address knowledge gaps in our physical understanding and provide validation datasets for models. The instruments, which sample at 10 Hz, are able to closely match expensive conventional instruments, and combined with skyview imagery, the spatial observations are directly linked to observed clouds. Information about atmospheric water content can be retrieved using the information from water vapour absorption bands. 

To complement these short term spatial data, long-term statistics of irradiance variability are derived from a 10-year 1 Hz resolution dataset from the Baseline Surface Radiation Network station in Cabauw, the Netherlands. Distributions and typical spatio-temporal scales of cloud shadows and irradiance peaks can be related to cloud type and meteorological conditions. The gathering and study of these datasets will lead to a better understanding of the physics. E.g., whether the dominant mechanism driving irradiance peaks is either forward scattering in transparent parts of clouds or 'reflections' from cloud sides. Furthermore, these datasets will help validate models, and ultimately improve our ability to accurately forecast irradiance variability at the small scales.
Curious long-term increase of the visual band of the solar spectrum in TAV2 and TSIS-1 SIM datasets
Authors: Mursula Kalevi , Teräsvuo Kalevi , Coddington Odele , Woods Tom
Published: June 2022

Total solar irradiance (TSI) has been measured by satellites for more than four decades, which provides an estimate of the amount of TSI variation over this time interval. Satellite measurements have established the variation of TSI in phase with sunspots by roughly 1000 ppm. This variation has slightly decreased during the last decades, following the related decrease of sunspot cycle amplitudes. However, opposing results have been found for a longer-term trend of TSI between recent cycle minima. In addition to TSI, some composite sets have been constructed to cover mainly the high-frequency part of the spectrum over several decades. A number of spectral lines have been observed for more than a century, yielding information on the centennial evolution of limited parts of the solar spectrum. However, the full solar spectrum has been measured only for about 20 years, first by the SORCE satellite from 2003 until 2020 and, more recently, by the TSIS-1 satellite since 2018. These observations allow to study not only by how much the different parts of the spectrum contribute to the TSI, but also their temporal evolution during the last 20 years.

We use here the TSIS-1 SIM (version 7) data and the SORCE SIM TSIS-1 adjusted SSI values (version 2, TAV2) to study the temporal evolution of several bands of the solar spectrum. We note that, according to TAV2, the contribution of the visual band of the spectrum to TSI (vis-TSI) depicts a curious, almost systematic increase from the late declining phase of solar cycle 23, through the ascending to maximum phase of solar cycle 24. Vis-TSI increases by about 1000 ppm during this time, reaching its maximum in 2015, at the same time as TSI has its cycle maximum. However, the subsequent decrease of vis-TSI is relatively smaller than that of TSI, with vis-TSI remaining some 0.4W/m**2 higher during the sunspot minimum of 2018 than during the previous minimum. TSIS-1 observations since 2018 show the continuing increase of vis-TSI in the ascending phase of cycle 25 roughly at the same rate as in the ascending phase of cycle 24. These observations exclude a purely solar cycle related variation of vis-TSI and indicate a curious longer-term increase.
Climate Monitoring Indices for Ireland
Authors: Griffin Seánie , Lambkin Keith
Published: June 2022

A key goal in the developing area of Climate Services at Met Éireann is the delivery of relevant climate information that can assist policy decision making in Ireland.

One such area is the provision of indices associated with climate extremes. Here the ETCCDI indices have been used to assess trends in extreme temperature and rainfall events at locations around Ireland, based on daily observations.  The primary aim is to provide easily accessible information for end users to assess the potential vulnerabilities/opportunities posed to their sectors by Ireland’s changing climate; be it in the form of longer growing seasons, less frosts or greater heat stress.

Reductions in cold extremes are found to be generally more significant than increases in warm extremes, with the warming of minimum temperature extremes being slightly more robust than for maximum temperatures. Warming trends pervade all the temperature indices and the results are generally consistent across the stations analysed.  This is not the case for rainfall indices, with large year-to-year variability and differences between stations. The majority of stations indicate increasing trends in total precipitation, though only a minority of these are found to be statistically significant. There is a large degree of uncertainty in the indices associated with extreme heavy rainfall events.

Specific drought monitoring products have also been developed for users in the water sector, in the form of maps of the Standardized Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI) using gridded observational datasets. While the Irish climate is typically associated with abundant amounts of rainfall, two recent drought events (July 2018 and May 2020) caused significant disruption for both agriculture and general water infrastructure. This has focused attention on providing relevant up-to-date information to relevant stakeholders, and resulted in the production of a near real-time operational product of catchment average SPI.

