scholarly journals Controlling the spatio-temporal dose distribution during STEM imaging by subsampled acquisition: In-situ observations of kinetic processes in liquids

2019 ◽  
Vol 115 (6) ◽  
pp. 063102 ◽  
Author(s):  
B. L. Mehdi ◽  
A. Stevens ◽  
L. Kovarik ◽  
N. Jiang ◽  
H. Mehta ◽  
...  
Keyword(s):  
Dose Distribution ◽  
In Situ Observations ◽  
Kinetic Processes ◽  
Spatio Temporal

scholarly journals Assessing spatio-temporal variability and trends in modelled and measured Greenland Ice Sheet albedo (2000–2013)

2014 ◽  
Vol 8 (6) ◽  
pp. 2293-2312 ◽  
Author(s):  
P. M. Alexander ◽  
M. Tedesco ◽  
X. Fettweis ◽  
R. S. W. van de Wal ◽  
C. J. P. P. Smeets ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Temporal Variability ◽  
Surface Albedo ◽  
Climate Model ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
High Elevation ◽  
In Situ Observations ◽  
Spatio Temporal

Abstract. Accurate measurements and simulations of Greenland Ice Sheet (GrIS) surface albedo are essential, given the role of surface albedo in modulating the amount of absorbed solar radiation and meltwater production. In this study, we assess the spatio-temporal variability of GrIS albedo during June, July, and August (JJA) for the period 2000–2013. We use two remote sensing products derived from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS), as well as outputs from the Modèle Atmosphérique Régionale (MAR) regional climate model (RCM) and data from in situ automatic weather stations. Our results point to an overall consistency in spatio-temporal variability between remote sensing and RCM albedo, but reveal a difference in mean albedo of up to ~0.08 between the two remote sensing products north of 70° N. At low elevations, albedo values simulated by the RCM are positively biased with respect to remote sensing products by up to ~0.1 and exhibit low variability compared with observations. We infer that these differences are the result of a positive bias in simulated bare ice albedo. MODIS albedo, RCM outputs, and in situ observations consistently indicate a decrease in albedo of −0.03 to −0.06 per decade over the period 2003–2013 for the GrIS ablation area. Nevertheless, satellite products show a decline in JJA albedo of −0.03 to −0.04 per decade for regions within the accumulation area that is not confirmed by either the model or in situ observations. These findings appear to contradict a previous study that found an agreement between in situ and MODIS trends for individual months. The results indicate a need for further evaluation of high elevation albedo trends, a reconciliation of MODIS mean albedo at high latitudes, and the importance of accurately simulating bare ice albedo in RCMs.

scholarly journals A synthetic satellite dataset of the spatio-temporal distributions of <i>Emiliania huxleyi</i> blooms and their impacts on Arctic and sub-Arctic marine environments (1998–2016)

2019 ◽  
Vol 11 (1) ◽  
pp. 119-128 ◽  
Author(s):  
Dmitry Kondrik ◽  
Eduard Kazakov ◽  
Dmitry Pozdnyakov
Keyword(s):  
Carbon Balance ◽  
Remote Sensing Data ◽  
Emiliania Huxleyi ◽  
Optical Remote Sensing ◽  
Arctic Seas ◽  
Co2 Partial Pressure ◽  
In Situ Observations ◽  
Spatio Temporal ◽  
Particulate Inorganic Carbon

Abstract. A 19-year (1998–2016) continuous dataset is presented of coccolithophore Emiliania huxleyi distributions and activity, i.e. the release of CaCO3 in water and the decrease of uptake of dissolved CO2 by Emiliania huxleyi cells (e.g. Kondrik et al., 2018a), in Arctic and sub-Arctic seas. The dataset is based on optical remote-sensing data (mostly OC CCI data) with assimilation of different relevant in situ observations, preprocessed with authorial algorithms. Alongside bloom locations, we provide both detailed information on E. huxleyi impacts on carbon balance and the sub-datasets of quantified coccolith concentrations, particulate inorganic carbon content and CO2 partial pressure in water driven by coccolithophores. All data are presented on a regular 4×4 km grid at a temporal resolution of 8 days. The paper describes the theoretical and methodological basis for all processing and modelling steps. The data are available on Zenodo: https://doi.org/10.5281/zenodo.1402033.

