bluuebunny/crossref_metadata_2025 huggingface dataset:
Projections for the Köppen-Geiger climate classification under future climate change for the Iberian Peninsula
Authors: Andrade Cristina , Contente Joana
Published: June 2021

Projections of the Köppen-Geiger climate classification under future climate change for the Iberian Peninsula (IP) are investigated by using a seven-ensemble mean of regional climate models (RCMs) attained from EURO-CORDEX. Maps with predicted future scenarios for temperature, precipitation and Köppen-Geiger classification are analyzed under RCP4.5 and RCP8.5 in Iberia. Widespread statistically significant shifts in temperature, precipitation and climate regimes are projected between 2041 and 2070, with higher expression under RCP8.5. An overall increase of temperatures and a decrease of precipitation in the south-southeast is predicted. Of the two climate types dry (B) and temperate (C), the dominant one was C in 86% of the Iberian territory for 1961-1990, predicted to decrease by 8.0% towards 2041-2070 under RCP4.5 (9.1% under RCP8.5). The hot-summer Mediterranean climate (CSa) will progressively replaces CSb (warm-summer) type towards north in the northwestern half of Iberia until 2070. This shift, depicted by the SSIM index, is noticeable in Portugal with a projected establishment of the CSa climate by 2041-2070. A predicted retreat of humid subtropical (Cfa) and temperate oceanic (Cfb) areas in the northeast towards Pyrenees region is noteworthy, alongside an increase of desert (BW) and semi-desert (BS) climates (7.8% and 9%) that progressively sets in the southeast (between Granada and Valencia). Climate types BSh and BWh (hot semi-desert and hot-desert, respectively), non-existent in 1961-1990 period, are projected to represent 2.8% of territory in 2041-2070 under RCP4.5 (5% under RCP8.5). The statistically significant projected changes hint at the disappearance of some vegetation species in certain regions of Iberia, with an expected increase of steppe, bush, grassland and wasteland vegetation cover, typical of dry climates in the southeast.

Funding: This research was funded by National Funds by FCT - Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020.
Learning of weather-type transitions for risk assessment
Authors: Hoffmann Peter
Published: June 2021

Persistence or sequences of critical weather patterns over Europe can trigger seasonally extreme hydroclimatic conditions in certain regions. In order to better estimate return periods of extremes across Europe, existing time series of sequences of weather-types over Europe were used to train monthly rules for the transition from one situation to another and their duration behaviour. This can be efficiently realized and tested by setting up decision trees and generating up to 10,000 year time series of weather-type sequences.

In an experiment carried out, large-scale weather situation types according to Hess/Brezowsky available from 1961 to 2020 were divided into two time periods and rules for the transition were derived for both by training decision trees. Based on the trained rules of transistions for the periods 1961-1990 and 1991-2020, 10,000-year weather-type sequences were then generated and analysed.

The comparison of the probability density functions of persistence for the 30 different large-scale weather situation types show that omega-like circultion patterns over Europe have a higher tendency to persist in the present time period. In connection with this, the risks of prolonged dry phases in Central Europe have increased. For the translation of different weather-types into local weather-type characteristics, long-term monthly mean daily precipitation values per weather-type was assigned from ERA5 reanalysis data and rearranged in a post-processing step according to the generated weather-type sequences. The analysis of the maximum duration of consecutive dry and wet months in Europe was the main focus and the identified long-term changes in hydroclimatic quantities can be thus exclusively attributed to dynamic factors.
Real time weather for autonomous driving and precise road weather forecasts based on floating car data and seamless integration of Ultra-Rapid Data Assimilation and Nowcasting
Authors: Paschalidi Zoi , Acevedo Walter , Hellweg Meike , Kratzsch Thomas , Potthast Roland , Nachtigall Jens
Published: June 2021

The growing availability of high resolved meteorological measurements coming from automobiles puts forward the possibility of developing real time weather forecast systems, which appears to be an essential key of autonomous driving enhancement. In this frame, the Fleet Weather Maps (Flotten-Wetter-Karte - FloWKar) project, a joint work of the German Meteorological Service (DWD) and the German car manufacturer AUDI AG, aims to explore how environmental data from sensors of vehicles on Germany’s roads, respecting data protection regulations, can be used in real time to improve weather forecast, nowcasting and warnings within DWD’s products. Regarding weather forecasting, an exceptionally fast data assimilation cycle with an update rate of the order of minutes is necessary. However, this cannot be achieved using standard assimilation systems. Hence, an ultra-rapid data assimilation (URDA) method has been developed. The URDA updates only a reduced version of the state variables in an existing model forecast, using different kind of observation data available, only after a standard assimilation cycle and a full model forecast. Moreover, the quality of the meteorological data collected by moving vehicles is vital and therefore a series of quality control and bias correction algorithms has been built for the correction of the raw observations, employing among others artificial intelligence techniques. The first preliminary results of both project partners are promising: the corrected measured variables of the mass-produced vehicle-based sensors match well with the ‘ground truth’ and real time maps are produced after the assimilation of the high resolved project data. The improved and detailed model outputs for road weather forecasting are a first necessary step towards the safety on roads especially in the winter conditions and the future autonomous driving.
A theory for surface-layer scaling of thermally-driven slope flows
Authors: Zardi Dino
Published: June 2021

Sloping terrains of any inclination favour the development, under the daily cycle of day time surface heating and night time cooling, of thermally-driven organised flows, displaying peculiar boundary layer structures, and eventually triggering the development of atmospheric convection.

