EMO-5: Copernicus pan-European high-resolution meteorological data set for large-scale hydrological modelling

2020 ◽  
Author(s):  
Vera Thiemig ◽  
Peter Salamon ◽  
Goncalo N. Gomes ◽  
Jon O. Skøien ◽  
Markus Ziese ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Large Scale ◽  
Interpolation Method ◽  
Meteorological Data ◽  
Hydrological Modelling ◽  
Data Set ◽  
Average Temperature ◽  
Time Period ◽  
Meteorological Observations

<p>We present EMO-5, a Pan-European high-resolution (5 km), (sub-)daily, multi-variable meteorological data set especially developed to the needs of an operational, pan-European hydrological service (EFAS; European Flood Awareness System). The data set is built on historic and real-time observations coming from 18,964 meteorological in-situ stations, collected from 24 data providers, and 10,632 virtual stations from four high-resolution regional observational grids (CombiPrecip, ZAMG - INCA, EURO4M-APGD and CarpatClim) as well as one global reanalysis product (ERA-Interim-land). This multi-variable data set covers precipitation, temperature (average, min and max), wind speed, solar radiation and vapor pressure; all at daily resolution and in addition 6-hourly resolution for precipitation and average temperature. The original observations were thoroughly quality controlled before we used the Spheremap interpolation method to estimate the variable values for each of the 5 x 5 km grid cells and their affiliated uncertainty. EMO-5 v1 grids covering the time period from 1990 till 2019 will be released as a free and open Copernicus product mid-2020 (with a near real-time release of the latest gridded observations in future). We would like to present the great potential EMO-5 holds for the hydrological modelling community.</p><p> </p><p>footnote: EMO = European Meteorological Observations</p>

scholarly journals EMO-5: A high-resolution multi-variable gridded meteorological data set for Europe

2021 ◽  
Author(s):  
Vera Thiemig ◽  
Goncalo N. Gomes ◽  
Jon O. Skøien ◽  
Markus Ziese ◽  
Armin Rauthe-Schöch ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Meteorological Data ◽  
Heavy Precipitation ◽  
Water Vapour Pressure ◽  
Data Set ◽  
High Resolution Data ◽  
Weather Stations ◽  
Resolution Data ◽  
Precipitation Events

Abstract. In this paper we present EMO-51, a European high-resolution, (sub-)daily, multi-variable meteorological data set built on historical and real-time observations obtained by integrating data from 18,964 ground weather stations, four high-resolution regional observational grids (i.e. CombiPrecip, ZAMG - INCA, EURO4M-APGD and CarpatClim) as well as one global reanalysis (ERA-Interim/Land). EMO-5 includes at daily resolution: total precipitation, temperatures (mean, minimum and maximum), wind speed, solar radiation and water vapour pressure. In addition, EMO-5 also makes available 6-hourly precipitation and mean temperature. The raw observations from the ground weather stations underwent a set of quality controls, before SPHEREMAP and Yamamoto interpolation methods were applied in order to estimate for each 5 x 5 km grid cell the variable value and its affiliated uncertainty, respectively. The quality of the EMO-5 precipitation data was evaluated through (1) comparison with two regional high resolution data sets (i.e. seNorge2 and seNorge2018), (2) analysis of 15 heavy precipitation events, and (3) examination of the interpolation uncertainty. Results show that EMO-5 successfully captured 80 % of the heavy precipitation events, and that it is of comparable quality to a regional high resolution data set. The availability of the uncertainty fields increases the transparency of the data set and hence the possible usage. EMO-5 (release 1) covers the time period from 1990 to 2019, with a near real-time release of the latest gridded observations foreseen soon. As a product of Copernicus, the EU's Earth observation programme, EMO-5 dataset is free and open, and can be accessed at https://doi.org/10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26 (Thiemig et al., 2021).1 EMO stands for “European Meteorological Observations”, whereas the 5 denotes the spatial resolution of 5 km.

