Chairpersons: Federico Fierli, Victor Venema, Barbara Chimani
Climatological studies require sufficiently long homogeneous time series. Long observational records are often subject to non-climatological changes, for example changes in location, measurement equipment or technique, or changes in the surrounding environment. In order to serve as basis for climatological studies, the homogeneity of observational data therefore must be tested, and if required, homogenised.
At the Swedish Meteorological and Hydrological Institute (SMHI) 35 key homogenised monthly average 2 m temperature data series has served as the basis for an indicator of climate change. The time series were homogenised manually with the Standard Normal Homogeneity Test. SMHI recently adopted the new climatological standard normal period 1991–2020, which called for an updated homogenised temperature data set.
In order to use a larger part of the observational data set and to enable systematic updating the monthly average temperature data set is homogenised with a new automated version of the homogenisation tool HOMER. Data from 836 individual time series (1860–2021) are merged into 456 time series with a novel automatic merging method. The expansion of the homogenised data set from 35 to 456 time series enables studies of regional climate. Merging limits the need of interpolation of data and increase the number of long time series without a net loss of data.
22 of the merged time series were found to be homogeneous. For the other time series, the median time per homogeneity break is 17 years which correspond well to the typical homogeneity break frequency of European temperature data sets. 40 % of the detected homogeneity breaks are supported in meta data. 27 % of the data points are corrected by ±0.5 °C or less, 2 % by ±1 °C or more. The average correction is negative, larger in the early periods, and larger in the summertime.
The average trend 1860–2021 in the resulting merged and homogenised data set is (0.13 ± 3) °C / 10 a, which does not significantly differ from that of the raw observational data or the previous homogenised data set. Extremely warm months defined as being outside of three times the standard deviation from the average of the full time series are most frequent and extremely cold months least frequent in the most recent 30-year period (1991–2020). In the homogenised data set, extremely warm months are even more frequent and extremely cold month even less frequent in 1991–2020, than in the raw observational data set.
In the presentation, the automation of HOMER and the novel automatic merging method is described. Results from the homogenised data set is presented in more detail and compared with the previous homogenised data set.
How to cite: Joelsson, M., Engström, E., and Kjellström, E.: Homogenisation of Swedish mean monthly temperature series 1860–2021, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-111, https://doi.org/10.5194/ems2022-111, 2022.
The measurement of sunshine duration has experienced a number of changes since the operational observation by the national meteorological service ZAMG in Austria. With the automatization of the stations the Campbell-Stokes sunshine recorders were replaced by Haenni Solar 111 instruments and later partly with Meteoservice SD6. While no calibration was/is necessary for Campbell-Stokes and Meteoservice SD6 (using a threshold of 120W/m2 as proposed by WMO), calibration was regularly done for the different versions of Haenni (in metadata the name of the company changed multiple times). First inquiries indicated that in the beginning they were calibrated to 200 W/m2 and that later on - during one of the service cycles around the year 2000 - the threshold was changed to 50 W/m2.
During the last years, six sun-tracking devices equipped with pyrheliometers have been installed in the Austrian network (ARAD station network - one BSRN site). By using those data the influence of the different sunshine recorder thresholds could be estimated and the meta information on the changes evaluated. For homogenization, ACMANT was used. The use of different choices for reference series have been analyzed and evaluated. Finally, additional to station data, satellite data from the EUMETSAT SARAH dataset from 1983 to 2015 was included in the final homogenization process of those time series.
The presentation will include information on the stations history, the correction and homogenisation methods explored and evaluated, information on the final homogenization procedure using ACMANT as well as the impact of the homogenization on the analyses of sunshine duration.
How to cite: Chimani, B., Paul, A., Olefs, M., Haslinger, K., and Tilg, A.-M.: Homogenized Sunshine duration data for the Austrian domain, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-265, https://doi.org/10.5194/ems2022-265, 2022.