The ubiquitous occurrence over the Earth of variously tilted surfaces - from gently sloping plains to steep cliffs, or valley and basin sidewalls – makes the understanding of such flows of utmost importance in view of the appropriate forecasting of the associated boundary layer transport processes. Also, they display a highly conceptual relevance, as they represent a prototypal situations for many other thermally driven-flows over complex terrain.   

An appropriate surface-layer scaling for slope wind is derived extending the classical analysis for flat horizontal terrain situations to the cover inclines. In the former, momentum and heat fluxes at the surface are two independent quantities, and vertical profiles of velocity and temperature can only be connected to them by means  of similiarity relationships, as fluxes are nearly invariant with height.

Instead, equations governing slope winds show that the mean wind and temperature profiles are closely connected to the flux structure normal to the slope, as this is not constant. Also, surface values of momentum flux and sensible heat flux are shown to be proportional to each other.

Based on the above relationships, suitable expressions are derived for the slope-normal profiles of velocity and temperature, both in the viscous sublayer and in the fully turbulent surface layer, as well as for the appropriate scaling factors in the two regions.
A generalized model between thermal conductivity and matric suction of soils
Authors: Lu Sen
Published: June 2021

Soil thermal conductivity (λ) is an important physical property in land surface parameterization. The soil thermal conductivity (λ) and matric suction of soil water (h, the negative of matric potential) relationship has been widely used in land surface models for estimating soil temperature and heat flux following the McCumber and Pielke (1981, MP81) λ-h model. However, few datasets are available for evaluating the accuracy and feasibility of the MP81 λ-h model under various soil and moisture conditions. In this study, we developed a new λ-h model and compared its performance with that of the MP81 model using measurements on 18 soils with a wide range of textures, water contents and bulk densities. The heat pulse technique was used to measure λ, and the suction table, micro-tensiometers, pressure plate device, and the dew point potentiometer were applied to obtain soil water retention curves at the appropriate suction ranges. In the range of pF (the common logarithm of h in cm)≤3, the λ-h relationships were highly nonlinear and varied strongly with soil texture and bulk density. In the dry range (i.e., pF > 3), there existed a universal λ-h relationship for all soil textures and bulk densities, and an exponential function was established to describe the relationship. Independent evaluations using λ-h data on five intact soil samples showed that the new model produced accurate λ data from pF values with root mean square errors (RMSE) with the range of 0.03–0.18Wm−1 K−1. While, large errors (RMSEs within 0.17–0.36Wm−1 K−1) were observed with λ estimates from the MP81 model. 
Cloud optical property benchmarking study
Authors: Nielsen Kristian Pagh
Published: June 2021

A benchmarking study to compare various cloud optical property schemes is proposed. The transmittance, reflectance and absorptance of clouds of cloud optical property schemes that are widely used in weather and climate have in recent years been shown to give different results. Not much attention has been paid to this, probably due to the fact that errors in cloud cover have larger impacts on the shortwave and longwave radiative fluxes than the cloud optical properties. Here the optical properties are the mass extinction coefficient, the single scattering albedo and the asymmetry factor. Cloud optical property schemes are typically based on detailed theoretical calculations, which are then parametrized. Furthermore, spectral band averaging is performed to get optical properties that match the spectral bands of the radiative transfer scheme. Here the 14 shortwave and 16 longwave spectral bands of the RRTM scheme are mostly used presently. Both the equations used in the parametrizations and the spectral averaging matter. For the spectral averaging it must be considered that both the shortwave (solar) and longwave (thermal) spectra change following the circumstances in the atmospheric environment. Thus, a dry atmosphere has different spectral characteristics from a moist atmosphere. In the ultraviolet part of the spectrum ozone is also important to account for, as is the impact of carbon dioxide, methane, nitrous oxide and lesser greenhose gasses in the longwave part of the spectrum. This also affects the spectral distribution of the available radiative flux within individual spectral bands. Thus, the impact of the atmospheric environment must also be accounted for in the benchmarking study. The setup and an online framework for sharing benchmarking results will be presented.
Downscaling global seasonal weather forecasts for crop yield forecasting over Zimbabwe
Authors: Chinyoka Sinclair , Steeneveld Gert-Jan
Published: June 2021

