scholarly journals Impacts of COVID-19 lockdown, Spring Festival and meteorology on the NO2 variations in early 2020 over China based on in-situ observations, satellite retrievals and model simulations

2021 ◽  
Vol 244 ◽  
pp. 117972
Author(s):  
Zhe Wang ◽  
Itsushi Uno ◽  
Keiya Yumimoto ◽  
Syuichi Itahashi ◽  
Xueshun Chen ◽  
...  
Keyword(s):  
Model Simulations ◽  
Spring Festival ◽  
Satellite Retrievals ◽  
In Situ Observations

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 Development of Observation-based Global Multi-layer Soil Moisture Products for 1970 to 2016

2021 ◽  
Author(s):  
Yaoping Wang ◽  
Jiafu Mao ◽  
Mingzhou Jin ◽  
Forrest M. Hoffman ◽  
Xiaoying Shi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Earth System Model ◽  
System Model ◽  
Surface Model ◽  
Earth System ◽  
Model Simulations ◽  
The Earth ◽  
Wide Range ◽  
In Situ Observations

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling/retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multi-layer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5° resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations and the latest gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Assessed against in situ observations, the global mean bias of the synthesized SM data ranged from −0.044 to 0.033 m3/m3, root mean squared error from 0.076 to 0.104 m3/m3, and Pearson correlation from 0.35 to 0.67. The merged SM datasets also showed the ability to capture historical large-scale drought events and physically plausible global sensitivities to observed meteorological factors. Three of the new SM products, produced by applying any of the three merging methods onto the source datasets excluding the Earth system models, were finally recommended for future applications because of their better performances than the Earth system model–dependent merged estimates. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

scholarly journals ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?

2018 ◽  
Vol 22 (6) ◽  
pp. 3515-3532 ◽  
Author(s):  
Clement Albergel ◽  
Emanuel Dutra ◽  
Simon Munier ◽  
Jean-Christophe Calvet ◽  
Joaquin Munoz-Sabater ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Snow Depth ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Model Simulations ◽  
In Situ Observations

Abstract. The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5 over the period 2010–2016. ERA-5 has important changes relative to the former ERA-Interim atmospheric reanalysis including higher spatial and temporal resolutions as well as a more recent model and data assimilation system. ERA-5 is foreseen to replace ERA-Interim reanalysis and one of the main goals of this study is to assess whether ERA-5 can enhance the simulation performances with respect to ERA-Interim when it is used to force a land surface model (LSM). To that end, both ERA-5 and ERA-Interim are used to force the ISBA (Interactions between Soil, Biosphere, and Atmosphere) LSM fully coupled with the Total Runoff Integrating Pathways (TRIP) scheme adapted for the CNRM (Centre National de Recherches Météorologiques) continental hydrological system within the SURFEX (SURFace Externalisée) modelling platform of Météo-France. Simulations cover the 2010–2016 period at half a degree spatial resolution. The ERA-5 impact on ISBA LSM relative to ERA-Interim is evaluated using remote sensing and in situ observations covering a substantial part of the land surface storage and fluxes over the continental US domain. The remote sensing observations include (i) satellite-driven model estimates of land evapotranspiration, (ii) upscaled ground-based observations of gross primary production, (iii) satellite-derived estimates of surface soil moisture and (iv) satellite-derived estimates of leaf area index (LAI). The in situ observations cover (i) soil moisture, (ii) turbulent heat fluxes, (iii) river discharges and (iv) snow depth. ERA-5 leads to a consistent improvement over ERA-Interim as verified by the use of these eight independent observations of different land status and of the model simulations forced by ERA-5 when compared with ERA-Interim. This is particularly evident for the land surface variables linked to the terrestrial hydrological cycle, while variables linked to vegetation are less impacted. Results also indicate that while precipitation provides, to a large extent, improvements in surface fields (e.g. large improvement in the representation of river discharge and snow depth), the other atmospheric variables play an important role, contributing to the overall improvements. These results highlight the importance of enhanced meteorological forcing quality provided by the new ERA-5 reanalysis, which will pave the way for a new generation of land-surface developments and applications.

Could an ocean climate atlas generated by a model compete with an observational one?

2020 ◽  
Author(s):  
Georgy I. Shapiro ◽  
Jose M. Gonzalez-Ondina ◽  
Xavier Francis ◽  
Hyee S. Lim ◽  
Ali Almehrezi
Keyword(s):  
Standard Error ◽  
Sea Surface ◽  
Vertical Profiles ◽  
Individual Data ◽  
Model Simulations ◽  
Ocean Climate ◽  
Data Points ◽  
In Situ Observations ◽  
The Mean