A new data set of quality-controlled and homogenized daily maximum (TX) and minimum (TN) temperature and precipitation (PPT) for Catalonia is presented. For temperature, the dataset includes 26 TX and TN long-term series, covering most of the period 1950–2021, while a subset of 7 additional series is also included as a support, but covering a shorter period. For precipitation, a set of 71 daily PPT series is considered, covering the period 1950-2021. Although the number of series is important enough for a geographical area such as Catalonia (32,000 square kilometres), the Pyrenean region is underrepresented. For this reason, the database incorporates two Andorran series.
First of all, a description of the quality control protocol implemented for the detection of erroneous daily observations is shown. It is based on a combination of both, semiautomatic filters and visual data inspection, and special attention is paid for correction of data displacement error.
Secondly, a short description of the methodology for creating long blended series is shown, based on completeness, continuity and location (both altitude and distance) criteria. Then, time series homogeneity is addressed by means of ACMANTv5, one of the best relative homogenization methods, and firstly used on a climate dataset in this work.
Finally, a description of the results of annual and seasonal trends for the whole dataset is provided. Trends are compared with the previous dataset used in Catalonia, that was homogenized using a combination of two approaches: HOMER (monthly time series) and Vincent (transfer of monthly adjustments to daily values). In addition, some climate extreme indicators based on daily time series are also calculated for Catalonia, as part of the Yearly Bulletin of Climate Indicators released by the Meteorological Service of Catalonia (SMC). The homogenized series will be regularly updated and are already available throughout the webpage of the SMC.
How to cite: Prohom, M. J., Cunillera, J., Domonkos, P., Herrero, M., Busto, M., Barrera-Escoda, A., and Reynés, J.: New daily homogenized database for Catalonia using ACMANTv5 (1950-2021), EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-376, https://doi.org/10.5194/ems2022-376, 2022.
The MASH (Multiple Analysis of Series for Homogenization) system was developed to complete, control and homogenize long-term monthly and daily data series. The data series homogenised by MASH sytem are the basis of other systems, since long data series represent the spatial and temporal samples for climate information. The daily average relative humidity data series are homogenised with using the MASHv3.03 software operationally at the Hungarian Meteorological Service.
In this study we present the verification statistics of the station data series and we show the representativity values of the station network. The data quality control part of MASH works efficiently for daily relative humidity values. We show the quality control results, including cases when the suspicious values are acceptable. Doubtful measurements come out due to the existence of e.g. inversion situations.
Recently we have homogenised not only the daily data but also the 6-hourly measurements. As the mathematical background of the MASH software allows us to use the homogenisation results of the past years, we homogenise these data series with using the results of the daily data. Since the hourly data series are highly inhomogeneous, moreover, their inhomogeneities are not the same as those of the daily series, therefore we cannot ignore the daily trend of the inhomogeneities.
Consequently, we decided to homogenise the 6-hourly series separately, using the usual MASH procedure. When homogenising the 6-hourly data series with the MASH system, the breakpoints detected in the daily data series can be automatically used as metadata. The results of the homogenisation of the 6-hourly values are also presented. These sub-daily data series are important for the accuracy of hazard warning, e.g. to study foggy situations.
How to cite: Izsak, B., Szentes, O., and Lakatos, M.: Homogenisation of relative humidity data series, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-463, https://doi.org/10.5194/ems2022-463, 2022.
Since its founding in 1989, the Global Precipitation Climatology Centre (GPCC) has been producing global precipitation analyses based on land surface in-situ measurements. In the now over 30 years the underlying database has been continuously expanded and includes a high station density and large temporal coverage. Due to the semi-automatic quality control routinely performed on the incoming station data, the GPCC database has a very high quality. Today, the GPCC holds data from more than 123,000 stations, about three quarters of them having long time series.
The core of the analyses is formed by data from the global meteorological and hydrological services, which provided their records to the GPCC, as well as national meteorological and hydrological services from all over the world. In addition, the GPCC receives SYNOP and CLIMAT reports via the WMO-GTS. These form a supplement for the high quality precipitation analyses and the basis for the near real-time evaluations.
