scholarly journals Systematic detection of local CH<sub>4</sub> emissions anomalies combining satellite measurements and high-resolution forecasts

2020 ◽  
Author(s):  
Jérôme Barré ◽  
Ilse Aben ◽  
Anna Agustí-Panareda ◽  
Gianpaolo Balsamo ◽  
Nicolas Bousserez ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Satellite Observations ◽  
Satellite Measurements ◽  
Satellite Retrieval ◽  
Quantitative Manner ◽  
Ch4 Concentration ◽  
Monitoring Service ◽  
Total Column

Abstract. In this study we present a novel monitoring methodology to detect local CH4 concentration anomalies worldwide that are related to rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget. The method uses high resolution (7 km × 7 km) retrievals of total column CH4 from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel 5 Precursor satellite. Observations are combined with high resolution CH4 forecasts (~ 9 km) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide departures (observations minus forecasts) close to the native satellite resolution at appropriate time. Investigating the departures is an effective way to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the large-scale biases on both forecasts and satellite observations. We then use a simple classification on the filtered departures to detect anomalies and plumes coming from CAMS emissions that are missing (e.g. pipeline or facility leaks), under-reported or over-reported (e.g. depleted drilling fields). Additionally, the classification helps to detect local satellite retrieval errors due to land surface albedo issues.

scholarly journals Systematic detection of local CH<sub>4</sub> anomalies by combining satellite measurements with high-resolution forecasts

2021 ◽  
Vol 21 (6) ◽  
pp. 5117-5136
Author(s):  
Jérôme Barré ◽  
Ilse Aben ◽  
Anna Agustí-Panareda ◽  
Gianpaolo Balsamo ◽  
Nicolas Bousserez ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Observations ◽  
Anthropogenic Emissions ◽  
Synoptic Scale ◽  
Satellite Measurements ◽  
Satellite Retrieval ◽  
Quantitative Manner ◽  
Ch4 Concentration ◽  
Monitoring Service ◽  
Total Column

Abstract. In this study, we present a novel monitoring methodology that combines satellite retrievals and forecasts to detect local CH4 concentration anomalies worldwide. These anomalies are caused by rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget and by biases in the satellite retrieval data. The method uses high-resolution (7 km × 7 km) retrievals of total column CH4 from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite. Observations are combined with high-resolution CH4 forecasts (∼ 9 km) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide departures (observations minus forecasts) at close to the satellite's native resolution at appropriate time. Investigating these departures is an effective way to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the synoptic-scale and meso-alpha-scale biases in both forecasts and satellite observations. We then apply a simple classification scheme to the filtered departures to detect anomalies and plumes that are missing (e.g. pipeline or facility leaks), underreported or overreported (e.g. depleted drilling fields) in the CAMS emissions. The classification method also shows some limitations to detect emission anomalies only due to local satellite retrieval biases linked to albedo and scattering issues.

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 A high-resolution global dataset of topographic index values for use in large-scale hydrological modelling

2014 ◽  
Vol 11 (6) ◽  
pp. 6139-6166 ◽  
Author(s):  
T. R. Marthews ◽  
S. J. Dadson ◽  
B. Lehner ◽  
S. Abele ◽  
N. Gedney
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Global Scale ◽  
Surface Fluxes ◽  
Data Sets ◽  
Topographic Index ◽  
Surface Water Flow ◽  
Land Surface Water ◽  
Compound Topographic Index

Abstract. Modelling land surface water flow is of critical importance for simulating land-surface fluxes, predicting runoff and water table dynamics and for many other applications of Land Surface Models. Many approaches are based on the popular hydrology model TOPMODEL, and the most important parameter of this model is the well-knowntopographic index. Here we present new, high-resolution parameter maps of the topographic index for all ice-free land pixels calculated from hydrologically-conditioned HydroSHEDS data sets using the GA2 algorithm. At 15 arcsec resolution, these layers are 4× finer than the resolution of the previously best-available topographic index layers, the Compound Topographic Index of HYDRO1k (CTI). In terms of the largest river catchments occurring on each continent, we found that in comparison to our revised values, CTI values were up to 20% higher in e.g. the Amazon. We found the highest catchment means were for the Murray-Darling and Nelson-Saskatchewan rather than for the Amazon and St. Lawrence as found from the CTI. We believe these new index layers represent the most robust existing global-scale topographic index values and hope that they will be widely used in land surface modelling applications in the future.

scholarly journals The STRatospheric Estimation Algorithm from Mainz (STREAM): estimating stratospheric NO<sub>2</sub> from nadir-viewing satellites by weighted convolution

