Validation of WRF-Chem Model and CAMS Performance in Estimating Near-Surface Atmospheric CO2 Mixing Ratio in the Area of Saint Petersburg (Russia)

Nowadays, different approaches for CO2 anthropogenic emission estimation are applied to control agreements on greenhouse gas reduction. Some methods are based on the inverse modelling of emissions using various measurements and the results of numerical chemistry transport models (CTMs). Since the accuracy and precision of CTMs largely determine errors in the approaches for emission estimation, it is crucial to validate the performance of such models through observations. In the current study, the near-surface CO2 mixing ratio simulated by the CTM Weather Research and Forecasting—Chemistry (WRF-Chem) at a high spatial resolution (3 km) using three different sets of CO2 fluxes (anthropogenic + biogenic fluxes, time-varying and constant anthropogenic emissions) and from Copernicus Atmosphere Monitoring Service (CAMS) datasets have been validated using in situ observations near the Saint Petersburg megacity (Russia) in March and April 2019. It was found that CAMS reanalysis data with a low spatial resolution (1.9 × 3.8°) can match the observations better than CAMS analysis data with a high resolution (0.15 × 0.15°). The CAMS analysis significantly overestimates the observed near-surface CO2 mixing ratio in Peterhof in March and April 2019 (by more than 10 ppm). The best match for the CAMS reanalysis and observations was observed in March, when the wind was predominantly opposite to the Saint Petersburg urbanized area. In contrast, the CAMS analysis fits the observed trend of the mixing ratio variation in April better than the reanalysis with the wind directions from the Saint Petersburg urban zone. Generally, the WRF-Chem predicts the observed temporal variations in the near-surface CO2 reasonably well (mean bias ≈ (−0.3) − (−0.9) ppm, RMSD ≈ 8.7 ppm, correlation coefficient ≈ 0.61 ± 0.04). The WRF-Chem data where anthropogenic and biogenic fluxes were used match the observations a bit better than the WRF-Chem data without biogenic fluxes. The diurnal time variation in the anthropogenic emissions influenced the WRF-Chem data insignificantly. However, in general, the data of all three WRF-Chem model runs give almost the same CO2 temporal variation in Peterhof in March and April 2019. This could be related to the late start of the growing season, which influences biogenic CO2 fluxes, inaccuracies in the estimation of the biogenic fluxes, and the simplified time variation pattern of the CO2 anthropogenic emissions.

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Validation of the capability of WRF-Chem model and CAMS to simulate near surface atmospheric CO2 mixing ratio for the territory of Saint-Petersburg

10.5194/egusphere-egu21-1497 ◽

2021 ◽

Author(s):

Georgy Nerobelov ◽

Yury Timofeyev ◽

Sergei Smyshlyaev ◽

Stefani Foka ◽

Ivan Mammarella ◽

...

Keyword(s):

Spatial Resolution ◽

Transport Model ◽

Weather Prediction ◽

Lower Atmosphere ◽

Radiation Balance ◽

Observation Data ◽

Mixing Ratio ◽

Top Down ◽

Near Surface ◽

Saint Petersburg

The growing content of greenhouse gases (GHGs) influences the radiation balance of the planet causing the rise of air temperature in lower atmosphere. This circumstance triggers researchers to create and develop the new methods of estimation of anthropogenic CO2 emissions. One of such method is top-down estimation which is based on measurements and chemical transport modelling. Since the errors of the top-down approach depend on quality of the modelled data it requires validation by complex observations. In current study we investigated the performance of regional numerical weather prediction and chemistry transport model WRF-Chem and CAMS service in simulating spatio-temporal variation of near surface atmospheric CO2 mixing ratio in March and April 2019 for the Saint-Petersburg area (Russia). To validate the modelled data, we used local observations obtained on Peterhof (St. Petersburg) station. The analysis demonstrates that WRF-Chem model can adequate simulate the transport of CO2 in near-surface layer with spatial resolution of 3 km. Average difference and correlation coefficient are in range 0.8-1.6% and 0.55-0.72 respectively. It was found that the WRF-Chem modelled data where biogenic and anthropogenic fluxes were considered fit the observation data worse than the WRF-Chem simulation where only anthropogenic emissions were used. It can be linked to the errors of the biogenic flux calculation. However, to prove that investigations for two contrast periods (in summer and winter) are needed. Despite the rude spatial resolution of the CAMS data (approximately 200x400 km) we found that in general the trend of surface atmospheric CO2 mixing ratio in March and April 2019 for the Saint-Petersburg area from the CAMS dataset fits the observations.