Parameter transferability between multiple gridded input datasets challenges hydrological model performance under changing climate

2021 ◽  
Author(s):  
Moctar Dembélé ◽  
Bettina Schaefli ◽  
Grégoire Mariéthoz
Keyword(s):  
West Africa ◽  
Large Scale ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Model Performance ◽  
Hydrologic Model ◽  
Hydrological Modelling ◽  
Climate Change Scenarios ◽  
Depth Analysis ◽  
Input Dataset

<p>The diversity of remotely sensed or reanalysis-based rainfall data steadily increases, which on one hand opens new perspectives for large scale hydrological modelling in data scarce regions, but on the other hand poses challenging question regarding parameter identification and transferability under multiple input datasets. This study analyzes the variability of hydrological model performance when (1) a set of parameters is transferred from the calibration input dataset to a different meteorological datasets and reversely, when (2) an input dataset is used with a parameter set, originally calibrated for a different input dataset.</p><p>The research objective is to highlight the uncertainties related to input data and the limitations of hydrological model parameter transferability across input datasets. An ensemble of 17 rainfall datasets and 6 temperature datasets from satellite and reanalysis sources (Dembélé et al., 2020), corresponding to 102 combinations of meteorological data, is used to force the fully distributed mesoscale Hydrologic Model (mHM). The mHM model is calibrated for each combination of meteorological datasets, thereby resulting in 102 calibrated parameter sets, which almost all give similar model performance. Each of the 102 parameter sets is used to run the mHM model with each of the 102 input datasets, yielding 10404 scenarios to that serve for the transferability tests. The experiment is carried out for a decade from 2003 to 2012 in the large and data-scarce Volta River basin (415600 km2) in West Africa.</p><p>The results show that there is a high variability in model performance for streamflow (mean CV=105%) when the parameters are transferred from the original input dataset to other input datasets (test 1 above). Moreover, the model performance is in general lower and can drop considerably when parameters obtained under all other input datasets are transferred to a selected input dataset (test 2 above). This underlines the need for model performance evaluation when different input datasets and parameter sets than those used during calibration are used to run a model. Our results represent a first step to tackle the question of parameter transferability to climate change scenarios. An in-depth analysis of the results at a later stage will shed light on which model parameterizations might be the main source of performance variability.</p><p>Dembélé, M., Schaefli, B., van de Giesen, N., & Mariéthoz, G. (2020). Suitability of 17 rainfall and temperature gridded datasets for large-scale hydrological modelling in West Africa. Hydrology and Earth System Sciences (HESS). https://doi.org/10.5194/hess-24-5379-2020</p>

scholarly journals Continuous high-resolution midlatitude-belt simulations for July–August 2013 with WRF

2017 ◽  
Vol 10 (5) ◽  
pp. 2031-2055 ◽  
Author(s):  
Thomas Schwitalla ◽  
Hans-Stefan Bauer ◽  
Volker Wulfmeyer ◽  
Kirsten Warrach-Sagi
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Temperature Fields ◽  
Surface Model ◽  
Limited Area ◽  
Data Set ◽  
Latitude Belt ◽  
Simulated Precipitation ◽  
The Impact

Abstract. Increasing computational resources and the demands of impact modelers, stake holders, and society envision seasonal and climate simulations with the convection-permitting resolution. So far such a resolution is only achieved with a limited-area model whose results are impacted by zonal and meridional boundaries. Here, we present the setup of a latitude-belt domain that reduces disturbances originating from the western and eastern boundaries and therefore allows for studying the impact of model resolution and physical parameterization. The Weather Research and Forecasting (WRF) model coupled to the NOAH land–surface model was operated during July and August 2013 at two different horizontal resolutions, namely 0.03 (HIRES) and 0.12° (LOWRES). Both simulations were forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis data at the northern and southern domain boundaries, and the high-resolution Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) data at the sea surface.The simulations are compared to the operational ECMWF analysis for the representation of large-scale features. To analyze the simulated precipitation, the operational ECMWF forecast, the CPC MORPHing (CMORPH), and the ENSEMBLES gridded observation precipitation data set (E-OBS) were used as references.Analyzing pressure, geopotential height, wind, and temperature fields as well as precipitation revealed (1) a benefit from the higher resolution concerning the reduction of monthly biases, root mean square error, and an improved Pearson skill score, and (2) deficiencies in the physical parameterizations leading to notable biases in distinct regions like the polar Atlantic for the LOWRES simulation, the North Pacific, and Inner Mongolia for both resolutions.In summary, the application of a latitude belt on a convection-permitting resolution shows promising results that are beneficial for future seasonal forecasting.

scholarly journals Impact of meteorological factors on number of new COVID-19 cases in Pune