This study focuses on the assessment of the impact of downscaling seasonal forecasts from the Climate Forecast System version 2 (CFSv2) using the Weather Research and Forecasting (WRF) mesoscale model over Zimbabwe on a spatial resolution of 21km and 7km for Southern Africa and Zimbabwe respectively. We used a 7-day re-initialization simulation strategy for 212 days per season and was repeated for eights seasons between 2010 and 2018. The impact of downscaling global seasonal forecasts was further evaluated in crop forecasting using the WOrld FOod STudies (WOFOST) model. Statistical analysis of the forecasted seasonal rainfall revealed a reduction of the bias from about -2 mm/day from CFSv2 forecasts to about 0.5mm/day from WRF forecasts in most parts of the country. We also found that an improvement in seasonal tercile rainfall prediction from 25%, 50%, and 75% by CFSv2 in three different regions to about 62.5% by WRF in all regions. Substantial improvement was achieved in Standard Precipitation Index-driven seasonal forecasts with two regions with a percent correct of 75% and region 2 with 100% by WRF compared to 62.5% by CFSv2 in all regions. Hence, the characterization of seasonal rainfall in terms of drought forecasts is better than the tercile rainfall prediction system and will be more beneficial to farmers in Zimbabwe. WRF seasonal rain forecasts improved both in magnitude and in forecasting the onset of the growing season. This was indicated by the accumulated absolute maize yield error which factored in a miss of onset of the growing season by each model. WRF outperformed CFSv2 for maize and sorghum yield forecasts in 6, 6, and 8 (out of 8) seasons in Karoi, Masvingo, and Gweru sites respectively. WRF forced crop simulations reduced mean absolute percent error of maize yield by 12.2% and sorghum yield by 9.3 % from CFSv2 forced simulations. Our results also show that maize will be more productive and less risky at Karoi and Masvingo and sorghum at the Gweru site. In our view, there should be no farming of both maize and sorghum at Beitbridge due to the high risk of crop failure unless a proper irrigation system is in place.
Weather forecasts - the islands of calmness in the rough TV sea
Authors: Tsalova Stanislava
Published: June 2021

People who are not involved in doing Weather forecast presentations, think that it is something easy to prepare. But it needs experience to present the weather data and forecast, which is scientific information in a way understandable for the TV viewers. Weather forecasts have always been islands of positive emotions in TV programs. 

The past year was very challeging for all TV stations around the world. In all the news and TV shows the main topic was Coronavirus disease. Now, more than ever TV weather forecast's role became to provide some positive emotions to the people who are so much got tired of the bad and scary news on their TVs. The fact is that during the pandemic the TV ratings are higher made our responsibility even bigger.

While preparing my weather presentations, even in cases of severe weather my top priority was not to scare people, who were scared enough. When showing weather videos, I avoided such with disasters. Instead I showed more wildlife and educational weather videos. Unlike before, in 2020/2021 years I definitely avoided climate change topic. 

While chatting about weather on air with the news and morning shows anchors, the chat had sometimes escalated to bursting into laughter. Unlike before, our viewers approved that highly, because everybody is under pressure now and such stress release things were more than welcome. The weather forecast now became more than ever an island of calmness and hope for a better tomorrow in the rough TV sea.

I want to share my experience and to exchange opinion on that topic with collegues from other countries and TV stations.
The urban fingerprint in the sea-breeze hodograph reveled by high resolution WRF simulations.
Authors: Avisar David , Pelta Ran , Chudnovsky Alexandra , Rostkier-Edelstein Dorita
Published: June 2021

We implement and verify for the first time four Weather Research and Forecasting (WRF) model urban configurations, focused on the coastal metropolitan area of Tel-Aviv (MTA) using updated land use and urban morphological maps. We analyze the mesoscale summertime flow and the urban canopy (UC) role in the occurrence of different hodograph dynamics observed within MTA at night. These events may be significant in the context of air quality research. The four configurations – bulk (MM), single-layer (SLUCM), multi-layer (BEP), and BEP coupled with the building energy model (BEPBEM) – reproduce the observed diurnal temperature and wind diurnal cycles, with similar 10m wind direction bias and RMSE (15° and ~30°, respectively), with preference for MM and SLUCM at night. However, the SLUCM shows the lowest skill for the 10m wind speed (WS) (bias and RMSE equal or larger than 1ms-1), and the BEP shows the largest underestimation of the 2m temperature, ~-2.5°C. In the SLUCM, the WS increases over an UC region and with increasing building heights. The simulations show that at night, a convergence line (CL) builds up with the urban heat island, downstream of the NW flow. West of the CL, the wind continues flowing from the sea, and rotates anti-clockwise to form a non-elliptical sea-breeze hodograph. Removing MTA UC restores an elliptical hodograph. East of the CL, the UC supports an elliptical hodograph with a clockwise rotation through the NE sector, previously reported as dynamically unstable. We expect such wind hodograph dynamics within similar coastal metropolitan areas.
How to develop and apply a model data standard on microscale model data.
Authors: Voss Vivien , Grawe David , Schlünzen K. Heinke
Published: June 2021