scholarly journals Evaluation of single-footprint AIRS CH<sub>4</sub> Profile Retrieval Uncertainties Using Aircraft Profile Measurements

2020 ◽  
Author(s):  
Susan S. Kulawik ◽  
John R. Worden ◽  
Vivienne H. Payne ◽  
Dejian Fu ◽  
Steve C. Wofsy ◽  
...  
Keyword(s):  
Lower Stratosphere ◽  
Optimal Estimation ◽  
Observation Error ◽  
Atmospheric Methane ◽  
Single Measurement ◽  
Atmospheric Infrared Sounder ◽  
Infrared Observations ◽  
In Situ Observations ◽  
Spatio Temporal

Abstract. We evaluate the uncertainties of methane optimal estimation retrievals from single footprint thermal infrared observations from the Atmospheric Infrared Sounder (AIRS). These retrievals are primarily sensitive to atmospheric methane in the mid-troposphere through the lower stratosphere (~2 to ~17 km). We compare to in situ observations made from aircraft during the Hiaper Pole to Pole Observations (HIPPO), the NASA Atmospheric Tomography Mission (ATom) campaigns, and from the NOAA ESRL aircraft network, between the surface and 5–13 km, across a range of years, latitudes between 60 S to 80 N, and over land and ocean. After a global, pressure dependent bias correction, we find that the land and ocean have similar biases and that the reported observation error (combined measurement and interference errors) of ~27  ppb is consistent with the standard deviation between aircraft and individual AIRS observations. A single measurement has measurement (noise related) uncertainty of ~17 ppb, a ~20 ppb uncertainty from radiative interferences (e.g. from water, temperature, etc.), and ~ 30 ppb due to smoothing error, which is partially removed when making comparisons to in situ measurements or models in a way that account for this regularization. We estimate a 16 ppb validation error because the aircraft typically did not measure methane at altitudes where the AIRS measurements have some sensitivity, e.g. the stratosphere. Daily averaged AIRS measurements of at least 9 observations over spatio-temporal domains of

scholarly journals Spatio-Temporal Variations of Atmospheric NH3 over East Asia by Comparison of Chemical Transport Model Results, Satellite Retrievals and Surface Observations

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 900 ◽  
Author(s):  
Zhe Wang ◽  
Itsushi Uno ◽  
Kazuo Osada ◽  
Syuichi Itahashi ◽  
Keiya Yumimoto ◽  
...  
Keyword(s):  
High Resolution ◽  
East Asia ◽  
Temporal Variations ◽  
Central China ◽  
Model Simulations ◽  
Satellite Retrievals ◽  
In Situ Observations ◽  
Spatio Temporal ◽  
Surface Observations

Atmospheric ammonia (NH3) plays an important role in the formation of secondary inorganic aerosols, the neutralization of acid rain, and the deposition to ecosystems, but has not been well understood yet, especially over East Asia. Based on the GEOS-Chem model results, the IASI satellite retrievals, the in-site surface observations of a nationwide filter pack (FP) network over Japan and the long-term high resolution online NH3 measurements at Fukuoka of western Japan, the spatio-temporal distributions of atmospheric NH3 over East Asia was analyzed comprehensively. A significant seasonal variation with a summer peak was found in all datasets. Comparison between the satellite retrievals and model simulations indicated that the IASI NH3 vertical column density (VCD) showed good consistency with GEOS-Chem results over North and central China, but had large differences over South China due to the effect of clouds. Over the Japan area, GEOS-Chem simulated NH3 concentrations successfully reproduced the spatio-temporal variations compared with in-situ observations, while IASI NH3 VCD retrievals were below or near the detection limit and difficult to obtain a reasonable correlation for with model results. The comprehensive analysis indicated that there were still some differences among different datasets, and more in-situ observations, improved satellite retrievals, and high-resolution model simulations with more accurate emissions are necessary for better understanding the atmospheric NH3 over East Asia.