Quality control activities include cross-referencing stations from different sources, flagging of data errors, and correcting temporally or spatially offset data. This data then forms the basis for the following interpolation and product generation.
In near real time, the 'First Guess Monthly', 'First Guess Daily', 'Monitoring Product', ‘Provisional Daily Precipitation Analysis’ and the 'GPCC Drought Index' are generated. These are based on WMO-GTS data and monthly data generated by the CPC (NOAA).
With a 2-3 year update cycle, the high quality data products are generated with intensive quality control and built on the entire GPCC data base. These non-real time products consist of the 'Full Data Monthly', 'Full Data Daily', 'Climatology', and 'HOMPRA-Europe' and are now available in the 2022 version.
All gridded datasets presented in this paper are freely available in netcdf format on the GPCC website https://gpcc.dwd.de and referenced by a digital object identifier (DOI). The site also provides an overview of all datasets, as well as a detailed description and further references for each dataset.
How to cite: Rustemeier, E., Schneider, U., Ziese, M., Finger, P., and Hänsel, S.: Updated gridded datasets version 2022 provided by the Global Precipitation Climatology Centre (GPCC) , EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-474, https://doi.org/10.5194/ems2022-474, 2022.
We will present our atmospheric physics contribution to the New Refined Observations of Climate Change from Spaceborne Gravity Missions (NEROGRAV) research group, which is funded by the German Research Foundation (DFG). The goal of NEROGRAV is to develop new analysis methods and modeling approaches to improve the current data analysis of the GRACE and GRACE-FO missions. Our contribution to this research unit is to is to (re)analyze data fields on horizontal grid sizes below 10 km from non-hydrostatic regional atmospheric models: the 1995-2019 COSMO-REA6 regional reanalysis and the July 2021 ICON-EU/D2 analyses. From these models, the mass densities of dry air and all water phases (gaseous, liquid clouds and rain, icy snow, sleet, and hail) are available in each of the 40-50 vertical layers, so that the total local and column-by-column atmospheric mass density can be calculated without using the hydrostatic assumption.
Current non-hydrostatic, high-resolution atmospheric models run under some idealizations (e.g., assumption of a perfect sphere, spatially constant gravitational acceleration, approximation of a flat atmosphere) that are inconsistent with the real measurements of the GRACE/GRACE-FO missions. Furthermore, high-resolution atmospheric model data are currently only available within the CORDEX-EU domain, which covers the North Atlantic-European region, or an even smaller domain. The results to be presented will answer the following questions: (1) Do the model idealizations cause discrepancies with respect to the GRACE/GRACE-FO measurements that are larger than the sum of the actual measurement uncertainties and the uncertainties resulting from the remaining dealiasing models? (2) Can the idealizations be corrected to achieve better agreement with the GRACE/GRACE-FO data without sacrificing the underlying model dynamics, e.g. by introducing additional gradients in the models? (3) Do extreme weather events, such as heavy rainfall observed in the Ruhr/Ahr/Erft/Maas basin in July 2021, cause mass variations that exceeds typical GRACE/GRACE-FO uncertainties? And finally, (4) does high-resolution but regional atmospheric mass variability leave a statistically significant fingerprint in the global loading coefficients?
How to cite: Friederichs, P., Dixit, S., Kracheletz, M., Springer, A., Mielke, C., Kusche, J., and Hense, A.: Atmospheric mass variations from high-resolution, non-hydrostatic atmospheric models and their influence on dealiasing of GRACE/GRACE-FO data sets, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-456, https://doi.org/10.5194/ems2022-456, 2022.
long-term tropospheric ozone time series has been generated for the tropical band (20°S to 20°N) based on convective cloud differential algorithm (CCD). Tropical tropospheric ozone columns were retrieved from several European sensors starting with observations by GOME in 1995 and including data from SCIAMACHY, OMI, GOME-2A and GOME-2B. The algorithm was developed within the ESA CCI project has been updated regularly.The time series has now been extended by DLR with data from GOME-2C and TROPOMI and now encompasses 25 years. The tropospheric ozone retrieval for all data sets is based on the total columns retrieved with the GODFIT algorithm and associated cloud products.