Numerical modeling makes it possible to represent complex processes in small-scale and complex areas like cities. For resolving obstacles, grid sizes in the order of meters are needed. Due to small grid sizes and numerical restrictions, such high-resolution investigations require a great deal of resources. Therefore, a re-use of the results by others would enhance the value of any of these model results. However, the subsequent use of model results is still poorly developed. Comparisons of model data, dissemination of results, or reproduction of simulations are hampered by inconsistent data structures, non-standardized variable names, and lack of information on model setup. In general, to ensure the reusability and accessibility of model data, data standards should be used. The most common data standard for atmospheric model output data are the CF conventions, a data standard for netCDF files, but this standard is currently not extended to cover the model output of obstacle resolving models (ORM).

The AtMoDat (Atmospheric Model Data) project developed a model data standard (ATMODAT standard) which ensure FAIR (Findability, Accessibility, Interoperability, and Reuse) and well documented data. We involved the micro-scale modelling community in this process with a web based survey (http://uhh.de/orm-survey) to find out which micro-scale ORMs are currently in use, their model specifics (e.g. used grid, coordinate system), and the handling of the model result data. Furthermore, the survey provides the opportunity to include suggestions and ideas, what we should consider in the development of the standard. We already identified typical variables used by ORMs (i.e. building structures, wall temperatures) and will propose them to be included in the CF convention.  The application of this standard is tested on the model output of the ORM MITRAS. The standard and experiences with its application will be presented.
A pragmatic re-engineering approach to extend short observation time series by climate anomaly gridding
Authors: Tveito Ole Einar
Published: June 2021

For many purposes, including the estimation of climate normals, requires long, continuous  and preferably homogeneous time series. Many observation series do not meet these requirements, especially due to modernisation and automation of the observation network. Despite the lack of long series there is still a need to provide climate parameters representing a longer time period than available. An actual problem is the calculation of new standard climate normals for the 1991-2020 period, where normal values need to be assigned also for observation series not meeting the requirements of WMO to estimate climate normals from observations. 

One possible approach to estimate monthly time series is to extract value from gridded climate anomaly fields. In this study this approach is applied to complete time series that will be the basis for calculation of long term reference values.

The calculation of the long term time series is a two step procedure. First monthly anomaly grids based on homogenised data series are produced. The homogenized series provide more stable and reliable spatial estimates than applying non homogenised data. The homogenised data set is also complete ensuring a spatially consistent input throughout the analysis period 1991-2020.

The monthly anomalies for the location of the series to be complete are extracted from the gridded fields. By combining the interpolated anomalies with the observations the long term mean value can be estimated. The study shows that this approach provides reliable estimates of long term values, even with just a few events for calibration. The precision of the estimates depend more on the representativity of the grid estimates than length of the observation series. At locations where the anomaly grids represent the spatial climate variability well, stable estimates are achieved. On the other hand will the estimates at locations where the anomaly grids are less accurate due to sparse data coverage or steep climate gradients lead to estimates with a larger variability, and  thus more uncertain estimates. 
DWD Geoportal – Converging open data, metadata and documentation in a user-friendly way
Authors: Reetz Björn , Riede Hella , Fuchs Dirk , Hagedorn Renate
Published: June 2021

Since 2017, Open Data has been a part of the DWD data distribution strategy. Starting with a small selection of meteorological products, the number of available datasets has grown continuously over the last years. Since the start, users can access datasets anonymously via the website https://opendata.dwd.de to download file-based meteorological products. Free access and the variety of products has been welcomed by the general public as well as private met service providers. The more datasets are provided in a directory structure, however, the more tedious it is to find and select among all available data. Also, metadata and documentation were available, but on separate public websites. This turned out to be an issue, especially for new users of DWD's open data.

To help users explore the available datasets as well as to quickly decide on their suitability for a certain use case, the Open Data team at DWD is developing a geoportal. It enables free-text search along with combined access to data, metadata, and description along with interactive previews via OGC WMS.

Cloud technology is a suitable way forward for hosting the geoportal along with the data in its operational state. Benefits are expected for the easy integration of rich APIs with the geoportal, and the flexible and fast deployment and scaling of optional or prototypical services such as WMS-based previews. Flexibility is also mandatory to respond to fluctuating user demands, depending on time of day and critical weather situations, which is supported by containerization. The growing overall volume of meteorological data at DWD may mandate to allow customers to bring their code to the data – for on-demand processing including slicing and interpolation –  instead of transferring files to every customer. Shared cloud instances are the ideal interface for this purpose.