<p>Modern numerical ocean models have matured over the last decades and are able to provide accurate fore- and hind-cast of the ocean state. The most accurate data could be obtained from the reanalysis where the model run in a hindcast mode with assimilation of available observational data. An obvious benefit of model simulation is that it provides the spatial density and temporal resolution which cannot be achieved by in-situ observations or satellite derived measurements. It is not unusual that even a relatively small area of the ocean model can have in access of 100,000 nodes in the horizontal, each containing vertical profiles of temperature, salinity, velocity and other ocean parameters with a temporal resolution theoretically as high as a few minutes. Remotely sensed (satellite) observations of sea surface temperature can compete with the models in terms of spatial resolution, however they only produce data at the sea surface not the vertical profiles. On the other hand, in-situ observations have a benefit of being much more precise than model simulations. For instance a widely used CTD profiler SBE 911plus has accuracy of about 0.001 °C, which is not achievable by models.</p><p>In the creation of a climatic atlas the higher accuracy of individual profiles provided by in-situ measurements may become less beneficial. Assuming the normal distribution of data at each location, the standard error of the mean (SEM) is calculated as SE=S/SQRT(N), where S is the standard deviation of individual data points around the mean, and N is the number of data points. The climatic data are obtained by averaging a large number of individual data points, and here the benefit of having more data points may become a greater advantage than the accuracy of a single observation.  </p><p>In this study we have created an ocean climate atlas for the northern part of the Indian Ocean including the Red Sea and the Arabian Gulf using model generated data. The data were taken from Copernicus Marine Environment Monitoring Service (CMEMS) reanalysis product GLOBAL_REANALYSIS_PHY_001_030 with 1/12° horizontal resolution and 50 vertical levels for the period 1998 to 2017. The model component is the NEMO platform driven at the surface by ECMWF ERA-Interim reanalysis. The model assimilates along track altimeter data, satellite Sea Surface Temperature, as well as in-situ temperature and salinity vertical profiles where available. The monthly data from CMEMS were then averaged over 20 years to produce an atlas at the surface, 10, 20, 30, 75, 100, 125, 150, 200, 250, 300, 400, and 500 m depths.  The standard error of the mean has been calculated for each point and each depth level on the native grid (1/12 degree).</p><p>The atlas based on model simulations was compared with the latest version of the World Ocean Atlas (WOA)  2018 published by the NCEI.  WOA has objectively analysed climatological mean fields on a ¼  degree grid. The differences between the mean values and SEMs from observational and simulated atlases are analysed, and the potential causes of mismatch are discussed.</p>

scholarly journals An initial assessment of SMAP soil moisture retrievals using high-resolution model simulations and in situ observations

2016 ◽  
Vol 43 (18) ◽  
pp. 9662-9668 ◽  
Author(s):  
Ming Pan ◽  
Xitian Cai ◽  
Nathaniel W. Chaney ◽  
Dara Entekhabi ◽  
Eric F. Wood
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Initial Assessment ◽  
Model Simulations ◽  
Resolution Model ◽  
High Resolution Model ◽  
In Situ Observations

scholarly journals Five Years of Spatially Resolved Ground-Based MAX-DOAS Measurements of Nitrogen Dioxide in the Urban Area of Athens: Synergies with In Situ Measurements and Model Simulations

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1634
Author(s):  
Myrto Gratsea ◽  
Eleni Athanasopoulou ◽  
Anastasia Kakouri ◽  
Andreas Richter ◽  
Andre Seyler ◽  
...  
Keyword(s):  
Nitrogen Dioxide ◽  
Urban Area ◽  
In Situ Measurements ◽  
Scale Model ◽  
Urban Background ◽  
Near Surface ◽  
Model Simulations ◽  
Different Seasons ◽  
In Situ Observations

Long-term nitrogen dioxide (NO2) slant column density measurements using the MAX-DOAS (multi-axis differential optical absorption spectroscopy) technique were analyzed in order to demonstrate the temporal and horizontal variability of the trace gas in Athens for the period October 2012–July 2017. The synergy with in situ measurements and model simulations was exploited for verifying the MAX-DOAS technique and its ability to assess the spatiotemporal characteristics of NO2 pollution in the city. Tropospheric NO2 columns derived from ground-based MAX-DOAS observations in two horizontal and five vertical viewing directions were compared with in situ chemiluminescence measurements representative of urban, urban background and suburban conditions; a satisfactory correlation was found for the urban (r ≈ 0.55) and remote areas (r ≈ 0.40). Mean tropospheric slant columns retrieved from measurements at the lowest elevation over the urban area ranged from 0.1 to 32 × 1016 molec cm−2. The interannual variability showed a rate of increase of 0.3 × 1016 molec cm−2 per year since 2012 in the urban area, leading to a total increase of 20%. The retrieved annual cycles captured the seasonal variability with lower NO2 levels in summer, highly correlated (r ≈ 0.85) with the urban background and suburban in situ observations. The NO2 diurnal variation for different seasons exhibited varied patterns, indicating the different role of photochemistry and anthropogenic activities in the different seasons. Compared to in situ observations, the MAX-DOAS NO2 morning peak occurred with a one-hour delay and decayed less steeply in winter. Measurements at different elevation angles are shown as a primary indicator of the vertical distribution of NO2 at the urban environment; the vertical convection of the polluted air masses and the enhanced NO2 near-surface concentrations are demonstrated by this analysis. The inhomogeneity of the NO2 spatial distribution was shown using a relevant inhomogeneity index; greater variability was found during the summer period. Comparisons with city-scale model simulations demonstrated that the horizontal light path length of MAX-DOAS covered a distance of 15 km. An estimation of urban sources’ contribution was also made by applying two simple methodologies on the MAX-DOAS measurements. The results were compared to NO2 predictions from the high resolution air quality model to infer the importance of vehicle emissions for the urban NO2 levels; 20–35% of the urban NO2 was found to be associated with road transport.

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