scholarly journals Assimilation of ground versus lidar observations for PM<sub>10</sub> forecasting

2013 ◽  
Vol 13 (1) ◽  
pp. 269-283 ◽  
Author(s):  
Y. Wang ◽  
K. N. Sartelet ◽  
M. Bocquet ◽  
P. Chazette
Keyword(s):  
Data Assimilation ◽  
Western Europe ◽  
Sensitivity Study ◽  
Optimal Interpolation ◽  
Temporal Influence ◽  
Potential Impact ◽  
Ground Based Lidar ◽  
Observing System Simulation Experiment ◽  
Lidar Observations ◽  
Do So

Abstract. This article investigates the potential impact of future ground-based lidar networks on analysis and short-term forecasts of particulate matter with a diameter smaller than 10 μm (PM10). To do so, an Observing System Simulation Experiment (OSSE) is built for PM10 data assimilation (DA) using optimal interpolation (OI) over Europe for one month from 15 July to 15 August 2001. First, using a lidar network with 12 stations and representing the "true" atmosphere by a simulation called "nature run", we estimate the efficiency of assimilating the lidar network measurements in improving PM10 concentration for analysis and forecast. It is compared to the efficiency of assimilating concentration measurements from the AirBase ground network, which includes about 500 stations in western Europe. It is found that assimilating the lidar observations decreases by about 54% the root mean square error (RMSE) of PM10 concentrations after 12 h of assimilation and during the first forecast day, against 59% for the assimilation of AirBase measurements. However, the assimilation of lidar observations leads to similar scores as AirBase's during the second forecast day. The RMSE of the second forecast day is improved on average over the summer month by 57% by the lidar DA, against 56% by the AirBase DA. Moreover, the spatial and temporal influence of the assimilation of lidar observations is larger and longer. The results show a potentially powerful impact of the future lidar networks. Secondly, since a lidar is a costly instrument, a sensitivity study on the number and location of required lidars is performed to help define an optimal lidar network for PM10 forecasts. With 12 lidar stations, an efficient network in improving PM10 forecast over Europe is obtained by regularly spacing the lidars. Data assimilation with a lidar network of 26 or 76 stations is compared to DA with the previously-used lidar network. During the first forecast day, the assimilation of 76 lidar stations' measurements leads to a better score (the RMSE decreased by about 65%) than AirBase's (the RMSE decreased by about 59%).

scholarly journals Assimilation of ground versus lidar observations for PM<sub>10</sub> forecasting

2012 ◽  
Vol 12 (9) ◽  
pp. 23291-23331
Author(s):  
Y. Wang ◽  
K. N. Sartelet ◽  
M. Bocquet ◽  
P. Chazette
Keyword(s):  
Western Europe ◽  
Sensitivity Study ◽  
Optimal Interpolation ◽  
Short Term ◽  
Temporal Influence ◽  
Potential Impact ◽  
Ground Based Lidar ◽  
Observing System Simulation Experiment ◽  
Lidar Observations ◽  
Do So

Abstract. This article investigates the potential impact of future ground-based lidar networks on analysis and short-term forecasts of particulate matter with a diameter smaller than 10 μg m−3 (PM10). To do so, an Observing System Simulation Experiment (OSSE) is built for PM10 data assimilation (DA) using optimal interpolation (OI) over Europe for one month in 2001. First, using a lidar network with 12 stations, we estimate the efficiency of assimilating the lidar network measurements in improving PM10 concentration analysis and forecast. It is compared to the efficiency of assimilating concentration measurements from the AirBase ground network, which includes about 500 stations in Western Europe. It is found that assimilating the lidar observations decreases by about 54% the root mean square error (RMSE) of PM10 concentrations after 12 h of assimilation and during the first forecast day, against 59% for the assimilation of AirBase measurements. However, the assimilation of lidar observations leads to similar scores as AirBase's during the second forecast day. The RMSE of the second forecast day is improved on average over the summer month by 57% by the lidar DA, against 56% by the AirBase DA. Moreover, the spatial and temporal influence of the assimilation of lidar observations is larger and longer. The results show a potentially powerful impact of the future lidar networks. Secondly, since a lidar is a costly instrument, a sensitivity study on the number and location of required lidars is performed to help defining an optimal lidar network for PM10 forecast. With 12 lidar stations, an efficient network in improving PM10 forecast over Europe is obtained by regularly spacing the lidars. DA with a lidar network of 26 or 76 stations is compared to DA with the previously-used lidar network. The assimilation of 76 lidar stations' measurements leads to a better score than AirBase's during the forecast days.