There are however some differences between the different tropospheric columns from the different sensors which have to be corrected for. The operational TROPOMI tropical tropospheric ozone data have a much higher spatial and temporal resolution. Therefore the data are averaged to 1.25° x 2.5° and 1 month to match the resolution of the other sensors. For the CCD time series, we used SCIAMACHY data as reference and fitted an offset and a trend correction to the data of the other sensors. For the sensors that do not have a temporal overlap with SCIAMACHY we used the harmonized version of GOME-2A and OMI as reference. We estimated the trend based on the merged long-term time series. For the tropics an overall trend of +0.9 DU/decade was found in the data set until 2021, varying locally between -0.5 and 1.8 DU/decade. We also looked for seasonal trends, although the uncertainty in the trend analysis increases. The strongest increase in tropospheric ozone was found for March-April-May and the smallest trends occurred during Oct-Dec. Besides the long term trend the data might also be used to investigate the influence of single events like extreme bio mass burning seasons.
How to cite: Heue, K.-P., Loyola, D., Coldewey-Egbers, M., Lerot, C., and van Roozendael, M.: Long-term Tropospheric ozone column data record from GOME to TROPOMI using CCD algorithm, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-477, https://doi.org/10.5194/ems2022-477, 2022.
In this study, we present the satellite-based GOME-type Total Ozone Essential Climate Variable (GTO-ECV) Climate Data Record (CDR), which has been developed in the framework of national and the European Space Agency’s Climate Change Initiative (ESA-CCI+) ozone projects. GTO-ECV covers the past 26 years (1995-2021), and it is regularly extended as part of the European Union Copernicus Climate Change Service (EU-C3S2) ozone project.
The GTO-ECV CDR combines space-based observations from a series of seven nadir-viewing low earth orbit sensors of the GOME-type (Global Ozone Monitoring Experiment). The latest additions were measurements from GOME-2/MetOp-C and from the TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5P. All instruments measure the upwelling solar radiation reflected or scattered in the Earth's atmosphere and from its surface in the ultraviolet, visible, and near-infrared spectral range. Total ozone columns are retrieved using the GOME Direct Fitting approach (GODFIT version 4) and the inter-sensor consistency is excellent. We combine the individual data sets into one homogenized record that provides monthly means with global coverage and a spatial resolution of 1°x1°.
The data record can be used for various climate applications regarding the long-term evolution of the atmospheric ozone layer including inter-annual variability and decadal trends on global and regional scales, or the evaluation of Chemistry-Climate Model simulations. Of particular interest is the search for signs of ozone recovery. Thanks to the Montreal Protocol the stratospheric concentrations of ozone depleting substances have been declining since the late 1990s and a slow healing of the ozone layer is expected. In this study, we report on the spatial and seasonal distribution of ozone trends and on the possible impact of climate change.
How to cite: Coldewey-Egbers, M., Loyola, D., Heue, K.-P., Lerot, C., and van Roozendael, M.: The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record for climate applications, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-492, https://doi.org/10.5194/ems2022-492, 2022.
Data repositories and archives play a critical role as source for observational data used in the study of past weather events and climate. Therefore, data storage is the first and essential step in the process of making the data usable. The Royal Meteorological Institute (RMI) has been digitizing its archives in image scan format for several years now, thanks to BELSPO's national DIGIT programme, in order to save them. This represents a significant amount of data that can be made available as soon as it has been encoded in a way that can be easily used by everyone. In terms of human resources, it would take a lot of time and people to carry out such a task. This is why RMI is going to launch a first campaign of keying by volunteers of its archives of climate bulletins from 1881 to 1900 via the Zooniverse platform. For this campaign, volunteers will be asked to encode daily precipitation and extreme temperature data. Zooniverse provides a popular way of developing citizen science projects. Using their Project Builder interface, a custom website was created to enable volunteers to transcribe the data from the images.
