scholarly journals High resolution modeling of CO<sub>2</sub> over Europe: implications for representation errors of satellite retrievals

2009 ◽  
Vol 9 (5) ◽  
pp. 20599-20630
Author(s):  
D. Pillai ◽  
C. Gerbig ◽  
J. Marshall ◽  
R. Ahmadov ◽  
R. Kretschmer ◽  
...  
Keyword(s):  
High Resolution ◽  
Horizontal Resolution ◽  
Co2 Fluxes ◽  
Modeling Framework ◽  
Global Models ◽  
Near Surface ◽  
Temporal Sampling ◽  
Inverse Models ◽  
Representation Error ◽  
Satellite Retrievals

Abstract. Satellite retrievals for column CO2 with better spatial and temporal sampling are expected to improve the current surface flux estimates of CO2 via inverse techniques. However, the spatial scale mismatch between remotely sensed CO2 and current generation inverse models can induce representation errors, which can cause systematic biases in flux estimates. This study is focused on estimating these representation errors associated with utilization of satellite measurements in global models with a horizontal resolution of about 1 degree or less. For this we used simulated CO2 from the high resolution modeling framework WRF-VPRM, which links CO2 fluxes from a diagnostic biosphere model to a weather forecasting model at 10×10 km2 horizontal resolution. Sub-grid variability of column averaged CO2, i.e. the variability not resolved by global models, reached up to 1.2 ppm. Statistical analysis of the simulation results indicate that orography plays an important role. Using sub-grid variability of orography and CO2 fluxes as well as resolved mixing ratio of CO2, a linear model can be formulated that could explain about 50% of the spatial patterns in the bias component of representation error in column and near-surface CO2 during day- and night-times. These findings give hints for a parameterization of representation error which would allow for the representation error to taken into account in inverse models or data assimilation systems.

scholarly journals High resolution modeling of CO<sub>2</sub> over Europe: implications for representation errors of satellite retrievals

2010 ◽  
Vol 10 (1) ◽  
pp. 83-94 ◽  
Author(s):  
D. Pillai ◽  
C. Gerbig ◽  
J. Marshall ◽  
R. Ahmadov ◽  
R. Kretschmer ◽  
...  
Keyword(s):  
High Resolution ◽  
Horizontal Resolution ◽  
Systematic Bias ◽  
Co2 Fluxes ◽  
Modeling Framework ◽  
Global Models ◽  
Near Surface ◽  
Inverse Models ◽  
Representation Error ◽  
Satellite Retrievals

Abstract. Satellite retrievals for column CO2 with better spatial and temporal sampling are expected to improve the current surface flux estimates of CO2 via inverse techniques. However, the spatial scale mismatch between remotely sensed CO2 and current generation inverse models can induce representation errors, which can cause systematic biases in flux estimates. This study is focused on estimating these representation errors associated with utilization of satellite measurements in global models with a horizontal resolution of about 1 degree or less. For this we used simulated CO2 from the high resolution modeling framework WRF-VPRM, which links CO2 fluxes from a diagnostic biosphere model to a weather forecasting model at 10×10 km2 horizontal resolution. Sub-grid variability of column averaged CO2, i.e. the variability not resolved by global models, reached up to 1.2 ppm with a median value of 0.4 ppm. Statistical analysis of the simulation results indicate that orography plays an important role. Using sub-grid variability of orography and CO2 fluxes as well as resolved mixing ratio of CO2, a linear model can be formulated that could explain about 50% of the spatial patterns in the systematic (bias or correlated error) component of representation error in column and near-surface CO2 during day- and night-times. These findings give hints for a parameterization of representation error which would allow for the representation error to taken into account in inverse models or data assimilation systems.

scholarly journals Tracking city CO<sub>2</sub> emissions from space using a high resolution inverse modeling approach: A case study for Berlin, Germany