scholarly journals Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign

2014 ◽  
Vol 14 (7) ◽  
pp. 3511-3532 ◽  
Author(s):  
Y. Wang ◽  
K. N. Sartelet ◽  
M. Bocquet ◽  
P. Chazette
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Data Assimilation ◽  
Transport Model ◽  
Interpolation Method ◽  
Optimal Interpolation ◽  
Atmospheric Conditions ◽  
Model Aerosol ◽  
Paris Area ◽  
Lidar Observations

Abstract. In this study, we investigate the ability of the chemistry transport model (CTM) Polair3D of the air quality modelling platform Polyphemus to simulate lidar backscattered profiles from model aerosol concentration outputs. This investigation is an important preprocessing stage of data assimilation (validation of the observation operator). To do so, simulated lidar signals are compared to hourly lidar observations performed during the MEGAPOLI (Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation) summer experiment in July 2009, when a ground-based mobile lidar was deployed around Paris on-board a van. The comparison is performed for six different measurement days, 1, 4, 16, 21, 26 and 29 July 2009, corresponding to different levels of pollution and different atmospheric conditions. Overall, Polyphemus well reproduces the vertical distribution of lidar signals and their temporal variability, especially for 1, 16, 26 and 29 July 2009. Discrepancies on 4 and 21 July 2009 are due to high-altitude aerosol layers, which are not well modelled. In the second part of this study, two new algorithms for assimilating lidar observations based on the optimal interpolation method are presented. One algorithm analyses PM10 (particulate matter with diameter less than 10 μm) concentrations. Another analyses PM2.5 (particulate matter with diameter less than 2.5 μm) and PM2.5–10 (particulate matter with a diameter higher than 2.5 μm and lower than 10 μm) concentrations separately. The aerosol simulations without and with lidar data assimilation (DA) are evaluated using the Airparif (a regional operational network in charge of air quality survey around the Paris area) database to demonstrate the feasibility and usefulness of assimilating lidar profiles for aerosol forecasts. The evaluation shows that lidar DA is more efficient at correcting PM10 than PM2.5, probably because PM2.5 is better modelled than PM10. Furthermore, the algorithm which analyses both PM2.5and PM2.5–10 provides the best scores for PM10. The averaged root-mean-square error (RMSE) of PM10 is 11.63 μg m−3 with DA (PM2.5 and PM2.5–10), compared to 13.69 μg m−3 with DA (PM10) and 17.74 μg m−3 without DA on 1 July 2009. The averaged RMSE of PM10 is 4.73 μg m−3 with DA (PM2.5 and PM2.5–10), against 6.08 μg m−3 with DA (PM10) and 6.67 μg m−3 without DA on 26 July 2009.

scholarly journals Multivariate Error Covariance Estimates by Monte Carlo Simulation for Assimilation Studies in the Pacific Ocean

2005 ◽  
Vol 133 (8) ◽  
pp. 2310-2334 ◽  
Author(s):  
Anna Borovikov ◽  
Michele M. Rienecker ◽  
Christian L. Keppenne ◽  
Gregory C. Johnson
Keyword(s):  
Monte Carlo ◽  
Data Assimilation ◽  
Forecast Error ◽  
Independent Set ◽  
Velocity Fields ◽  
Optimal Interpolation ◽  
Model Errors ◽  
Error Statistics ◽  
Practical Applications ◽  
Monte Carlo Techniques

Abstract One of the most difficult aspects of ocean-state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model–observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross covariances between different model variables used. Here a comparison is made between a univariate optimal interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature profiles. In the UOI case only temperature is updated using a Gaussian covariance function. In the MvOI, salinity, zonal, and meridional velocities as well as temperature are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimate of the forecast error statistics is made by Monte Carlo techniques from an ensemble of model forecasts. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross covariances between the fields of different physical variables constituting the model-state vector, at the same time incorporating the model’s dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere–Ocean array have been assimilated in this study. To investigate the efficacy of the multivariate scheme, two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity, and temperature. For reference, a control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the UOI and MvOI is similar with respect to the temperature field, the salinity and velocity fields are greatly improved when the multivariate correction is used, as is evident from the analyses of the rms differences between these fields and independent observations. The MvOI assimilation is found to improve upon the control run in generating water masses with properties close to the observed, while the UOI fails to maintain the temperature and salinity structure.