This first campaign will assess the usefulness and reliability of the results collected through volunteer participation. The focus will be on the design of the encoding page, the feedback from volunteers on the tool provided, the quality of the results but also the popularity of such a process. An analysis of the results will be carried out to assess whether RMI will carry out other campaigns in the future on a wider range of climate parameters.
How to cite: Ingels, R. and Journée, M.: Digitizing observations of precipitation and temperature extremes from the meteorological reports of the Royal Meteorological Institute (1881-1900) by volunteer citizen scientists., EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-353, https://doi.org/10.5194/ems2022-353, 2022.
The ongoing climate change raises the question if, how and why the wind climate is changing. IPCC stated in AR6WG1 that the confidence in wind changes is “low” to “medium”; there is limited knowledge of historical wind changes and multidecadal variability. A fundamental challenge for the climate research community is to rescue old climate observations from weather archives at the National Weather Services and derive homogeneous and complete data series. At SMHI most of the meteorological data is available digitally since the 1960s, but before that only a minor part of the data is previously digitized.
Historical wind speed and direction observations from 1920th to 1940th from 13 stations in Sweden have been rescued and digitized making 165 additional station years of wind data available through Swedish Meteorological and Hydrological Institute (SMHI) open data portal. Stations with instrumental measurements of wind were selected and in the early 1900-century the accordingly equipped stations were mainly found at lighthouses along the coast and at airports. The dominant type of anemometer was of cup-type and different versions are described in the article. The work followed the protocol “Guidelines on Best Practices for Climate Data Rescue” of the World Meteorological Organization. Along with digitize the wind observations meta data of the measurements done at the stations was collected and compiled as a support to the following quality control and homogenization of the wind data. The meta data showed that the most common identified possible homogeneity break was change of observer, but also change of instrument type and position were found in the records.
The presentation is a part of the first work package of the WINDGUST project, which is a collaboration between the SMHI, the University of Gothenburg - Regional Climate Group (GU-RCG) and the Spanish National Research Council (CSIC). The project aim is to fill the key gap of short availability (since 1939) and temporal inhomogeneity of wind datasets in Sweden. Especially, the results could contribute to futures studies on the causes driving the current “stilling” and “reversal” debate in a global warming climate.
Since previous presentations of the current work package, a data screening has been performed for the wind observations from the 13 digitized stations to visualize the data cover and monthly variability and wind speed range. Two distinct categories of stations with separate wind patterns can be established: coastal and inland stations where inland stations typically has a weak annual variation while coastal stationstypically experience the highest wind speed in November and December.
How to cite: Engström, E., Azorin-Molina, C., Wern, L., Hellström, S., Zhou, C., Södling, J., Joelsson, M., and Chen, D.: Data rescue of historical wind observations in Sweden since the 1920s, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-436, https://doi.org/10.5194/ems2022-436, 2022.
The ECA&D data-set collects over 81000 series of observations for the Essential Climate Variables more than 22400 stations from all countries in Europe. Large number of these series are affected by outliers, repeated values and other issues in the measurements. These quality issues need to be identified prior to further processing of the data into e.g. the production of the gridded E-OBS datasets as these issues may lead to erroneous estimates of climate impact indices and trends. In the context of the Copernicus contract C3S2_311 Lot3, the MetQC method developed at GCRI has been implemented for operational use at ECA&D. This new method combines checks on duplicate series, repetitive values and outliers with an inter-stations comparison. Earlier work compared five different quality check methods (MetQC, MASH, ACMANT, NOAA, C3QC) on four benchmark data-sets covered by ECA&D. This work indicated that the MetQC approach performed well in comparison against the other methods and the new method replaces the more primitive approach at ECA&D in which straightforward stand-alone tests were conducted. A strong aspect of the MetQC method is that it provides an estimate for an alternative value of a suspect value based on values of surrounding stations, with a quantification of the reliability of this alternative. The MetQC method has been further refined and tailored to the application at ECA&D to be capable of handling the vast dataset in a reasonable time. The presentation will focus on the improvement in quality for ECA&D and comparisons in terms of numbers of flagged data and given between the new approach and the approach it replaces.