2016 ◽  
Vol 9 (7) ◽  
pp. 2753-2779 ◽  
Author(s):  
Steffen Beirle ◽  
Christoph Hörmann ◽  
Patrick Jöckel ◽  
Song Liu ◽  
Marloes Penning de Vries ◽  
...  
Keyword(s):  
Input Data ◽  
Polar Vortex ◽  
Chemical Transport ◽  
Estimation Algorithm ◽  
Satellite Observations ◽  
Satellite Measurements ◽  
Transport Models ◽  
Verification Algorithm ◽  
Chemical Transport Models ◽  
Total Column

Abstract. The STRatospheric Estimation Algorithm from Mainz (STREAM) determines stratospheric columns of NO2 which are needed for the retrieval of tropospheric columns from satellite observations. It is based on the total column measurements over clean, remote regions as well as over clouded scenes where the tropospheric column is effectively shielded. The contribution of individual satellite measurements to the stratospheric estimate is controlled by various weighting factors. STREAM is a flexible and robust algorithm and does not require input from chemical transport models. It was developed as a verification algorithm for the upcoming satellite instrument TROPOMI, as a complement to the operational stratospheric correction based on data assimilation. STREAM was successfully applied to the UV/vis satellite instruments GOME 1/2, SCIAMACHY, and OMI. It overcomes some of the artifacts of previous algorithms, as it is capable of reproducing gradients of stratospheric NO2, e.g., related to the polar vortex, and reduces interpolation errors over continents. Based on synthetic input data, the uncertainty of STREAM was quantified as about 0.1–0.2 × 1015 molecules cm−2, in accordance with the typical deviations between stratospheric estimates from different algorithms compared in this study.

Rapid dehazing algorithm based on large-scale median filtering for high-resolution visible near-infrared remote sensing images

2014 ◽  
Vol 12 (05) ◽  
pp. 1461010 ◽  
Author(s):  
Changmiao Hu ◽  
Ping Tang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Near Infrared ◽  
Low Frequency ◽  
Median Filtering ◽  
Remote Sensing Images ◽  
Infrared Band ◽  
Remote Sensing Image Processing

In recent years, China's demand for satellite remote sensing images increased. Thus, the country launched a series of satellites equipped with high-resolution sensors. The resolutions of these satellites range from 30 m to a few meters, and the spectral range covers the visible to the near-infrared band. These satellite images are mainly used for environmental monitoring, mapping, land surface classification and other fields. However, haze is an important factor that often affects image quality. Thus, dehazing technology is becoming a critical step in high-resolution remote sensing image processing. This paper presents a rapid algorithm for dehazing based on a semi-physical haze model. Large-scale median filtering technique is used to extract large areas of bright, low-frequency information from images to estimate the distribution and thickness of the haze. Four images from different satellites are used for experiment. Results show that the algorithm is valid, fast, and suitable for the rapid dehazing of numerous large-sized high-resolution remote sensing images in engineering applications.

Impact of an Exponential Profile of Saturated Hydraulic Conductivity within the ISBA LSM: Simulations over the Rhône Basin

2006 ◽  
Vol 7 (1) ◽  
pp. 61-80 ◽  
Author(s):  
B. Decharme ◽  
H. Douville ◽  
A. Boone ◽  
F. Habets ◽  
J. Noilhan
Keyword(s):  
High Resolution ◽  
Hydraulic Conductivity ◽  
Land Surface ◽  
Large Scale ◽  
Saturated Hydraulic Conductivity ◽  
Rooting Depth ◽  
Surface Model ◽  
Low Resolution ◽  
The Impact ◽  
Hydrological Applications

Abstract This study focuses on the influence of an exponential profile of saturated hydraulic conductivity, ksat, with soil depth on the water budget simulated by the Interaction Soil Biosphere Atmosphere (ISBA) land surface model over the French Rhône River basin. With this exponential profile, the saturated hydraulic conductivity at the surface increases by approximately a factor of 10, and its mean value increases in the root zone and decreases in the deeper region of the soil in comparison with the values given by Clapp and Hornberger. This new version of ISBA is compared to the original version in offline simulations using the Rhône-Aggregation high-resolution database. Low-resolution simulations, where all atmospheric data and surface parameters have been aggregated, are also performed to test the impact of the modified ksat profile at the typical scale of a climate model. The simulated discharges are compared to observations from a dense network consisting of 88 gauging stations. Results of the high-resolution experiments show that the exponential profile of ksat globally improves the simulated discharges and that the assumption of an increase in saturated hydraulic conductivity from the soil surface to a depth close to the rooting depth in comparison with values given by Clapp and Hornberger is reasonable. Results of the scaling experiments indicate that this parameterization is also suitable for large-scale hydrological applications. Nevertheless, low-resolution simulations with both model versions overestimate evapotranspiration (especially from the plant transpiration and the wet fraction of the canopy) to the detriment of total runoff, which emphasizes the need for implementing subgrid distribution of precipitation and land surface properties in large-scale hydrological applications.