Balanced Ocean-Data Assimilation near the Equator

2002 ◽  
Vol 32 (9) ◽  
pp. 2509-2519 ◽  
Author(s):  
Gerrit Burgers ◽  
Magdalena A. Balmaseda ◽  
Femke C. Vossepoel ◽  
Geert Jan van Oldenborgh ◽  
Peter Jan van Leeuwen
Keyword(s):  
Data Assimilation ◽  
Shallow Water ◽  
General Circulation ◽  
General Circulation Models ◽  
Density Field ◽  
Water Model ◽  
Optimal Interpolation ◽  
Shallow Water Model ◽  
Ocean Data Assimilation ◽  
Zonal Velocity

Abstract The question is addressed whether using unbalanced updates in ocean-data assimilation schemes for seasonal forecasting systems can result in a relatively poor simulation of zonal currents. An assimilation scheme, where temperature observations are used for updating only the density field, is compared to a scheme where updates of density field and zonal velocities are related by geostrophic balance. This is done for an equatorial linear shallow-water model. It is found that equatorial zonal velocities can be detoriated if velocity is not updated in the assimilation procedure. Adding balanced updates to the zonal velocity is shown to be a simple remedy for the shallow-water model. Next, optimal interpolation (OI) schemes with balanced updates of the zonal velocity are implemented in two ocean general circulation models. First tests indicate a beneficial impact on equatorial upper-ocean zonal currents.

Investigation of the impact of thermodynamic profiles of ground based lidar systems on short-range forecast skill by means of ETKF-hybrid 3DVAR data assimilation

2021 ◽  
Author(s):  
Rohith Thundathil ◽  
Thomas Schwitalla ◽  
Andreas Behrendt ◽  
Diego Lange ◽  
Florian Späth ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Wrf Model ◽  
Absolute Humidity ◽  
Raman Lidar ◽  
High Definition ◽  
Background Error ◽  
Ground Based Lidar ◽  
Ensemble Transform Kalman Filter ◽  
Definition Of ◽  
The Impact

&lt;p&gt;Ground based active remote-sensing instruments have proved its potential through its high quality observations of thermodynamic profiles. In this study, thermodynamic profiles obtained from the temperature Raman lidar (TRL) and the water-vapour differential absorption lidar (DIAL) of the University of Hohenheim (UHOH) are assimilated into the Weather Research and Forecasting model data assimilation (WRFDA) system through a new forward operator for absolute humidity and mixing ratio developed in-house.&lt;br&gt;Thermodynamic DA was performed either with the deterministic 3-dimensional variational (3DVAR) DA system or with the hybrid 3DVAR-Ensemble Transform Kalman Filter (ETKF) approach. We used data of the High Definition of Clouds and Precipitation for advancing Climate Prediction (HD(CP)2 project Observation Prototype Experiment (HOPE). The WRF model was configured for a central European domain at a convection permitting resolution of 2.5 km spatial grid increment and 100 levels in the vertical with fine resolution in the planetary boundary layer (PBL). The assimilation experiments were conducted in a rapid update cycle (RUC) mode with an hourly update frequency. The hybrid 3DVAR-ETKF DA system was incorporated with an adaptive inflation scheme using a set of 10 ensemble members each with the same configuration as the previous experiments for the 3DVAR.&amp;#160; We will present the results of three DA experiments. In the first experiment (CONV_DA), or the control run, only assimilation of the conventional observations was carried out with 3DVAR DA. The second experiment (QT_DA) was a 3DVAR DA assimilating WVMR and temperature together in addition to the conventional dataset. The third experiment (QT_HYB_DA) assimilated WVMR and temperature together in addition to the conventional dataset with Hybrid DA.&lt;br&gt;The WVMR RMSE with respect to the WVDIAL reduced by 41 % in 3DVAR and still reduced to 51 % in QT_HYB_DA compared to CONV_DA. Although temperature RMSE with respect to TRL increased by 5 % in QT_DA, RMSE significantly reduced to 47 % in QT_HYB_DA compared to CONV_DA. The correlation between the temperature and WVMR variables in the background error covariance matrix of the 3DVAR, which is static and not flow-dependent, limited the improvement in temperature. Flow-dependency in Hybrid DA improved the error correlations.&lt;br&gt;We also present results of a collaborative effort with the Raman lidar for meteorological observation (RALMO) from the MeteoSwiss and the Atmospheric Raman Temperature and Humidity Sounder (ARTHUS) using even finer model resolution. The initial results of the new study will also be presented here.&lt;/p&gt;