2021 ◽  
Vol 8 (9) ◽  
pp. 4307
Author(s):  
Saurabh Mahajan ◽  
Ravi Devarakonda ◽  
Gautam Mukherjee ◽  
Nisha Verma ◽  
Kumar Pushkar
Keyword(s):  
Meteorological Data ◽  
Secondary Data ◽  
Significant Negative Correlation ◽  
Meteorological Variables ◽  
Strong Positive Correlation ◽  
Daily Number ◽  
Average Temperature ◽  
Time Period ◽  
Indian Meteorological Department ◽  
The Impact

Background: Coronaviruses are a family of viruses that can result in different types of illnesses, most commonly, as Severe acute respiratory syndrome (SARS). Researches have shown that the atmospheric variables and the density of population have affected the transmission of the disease. Meteorological variables like temperature, humidity among others have found to affect the rise of pandemic in positive or negative ways.  Respiratory virus illnesses have shown seasonal variability since the time they have been discovered and managed. This study investigated the relationship between the meteorological variables of temperature, humidity and precipitation in the spread of COVID-19 disease in the city of Pune.Methods: This record based descriptive study is conducted after secondary data analysis of number of new cases of COVID-19 per day from the period 01 May to 24 December 2020 in Pune. Meteorological data of maximum (Tmax), minimum (Tmin) and daily average temperature (Tavg), humidity and precipitation were daily noted from Indian meteorological department website. Trend was identified plotting the daily number of clinically diagnosed cases over time period. Pearson’s correlation was used to estimate association between meteorological variables and daily detected fresh cases of COVID-19 disease.  Results: Analysis revealed significant negative correlation (r=-0.3563, p<0.005) between daily detected number of cases and maximum daily temperature. A strong positive correlation was seen between humidity and daily number of cases (r=0.5541, p<0.005).Conclusions: The findings of this study will aid in forecasting epidemics and in preparing for the impact of climate change on the COVID epidemiology through the implementation of public health preventive measures.

Real-Time Prediction of Choke Health Using Production Data Integrated with AI

2021 ◽  
Author(s):  
Ahmed Alghamdi ◽  
Olakunle Ayoola ◽  
Khalid Mulhem ◽  
Mutlaq Otaibi ◽  
Abdulazeez Abdulraheem
Keyword(s):  
High Pressure ◽  
Real Time ◽  
Large Scale ◽  
Production Systems ◽  
Rate Of Change ◽  
Production Data ◽  
Ann Model ◽  
Sand Production ◽  
Data Set ◽  
Pressure Drops

Abstract Chokes are an integral part of production systems and are crucial surface equipment that faces rough conditions such as high-pressure drops and erosion due to solids. Predicting choke health is usually achieved by analyzing the relationship of choke size, pressure, and flow rate. In large-scale fields, this process requires extensive-time and effort using the conventional techniques. This paper presents a real-time proactive approach to detect choke wear utilizing production data integrated with AI analytics. Flowing parameters data were collected for more than 30 gas wells. These wells are producing gas with slight solids production from a high-pressure high-temperature field. In addition, these wells are equipped with a multi-stage choke system. The approach of determining choke wear relies on training the AI model on a dataset constructed by comparison of the choke valve rate of change with respect to a smoother slope of the production rate. If the rate of change is not within a tolerated range of divergence, an abnormal choke behavior is detected. The data set was divided into 70% for training and 30% for testing. Artificial Neural Network (ANN) was trained on data that has the following inputs: gas specific gravity, upstream & downstream pressure and temperature, and choke size. This ANN model achieved a correlation coefficient above 0.9 with an excellent prediction on the data points exhibiting normal or abnormal choke behaviors. Piloting this application on large fields, where manual analysis is often impractical, saves a substantial man-hour and generates significant cost-avoidance. Areas for improvement in such an application depends on equipping the ANN network with long-term production profile prediction abilities, such as water production, and this analysis relies on having an accurate reading from the venturi meters, which is often the case in single-phase flow. The application of this AI-driven analytics provides tremendous improvement for remote offshore production operations surveillance. The novel approach presented in this paper capitalizes on the AI analytics for estimating proactively detecting choke health conditions. The advantages of such a model are that it harnesses AI analytics to help operators improve asset integrity and production monitoring compliance. In addition, this approach can be expanded to estimate sand production as choke wear is a strong function of sand production.

scholarly journals A new global real-time Lagrangian diagnostic system for stratosphere-troposphere exchange: evaluation during a balloon sonde campaign in eastern Canada