2016 ◽  
Author(s):  
Dhanyalekshmi Pillai ◽  
Michael Buchwitz ◽  
Christoph Gerbig ◽  
Thomas Koch ◽  
Maximilian Reuter ◽  
...  
Keyword(s):  
High Resolution ◽  
Co2 Emissions ◽  
Inverse Modeling ◽  
Co2 Emission ◽  
Bayesian Inversion ◽  
Co2 Fluxes ◽  
Modeling Framework ◽  
Worst Case ◽  
Urban Emissions ◽  
The City

Abstract. Currently 52 % of the world's population resides in urban areas and as a consequence, approximately 70 % of fossil fuel emissions of CO2 arise from cities. This fact in combination with large uncertainties associated with quantifying urban emissions due to lack of appropriate measurements makes it crucial to obtain new measurements useful to identify and quantify urban emissions. This is required, for example, for the assessment of emission mitigation strategies and their effectiveness. Here we investigate the potential of a satellite mission like Carbon Monitoring Satellite (CarbonSat), proposed to the European Space Agency (ESA) – to retrieve the city emissions globally, taking into account a realistic description of the expected retrieval errors, the spatiotemporal distribution of CO2 fluxes, and atmospheric transport. To achieve this we use (i) a high-resolution modeling framework consisting of the Weather Research Forecasting model with a greenhouse gas module (WRF-GHG), which is used to simulate the atmospheric observations of column averaged CO2 dry air mole fractions (XCO2), and (ii) a Bayesian inversion method to derive anthropogenic CO2 emissions and their errors from the CarbonSat XCO2 observations. We focus our analysis on Berlin in Germany using CarbonSat's cloud-free overpasses for one reference year. The dense (wide swath) CarbonSat simulated observations with high-spatial resolution (approx. 2 km × 2 km) permits one to map the city CO2 emission plume with a peak enhancement of typically 0.8–1.35 ppm relative to the background. By performing a Bayesian inversion, it is shown that the random error (RE) of the retrieved Berlin CO2 emission for a single overpass is typically less than 8 to 10 MtCO2 yr−1 (about 15 to 20 % of the total city emission). The range of systematic errors (SE) of the retrieved fluxes due to various sources of error (measurement, modeling, and inventories) is also quantified. Depending on the assumptions made, the SE is less than about 6 to 10 MtCO2 yr−1 for most cases. We find that in particular systematic modeling-related errors can be quite high during the summer months due to substantial XCO2 variations caused by biogenic CO2 fluxes at and around the target region. When making the extreme worst-case assumption that biospheric XCO2 variations cannot be modeled at all (which is overly pessimistic), the SE of the retrieved emission is found to be larger than 10 MtCO2 yr−1 for about half of the sufficiently cloud-free overpasses, and for some of the overpasses we found that SE may even be on the order of magnitude of the anthropogenic emission. This indicates that biogenic XCO2 variations cannot be neglected but must be considered during forward and/or inverse modeling. Overall, we conclude that CarbonSat is well suited to obtain city-scale CO2 emissions as needed to enhance our current understanding of anthropogenic carbon fluxes and that CarbonSat or CarbonSat-like satellites should be an important component of a future global carbon emission monitoring system.

scholarly journals Assessing the CAM5 physics suite in the WRF-Chem model: implementation, resolution sensitivity, and a first evaluation for a regional case study

2014 ◽  
Vol 7 (3) ◽  
pp. 755-778 ◽  
Author(s):  
P.-L. Ma ◽  
P. J. Rasch ◽  
J. D. Fast ◽  
R. C. Easter ◽  
W. I. Gustafson Jr. ◽  
...  
Keyword(s):  
Climate Model ◽  
Mesoscale Model ◽  
Global Climate Model ◽  
Cloud Microphysics ◽  
Horizontal Resolution ◽  
Surface Fluxes ◽  
The Arctic ◽  
Shallow Convection ◽  
Modeling Framework ◽  
Near Surface