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Assessment of Tropospheric Concentrations of NO2 from the TROPOMI/Sentinel-5 Precursor for the Estimation of Long-Term Exposure to Surface NO2 over South Korea

Remote Sensing ◽

10.3390/rs13101877 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1877

Author(s):

Ukkyo Jeong ◽

Hyunkee Hong

Keyword(s):

South Korea ◽

Spatial Resolution ◽

Atmospheric Composition ◽

Mixing Ratio ◽

Mean Values ◽

Mixing Ratios ◽

Tropospheric No2 ◽

Korean Ministry ◽

Surface Mixing ◽

Rural Sites

Since April 2018, the TROPOspheric Monitoring Instrument (TROPOMI) has provided data on tropospheric NO2 column concentrations (CTROPOMI) with unprecedented spatial resolution. This study aims to assess the capability of TROPOMI to acquire high spatial resolution data regarding surface NO2 mixing ratios. In general, the instrument effectively detected major and moderate sources of NO2 over South Korea with a clear weekday–weekend distinction. We compared the CTROPOMI with surface NO2 mixing ratio measurements from an extensive ground-based network over South Korea operated by the Korean Ministry of Environment (SKME; more than 570 sites), for 2019. Spatiotemporally collocated CTROPOMI and SKME showed a moderate correlation (correlation coefficient, r = 0.67), whereas their annual mean values at each site showed a higher correlation (r = 0.84). The CTROPOMI and SKME were well correlated around the Seoul metropolitan area, where significant amounts of NO2 prevailed throughout the year, whereas they showed lower correlation at rural sites. We converted the tropospheric NO2 from TROPOMI to the surface mixing ratio (STROPOMI) using the EAC4 (ECMWF Atmospheric Composition Reanalysis 4) profile shape, for quantitative comparison with the SKME. The estimated STROPOMI generally underestimated the in-situ value obtained, SKME (slope = 0.64), as reported in previous studies.

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The Impacts of Spatial Resolution, Viewing Angle, and Spectral Vegetation Indices on the Quantification of Woody Mediterranean Species Seasonality Using Remote Sensing

Remote Sensing ◽

10.3390/rs13101958 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1958

Author(s):

Shelly Elbaz ◽

Efrat Sheffer ◽

Itamar M. Lensky ◽

Noam Levin

Keyword(s):

Spatial Resolution ◽

Seasonal Changes ◽

Dominant Role ◽

Vegetation Indices ◽

Individual Species ◽

Viewing Angle ◽

Pixel Size ◽

Mediterranean Species ◽

Near Surface ◽

Nadir Point

Discriminating between woody plant species using a single image is not straightforward due to similarity in their spectral signatures, and limitations in the spatial resolution of many sensors. Seasonal changes in vegetation indices can potentially improve vegetation mapping; however, for mapping at the individual species level, very high spatial resolution is needed. In this study we examined the ability of the Israel/French satellite of VENμS and other sensors with higher spatial resolutions, for identifying woody Mediterranean species, based on the seasonal patterns of vegetation indices (VIs). For the study area, we chose a site with natural and highly heterogeneous vegetation in the Judean Mountains (Israel), which well represents the Mediterranean maquis vegetation of the region. We used three sensors from which the indices were derived: a consumer-grade ground-based camera (weekly images at VIS-NIR; six VIs; 547 individual plants), UAV imagery (11 images, five bands, seven VIs) resampled to 14, 30, 125, and 500 cm to simulate the spatial resolutions available from some satellites, and VENμS Level 1 product (with a nominal spatial resolution of 5.3 m at nadir; seven VIs; 1551 individual plants). The various sensors described seasonal changes in the species’ VIs at different levels of success. Strong correlations between the near-surface sensors for a given VI and species mostly persisted for all spatial resolutions ≤125 cm. The UAV ExG index presented high correlations with the ground camera data in most species (pixel size ≤125 cm; 9 of 12 species with R ≥ 0.85; p < 0.001), and high classification accuracies (pixel size ≤30 cm; 8 species with >70%), demonstrating the possibility for detailed species mapping from space. The seasonal dynamics of the species obtained from VENμS demonstrated the dominant role of ephemeral herbaceous vegetation on the signal recorded by the sensor. The low variance between the species as observed from VENμS may be explained by its coarse spatial resolution (effective ground spatial resolution of 7.5) and its non-nadir viewing angle (29.7°) over the study area. However, considering the challenging characteristics of the research site, it may be that using a VENμS type sensor (with a spatial resolution of ~1 m) from a nadir point of view and in more homogeneous and dense areas would allow for detailed mapping of Mediterranean species based on their seasonality.