The contribution will outline a protoype version of the new geoportal and discuss further steps for launching it to the public.
Quantification of the Emission Changes in Europe During 2020 Due to the COVID-19 Mobility Restrictions
Authors: Guevara Marc , Jorba Oriol , Petetin Hervé , Denier Van Der Gon Hugo , Kuenen Jeroen , Super Ingrid , Peuch Vincent-Henri , Pérez García-Pando Carlos
Published: June 2021

To hinder the circulation of the COVID-19 virus, European governments implemented emergency measures going from light social distancing to strict lockdowns, depending on the country. As a consequence, many industries, businesses and transport networks were forced to either close down or drastically reduce their activity, which resulted in an unprecedented drop of anthropogenic emissions. This work presents the Copernicus Atmosphere Monitoring Service (CAMS) European regional emission adjustment factors associated to the COVID-19 mobility restrictions, an open source dataset of daily-, sector-, pollutant- and country-dependent emission adjustment factors for Europe linked to the COVID-19 pandemic. The resulting dataset covers a total of six emission sectors, including: road transport, energy industry, manufacturing industry, residential and commercial combustion, aviation and shipping. The time period covered by the dataset includes the first and second waves of the disease ocurred during 2020, starting from 21 February, when the first European localised lockdown was implemented in the region of Lombardy (Italy), until 31 December, when COVID-19 transmission remained widespread and several countries had nationwide restrictions still in place. The adjustment factor dataset is based on a wide range of information sources and approaches, including open access and measured activity data and meteorological data, as well as the use of machine learning techniques. We combined the computed emission adjustment factors with the CAMS European gridded emission inventory to spatially (0.1x0.05 degrees) and temporally (daily) quantify reductions in 2020 emissions from both criteria pollutants (NOx, SO2, NMVOC, NH3, CO, PM10 and PM2.5) and greenhouse gases (CO2 fossil fuel, CO2 biofuel and CH4) as compared to a business-as-usual scenario, as well as to assess the contribution of each sector and country to the overall reductions. The resulting gridded and time-resolved emission reductions suggest an heterogeneous impact of the COVID-19 restrictions across pollutants, sectors and countries.
An integrated climate and water resources management (iC-WRM) prototype for long term water allocation in the Upper Yellow River Region of China
Authors: Siu Yim Ling , Willis Thomas , Turner Andy , Dessai Suraje , Taylor Andrea
Published: June 2021

Water resources management is often regarded as a complex issue which requires the process of planning, developing, allocating, distributing and managing the use of water resources. Climate change poses challenges (and risks) to the water sector, especially when the nation state is vast and has uneven distribution of water sources such as China. Furthermore, water management still largely relies on the use of historic, seasonal and annual climate data. There is limited demand from water managers for longer term climate information such as multi-annual and multi-decadal data. To promote the use of longer term climate information in the water sector in China, in this research, we have adopted an interdisciplinary approach and have applied a user-centred, co-production method to develop an integrated climate and water resources climate service prototype (iC-WRM) with water managers and their intermediaries. The Upper Yellow River Basin was used as a demonstration in iC-WRM to provide water managers with different scenario-based simulations to gain insights to the impacts of climate change on the region. Noticeably, the development of the prototype was constructed, tested and evaluated by water managers under Coronavirus restrictions which had prevented the typical co-development and user-evaluation processes to be undertaken. iC-WRM was shown to be successful, as key messages relating to be the impact of climate change could be effectively communicated through the prototype interface. Also, it has promoted a degree of understanding about the potential impact of climate change in terms of water resources management in China. This will encourage the development of other climate services to understand and implement the key outputs of this climate service prototype to other sectors (e.g. agriculture/food production, regional planning).
Next-generation EUMETSAT satellite missions: key features, improvement to meteorological forecasting, and user preparation
Authors: Bojinski Stephan , Thonipparambil Sreerekha
Published: June 2021

Starting in 2022, EUMETSAT is launching its next generation satellites Meteosat Third Generation (MTG) and EUMETSAT Polar System – Second Generation (EPS-SG) as a follow on to its current operational Meteosat and EPS programmes. Data from these new missions will provide enhancement to operational forecasters as well as offer great research potential for better characterising convection, clouds, aerosols, atmospheric chemistry, and other parameters. Meteorological applications based on nowcasting and NWP are expected to benefit significantly.

EUMETSAT is supporting users in operational services as well as in research and academia in their preparation for next-generation satellite data through the MTG User Preparation (MTGUP) and EPS-SG User Preparation (EPS-SG UP) projects. The main objective are to ensure an early uptake of data from the heritage instruments of MTG and EPS-SG thus ensuring a smooth transition and continuity of operations for the National Meteorological Services. A second objective of the UP projects is to support the users in their preparation to gain advantage from the enhanced capabilities of the heritage missions and novel missions that are part of MTG and EPS-SG.  These projects also provide a platform for the user communities to share their experiences and cross-fertilise their user preparation activities.  The User preparation activities are centered around five core themes: Science support, Test data and format support, User information and communication, training and data access support. 