Abstract. A suite of physical parameterizations (deep and shallow convection, turbulent boundary layer, aerosols, cloud microphysics, and cloud fraction) from the global climate model Community Atmosphere Model version 5.1 (CAM5) has been implemented in the regional model Weather Research and Forecasting with chemistry (WRF-Chem). A downscaling modeling framework with consistent physics has also been established in which both global and regional simulations use the same emissions and surface fluxes. The WRF-Chem model with the CAM5 physics suite is run at multiple horizontal resolutions over a domain encompassing the northern Pacific Ocean, northeast Asia, and northwest North America for April 2008 when the ARCTAS, ARCPAC, and ISDAC field campaigns took place. These simulations are evaluated against field campaign measurements, satellite retrievals, and ground-based observations, and are compared with simulations that use a set of common WRF-Chem parameterizations. This manuscript describes the implementation of the CAM5 physics suite in WRF-Chem, provides an overview of the modeling framework and an initial evaluation of the simulated meteorology, clouds, and aerosols, and quantifies the resolution dependence of the cloud and aerosol parameterizations. We demonstrate that some of the CAM5 biases, such as high estimates of cloud susceptibility to aerosols and the underestimation of aerosol concentrations in the Arctic, can be reduced simply by increasing horizontal resolution. We also show that the CAM5 physics suite performs similarly to a set of parameterizations commonly used in WRF-Chem, but produces higher ice and liquid water condensate amounts and near-surface black carbon concentration. Further evaluations that use other mesoscale model parameterizations and perform other case studies are needed to infer whether one parameterization consistently produces results more consistent with observations.

scholarly journals Improving Week-2 Forecasts with Multimodel Reforecast Ensembles

2006 ◽  
Vol 134 (8) ◽  
pp. 2279-2284 ◽  
Author(s):  
Jeffrey S. Whitaker ◽  
Xue Wei ◽  
Frédéric Vitart
Keyword(s):  
High Resolution ◽  
Real Time ◽  
State Of The Art ◽  
Horizontal Resolution ◽  
Model Output ◽  
Single Model ◽  
Global Models ◽  
Operational Forecast ◽  
Model Output Statistics ◽  
Ecmwf Model

Abstract It has recently been demonstrated that model output statistics (MOS) computed from a long retrospective dataset of ensemble “reforecasts” from a single model can significantly improve the skill of probabilistic week-2 forecasts (with the same model). In this study the technique is extended to a multimodel reforecast dataset consisting of forecasts from ECMWF and NCEP global models. Even though the ECMWF model is more advanced than the version of the NCEP model used (it has more than double the horizontal resolution and is about five years newer), the forecasts produced by the multimodel MOS technique are more skillful than those produced by the MOS technique applied to either the NCEP or ECMWF forecasts alone. These results demonstrate that the MOS reforecast approach yields benefits for week-2 forecasts that are just as large for high-resolution state-of-the-art models as they are for relatively low resolution out-of-date models. Furthermore, operational forecast centers can benefit by sharing both retrospective reforecast datasets and real-time forecasts.

scholarly journals Modelling CO<sub>2</sub> weather – why horizontal resolution matters

2019 ◽  
Vol 19 (11) ◽  
pp. 7347-7376 ◽  
Author(s):  
Anna Agustí-Panareda ◽  
Michail Diamantakis ◽  
Sébastien Massart ◽  
Frédéric Chevallier ◽  
Joaquín Muñoz-Sabater ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
High Resolution ◽  
Greenhouse Gas ◽  
Real Time ◽  
Horizontal Resolution ◽  
Surface Fluxes ◽  
Atmospheric Co2 ◽  
Small Scale ◽  
Co2 Fluxes