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Estimating daily full-coverage near surface O3, CO, and NO2 concentrations at a high spatial resolution over China based on S5P-TROPOMI and GEOS-FP

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2021.03.018 ◽

2021 ◽

Vol 175 ◽

pp. 311-325

Author(s):

Yuan Wang ◽

Qiangqiang Yuan ◽

Tongwen Li ◽

Liye Zhu ◽

Liangpei Zhang

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

Near Surface ◽

Full Coverage ◽

Surface O3

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Important factors in the tracking of tropical cyclones in operational models

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-20-0175.1 ◽

2021 ◽

Author(s):

Timothy Marchok

Keyword(s):

Geophysical Fluid Dynamics Laboratory ◽

Surface Layers ◽

Individual Parameter ◽

Near Surface ◽

Parameter Configuration ◽

Multiple Variables ◽

The Mean ◽

Tropical Cyclone Center ◽

Multiple Configurations ◽

Better Than

AbstractMultiple configurations of the Geophysical Fluid Dynamics Laboratory vortex tracker are tested to determine a setup that produces the best representation of a model forecast tropical cyclone center fix for the purpose of providing track guidance with the highest degree of accuracy and availability. Details of the tracking algorithms are provided, including descriptions of both the Barnes analysis used for center-fixing most variables and a separate scheme used for center-fixing wind circulation. The tracker is tested by running multiple configurations on all storms from the 2015-2017 hurricane seasons in the Atlantic and eastern Pacific Basins using forecasts from two operational National Weather Service models, the Global Forecast System (GFS) and the Hurricane Weather Research and Forecast (HWRF) model. A configuration that tracks only 850 mb geopotential height has the smallest forecast track errors of any configuration based on an individual parameter. However, a configuration composed of the mean of eleven parameters outperforms any of the configurations that are based on individual parameters. Configurations composed of subsets of the eleven parameters and including both mass and momentum variables provide results comparable to or better than the full 11-parameter configuration. In particular, a subset configuration with thickness variables excluded generally outperforms the 11-parameter mean, while one composed of variables from only the 850 mb and near-surface layers performs nearly as well as the 11-parameter mean. Tracker configurations composed of multiple variables are more reliable in providing guidance through the end of a forecast period than are tracker configurations based on individual parameters.

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A method of precise misorientation determination

Journal of Applied Crystallography ◽

10.1107/s0021889803015905 ◽

2003 ◽

Vol 36 (6) ◽

pp. 1319-1323 ◽

Cited By ~ 5

Author(s):

A. Morawiec

Keyword(s):

Electron Microscopy ◽

Transmission Electron Microscopy ◽

Spatial Resolution ◽

Standard Procedure ◽

Good Spatial Resolution ◽

Transmission Electron ◽

Order Of Magnitude ◽

Achievable Accuracy ◽

Better Than

A method that improves the accuracy of misorientations determined from Kikuchi patterns is described. It is based on the fact that some parameters of a misorientation calculated from two orientations are more accurate than other parameters. A procedure which eliminates inaccurate elements is devised. It requires at least two foil inclinations. The quality of the approach relies on the possibility to set large sample-to-detector distances and the availability of good spatial resolution of transmission electron microscopy. Achievable accuracy is one order of magnitude better than the accuracy of the standard procedure.

