OSA3.2

Spatial climatology

Spatially comprehensive representations of past weather and climate, for example in the form of gridded datasets, are an important basis for analyzing climate variations and for modelling weather-related impacts on the environment and natural resources. They are also indispensable for validation and downscaling of climate models. Increasing demands for, and widespread application of grid data, call for efficient methods of spatial analysis from observations, and profound knowledge of the potential and limitations of these datasets in applications. At the same time, the growing pool of observational data (radar data, satellite based data…) offers the opportunity to improve the accuracy and reduce uncertainty of gridded climate data. Modern spatial climatology therefore deals with a wide range of space and time scales. As a result, actual developments in the field are concerned with a range of challenging issues. These include for example the spatial characteristics and representation of extremes, the representation of small-scale processes (auxiliary variables), the integration of several observational data sources (e.g. station, radar, satellite, re-analysis data), the quantification of uncertainties, the analysis at sub-daily time scales, and the long-term consistency as well as cross-variable consistency in grid datasets.

This session addresses topics related to the development, production, quality assessment and application of gridded climate data with an emphasis on statistical methods for spatial analysis and interpolation applied on observational data. Contributions dealing with modern methodological challenges and applications giving pertinent insights are particularly encouraged. Spatial analysis by applying e.g. GIS is a very strong tool for visualizing and disseminating climate information. Examples showing developments, application and dissemination of products from such analyses for climate services are also very welcome.

The session intends to bring together experts, scientists and other interested people analyzing spatio-temporal characteristics of climatological elements, including spatial interpolation and GIS modeling within meteorology, climatology and other related environmental sciences.

Convener: Ole Einar Tveito | Co-conveners: Mojca Dolinar, Christoph Frei
Lightning talks
| Fri, 10 Sep, 14:00–15:30 (CEST)

Lightning talks: Fri, 10 Sep

Chairperson: Ole Einar Tveito
14:00–14:05
14:05–14:20
|
EMS2021-365
|
solicited
Gerard van der Schrier, Wouter Knap, Marieke Dirksen, Else J.M. van den Besselaar, and Albert M.G. Klein Tank

Within the EOBS project, one of the objectives is to provide an (ensemble) gridded data set of global radiation. In-situ observations of daily sums of global radiation are combined with daily sunshine duration records to construct a dataset for daily global radiation that goes back to 1950. A generalization of the commonly used Angstrom-Prescott formula is used to relate daily values of sunshine duration to global radiation, where optimal values of the parameters in this model are found by allowing for variations in the latitude and with the seasons. A quality control procedure based on the physical limits of  global radiation - latitude and yearday dependent - is applied to the data.

Based on this dataset, a gridded dataset for daily global radiation is produced with a resolution of 0.1 degree, covering Europe. The density of the combined networks of radiation and sunshine duration measurements hugely varies in space and time and this inhomogeneity is likely to give variations in space and time of the confidence of the gridded dataset. A method for enhancing the spatial analysis of daily global radiation from a sparse network is by incorporating information on the spatial covariance in the global radiation fields determined from high‐resolution measurements available in the past. Here we use satellite-based daily observations of downwards surface shortwave radiation from the CERES (Clouds and the Earth's Radiant Energy System) dataset for this purpose.

This approach is inspired by the reduced space optimal interpolation (RSOI) method, and the dominant patterns of variability are calculated using Self Organizing Maps (SOMs). Before reducing the dimension of the CERES dataset to 15 patterns, seasonal trends were removed. SOMs comprise a class of unsupervised neural networks that organize input geospatial data into a user-defined number of outputs (nodes) obtained by iteratively adjusting the nodes to resemble the input data. The training of this unsupervised artificial neural network is entirely data driven.

In the presentation, the similarity between the gridded dataset and the underlying station data is quantified, and a comparison against the CMSAF SARAH dataset is presented.

How to cite: van der Schrier, G., Knap, W., Dirksen, M., van den Besselaar, E. J. M., and Klein Tank, A. M. G.: A gridded European global dataset based on in-situ observations, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-365, https://doi.org/10.5194/ems2021-365, 2021.