Abstract. Climate change mitigation efforts require information on the current greenhouse gas atmospheric concentrations and their sources and sinks. Carbon dioxide (CO2) is the most abundant anthropogenic greenhouse gas. Its variability in the atmosphere is modulated by the synergy between weather and CO2 surface fluxes, often referred to as CO2 weather. It is interpreted with the help of global or regional numerical transport models, with horizontal resolutions ranging from a few hundreds of kilometres to a few kilometres. Changes in the model horizontal resolution affect not only atmospheric transport but also the representation of topography and surface CO2 fluxes. This paper assesses the impact of horizontal resolution on the simulated atmospheric CO2 variability with a numerical weather prediction model. The simulations are performed using the Copernicus Atmosphere Monitoring Service (CAMS) CO2 forecasting system at different resolutions from 9 to 80 km and are evaluated using in situ atmospheric surface measurements and atmospheric column-mean observations of CO2, as well as radiosonde and SYNOP observations of the winds. The results indicate that both diurnal and day-to-day variability of atmospheric CO2 are generally better represented at high resolution, as shown by a reduction in the errors in simulated wind and CO2. Mountain stations display the largest improvements at high resolution as they directly benefit from the more realistic orography. In addition, the CO2 spatial gradients are generally improved with increasing resolution for both stations near the surface and those observing the total column, as the overall inter-station error is also reduced in magnitude. However, close to emission hotspots, the high resolution can also lead to a deterioration of the simulation skill, highlighting uncertainties in the high-resolution fluxes that are more diffuse at lower resolutions. We conclude that increasing horizontal resolution matters for modelling CO2 weather because it has the potential to bring together improvements in the surface representation of both winds and CO2 fluxes, as well as an expected reduction in numerical errors of transport. Modelling applications like atmospheric inversion systems to estimate surface fluxes will only be able to benefit fully from upgrades in horizontal resolution if the topography, winds and prior flux distribution are also upgraded accordingly. It is clear from the results that an additional increase in resolution might reduce errors even further. However, the horizontal resolution sensitivity tests indicate that the change in the CO2 and wind modelling error with resolution is not linear, making it difficult to quantify the improvement beyond the tested resolutions. Finally, we show that the high-resolution simulations are useful for the assessment of the small-scale variability of CO2 which cannot be represented in coarser-resolution models. These representativeness errors need to be considered when assimilating in situ data and high-resolution satellite data such as Greenhouse gases Observing Satellite (GOSAT), Orbiting Carbon Observatory-2 (OCO-2), the Chinese Carbon Dioxide Observation Satellite Mission (TanSat) and future missions such as the Geostationary Carbon Observatory (GeoCarb) and the Sentinel satellite constellation for CO2. For these reasons, the high-resolution CO2 simulations provided by the CAMS in real time can be useful to estimate such small-scale variability in real time, as well as providing boundary conditions for regional modelling studies and supporting field experiments.

Subgrid-Scale Variability for Thermodynamic Variables in an Offline Land Surface Prediction System

2015 ◽  
Vol 17 (1) ◽  
pp. 171-193 ◽  
Author(s):  
Mélanie C. Rochoux ◽  
Stéphane Bélair ◽  
Maria Abrahamowicz ◽  
Pierre Pellerin
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Horizontal Resolution ◽  
Prediction System ◽  
Subgrid Scale ◽  
Grid Spacing ◽  
Model Bias ◽  
Near Surface ◽  
Surface Prediction ◽  
The Impact

Abstract This study presents a numerical analysis of the impact of the horizontal resolution on the forecast capability of the Canadian offline land surface prediction system (SPS; formerly known as GEM-Surf) forced by the 15-km Global Environmental Multiscale (GEM) atmospheric model. This system is used to quantify on a statistical basis the subgrid-scale variability of (near-)surface variables for 25-km grid spacing based on the 2.5- or 10-km SPS run at regional scale over the 2012 summer season. The model bias and the distributions characterizing the subgrid-scale variability drastically depend on the geographic areas as well as on the diurnal cycle. These results show the benefits of high-resolution land surface simulations to account for length scales that are more consistent with the scales at which the actual land surface balance is affected by the heterogeneous geophysical fields (i.e., roughness length, land–water mask, glacier mask, and soil texture). The model bias results highlight the potential of an SPS–GEM two-way coupling strategy for refining predictions near the surface through the upscaling of high-resolution surface heat fluxes to the coarser atmospheric grid spacing, with these fluxes being significantly different from those explicitly resolved at 25 km and featuring nonlinear behavior with respect to the horizontal resolution. Since the computational power of meteorological operational centers progressively increases, making it possible to run high-resolution limited-area models, solving the surface at high resolution in a surface–atmosphere fully coupled system becomes a key aspect for improving numerical weather and environmental forecast performance.