This paper will highlight key features of the next-generation operational EUMETSAT missions, cover the achievements of the user preparation projects over the last two years and the plans for the period 2021-2025. EUMETSAT joins hands with partners and Member States, particularly in national meteorological and hydrological services, in the user preparation activities.
Evaluation and Post-Processing of Reanalysis Wind Speeds for Renewable Energy Applications
Authors: Brune Sebastian , Keller Jan D. , Wahl Sabrina
Published: June 2021

The correct spatio-temporal representation of wind speed is of large interest for the wind energy sector. Therefore, this study compares wind measurements in different heights from several locations in Central Europe with two global (ERA5, MERRA-2) and one regional reanalysis (COSMO-REA6). Employing a two-parameter Weibull distribution, the shape and scale parameters as well as mean, standard deviation and RMSE are investigated at and around common wind turbine hub height. We find that COSMO-REA6 best describes wind fields closer to the surface possibly due to its high horizontal resolution. Here, it also exhibits a good alignment with the diurnal cycle. However, for common wind turbine hub heights and above, ERA5 outperforms the other two reanalyses possibly due to its higher vertical resolution. MERRA-2 overestimates wind speed in the lower boundary layer at nearly all sites.

In the next step, a diagnostic and mass-consistent wind model is applied to the COSMO-REA6 wind field. The resolution of the wind field will be increased by a factor of 8 from originally 6 km to approximately 800 m. In addition to the vertical stability of the lower atmosphere, the orography on the finer grid and the corresponding effects are taken into account. We expect that especially in complex terrain the wind field will be corrected and thus should fit better to the observations. Channeling effects, shadowing and increased wind speed in exposed locations can be better represented. The new high-resolution wind field forms the basis for a statistical wind model to obtain post-processed wind estimates in the lower boundary layer. This approach will utilize generalized linear model and/or an artificial neural network techniques.
Validation of manual, surface-based detections of Cirrus with CALIPSO space lidar
Authors: Kotarba Andrzej , Nguyen Huu Żaneta
Published: June 2021

Clouds generally cool the atmosphere due to a negative net radiative forcing. However the Cirrus clouds warm the climate, despite they co-occur with other cloud types, or are the only cloud layer present. Consequently, changes in Cirrus frequency are important for understanding the climate change and climate variations. Manual, ground-based observations of Cirrus are the longest climate record on that variable. The reliability of those observations is questionable, because of viewing geometry (middle/low-level clouds obscure the high-level clouds), and observers ability to detect optically thin media in the upper atmosphere.

We have validated the reliability of ground-based observations of Cirrus using state-of-the-art lidar data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. Focusing on the observer’s sensitivity to Cirrus, we only evaluated the observations under a perfect conditions: no clouds at middle/low level. We found that probability of Cirrus detection was 67% during the day time, and was controlled by the Cirrus optical depth (60% for subvisual clouds, and >95% for depth >5). During the night the probability of detection decreased to 35%. Presence of the illuminated Moon positively impacted the hit rate, but only when lunar phase was greater than 50%. Evaluation of the visual method, as a whole, considered clouds at any level, as they occur in real, true-weather conditions. We found that probability of Cirrus detection was 48% daytime, and as little as 28% nighttime. The increase of the sky sealing by low/middle-level clouds negatively impacted the accuracy. Sharp decrease in Cirrus detection efficiency was observed when cloud fraction for middle/low clouds exceeded 50%-60% (daytime), and 10%-25% (nighttime).

Based on the results, we conclude that in the majority of cases, accuracy we found can be achieved just by an accident: "Cirrus"/"no Cirrus" can be reported with no looking at the sky, and the resulting accuracy will be the same as for the real, empirical data from stations. Consequently, the manual observations are highly uncertain and may be unreliable for deriving a long-term climate trends. Even at best, the surface-based, manual observations are less reliable than those collected with a satellite imagers (specifically the MODIS-VIIRS cloud product).
Evaluation of statistical downscaling methods for climate change projections over Spain: present conditions with perfect predictors.
Authors: Hernanz Alfonso , García-Valero Juan-Andres , Domínguez Marta , Ramos-Calzado Petra , Pastor-Saavedra María-Asunción , Rodríguez-Camino Ernesto
Published: June 2021