14:20–14:25
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EMS2021-466
Jaqueline Drücke, Uwe Pfeifroth, Jörg Trentmann, and Rainer Hollmann

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.

How to cite: Drücke, J., Pfeifroth, U., Trentmann, J., and Hollmann, R.: Satellite-derived sunshine duration data in the CM SAF SARAH-3 climate data record , EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-466, https://doi.org/10.5194/ems2021-466, 2021.

14:25–14:30
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EMS2021-206
Christoph Frei and Sophie Fukutome

Knowledge of the magnitude of rare precipitation extremes is important for civil protection and infrastructure design. But the preparation of related datasets is methodologically complex. The estimation of, say, the 100-year return value at a location without measurement crucially depends on how much information can be drawn from instrumented locations nearby. Are there prospects for this “borrowing information over space”, when the climate of the region is spatially complex? In this presentation we illustrate that a method of spatial extreme value analysis has great potential, if it can be carefully configured and equipped with climatological knowledge. We also illustrate that spatial integration may be less efficient when the modelling is limited by poor observational coverage. In both cases, it is highly desirable to quantify uncertainties reliably.

Here, we focus on the domain of Switzerland, with a complex topography and climatology. A Bayesian Hierarchical Model, combining the GEV model for block maxima and the Gaussian Random Fields model for spatial dependence, is used to derive km-scale maps of return levels for 24-hour and 1-hour precipitation extremes. The observational coverage is very different between the two durations, with more than 400 stations over 60 years for the 24-hour, but less than 100 stations over 40 years for the 1-hour case. Accordingly, the model configuration for the data-rich case can involve numerous and verifiably informative covariates, whereas the modelling for the data-sparse case is left to be more scrimpy. Our results illustrate the difference in the information gain over space using a set of independent test stations. An additional finding of this study is, that efficient information gain over space is not a given from rich data alone, but depends on a careful model configuration. Advanced methods of data science do not replace a knowledgeable climatologist and patience with simple data exploration.

How to cite: Frei, C. and Fukutome, S.: Spatial analysis of extreme hourly and daily precipitation return levels – Borrowing information over space in spite of complex topography, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-206, https://doi.org/10.5194/ems2021-206, 2021.

14:30–14:35
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EMS2021-56
Cristian Lussana, Thomas N. Nipen, Ivar A. Seierstad, and Christoffer A. Elo

Hourly precipitation is often simultaneously simulated by numerical models and observed by multiple data sources. Accurate precipitation fields based on all available information are valuable input for numerous applications and a critical aspect of climate monitoring. 

Inverse problem theory offers an ideal framework for the combination of observations with a numerical model background. In particular, we have considered a modified ensemble optimal interpolation scheme. The deviations between background and observations are used to adjust for deficiencies in the ensemble. A data transformation based on Gaussian anamorphosis has been used to optimally exploit the potential of the spatial analysis, given that precipitation is approximated with a gamma distribution and the spatial analysis requires normally distributed variables. For each point, the spatial analysis returns the shape and rate parameters of its gamma distribution. 

The ensemble-based statistical interpolation scheme with Gaussian anamorphosis for precipitation (EnSI-GAP) is implemented in a way that the covariance matrices are locally stationary, and the background error covariance matrix undergoes a localization process. Concepts and methods that are usually found in data assimilation are here applied to spatial analysis, where they have been adapted in an original way to represent precipitation at finer spatial scales than those resolved by the background, at least where the observational network is dense enough.

The EnSI-GAP setup requires the specification of a restricted number of parameters, and specifically, the explicit values of the error variances are not needed, since they are inferred from the available data. 

The examples of applications presented over Norway provide a better understanding of EnSI-GAP. The data sources considered are those typically used at national meteorological services, such as local area models, weather radars, and in situ observations. For this last data source, measurements from both traditional and opportunistic sensors have been considered.

How to cite: Lussana, C., Nipen, T. N., Seierstad, I. A., and Elo, C. A.: Spatial analysis of hourly precipitation combining observations and ensemble forecasts, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-56, https://doi.org/10.5194/ems2021-56, 2021.