scholarly journals Evaluation of WRF model seasonal forecasts for tropical region of Singapore

2015 ◽  
Vol 12 (1) ◽  
pp. 69-72 ◽  
Author(s):  
J. Singh ◽  
K. Yeo ◽  
X. Liu ◽  
R. Hosseini ◽  
J. R. Kalagnanam
Keyword(s):  
High Resolution ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Tropical Region ◽  
Seasonal Forecasts ◽  
Near Surface ◽  
High Resolution Data ◽  
Model Configuration ◽  
And Performance ◽  
Resolution Data

Abstract. The Weather and Research Forecast (WRF) model is evaluated for the monsoon and inter-monsoon seasons over the tropical region of Singapore. The model configuration, physical parameterizations and performance results are described in this paper. In addition to the ready-to-use data available with the WRF model, the model configuration includes high resolution MODIS land use (500 m horizontal resolution) and JPL-NASA sea surface temperature (1 km horizontal resolution) data. The model evaluation is performed against near surface observations for temperature, relative humidity, wind speed and direction, available from a dense network of weather monitoring stations across Singapore. It is found that the high resolution data sets bring significant improvement in the model forecasts. The results also indicate that the model forecasts are more accurate in the monsoon seasons compared to the inter-monsoon seasons.

scholarly journals Merits of novel high-resolution estimates and existing long-term estimates of humidity and incident radiation in a complex domain

2019 ◽  
Author(s):  
Helene Birkelund Erlandsen ◽  
Lena Merete Tallaksen ◽  
Jørn Kristiansen
Keyword(s):  
High Resolution ◽  
Incident Radiation ◽  
Reanalysis Data ◽  
Horizontal Resolution ◽  
Ecological Modelling ◽  
The Novel ◽  
Near Surface ◽  
Air Mass ◽  
Humidity Measurements ◽  
Mass Type

Abstract. To provide better and more robust estimates of evaporation and snow-melt in a changing climate, hydrological and ecological modelling practices are shifting towards solving the surface energy balance. In addition to precipitation and near-surface temperature (T2), which often is available at high resolution by national providers, high quality estimates of 2-meter humidity, surface incident shortwave (SW ↓) and longwave (LW ↓) radiation are also required. Novel, gridded estimates of humidity and incident radiation are constructed using a methodology similar to that used in the development of the WATCH forcing data, however, a national 1 × 1 km gridded, observation-based T2 data is consulted in the downscaling rather than the 0.5 × 0.5 degree CRU T2 data. The novel dataset, HySN, is archived in Zenodo (https://doi.org/10.5281/zenodo.1970170). The HySN estimates, existing estimates from reanalysis data, post-processed reanalysis data, and VIC-type forcing data are compared with observations from the Norwegian mainland between 1982 and 2000. Humidity measurements from 84 stations are used, and, by employing quality control routines and including agricultural stations, SW ↓ observations from 10 stations are made available. Meanwhile, only two stations have observations of LW ↓. Vertical gradients, differences when compared at common altitudes, daily correlations, sensitivities to air mass type, and, where possible, trends and geographical gradients in seasonal means are assessed. At individual stations differences in seasonal means from the observations are as large as 7 °C for Td, 62 W m−2 for SW ↓, and 24 W m−2 for LW ↓. Most models overestimate SW ↓, and underestimate LW ↓. Horizontal resolution is not a predictor of the model's efficiency. Daily correlation is better captured in the products based on newer reanalysis data. Certain model estimates show different dependencies on geographical features, diverging trends, or a different sensitivity to air mass type than the observations.