scholarly journals Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

2020 ◽  
Vol 12 (21) ◽  
pp. 3503 ◽  
Author(s):  
Volkan Senyurek ◽  
Fangni Lei ◽  
Dylan Boyd ◽  
Ali Cafer Gurbuz ◽  
Mehmet Kurum ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Correlation Coefficient ◽  
Root Mean Square ◽  
Temporal Resolution ◽  
Mean Square ◽  
In Situ Observations ◽  
Spatio Temporal ◽  
Mean Square Errors

This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)’s high spatio-temporal resolution observations over the tropics (within ±38° latitudes) at L-band. The learning model is trained by using in-situ SM data from the International Soil Moisture Network (ISMN) sites and various space-borne ancillary data. The approach produces daily SM retrievals that are gridded to 3 km and 9 km within the CYGNSS spatial coverage. The performance of the model is independently evaluated at various temporal scales (daily, 3-day, weekly, and monthly) against Soil Moisture Active Passive (SMAP) mission’s enhanced SM products at a resolution of 9 km × 9 km. The mean unbiased root-mean-square difference (ubRMSD) between concurrent (same calendar day) CYGNSS and SMAP SM retrievals for about three years (from 2017 to 2019) is 0.044 cm3 cm−3 with a correlation coefficient of 0.66 over SMAP recommended grids. The performance gradually improves with temporal averaging and degrades over regions regularly flagged by SMAP such as dense forest, high topography, and coastlines. Furthermore, CYGNSS and SMAP retrievals are evaluated against 170 ISMN in-situ observations that result in mean unbiased root-mean-square errors (ubRMSE) of 0.055 cm3 cm−3 and 0.054 cm3 cm−3, respectively, and a higher correlation coefficient with CYGNSS retrievals. It is important to note that the proposed approach is trained over limited in-situ observations and is independent of SMAP observations in its training. The retrieval performance indicates current applicability and future growth potential of GNSS-R-based, directly measured spaceborne SM products that can provide improved spatio-temporal resolution than currently available datasets.

scholarly journals Temporal evolution of temperatures in the Red Sea and the Gulf of Aden based on in situ observations (1958–2017)

Ocean Science ◽  
2020 ◽  
Vol 16 (1) ◽  
pp. 149-166
Author(s):  
Miguel Agulles ◽  
Gabriel Jordà ◽  
Burt Jones ◽  
Susana Agustí ◽  
Carlos M. Duarte
Keyword(s):  
Red Sea ◽  
Temporal Evolution ◽  
Optimal Interpolation ◽  
Strong Impact ◽  
Gulf Of Aden ◽  
In Situ Observations ◽  
Spatio Temporal ◽  
Good Agreement

Abstract. The Red Sea holds one of the most diverse marine ecosystems in the world, although fragile and vulnerable to ocean warming. Several studies have analysed the spatio-temporal evolution of temperature in the Red Sea using satellite data, thus focusing only on the surface layer and covering the last ∼30 years. To better understand the long-term variability and trends of temperature in the whole water column, we produce a 3-D gridded temperature product (TEMPERSEA) for the period 1958–2017, based on a large number of in situ observations, covering the Red Sea and the Gulf of Aden. After a specific quality control, a mapping algorithm based on optimal interpolation have been applied to homogenize the data. Also, an estimate of the uncertainties of the product has been generated. The calibration of the algorithm and the uncertainty computation has been done through sensitivity experiments based on synthetic data from a realistic numerical simulation. TEMPERSEA has been compared to satellite observations of sea surface temperature for the period 1981–2017, showing good agreement especially in those periods when a reasonable number of observations were available. Also, very good agreement has been found between air temperatures and reconstructed sea temperatures in the upper 100 m for the whole period 1958–2017, enhancing confidence in the quality of the product. The product has been used to characterize the spatio-temporal variability of the temperature field in the Red Sea and the Gulf of Aden at different timescales (seasonal, interannual and multidecadal). Clear differences have been found between the two regions suggesting that the Red Sea variability is mainly driven by air–sea interactions, while in the Gulf of Aden the lateral advection of water plays a relevant role. Regarding long-term evolution, our results show only positive trends above 40 m depth, with maximum trends of 0.045 + 0.016 ∘C decade−1 at 15 m, and the largest negative trends at 125 m (-0.072+0.011 ∘C decade−1). Multidecadal variations have a strong impact on the trend computation and restricting them to the last 30–40 years of data can bias high the trend estimates.