14:35–14:40
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EMS2021-358
Jouke de Baar, Gerard van der Schrier, Irene Garcia-Marti, and Else van den Besselaar

Objective

The purpose of the European Copernicus Climate Change Service (C3S) is to support society by providing information about the past, present and future climate. For the service related to in-situ observations, one of the objectives is to provide high-resolution (0.1x0.1 and 0.25x0.25 degrees) gridded wind speed fields. The gridded wind fields are based on ECA&D daily average station observations for the period 1970-2020.

Research question 

We address the following research questions: [1] How efficiently can we provide the gridded wind fields as a statistically reliable ensemble, in order to represent the uncertainty of the gridding? [2] How efficiently can we exploit high-resolution geographical auxiliary variables (e.g. digital elevation model, terrain roughness) to augment the station data from a sparse network, in order to provide gridded wind fields with high-resolution local features?

Approach

In our analysis, we apply greedy forward selection linear regression (FSLR) to include the high-resolution effects of the auxiliary variables on monthly-mean data. These data provide a ‘background’ for the daily estimates. We apply cross-validation to avoid FSLR over-fitting and use full-cycle bootstrapping to create FSLR ensemble members. Then, we apply Gaussian process regression (GPR) to regress the daily anomalies. We consider the effect of the spatial distribution of station locations on the GPR gridding uncertainty.

The goal of this work is to produce several decades of daily gridded wind fields, hence, computational efficiency is of utmost importance. We alleviate the computational cost of the FSLR and GPR analyses by incorporating greedy algorithms and sparse matrix algebra in the analyses.

Novelty   

The gridded wind fields are calculated as a statistical ensemble of realizations. In the present analysis, the ensemble spread is based on uncertainties arising from the auxiliary variables as well as from the spatial distribution of stations.

Cross-validation is used to tune the GPR hyper parameters. Where conventional GPR hyperparameter tuning aims at an optimal prediction of the gridded mean, instead, we tune the GPR hyperparameters for optimal prediction of the gridded ensemble spread.

Building on our experience with providing similar gridded climate data sets, this set of gridded wind fields is a novel addition to the E-OBS climate data sets.

How to cite: de Baar, J., van der Schrier, G., Garcia-Marti, I., and van den Besselaar, E.: A new pan-European dataset for gridded daily average wind speed based on in-situ observations, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-358, https://doi.org/10.5194/ems2021-358, 2021.

14:40–14:45
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EMS2021-213
Cristina Lavecchia, Enea Montoli, Samantha Pilati, and Giuseppe Frustaci

With the growing relevance of urbanized environments in the framework of adaptation and mitigation plans, improvements in monitoring the urban weather, and specially in the knowledge of the urban climatology and its evolution, are urgently needed. A basic difficulty arises from the fact that dedicated surface observational networks with the desired characteristics of measurement quality and continuity are often lacking in cities, while remote sensing data are mainly used for specific aspects, as for instance the Surface Urban Heat Island, while air temperature is more important for applications. After the experience gained, and the methodologies developed in Milan during a locally co-funded project (ClimaMi: https://www.progettoclimami.it/), the possibility was investigated of a medium- to high-resolution urban climatology mainly derived from observed air temperature and precipitation data.

The urban specialized surface network (by Fondazione Osservatorio Meteorologico Milano Duomo: FOMD), in operation since 2011 and “metrologically” tested during MeteoMet Project (Merlone et al., 2015), was considered as a reliable basis for a new and more detailed analysis of the most recent urban climate in Milan. To complement the necessarily limited number of high quality measurements by this urban Climate Network (CN),  other  automatic weather stations  (as homogenous as possible to CN) were accurately selected from third-party networks, in particular from the regional (ARPA Lombardy) meso-synoptic one, and from a private citizens association (MeteoNetwork): this helped in setting up a database of reliable hourly observational data (and metadata) in urban and peri-urban environments, dense enough for a mesoscale description of the city main statistical characteristics and for an already significative time span of 5 years.