2012 ◽  
Vol 12 (5) ◽  
pp. 2661-2679 ◽  
Author(s):  
M. S. Bourqui ◽  
A. Yamamoto ◽  
D. Tarasick ◽  
M. D. Moran ◽  
L.-P. Beaudoin ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Diagnostic System ◽  
Weather Forecast ◽  
Identification Algorithm ◽  
Eastern Canada ◽  
Data Set ◽  
Predictive Skill ◽  
Stratospheric Intrusions ◽  
Archived Data

Abstract. A new global real-time Lagrangian diagnostic system for stratosphere-troposphere exchange (STE) developed for Environment Canada (EC) has been delivering daily archived data since July 2010. The STE calculations are performed following the Lagrangian approach proposed in Bourqui (2006) using medium-range, high-resolution operational global weather forecasts. Following every weather forecast, trajectories are started from a dense three-dimensional grid covering the globe, and are calculated forward in time for six days of the forecast. All trajectories crossing either the dynamical tropopause (±2 PVU) or the 380 K isentrope and having a residence time greater than 12 h are archived, and also used to calculate several diagnostics. This system provides daily global STE forecasts that can be used to guide field campaigns, among other applications. The archived data set offers unique high-resolution information on transport across the tropopause for both extra-tropical hemispheres and the tropics. This will be useful for improving our understanding of STE globally, and as a reference for the evaluation of lower-resolution models. This new data set is evaluated here against measurements taken during a balloon sonde campaign with daily launches from three stations in eastern Canada (Montreal, Egbert, and Walsingham) for the period 12 July to 4 August 2010. The campaign found an unexpectedly high number of observed stratospheric intrusions: 79% (38%) of the profiles appear to show the presence of stratospheric air below than 500 hPa (700 hPa). An objective identification algorithm developed for this study is used to identify layers in the balloon-sonde profiles affected by stratospheric air and to evaluate the Lagrangian STE forecasts. We find that the predictive skill for the overall intrusion depth is very good for intrusions penetrating down to 300 and 500 hPa, while it becomes negligible for intrusions penetrating below 700 hPa. Nevertheless, the statistical representation of these deep intrusions is reasonable, with an average bias of 24%. Evaluation of the skill at representing the detailed structures of the stratospheric intrusions shows good predictive skill down to 500 hPa, reduced predictive skill between 500 and 700 hPa, and none below. A significant low statistical bias of about 30% is found in the layer between 500 to 700 hPa. However, analysis of missed events at one site, Montreal, shows that 70% of them coincide with candidate clusters of trajectories that pass through Montreal, but that are too dispersed to be detected in the close neighbourhood of the station. Within the limits of this study, this allows us to expect a negligible bias throughout the troposphere in the spatially averaged STE frequency derived from this data set, for example in climatological maps of STE mass fluxes. This first evaluation is limited to eastern Canada in one summer month with a high frequency of stratospheric intrusions, and further work is needed to evaluate this STE data set in other months and locations.

scholarly journals Chloroplast Genome Sequence ofArabidopsis thalianaAccession Landsbergerecta, Assembled from Single-Molecule, Real-Time Sequencing Data

2016 ◽  
Vol 4 (5) ◽  
Author(s):  
Kai Bernd Stadermann ◽  
Daniela Holtgräwe ◽  
Bernd Weisshaar
Keyword(s):  
Arabidopsis Thaliana ◽  
High Resolution ◽  
Chloroplast Genome ◽  
Real Time ◽  
Genome Sequence ◽  
Single Molecule ◽  
Sequencing Data ◽  
Data Set ◽  
Chloroplast Genomes ◽  
Chloroplast Genome Sequence

A publicly available data set from Pacific Biosciences was used to create an assembly of the chloroplast genome sequence of theArabidopsis thalianagenotype Landsbergerecta. The assembly is solely based on single-molecule, real-time sequencing data and hence provides high resolution of the two inverted repeat regions typically contained in chloroplast genomes.

