Using a massive high‐resolution ensemble climate data set to examine dynamic and thermodynamic aspects of heavy precipitation change

A 40-year global data set of visible-channel remote-sensing reflectances and coccolithophore bloom occurrence derived from the Advanced Very High Resolution Radiometer catalogue

Earth System Science Data ◽

10.5194/essd-10-2043-2018 ◽

2018 ◽

Vol 10 (4) ◽

pp. 2043-2054 ◽

Cited By ~ 2

Author(s):

Benjamin Roger Loveday ◽

Timothy Smyth

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Atmospheric Correction ◽

Global Ocean ◽

Climate Data ◽

Ancillary Data ◽

Data Set ◽

Remote Sensing Reflectance ◽

Data Files ◽

Very High

Abstract. A consistently calibrated 40-year-long data set of visible-channel remote-sensing reflectance has been derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor global time series. The data set uses as its source the Pathfinder Atmospheres – Extended (PATMOS-x) v5.3 Climate Data Record for top-of-atmosphere (TOA) visible-channel reflectances. This paper describes the theoretical basis for the atmospheric correction procedure and its subsequent implementation, including the necessary ancillary data files used and quality flags applied, in order to determine remote-sensing reflectance. The resulting data set is produced at daily, and archived at monthly, resolution, on a 0.1∘×0.1∘ grid at https://doi.org/10.1594/PANGAEA.892175. The primary aim of deriving this data set is to highlight regions of the global ocean affected by highly reflective blooms of the coccolithophorid Emiliania huxleyi (where lith concentration >2–5×104 mL−1) over the past 40 years.

Development and validation of the CHIRTS-daily quasi-global high-resolution daily temperature data set

Scientific Data ◽

10.1038/s41597-020-00643-7 ◽

2020 ◽

Vol 7 (1) ◽

Cited By ~ 2

Author(s):

Andrew Verdin ◽

Chris Funk ◽

Pete Peterson ◽

Martin Landsfeld ◽

Cascade Tuholske ◽

...

Keyword(s):

High Resolution ◽

Remotely Sensed ◽

Temperature Data ◽

Daily Temperature ◽

Good Representation ◽

Climate Data ◽

Data Set ◽

Weather Forecasts ◽

Development And Validation

Abstract We present a high-resolution daily temperature data set, CHIRTS-daily, which is derived by merging the monthly Climate Hazards center InfraRed Temperature with Stations climate record with daily temperatures from version 5 of the European Centre for Medium-Range Weather Forecasts Re-Analysis. We demonstrate that remotely sensed temperature estimates may more closely represent true conditions than those that rely on interpolation, especially in regions with sparse in situ data. By leveraging remotely sensed infrared temperature observations, CHIRTS-daily provides estimates of 2-meter air temperature for 1983–2016 with a footprint covering 60°S-70°N. We describe this data set and perform a series of validations using station observations from two prominent climate data sources. The validations indicate high levels of accuracy, with CHIRTS-daily correlations with observations ranging from 0.7 to 0.9, and very good representation of heat wave trends.

Narrowband-to-Broadband Conversions for Top-of-Atmosphere Reflectance from the Advanced Very High Resolution Radiometer (AVHRR)

Remote Sensing ◽

10.3390/rs12020305 ◽

2020 ◽

Vol 12 (2) ◽

pp. 305 ◽

Cited By ~ 3

Author(s):

Tom Akkermans ◽

Nicolas Clerbaux

Keyword(s):

High Resolution ◽

Global Climate ◽

Regional Scale ◽

Radiant Energy ◽

Large Data ◽

Energy System ◽

Climate Data ◽

Data Set ◽

Regional Analyses ◽

Very High

The current lack of a long, 30+ year, global climate data record of reflected shortwave top-of-atmosphere (TOA) radiation could be tackled by relying on existing narrowband records from the Advanced Very High Resolution Radiometer (AVHRR) instruments, and transform these measurements into broadband quantities like provided by the Clouds and the Earth’s Radiant Energy System (CERES). This paper presents the methodology of an AVHRR-to-CERES narrowband-to-broadband conversion for shortwave TOA reflectance, including the ready-to-use results in the form of scene-type dependent regression coefficients, allowing a calculation of CERES-like shortwave broadband reflectance from AVHRR channels 1 and 2. The coefficients are obtained using empirical relations in a large data set of collocated, coangular and simultaneous AVHRR-CERES observations, requiring specific orbital conditions for the AVHRR- and CERES-carrying satellites, from which our data analysis uses all available data for an unprecedented observation matching between both instruments. The multivariate linear regressions were found to be robust and well-fitting, as demonstrated by the regression statistics on the calibration subset (80% of data): adjusted R 2 higher than 0.9 and relative RMS residual mostly below 3%, which is a significant improvement compared to previous regressions. Regression models are validated by applying them on a validation subset (20% of data), indicating a good performance overall, roughly similar to the calibration subset, and a negligible mean bias. A second validation approach uses an expanded data set with global coverage, allowing regional analyses. In the error analysis, instantaneous accuracy is quantified at regional scale between 1.8 Wm − 2 and 2.3 Wm − 2 (resp. clear-sky and overcast conditions) at 1 standard deviation (RMS bias). On daily and monthly time scales, these errors correspond to 0.7 and 0.9 Wm − 2 , which is compliant with the GCOS requirement of 1 Wm − 2 .