An outline of the framework for producing and disseminating these indices will be presented, along with the main findings of dominant trends in climate extremes for Ireland.
Climate factsheets for world heritage sites in Norway
Authors: Hygen Hans Olav , Nilsen Irene Brox , Dalen Elin
Published: June 2022

UNESCO has defined 1154 properties of world heritage, which all face an increased threat of degradation of their values due to climate change. Eight world heritage sitesare based in Norway (with one transboundary). As a response to this known threat, The Directorate for Cultural Heritage in Norway has started to assess the threat, and effect, of climate change on the sites and the communities around the sites by applying a method called CVI (https://cvi-heritage.org/) to the world heritage site on Vega archipelago. As part of this process, an assessment of the known climate changes affecting Vega was developed.

The Norwegian Ccentre for Climate Services has delivered two national assessments for Norway. Based on the later assessment, climate factsheets (Klimaprofiler) have been issued for every county, describing current conditions as well as the most important changes from the reference period to 2100. These fact sheets are tailored to meet the county and municipal needs of climate information for climate adaptation. The workshops regarding the climate vulnerability Vega archipelago demonstrated the need of a tailored interpretation of the fact sheet highlighting the threats affecting the world heritage sites. Whereas the county-wise climate change factsheets are developed to serve climate change adaptation needs related to design and planning, and therefore present results towards 2100, the world heritage sites preferred site specific information towards the middle of this century. During the winter and spring of 2022, a prototype for climate factsheets for the world heritage of Rock art in Alta was developed, and a first version presented at a workshop on Vega in april 2022. Based on the response of this presentation, similar factsheets will be co-produced for each of the eight world heritage properties in Norway. These factsheets are meant as a supplement to the county-wise factsheets, not a replacement. The comments provided in the process will inspire the next generation of county-wise factsheets which are planned to be issued by the Norwegian Centre of Climate Services in 2025.
Surface urban heat island characteristics in Budapest (Hungary) during the summer period
Authors: Dezső Zsuzsanna , Pongrácz Rita , Bartholy Judit
Published: June 2022

Budapest is the capital and the largest city of Hungary, with 1.7 million inhabitants, surrounded by a large agglomeration area. Using a 20-year surface temperature database measured by NASA's Terra and Aqua satellites, we analysed the spatial distribution, inter-annual variability and 20-year trends of the urban surface heat island in Budapest. As the presence of the urban heat island causes the most adverse effects in summer, we investigated the evolution of surface temperature and heat island intensity in details for the late spring and summer months, with a special focus on heat waves.

Our results show that the maximum surface heat island intensity usually occurs in June, during the daytime. It was found that during the studied months, the monthly average surface temperature in the urban area exceeds 30 °C in the early afternoon and approaches 40 °C in the summer months of the hottest years. During periods of intense heat waves, surface temperatures above 50 °C can be detected on some days in a large area of the city. Our studies show that in dry, heat-wave weather situations, when surface temperatures are extremely high, relatively low heat island intensities occur. In these cases, the lower heat island intensities are not caused by a decrease in urban surface temperatures, but by a more intense warming of the areas outside the city, which is a consequence of the lower latent heat content of the energy balance above natural surfaces in these hot, dry situations.

As the Intergovernmental Panel on Climate Change (IPCC) assessment report states, extreme weather events, including heat waves, will become more frequent, longer, and more intense as global warming increases, therefore, it is important to gain a better understanding of the urban heat island effect to help developing effective climate adaptation strategies on local/regional scale.
Assimilation of ground-based microwave radiometer observations into convection resolving ICON model using an observing system simulation experiment
Authors: Toporov Maria , Löhnert Ulrich , Schemann Vera , Schomburg Annika , Vural Jasmin
Published: June 2022

