TITAN automatic spatial quality control  of meteorological in-situ observations

Båserud, Line; Lussana, Cristian; Nipen, Thomas N.; Seierstad, Ivar A.; Oram, Louise; Aspelien, Trygve

doi:10.5194/asr-17-153-2020

Cited by 25 publications

(30 citation statements)

References 16 publications

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“…Finally, a fifth step (L5) is added to remove too high spatial variability among closely located CWSs within a 3-km distance, following the idea of a "buddy check" from Båserud et al (2020) and Nipen et al (2020). The criteria for the L5 step for the temperature value for j-th CWS at i-th moment is based on its deviation from the mean value over the neighboring CWS:…”

Section: Citizen Weather Stationsmentioning

confidence: 99%

Quantifying Local and Mesoscale Drivers of the Urban Heat Island of Moscow with Reference and Crowdsourced Observations

Varentsov

Fenner

Meier

et al. 2021

Front. Environ. Sci.

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Urban climate features, such as the urban heat island (UHI), are determined by various factors characterizing the modifications of the surface by the built environment and human activity. These factors are often attributed to the local spatial scale (hundreds of meters up to several kilometers). Nowadays, more and more urban climate studies utilize the concept of the local climate zones (LCZs) as a proxy for urban climate heterogeneity. However, for modern megacities that extend to dozens of kilometers, it is reasonable to suggest a significant contribution of the larger-scale factors to the temperature and UHI climatology. In this study, we investigate the contribution of local-scale and mesoscale driving factors of the nocturnal canopy layer UHI of the Moscow megacity in Russia. The study is based on air temperature observations from a dense network consisting of around 80 reference and more than 1,500 crowdsourced citizen weather stations for a summer and a winter season. For the crowdsourcing data, an advanced quality control algorithm is proposed. Based on both types of data, we show that the spatial patterns of the UHI are shaped both by local-scale and mesoscale driving factors. The local drivers represent the surface features in the vicinity of a few hundred meters and can be described by the LCZ concept. The mesoscale drivers represent the influence of the surrounding urban areas in the vicinity of 2–20 km around a station, transformed by diffusion, and advection in the atmospheric boundary layer. The contribution of the mesoscale drivers is reflected in air temperature differences between similar LCZs in different parts of the megacity and in a dependence between the UHI intensity and the distance from the city center. Using high-resolution city-descriptive parameters and different statistical analysis, we quantified the contributions of the local- and mesoscale driving factors. For selected cases with a pronounced nocturnal UHI, their respective contributions are of similar magnitude. Our findings highlight the importance of taking both local- and mesoscale effects in urban climate studies for megacities into account. Furthermore, they underscore a need for an extension of the LCZ concept to take mesoscale settings of the urban environment into account.

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Section: Citizen Weather Stationsmentioning

confidence: 99%

Quantifying Local and Mesoscale Drivers of the Urban Heat Island of Moscow with Reference and Crowdsourced Observations

Varentsov

Fenner

Meier

et al. 2021

Front. Environ. Sci.

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“…Integrating citizen observations into operational systems comes with a number of challenges. The operational systems must be robust and, therefore, rely on strict quality control procedures, such as those described by Båserud et al (2020). In this study, hourly precipitation observations from the same network of opportunistic sensors are considered and used both in Sect.…”

Section: Intense Precipitation Case Over South Norwaymentioning

confidence: 99%

Ensemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitation

Lussana

Nipen

Seierstad

et al. 2021

Nonlin. Processes Geophys.

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Abstract. Hourly precipitation over a region is often simultaneously simulated by numerical models and observed by multiple data sources. An accurate precipitation representation based on all available information is a valuable result for numerous applications and a critical aspect of climate monitoring. The 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.

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“…Met Éireann, the Irish Meteorological Service, uses the HARMONIE-AROME canonical model configuration (CMC) of the shared ALADIN-HIRLAM NWP system for short-range operational weather forecasting (see Appendix E for more information on HARMONIE-AROME and ALADIN-HIRLAM). The default surface land-cover in HARMONIE-AROME is the ECOCLIMAP (Bengtsson et al, 2017) global land-cover database developed by Météo-France in partnership with the scientific community (Masson et al, 2003;Faroux et al, 2013;CNRM, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…An evaluation of ECO-SG showed an improvement in the representation of water bodies compared to ECOCLIMAP-II (Samuelsson et al, 2020). However, some limitations in the performance of the current HARMONIE-AROME configuration are attributed to surface processes and physiography issues (Bengtsson et al, 2017). The use of local physiographic datasets to identify and correct ECOCLIMAP-II inaccuracies over Iceland led to an improvement in wind forecasts (Petersen et al, 2017) and motivated us to compare the different iterations of ECO-CLIMAP against local physiographic datasets for Ireland.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning to produce a very high resolution land-cover map for Ireland

et al. 2021

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Abstract. Land-cover classifications in the form of maps are required for numerical modelling of weather and climate. Such maps are often of coarse resolution and are infrequently updated. Here we propose a novel approach for land-cover classification using a Convolutional Neural Network machine learning algorithm to segment satellite images into various land-cover classes. Sentinel-2 satellite imagery, the CORINE land-cover database and the BigEarthNet dataset are used. A 10 m resolution map, called the Ulmas-Walsh map, has been created for Ireland that outperforms ECO-SG in terms of accuracy, as well as demonstrating a capacity for identifying features not labelled correctly in CORINE. The map can be updated on demand for any time of the year, subject to cloud cover. This is particularly useful for regions with large seasonal variation in land classifications such as Turloughs – seasonal lakes, flood plains and rotational crops.

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TITAN automatic spatial quality control of meteorological in-situ observations

Cited by 25 publications

References 16 publications

Quantifying Local and Mesoscale Drivers of the Urban Heat Island of Moscow with Reference and Crowdsourced Observations

Quantifying Local and Mesoscale Drivers of the Urban Heat Island of Moscow with Reference and Crowdsourced Observations

Ensemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitation

Using machine learning to produce a very high resolution land-cover map for Ireland

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