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

10.5194/essd-2021-339 ◽

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.

Algorithms for automated montage synthesis of images from laser-scanning confocal microscopes

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100139627 ◽

1995 ◽

Vol 53 ◽

pp. 650-651

Author(s):

D. E. Becker

Keyword(s):

Image Analysis ◽

High Resolution ◽

Laser Scanning ◽

Cell Counting ◽

Wide Area ◽

Data Set ◽

Computational Synthesis ◽

Automated Cell Counting ◽

Partial View ◽

The Individual

An efficient, robust, and widely-applicable technique is presented for computational synthesis of high-resolution, wide-area images of a specimen from a series of overlapping partial views. This technique can also be used to combine the results of various forms of image analysis, such as segmentation, automated cell counting, deblurring, and neuron tracing, to generate representations that are equivalent to processing the large wide-area image, rather than the individual partial views. This can be a first step towards quantitation of the higher-level tissue architecture. The computational approach overcomes mechanical limitations, such as hysterisis and backlash, of microscope stages. It also automates a procedure that is currently done manually. One application is the high-resolution visualization and/or quantitation of large batches of specimens that are much wider than the field of view of the microscope.The automated montage synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the images of interest. In many cases, image analysis performed on each data set can provide useful landmarks. Even when no such “natural” landmarks are available, image processing can often provide useful landmarks.

PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

Scientific Data ◽

10.1038/s41597-021-00940-9 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Mojtaba Sadeghi ◽

Phu Nguyen ◽

Matin Rahnamay Naeini ◽

Kuolin Hsu ◽

Dan Braithwaite ◽

...

Keyword(s):

Spatial Resolution ◽

Heavy Precipitation ◽

Climate Data ◽

Spatiotemporal Resolution ◽

Data Record ◽

Precipitation Estimation ◽

Precipitation Estimates ◽

Global Precipitation ◽

Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

A high-resolution model of the inner organs based on the visible muman data set

Journal of Visualization ◽

10.1007/bf03182328 ◽

2002 ◽

Vol 5 (3) ◽

pp. 212-212 ◽

Cited By ~ 1

Author(s):

U. Tiede ◽

A. Pommert ◽

B. Pflesser ◽

E. Richter ◽

M. Riemer ◽

...

Keyword(s):

High Resolution ◽

Data Set ◽

Inner Organs ◽

Resolution Model ◽

High Resolution Model

TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-10963-2014 ◽

2014 ◽

Vol 14 (20) ◽

pp. 10963-10976 ◽

Cited By ~ 200

Author(s):

J. J. P. Kuenen ◽

A. J. H. Visschedijk ◽

M. Jozwicka ◽

H. A. C. Denier van der Gon

Keyword(s):

Particulate Matter ◽

Air Quality ◽

High Resolution ◽

Emission Inventory ◽

Point Sources ◽

Air Quality Modelling ◽

Emission Data ◽

Data Set ◽

Spatially Distributed ◽

Reported Data

Abstract. Emissions to air are reported by countries to EMEP. The emissions data are used for country compliance checking with EU emission ceilings and associated emission reductions. The emissions data are also necessary as input for air quality modelling. The quality of these "official" emissions varies across Europe. As alternative to these official emissions, a spatially explicit high-resolution emission inventory (7 × 7 km) for UNECE-Europe for all years between 2003 and 2009 for the main air pollutants was made. The primary goal was to supply air quality modellers with the input they need. The inventory was constructed by using the reported emission national totals by sector where the quality is sufficient. The reported data were analysed by sector in detail, and completed with alternative emission estimates as needed. This resulted in a complete emission inventory for all countries. For particulate matter, for each source emissions have been split in coarse and fine particulate matter, and further disaggregated to EC, OC, SO4, Na and other minerals using fractions based on the literature. Doing this at the most detailed sectoral level in the database implies that a consistent set was obtained across Europe. This allows better comparisons with observational data which can, through feedback, help to further identify uncertain sources and/or support emission inventory improvements for this highly uncertain pollutant. The resulting emission data set was spatially distributed consistently across all countries by using proxy parameters. Point sources were spatially distributed using the specific location of the point source. The spatial distribution for the point sources was made year-specific. The TNO-MACC_II is an update of the TNO-MACC emission data set. Major updates included the time extension towards 2009, use of the latest available reported data (including updates and corrections made until early 2012) and updates in distribution maps.

Elemental ratio measurements of organic compounds using aerosol mass spectrometry: characterization, improved calibration, and implications

Atmospheric Chemistry and Physics ◽

10.5194/acp-15-253-2015 ◽

2015 ◽

Vol 15 (1) ◽

pp. 253-272 ◽

Cited By ~ 418

Author(s):

M. R. Canagaratna ◽

J. L. Jimenez ◽

J. H. Kroll ◽

Q. Chen ◽

S. H. Kessler ◽

...