State-of-the-art high resolution, convection resolving NWP models and reanalysis typically operate on a horizontal resolution of 1-5 km. These models require specific data assimilation schemes with frequent analysis (every 1-6 h) and corresponding dense and frequent observations to define the detailed initial conditions. Key variables needed for convection-resolving data assimilation are, among others, the 3-dimensional fields of temperature and humidity. Both variables are not adequately (vertically, horizontally and temporally) measured by current observing systems. The vertical resolution of atmospheric profiles provided by satellite sensors is poor, especially in the atmospheric boundary layer, where convection resolving models have many layers close to the surface to better describe the surface-atmosphere exchange processes. To the necessary information, a new generation of observations through the lowest few kilometers of the atmosphere is required. A network of ground-based remote sensing sensors (e.g., microwave radiometer, MWR, or water vapor DIAL) has the potential to provide real time profile observations to forecasting centers. Maintaining an operational observing network is a difficult and expensive task. Therefore, it is essential to evaluate the impact of different components of the current observing system and to assess the potential contribution of a new observing components to the analysis of the atmospheric state.
In our study we perform an Observing System Simulation Experiment (OSSE) to show the potential benefit of ground-based MWR for improving the initial thermodynamic state of the atmosphere. The Nature Run (NR), representing the “true” atmosphere, is performed using the ICON-LES model for a 150x150 km domain in the western part of Germany with 500 m horizontal resolution for summer convective cases. The MWR observations, dependent on cloudiness, temperature and humidity profiles, are simulated with the radiative transfer model RTTOV-gb and assimilated into the convection resolving ICON model (2km horizontal resolution). In this contribution, we will present first impact studies of assimilating synthetic observations of single MWR instruments on the initial temperature and humidity fields and extend the approach for evaluating the effect of a network of instruments with respect to other variables relevant for solar power applications, such as precipitation and solar radiation.
Spatial regression of multi-fidelity meteorological observations using a proxy-based measurement error model
Authors: de Baar Jouke , Garcia-Marti Irene , van der Schrier Gerard
Published: June 2022

Challenge. Meteorological observations are fundamental to sustain a wide range of applications at national meteorological and hydrological services (NMHSs), including gridded climate datasets. Typically, NMHSs use high-quality observations from the networks they operate. However, in recent years, alternative weather data sources are becoming available. Government agencies, organizations, or citizens are joining the effort of monitoring the weather by placing sensors in public or private spaces. The promise of such alternative networks is that they are available at higher spatial resolution that the official ones, which implies the weather observations could contribute to add more spatial detail to the Climate Services. Although the alternative measurements are indeed provided at a higher spatial resolution, they also contain substantial bias and noise, which needs to be addressed.

Approach. The quantification of bias and noise, or measurement errors, is becoming a central theme in science. Measurement error can have a large effect on reliable spatial regression of multifidelity data, therefore it is important to provide a quantified bias and noise level to the regression algorithm. This proper treatment of bias and noise in multi-fidelity data is required, for example, when delivering gridded datasets. Such error treatment does not only improve the reliability of the predicted mean grid (ensemble mean), but also has a notable effect on the predicted uncertainty in the grid (ensemble spread). Since in most cases no quantified bias or noise level is available from the datasets, we propose to use a proxy-based model to learn the bias and noise from the data. Proxies for bias and noise of multi-fidelity meteorological data can be ‘covariates’ like population density, forest cover, radiation intensity, or similar.

Results. In the present work, we use multi-fidelity kriging to combine three datasets in the Netherlands: the network of the National Meteorological Service KNMI, data measured along the national road network by Rijkswaterstaat (Directorate-General for Public Works and Water Management), and crowd-sourced data from the WOW-NL network (http://wow.knmi.nl). In our study, we test our methods on synthetic data and then apply those methods to observed data. We investigate and quantify the improvements in gridded temperature (mean and uncertainty of grid) which we observe when applying a proxy-based error model.
A framework for comparative cluster analysis of ensemble weather prediction data      
Authors: Modali Kameswarrao , Sander Dominik , Brune Sebastian , Rupp Philip , Garny Hella , Baehr Johanna , Rautenhaus Marc
Published: June 2022

Ensemble forecasting has become a standard means to obtain information about forecast uncertainties in meteorological centres across the world. The large datasets generated by ensemble prediction systems carry much information that is difficult to analyse manually – here, techniques from the field of artificial intelligence can be beneficial to aid the analysis. Cluster analysis is one commonly used (unsupervised machine learning) approach to automatically determine distinct scenarios in numerical weather forecasting ensembles, both in atmospheric research and operational forecasting. Typically, a cluster analysis focusses on a selected meteorological forecast variable, a specific region, and time (or a time window). The dimensionality of the data is reduced by techniques like principal component analysis, and a clustering algorithm – typically k-means – is applied to the reduced data set. Challenges with such an approach arise through the determined clusters often being sensitive to factors including the selected region, forecast variable, and algorithm parameters, and also through the employed algorithms often appearing as a “black box” to the user. In our work, we attempt to make the clustering process more transparent by providing a visual analysis framework to analyse the sensitivity of generated clusters with respect to various factors. The presented framework is coupled to the open-source meteorological ensemble visualization software Met.3D, allowing for interactive specification of clustering parameters and for interactive visual analysis, including 3-D elements. A case study using ensemble prediction data of sudden stratospheric warmings (SSWs) is presented, demonstrating how visualizing similarity between clusterings with different parameters can aid the interpretation of the data.
The impact of clouds and aerosols on the radiative fluxes in Germany over the past 70 years from a surface perspective
Authors: Wacker Stefan , Doppler Lionel , Becker Ralf , Filipitsch Florian
Published: June 2022

The German Weather Service (DWD) initiated the operational observation of solar global and diffuse radiation at the Observatory Potsdam in 1937. Nowadays, these components are observed at 35 stations using thermopile-based pyranometers. Most of the stations are also equipped with a pyrgeometer to record the downwelling long-wave radiation.