scholarly journals A qualitative account of young people’s experiences of alcohol screening and brief interventions in schools: SIPS Jr-HIGH trial findings

2019 ◽  
Vol 42 (3) ◽  
pp. e259-e267
Author(s):  
E L Giles ◽  
G J McGeechan ◽  
S J Scott ◽  
R McGovern ◽  
S Boniface ◽  
...  
Keyword(s):  
Alcohol Use ◽  
Young People ◽  
Western Europe ◽  
Controlled Trial ◽  
Alcohol Screening ◽  
Screening And Brief Intervention ◽  
The United Kingdom ◽  
Analysis Process ◽  
Potential Impact ◽  
The Uk

Abstract Background The United Kingdom (UK) has seen a decrease in the number of young people drinking alcohol. However, the UK prevalence of underage drinking still ranks amongst the highest in Western Europe. Whilst there is a wealth of evidence reporting on the effectiveness of both primary, and secondary interventions, there are few reports of the experiences of young people who receive them. Methods The present study reports findings from interviews with 33 young people who were involved in an alcohol screening and brief intervention randomized controlled trial in schools in England. All interviews were analysed using inductive applied thematic analysis. Results Three major themes were identified following the analysis process: 1) drinking identities and awareness of risk; 2) access to support and advice in relation to alcohol use; and 3) appraisal of the intervention and potential impact on alcohol use. Conclusions There appeared to be a reluctance from participants to describe themselves as someone who drinks alcohol. Furthermore, those who did drink alcohol often did so with parental permission. There was variation amongst participants as to how comfortable they felt talking about alcohol issues with school staff. Overall participants felt the intervention was useful, but would be better suited to ‘heavier’ drinkers.

scholarly journals Physical-biogeochemical regional ocean model uncertainties stemming from stochastic parameterizations and potential impact on data assimilation

2019 ◽  
Author(s):  
Vassilios D. Vervatis ◽  
Pierre De Mey-Frémaux ◽  
Nadia Ayoub ◽  
Sarantis Sofianos ◽  
Charles-Emmanuel Testut ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Spatial Scales ◽  
Ecosystem Model ◽  
Ocean Model ◽  
Open Ocean ◽  
Small Scale ◽  
Biogeochemical Model ◽  
Frequency Error ◽  
Model Errors ◽  
Potential Impact

Abstract. We generate ocean biogeochemical model ensembles including several kinds of stochastic parameterizations. The NEMO stochastic modules are complemented by integrating a subroutine to calculate variable anisotropic spatial scales, which are of particular importance in high-resolution coastal configurations. The domain covers the Bay of Biscay at 1/36° resolution, as a case study for open-ocean and coastal shelf dynamics. At first, we identify uncertainties from assumptions subject to erroneous atmospheric forcing, ocean model improper parameterizations and ecosystem state uncertainties. The error regimes are found to be mainly driven by the wind forcing, with the rest of the perturbed tendencies locally augmenting the ensemble spread. Biogeochemical uncertainties arise from inborn ecosystem model errors and from errors in the physical state. Model errors in physics are found to have larger impact on chlorophyll spread than those of the ecosystem. In a second step, the ensembles undergo verification with respect to observations, focusing on upper-ocean properties. We investigate the statistical consistency of prior model errors and observation estimates, in view of joint uncertainty vicinities, associated with both sources of information. OSTIA-SST L4 distribution appears to be compatible with ensembles perturbing physics, since vicinities overlap, enabling data assimilation. The most consistent configuration for SLA along-track L3 data is in the Abyssal plain, where the spread is increased due to mesoscale eddy decorrelation. The largest statistical SLA biases are observed in coastal regions, sometimes to the point that vicinities become disjoint. Missing error processes in relation to SLA hint at the presence of high-frequency error sources currently unaccounted for, potentially leading to ill-posed assimilation problems. Ecosystem model-data samples with respect to Ocean Colour L4 appear to be compatible with each other only at times, with data assimilation being marginally well-posed. In a third step, we illustrate the potential influence of those uncertainties on data assimilation impact exercise, by means of multivariate representers and EnKF-type incremental analysis for a few members. Corrections on physical properties are associated with large-scale biases between model and data, with diverse characteristics in the open-ocean and the shelves. The increments are often characteristic of the underlying mesoscale features, chlorophyll included due to the vertical velocity field. Small scale local corrections are visible over the shelves. Chlorophyll information seems to have a very measurable potential impact on physical variables.