Keyword(s):

High Resolution ◽

Vacuum Ultraviolet ◽

Laboratory Data ◽

Detailed Examination ◽

Relative Difference ◽

Data Set ◽

Elemental Ratios ◽

Standard Data ◽

Aerosol Mass ◽

Relative Errors

Abstract. Elemental compositions of organic aerosol (OA) particles provide useful constraints on OA sources, chemical evolution, and effects. The Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) is widely used to measure OA elemental composition. This study evaluates AMS measurements of atomic oxygen-to-carbon (O : C), hydrogen-to-carbon (H : C), and organic mass-to-organic carbon (OM : OC) ratios, and of carbon oxidation state (OS C) for a vastly expanded laboratory data set of multifunctional oxidized OA standards. For the expanded standard data set, the method introduced by Aiken et al. (2008), which uses experimentally measured ion intensities at all ions to determine elemental ratios (referred to here as "Aiken-Explicit"), reproduces known O : C and H : C ratio values within 20% (average absolute value of relative errors) and 12%, respectively. The more commonly used method, which uses empirically estimated H2O+ and CO+ ion intensities to avoid gas phase air interferences at these ions (referred to here as "Aiken-Ambient"), reproduces O : C and H : C of multifunctional oxidized species within 28 and 14% of known values. The values from the latter method are systematically biased low, however, with larger biases observed for alcohols and simple diacids. A detailed examination of the H2O+, CO+, and CO2+ fragments in the high-resolution mass spectra of the standard compounds indicates that the Aiken-Ambient method underestimates the CO+ and especially H2O+ produced from many oxidized species. Combined AMS–vacuum ultraviolet (VUV) ionization measurements indicate that these ions are produced by dehydration and decarboxylation on the AMS vaporizer (usually operated at 600 °C). Thermal decomposition is observed to be efficient at vaporizer temperatures down to 200 °C. These results are used together to develop an "Improved-Ambient" elemental analysis method for AMS spectra measured in air. The Improved-Ambient method uses specific ion fragments as markers to correct for molecular functionality-dependent systematic biases and reproduces known O : C (H : C) ratios of individual oxidized standards within 28% (13%) of the known molecular values. The error in Improved-Ambient O : C (H : C) values is smaller for theoretical standard mixtures of the oxidized organic standards, which are more representative of the complex mix of species present in ambient OA. For ambient OA, the Improved-Ambient method produces O : C (H : C) values that are 27% (11%) larger than previously published Aiken-Ambient values; a corresponding increase of 9% is observed for OM : OC values. These results imply that ambient OA has a higher relative oxygen content than previously estimated. The OS C values calculated for ambient OA by the two methods agree well, however (average relative difference of 0.06 OS C units). This indicates that OS C is a more robust metric of oxidation than O : C, likely since OS C is not affected by hydration or dehydration, either in the atmosphere or during analysis.

Nowcasting heavy precipitation over the Netherlands using a 13-year radar archive: a machine learning approach

10.5194/egusphere-egu21-12814 ◽

2021 ◽

Author(s):

Eva van der Kooij ◽

Marc Schleiss ◽

Riccardo Taormina ◽

Francesco Fioranelli ◽

Dorien Lugt ◽

...

Keyword(s):

Machine Learning ◽

The Netherlands ◽

Heavy Rainfall ◽

Predictive Performance ◽

Heavy Precipitation ◽

Early Warning Systems ◽

Training Data ◽

Short Term ◽

Data Set ◽

Radar Images

Accurate short-term forecasts, also known as nowcasts, of heavy precipitation are desirable for creating early warning systems for extreme weather and its consequences, e.g. urban flooding. In this research, we explore the use of machine learning for short-term prediction of heavy rainfall showers in the Netherlands.We assess the performance of a recurrent, convolutional neural network (TrajGRU) with lead times of 0 to 2 hours. The network is trained on a 13-year archive of radar images with 5-min temporal and 1-km spatial resolution from the precipitation radars of the Royal Netherlands Meteorological Institute (KNMI). We aim to train the model to predict the formation and dissipation of dynamic, heavy, localized rain events, a task for which traditional Lagrangian nowcasting methods still come up short.We report on different ways to optimize predictive performance for heavy rainfall intensities through several experiments. The large dataset available provides many possible configurations for training. To focus on heavy rainfall intensities, we use different subsets of this dataset through using different conditions for event selection and varying the ratio of light and heavy precipitation events present in the training data set and change the loss function used to train the model.To assess the performance of the model, we compare our method to current state-of-the-art Lagrangian nowcasting system from the pySTEPS library, like S-PROG, a deterministic approximation of an ensemble mean forecast. The results of the experiments are used to discuss the pros and cons of machine-learning based methods for precipitation nowcasting and possible ways to further increase performance.