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Global downscaling of remotely sensed soil moisture using neural networks

Hydrology and Earth System Sciences ◽

10.5194/hess-22-5341-2018 ◽

2018 ◽

Vol 22 (10) ◽

pp. 5341-5356 ◽

Cited By ~ 10

Author(s):

Seyed Hamed Alemohammad ◽

Jana Kolassa ◽

Catherine Prigent ◽

Filipe Aires ◽

Pierre Gentine

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Vegetation Index ◽

Spatial Scales ◽

Ancillary Data ◽

Repeat Time ◽

Spatiotemporal Scales ◽

Better Than

Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

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Air quality impacts of COVID-19 lockdown measures detected from space using high spatial resolution observations of multiple trace gases from Sentinel-5P/TROPOMI

10.5194/acp-2021-534 ◽

2021 ◽

Author(s):

Pieternel F. Levelt ◽

Deborah C. Stein Zweers ◽

Ilse Aben ◽

Maite Bauwens ◽

Tobias Borsdorff ◽

...

Keyword(s):

Air Quality ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Trace Gases ◽

Trace Gas ◽

Joint Analysis ◽

Anthropogenic Emissions ◽

China And India ◽

Societal Relevance ◽

The Impact

Abstract. The aim of this paper is two-fold: to provide guidance on how to best interpret TROPOMI trace gas retrievals and to highlight how TROPOMI trace gas data can be used to understand event-based impacts on air quality from regional to city-scales around the globe. For this study, we present the observed changes in the atmospheric column amounts of five trace gases (NO2, SO2, CO, HCHO and CHOCHO) detected by the Sentinel-5P TROPOMI instrument, driven by reductions of anthropogenic emissions due to COVID-19 lockdown measures in 2020. We report clear COVID-19-related decreases in NO2 concentrations on all continents. For megacities, reductions in column amounts of tropospheric NO2 range between 14 % and 63 %. For China and India supported by NO2 observations, where the primary source of anthropogenic SO2 is coal-fired power generation, we were able to detect sector-specific emission changes using the SO2 data. For HCHO and CHOCHO, we consistently observe anthropogenic changes in two-week averaged column amounts over China and India during the early phases of the lockdown periods. That these variations over such a short time scale are detectable from space, is due to the high resolution and improved sensitivity of the TROPOMI instrument. For CO, we observe a small reduction over China which is in concert with the other trace gas reductions observed during lockdown, however large, interannual differences prevent firm conclusions from being drawn. The joint analysis of COVID-19 lockdown-driven reductions in satellite observed trace gas column amounts, using the latest operational and scientific retrieval techniques for five species concomitantly is unprecedented. However, the meteorologically and seasonally driven variability of the five trace gases does not allow for drawing fully quantitative conclusions on the reduction of anthropogenic emissions based on TROPOMI observations alone. We anticipate that in future, the combined use of inverse modelling techniques with the high spatial resolution data from S5P/TROPOMI for all observed trace gases presented here, will yield a significantly improved sector-specific, space-based analysis of the impact of COVID-19 lockdown measures as compared to other existing satellite observations. Such analyses will further enhance the scientific impact and societal relevance of the TROPOMI mission.

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Rings of Uranus: A Review of Occultation Results

International Astronomical Union Colloquium ◽

10.1017/s0252921100082464 ◽

1982 ◽

Vol 60 ◽

pp. 237-256 ◽

Cited By ~ 1

Author(s):

James L. Elliot

Keyword(s):

Spatial Resolution ◽

Particle Collisions ◽

Dynamical Models ◽

High Resolution Data ◽

Unique System ◽

Structural Details ◽

The Mean ◽

Geometric Albedo ◽

Resolution Data ◽

Better Than

Since their discovery in 1977 (Elliot 1979), the dark, narrow rings of Uranus have intrigued dynamicists. The main enigma has been how the rings can remain so narrow - only a few km wide - when particle collisions and the Poynting-Robertson effect should cause the particles to disperse. The Uranian rings have posed other problems as well, and have proved to be a unique system for developing dynamical models of rings. The reason for this theoretical interest is the high precision and time coverage of the data available from occultation observations. With occultations we obtain a spatial resolution of 1 km in the position of ring segments and a resolution of 4 km in their structural details. These high-resolution data are available sufficiently often to be useful for dynamical purposes - at the rate of 1-2 events per year. This spatial resolution is somewhat better than that obtained by Voyager imaging of Jupiter’s and Saturn’s rings (Owen et al. 1979; Smith et al. 1981). Ground-based images of the Uranian rings, obtained by Matthews, Nicholson, and Neugebauer (1981), have a spatial resolution of ~50,000 km. Although unable to resolve individual rings, these data have established the mean geometric albedo of the rings at 0.030 ± 0.005.

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