The solar radiation experienced major decadal variations in Germany over the past 80 years. While a significant decrease between 1950 and 1985 occurred, a continuous increase can be observed since 1985 with some of the highest annual means ever recorded in the past five years. The decrease in the second half of the 20th century with the subsequent increase is commonly referred to as dimming and brightening.  While these variations were most likely due to anthropogenically caused variations in the aerosol load, the recent increase is not yet fully understood. Since aerosol loads have stabilized at low levels at the beginning of the 21th century, the direct aerosol effect might be less prominent at present. Instead, changes in cloudiness may have become more important.

In order to disentangle the observed trends in the radiative fluxes and to determine the contribution from aerosols and clouds separately we also use radiation data from the BSRN station at the Meteorological Observatory Lindenberg. The station was established in 1994 and high accuracy measurements of downwelling long-wave radiation and all three components of downwelling short-wave radiation (global, diffuse and direct) have been recorded continuously since then. We use a 1-dim. Radiative Transfer Model (RTM) to calculate the short- and long-wave cloud-free fluxes in order to quantify the cloud radiative effect. Similar, we estimate the direct aerosol effect. Preliminary results indicate a decrease in the cloud radiative effect over the past 20 year, which can imply a decrease in cloud cover but may also include a shift towards a different cloud type and/or a change in microphysical cloud properties. Finally, we present methods which can be used to determine the spectral AOD and the cloud-radiative effect for sites or periods when no direct observations of the spectral AOD and no information about the vertical temperature and humidity structure of the atmosphere for thermal RTM calculations are available.
Estimating the possibility of thermal stress with computer vision and neural networks based on Local Climate Zone and terrain.
Authors: Lau Tsz Kin , Chen Yu Cheng , Lin Tzu Ping
Published: June 2022

Urban Heat Island (UHI) is an evident phenomenon in Taiwan, especially the capital Taipei city, owing to the highly developed with numerous high-rise buildings and the basin terrain. Due to the problem above, a method to estimate the thermal stress possibility based on the urban pattern is necessary. In recent years, machine learning and deep learning developed quickly and achieved in many fields like image recognition, machine translation, and predicting environmental characteristics. With the excellent development of machine learning and deep learning, a novel method for estimating the thermal stress possibility was presented in this work. In this study, the average temperature distribution in Taipei at 1 pm in August 2021 was calculated based on the information from measurement points of the high-density street-level air temperature observation network (HiSAN) and the Central Weather Bureau (CWB). And the thermal condition was classified into three classes: the low, medium, and high possibility of thermal stress based on the median, upper quartile, and top 90% of the data. Simultaneously, Taipei city was classified with Local Climate Zone (LCZ) based on the satellite image from Landsat 8. And this study also collected the terrain data to correct the effect of altitude on temperature. The LCZ map and terrain were resampled into 100m spatial resolution for the subsequent work. Artificial Neural Network (ANN) and Deep Neural Network (DNN) were used and compared this work’s performances for estimating the thermal stress possibilities in Taipei city. The LCZ map and terrain data were reshaped as the images with 64 pixels to quantize pattern features within the areas of 800m2 and inputted into the models for training and predicting. Both ANN and DNN models have the same learning rate and training epochs which were 0.001 and 500 epochs. After the training processes of the models, ANN and DNN models were able to estimate the possibility of thermal stress for each area by LCZ map and terrain data. The results of ANN and DNN models proved the feasibility of using algorithms of machine learning and computer vision to assess microclimate conditions. Also, the novel method presented in this work can help estimate the thermal stress possible for the areas without measurement points.
Global Historical Climate Database - HCLIM
Authors: Lundstad Elin
Published: June 2022