scholarly journals Inverse modeling of black carbon emissions over China using ensemble data assimilation

2016 ◽  
Vol 16 (2) ◽  
pp. 989-1002 ◽  
Author(s):  
P. Wang ◽  
H. Wang ◽  
Y. Q. Wang ◽  
X. Y. Zhang ◽  
S. L. Gong ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Black Carbon ◽  
Atmospheric Chemistry ◽  
Inverse Modeling ◽  
Optimal Interpolation ◽  
Top Down ◽  
Bottom Up ◽  
Error Matrix ◽  
Ensemble Data Assimilation ◽  
Ensemble Data

Abstract. Emissions inventories of black carbon (BC), which are traditionally constructed using a bottom-up approach based on activity data and emissions factors, are considered to contain a large level of uncertainty. In this paper, an ensemble optimal interpolation (EnOI) data assimilation technique is used to investigate the possibility of optimally recovering the spatially resolved emissions bias of BC. An inverse modeling system for emissions is established for an atmospheric chemistry aerosol model and two key problems related to ensemble data assimilation in the top-down emissions estimation are discussed: (1) how to obtain reasonable ensembles of prior emissions and (2) establishing a scheme to localize the background-error matrix. An experiment involving 1-year-long simulation cycle with EnOI inversion of BC emissions is performed for 2008. The bias of the BC emissions intensity in China at each grid point is corrected by this inverse system. The inverse emission over China in January is 240.1 Gg, and annual emission is about 2539.3 Gg, which is about 1.8 times of bottom-up emission inventory. The results show that, even though only monthly mean BC measurements are employed to inverse the emissions, the accuracy of the daily model simulation improves. Using top-down emissions, the average root mean square error of simulated daily BC is decreased by nearly 30 %. These results are valuable and promising for a better understanding of aerosol emissions and distributions, as well as aerosol forecasting.

scholarly journals Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation

2019 ◽  
Vol 11 (7) ◽  
pp. 858 ◽  
Author(s):  
Redouane Lguensat ◽  
Phi Huynh Viet ◽  
Miao Sun ◽  
Ge Chen ◽  
Tian Fenglin ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Level ◽  
Large Scale ◽  
Empirical Orthogonal Functions ◽  
Data Driven ◽  
Optimal Interpolation ◽  
Observation System ◽  
Orthogonal Functions ◽  
Sea Level Anomalies ◽  
Recent Developments

From the recent developments of data-driven methods as a means to better exploit large-scale observation, simulation and reanalysis datasets for solving inverse problems, this study addresses the improvement of the reconstruction of higher-resolution Sea Level Anomaly (SLA) fields using analog strategies. This reconstruction is stated as an analog data assimilation issue, where the analog models rely on patch-based and Empirical Orthogonal Functions (EOF)-based representations to circumvent the curse of dimensionality. We implement an Observation System Simulation Experiment (OSSE) in the South China Sea. The reported results show the relevance of the proposed framework with a significant gain in terms of Root Mean Square Error (RMSE) for scales below 100 km. We further discuss the usefulness of the proposed analog model as a means to exploit high-resolution model simulations for the processing and analysis of current and future satellite-derived altimetric data with regard to conventional interpolation schemes, especially optimal interpolation.

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