Nevertheless, resilience plans by local authorities and professionals often require a spatial resolution of the order of tens of meters: to significantly improve the spatial resolution, space-borne sensors are an obvious and nowadays practical possibility. Furthermore, to make the best use of the quality of (under sampled) surface measurements, and of the high spatial resolution offered by remote sensed data, a cokriging-based methodology (Goovaerts, 1999) was developed and tested for air temperature. While direct correlation methods between Land Surface Temperature (LST) and the (more interesting and required) near-surface air temperature are not straightforward and generally unreliable, the encouraging results obtained in reconstructing air temperature fields by cokriging allowed an analysis of the recent climate of the cities and neighborhoods at medium to high spatial resolution for selected weather types of particular relevance in the definition of resilience measures.

The same methodology is now under test for precipitation measurements by different sensors and networks, and first results will be presented together with the unprecedented climatological description of temperature in the greater Milan, and analysis of micro-scale urban climate variations in consideration of (present and future) climate monitoring and assessment needs.

How to cite: Lavecchia, C., Montoli, E., Pilati, S., and Frustaci, G.: Spatially detailed urban climatology for temperature and precipitation., EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-213, https://doi.org/10.5194/ems2021-213, 2021.

14:45–14:50
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EMS2021-89
Vlad-Alexandru Amihăesei, Lucian Sfîcă, and Alexandru Dumitrescu

The south-eastern part of the European continent is known as a region where the types of climate are hard to be delimited, being indicated by Trewartha since 1961 among the so-called Earth's Climate Problem regions of the world. This is given especially by its position at the merges of arid and cold climate of the temperate zone in Europe. Taking to account this aspect, it is not surprisingly that after almost 100 years of climate classification attempts, there is still no agreement regarding the climate type of Romania and its corresponding subdivisions. Even if a weak majority of the Romanian climatologists plead for a temperate continental climate, some others consider that Romania has a typically temperate transitional climate specific for central Europe. However, most of previous regionalizations are highly subjective with no proper quantitative assessment of climate conditions. 

In our study a climate regionalization of Romania’s territory is proposed, based on an objective approach. For this purpose, 9 monthly climate parameters extracted from interpolation gridded data sets (ERA-5 land and ROCADA) were used.

The regionalization was performed by mixing two objective methods. Firstly, all the 108 input variables were reduced at 8 major factors using factor analysis. Secondly, those factors were used in a k-means clustering method and a new scheme of climate regionalization of Romanian territory was obtained. Through this, we succeed to delimitate 8 different climate subtypes within Romania's territory which we aggregated firstly in 2 major zonal climate types: (i) temperate transitional climate (TTC) from maritime to continental type, extended in the north-east part of Romania and (ii) temperate orographically sheltered climate (TOSC) with 2 major subtypes. The first sub-type of TOSC is extended within the Carpathian mountain arch (an extension of pannonian climate) and the second one covers the romanian part of the region between Carpathian and Balkan Mountain (lower danubian climate). Besides these two zonal types the major landforms of Romania impose specific climate conditions: (iii) the Carpathian mountains and sub-mountains area have their own climate features (CMSC) with 3 climate subtypes (precarpathian, eastern Carpathian and alpine climates), while the (iv) Black Sea shapes the main climate conditions of the south-eastern side of the country especially along the coast with 2 climate subtypes (ponto-deltaic and western pontic type). The main features of these climate types/subtypes are presented in detailed in the study.

In the meantime, the proposed climate regionalization covers partially the neighbor countries in an attempt to homogenize the different national perspectives on the climate types along the states boundaries in central and south-eastern Europe.

How to cite: Amihăesei, V.-A., Sfîcă, L., and Dumitrescu, A.: Redefining the climate regions of Romania through objective methods, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-89, https://doi.org/10.5194/ems2021-89, 2021.

14:50–14:55
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EMS2021-421
Ole Einar Tveito

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. 

How to cite: Tveito, O. E.: A pragmatic re-engineering approach to extend short observation time series by climate anomaly gridding, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-421, https://doi.org/10.5194/ems2021-421, 2021.

14:55–15:30

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