scholarly journals Skill assessment of global, regional and coastal circulation forecast models: evaluating the benefits of dynamical downscaling in IBI surface waters

2019 ◽  
Author(s):  
Pablo Lorente ◽  
Marcos García-Sotillo ◽  
Arancha Amo-Baladrón ◽  
Roland Aznar ◽  
Bruno Levier ◽  
...  
Keyword(s):  
Dynamical Downscaling ◽  
Horizontal Resolution ◽  
Skill Assessment ◽  
Added Value ◽  
Two Phase ◽  
Forecast Models ◽  
In Situ Observations ◽  
Spatio Temporal ◽  
Freshwater Plume

Abstract. In this work, a multi-parameter inter-comparison of diverse ocean forecast models was conducted at the sea surface, ranging from global to local scales in a two-phase strategy. Firstly, a comparison of CMEMS-GLOBAL and the nested CMEMS-IBI regional system was performed against satellite-derived and in situ observations. Results highlighted the overall benefits of both the GLOBAL data assimilation in open-waters and the increased horizontal resolution of IBI in coastal areas, respectively. Besides, IBI proved to capture shelf dynamics by better representing the horizontal extent and strength of a river freshwater plume, according to the results derived from the validation against in situ observations from a buoy moored in NW Spain. Secondly, a multi-model inter-comparison exercise for 2017 was performed in the Strait of Gibraltar among GLOBAL, IBI and the nested SAMPA high-resolution coastal forecast system in order to elucidate the accuracy of each system to characterize the Atlantic Jet (AJ) inflow dynamic. A quantitative validation against High Frequency radar (HFR) hourly currents highlighted both the steady improvement in AJ representation in terms of speed and direction when zooming from global to coastal scales though a multi-nesting model approach and also the relevance of a variety of factors at local scale such as a refined horizontal resolution, a tailored bathymetry and a higher spatio-temporal resolution of the atmospheric forcing. The ability of each model to reproduce a 2-day quasi-permanent full reversal of the AJ surface inflow was examined in terms of wind-induced circulation patterns. SAMPA appeared to better reproduce the reversal events detected with HFR estimations, demonstrating the potential added value of coastal models with respect to coarser parent regional systems. Finally, SAMPA coastal model outputs were also qualitatively analysed in the Western Alboran Sea to put in a broader perspective the context of the onset, development and end of such flow reversal episodes.

scholarly journals A synthetic satellite dataset of <i>E. huxleyi</i> spatio-temporal distributions and their impacts on Arctic and Subarctic marine environments (1998–2016)

2018 ◽  
Author(s):  
Dmitry Kondrik ◽  
Eduard Kazakov ◽  
Dmitry Pozdnyakov
Keyword(s):  
Remote Sensing ◽  
Inorganic Carbon ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Marine Environments ◽  
Co2 Partial Pressure ◽  
In Situ Observations ◽  
Spatio Temporal ◽  
Particulate Inorganic Carbon

Abstract. A 19-year (1998–2016) continuous dataset of coccolithophore E. huxleyi distributions and activity in Arctic and Subarctic seas is presented. The dataset is based on optical remote sensing data (mostly OC CCI data) with assimilation of different relevant in-situ observations, preprocessed with authorial algorithms. Alongside with bloom locations, we also provide both detailed information on E. huxleyi impacts within the bloom area on marine environments and the subdatasets of quantified coccolith concentrations, particulate inorganic carbon content and CO2 partial pressure in water driven by coccolithophores. All data are presented on a regular 4×4 km grid at a temporal resolution of 8 days. The paper describes the theoretical and methodological basis for all processing and modeling steps. The data are available on Zenodo: https://doi.org/10.5281/zenodo.1402033.

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