How to cite: Stepanek, P., Van der Schrier, G., and Zahradníček, P.: A new quality Control procedure for ECA&D, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-716, https://doi.org/10.5194/ems2022-716, 2022.
Air temperature is historically monitored in Belgium by a network of climatological stations. Most of these stations are till nowadays mainly manual: they rely on a volunteer observer who records every morning the daily extreme air temperatures by reading liquid-in-glass thermometers (including mercury thermometers) placed in Stevenson screens. Since 2020, the UNEP Minamata Convention on Mercury bans all production, import and export of observing instruments containing mercury. In the recent years, various alternatives to mercury thermometers have therefore been considered and tested by the Royal Meteorological Institute of Belgium (RMI). Parallel measurements have been performed with several synoptic weather stations in Belgium. Based on these comparisons as well as on practical arguments (e.g., the price as well as the ease of installation, maintenance and operation), a compact low-cost automatic weather station was selected by RMI to transition away from the conventional manual climatological stations. In 2022, almost 50 of these compact stations have already been installed and around 50 further ones are planned in the coming months. These stations provide 10-min observations in real-time through wireless data transmission.
In practice, the communication of these compact stations is however not perfectly robust as data are regularly missing due to frequent down-time of usually short duration. As these gaps in the time series can be problematic for end-users applications, a procedure to automatically provide estimations for all missing data is needed. In the complete data processing chain (i.e., from the sensor to the end-user), this automatic data completion step is performed just after the real-time data quality control (i.e., quality tests that directly eliminate obvious errors). It is followed by a final data validation (i.e., detailed but delayed and partly manual verification of the data to ensure the quality of the climate archives). Observations and estimations are labeled by distinct quality flags in the climate archives.
This contribution provides an overview of the recent developments regarding the automatic completion of gaps in 10-min air temperature series. Various approaches including inverse distance weighted interpolation (IDW), principal component analysis (PCA), linear interpolation and linear regression have been compared in order to select the method to be implemented in the operational data processing chain.
How to cite: Journée, M., Loucheur, B., Absil, P.-A., and Bertrand, C.: Completion of gaps in 10-min air temperature series from automatic weather stations in Belgium, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-68, https://doi.org/10.5194/ems2022-68, 2022.
The Berchtesgaden National Park located in the Bavarian Alps in Southeastern Germany is a highly interesting study field due to its extreme topography (~ 600 - 2700 m.a.s.l.) and locally most variable climate conditions.
Due to the research objective of the national park and the cooperation with the German Meteorological Service (DWD) we find a dense measurement infrastructure compared to other alpine regions. Ground measurements of temperature, relative humidity, precipitation and wind conducted by both partners go back as long as the 1980s with stations in different altitudes covering the complex terrain. Altogether, data of almost 100 stations is available, albeit with different record lengths (some only have short records of a few years). However, up to now, the datasets of the two institutions have only been used separately and lack a stringent quality control which takes into account the challenges of measurements in complex terrain.
New efforts are now underway to create a quality controlled, homogenized dataset for the Berchtesgaden National Park which will be openly available for the research community. Additionally, a gridding of the station data is planned which is of high importance to cover complex high mountain areas as adequately as less complex regions. The efforts also include the assessment of uncertainties, both of the measurement devices and of the digitization of thermohygrograph charts, as well as the compilation of metadata. Data from the operational Networks of the German and Austrian Meteorological Services, as well as other available station networks in the region are included in the processing of data.
This contribution will introduce this new dataset which is currently in the making and which in future can be used as common historical reference e.g. for climate change studies, as boundary conditions for impact models or for the validation of climate models in complex terrain. It will also highlight some of the challenges of measurements in complex terrain and their data processing.
How to cite: Sedlmeier, K., Lotz, A., Nitsche, O., Heiser, S., Paunovic, I., Bock, L., and Mühlbacher, G.: A new dataset for climate change studies in complex alpine terrain (in the making), EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-490, https://doi.org/10.5194/ems2022-490, 2022.