scholarly journals Using High-Resolution Simulations to Quantify Errors in Radar Estimates of Tornado Intensity

2018 ◽  
Vol 146 (7) ◽  
pp. 2271-2296 ◽  
Author(s):  
Nathan A. Dahl ◽  
David S. Nolan
Keyword(s):  
High Resolution ◽  
Wind Speed ◽  
Wind Direction ◽  
Total Error ◽  
Global Maximum ◽  
Error Type ◽  
Pronounced Increase ◽  
Near Surface ◽  
Temporal Sampling ◽  
Radar Beam

Abstract Observation experiments are performed on a set of high-resolution large-eddy simulations of translating tornado-like vortices. Near-surface Doppler wind measurements are taken by emulating a mobile radar positioned from 1 to 10 km south of each vortex track and conducting single-level scans every 2 s. The departure of each observed gust (wind measurement averaged over two successive scans) from the corresponding true maximum 3-s gust at 10 m AGL (“S10–3s”) is partitioned into error sources associated with resolution volume size, wind direction relative to the radar beam, beam elevation, and temporal sampling. The distributions of each error type are diagrammed as functions of range, observed wind speed, and predicted deviation between the wind direction and the radar beam. The results indicate that the deviation between the wind direction and the radar beam is the predominant source of error in these rapid scan scenarios, although range is also a substantial factor. The median total error is ~10% for small deviation at close range, but it approximately doubles if the range is increased from 1 to 10 km; a more pronounced increase in both the median value and the variance of the total error is seen as the deviation becomes large. Because of this, the underestimate of the global maximum S10–3s approaches 30–40 m s−1 at a longer range, although the global maximum of the time-averaged observed wind speed gives a reasonable approximation of the time-mean maximum S10–3s in many cases. Because of simplifying assumptions and the limited number of cases examined, these results are intended as a baseline for further research.

A Next-Generation Coastal Ocean Operational System: Probabilistic Flood Forecasting at Street Scale

2019 ◽  
Vol 100 (1) ◽  
pp. 41-54 ◽  
Author(s):  
Antoni Jordi ◽  
Nickitas Georgas ◽  
Alan Blumberg ◽  
Larry Yin ◽  
Ziyu Chen ◽  
...  
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
High Water ◽  
Flood Forecasting ◽  
Coastal Ocean ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Ocean Modeling ◽  
Metropolitan Region ◽  
Modeling Framework

AbstractRecent hurricanes have demonstrated the need for real-time flood forecasting at street scale in coastal urban areas. Here, we describe the high-impact high-resolution (HIHR) system that operationally forecasts flooding at very high resolution in the New York–New Jersey metropolitan region. HIHR is the latest upgrade of the Stevens Flood Advisory System (SFAS), a highly detailed operational coastal ocean modeling system. SFAS, based on the Hydrologic–Hydraulic–Hydrodynamic Ensemble (H3E) modeling framework, consists of four sets of nested coastal and inland flood models that provide ensemble flood forecasts with a horizon of at least 96 h from regional to street scales based on forcing from 100 different meteorological output fields. HIHR includes nine model domains with horizontal resolution ranging from 3 to 10 m around critical infrastructure sites in the region. HIHR models are based on an advanced hydrodynamic code [the Stevens Estuarine and Coastal Ocean Model (sECOM), a derivative of the Princeton Ocean Model] and nested into the H3E models. HIHR was retrospectively evaluated by forecasting the coastal flooding caused by Superstorm Sandy in 2012 using water-level sensors, high-water marks, and flood maps. The forecasts for the 95th percentile show a good agreement with these observations even three days before the peak flood, while the 50th percentile is negatively biased because of the lack of resolution on the meteorological forcing. Forecasts became more accurate and less uncertain as the forecasts were issued closer to the peak flooding.

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