Small-scale processes with large-scale impacts: Investigating canopy structure controls on energy fluxes to the forest snowpack

2020 ◽  
Author(s):  
Giulia Mazzotti ◽  
Richard Essery ◽  
Johanna Malle ◽  
Clare Webster ◽  
Tobias Jonas
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Meteorological Data ◽  
Canopy Structure ◽  
Model Performance ◽  
Turbulent Fluxes ◽  
Temporal Variations ◽  
Small Scale ◽  
Forest Stands

&lt;p&gt;Forest canopies strongly affect snowpack energetics during wintertime. In discontinuous forest stands, spatio-temporal variations in radiative and turbulent fluxes create complex snow distribution and melt patterns, with further impacts on the hydrological regimes and on the land surface properties of seasonally snow-covered forested environments.&lt;/p&gt;&lt;p&gt;As increasingly detailed canopy structure datasets are becoming available, canopy-induced energy exchange processes can be explicitly represented in high-resolution snow models. We applied the modelling framework FSM2 to obtain spatially distributed simulations of the forest snowpack in subalpine and boreal forest stands at high spatial (2m) and temporal (10min) resolution. Modelled sub-canopy radiative and turbulent fluxes were compared to detailed meteorological data of incoming irradiances, air and snow surface temperatures. These were acquired with novel observational systems, including 1) a motorized cable car setup recording spatially and temporally resolved data along a transect and 2) a handheld setup designed to capture temporal snapshots of 2D spatial distributions across forest discontinuities.&lt;/p&gt;&lt;p&gt;The combination of high-resolution modelling and multi-dimensional datasets allowed us to assess model performance at the level of individual energy balance components, under various meteorological conditions and across canopy density gradients. We showed which canopy representation strategies within FSM2 best succeeded in reproducing snowpack energy transfer dynamics in discontinuous forests, and derived implications for implementing forest snow processes in coarser-resolution models.&lt;/p&gt;

Visualising large-scale geodynamic simulations: How to Dive into Earth's Mantle with Virtual Reality

2020 ◽  
Author(s):  
Markus Wiedemann ◽  
Bernhard S.A. Schuberth ◽  
Lorenzo Colli ◽  
Hans-Peter Bunge ◽  
Dieter Kranzlmüller
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
High Performance ◽  
Large Scale ◽  
Large Data ◽  
Optimization Techniques ◽  
Physical Parameters ◽  
Mantle Flow ◽  
Data Set ◽  
Precise Knowledge

&lt;p&gt;Precise knowledge of the forces acting at the base of tectonic plates is of fundamental importance, but models of mantle dynamics are still often qualitative in nature to date. One particular problem is that we cannot access the deep interior of our planet and can therefore not make direct in situ measurements of the relevant physical parameters. Fortunately, modern software and powerful high-performance computing infrastructures allow us to generate complex three-dimensional models of the time evolution of mantle flow through large-scale numerical simulations.&lt;/p&gt;&lt;p&gt;In this project, we aim at visualizing the resulting convective patterns that occur thousands of kilometres below our feet and to make them &quot;accessible&quot; using high-end virtual reality techniques.&lt;/p&gt;&lt;p&gt;Models with several hundred million grid cells are nowadays possible using the modern supercomputing facilities, such as those available at the Leibniz Supercomputing Centre. These models provide quantitative estimates on the inaccessible parameters, such as buoyancy and temperature, as well as predictions of the associated gravity field and seismic wavefield that can be tested against Earth observations.&lt;/p&gt;&lt;p&gt;3-D visualizations of the computed physical parameters allow us to inspect the models such as if one were actually travelling down into the Earth. This way, convective processes that occur thousands of kilometres below our feet are virtually accessible by combining the simulations with high-end VR techniques.&lt;/p&gt;&lt;p&gt;The large data set used here poses severe challenges for real time visualization, because it cannot fit into graphics memory, while requiring rendering with strict deadlines. This raises the necessity to balance the amount of displayed data versus the time needed for rendering it.&lt;/p&gt;&lt;p&gt;As a solution, we introduce a rendering framework and describe our workflow that allows us to visualize this geoscientific dataset. Our example exceeds 16 TByte in size, which is beyond the capabilities of most visualization tools. To display this dataset in real-time, we reduce and declutter the dataset through isosurfacing and mesh optimization techniques.&lt;/p&gt;&lt;p&gt;Our rendering framework relies on multithreading and data decoupling mechanisms that allow to upload data to graphics memory while maintaining high frame rates. The final visualization application can be executed in a CAVE installation as well as on head mounted displays such as the HTC Vive or Oculus Rift. The latter devices will allow for viewing our example on-site at the EGU conference.&lt;/p&gt;

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