The Spanish Meteorological Agency (AEMET) is responsible for the elaboration of downscaled climate projections over Spain to feed the Second National Plan of Adaptation to Climate Change (PNACC-2). The main objective of this work is to establish a comparison among five statistical downscaling methods developed at AEMET: 1) Analog, 2) Regression, 3) Artificial Neural Networks, 4) Support Vector Machines and 5) Kernel Ridge Regression. All the five methods have been applied with a Perfect Prog approach to downscale daily maximum/minimum temperatures and daily precipitation on a high resolution observational grid (0.05o) over mainland Spain and the Balearic Islands, a region particularly challenging due to its large regional spatio-temporal variabilities. The comparison has been carried out under present conditions and with perfect predictors, based on the framework established by the VALUE network, in particular, on its perfect predictor experiment. The evaluation here performed is focused on marginal aspects, through an analysis of the four seasonal distributions of each variable. In order to enable a manageable comparison among all methods three indexes commonly used for climate change adaptation and impact studies have been used: the mean value and the 10th and 90th percentiles for daily maximum/minimum temperatures and the total precipitation amount (PRCPTOT), the total precipitation on very wet days (R95p) and the number of wet days (R01) for precipitation. For maximum/minimum temperatures, all methods display a similar behavior. They capture very satisfactorily the mean values although slight biases are detected on the extremes. In general, results for maximum temperature appear to be more accurate than for minimum temperature, and the non-linear methods display certain added value. For precipitation, remarkable differences are found among all methods: most of them are capable of reproducing the total precipitation amount quite satisfactorily, while other aspects such as intense precipitations and the precipitation occurrence are captured with more accuracy by the Analog method.

 
Spatio-temporal patterns and extremity assessment of heavy rainfall events in Germany, derived from a radar-based catalogue (CatRaRE, 2001-2020)
Authors: Walawender Ewelina , Lengfeld Katharina , Winterrath Tanja , Weigl Elmar , Becker Andreas
Published: June 2021

One of the predicted effects of climate change in Central Europe is a growing number and increasing extremity of heavy rainfalls. Thus, it is of a great importance not only to develop best possible nowcasting methods and long-term forecasting models, but also to look closer at the structure and detailed characteristics of extreme events that have already taken place.

With this objective, the German Weather Service (DWD) has developed a Catalogue of Radar-based Heavy Rainfall Events (CatRaRE), derived from 20 years of climatological radar data for the area of Germany.

Using hourly data of about 1 km spatial resolution, an object-oriented analysis is performed to classify spatially and timely independent rainfall events exceeding the official warning level for heavy precipitation. Events with duration between 1 and 72 hours are investigated and statistically analysed. Apart from various extremity attributes, like return period, heavy precipitation, and weather extremity indices, the catalogue is enriched with additional variables (e.g. weather type, antecedent precipitation index, population density, land cover, imperviousness degree, Topographic Position Index), providing the meteorological background and helping to estimate the possible impact, each event could provoke.

The Catalogue is freely available via DWD’s Open Data Portal in both a tabular and spatial (GIS) format. In addition, a user friendly online Dashboard was developed to visualize the data and communicate our results to a broader audience. 

We will present the CatRaRE Catalogue and results of a comprehensive analysis of all classified heavy precipitation events that occurred in Germany between 2001 and 2020. Different time scales from diurnal to multi-annual, as well as identified spatial patterns in connection with event attributes will be illustrated. Most common weather types, favouring occurrence of detected events will be outlined. Finally, we will demonstrate selected application possibilities by combining the catalogue with other datasets (e.g. fire brigade operations).
Satellite-derived sunshine duration data in the CM SAF SARAH-3 climate data record 
Authors: Drücke Jaqueline , Pfeifroth Uwe , Trentmann Jörg , Hollmann Rainer
Published: June 2021

Sunshine Duration (SDU) is an important parameter in climate monitoring (e.g., due to the availability of long term measurements) and weather application. The exceptional sunny years in Europe since 2018 have raised also the attention of the general public towards this parameter.

The definition of SDU by WMO via the threshold of 120 W/m2 for the Direct Normal Irradiance (DNI) allows the estimation of sunshine duration from satellite-derived surface irradiance data. Sunshine duration is part of the climate data record (CDR) “Surface Solar Radiation data set – Heliosat” (SARAH-2.1, doi: 10.5676/EUM_SAF_CM/SARAH/V002_01) by EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), which is based on observations from the series of Meteosat satellites. The provided temporal resolutions are daily and monthly sums with a grid space of 0.05°; the data are available from 1983 to 2017 at www.cmsaf.eu. This climate data record is temporally extended by the so-called SARAH-ICDR (Interim Climate Data record) with an average timeliness of 3 days to allow climate monitoring. An updated, improved, and extended version of the SARAH-2.1 CDR is currently being developed and will be made available in early 2022. The SARAH-3 CDR of sunshine duration, covering 1983 to 2020, will be improved compared to the current version, in particular during situations with snow-covered surfaces.