There is a growing need for past weather and climate data to support science and decision-making. This presentation describes the compilation and the construction of global monthly instrumental climate data with a focus on the 18th and early 19th centuries. This database provides early instrumental data, and it is multivariable (air temperature, pressure, precipitation sum, number of precipitation days) are recovered for thousands of locations around the world for that encompasses a substantial body of the known early instrumental time series. Instrumental meteorological measurements from periods prior to the start of national weather services are designated “early instrumental data”. Much of the data is taken from repositories we know (GHCN, ISTI, CRUTEM, Berkeley Earth, HISTALP). In addition, many of these stations have not been digitized before. It ends up in one database with the same format, so it is easy to use the data with a good overview of metadata. The dataset contains series compiled from existing databases that start before 1890 (though continuing to the present) as well as a large amount of newly rescued data. The first record is from 1586. All series underwent a quality control procedure and subdaily series were processed to monthly mean values. An inventory was compiled, and the collection was deduplicated based on coordinates and mutual correlations. The data are provided in a common format accompanied by the inventory. The collection totals 12452 meteorological records in 118 countries. The data can be used for climate reconstructions and analyses. It is the most comprehensive global monthly climate data set for the preindustrial period.
Estimation of the spectral aerosol optical depth from broadband surface radiation measurements and quantification of the direct aerosol effect in Germany
Authors: Filipitsch Florian , Doppler Lionel , Seifert Axel , Wacker Stefan
Published: June 2022

For more than 70 years measurements of the global and diffuse solar surface radiation have been taken within the DWD measurement network. Since 2006 those two radiation quantities are recorded with a temporal resolution of 1 minute at currently more than 30 stations.

At Meteorological Observatory in Lindenberg, we developed a new optimal estimation-based retrieval algorithm to estimate the AOD from ground-based global and diffuse broadband radiation measurements, measurements of the total column water vapor from GNNS, and ozone from the satellite-borne instrument OMI. The suitability of using the global surface radiation as a source to estimate the Aerosol Optical Depth (AOD) at certain wavelength based on radiative transfer simulations was already shown in several studies.  Adding the information on the diffuse surface radiation increases the information content on the aerosol size distribution. Thereby we could reduce the error between the AOD derived from broadband measurements and the AOD derived from photometric measurements. Comparisons of the derived AOD with 7 collocated AERONET sites in Germany show a very good agreement with a monthly mean difference below 0.02 in the AOD. The differences in the AODs can be simply described by the differences in observed airmass and larger measurement errors of the broadband observations.

With the radiative transfer model, used in the retrieval algorithm as a forward model, we calculate for each measurement the incoming surface radiation for a cloud and aerosol-free atmosphere to estimate the direct aerosol effect for the daily mean AOD. For this reason, the estimated direct aerosol effect is limited to days with at least 10 minutes of cloud-free observations.

In our presentation, we will show validation results of the algorithm as well as trend analysis of the AOD and direct aerosol effect in Germany at available pyranometer sites since 2006.

 

 

 
The effect of Chinese Gigacity on weather fronts
Authors: Kokkonen Tom , Sinclair Victoria , Xue Lian , Ding Aijun , Kulmala Markku
Published: June 2022

The rapid, large-scale urbanization in northeast China during the last few decades has been unique. Recently, Kulmala et al (2021) proposed that in eastern China, a cluster of individual megacities, located roughly inside a triangle Beijing-Xian-Shanghai, could be classified as one huge continuous urban area – a gigacity. In a such gigacity region, combined effect of pollution, vast area of increased surface roughness and urban heat island can strongly interact with the local, synoptic, and even continental scale meteorological conditions. Therefore, the complex interactions between multi-scale meteorological processes and the anthropogenic effects of gigacity bring great challenges to the understanding of environmental and climate systems, air quality forecast and weather prediction in the gigacity region. There are many studies about the interaction of urban areas and local and regional scale atmospheric circulation. However, it is still poorly understood how these very large urban areas interact with meso- and synoptic-scale meteorology. The objective of this study was to analyse the effect of gigacity area to the weather fronts. The study period was 1980-2010 when most of the rapid urbanization occurred in the gigacity region. The identification of weather fronts was performed following the principles by Jenkner et al (2010) and Berry et al (2011). However, we further developed the method to utilize the higher resolution of ERA5 data, whereas previously it was using the ERA-Interim data with coarser resolution for front identification (0.25° v.s. 2.5°, respectively). In addition, to identify the key chemical and physical processes, and to quantify their individual contributions, the mesoscale Weather Research and Forecasting (WRF) model coupled with chemistry and the urban canopy model (WRF–UCM–Chem) was used for individual case studies. Based on the results, a statistically significant decreasing trend on number of cold fronts was found in large areas inside the gigacity, most likely due to the increasing surface frictional drag caused by the increasing surface roughness over the very large gigacity area. The results gained in this study will improve the understanding of the gigacity climate and will potentially have strong implications for air quality and weather forecasting in this region.

 

Berry et al., Geophys. Res. Lett. 38, L04809, 2011.

Jenkner et al., Meteorol. Appl. 17, 1–18, 2010.

Kulmala et al., Atmos. Chem. Phys. 21, 8313–8322, 2021.