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;

scholarly journals A method for colocating satellite <i>X</i><sub>CO<sub>2</sub></sub> data to ground-based data and its application to ACOS-GOSAT and TCCON

2014 ◽  
Vol 7 (8) ◽  
pp. 2631-2644 ◽  
Author(s):  
H. Nguyen ◽  
G. Osterman ◽  
D. Wunch ◽  
C. O'Dell ◽  
L. Mandrake ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Weighted Average ◽  
Satellite Observations ◽  
Total Carbon ◽  
Satellite Measurements ◽  
Temporal Window ◽  
Squared Error ◽  
Scanning Imaging ◽  
Total Column ◽  
Absorption Spectrometer

Abstract. Satellite measurements are often compared with higher-precision ground-based measurements as part of validation efforts. The satellite soundings are rarely perfectly coincident in space and time with the ground-based measurements, so a colocation methodology is needed to aggregate "nearby" soundings into what the instrument would have seen at the location and time of interest. We are particularly interested in validation efforts for satellite-retrieved total column carbon dioxide (XCO2), where XCO2 data from Greenhouse Gas Observing Satellite (GOSAT) retrievals (ACOS, NIES, RemoteC, PPDF, etc.) or SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) are often colocated and compared to ground-based column XCO2 measurement from Total Carbon Column Observing Network (TCCON). Current colocation methodologies for comparing satellite measurements of total column dry-air mole fractions of CO2 (XCO2) with ground-based measurements typically involve locating and averaging the satellite measurements within a latitudinal, longitudinal, and temporal window. We examine a geostatistical colocation methodology that takes a weighted average of satellite observations depending on the "distance" of each observation from a ground-based location of interest. The "distance" function that we use is a modified Euclidian distance with respect to latitude, longitude, time, and midtropospheric temperature at 700 hPa. We apply this methodology to XCO2 retrieved from GOSAT spectra by the ACOS team, cross-validate the results to TCCON XCO2 ground-based data, and present some comparisons between our methodology and standard existing colocation methods showing that, in general, geostatistical colocation produces smaller mean-squared error.

scholarly journals Online dynamical downscaling of temperature and precipitation within the <i>i</i>LOVECLIM model (version 1.1)

2018 ◽  
Vol 11 (1) ◽  
pp. 453-466
Author(s):  
Aurélien Quiquet ◽  
Didier M. Roche ◽  
Christophe Dumas ◽  
Didier Paillard
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Atmospheric Model ◽  
Scale Model ◽  
Model Parameters ◽  
Temperature And Precipitation ◽  
Large Scale Model

Abstract. This paper presents the inclusion of an online dynamical downscaling of temperature and precipitation within the model of intermediate complexity iLOVECLIM v1.1. We describe the following methodology to generate temperature and precipitation fields on a 40 km  ×  40 km Cartesian grid of the Northern Hemisphere from the T21 native atmospheric model grid. Our scheme is not grid specific and conserves energy and moisture in the same way as the original climate model. We show that we are able to generate a high-resolution field which presents a spatial variability in better agreement with the observations compared to the standard model. Although the large-scale model biases are not corrected, for selected model parameters, the downscaling can induce a better overall performance compared to the standard version on both the high-resolution grid and on the native grid. Foreseen applications of this new model feature include the improvement of ice sheet model coupling and high-resolution land surface models.

scholarly journals A High-Resolution Dataset of Water Fluxes and States for Germany Accounting for Parametric Uncertainty

2016 ◽  
Author(s):  
Matthias Zink ◽  
Rohini Kumar ◽  
Matthias Cuntz ◽  
Luis Samaniego
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Groundwater Recharge ◽  
Land Surface ◽  
Large Scale ◽  
Parametric Uncertainty ◽  
Model Parameters ◽  
High Resolution Data ◽  
Daily Discharge ◽  
Resolution Dataset

Abstract. Long term, high-resolution data about hydrologic fluxes and states are needed for many hydrological applications. Because continuous large-scale observations of such variables are not feasible, hydrologic or land surface models are applied to derive them. This study aims to analyze and provide a consistent high-resolution dataset of land surface variables over Germany, accounting for uncertainties caused by equifinal model parameters. The mesoscale Hydrological Model (mHM) is employed to derive an ensemble (100 members) of evapotranspiration, groundwater recharge, soil moisture and generated runoff at high spatial and temporal resolutions (4 km and daily, respectively) for the period 1951–2010. The model is cross-evaluated against the observed runoff in 222 catchments, which are not used for model calibration. The mean (standard deviation) of the ensemble median NSE estimated for these catchments is 0.68 (0.09) for daily discharge simulations. The modeled evapotranspiration and soil moisture reasonably represent the observations from eddy covariance stations. Our analysis indicates the lowest parametric uncertainty for evapotranspiration, and the largest is observed for groundwater recharge. The uncertainty of the hydrologic variables varies over the course of a year, with the exception of evapotranspiration, which remains almost constant. This study emphasizes the role of accounting for the parametric uncertainty in model-derived hydrological datasets.

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