Here, the algorithm, improvements compared to SARAH-2.1 and a first validation will be presented for sunshine duration, especially for Germany and Europe. The validation is based on station data from Climate Data Center (CDC) for Germany and European Climate Assessment & Dataset (ECA&D) for Europe.
Response of surface ozone concentration to emission reduction and meteorology during the COVID-19 lockdown
Authors: Deroubaix Adrien , Gaubert Benjamin , Bouarar Idir , Doumbia Thierno , Liu Yiming , Stavrakou Trissevgeni , Darras Sabine , Elguindi Nellie , Granier Claire , Lacey Forrest , Müller Jean-François , Shi Xiaoqin , Tilmes Simone , Wang Tao , Brasseur Guy
Published: June 2021

During the COVID-19 pandemic, the first lockdown period (March-May 2020) has led to an unprecedented reduction in pollutant emissions. For 3⁄4 of the more than 1,100 available monitoring stations in Europe, the average NO2 concentrations decreased by at least 25% (2.7 μg.m-3) compared to the average concentrations recorded during the same period of the previous seven years. The relative reduction was of similar magnitude in both urban and rural areas.

We further investigate the spatial distribution of the O3 change. Relative to the seven years average, positive anomalies were observed in northern Europe and negative anomalies in southwestern Europe. However, the level of total oxidant (Ox = O3 + NO2) remained unchanged except in southwestern Europe where it decreased.

At the global scale, the ozone concentration increased only in a few NOx-saturated regions. After presenting data from monitoring stations in Europe, we analyze the drivers of the change in surface ozone concentrations using the global Community Earth System Model. We contrast global simulations of the atmospheric composition with and without lockdown adjusted anthropogenic emissions for the COVID-19 period.

By comparing the situation in Europe with that of the United States and China, we show that the reduced cloudiness in northern Europe played a significant role by shifting the photochemical partitioning between NO2 and O3 toward more ozone, while in the North China Plain, enhanced ozone concentrations resulted primarily from reduced emissions of primary pollutants.

These results illustrate the complexity of the processes affecting ozone in the troposphere and hence the difficulty of implementing efficient regulations targeting air quality impacts.
Improving surface representation and consequences in NWP forecast
Authors: Tudor Martina , Ivatek-Šahdan Stjepan
Published: June 2021

The fields that describe surface properties, from terrain height to vegetation types can have substantial impact on NWP model forecast, especially on the model variables close to the surface. These fields can be computed from different databases. Higher resolution of the terrain height database and higher quality of input data leads to a better representation of the terrain height and other surface fields, especially as NWP models move to a higher resolution. Here we use ALARO configuration of the ALADIN System with TOUCANS turbulence scheme (prognostic TKE) with nonhydrostatic dynamics in 2km resolution over Croatia. The model domain contains Dinaric Alps mountains and Adriatic sea.  The existing operational NWP application uses fields from an old database that is insufficient to properly describe the surface in 2km grid spacing. The fields describing topography, such as terrain height, land sea mask, subgrid terrain variability including surface roughness are computed from a new database in substantially higher resolution. The new fields describing the surface characteristics are more realistic, but also substantially different from the fields used before.  However, the model, including the turbulence parametrisation, was tuned using the old database. Therefore, the subsequent model forecast was not automatically improved when the fields from the new database were used. Tuning only one parameter in a scheme is substantial work, but tuning the whole model with a large number of tuning parametres is daunting. Therefore, the computation of surface roughness and other parameters was tuned in order to improve the 10m wind forecast. Decreased surface roughness does not always lead to higher surface wind speeds and vice versa.
Precipitation Postprocessing using an 2D Analog Ensemble based on Wavelet-Metric
Authors: Wittenbrink Martin , Keller Jan
Published: June 2021

In recent years, several studies showed that the Analog Ensemble (AnEn) method can be a powerful postprocessing tool for meteorological applications. While being mostly applied on station observation data, we expand the AnEn method to gridded 2D-data by running it on overlapping subsets of the NWP model (COSMO-DE-EPS) and using satellite precipitation observation data (RADKLIM) to build up multiple AnEns across the NWP grid.

In general, the AnEn method uses a data set of predictors from NWP model output and corresponding observations. Specifically, the approach is to identify the most similar cases in the training data set to the time step for which the AnEn should be determined. The similarity is calculated based on a metric which takes into account the differences between the predictor data at the current time step and all time steps in the training data. For the most similar cases, the corresponding observations are then chosen as the AnEn members.

In our implementation for a 2D-AnEn, we employ a metric based on wavelet transformations. This allows for an estimation of similarity based on spatial structures and thus overcomes the so-called “double penalty” problem induced by spatial displacement of forecasts (especially found in precipitation forecasts). We use a “Dual Tree Complex Wavelet Transform” (DTCWT) which allows for an efficient extraction of structural information of a 2D field on various scales and angles. Further, we determine weights for the various predictor variables using the so called “Simplified Brute Force” approach.

The results from our experiments show that AnEn provides a reasonable approach to estimate spatially consistent post-processed precipitation fields.